From 2885f7ae2dbdde5706ac9cc772e3cb005323626e Mon Sep 17 00:00:00 2001
From: jenkins-doc-updater <jenkins@example.com>
Date: Sat, 3 Apr 2021 17:21:18 +0000
Subject: [PATCH 1/7] [skip ci] Update documentation

---
 .buildinfo                                    |     4 +
 .nojekyll                                     |     0
 .../examples_hyperparameter_tuning_24_0.png   |   Bin 0 -> 9469 bytes
 .../examples_hyperparameter_tuning_4_1.png    |   Bin 0 -> 4565 bytes
 _images/examples_logistic_falkon_22_0.png     |   Bin 0 -> 211967 bytes
 _images/examples_logistic_falkon_4_0.png      |   Bin 0 -> 154507 bytes
 _images/examples_simple_regression_17_1.png   |   Bin 0 -> 6082 bytes
 .../api_reference/center_selector.rst.txt     |    31 +
 _sources/api_reference/gsc_losses.rst.txt     |    30 +
 _sources/api_reference/index.rst.txt          |    18 +
 _sources/api_reference/kernels.rst.txt        |    61 +
 _sources/api_reference/mmv_ops.rst.txt        |   133 +
 _sources/api_reference/models.rst.txt         |    29 +
 _sources/api_reference/optimization.rst.txt   |    27 +
 _sources/api_reference/options.rst.txt        |    14 +
 _sources/api_reference/outofcore.rst.txt      |    25 +
 _sources/api_reference/preconditioner.rst.txt |    29 +
 _sources/api_reference/sparse.rst.txt         |    24 +
 _sources/examples/examples.rst.txt            |    34 +
 .../examples/hyperparameter_tuning.ipynb.txt  |   430 +
 _sources/examples/logistic_falkon.ipynb.txt   |   436 +
 .../examples/performance_tuning.ipynb.txt     |    49 +
 _sources/examples/simple_regression.ipynb.txt |   403 +
 _sources/get_started.rst.txt                  |    53 +
 _sources/index.rst.txt                        |    62 +
 _sources/install.rst.txt                      |    72 +
 _static/basic.css                             |   856 ++
 _static/css/badge_only.css                    |     1 +
 _static/css/fonts/Roboto-Slab-Bold.woff       |   Bin 0 -> 87624 bytes
 _static/css/fonts/Roboto-Slab-Bold.woff2      |   Bin 0 -> 67312 bytes
 _static/css/fonts/Roboto-Slab-Regular.woff    |   Bin 0 -> 86288 bytes
 _static/css/fonts/Roboto-Slab-Regular.woff2   |   Bin 0 -> 66444 bytes
 _static/css/fonts/fontawesome-webfont.eot     |   Bin 0 -> 165742 bytes
 _static/css/fonts/fontawesome-webfont.svg     |  2671 ++++
 _static/css/fonts/fontawesome-webfont.ttf     |   Bin 0 -> 165548 bytes
 _static/css/fonts/fontawesome-webfont.woff    |   Bin 0 -> 98024 bytes
 _static/css/fonts/fontawesome-webfont.woff2   |   Bin 0 -> 77160 bytes
 _static/css/fonts/lato-bold-italic.woff       |   Bin 0 -> 323344 bytes
 _static/css/fonts/lato-bold-italic.woff2      |   Bin 0 -> 193308 bytes
 _static/css/fonts/lato-bold.woff              |   Bin 0 -> 309728 bytes
 _static/css/fonts/lato-bold.woff2             |   Bin 0 -> 184912 bytes
 _static/css/fonts/lato-normal-italic.woff     |   Bin 0 -> 328412 bytes
 _static/css/fonts/lato-normal-italic.woff2    |   Bin 0 -> 195704 bytes
 _static/css/fonts/lato-normal.woff            |   Bin 0 -> 309192 bytes
 _static/css/fonts/lato-normal.woff2           |   Bin 0 -> 182708 bytes
 _static/css/theme.css                         |     4 +
 _static/doctools.js                           |   321 +
 _static/documentation_options.js              |    12 +
 _static/file.png                              |   Bin 0 -> 286 bytes
 _static/jquery-3.5.1.js                       | 10872 ++++++++++++++++
 _static/jquery.js                             |     2 +
 _static/js/badge_only.js                      |     1 +
 _static/js/html5shiv-printshiv.min.js         |     4 +
 _static/js/html5shiv.min.js                   |     4 +
 _static/js/theme.js                           |     1 +
 _static/language_data.js                      |   297 +
 _static/minus.png                             |   Bin 0 -> 90 bytes
 _static/plus.png                              |   Bin 0 -> 90 bytes
 _static/pygments.css                          |    69 +
 _static/searchtools.js                        |   522 +
 _static/underscore-1.12.0.js                  |  2027 +++
 _static/underscore.js                         |     6 +
 api_reference/center_selector.html            |   601 +
 api_reference/gsc_losses.html                 |   715 +
 api_reference/index.html                      |   464 +
 api_reference/kernels.html                    |  1428 ++
 api_reference/mmv_ops.html                    |   597 +
 api_reference/models.html                     |   882 ++
 api_reference/optimization.html               |   439 +
 api_reference/options.html                    |   483 +
 api_reference/outofcore.html                  |   441 +
 api_reference/preconditioner.html             |   828 ++
 api_reference/sparse.html                     |   500 +
 examples/examples.html                        |   389 +
 examples/hyperparameter_tuning.html           |   932 ++
 examples/hyperparameter_tuning.ipynb          |   430 +
 examples/logistic_falkon.html                 |   953 ++
 examples/logistic_falkon.ipynb                |   436 +
 examples/performance_tuning.html              |   380 +
 examples/performance_tuning.ipynb             |    49 +
 examples/simple_regression.html               |   845 ++
 examples/simple_regression.ipynb              |   403 +
 genindex.html                                 |   860 ++
 get_started.html                              |   419 +
 index.html                                    |   516 +
 install.html                                  |   429 +
 objects.inv                                   |   Bin 0 -> 4378 bytes
 py-modindex.html                              |   405 +
 search.html                                   |   360 +
 searchindex.js                                |     1 +
 90 files changed, 34819 insertions(+)
 create mode 100644 .buildinfo
 create mode 100644 .nojekyll
 create mode 100644 _images/examples_hyperparameter_tuning_24_0.png
 create mode 100644 _images/examples_hyperparameter_tuning_4_1.png
 create mode 100644 _images/examples_logistic_falkon_22_0.png
 create mode 100644 _images/examples_logistic_falkon_4_0.png
 create mode 100644 _images/examples_simple_regression_17_1.png
 create mode 100644 _sources/api_reference/center_selector.rst.txt
 create mode 100644 _sources/api_reference/gsc_losses.rst.txt
 create mode 100644 _sources/api_reference/index.rst.txt
 create mode 100644 _sources/api_reference/kernels.rst.txt
 create mode 100644 _sources/api_reference/mmv_ops.rst.txt
 create mode 100644 _sources/api_reference/models.rst.txt
 create mode 100644 _sources/api_reference/optimization.rst.txt
 create mode 100644 _sources/api_reference/options.rst.txt
 create mode 100644 _sources/api_reference/outofcore.rst.txt
 create mode 100644 _sources/api_reference/preconditioner.rst.txt
 create mode 100644 _sources/api_reference/sparse.rst.txt
 create mode 100644 _sources/examples/examples.rst.txt
 create mode 100644 _sources/examples/hyperparameter_tuning.ipynb.txt
 create mode 100644 _sources/examples/logistic_falkon.ipynb.txt
 create mode 100644 _sources/examples/performance_tuning.ipynb.txt
 create mode 100644 _sources/examples/simple_regression.ipynb.txt
 create mode 100644 _sources/get_started.rst.txt
 create mode 100644 _sources/index.rst.txt
 create mode 100644 _sources/install.rst.txt
 create mode 100644 _static/basic.css
 create mode 100644 _static/css/badge_only.css
 create mode 100644 _static/css/fonts/Roboto-Slab-Bold.woff
 create mode 100644 _static/css/fonts/Roboto-Slab-Bold.woff2
 create mode 100644 _static/css/fonts/Roboto-Slab-Regular.woff
 create mode 100644 _static/css/fonts/Roboto-Slab-Regular.woff2
 create mode 100644 _static/css/fonts/fontawesome-webfont.eot
 create mode 100644 _static/css/fonts/fontawesome-webfont.svg
 create mode 100644 _static/css/fonts/fontawesome-webfont.ttf
 create mode 100644 _static/css/fonts/fontawesome-webfont.woff
 create mode 100644 _static/css/fonts/fontawesome-webfont.woff2
 create mode 100644 _static/css/fonts/lato-bold-italic.woff
 create mode 100644 _static/css/fonts/lato-bold-italic.woff2
 create mode 100644 _static/css/fonts/lato-bold.woff
 create mode 100644 _static/css/fonts/lato-bold.woff2
 create mode 100644 _static/css/fonts/lato-normal-italic.woff
 create mode 100644 _static/css/fonts/lato-normal-italic.woff2
 create mode 100644 _static/css/fonts/lato-normal.woff
 create mode 100644 _static/css/fonts/lato-normal.woff2
 create mode 100644 _static/css/theme.css
 create mode 100644 _static/doctools.js
 create mode 100644 _static/documentation_options.js
 create mode 100644 _static/file.png
 create mode 100644 _static/jquery-3.5.1.js
 create mode 100644 _static/jquery.js
 create mode 100644 _static/js/badge_only.js
 create mode 100644 _static/js/html5shiv-printshiv.min.js
 create mode 100644 _static/js/html5shiv.min.js
 create mode 100644 _static/js/theme.js
 create mode 100644 _static/language_data.js
 create mode 100644 _static/minus.png
 create mode 100644 _static/plus.png
 create mode 100644 _static/pygments.css
 create mode 100644 _static/searchtools.js
 create mode 100644 _static/underscore-1.12.0.js
 create mode 100644 _static/underscore.js
 create mode 100644 api_reference/center_selector.html
 create mode 100644 api_reference/gsc_losses.html
 create mode 100644 api_reference/index.html
 create mode 100644 api_reference/kernels.html
 create mode 100644 api_reference/mmv_ops.html
 create mode 100644 api_reference/models.html
 create mode 100644 api_reference/optimization.html
 create mode 100644 api_reference/options.html
 create mode 100644 api_reference/outofcore.html
 create mode 100644 api_reference/preconditioner.html
 create mode 100644 api_reference/sparse.html
 create mode 100644 examples/examples.html
 create mode 100644 examples/hyperparameter_tuning.html
 create mode 100644 examples/hyperparameter_tuning.ipynb
 create mode 100644 examples/logistic_falkon.html
 create mode 100644 examples/logistic_falkon.ipynb
 create mode 100644 examples/performance_tuning.html
 create mode 100644 examples/performance_tuning.ipynb
 create mode 100644 examples/simple_regression.html
 create mode 100644 examples/simple_regression.ipynb
 create mode 100644 genindex.html
 create mode 100644 get_started.html
 create mode 100644 index.html
 create mode 100644 install.html
 create mode 100644 objects.inv
 create mode 100644 py-modindex.html
 create mode 100644 search.html
 create mode 100644 searchindex.js

diff --git a/.buildinfo b/.buildinfo
new file mode 100644
index 00000000..b65534f0
--- /dev/null
+++ b/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: e068a7f093f5a5cedc111cb6bdc449ad
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/.nojekyll b/.nojekyll
new file mode 100644
index 00000000..e69de29b
diff --git a/_images/examples_hyperparameter_tuning_24_0.png b/_images/examples_hyperparameter_tuning_24_0.png
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diff --git a/_sources/api_reference/center_selector.rst.txt b/_sources/api_reference/center_selector.rst.txt
new file mode 100644
index 00000000..6b251150
--- /dev/null
+++ b/_sources/api_reference/center_selector.rst.txt
@@ -0,0 +1,31 @@
+falkon.center_selection
+=======================
+
+.. automodule:: falkon.center_selection
+
+.. py:currentmodule:: falkon.center_selection
+
+
+CenterSelector
+--------------
+
+.. autoclass:: falkon.center_selection.CenterSelector
+    :members:
+
+
+UniformSelector
+---------------
+
+.. autoclass:: falkon.center_selection.UniformSelector
+    :members:
+    :inherited-members:
+    :show-inheritance:
+
+
+FixedSelector
+-------------
+
+.. autoclass:: falkon.center_selection.FixedSelector
+    :members:
+    :inherited-members: ABC
+    :show-inheritance:
diff --git a/_sources/api_reference/gsc_losses.rst.txt b/_sources/api_reference/gsc_losses.rst.txt
new file mode 100644
index 00000000..f2be03ef
--- /dev/null
+++ b/_sources/api_reference/gsc_losses.rst.txt
@@ -0,0 +1,30 @@
+falkon.gsc_losses
+=================
+
+.. automodule:: falkon
+
+.. py:currentmodule:: falkon
+
+
+Loss
+----
+
+.. autoclass:: falkon.gsc_losses.Loss
+    :members:
+    :special-members: __call__
+
+
+Logistic loss
+-------------
+
+.. autoclass:: falkon.gsc_losses.LogisticLoss
+    :members:
+    :special-members: __call__
+
+
+Weighted binary cross entropy loss
+----------------------------------
+
+.. autoclass:: falkon.gsc_losses.WeightedCrossEntropyLoss
+    :members:
+    :special-members: __call__
diff --git a/_sources/api_reference/index.rst.txt b/_sources/api_reference/index.rst.txt
new file mode 100644
index 00000000..7e410b4d
--- /dev/null
+++ b/_sources/api_reference/index.rst.txt
@@ -0,0 +1,18 @@
+.. _api_reference:
+
+API Reference
+=============
+
+.. toctree::
+    :maxdepth: 3
+
+    models
+    kernels
+    options
+    gsc_losses
+    preconditioner
+    optimization
+    outofcore
+    mmv_ops
+    sparse
+    center_selector
diff --git a/_sources/api_reference/kernels.rst.txt b/_sources/api_reference/kernels.rst.txt
new file mode 100644
index 00000000..2c1ca868
--- /dev/null
+++ b/_sources/api_reference/kernels.rst.txt
@@ -0,0 +1,61 @@
+
+falkon.kernels
+==============
+
+.. automodule:: falkon.kernels
+.. py:currentmodule:: falkon.kernels
+
+
+Kernel
+------
+
+.. autoclass:: falkon.kernels.kernel.Kernel
+    :members:
+    :private-members: _prepare, _apply, _finalize, _prepare_sparse, _apply_sparse, _decide_mm_impl, _decide_mmv_impl, _decide_dmmv_impl
+    :special-members: __call__
+
+.. autoclass:: falkon.kernels.keops_helpers.KeopsKernelMixin
+    :members:
+
+
+Radial kernels
+--------------
+
+Gaussian kernel
+~~~~~~~~~~~~~~~
+
+.. autoclass:: GaussianKernel
+    :members: mmv, dmmv
+    :special-members: __call__
+
+Laplacian kernel
+~~~~~~~~~~~~~~~~
+
+.. autoclass:: LaplacianKernel
+    :members: mmv, dmmv
+    :special-members: __call__
+
+
+Dot-Product kernels
+-------------------
+
+Polynomial kernel
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: PolynomialKernel
+    :members: mmv, dmmv
+    :special-members: __call__
+
+Linear kernel
+~~~~~~~~~~~~~
+
+.. autoclass:: LinearKernel
+    :members: mmv, dmmv
+    :special-members: __call__
+
+Sigmoid kernel
+~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: SigmoidKernel
+    :members: mmv, dmmv
+    :special-members: __call__
\ No newline at end of file
diff --git a/_sources/api_reference/mmv_ops.rst.txt b/_sources/api_reference/mmv_ops.rst.txt
new file mode 100644
index 00000000..18b7bb00
--- /dev/null
+++ b/_sources/api_reference/mmv_ops.rst.txt
@@ -0,0 +1,133 @@
+falkon.mmv_ops
+==============
+
+The algorithms to compute kernels and kernel-vector products blockwise on GPUs and CPU. The algorithms in this module
+are kernel agnostic. Refer to :mod:`falkon.kernels` for the actual kernel implementations.
+
+The KeOps wrapper only supports the `mmv` operation (kernel-vector products). The matrix-multiplication implementations
+instead support three different operations:
+
+ - `mm` which calculates the full kernel
+ - `mmv` which calculates kernel-vector products
+ - `dmmv` which calculates double kernel-vector products (which are operations like :math:`K^\top (K v)` where
+   :math:`K` is a kernel matrix and :math:`v` is a vector).
+
+.. automodule:: falkon.mmv_ops
+.. py:currentmodule:: falkon.mmv_ops
+
+run_keops_mmv
+-------------
+
+A thin wrapper to KeOps is provided to allow for block-splitting and multiple GPU usage. This only supports
+kernel-vector products.
+
+.. autofunction:: falkon.mmv_ops.keops.run_keops_mmv
+
+
+fmm_cpu
+-------
+
+Blocked kernel calculation on the CPU.
+
+.. autofunction:: falkon.mmv_ops.fmm_cpu.fmm_cpu
+
+
+fmm_cpu_sparse
+--------------
+
+Blocked kernel calculation on the CPU for sparse datasets.
+
+.. autofunction:: falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse
+
+
+fmm_cuda
+--------
+
+Blocked kernel calculation on GPUs.
+
+.. autofunction:: falkon.mmv_ops.fmm_cuda.fmm_cuda
+
+
+fmm_cuda_sparse
+---------------
+
+Blocked kernel calculation on GPUs for sparse datasets.
+
+.. autofunction:: falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse
+
+
+fmmv_cpu
+---------------
+
+Blocked kernel-vector products on CPU.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cpu.fmmv_cpu
+
+
+fmmv_cpu_sparse
+---------------
+
+Blocked kernel-vector products on CPU for sparse datasets.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse
+
+
+fdmmv_cpu
+---------------
+
+Blocked double kernel-vector products on CPU.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cpu.fdmmv_cpu
+
+
+fdmmv_cpu_sparse
+----------------
+
+Blocked double kernel-vector products on CPU for sparse datasets.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse
+
+
+fmmv_cuda
+----------------
+
+Blocked kernel-vector products on GPUs.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cuda.fmmv_cuda
+
+
+fmmv_cuda_sparse
+----------------
+
+Blocked kernel-vector products on GPUs for sparse datasets.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse
+
+
+fdmmv_cuda
+----------------
+
+Blocked double kernel-vector products on GPUs.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cuda.fdmmv_cuda
+
+
+fdmmv_cuda_sparse
+-----------------
+
+Blocked double kernel-vector products on GPUs for sparse datasets.
+
+.. autofunction:: falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse
+
+
+incore_fmmv
+-----------
+
+.. autofunction:: falkon.mmv_ops.fmmv_incore.incore_fmmv
+
+
+incore_fdmmv
+------------
+
+.. autofunction:: falkon.mmv_ops.fmmv_incore.incore_fdmmv
+
diff --git a/_sources/api_reference/models.rst.txt b/_sources/api_reference/models.rst.txt
new file mode 100644
index 00000000..05aecc37
--- /dev/null
+++ b/_sources/api_reference/models.rst.txt
@@ -0,0 +1,29 @@
+falkon.models
+=============
+
+.. automodule:: falkon.models
+.. currentmodule:: falkon.models
+
+
+Falkon
+------
+
+.. autoclass:: Falkon
+    :members:
+    :inherited-members:
+
+
+LogisticFalkon
+--------------
+
+.. autoclass:: LogisticFalkon
+    :members:
+    :inherited-members:
+
+
+InCoreFalkon
+------------
+
+.. autoclass:: InCoreFalkon
+    :members:
+    :inherited-members:
diff --git a/_sources/api_reference/optimization.rst.txt b/_sources/api_reference/optimization.rst.txt
new file mode 100644
index 00000000..fb063aaa
--- /dev/null
+++ b/_sources/api_reference/optimization.rst.txt
@@ -0,0 +1,27 @@
+falkon.optim
+============
+
+.. automodule:: falkon.optim
+.. py:currentmodule:: falkon.optim
+
+Optimizer
+---------
+
+.. autoclass:: Optimizer
+    :members:
+
+
+Conjugate gradient methods
+--------------------------
+
+ConjugateGradient
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: ConjugateGradient
+    :members:
+
+FalkonConjugateGradient
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: FalkonConjugateGradient
+    :members:
diff --git a/_sources/api_reference/options.rst.txt b/_sources/api_reference/options.rst.txt
new file mode 100644
index 00000000..964d820b
--- /dev/null
+++ b/_sources/api_reference/options.rst.txt
@@ -0,0 +1,14 @@
+.. _api_options:
+
+falkon.options
+==============
+
+.. automodule:: falkon.options
+.. py:currentmodule:: falkon.options
+
+
+FalkonOptions
+-------------
+
+.. autoclass:: FalkonOptions
+    :members:
diff --git a/_sources/api_reference/outofcore.rst.txt b/_sources/api_reference/outofcore.rst.txt
new file mode 100644
index 00000000..2cb30f75
--- /dev/null
+++ b/_sources/api_reference/outofcore.rst.txt
@@ -0,0 +1,25 @@
+falkon.ooc_ops
+==============
+
+The out-of-core algorithms for the Cholesky decomposition and the LAUUM operation are crucial for speeding up our
+library. To find out more about how they work, check the source code:
+
+ - `Out of core Cholesky <https://github.com/FalkonML/falkon/blob/master/falkon/ooc_ops/multigpu/cuda/multigpu_potrf.cu>`_ (CUDA code)
+ - `Out of core LAUUM <https://github.com/FalkonML/falkon/blob/master/falkon/ooc_ops/parallel_lauumm.py>`_ (Python code)
+
+The following functions provide a higher-level interface to the two operations.
+
+.. automodule:: falkon.ooc_ops
+.. py:currentmodule:: falkon.ooc_ops
+
+
+gpu_cholesky
+------------
+
+.. autofunction:: gpu_cholesky
+
+
+gpu_lauum
+---------
+
+.. autofunction:: gpu_lauum
diff --git a/_sources/api_reference/preconditioner.rst.txt b/_sources/api_reference/preconditioner.rst.txt
new file mode 100644
index 00000000..effcf028
--- /dev/null
+++ b/_sources/api_reference/preconditioner.rst.txt
@@ -0,0 +1,29 @@
+falkon.preconditioner
+=====================
+
+.. automodule:: falkon.preconditioner
+.. py:currentmodule:: falkon.preconditioner
+
+Preconditioner
+--------------
+
+.. autoclass:: falkon.preconditioner.preconditioner.Preconditioner
+    :members:
+
+
+Cholesky preconditioners
+------------------------
+
+FalkonPreconditioner
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: FalkonPreconditioner
+    :members:
+    :inherited-members: ABC
+
+LogisticPreconditioner
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: LogisticPreconditioner
+    :members:
+    :inherited-members: ABC
diff --git a/_sources/api_reference/sparse.rst.txt b/_sources/api_reference/sparse.rst.txt
new file mode 100644
index 00000000..13e13b26
--- /dev/null
+++ b/_sources/api_reference/sparse.rst.txt
@@ -0,0 +1,24 @@
+falkon.sparse
+=============
+
+.. automodule:: falkon.sparse
+.. currentmodule:: falkon.sparse
+
+SparseTensor
+------------
+
+.. autoclass:: falkon.sparse.sparse_tensor.SparseTensor
+    :members:
+
+.. autoclass:: falkon.sparse.sparse_tensor.SparseType
+    :members:
+
+
+Sparse operations
+-----------------
+
+.. autofunction:: sparse_matmul
+
+.. autofunction:: sparse_square_norm
+
+.. autofunction:: sparse_norm
diff --git a/_sources/examples/examples.rst.txt b/_sources/examples/examples.rst.txt
new file mode 100644
index 00000000..56f7d26f
--- /dev/null
+++ b/_sources/examples/examples.rst.txt
@@ -0,0 +1,34 @@
+.. _examples:
+
+Examples
+========
+
+Starting with simple kernel ridge regression, via classification, hyperparameter tuning, to large-scale GPU experiments,
+these notebooks cover all there is to know about Falkon.
+
+ - `Kernel ridge regression`_ goes through the basic notions of the library with a simple example;
+ - `Logistic Falkon tutorial`_ shows how to use the Logistic Falkon estimator, comparing the results with normal Falkon;
+ - `Hyperparameter tuning`_ is a fully worked out example of parameter optimization with Falkon for a real-world multi-class problem;
+ - `GPU training`_ details the options to maximize performance on multiple GPU systems.
+
+.. toctree::
+    :maxdepth: 1
+    :hidden:
+
+    ./simple_regression.ipynb
+    ./logistic_falkon.ipynb
+    ./hyperparameter_tuning.ipynb
+    ./performance_tuning.ipynb
+
+
+.. _Kernel ridge regression:
+    ./simple_regression.ipynb
+
+.. _Logistic Falkon tutorial:
+    ./logistic_falkon.ipynb
+
+.. _Hyperparameter tuning:
+    ./hyperparameter_tuning.ipynb
+
+.. _GPU training:
+    ./performance_tuning.ipynb
diff --git a/_sources/examples/hyperparameter_tuning.ipynb.txt b/_sources/examples/hyperparameter_tuning.ipynb.txt
new file mode 100644
index 00000000..aef681c6
--- /dev/null
+++ b/_sources/examples/hyperparameter_tuning.ipynb.txt
@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Hyperparameter Tuning with Falkon\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "We use Falkon for a multi-class problem (on the [digits](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits) dataset), showing how to integrate it into scikit-learn\n",
+    "for hyperparameter optimization.\n",
+    "\n",
+    "Since both `Falkon` and `LogisticFalkon` are estimators, and follow scikit-learn's API, integration is seamless."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection, metrics\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use the **digits** dataset, which is distributed alongside scikit-learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_digits(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "_ = ax.matshow(X[1].reshape((8, 8)), cmap='gray')\n",
+    "print(\"Label: %d\" % Y[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "As always with Falkon we must:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train)\n",
+    "Y_test = torch.from_numpy(Y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalizing the data is always a good idea. Here we use the global mean and standard deviation of the training set for z-score normalization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = X_train.mean()\n",
+    "train_std = X_train.std()\n",
+    "X_train -= train_mean\n",
+    "X_train /= train_std\n",
+    "X_test -= train_mean\n",
+    "X_test /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert labels to 1-hot\n",
+    "eye = torch.eye(10, dtype=torch.float32)\n",
+    "Y_train = eye[Y_train]\n",
+    "Y_test = eye[Y_test]\n",
+    "print(\"First label vector: \", Y_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search for the optimal parameters\n",
+    "\n",
+    "Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.\n",
+    "\n",
+    "Scikit-learn has great support for this, with the `GridSearchCV` class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.\n",
+    "\n",
+    "Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mclass_loss(true, pred):\n",
+    "    true = torch.argmax(true, dim=1)\n",
+    "    pred = torch.argmax(pred, dim=1)\n",
+    "    return torch.mean((true != pred).to(torch.float32))\n",
+    "mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter settings which will be explored by the grid-search are:\n",
+    " - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data)\n",
+    " - four different regularization values\n",
+    " - two different values for M: the number of inducing points. As we will see, a larger `M` is almost always better than a smaller one (but it leads to longer training times). \n",
+    "   Of course this is not the case if the dataset is easy to overfit, since reducing `M` may also provide additional regularization.\n",
+    "\n",
+    "When we create the estimator we pass it additional parameters via the `FalkonOptions` class.\n",
+    "In our case we want to ensure that the model runs on the CPU by setting `use_cpu=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/helpers.py:31: UserWarning: Failed to initialize cuBLAS library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "Error encountered was cublas library not found\n",
+      "  warnings.warn(get_error_str(\"cuBLAS\", e))\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameter_grid = {\n",
+    "    'kernel': [falkon.kernels.GaussianKernel(sigma=1), \n",
+    "               falkon.kernels.GaussianKernel(sigma=5), \n",
+    "               falkon.kernels.GaussianKernel(sigma=10),\n",
+    "               falkon.kernels.GaussianKernel(sigma=15),],\n",
+    "    'penalty': [1e-3, 1e-5, 1e-7, 1e-9],\n",
+    "    'M': [500, 1000],\n",
+    "}\n",
+    "estimator = falkon.Falkon(\n",
+    "    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000, maxiter=10,\n",
+    "    options=falkon.FalkonOptions(use_cpu=True))\n",
+    "\n",
+    "grid_search = model_selection.GridSearchCV(estimator, parameter_grid, mclass_scorer, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The best parameters are:  {'M': 1000, 'kernel': GaussianKernel(sigma=5.0), 'penalty': 1e-07}\n",
+      "CPU times: user 2min 5s, sys: 17 s, total: 2min 22s\n",
+      "Wall time: 35.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "grid_search.fit(X_train, Y_train)\n",
+    "print(\"The best parameters are: \", grid_search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluating the model\n",
+    "We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.14 s, sys: 143 ms, total: 1.29 s\n",
+      "Wall time: 323 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "flk = grid_search.best_estimator_\n",
+    "flk.fit(X_train, Y_train)\n",
+    "test_pred = flk.predict(X_test)\n",
+    "train_pred = flk.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training error: 0.00%\n",
+      "Test error: 1.11%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training error: %.2f%%\" % (mclass_loss(Y_train, train_pred) * 100))\n",
+    "print(\"Test error: %.2f%%\" % (mclass_loss(Y_test, test_pred) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot grid-search results\n",
+    "\n",
+    "Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful.\n",
+    "If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.\n",
+    "\n",
+    "In the plot red indicates a high error, while darker blue indicates a low error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_df = pd.DataFrame.from_dict(grid_search.cv_results_)\n",
+    "res_df[\"M\"] = res_df.params.apply(lambda x: x.get(\"M\"))\n",
+    "res_df[\"penalty\"] = res_df.params.apply(lambda x: x.get(\"penalty\"))\n",
+    "res_df[\"sigma\"] = res_df.params.apply(lambda x: x.get(\"kernel\").sigma.item())\n",
+    "res_df = res_df[[\"mean_test_score\", \"M\", \"penalty\", \"sigma\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_heatmap(ax, df, xlabel, ylabel, value, scale):\n",
+    "    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)\n",
+    "    cmap = plt.cm.get_cmap('coolwarm_r', 20)\n",
+    "    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])\n",
+    "    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)\n",
+    "    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)\n",
+    "    ax.set_xticklabels(piv.columns, minor=False)\n",
+    "    ax.set_yticklabels(piv.index, minor=False)\n",
+    "    ax.set_xlabel(xlabel)\n",
+    "    ax.set_ylabel(ylabel)\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
+    "scale = (res_df[\"mean_test_score\"].min(), res_df[\"mean_test_score\"].max())\n",
+    "\n",
+    "c = plot_heatmap(axs[0], res_df[res_df.M == 500], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "axs[0].set_title(\"M = 500\")\n",
+    "c = plot_heatmap(axs[1], res_df[res_df.M == 1000], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "_ = axs[1].set_title(\"M = 1000\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/_sources/examples/logistic_falkon.ipynb.txt b/_sources/examples/logistic_falkon.ipynb.txt
new file mode 100644
index 00000000..c157bfb6
--- /dev/null
+++ b/_sources/examples/logistic_falkon.ipynb.txt
@@ -0,0 +1,436 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introducing Logistic Falkon\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this notebook we use a synthetic dataset to show how the `LogisticFalkon` estimator works.\n",
+    "\n",
+    "We compare Falkon -- which is trained on the squared loss -- to its logistic loss version, and verify that on a binary classification problem logistic falkon achieves better accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use a synthetic dataset in two dimensions, which consists of two concentric circles labeled with different classes.\n",
+    "\n",
+    "We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 100000 samples to demonstrate the speed of the `Falkon` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.make_circles(\n",
+    "    n_samples=100_000, shuffle=False, noise=0.1, random_state=122)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 7))\n",
+    "ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.1, marker='.')\n",
+    "_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.1, marker='.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "The minimal preprocessing steps are:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)\n",
+    " 3. Reshape the labels so that they have two dimensions. This is not strictly necessary, but Falkon internally works with 2D tensors, so the output of the `Falkon.predict` method will always be 2D.\n",
+    " 4. Change the labels from 0, 1 to -1, 1. Note that Logistic Falkon uses the following formula for the logistic loss:\n",
+    "     $$\\log(1 + e^{-y_1 y_2})$$\n",
+    "     \n",
+    "     where $y_1$ and $y_2$ are labels and predictions respectively, which only makes sense if the labels are -1, 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train).to(dtype=torch.float32).reshape(-1, 1)\n",
+    "Y_test = torch.from_numpy(Y_test).to(dtype=torch.float32).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y_train[Y_train == 0] = -1\n",
+    "Y_test[Y_test == 0] = -1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the Falkon model\n",
+    "\n",
+    "We use the same base parameters for both models: a small amount of regularization ($10^{-7}$) and a Gaussian kernel with $\\sigma = 5$.\n",
+    "The number of inducing points is set to 1000 which is adequate for the problem which is very easy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binary_loss(true, pred):\n",
+    "    return torch.mean((true != torch.sign(pred)).to(torch.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
+     ]
+    }
+   ],
+   "source": [
+    "flk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "\n",
+    "flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Logistic Falkon model\n",
+    "\n",
+    "The logistic Falkon estimator uses the same base parameters as Falkon.\n",
+    "\n",
+    "However, instead of specifying a single value for regularization, we need to specify a *regularization path*: a series of decreasing amounts of regularization. \n",
+    "For each regularization value we also need to specify the *number of iterations* of conjugate gradient descent, which will be performed for that specific regularization value.\n",
+    "\n",
+    "We validated empirically on a wide number of binary classification problems that a good scheme to set the regularization path is to use three short (i.e. 3 iterations) runs with increasing regularization, and then a few longer (here we used 8 iterations) runs with the final regularization value (here `1e-7 `, the same as for Falkon).\n",
+    "\n",
+    "The `LogisticFalkon` estimator also accepts a mandatory `loss` parameter, which should be set to an instance of the `LogisticLoss` class. While the `LogisticLoss` is the only implemented loss at the moment, the learning algorithm is defined for any *generalized self-concordant* loss, and we plan to extend the library to support more functions.\n",
+    "\n",
+    "An additional feature we show here is **error monitoring**: By passing an error function to the estimator (see parameter `error_fn`), the estimator will print the training error at every iteration (how often such prints occur is governed by the `error_every` parameter).\n",
+    "This can be very useful to determine if it is possible to stop training early, and in general to monitor the training process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logflk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "logloss = falkon.gsc_losses.LogisticLoss(logflk_kernel)\n",
+    "\n",
+    "penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7, 1e-7]\n",
+    "iter_list = [3, 3, 3, 8, 8, 8]\n",
+    "\n",
+    "logflk = falkon.LogisticFalkon(\n",
+    "    kernel=logflk_kernel, penalty_list=penalty_list, iter_list=iter_list, M=1000, loss=logloss,\n",
+    "    error_fn=binary_loss, error_every=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train both models\n",
+    "\n",
+    "Training Falkon for 20 iterations (default value) takes about 20s on a laptop.\n",
+    "\n",
+    "Clearly, since the logistic falkon runs about 33 iterations (the sum of values in `iter_list`), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns.\n",
+    "On the same laptop, the logistic falkon algorithm takes around 50s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 41s, sys: 66.6 ms, total: 1min 41s\n",
+      "Wall time: 25.4 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Falkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f22222fc650>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-07, seed=None, weight_fn=None)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "flk.fit(X_train, Y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0 - penalty 1.000000e-03 - sub-iterations 3\n",
+      "Iteration   0 - Elapsed 4.02s - training loss 0.6910 - training error 0.2550 \n",
+      "Iteration 1 - penalty 1.000000e-05 - sub-iterations 3\n",
+      "Iteration   1 - Elapsed 9.15s - training loss 0.5639 - training error 0.1623 \n",
+      "Iteration 2 - penalty 1.000000e-07 - sub-iterations 3\n",
+      "Iteration   2 - Elapsed 14.68s - training loss 0.4032 - training error 0.1627 \n",
+      "Iteration 3 - penalty 1.000000e-07 - sub-iterations 8\n",
+      "Iteration   3 - Elapsed 27.14s - training loss 0.3696 - training error 0.1607 \n",
+      "Iteration 4 - penalty 1.000000e-07 - sub-iterations 8\n",
+      "Iteration   4 - Elapsed 39.60s - training loss 0.3655 - training error 0.1605 \n",
+      "Iteration 5 - penalty 1.000000e-07 - sub-iterations 8\n",
+      "Iteration   5 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
+      "Iteration   6 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
+      "CPU times: user 3min 46s, sys: 48 ms, total: 3min 46s\n",
+      "Wall time: 56.7 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LogisticFalkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f2222305590>, error_every=1, error_fn=<function binary_loss at 0x7f22a05ec950>, iter_list=[3, 3, 3, 8, 8, 8], kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07, 1e-07], seed=None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "logflk.fit(X_train, Y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing\n",
+    "\n",
+    "However, the price paid for with a higher training time, leads to lower training error.\n",
+    "\n",
+    "We found that, on a variety of binary classification datasets, logistic falkon obtains a slightly lower error than\n",
+    "the vanilla Falkon algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Falkon model          -- Error: 15.81%\n",
+      "Logistic Falkon model -- Error: 15.63%\n"
+     ]
+    }
+   ],
+   "source": [
+    "flk_pred = flk.predict(X_test)\n",
+    "flk_err = binary_loss(Y_test, flk_pred)\n",
+    "\n",
+    "logflk_pred = logflk.predict(X_test)\n",
+    "logflk_err = binary_loss(Y_test, logflk_pred)\n",
+    "\n",
+    "print(\"Falkon model          -- Error: %.2f%%\" % (flk_err * 100))\n",
+    "print(\"Logistic Falkon model -- Error: %.2f%%\" % (logflk_err * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot predictions\n",
+    "\n",
+    "In the plot we have the outer and inner circles which are correct predictions, and the circles in the middle which are the mispredicted points.\n",
+    "Since we added lots of noise to the dataset, perfect predictions are not possible (there is no clear boundary between the two classes).\n",
+    "\n",
+    "Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where a slightly lower classification error is desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_predictions(preds, ax):\n",
+    "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 0], \n",
+    "               X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 1], \n",
+    "               alpha=0.1, marker='.', color='b')\n",
+    "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),0], \n",
+    "               X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),1], \n",
+    "               alpha=0.1, marker='.', color='r')\n",
+    "\n",
+    "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 0], \n",
+    "               X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 1], \n",
+    "               alpha=0.2, marker='x', color='b')\n",
+    "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),0], \n",
+    "               X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),1], \n",
+    "               alpha=0.2, marker='x', color='r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(ncols=2, figsize=(13, 5))\n",
+    "plot_predictions(flk_pred, ax[0])\n",
+    "ax[0].set_title(\"Falkon\")\n",
+    "plot_predictions(logflk_pred, ax[1])\n",
+    "_ = ax[1].set_title(\"Logistic Falkon\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/examples/performance_tuning.ipynb.txt b/_sources/examples/performance_tuning.ipynb.txt
new file mode 100644
index 00000000..0be2e993
--- /dev/null
+++ b/_sources/examples/performance_tuning.ipynb.txt
@@ -0,0 +1,49 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Training on the GPU\n",
+    "\n",
+    "This notebook will be available soon"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/_sources/examples/simple_regression.ipynb.txt b/_sources/examples/simple_regression.ipynb.txt
new file mode 100644
index 00000000..68b25646
--- /dev/null
+++ b/_sources/examples/simple_regression.ipynb.txt
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Falkon Regression Tutorial\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook introduces the main interface of the Falkon library, \n",
+    "using a toy regression problem.\n",
+    "\n",
+    "We will be using the Boston housing dataset which is included in `scikit-learn` to train a Falkon model.\n",
+    "Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions.\n",
+    "The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.\n",
+    "\n",
+    "After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test \n",
+    "set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the [model_selection module](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_boston(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "num_train = int(X.shape[0] * 0.8)\n",
+    "num_test = X.shape[0] - num_train\n",
+    "shuffle_idx = np.arange(X.shape[0])\n",
+    "np.random.shuffle(shuffle_idx)\n",
+    "train_idx = shuffle_idx[:num_train]\n",
+    "test_idx = shuffle_idx[num_train:]\n",
+    "\n",
+    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
+    "Xtest, Ytest = X[test_idx], Y[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Pre-process the data\n",
+    "\n",
+    "We must convert the numpy arrays to PyTorch tensors before using them in Falkon.\n",
+    "This is very easy and fast with the `torch.from_numpy` function.\n",
+    "\n",
+    "Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data:\n",
+    "convert it to have zero-mean and unit standard deviation.\n",
+    "We use the statistics of the training data to avoid leakage between the two sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# convert numpy -> pytorch\n",
+    "Xtrain = torch.from_numpy(Xtrain)\n",
+    "Xtest = torch.from_numpy(Xtest)\n",
+    "Ytrain = torch.from_numpy(Ytrain)\n",
+    "Ytest = torch.from_numpy(Ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = Xtrain.mean(0, keepdim=True)\n",
+    "train_std = Xtrain.std(0, keepdim=True)\n",
+    "Xtrain -= train_mean\n",
+    "Xtrain /= train_std\n",
+    "Xtest -= train_mean\n",
+    "Xtest /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Create the Falkon model\n",
+    "\n",
+    "The Falkon object is the main API of this library. \n",
+    "It is similar in spirit to the fit-transform API of scikit-learn, while supporting some\n",
+    "additional features such as monitoring of validation error.\n",
+    "\n",
+    "While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much \n",
+    "larger datasets.\n",
+    "Here we only showcase some of the more basic options.\n",
+    "\n",
+    "Mandatory parameters are:\n",
+    " - the kernel function (here we use a linear kernel)\n",
+    " - the amount of regularization, which we set to some small positive value\n",
+    " - the number of inducing points M. \n",
+    "   Since the dataset is already very small, we use set `M=N` (every data-point is an inducing point). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
+     ]
+    }
+   ],
+   "source": [
+    "options = falkon.FalkonOptions()\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=5)\n",
+    "flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=Xtrain.shape[0], options=options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Training the model\n",
+    "\n",
+    "The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are\n",
+    "two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed.\n",
+    "To gain more insight in the various steps of the algorithm you can pass `debug=True` when creating the Falkon object. \n",
+    "\n",
+    "Model training will occur on the GPU, if it is available, and CUDA is properly installed, \n",
+    "or on the CPU as a fallback. \n",
+    "\n",
+    "If KeOps has been installed alongside Falkon, the first run may take\n",
+    "a bit longer due to the compilation step of KeOps (to avoid using KeOps you can pass the argument `keops_active=False` to the `FalkonOptions` object)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stopping conjugate gradient descent at iteration 1. Solution has converged.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Falkon(M=404, center_selection=<falkon.center_selection.UniformSelector object at 0x7f31c8279190>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-05, seed=None, weight_fn=None)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flk.fit(Xtrain, Ytrain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Evaluating model performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the `predict` method to obtain predictions on new data.\n",
+    "\n",
+    "Here we print the error on both train and test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training RMSE: 1.794\n",
+      "Test RMSE: 3.534\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_pred = flk.predict(Xtrain).reshape(-1, )\n",
+    "test_pred = flk.predict(Xtest).reshape(-1, )\n",
+    "\n",
+    "def rmse(true, pred):\n",
+    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
+    "\n",
+    "print(\"Training RMSE: %.3f\" % (rmse(train_pred, Ytrain)))\n",
+    "print(\"Test RMSE: %.3f\" % (rmse(test_pred, Ytest)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f3148f13a50>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(Ytest, alpha=0.7, label=\"True\")\n",
+    "ax.hist(test_pred, alpha=0.7, label=\"Pred\")\n",
+    "ax.legend(loc=\"best\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/_sources/get_started.rst.txt b/_sources/get_started.rst.txt
new file mode 100644
index 00000000..db1b63e3
--- /dev/null
+++ b/_sources/get_started.rst.txt
@@ -0,0 +1,53 @@
+.. _get_started:
+
+Getting Started
+===============
+
+Once Falkon is installed, getting started is easy. The basic setup to use the `Falkon` estimator only requires
+few lines of code:
+
+.. code-block:: python
+
+    import torch
+    from sklearn.datasets import load_boston
+    from falkon import Falkon, kernels
+
+    X, Y = load_boston(return_X_y=True)
+    X = torch.from_numpy(X)
+    Y = torch.from_numpy(Y).reshape(-1, 1)
+
+    kernel = kernels.GaussianKernel(sigma=1.0)
+    model = Falkon(
+        kernel=kernel,
+        penalty=1e-6,
+        M=100,
+    )
+    model.fit(X, Y)
+    predictions = model.predict(X)
+
+
+Passing Options
+~~~~~~~~~~~~~~~
+
+A number of different options exist for both the `Falkon` and `LogisticFalkon` estimators (see :ref:`falkon.Options <api_options>`).
+All options can be passed to the estimator through the `FalkonOptions` class, like so:
+
+.. code-block:: python
+
+    from falkon import FalkonOptions, Falkon, kernels
+
+    # Options to: increase the amount of output information; avoid using the KeOps library
+    options = FalkonOptions(debug=True, no_keops=True)
+    kernel = kernels.GaussianKernel(sigma=1.0)
+
+    model = Falkon(kernel=kernel,
+                   penalty=1e-6,
+                   M=100,
+                   maxiter=10,   # Set the maximum number of conjugate gradient iterations to 10
+                   options=options)
+
+
+More Examples
+~~~~~~~~~~~~~
+
+For more detailed examples, have a look at the :ref:`example notebooks <examples>`.
\ No newline at end of file
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
new file mode 100644
index 00000000..a72bb123
--- /dev/null
+++ b/_sources/index.rst.txt
@@ -0,0 +1,62 @@
+.. falkon documentation master file, created by
+   sphinx-quickstart on Thu Jun 18 10:38:58 2020.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+==================================
+Falkon
+==================================
+A Python library for large-scale kernel methods, with optional (multi-)GPU acceleration.
+
+The library currently includes two solvers:
+one for approximate kernel ridge regression [2]_ which is extremely fast, and one for kernel logistic
+regression [3]_ which trades off lower speed for better accuracy on binary classification problems.
+
+The main features of Falkon are:
+
+ * *Full multi-GPU support* - All compute-intensive parts of the algorithms are multi-GPU capable.
+ * *Extreme scalability* - Unlike other kernel solvers, we keep memory usage in check. We have tested the library with
+   datasets of billions of points.
+ * *Sparse data support*
+ * *Scikit-learn integration* - Our estimators follow the scikit-learn API
+
+For more details about the algorithms used, you can read our paper [1]_, or look at the source code
+`github.com/FalkonML/falkon <https://github.com/falkonml/falkon>`_ and at the :ref:`documentation <api_reference>`.
+Also, make sure to follow :ref:`the example notebooks <examples>` to find out about all of Falkon's features.
+
+Falkon is built on top of `PyTorch <https://pytorch.org/>`__ which is used to support both CPU and GPU tensor
+calculations, and `KeOps <https://www.kernel-operations.io/keops/index.html>`__ for fast kernel evaluations on the GPU.
+
+If you find this library useful for your research, please cite us [1]_!
+
+Contents
+========
+
+.. toctree::
+   :maxdepth: 3
+
+   install
+   get_started
+   examples/examples
+   api_reference/index
+
+
+References
+==========
+
+.. [1] Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+       "Kernel methods through the roof: handling billions of points efficiently,"
+       arXiv:2006.10350, 2020.
+.. [2] Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, "FALKON: An optimal large
+       scale kernel method," Advances in Neural Information Processing Systems, 2017.
+.. [3] Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, "Globally Convergent
+      Newton Methods for Ill-conditioned Generalized Self-concordant Losses,"
+      Advances in Neural Information Processing Systems, 2019
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt
new file mode 100644
index 00000000..34430468
--- /dev/null
+++ b/_sources/install.rst.txt
@@ -0,0 +1,72 @@
+.. _install:
+
+Install
+=======
+
+Supported Platforms
+-------------------
+
+Falkon is only tested on Linux.
+
+GPU support is achieved via CUDA, so using Falkon from Windows will be tricky.
+
+Using the library from the CPU only is much more portable since there is no CUDA requirement. CPU-only Falkon
+is thus likely to work on Windows and Mac OS easily.
+
+Prerequisites
+-------------
+
+PyTorch and CUDA
+~~~~~~~~~~~~~~~~
+Falkon depends on PyTorch, and on the NVIDIA Toolkit (for NVIDIA GPU support) which is usually installed
+alongside PyTorch.
+PyTorch can be installed in several ways by following the instructions at `Install PyTorch <https://pytorch.org/get-started/locally/>`__.
+If GPU support is desired, make sure that PyTorch CUDA bindings are working:
+
+.. code-block:: python
+
+    import torch
+    assert torch.cuda.is_available()
+
+Intel MKL
+~~~~~~~~~
+If Falkon is not installed with GPU support, it will try to link to `Intel MKL <https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html>`__
+to speed-up certain sparse operations. MKL shared libraries are usually distributed with numpy, so this should not be a problem.
+In case sparse matrix support is not needed, the MKL library will not be loaded.
+
+KeOps
+~~~~~
+Falkon uses a patched version of `KeOps <https://www.kernel-operations.io/keops/index.html>`__, therefore until the
+patches are merged in the KeOps library, users will need to uninstall existing KeOps installations (if present), and
+install our patched version.
+
+The patched version is available as a submodule of the Falkon repository. To install it, you can do the following:
+
+.. code-block:: bash
+
+    $ git clone --recurse-submodules https://github.com/FalkonML/falkon.git
+    $ cd falkon/
+    $ pip install ./keops
+
+
+Installing
+----------
+
+After having installed KeOps, you are ready to install Falkon with `pip` (from the root of the git tree):
+
+.. code-block:: bash
+
+   $ pip install .
+
+This will take a while since there are both Cython extensions which need to be compiled (if Cython is installed),
+and PyTorch extensions which take some time to compile.
+
+
+Testing the installation
+------------------------
+
+To check that everything works correctly you can follow the `Kernel ridge regression`_ notebook.
+
+
+.. _Kernel ridge regression:
+    examples/simple_regression.ipynb
diff --git a/_static/basic.css b/_static/basic.css
new file mode 100644
index 00000000..be19270e
--- /dev/null
+++ b/_static/basic.css
@@ -0,0 +1,856 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
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+div.sphinxsidebar ul ul {
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+    margin-bottom: 0;
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+div.sphinxsidebar form {
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+div.sphinxsidebar input {
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+    font-family: sans-serif;
+    font-size: 1em;
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+div.sphinxsidebar #searchbox form.search {
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+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
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+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li div.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
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+table.contentstable p.biglink {
+    line-height: 150%;
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+a.biglink {
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+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
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+
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+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
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+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
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+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 450px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
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+a.brackets:before,
+span.brackets > a:before{
+    content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+    content: "]";
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
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+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
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+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
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+div.body td {
+    text-align: left;
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+.first {
+    margin-top: 0 !important;
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+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
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+
+img.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
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+    margin-left: auto;
+    margin-right: auto;
+}
+
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+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
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+    border: 0 !important;
+}
+
+th {
+    text-align: left;
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+}
+
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+    margin-left: 1px;
+}
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+th > :first-child,
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+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
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+}
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+}
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+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
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+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
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+}
+
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+ul.simple > li:not(:first-child) > p {
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+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
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+dl.citation > dt {
+    float: left;
+    margin-right: 0.5em;
+}
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+dl.footnote > dd,
+dl.citation > dd {
+    margin-bottom: 0em;
+}
+
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+    clear: both;
+}
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+}
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+}
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+    margin-bottom: 0em;
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+    margin-bottom: 15px;
+}
+
+dd > :first-child {
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+}
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+dd ul, dd table {
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+}
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+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
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+}
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+}
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+}
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+    padding: 5px;
+    border: 3px solid red;
+}
+
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+}
+
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+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
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+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
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+}
+
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+}
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+}
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+    margin: 0.5em;
+    content: ":";
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
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+    -ms-hyphens: none;
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+}
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+}
+
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+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
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+table.highlighttable {
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+}
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+}
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+}
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+    padding: 0;
+}
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+}
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+    overflow: hidden;
+}
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+}
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+div.highlight pre,
+table.highlighttable pre {
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+}
+
+div.code-block-caption + div {
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+}
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+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
+    user-select: none;
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
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+}
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+    font-size: 1.2em;
+}
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+code.descclassname {
+    background-color: transparent;
+}
+
+code.xref, a code {
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+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css
new file mode 100644
index 00000000..e380325b
--- /dev/null
+++ b/_static/css/badge_only.css
@@ -0,0 +1 @@
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new file mode 100644
index 00000000..855c845e
--- /dev/null
+++ b/_static/css/fonts/fontawesome-webfont.svg
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+ By ,,,
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diff --git a/_static/css/theme.css b/_static/css/theme.css
new file mode 100644
index 00000000..8cd4f101
--- /dev/null
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diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 00000000..61ac9d26
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,321 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for all documentation.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+  if (!x) {
+    return x
+  }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s === 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node, addItems) {
+    if (node.nodeType === 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 &&
+          !jQuery(node.parentNode).hasClass(className) &&
+          !jQuery(node.parentNode).hasClass("nohighlight")) {
+        var span;
+        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+        if (isInSVG) {
+          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+        } else {
+          span = document.createElement("span");
+          span.className = className;
+        }
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
+        if (isInSVG) {
+          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+          var bbox = node.parentElement.getBBox();
+          rect.x.baseVal.value = bbox.x;
+          rect.y.baseVal.value = bbox.y;
+          rect.width.baseVal.value = bbox.width;
+          rect.height.baseVal.value = bbox.height;
+          rect.setAttribute('class', className);
+          addItems.push({
+              "parent": node.parentNode,
+              "target": rect});
+        }
+      }
+    }
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this, addItems);
+      });
+    }
+  }
+  var addItems = [];
+  var result = this.each(function() {
+    highlight(this, addItems);
+  });
+  for (var i = 0; i < addItems.length; ++i) {
+    jQuery(addItems[i].parent).before(addItems[i].target);
+  }
+  return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
+      this.initOnKeyListeners();
+    }
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
+  LOCALE : 'unknown',
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated === 'undefined')
+      return string;
+    return (typeof translated === 'string') ? translated : translated[0];
+  },
+
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated === 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
+  },
+
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
+  },
+
+  /**
+   * add context elements like header anchor links
+   */
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
+  },
+
+  /**
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   */
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash && $.browser.mozilla)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
+  },
+
+  /**
+   * highlight the search words provided in the url in the text
+   */
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
+  },
+
+  /**
+   * init the domain index toggle buttons
+   */
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) === 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+  },
+
+  /**
+   * make the url absolute
+   */
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  },
+
+  /**
+   * get the current relative url
+   */
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this === '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  },
+
+  initOnKeyListeners: function() {
+    $(document).keydown(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box, textarea, dropdown or button
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
+          && !event.shiftKey) {
+        switch (event.keyCode) {
+          case 37: // left
+            var prevHref = $('link[rel="prev"]').prop('href');
+            if (prevHref) {
+              window.location.href = prevHref;
+              return false;
+            }
+          case 39: // right
+            var nextHref = $('link[rel="next"]').prop('href');
+            if (nextHref) {
+              window.location.href = nextHref;
+              return false;
+            }
+        }
+      }
+    });
+  }
+};
+
+// quick alias for translations
+_ = Documentation.gettext;
+
+$(document).ready(function() {
+  Documentation.init();
+});
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 00000000..567cdf71
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,12 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: '0.6.3',
+    LANGUAGE: 'None',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false
+};
\ No newline at end of file
diff --git a/_static/file.png b/_static/file.png
new file mode 100644
index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3
GIT binary patch
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z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHD<M{a4P!N^sPbQKi=?mBx
zoos%BSoiGXjr-;%$QixXMOVNSUNp6L0a1Oz&cgu)wqE?07u5I7qrQIu4Fij)Y3c&0
z@0u_#NH6I?Mk(n;dT}d~^J<WkTLqp|RW-hV56tKpXqu)k@V{?amI+5DOlEU@funz+
kySsbM>fiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8(

literal 0
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diff --git a/_static/jquery-3.5.1.js b/_static/jquery-3.5.1.js
new file mode 100644
index 00000000..50937333
--- /dev/null
+++ b/_static/jquery-3.5.1.js
@@ -0,0 +1,10872 @@
+/*!
+ * jQuery JavaScript Library v3.5.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2020-05-04T22:49Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.5.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.5
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2020-03-14
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/_static/jquery.js b/_static/jquery.js
new file mode 100644
index 00000000..b0614034
--- /dev/null
+++ b/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var 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+++ b/_static/js/html5shiv-printshiv.min.js
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+* @preserve HTML5 Shiv 3.7.3-pre | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=y.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=y.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),y.elements=c+" "+a,j(b)}function f(a){var b=x[a[v]];return b||(b={},w++,a[v]=w,x[w]=b),b}function g(a,c,d){if(c||(c=b),q)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():u.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||t.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),q)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return y.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(y,b.frag)}function j(a){a||(a=b);var d=f(a);return!y.shivCSS||p||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),q||i(a,d),a}function k(a){for(var b,c=a.getElementsByTagName("*"),e=c.length,f=RegExp("^(?:"+d().join("|")+")$","i"),g=[];e--;)b=c[e],f.test(b.nodeName)&&g.push(b.applyElement(l(b)));return g}function l(a){for(var b,c=a.attributes,d=c.length,e=a.ownerDocument.createElement(A+":"+a.nodeName);d--;)b=c[d],b.specified&&e.setAttribute(b.nodeName,b.nodeValue);return e.style.cssText=a.style.cssText,e}function m(a){for(var b,c=a.split("{"),e=c.length,f=RegExp("(^|[\\s,>+~])("+d().join("|")+")(?=[[\\s,>+~#.:]|$)","gi"),g="$1"+A+"\\:$2";e--;)b=c[e]=c[e].split("}"),b[b.length-1]=b[b.length-1].replace(f,g),c[e]=b.join("}");return c.join("{")}function n(a){for(var b=a.length;b--;)a[b].removeNode()}function o(a){function b(){clearTimeout(g._removeSheetTimer),d&&d.removeNode(!0),d=null}var d,e,g=f(a),h=a.namespaces,i=a.parentWindow;return!B||a.printShived?a:("undefined"==typeof h[A]&&h.add(A),i.attachEvent("onbeforeprint",function(){b();for(var f,g,h,i=a.styleSheets,j=[],l=i.length,n=Array(l);l--;)n[l]=i[l];for(;h=n.pop();)if(!h.disabled&&z.test(h.media)){try{f=h.imports,g=f.length}catch(o){g=0}for(l=0;g>l;l++)n.push(f[l]);try{j.push(h.cssText)}catch(o){}}j=m(j.reverse().join("")),e=k(a),d=c(a,j)}),i.attachEvent("onafterprint",function(){n(e),clearTimeout(g._removeSheetTimer),g._removeSheetTimer=setTimeout(b,500)}),a.printShived=!0,a)}var p,q,r="3.7.3",s=a.html5||{},t=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,u=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,v="_html5shiv",w=0,x={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",p="hidden"in a,q=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){p=!0,q=!0}}();var y={elements:s.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:r,shivCSS:s.shivCSS!==!1,supportsUnknownElements:q,shivMethods:s.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=y,j(b);var z=/^$|\b(?:all|print)\b/,A="html5shiv",B=!q&&function(){var c=b.documentElement;return!("undefined"==typeof b.namespaces||"undefined"==typeof b.parentWindow||"undefined"==typeof c.applyElement||"undefined"==typeof c.removeNode||"undefined"==typeof a.attachEvent)}();y.type+=" print",y.shivPrint=o,o(b),"object"==typeof module&&module.exports&&(module.exports=y)}("undefined"!=typeof window?window:this,document);
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diff --git a/_static/js/html5shiv.min.js b/_static/js/html5shiv.min.js
new file mode 100644
index 00000000..cd1c674f
--- /dev/null
+++ b/_static/js/html5shiv.min.js
@@ -0,0 +1,4 @@
+/**
+* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
+*/
+!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3-pre",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b),"object"==typeof module&&module.exports&&(module.exports=t)}("undefined"!=typeof window?window:this,document);
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diff --git a/_static/js/theme.js b/_static/js/theme.js
new file mode 100644
index 00000000..839d07e2
--- /dev/null
+++ b/_static/js/theme.js
@@ -0,0 +1 @@
+!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return n[i].call(o.exports,o,o.exports,t),o.l=!0,o.exports}t.m=n,t.c=e,t.d=function(n,e,i){t.o(n,e)||Object.defineProperty(n,e,{enumerable:!0,get:i})},t.r=function(n){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(n,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(n,"__esModule",{value:!0})},t.t=function(n,e){if(1&e&&(n=t(n)),8&e)return n;if(4&e&&"object"==typeof n&&n&&n.__esModule)return n;var i=Object.create(null);if(t.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:n}),2&e&&"string"!=typeof n)for(var o in n)t.d(i,o,function(e){return n[e]}.bind(null,o));return i},t.n=function(n){var e=n&&n.__esModule?function(){return n.default}:function(){return n};return t.d(e,"a",e),e},t.o=function(n,e){return Object.prototype.hasOwnProperty.call(n,e)},t.p="",t(t.s=0)}([function(n,e,t){t(1),n.exports=t(3)},function(n,e,t){(function(){var e="undefined"!=typeof window?window.jQuery:t(2);n.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(n){var t=this;void 0===n&&(n=!0),t.isRunning||(t.isRunning=!0,e((function(e){t.init(e),t.reset(),t.win.on("hashchange",t.reset),n&&t.win.on("scroll",(function(){t.linkScroll||t.winScroll||(t.winScroll=!0,requestAnimationFrame((function(){t.onScroll()})))})),t.win.on("resize",(function(){t.winResize||(t.winResize=!0,requestAnimationFrame((function(){t.onResize()})))})),t.onResize()})))},enableSticky:function(){this.enable(!0)},init:function(n){n(document);var e=this;this.navBar=n("div.wy-side-scroll:first"),this.win=n(window),n(document).on("click","[data-toggle='wy-nav-top']",(function(){n("[data-toggle='wy-nav-shift']").toggleClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift")})).on("click",".wy-menu-vertical .current ul li a",(function(){var t=n(this);n("[data-toggle='wy-nav-shift']").removeClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift"),e.toggleCurrent(t),e.hashChange()})).on("click","[data-toggle='rst-current-version']",(function(){n("[data-toggle='rst-versions']").toggleClass("shift-up")})),n("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),n("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),n("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n('<span class="toctree-expand"></span>'),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}t.length>0&&($(".wy-menu-vertical .current").removeClass("current"),t.addClass("current"),t.closest("li.toctree-l1").addClass("current"),t.closest("li.toctree-l1").parent().addClass("current"),t.closest("li.toctree-l1").addClass("current"),t.closest("li.toctree-l2").addClass("current"),t.closest("li.toctree-l3").addClass("current"),t.closest("li.toctree-l4").addClass("current"),t.closest("li.toctree-l5").addClass("current"),t[0].scrollIntoView())}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current"),e.siblings().find("li.current").removeClass("current"),e.find("> ul li.current").removeClass("current"),e.toggleClass("current")}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return n=i+o,r}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()}).call(window)},function(n,e){n.exports=jQuery},function(n,e,t){}]);
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diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 00000000..863704b3
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,297 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+
diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d
GIT binary patch
literal 90
zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj
kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK

literal 0
HcmV?d00001

diff --git a/_static/plus.png b/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
GIT binary patch
literal 90
zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu>-2
m3q%Vub%g%s<8sJhVPMczOq}xhg9DJoz~JfX=d#Wzp$Pyb1r*Kz

literal 0
HcmV?d00001

diff --git a/_static/pygments.css b/_static/pygments.css
new file mode 100644
index 00000000..20c4814d
--- /dev/null
+++ b/_static/pygments.css
@@ -0,0 +1,69 @@
+.highlight .hll { background-color: #ffffcc }
+.highlight  { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 00000000..1a90152e
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,522 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+if (!Scorer) {
+  /**
+   * Simple result scoring code.
+   */
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [filename, title, anchor, descr, score]
+    // and returns the new score.
+    /*
+    score: function(result) {
+      return result[4];
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {0:  15,   // used to be importantResults
+              1:  5,   // used to be objectResults
+              2: -5},  // used to be unimportantResults
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2
+  };
+}
+
+if (!splitQuery) {
+  function splitQuery(query) {
+    return query.split(/\s+/);
+  }
+}
+
+/**
+ * Search Module
+ */
+var Search = {
+
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  htmlToText : function(htmlString) {
+      var virtualDocument = document.implementation.createHTMLDocument('virtual');
+      var htmlElement = $(htmlString, virtualDocument);
+      htmlElement.find('.headerlink').remove();
+      docContent = htmlElement.find('[role=main]')[0];
+      if(docContent === undefined) {
+          console.warn("Content block not found. Sphinx search tries to obtain it " +
+                       "via '[role=main]'. Could you check your theme or template.");
+          return "";
+      }
+      return docContent.textContent || docContent.innerText;
+  },
+
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
+  },
+
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
+  },
+
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
+    }
+  },
+
+  hasIndex : function() {
+      return this._index !== null;
+  },
+
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
+
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
+
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch : function(query) {
+    // create the required interface elements
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
+
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query : function(query) {
+    var i;
+
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
+      }
+
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
+      // stem the word
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      // prevent stemmer from cutting word smaller than two chars
+      if(word.length < 3 && tmp[i].length >= 3) {
+        word = tmp[i];
+      }
+      var toAppend;
+      // select the correct list
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
+      else {
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
+      }
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
+
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
+
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
+
+    // lookup as object
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
+
+    // lookup as search terms in fulltext
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
+        // same score: sort alphabetically
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+      }
+    });
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    //console.info('search results:', Search.lastresults);
+
+    // print the results
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li></li>');
+        var requestUrl = "";
+        var linkUrl = "";
+        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
+          linkUrl = requestUrl;
+
+        } else {
+          // normal html builders
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+        }
+        listItem.append($('<a/>').attr('href',
+            linkUrl +
+            highlightstring + item[2]).html(item[1]));
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+          $.ajax({url: requestUrl,
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
+                    }
+                    Search.output.append(listItem);
+                    setTimeout(function() {
+                      displayNextItem();
+                    }, 5);
+                  }});
+        } else {
+          // no source available, just display title
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
+
+    var i;
+    var results = [];
+
+    for (var prefix in objects) {
+      for (var name in objects[prefix]) {
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        var fullnameLower = fullname.toLowerCase()
+        if (fullnameLower.indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullnameLower.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullnameLower == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var match = objects[prefix][name];
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
+
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
+      }
+    }
+
+    return results;
+  },
+
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
+   */
+  escapeRegExp : function(string) {
+    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
+
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
+
+    // perform the search on the required terms
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        var word_regex = this.escapeRegExp(word);
+        for (var w in terms) {
+          if (w.match(word_regex) && !terms[word]) {
+            _o.push({files: terms[w], score: Scorer.partialTerm})
+          }
+        }
+        for (var w in titleterms) {
+          if (w.match(word_regex) && !titleterms[word]) {
+              _o.push({files: titleterms[w], score: Scorer.partialTitle})
+          }
+        }
+      }
+
+      // no match but word was a required one
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
+      // found search word in contents
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
+
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
+
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {};
+          scoreMap[file][word] = o.score;
+        }
+      });
+
+      // create the mapping
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap && fileMap[file].indexOf(word) === -1)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
+
+    // now check if the files don't contain excluded terms
+    for (file in fileMap) {
+      var valid = true;
+
+      // check if all requirements are matched
+      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
+        searchterms.filter(function(term){return term.length > 2}).length
+      if (
+        fileMap[file].length != searchterms.length &&
+        fileMap[file].length != filteredTermCount
+      ) continue;
+
+      // ensure that none of the excluded terms is in the search result
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
+
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words, hlwords is the list of normal, unstemmed
+   * words. the first one is used to find the occurrence, the
+   * latter for highlighting it.
+   */
+  makeSearchSummary : function(htmlText, keywords, hlwords) {
+    var text = Search.htmlToText(htmlText);
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<div class="context"></div>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
+};
+
+$(document).ready(function() {
+  Search.init();
+});
diff --git a/_static/underscore-1.12.0.js b/_static/underscore-1.12.0.js
new file mode 100644
index 00000000..3af6352e
--- /dev/null
+++ b/_static/underscore-1.12.0.js
@@ -0,0 +1,2027 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.12.0
+  //     https://underscorejs.org
+  //     (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.12.0';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _(obj) {
+    if (obj instanceof _) return obj;
+    if (!(this instanceof _)) return new _(obj);
+    this._wrapped = obj;
+  }
+
+  _.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _.prototype.valueOf = _.prototype.toJSON = _.prototype.value;
+
+  _.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _) a = a._wrapped;
+    if (b instanceof _) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath(path) {
+    return isArray(path) ? path : [path];
+  }
+  _.toPath = toPath;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath$1(path) {
+    return _.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath$1(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has$1(obj, path) {
+    path = toPath$1(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath$1(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_.iteratee !== iteratee) return _.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    // If a variable is not specified, place data values in local scope.
+    if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n';
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(settings.variable || 'obj', '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    var argument = settings.variable || 'obj';
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath$1(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath$1(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten$1(array, depth) {
+    return flatten(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _[name] = obj[name];
+      _.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_, args));
+      };
+    });
+    return _;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has$1,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten$1,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _$1 = mixin(allExports);
+  // Legacy Node.js API.
+  _$1._ = _$1;
+
+  return _$1;
+
+})));
+//# sourceMappingURL=underscore.js.map
diff --git a/_static/underscore.js b/_static/underscore.js
new file mode 100644
index 00000000..166240ef
--- /dev/null
+++ b/_static/underscore.js
@@ -0,0 +1,6 @@
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n=n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
+//     Underscore.js 1.12.0
+//     https://underscorejs.org
+//     (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+//     Underscore may be freely distributed under the MIT license.
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diff --git a/api_reference/center_selector.html b/api_reference/center_selector.html
new file mode 100644
index 00000000..9f6237ac
--- /dev/null
+++ b/api_reference/center_selector.html
@@ -0,0 +1,601 @@
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+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fixedselector">FixedSelector</a></li>
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+<section id="module-falkon.center_selection">
+<span id="falkon-center-selection"></span><h1>falkon.center_selection<a class="headerlink" href="#module-falkon.center_selection" title="Permalink to this headline">¶</a></h1>
+<section id="centerselector">
+<h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.center_selection.CenterSelector">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></code><code class="sig-name descname"><span class="pre">CenterSelector</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.CenterSelector" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create the center selector with a random number generator</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>random_gen</strong> – A numpy random number generator object or a random seed.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.center_selection.CenterSelector.select">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">select</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The full input dataset (or a representation of it)</p></li>
+<li><p><strong>Y</strong> – The full input labels (this may be None)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>X_centers</em> – If Y is None this is the only output: M centers selected from the data.</p></li>
+<li><p><em>Y_centers</em> – If Y is not empty, a set of label centers shall be returned as well.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.center_selection.CenterSelector.select_indices">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">select_indices</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select_indices" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The full input dataset (or a representation of it)</p></li>
+<li><p><strong>Y</strong> – The full input labels (this may be None)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>X_centers</em> – If Y is None this is the only output: M centers selected from the data.</p></li>
+<li><p><em>Y_centers</em> – If Y is not empty, a set of label centers shall be returned as well.</p></li>
+<li><p><em>indices</em> – The indices in X associated with the chosen centers. Subclasses may not implement
+this method, in which case a <cite>NotImplementedError</cite> will be raised.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="uniformselector">
+<h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.center_selection.UniformSelector">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></code><code class="sig-name descname"><span class="pre">UniformSelector</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.UniformSelector" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.center_selection.CenterSelector</span></code></a></p>
+<p>Center selector which chooses from the full dataset uniformly at random (without replacement)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>random_gen</strong> – A numpy random number generator object or a random seed.</p></li>
+<li><p><strong>num_centers</strong> – The number of centers which should be selected by this class.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.center_selection.UniformSelector.select">
+<code class="sig-name descname"><span class="pre">select</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select" title="Permalink to this definition">¶</a></dt>
+<dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
+<p>The selection strategy is uniformly at random. To control the randomness,
+pass an appropriate numpy random generator to this class’s constructor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – N x D tensor containing the whole input dataset. If N is lower than the number of
+centers this class is programmed to pick, a warning will be raised and only N centers
+will be returned.</p></li>
+<li><p><strong>Y</strong> – Optional N x T tensor containing the input targets. If <cite>Y</cite> is provided,
+the same observations selected for <cite>X</cite> will also be selected from <cite>Y</cite>.
+Certain models (such as <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.LogisticFalkon</span></code></a>) require centers to be
+extracted from both predictors and targets, while others (such as
+<a class="reference internal" href="models.html#falkon.models.Falkon" title="falkon.models.Falkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.Falkon</span></code></a>) only require the centers from the predictors.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>X_M</em> – The randomly selected centers. They will be in a new, memory-contiguous tensor.
+All characteristics of the input tensor will be preserved.</p></li>
+<li><p><em>(X_M, Y_M)</em> – If <cite>Y</cite> was different than <cite>None</cite> then the entries of <cite>Y</cite> corresponding to the
+selected centers of <cite>X</cite> will also be returned.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.center_selection.UniformSelector.select_indices">
+<code class="sig-name descname"><span class="pre">select_indices</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select_indices" title="Permalink to this definition">¶</a></dt>
+<dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
+<p>The selection strategy is uniformly at random. To control the randomness,
+pass an appropriate numpy random generator to this class’s constructor.</p>
+<p>This method behaves the same as <a class="reference internal" href="#falkon.center_selection.UniformSelector.select" title="falkon.center_selection.UniformSelector.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select()</span></code></a> but additionally returns a <cite>LongTensor</cite>
+containing the indices of the chosen points.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – N x D tensor containing the whole input dataset. If N is lower than the number of
+centers this class is programmed to pick, a warning will be raised and only N centers
+will be returned.</p></li>
+<li><p><strong>Y</strong> – Optional N x T tensor containing the input targets. If <cite>Y</cite> is provided,
+the same observations selected for <cite>X</cite> will also be selected from <cite>Y</cite>.
+Certain models (such as <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.LogisticFalkon</span></code></a>) require centers to be
+extracted from both predictors and targets, while others (such as
+<a class="reference internal" href="models.html#falkon.models.Falkon" title="falkon.models.Falkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.Falkon</span></code></a>) only require the centers from the predictors.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>(X_M, indices)</em> – The randomly selected centers and the corresponding indices.
+The centers will be stored in a new, memory-contiguous tensor and all
+characteristics of the input tensor will be preserved.</p></li>
+<li><p><em>(X_M, Y_M, indices)</em> – If parameter`Y` is not <cite>None</cite> then the entries of <cite>Y</cite> corresponding to the
+selected centers of <cite>X</cite> will also be returned.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="fixedselector">
+<h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.center_selection.FixedSelector">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></code><code class="sig-name descname"><span class="pre">FixedSelector</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.FixedSelector" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.center_selection.CenterSelector</span></code></a></p>
+<p>Center selector which always picks the same centers.</p>
+<p>The fixed centers are specified at class initialization time.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>centers</strong> – Tensor of data-centers to be used.</p></li>
+<li><p><strong>y_centers</strong> – Optional tensor of label-centers to be used. If this is <cite>None</cite>, calling <a class="reference internal" href="#falkon.center_selection.FixedSelector.select" title="falkon.center_selection.FixedSelector.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select()</span></code></a> with
+a non-empty <cite>Y</cite> argument will throw an exception</p></li>
+<li><p><strong>idx_centers</strong> – Optional tensor containing the indices which correspond to the given centers. This tensor
+is used in the <a class="reference internal" href="#falkon.center_selection.FixedSelector.select_indices" title="falkon.center_selection.FixedSelector.select_indices"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select_indices()</span></code></a> method.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.center_selection.FixedSelector.select">
+<code class="sig-name descname"><span class="pre">select</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the fixed centers with which this instance was created</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – This parameter is ignored. The centers returned are the ones passed in the class’s
+constructor.</p></li>
+<li><p><strong>Y</strong> – Optional N x T tensor containing the input targets. The value of the parameter is
+ignored, but if it is not <cite>None</cite>, this method will return a tuple of X-centers
+and Y-centers.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>X_M</em> – The fixed centers as given in the class constructor</p></li>
+<li><p><em>(X_M, Y_M)</em> – The X-centers and Y-centers as given in the class constructor. This tuple is only
+returned if <cite>Y</cite> is not None.</p></li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Raises</dt>
+<dd class="field-odd"><p><strong>RuntimeError</strong> – If parameter <cite>Y</cite> is not None but the <cite>y_centers</cite> tensor passed to the class constructor
+    is <cite>None</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.center_selection.FixedSelector.select_indices">
+<code class="sig-name descname"><span class="pre">select_indices</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select_indices" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns the fixed centers, and their indices with which this instance was created</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – This parameter is ignored. The centers returned are the ones passed in the class’s
+constructor.</p></li>
+<li><p><strong>Y</strong> – Optional N x T tensor containing the input targets. The value of the parameter is
+ignored, but if it is not <cite>None</cite>, this method will return a tuple of X-centers
+Y-centers, and indices.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>(X_M, indices)</em> – The fixed centers and the indices as given in the class constructor</p></li>
+<li><p><em>(X_M, Y_M, indices)</em> – The X-centers, Y-centers and indices as given in the class constructor.
+This tuple is only returned if <cite>Y</cite> is not None.</p></li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Raises</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>RuntimeError</strong> – If the indices passed to the class constructor are <cite>None</cite>.</p></li>
+<li><p><strong>RuntimeError</strong> – If parameter <cite>Y</cite> is not None but the <cite>y_centers</cite> tensor passed to the class constructor
+    is <cite>None</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+
+
+           </div>
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+          </div>
+          <footer>
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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+<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
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+<section id="module-falkon">
+<span id="falkon-gsc-losses"></span><h1>falkon.gsc_losses<a class="headerlink" href="#module-falkon" title="Permalink to this headline">¶</a></h1>
+<section id="loss">
+<h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.gsc_losses.Loss">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></code><code class="sig-name descname"><span class="pre">Loss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.Loss" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract generalized self-concordant loss function class.</p>
+<p>Such loss functions must be three times differentiable; but for the logistic Falkon algorithm
+only the first two derivatives are used.
+Subclasses must implement the <a class="reference internal" href="#falkon.gsc_losses.Loss.__call__" title="falkon.gsc_losses.Loss.__call__"><code class="xref py py-meth docutils literal notranslate"><span class="pre">__call__()</span></code></a> method which calculates the loss function
+given two input vectors (the inputs could also be matrices e.g. for the softmax loss),
+the <a class="reference internal" href="#falkon.gsc_losses.Loss.df" title="falkon.gsc_losses.Loss.df"><code class="xref py py-meth docutils literal notranslate"><span class="pre">df()</span></code></a> method which calculates the first derivative of the function and <a class="reference internal" href="#falkon.gsc_losses.Loss.ddf" title="falkon.gsc_losses.Loss.ddf"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ddf()</span></code></a>
+which calculates the second derivative.</p>
+<p>Additionally, this class provides two methods (<a class="reference internal" href="#falkon.gsc_losses.Loss.knmp_grad" title="falkon.gsc_losses.Loss.knmp_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">knmp_grad()</span></code></a> and <a class="reference internal" href="#falkon.gsc_losses.Loss.knmp_hess" title="falkon.gsc_losses.Loss.knmp_hess"><code class="xref py py-meth docutils literal notranslate"><span class="pre">knmp_hess()</span></code></a>) which
+calculate kernel-vector products using the loss derivatives for vectors. These functions are
+specific to the logistic Falkon algorithm.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> – A descriptive name for the loss function (e.g. “logistic”, “softmax”)</p></li>
+<li><p><strong>kernel</strong> – The kernel function used for training a LogFalkon model</p></li>
+<li><p><strong>opt</strong> – Falkon options container. Will be passed to the kernel when computing kernel-vector
+products.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#falkon.gsc_losses.LogisticLoss" title="falkon.gsc_losses.LogisticLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></dt><dd><p>a concrete implementation of this class for the logistic loss.</p>
+</dd>
+<dt><a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.LogisticFalkon</span></code></a></dt><dd><p>the logistic Falkon model which uses GSC losses.</p>
+</dd>
+</dl>
+</div>
+<dl class="py method">
+<dt id="falkon.gsc_losses.Loss.__call__">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract method. Should return the loss for predicting <cite>y2</cite> with true labels <cite>y1</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y1</strong> (<em>torch.Tensor</em>) – One of the two inputs to the loss. This should be interpreted as the <cite>true</cite> labels.</p></li>
+<li><p><strong>y2</strong> (<em>torch.Tensor</em>) – The other loss input. Should be interpreted as the predicted labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>torch.Tensor</em> – The loss calculated for the two inputs.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.Loss.ddf">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">ddf</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.ddf" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract method. Should return the second derivative of the loss wrt <cite>y2</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y1</strong> (<em>torch.Tensor</em>) – One of the two inputs to the loss. This should be interpreted as the <cite>true</cite> labels.</p></li>
+<li><p><strong>y2</strong> (<em>torch.Tensor</em>) – The other loss input. Should be interpreted as the predicted labels. The derivative
+should be computed with respect to this tensor.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>torch.Tensor</em> – The second derivative of the loss with respect to <cite>y2</cite>. It will be a tensor of the
+same shape as the two inputs.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.Loss.df">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">df</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.df" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract method. Should return the derivative of the loss wrt <cite>y2</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y1</strong> (<em>torch.Tensor</em>) – One of the two inputs to the loss. This should be interpreted as the <cite>true</cite> labels.</p></li>
+<li><p><strong>y2</strong> (<em>torch.Tensor</em>) – The other loss input. Should be interpreted as the predicted labels. The derivative
+should be computed with respect to this tensor.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>torch.Tensor</em> – The derivative of the loss with respect to <cite>y2</cite>. It will be a tensor of the same shape
+as the two inputs.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.Loss.knmp_grad">
+<code class="sig-name descname"><span class="pre">knmp_grad</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_grad" title="Permalink to this definition">¶</a></dt>
+<dd><p>Computes a kernel vector product where the vector is the first derivative of this loss</p>
+<p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
+number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
+<div class="math notranslate nohighlight">
+\[\dfrac{1}{n} K(X_c, X) &#64; (\mathcal{l}'(Y, K(X, X_c) &#64; u))\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>torch.Tensor</em>) – Data matrix of shape (n x d) with <cite>n</cite> samples in <cite>d</cite> dimensions.</p></li>
+<li><p><strong>Xc</strong> (<em>torch.Tensor</em>) – Center matrix of shape (m x d) with <cite>m</cite> centers in <cite>d</cite> dimensions.</p></li>
+<li><p><strong>Y</strong> (<em>torch.Tensor</em>) – Label matrix of shape (n x t) with <cite>n</cite> samples. Depending on the loss, the labels may or may not
+have more than one dimension.</p></li>
+<li><p><strong>u</strong> (<em>torch.Tensor</em>) – A vector (or matrix if the labels are multi-dimensional) of weights of shape (m x t).
+The product <cite>K(X, Xc) &#64; u</cite>, where <cite>K</cite> is the kernel associated to this loss, should
+produce label predictions.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em> or </em><em>None</em>) – Options to be passed to the mmv function for the kernel associated to this loss.
+Options passed as an argument take precedence over the options used to build this
+class instance.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>grad_mul</strong> (<em>torch.Tensor</em>) – A tensor of shape (m x 1) coming from the multiplication of the kernel matrix
+<cite>K(Xc, X)</cite> and the loss calculated on predictions with weights <cite>u</cite>.
+The formula followed is: <cite>(1/n) * K(Xc, X) &#64; df(Y, K(X, Xc) &#64; u)</cite>.</p></li>
+<li><p><strong>func_val</strong> (<em>torch.Tensor</em>) – A tensor of shape (n x t) of predictions obtained with weights <cite>u</cite>.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.Loss.knmp_hess">
+<code class="sig-name descname"><span class="pre">knmp_hess</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_hess" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute a kernel-vector product with a rescaling with the second derivative</p>
+<p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
+number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
+<div class="math notranslate nohighlight">
+\[\dfrac{1}{n} K(X_c, X) &#64; (\mathcal{l}''(Y, f) * K(X, X_c) &#64; u)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>torch.Tensor</em>) – Data matrix of shape (n x d) with <cite>n</cite> samples in <cite>d</cite> dimensions.</p></li>
+<li><p><strong>Xc</strong> (<em>torch.Tensor</em>) – Center matrix of shape (m x d) with <cite>m</cite> centers in <cite>d</cite> dimensions.</p></li>
+<li><p><strong>Y</strong> (<em>torch.Tensor</em>) – Label matrix of shape (n x t) with <cite>n</cite> samples. Depending on the loss, the labels may
+or may not have more than one dimension.</p></li>
+<li><p><strong>f</strong> (<em>torch.Tensor</em>) – Tensor of shape (n x t) of predictions. Typically this will be the second output of
+the <a class="reference internal" href="#falkon.gsc_losses.Loss.knmp_grad" title="falkon.gsc_losses.Loss.knmp_grad"><code class="xref py py-meth docutils literal notranslate"><span class="pre">knmp_grad()</span></code></a> method.</p></li>
+<li><p><strong>u</strong> (<em>torch.Tensor</em>) – A vector (or matrix if the labels are multi-dimensional) of weights of shape (m x t).
+The product <cite>K(X, Xc) &#64; u</cite>, where <cite>K</cite> is the kernel associated to this loss, should
+produce label predictions.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em> or </em><em>None</em>) – Options to be passed to the mmv function for the kernel associated to this loss.
+Options passed as an argument take precedence over the options used to build this
+class instance.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>A tensor of shape (m x t), the output of the computation.</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="logistic-loss">
+<h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.gsc_losses.LogisticLoss">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></code><code class="sig-name descname"><span class="pre">LogisticLoss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition">¶</a></dt>
+<dd><p>Wrapper for the logistic loss, to be used in conjunction with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
+<p>Usage of this loss assumes a binary classification problem with labels -1 and +1. For different
+choices of labels, see <a class="reference internal" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel function used for training a <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> model</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options container. Will be passed to the kernel when computing kernel-vector
+products.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">log_loss</span> <span class="o">=</span> <span class="n">LogisticLoss</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">LogisticFalkon</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="p">[</span><span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-4</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">loss</span><span class="o">=</span><span class="n">log_loss</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt id="falkon.gsc_losses.LogisticLoss.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the logistic loss between two 1-dimensional tensors</p>
+<p>The formula used is <span class="math notranslate nohighlight">\(\log(1 + \exp(-y_1 * y_2))\)</span></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y1</strong> – The first input tensor. Must be 1D</p></li>
+<li><p><strong>y2</strong> – The second input tensor. Must be 1D</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>loss</em> – The logistic loss between the two input vectors.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.LogisticLoss.ddf">
+<code class="sig-name descname"><span class="pre">ddf</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.ddf" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the second derivative of the logistic loss with respect to <cite>y2</cite></p>
+<p>The formula used is</p>
+<div class="math notranslate nohighlight">
+\[y_1^2 \dfrac{1}{1 + \exp(-y_1 * y_2)} \dfrac{1}{1 + \exp(y_1 * y_2)}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y1</strong> – The first input tensor. Must be 1D</p></li>
+<li><p><strong>y2</strong> – The second input tensor. Must be 1D</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>dd_loss</em> – The second derivative of the logistic loss, calculated between the two input vectors.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.LogisticLoss.df">
+<code class="sig-name descname"><span class="pre">df</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.df" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the derivative of the logistic loss with respect to <cite>y2</cite></p>
+<p>The formula used is</p>
+<div class="math notranslate nohighlight">
+\[\dfrac{-y_1}{1 + \exp(y_1 * y_2)}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y1</strong> – The first input tensor. Must be 1D</p></li>
+<li><p><strong>y2</strong> – The second input tensor. Must be 1D</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>d_loss</em> – The derivative of the logistic loss, calculated between the two input vectors.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="weighted-binary-cross-entropy-loss">
+<h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-binary-cross-entropy-loss" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></code><code class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition">¶</a></dt>
+<dd><p>Wrapper for the weighted binary cross-entropy loss, to be used with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
+<p>Using this loss assumes a binary classification problem with labels 0 and +1. Additionally,
+this loss allows to place a different weight to samples belonging to one of the two classes
+(see the <cite>neg_weight</cite> parameter).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel function used for training a <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> model</p></li>
+<li><p><strong>neg_weight</strong> (<em>float</em>) – The weight to be assigned to samples belonging to the negative (0-labeled) class.
+By setting <cite>neg_weight</cite> to 1, the classes are equally weighted and this loss is
+equivalent to the <a class="reference internal" href="#falkon.gsc_losses.LogisticLoss" title="falkon.gsc_losses.LogisticLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a> loss, but with a different
+choice of labels.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options container. Will be passed to the kernel when computing kernel-vector
+products.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wce_loss</span> <span class="o">=</span> <span class="n">WeightedCrossEntropyLoss</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">LogisticFalkon</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="p">[</span><span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-4</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">loss</span><span class="o">=</span><span class="n">wce_loss</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the weighted BCE loss between two 1-dimensional tensors</p>
+<p>The formula used is</p>
+<div class="math notranslate nohighlight">
+\[\mathrm{true} * \log(1 + e^{-\mathrm{pred}}) + w * (1 - \mathrm{true}) * \log(1 + e^{\mathrm{pred}})\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>true</strong> – The label tensor. Must be 1D, with values 0 or 1.</p></li>
+<li><p><strong>pred</strong> – The prediction tensor. Must be 1D. These are “logits” so need not be scaled before
+hand.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>loss</em> – The weighted BCE loss between the two input vectors.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss.ddf">
+<code class="sig-name descname"><span class="pre">ddf</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.ddf" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the second derivative of the weighted BCE loss with respect to <cite>pred</cite></p>
+<p>The formula used is</p>
+<div class="math notranslate nohighlight">
+\[\dfrac{-(\mathrm{true} * (w - 1) - w) * e^{\mathrm{pred}}}{(e^{\mathrm{pred}} + 1)^2}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>true</strong> – The label tensor. Must be 1D</p></li>
+<li><p><strong>pred</strong> – The prediction tensor. Must be 1D</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>dd_loss</em> – The second derivative of the weighted BCE loss between the two input vectors.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss.df">
+<code class="sig-name descname"><span class="pre">df</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.df" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the derivative of the weighted BCE loss with respect to <cite>pred</cite></p>
+<p>The formula used is</p>
+<div class="math notranslate nohighlight">
+\[\dfrac{-(w * \mathrm{true} - w) * e^{\mathrm{pred}} - \mathrm{true}}{e^{\mathrm{pred}} + 1}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>true</strong> – The label tensor. Must be 1D</p></li>
+<li><p><strong>pred</strong> – The prediction tensor. Must be 1D</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>d_loss</em> – The derivative of the weighted BCE loss between the two input vectors.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+
+
+           </div>
+           
+          </div>
+          <footer>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
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+<li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
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+</li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l2"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l2"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
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+<section id="module-falkon.kernels">
+<span id="falkon-kernels"></span><h1>falkon.kernels<a class="headerlink" href="#module-falkon.kernels" title="Permalink to this headline">¶</a></h1>
+<section id="kernel">
+<h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.kernels.kernel.Kernel">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.kernel.</span></code><code class="sig-name descname"><span class="pre">Kernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_type</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Abstract kernel class. Kernels should inherit from this class, overriding appropriate methods.</p>
+<p>To extend Falkon with new kernels, you should read the documentation of this class
+carefully. In particular, you will <strong>need</strong> to implement <a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare" title="falkon.kernels.kernel.Kernel._prepare"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare()</span></code></a>, <a class="reference internal" href="#falkon.kernels.kernel.Kernel._apply" title="falkon.kernels.kernel.Kernel._apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_apply()</span></code></a> and
+<a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> methods.</p>
+<p>Other methods which should be optionally implemented are the sparse versions
+<a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare_sparse" title="falkon.kernels.kernel.Kernel._prepare_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare_sparse()</span></code></a> and <a class="reference internal" href="#falkon.kernels.kernel.Kernel._apply_sparse" title="falkon.kernels.kernel.Kernel._apply_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_apply_sparse()</span></code></a> (note that there is no <cite>_finalize_sparse</cite>,
+since the <cite>_finalize</cite> takes as input a partial kernel matrix, and even with sparse data,
+kernel matrices are assumed to be dense. Therefore, even for sparse data, the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a>
+method will be used.</p>
+<p>To provide a KeOps implementation, you will have to inherit also from the
+<a class="reference internal" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a> class, and implement its abstract methods. In case
+a KeOps implementation is provided, you should make sure to override the
+<a class="reference internal" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="falkon.kernels.kernel.Kernel._decide_mmv_impl"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_decide_mmv_impl()</span></code></a> and <a class="reference internal" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="falkon.kernels.kernel.Kernel._decide_dmmv_impl"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_decide_dmmv_impl()</span></code></a> so that the KeOps implementation is
+effectively used. Have a look at the <a class="reference internal" href="#falkon.kernels.PolynomialKernel" title="falkon.kernels.PolynomialKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.kernels.PolynomialKernel</span></code></a> class for
+an example of how to integrate KeOps in the kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> – A short name for the kernel (e.g. “Gaussian”)</p></li>
+<li><p><strong>kernel_type</strong> – A short string describing the type of kernel. This may be used to create specialized
+functions in <a class="reference internal" href="mmv_ops.html#module-falkon.mmv_ops" title="falkon.mmv_ops"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.mmv_ops</span></code></a> which optimize for a specific kernel type.</p></li>
+<li><p><strong>opt</strong> – Base set of options to be used for operations involving this kernel.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._apply">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_apply</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._apply" title="Permalink to this definition">¶</a></dt>
+<dd><p>Main kernel operation, usually matrix multiplication.</p>
+<p>This function will be called with two blocks of data which may be subsampled on the
+first and second dimension (i.e. X1 may be of size <cite>n x d</cite> where <cite>n &lt;&lt; N</cite> and <cite>d &lt;&lt; D</cite>).
+The output shall be stored in the <cite>out</cite> argument, and not be returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – (n x d) tensor. It is a block of the <cite>X1</cite> input matrix, possibly subsampled in the
+first dimension.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – (m x d) tensor. It is a block of the <cite>X2</cite> input matrix, possibly subsampled in the
+first dimension.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em>) – (n x m) tensor. A tensor in which the output of the operation shall be accumulated.
+This tensor is initialized to 0 before calling <cite>_apply</cite>, but in case of subsampling
+of the data along the second dimension, multiple calls will be needed to compute a
+single (n x m) output block. In such case, the first call to this method will have
+a zeroed tensor, while subsequent calls will simply reuse the same object.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._apply_sparse">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_apply_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._apply_sparse" title="Permalink to this definition">¶</a></dt>
+<dd><p>Main kernel computation for sparse tensors.</p>
+<p>Unlike the :meth`_apply` method, the <cite>X1</cite> and <cite>X2</cite> tensors are only subsampled along
+the first dimension. Take note that the <cite>out</cite> tensor <strong>is not sparse</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (n x D), with possibly n &lt;&lt; N.</p></li>
+<li><p><strong>X2</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (m x D), with possibly m &lt;&lt; M.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em>) – Tensor of shape (n x m) which should hold a tile of the kernel. The output of this
+function (typically a matrix multiplication) should be placed in this tensor.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._decide_dmmv_impl">
+<code class="sig-name descname"><span class="pre">_decide_dmmv_impl</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="Permalink to this definition">¶</a></dt>
+<dd><p>Choose which <cite>dmmv</cite> function to use for this data.</p>
+<p>Note that <cite>dmmv</cite> functions compute double kernel-vector products (see <a class="reference internal" href="#falkon.kernels.kernel.Kernel.dmmv" title="falkon.kernels.kernel.Kernel.dmmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dmmv()</span></code></a> for
+an explanation of what they are).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – First data matrix, of shape (N x D)</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – Second data matrix, of shape (M x D)</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Vector for the matrix-vector multiplication (M x T)</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Vector for the matrix-vector multiplicatoin (N x T)</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options. Options may be specified to force GPU or CPU usage.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>dmmv_fn</em> – A function which allows to perform the <cite>mmv</cite> operation.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This function decides based on the inputs: if the inputs are sparse, it will choose
+the sparse implementations; if CUDA is detected, it will choose the CUDA implementation;
+otherwise it will simply choose the basic CPU implementation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._decide_mm_impl">
+<code class="sig-name descname"><span class="pre">_decide_mm_impl</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mm_impl" title="Permalink to this definition">¶</a></dt>
+<dd><p>Choose which <cite>mm</cite> function to use for this data.</p>
+<p>Note that <cite>mm</cite> functions compute the kernel itself so <strong>KeOps may not be used</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – First data matrix, of shape (N x D)</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – Second data matrix, of shape (M x D)</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options. Options may be specified to force GPU or CPU usage.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>mm_fn</em> – A function which allows to perform the <cite>mm</cite> operation.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This function decides based on the inputs: if the inputs are sparse, it will choose
+the sparse implementations; if CUDA is detected, it will choose the CUDA implementation;
+otherwise it will simply choose the basic CPU implementation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._decide_mmv_impl">
+<code class="sig-name descname"><span class="pre">_decide_mmv_impl</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="Permalink to this definition">¶</a></dt>
+<dd><p>Choose which <cite>mmv</cite> function to use for this data.</p>
+<p>Note that <cite>mmv</cite> functions compute the kernel-vector product</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – First data matrix, of shape (N x D)</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – Second data matrix, of shape (M x D)</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – Vector for the matrix-vector multiplication (M x T)</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options. Options may be specified to force GPU or CPU usage.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>mmv_fn</em> – A function which allows to perform the <cite>mmv</cite> operation.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This function decides based on the inputs: if the inputs are sparse, it will choose
+the sparse implementations; if CUDA is detected, it will choose the CUDA implementation;
+otherwise it will simply choose the basic CPU implementation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._finalize">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_finalize</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._finalize" title="Permalink to this definition">¶</a></dt>
+<dd><p>Final actions to be performed on a partial kernel matrix.</p>
+<p>All elementwise operations on the kernel matrix should be performed in this method.
+Operations should be performed inplace by modifying the matrix <cite>A</cite>, to improve memory
+efficiency. If operations are not in-place, out-of-memory errors are possible when
+using the GPU.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<em>torch.Tensor</em>) – A (m x n) tile of the kernel matrix, as obtained by the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._apply" title="falkon.kernels.kernel.Kernel._apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_apply()</span></code></a> method.</p></li>
+<li><p><strong>d</strong> – Additional data, as computed by the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare" title="falkon.kernels.kernel.Kernel._prepare"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare()</span></code></a> method.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>A</em> – The same tensor as the input, if operations are performed in-place. Otherwise
+another tensor of the same shape.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._prepare">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_prepare</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Any</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._prepare" title="Permalink to this definition">¶</a></dt>
+<dd><p>Pre-processing operations necessary to compute the kernel.</p>
+<p>This function will be called with two blocks of data which may be subsampled on the
+first dimension (i.e. X1 may be of size <cite>n x D</cite> where <cite>n &lt;&lt; N</cite>). The function should
+not modify <cite>X1</cite> and <cite>X2</cite>. If necessary, it may return some data which is then made available
+to the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> method.</p>
+<p>For example, in the Gaussian kernel, this method is used to compute the squared norms
+of the datasets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – (n x D) tensor. It is a block of the <cite>X1</cite> input matrix, possibly subsampled in the
+first dimension.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – (m x D) tensor. It is a block of the <cite>X2</cite> input matrix, possibly subsampled in the
+first dimension.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p>Data which may be used for the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> method. If no such information is</p></li>
+<li><p><em>necessary, returns None.</em></p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel._prepare_sparse">
+<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_prepare_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._prepare_sparse" title="Permalink to this definition">¶</a></dt>
+<dd><p>Data preprocessing for sparse tensors.</p>
+<p>This is an equivalent to the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare" title="falkon.kernels.kernel.Kernel._prepare"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare()</span></code></a> method for sparse tensors.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (n x D), with possibly n &lt;&lt; N.</p></li>
+<li><p><strong>X2</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (m x D), with possibly m &lt;&lt; M.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p>Data derived from <cite>X1</cite> and <cite>X2</cite> which is needed by the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> method when</p></li>
+<li><p><em>finishing to compute a kernel tile.</em></p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel.dmmv">
+<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
+<p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
+where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
+we remove the additive factor.
+<strong>At least one of `w` and `v` must be provided</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute matrix-vector products. This may also be a matrix of shape
+(N x T) but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (M x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([150, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel.extra_mem">
+<code class="sig-name descname"><span class="pre">extra_mem</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.extra_mem" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the amount of extra memory which will be needed when computing this kernel.</p>
+<p>Often kernel computation needs some extra memory allocations. To avoid using too large
+block-sizes which may lead to OOM errors, you should declare any such extra allocations
+for your kernel here.</p>
+<p>Indicate extra allocations as coefficients on the required dimensions. For example,
+if computing a kernel needs to re-allocate the data-matrix (which is of size n * d),
+the return dictionary will be: <cite>{‘nd’: 1}</cite>. Other possible coefficients are on <cite>d</cite>, <cite>n</cite>, <cite>m</cite>
+which are respectively the data-dimension, the number of data-points in the first data
+matrix and the number of data-points in the second matrix. Pairwise combinations of the
+three dimensions are possible (i.e. <cite>nd</cite>, <cite>nm</cite>, <cite>md</cite>).
+Make sure to specify the dictionary keys as is written here since they will not be
+recognized otherwise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><strong>extra_allocs</strong> (<em>dictionary</em>) – A dictionary from strings indicating on which dimensions the extra-allocation is
+needed (allowed strings: <cite>‘n’, ‘m’, ‘d’, ‘nm’, ‘nd’, ‘md’</cite>) to floating-point numbers
+indicating how many extra-allocations are needed.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.kernel.Kernel.mmv">
+<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([100, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="falkon.kernels.keops_helpers.KeopsKernelMixin">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.keops_helpers.</span></code><code class="sig-name descname"><span class="pre">KeopsKernelMixin</span></code><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="py method">
+<dt id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_dmmv_helper">
+<code class="sig-name descname"><span class="pre">keops_dmmv_helper</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmv_fn</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_dmmv_helper" title="Permalink to this definition">¶</a></dt>
+<dd><p>performs fnc(X1*X2’, X1, X2)’ * ( fnc(X1*X2’, X1, X2) * v  +  w )</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – N x D tensor</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – M x D tensor</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – M x T tensor. Often, T = 1 and this is a vector.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em>) – N x T tensor. Often, T = 1 and this is a vector.</p></li>
+<li><p><strong>kernel</strong> (<a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – Kernel instance to calculate this kernel. This is only here to preserve API
+structure.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor in which to store the output (M x T)</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Options to be passed downstream</p></li>
+<li><p><strong>mmv_fn</strong> (<em>Callable</em>) – The function which performs the mmv operation. Two mmv operations are (usually)
+needed for a dmmv operation.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The double MMV is implemented as two separate calls to the user-supplied
+<cite>mmv_fn</cite>. The first one calculates the inner part of the formula (NxT)
+while the second calculates the outer matrix-vector multiplication which</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="radial-kernels">
+<h2>Radial kernels<a class="headerlink" href="#radial-kernels" title="Permalink to this headline">¶</a></h2>
+<section id="gaussian-kernel">
+<h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.kernels.GaussianKernel">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">GaussianKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Class for computing the Gaussian kernel and related kernel-vector products</p>
+<p>The Gaussian kernel is one of the most common and effective kernel embeddings
+since it is infinite dimensional, and governed by a single parameter. The kernel length-scale
+determines the width of the Gaussian distribution which is placed on top of each point.
+A larger sigma corresponds to a wide Gaussian, so that the relative influence of far away
+points will be high for computing the kernel at a given datum.
+On the opposite side of the spectrum, a small sigma means that only nearby points will
+influence the kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sigma</strong> – The length-scale of the kernel.
+This can be a scalar, and then it corresponds to the standard deviation
+of the Gaussian distribution from which the kernel is derived.
+If <cite>sigma</cite> is a vector of size <cite>d</cite> (where <cite>d</cite> is the dimensionality of the data), it is
+interpreted as the diagonal standard deviation of the Gaussian distribution.
+It can also be a matrix of  size <cite>d*d</cite> where <cite>d</cite>, in which case sigma will be the precision
+matrix (inverse covariance).</p></li>
+<li><p><strong>opt</strong> – Additional options to be forwarded to the matrix-vector multiplication
+routines.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Creating a Gaussian kernel with a single length-scale. Operations on this kernel will not
+use KeOps.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">K</span> <span class="o">=</span> <span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mf">3.0</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>Creating a Gaussian kernel with a different length-scale per dimension</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">K</span> <span class="o">=</span> <span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">,</span> <span class="mf">7.0</span><span class="p">]))</span>
+</pre></div>
+</div>
+<p>Creating a Gaussian kernel object with full covariance matrix (randomly chosen)</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">mat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sym_mat</span> <span class="o">=</span> <span class="n">mat</span> <span class="o">@</span> <span class="n">mat</span><span class="o">.</span><span class="n">T</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">K</span> <span class="o">=</span> <span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="n">sym_mat</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">K</span>
+<span class="go">GaussianKernel(sigma=tensor([[ 2.0909,  0.0253, -0.2490],</span>
+<span class="go">        [ 0.0253,  0.3399, -0.5158],</span>
+<span class="go">        [-0.2490, -0.5158,  4.4922]], dtype=torch.float64))  #random</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>The Gaussian kernel with a single length-scale follows</p>
+<div class="math notranslate nohighlight">
+\[k(x, x') = \exp{-\dfrac{\lVert x - x' \rVert^2}{2\sigma^2}}\]</div>
+<p>When the length-scales are specified as a matrix, the RBF kernel is determined by</p>
+<div class="math notranslate nohighlight">
+\[k(x, x') = \exp{-\dfrac{1}{2}x\Sigma x'}\]</div>
+<p>In both cases, the actual computation follows a different path, working on the expanded
+norm.</p>
+<dl class="py method">
+<dt id="falkon.kernels.GaussianKernel.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.GaussianKernel.dmmv">
+<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
+<p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
+where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
+we remove the additive factor.
+<strong>At least one of `w` and `v` must be provided</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute matrix-vector products. This may also be a matrix of shape
+(N x T) but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (M x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([150, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.GaussianKernel.mmv">
+<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([100, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="laplacian-kernel">
+<h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.kernels.LaplacianKernel">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">LaplacianKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Class for computing the Laplacian kernel, and related kernel-vector products.</p>
+<p>The Laplacian kernel is similar to the Gaussian kernel, but less sensitive to changes
+in the parameter <cite>sigma</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>sigma</strong> – The length-scale of the Laplacian kernel</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The Laplacian kernel is determined by the following formula</p>
+<div class="math notranslate nohighlight">
+\[k(x, x') = \exp{-\frac{\lVert x - x' \rVert}{\sigma}}\]</div>
+<dl class="py method">
+<dt id="falkon.kernels.LaplacianKernel.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.LaplacianKernel.dmmv">
+<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
+<p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
+where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
+we remove the additive factor.
+<strong>At least one of `w` and `v` must be provided</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute matrix-vector products. This may also be a matrix of shape
+(N x T) but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (M x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([150, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.LaplacianKernel.mmv">
+<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([100, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+<section id="dot-product-kernels">
+<h2>Dot-Product kernels<a class="headerlink" href="#dot-product-kernels" title="Permalink to this headline">¶</a></h2>
+<section id="polynomial-kernel">
+<h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.kernels.PolynomialKernel">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">PolynomialKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Polynomial kernel with multiplicative and additive constants.</p>
+<p>Follows the formula</p>
+<div class="math notranslate nohighlight">
+\[(\alpha * X_1^\top X_2 + \beta)^{\mathrm{degree}}\]</div>
+<p>Where all operations apart from the matrix multiplication are taken element-wise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
+<li><p><strong>beta</strong> (<em>float-like</em>) – Additive constant</p></li>
+<li><p><strong>degree</strong> (<em>float-like</em>) – Power of the polynomial kernel</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options which will be used in downstream kernel operations.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.kernels.PolynomialKernel.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.PolynomialKernel.dmmv">
+<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
+<p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
+where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
+we remove the additive factor.
+<strong>At least one of `w` and `v` must be provided</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute matrix-vector products. This may also be a matrix of shape
+(N x T) but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (M x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([150, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.PolynomialKernel.mmv">
+<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([100, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="linear-kernel">
+<h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.kernels.LinearKernel">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">LinearKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Linear Kernel with optional scaling and translation parameters.</p>
+<p>The kernel implemented here is the covariance function in the original
+input space (i.e. <cite>X &#64; X.T</cite>) with optional parameters to translate
+and scale the kernel: <cite>beta + 1/(sigma**2) * X &#64; X.T</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>beta</strong> (<em>float-like</em>) – Additive constant for the kernel, default: 0.0</p></li>
+<li><p><strong>sigma</strong> (<em>float-like</em>) – Multiplicative constant for the kernel. The kernel will
+be multiplied by the inverse of sigma squared. Default: 1.0</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options which will be used in downstream kernel operations.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">LinearKernel</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># 100 samples in 3 dimensions</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel_matrix</span> <span class="o">=</span> <span class="n">k</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">kernel_matrix</span><span class="p">,</span> <span class="n">X</span> <span class="o">@</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt id="falkon.kernels.LinearKernel.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.LinearKernel.dmmv">
+<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
+<p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
+where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
+we remove the additive factor.
+<strong>At least one of `w` and `v` must be provided</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute matrix-vector products. This may also be a matrix of shape
+(N x T) but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (M x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([150, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.LinearKernel.mmv">
+<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([100, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="sigmoid-kernel">
+<h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.kernels.SigmoidKernel">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">SigmoidKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sigmoid (or hyperbolic tangent) kernel function, with additive and multiplicative constants.</p>
+<p>Follows the formula</p>
+<div class="math notranslate nohighlight">
+\[k(x, y) = \tanh(\alpha x^\top y + \beta)\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
+<li><p><strong>beta</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options which will be used in downstream kernel operations.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.kernels.SigmoidKernel.__call__">
+<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.SigmoidKernel.dmmv">
+<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
+<p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
+where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
+we remove the additive factor.
+<strong>At least one of `w` and `v` must be provided</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute matrix-vector products. This may also be a matrix of shape
+(N x T) but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (M x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([150, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.kernels.SigmoidKernel.mmv">
+<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([100, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
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+<li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
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+<section id="falkon-mmv-ops">
+<h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink to this headline">¶</a></h1>
+<p>The algorithms to compute kernels and kernel-vector products blockwise on GPUs and CPU. The algorithms in this module
+are kernel agnostic. Refer to <a class="reference internal" href="kernels.html#module-falkon.kernels" title="falkon.kernels"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.kernels</span></code></a> for the actual kernel implementations.</p>
+<p>The KeOps wrapper only supports the <cite>mmv</cite> operation (kernel-vector products). The matrix-multiplication implementations
+instead support three different operations:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><cite>mm</cite> which calculates the full kernel</p></li>
+<li><p><cite>mmv</cite> which calculates kernel-vector products</p></li>
+<li><p><cite>dmmv</cite> which calculates double kernel-vector products (which are operations like <span class="math notranslate nohighlight">\(K^\top (K v)\)</span> where
+<span class="math notranslate nohighlight">\(K\)</span> is a kernel matrix and <span class="math notranslate nohighlight">\(v\)</span> is a vector).</p></li>
+</ul>
+</div></blockquote>
+<span class="target" id="module-falkon.mmv_ops"></span><section id="run-keops-mmv">
+<h2>run_keops_mmv<a class="headerlink" href="#run-keops-mmv" title="Permalink to this headline">¶</a></h2>
+<p>A thin wrapper to KeOps is provided to allow for block-splitting and multiple GPU usage. This only supports
+kernel-vector products.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.keops.run_keops_mmv">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.keops.</span></code><code class="sig-name descname"><span class="pre">run_keops_mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'Sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.keops.run_keops_mmv" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fmm-cpu">
+<h2>fmm_cpu<a class="headerlink" href="#fmm-cpu" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel calculation on the CPU.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmm_cpu.fmm_cpu">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cpu.</span></code><code class="sig-name descname"><span class="pre">fmm_cpu</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">falkon.options.BaseOptions</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cpu.fmm_cpu" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute kernel value on matrices X1 and X2: <code class="docutils literal notranslate"><span class="pre">out</span> <span class="pre">=</span> <span class="pre">kernel(X1,</span> <span class="pre">X2)</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> – [N, D] array</p></li>
+<li><p><strong>X2</strong> – [M, D] array</p></li>
+<li><p><strong>kernel</strong> – Class representing the desired kernel function</p></li>
+<li><p><strong>out</strong> – Array for storing the kernel output. If None, will be allocated within the function.</p></li>
+<li><p><strong>opt</strong> – Basic options dictionary, used for determining available memory. Additionally, the
+<code class="xref py py-attr docutils literal notranslate"><span class="pre">no_single_kernel</span></code> option is used to determine the
+accumulator data type.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>out</em> – [N, M] array. The kernel between X1 and X2.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="fmm-cpu-sparse">
+<h2>fmm_cpu_sparse<a class="headerlink" href="#fmm-cpu-sparse" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel calculation on the CPU for sparse datasets.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cpu.</span></code><code class="sig-name descname"><span class="pre">fmm_cpu_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">falkon.options.BaseOptions</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fmm-cuda">
+<h2>fmm_cuda<a class="headerlink" href="#fmm-cuda" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel calculation on GPUs.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmm_cuda.fmm_cuda">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cuda.</span></code><code class="sig-name descname"><span class="pre">fmm_cuda</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cuda.fmm_cuda" title="Permalink to this definition">¶</a></dt>
+<dd><p>performs fnc(X1*X2’, X1, X2) in blocks on multiple GPUs</p>
+</dd></dl>
+
+</section>
+<section id="fmm-cuda-sparse">
+<h2>fmm_cuda_sparse<a class="headerlink" href="#fmm-cuda-sparse" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel calculation on GPUs for sparse datasets.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cuda.</span></code><code class="sig-name descname"><span class="pre">fmm_cuda_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fmmv-cpu">
+<h2>fmmv_cpu<a class="headerlink" href="#fmmv-cpu" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel-vector products on CPU.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cpu.fmmv_cpu">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fmmv_cpu</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fmmv_cpu" title="Permalink to this definition">¶</a></dt>
+<dd><p>Blockwise kernel-vector product</p>
+<p>This function computes <code class="docutils literal notranslate"><span class="pre">kernel(X1,</span> <span class="pre">X2)</span> <span class="pre">&#64;</span> <span class="pre">v</span></code> in a blockwise fashion, to avoid having the
+whole N*M kernel matrix in memory at once.
+Note that while the principle is that of matrix-vector product, <cite>v</cite> can have more than
+one column.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> – [N, D] array</p></li>
+<li><p><strong>X2</strong> – [M, D] array</p></li>
+<li><p><strong>v</strong> – [M, T] array</p></li>
+<li><p><strong>kernel</strong> – Class representing the desired kernel function</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [N, T] array for storing the kernel-vector product output.
+If None, will be allocated within the function.</p></li>
+<li><p><strong>opt</strong> – Basic options dictionary, used for determining available memory.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="fmmv-cpu-sparse">
+<h2>fmmv_cpu_sparse<a class="headerlink" href="#fmmv-cpu-sparse" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel-vector products on CPU for sparse datasets.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fmmv_cpu_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">falkon.options.BaseOptions</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fdmmv-cpu">
+<h2>fdmmv_cpu<a class="headerlink" href="#fdmmv-cpu" title="Permalink to this headline">¶</a></h2>
+<p>Blocked double kernel-vector products on CPU.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cpu.fdmmv_cpu">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cpu</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu" title="Permalink to this definition">¶</a></dt>
+<dd><p>Calculate a double kernel-vector product.</p>
+<p>This function computes the following quantity: <code class="docutils literal notranslate"><span class="pre">kernel(X1,</span> <span class="pre">X2).T</span> <span class="pre">&#64;</span> <span class="pre">(kernel(X1,</span> <span class="pre">X2)</span> <span class="pre">&#64;</span> <span class="pre">v</span> <span class="pre">+</span> <span class="pre">w)</span></code>
+Where one of <cite>v</cite> or <cite>w</cite> can be empty.
+All arrays passed to this function must be 2-dimensional, although
+the second dimension can be unitary.</p>
+<p>The expression is not computed directly. We separate the computation
+into smaller blocks so as to reduce the total memory consumption (the
+large N*M kernel matrix is never wholly stored in RAM.)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> – [N, D] array</p></li>
+<li><p><strong>X2</strong> – [M, D] array</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [M, T] array. But note that at least one of v or w must be specified.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [N, T] array. But note that at least one of v or w must be specified.</p></li>
+<li><p><strong>kernel</strong> – Class representing the desired kernel function</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [M, T] array for storing the kernel-vector product output.
+If None, will be allocated within the function.</p></li>
+<li><p><strong>opt</strong> – Basic options dictionary, used for determining available memory.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="fdmmv-cpu-sparse">
+<h2>fdmmv_cpu_sparse<a class="headerlink" href="#fdmmv-cpu-sparse" title="Permalink to this headline">¶</a></h2>
+<p>Blocked double kernel-vector products on CPU for sparse datasets.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cpu_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fmmv-cuda">
+<h2>fmmv_cuda<a class="headerlink" href="#fmmv-cuda" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel-vector products on GPUs.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cuda.fmmv_cuda">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fmmv_cuda</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fmmv_cuda" title="Permalink to this definition">¶</a></dt>
+<dd><p>X1 : N x D
+X2 : M x D
+v  : M x T</p>
+<p>performs  fnc(X1*X2’, X1, X2) * v   : N x T
+in blocks on multiple GPUs</p>
+</dd></dl>
+
+</section>
+<section id="fmmv-cuda-sparse">
+<h2>fmmv_cuda_sparse<a class="headerlink" href="#fmmv-cuda-sparse" title="Permalink to this headline">¶</a></h2>
+<p>Blocked kernel-vector products on GPUs for sparse datasets.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fmmv_cuda_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fdmmv-cuda">
+<h2>fdmmv_cuda<a class="headerlink" href="#fdmmv-cuda" title="Permalink to this headline">¶</a></h2>
+<p>Blocked double kernel-vector products on GPUs.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cuda.fdmmv_cuda">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cuda</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda" title="Permalink to this definition">¶</a></dt>
+<dd><p>X1 : N x D
+X2 : M x D
+v  : M x T
+w  : N x T</p>
+<p>performs fnc(X1*X2’, X1, X2)’ * ( fnc(X1*X2’, X1, X2) * v  +  w )  : M x T
+in blocks on multiple GPUs</p>
+<p>Assume all inputs have the same data type</p>
+</dd></dl>
+
+</section>
+<section id="fdmmv-cuda-sparse">
+<h2>fdmmv_cuda_sparse<a class="headerlink" href="#fdmmv-cuda-sparse" title="Permalink to this headline">¶</a></h2>
+<p>Blocked double kernel-vector products on GPUs for sparse datasets.</p>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cuda_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="incore-fmmv">
+<h2>incore_fmmv<a class="headerlink" href="#incore-fmmv" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_incore.incore_fmmv">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></code><code class="sig-name descname"><span class="pre">incore_fmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="incore-fdmmv">
+<h2>incore_fdmmv<a class="headerlink" href="#incore-fdmmv" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="falkon.mmv_ops.fmmv_incore.incore_fdmmv">
+<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></code><code class="sig-name descname"><span class="pre">incore_fdmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+</section>
+
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+<li class="toctree-l1"><a class="reference internal" href="../install.html">Install</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.models</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#incorefalkon">InCoreFalkon</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
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+<section id="module-falkon.models">
+<span id="falkon-models"></span><h1>falkon.models<a class="headerlink" href="#module-falkon.models" title="Permalink to this headline">¶</a></h1>
+<section id="falkon">
+<h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.models.Falkon">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.models.</span></code><code class="sig-name descname"><span class="pre">Falkon</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition">¶</a></dt>
+<dd><p>Falkon Kernel Ridge Regression solver.</p>
+<p>This estimator object solves approximate kernel ridge regression problems with Nystroem
+projections and a fast optimization algorithm as described in <a class="reference internal" href="#flk-1" id="id1"><span>[flk_1]</span></a>, <a class="reference internal" href="#flk-2" id="id2"><span>[flk_2]</span></a>.</p>
+<p>Multiclass and multiple regression problems can all be tackled
+with this same object, for example by encoding multiple classes
+in a one-hot target matrix.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel</strong> – Object representing the kernel function used for KRR.</p></li>
+<li><p><strong>penalty</strong> (<em>float</em>) – Amount of regularization to apply to the problem.
+This parameter must be greater than 0.</p></li>
+<li><p><strong>M</strong> (<em>int</em>) – The number of Nystrom centers to pick. <cite>M</cite> must be positive,
+and lower than the total number of training points. A larger
+<cite>M</cite> will typically lead to better accuracy but will use more
+computational resources. You can either specify the number of centers
+by setting this parameter, or by passing to this constructor a <cite>CenterSelctor</cite> class
+instance.</p></li>
+<li><p><strong>center_selection</strong> (<em>str</em><em> or </em><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><em>falkon.center_selection.CenterSelector</em></a>) – The center selection algorithm. Implemented is only ‘uniform’
+selection which can choose each training sample with the same
+probability.</p></li>
+<li><p><strong>maxiter</strong> (<em>int</em>) – The number of iterations to run the optimization for. Usually
+fewer than 20 iterations are necessary, however this is problem
+dependent.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em> or </em><em>None</em>) – Random seed. Can be used to make results stable across runs.
+Randomness is present in the center selection algorithm, and in
+certain optimizers.</p></li>
+<li><p><strong>error_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – A function with two arguments: targets and predictions, both <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code>
+objects which returns the error incurred for predicting ‘predictions’ instead of
+‘targets’. This is used to display the evolution of the error during the iterations.</p></li>
+<li><p><strong>error_every</strong> (<em>int</em><em> or </em><em>None</em>) – Evaluate the error (on training or validation data) every
+<cite>error_every</cite> iterations. If set to 1 then the error will be
+calculated at each iteration. If set to None, it will never be
+calculated.</p></li>
+<li><p><strong>weight_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – <p>A function which appropriately weighs the target tensor (i.e. Y). This function is used
+for weighted least-squares, it should accept a single argument which represents a subset
+of the targets, and return a vector of weights corresponding to the input targets.</p>
+<p>As an example, in the setting of binary classification Y can be -1 or +1. To give more
+importance to errors on the negative class, pass a <cite>weight_fn</cite> which returns 2 whenever
+the target is -1.</p>
+</p></li>
+<li><p><strong>options</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Additional options used by the components of the Falkon solver. Individual options
+are documented in <a class="reference internal" href="options.html#module-falkon.options" title="falkon.options"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.options</span></code></a>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Running Falkon on a random dataset</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">options</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="rubric">References</p>
+<dl class="citation">
+<dt class="label" id="flk-1"><span class="brackets"><a class="fn-backref" href="#id1">flk_1</a></span></dt>
+<dd><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
+scale kernel method,” Advances in Neural Information Processing Systems 29, 2017.</p>
+</dd>
+<dt class="label" id="flk-2"><span class="brackets"><a class="fn-backref" href="#id2">flk_2</a></span></dt>
+<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+“Kernel methods through the roof: handling billions of points efficiently,”
+arXiv:2006.10350, 2020.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.models.Falkon.fit">
+<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Fits the Falkon KRR model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>torch.Tensor</em>) – The tensor of training data, of shape [num_samples, num_dimensions].
+If X is in Fortran order (i.e. column-contiguous) then we can avoid
+an extra copy of the data.</p></li>
+<li><p><strong>Y</strong> (<em>torch.Tensor</em>) – The tensor of training targets, of shape [num_samples, num_outputs].
+If X and Y represent a classification problem, Y can be encoded as a one-hot
+vector.
+If Y is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data.</p></li>
+<li><p><strong>Xts</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Tensor of validation data, of shape [num_test_samples, num_dimensions].
+If validation data is provided and <cite>error_fn</cite> was specified when
+creating the model, they will be used to print the validation error
+during the optimization iterations.
+If Xts is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data.</p></li>
+<li><p><strong>Yts</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Tensor of validation targets, of shape [num_test_samples, num_outputs].
+If validation data is provided and <cite>error_fn</cite> was specified when
+creating the model, they will be used to print the validation error
+during the optimization iterations.
+If Yts is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>model</strong> (<em>Falkon</em>) – The fitted model</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.Falkon.get_params">
+<code class="sig-name descname"><span class="pre">get_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.get_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>params</strong> (<em>dict</em>) – Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.Falkon.predict">
+<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.models.Falkon.predict" title="Permalink to this definition">¶</a></dt>
+<dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>torch.Tensor</em>) – Tensor of test data points, of shape [num_samples, num_dimensions].</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>predictions</strong> (<em>torch.Tensor</em>) – Prediction tensor of shape [num_samples, num_outputs] for all
+data points.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.Falkon.set_params">
+<code class="sig-name descname"><span class="pre">set_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.set_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s
+possible to update each component of a nested object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>self</strong> (<em>estimator instance</em>) – Estimator instance.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="logisticfalkon">
+<h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.models.LogisticFalkon">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.models.</span></code><code class="sig-name descname"><span class="pre">LogisticFalkon</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_list</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_list</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><span class="pre">falkon.gsc_losses.Loss</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon" title="Permalink to this definition">¶</a></dt>
+<dd><p>Falkon Logistic regression solver.</p>
+<p>This estimator object solves approximate logistic regression problems with Nystroem
+projections and a fast optimization algorithm as described in <a class="footnote-reference brackets" href="#id5" id="id3">1</a>, <a class="footnote-reference brackets" href="#id6" id="id4">2</a>.</p>
+<p>This model can handle logistic regression, so it may be used in place of
+<a class="reference internal" href="#falkon.models.Falkon" title="falkon.models.Falkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.Falkon</span></code></a> (which uses the squared loss) when tackling binary
+classification problems.</p>
+<p>The algorithm works by repeated applications of the base falkon algorithm with decreasing
+amounts of regularization; therefore the class accepts slightly different parameters from
+<a class="reference internal" href="#falkon.models.Falkon" title="falkon.models.Falkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.Falkon</span></code></a>: a <cite>penalty_list</cite> which should contain a list of decreasing
+regularization amounts, and an <cite>iter_list</cite> which should specify for each application
+of the base algorithm, how many CG iterations to use. For guidance on how to set these
+parameters, see below.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel</strong> – Object representing the kernel function used for KRR.</p></li>
+<li><p><strong>penalty_list</strong> (<em>List</em><em>[</em><em>float</em><em>]</em>) – Amount of regularization to use for each iteration of the base algorithm. The length
+of this list determines the number of base algorithm iterations.</p></li>
+<li><p><strong>iter_list</strong> (<em>List</em><em>[</em><em>int</em><em>]</em>) – Number of conjugate gradient iterations used in each iteration of the base algorithm.
+The length of this list must be identical to that of <cite>penalty_list</cite>.</p></li>
+<li><p><strong>loss</strong> (<a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><em>Loss</em></a>) – This parameter must be set to an instance of <a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss" title="falkon.gsc_losses.LogisticLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.gsc_losses.LogisticLoss</span></code></a>,
+initialized with the same kernel as this class.</p></li>
+<li><p><strong>M</strong> (<em>int</em>) – The number of Nystrom centers to pick. <cite>M</cite> must be positive,
+and lower than the total number of training points. A larger
+<cite>M</cite> will typically lead to better accuracy but will use more
+computational resources.</p></li>
+<li><p><strong>center_selection</strong> (<em>str</em><em> or </em><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><em>falkon.center_selection.CenterSelector</em></a>) – The center selection algorithm. Implemented is only ‘uniform’
+selection which can choose each training sample with the same
+probability.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em> or </em><em>None</em>) – Random seed. Can be used to make results stable across runs.
+Randomness is present in the center selection algorithm, and in
+certain optimizers.</p></li>
+<li><p><strong>error_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – A function with two arguments: targets and predictions, both <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code>
+objects which returns the error incurred for predicting ‘predictions’ instead of
+‘targets’. This is used to display the evolution of the error during the iterations.</p></li>
+<li><p><strong>error_every</strong> (<em>int</em><em> or </em><em>None</em>) – Evaluate the error (on training or validation data) every
+<cite>error_every</cite> iterations. If set to 1 then the error will be
+calculated at each iteration. If set to None, it will never be
+calculated.</p></li>
+<li><p><strong>options</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Additional options used by the components of the Falkon solver. Individual options
+are documented in <a class="reference internal" href="options.html#module-falkon.options" title="falkon.options"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.options</span></code></a>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Running Logistic Falkon on a random dataset</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span><span class="p">[</span><span class="n">Y</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span><span class="p">[</span><span class="n">Y</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">options</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">LogisticFalkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty_list</span><span class="o">=</span><span class="p">[</span><span class="mf">1e-2</span><span class="p">,</span> <span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-6</span><span class="p">,</span> <span class="mf">1e-6</span><span class="p">,</span> <span class="mf">1e-6</span><span class="p">],</span>
+<span class="gp">&gt;&gt;&gt; </span>                       <span class="n">iter_list</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">M</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+<p class="rubric">References</p>
+<dl class="footnote brackets">
+<dt class="label" id="id5"><span class="brackets"><a class="fn-backref" href="#id3">1</a></span></dt>
+<dd><p>Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, “Globally Convergent Newton Methods
+for Ill-conditioned Generalized Self-concordant Losses,” NeurIPS 32, 2019.</p>
+</dd>
+<dt class="label" id="id6"><span class="brackets"><a class="fn-backref" href="#id4">2</a></span></dt>
+<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+“Kernel methods through the roof: handling billions of points efficiently,”
+arXiv:2006.10350, 2020.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>A rule of thumb for <strong>setting the `penalty_list`</strong> is to keep in mind the desired final
+regularization (1e-6 in the example above), and then create a short path of around three
+iterations where the regularization is decreased down to the desired value. The decrease can
+be of 10^2 or 10^3 at each step. Then a certain number of iterations at the desired
+regularization may be necessary to achieve good performance.
+The <cite>iter_list</cite> attribute  follows a similar reasoning: use 3 inner-steps for the first three
+iterations where the regularization is decreased, and then switch to a higher number of
+inner-steps (e.g. 8) for the remaining iterations.</p>
+<dl class="py method">
+<dt id="falkon.models.LogisticFalkon.fit">
+<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Fits the Falkon Kernel Logistic Regression model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>torch.Tensor</em>) – The tensor of training data, of shape [num_samples, num_dimensions].
+If X is in Fortran order (i.e. column-contiguous) then we can avoid
+an extra copy of the data.</p></li>
+<li><p><strong>Y</strong> (<em>torch.Tensor</em>) – The tensor of training targets, of shape [num_samples, num_outputs].
+If X and Y represent a classification problem, Y can be encoded as a one-hot
+vector.
+If Y is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data.</p></li>
+<li><p><strong>Xts</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Tensor of validation data, of shape [num_test_samples, num_dimensions].
+If validation data is provided and <cite>error_fn</cite> was specified when
+creating the model, they will be used to print the validation error
+during the optimization iterations.
+If Xts is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data.</p></li>
+<li><p><strong>Yts</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Tensor of validation targets, of shape [num_test_samples, num_outputs].
+If validation data is provided and <cite>error_fn</cite> was specified when
+creating the model, they will be used to print the validation error
+during the optimization iterations.
+If Yts is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>model</strong> (<em>LogisticFalkon</em>) – The fitted model</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.LogisticFalkon.get_params">
+<code class="sig-name descname"><span class="pre">get_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.get_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>params</strong> (<em>dict</em>) – Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.LogisticFalkon.predict">
+<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.models.LogisticFalkon.predict" title="Permalink to this definition">¶</a></dt>
+<dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>torch.Tensor</em>) – Tensor of test data points, of shape [num_samples, num_dimensions].</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>predictions</strong> (<em>torch.Tensor</em>) – Prediction tensor of shape [num_samples, num_outputs] for all
+data points.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.LogisticFalkon.set_params">
+<code class="sig-name descname"><span class="pre">set_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.set_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s
+possible to update each component of a nested object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>self</strong> (<em>estimator instance</em>) – Estimator instance.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="incorefalkon">
+<h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.models.InCoreFalkon">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.models.</span></code><code class="sig-name descname"><span class="pre">InCoreFalkon</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition">¶</a></dt>
+<dd><p>In GPU core Falkon Kernel Ridge Regression solver.</p>
+<p>This estimator object solves approximate kernel ridge regression problems with Nystroem
+projections and a fast optimization algorithm as described in <a class="reference internal" href="#flkk-1" id="id7"><span>[flkk_1]</span></a>, <a class="reference internal" href="#flkk-2" id="id8"><span>[flkk_2]</span></a>.</p>
+<p>Multiclass and multiple regression problems can all be tackled
+with this same object, for example by encoding multiple classes
+in a one-hot target matrix.</p>
+<p>Compared to the base <cite>falkon.models.Falkon</cite> estimator, the <cite>InCoreFalkon</cite> estimator
+is designed to work fully within the GPU, performing no data-copies between CPU and GPU. As
+such, it is more constraining than the base estimator, but <strong>has better performance on smaller
+problems</strong>.
+In particular, the constraints are that:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>the input data must be on a single GPU, when calling <cite>InCoreFalkon.fit</cite>;</p></li>
+<li><p>the data, preconditioner, kernels, etc. must all fit on the same GPU.</p></li>
+</ul>
+</div></blockquote>
+<p>Using multiple GPUs is not possible with this model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel</strong> – Object representing the kernel function used for KRR.</p></li>
+<li><p><strong>penalty</strong> (<em>float</em>) – Amount of regularization to apply to the problem.
+This parameter must be greater than 0.</p></li>
+<li><p><strong>M</strong> (<em>int</em>) – The number of Nystrom centers to pick. <cite>M</cite> must be positive,
+and lower than the total number of training points. A larger
+<cite>M</cite> will typically lead to better accuracy but will use more
+computational resources. You can either specify the number of centers
+by setting this parameter, or by passing to this constructor a <cite>CenterSelctor</cite> class
+instance.</p></li>
+<li><p><strong>center_selection</strong> (<em>str</em><em> or </em><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><em>falkon.center_selection.CenterSelector</em></a>) – The center selection algorithm. Implemented is only ‘uniform’
+selection which can choose each training sample with the same
+probability.</p></li>
+<li><p><strong>maxiter</strong> (<em>int</em>) – The number of iterations to run the optimization for. Usually
+fewer than 20 iterations are necessary, however this is problem
+dependent.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em> or </em><em>None</em>) – Random seed. Can be used to make results stable across runs.
+Randomness is present in the center selection algorithm, and in
+certain optimizers.</p></li>
+<li><p><strong>error_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – A function with two arguments: targets and predictions, both <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code>
+objects which returns the error incurred for predicting ‘predictions’ instead of
+‘targets’. This is used to display the evolution of the error during the iterations.</p></li>
+<li><p><strong>error_every</strong> (<em>int</em><em> or </em><em>None</em>) – Evaluate the error (on training or validation data) every
+<cite>error_every</cite> iterations. If set to 1 then the error will be
+calculated at each iteration. If set to None, it will never be
+calculated.</p></li>
+<li><p><strong>weight_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – <p>A function which appropriately weighs the target tensor (i.e. Y). This function is used
+for weighted least-squares, it should accept a single argument which represents a subset
+of the targets, and return a vector of weights corresponding to the input targets.</p>
+<p>As an example, in the setting of binary classification Y can be -1 or +1. To give more
+importance to errors on the negative class, pass a <cite>weight_fn</cite> which returns 2 whenever
+the target is -1.</p>
+</p></li>
+<li><p><strong>options</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Additional options used by the components of the Falkon solver. Individual options
+are documented in <a class="reference internal" href="options.html#module-falkon.options" title="falkon.options"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.options</span></code></a>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Running Falkon on a random dataset</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">options</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">InCoreFalkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">assert</span> <span class="n">preds</span><span class="o">.</span><span class="n">is_cuda</span>
+</pre></div>
+</div>
+<p class="rubric">References</p>
+<dl class="citation">
+<dt class="label" id="flkk-1"><span class="brackets"><a class="fn-backref" href="#id7">flkk_1</a></span></dt>
+<dd><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
+scale kernel method,” Advances in Neural Information Processing Systems 29, 2017.</p>
+</dd>
+<dt class="label" id="flkk-2"><span class="brackets"><a class="fn-backref" href="#id8">flkk_2</a></span></dt>
+<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+“Kernel methods through the roof: handling billions of points efficiently,”
+arXiv:2006.10350, 2020.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.models.InCoreFalkon.fit">
+<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Fits the Falkon KRR model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>torch.Tensor</em>) – The tensor of training data, of shape [num_samples, num_dimensions].
+If X is in Fortran order (i.e. column-contiguous) then we can avoid
+an extra copy of the data. Must be a CUDA tensor.</p></li>
+<li><p><strong>Y</strong> (<em>torch.Tensor</em>) – The tensor of training targets, of shape [num_samples, num_outputs].
+If X and Y represent a classification problem, Y can be encoded as a one-hot
+vector.
+If Y is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data. Must be a CUDA tensor.</p></li>
+<li><p><strong>Xts</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Tensor of validation data, of shape [num_test_samples, num_dimensions].
+If validation data is provided and <cite>error_fn</cite> was specified when
+creating the model, they will be used to print the validation error
+during the optimization iterations.
+If Xts is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data. Must be a CUDA tensor.</p></li>
+<li><p><strong>Yts</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Tensor of validation targets, of shape [num_test_samples, num_outputs].
+If validation data is provided and <cite>error_fn</cite> was specified when
+creating the model, they will be used to print the validation error
+during the optimization iterations.
+If Yts is in Fortran order (i.e. column-contiguous) then we can avoid an
+extra copy of the data. Must be a CUDA tensor.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>model</strong> (<em>InCoreFalkon</em>) – The fitted model</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.InCoreFalkon.get_params">
+<code class="sig-name descname"><span class="pre">get_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.get_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>params</strong> (<em>dict</em>) – Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.InCoreFalkon.predict">
+<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.models.InCoreFalkon.predict" title="Permalink to this definition">¶</a></dt>
+<dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>torch.Tensor</em>) – Tensor of test data points, of shape [num_samples, num_dimensions].</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>predictions</strong> (<em>torch.Tensor</em>) – Prediction tensor of shape [num_samples, num_outputs] for all
+data points.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.models.InCoreFalkon.set_params">
+<code class="sig-name descname"><span class="pre">set_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.set_params" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s
+possible to update each component of a nested object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>self</strong> (<em>estimator instance</em>) – Estimator instance.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+
+
+           </div>
+           
+          </div>
+          <footer>
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+        <a href="kernels.html" class="btn btn-neutral float-right" title="falkon.kernels" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="index.html" class="btn btn-neutral float-left" title="API Reference" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+    </div>
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+  <hr/>
+
+  <div role="contentinfo">
+    <p>
+        &#169; Copyright 2020, Giacomo Meanti.
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+<section id="module-falkon.optim">
+<span id="falkon-optim"></span><h1>falkon.optim<a class="headerlink" href="#module-falkon.optim" title="Permalink to this headline">¶</a></h1>
+<section id="optimizer">
+<h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.optim.Optimizer">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.optim.</span></code><code class="sig-name descname"><span class="pre">Optimizer</span></code><a class="headerlink" href="#falkon.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="conjugate-gradient-methods">
+<h2>Conjugate gradient methods<a class="headerlink" href="#conjugate-gradient-methods" title="Permalink to this headline">¶</a></h2>
+<section id="conjugategradient">
+<h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.optim.ConjugateGradient">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.optim.</span></code><code class="sig-name descname"><span class="pre">ConjugateGradient</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.ConjugateGradientOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.ConjugateGradient" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="falkonconjugategradient">
+<h3>FalkonConjugateGradient<a class="headerlink" href="#falkonconjugategradient" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.optim.FalkonConjugateGradient">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.optim.</span></code><code class="sig-name descname"><span class="pre">FalkonConjugateGradient</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner" title="falkon.preconditioner.preconditioner.Preconditioner"><span class="pre">falkon.preconditioner.preconditioner.Preconditioner</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.FalkonConjugateGradient" title="Permalink to this definition">¶</a></dt>
+<dd><p>Preconditioned conjugate gradient solver, optimized for the Falkon algorithm.</p>
+<p>The linear system solved is</p>
+<div class="math notranslate nohighlight">
+\[\widetilde{B}^\top H \widetilde{B} \beta = \widetilde{B}^\top K_{nm}^\top Y\]</div>
+<p>where <span class="math notranslate nohighlight">\(\widetilde{B}\)</span> is the approximate preconditioner</p>
+<div class="math notranslate nohighlight">
+\[\widetilde{B} = 1/\sqrt{n}T^{-1}A^{-1}\]</div>
+<p><span class="math notranslate nohighlight">\(\beta\)</span> is the preconditioned solution vector (from which we can get <span class="math notranslate nohighlight">\(\alpha = \widetilde{B}\beta\)</span>),
+and <span class="math notranslate nohighlight">\(H\)</span> is the <span class="math notranslate nohighlight">\(m\times m\)</span> sketched matrix</p>
+<div class="math notranslate nohighlight">
+\[H = K_{nm}^\top K_{nm} + \lambda n K_{mm}\]</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel</strong> – The kernel class used for the CG algorithm</p></li>
+<li><p><strong>preconditioner</strong> – The approximate Falkon preconditioner. The class should allow triangular solves with
+both <span class="math notranslate nohighlight">\(T\)</span> and <span class="math notranslate nohighlight">\(A\)</span> and multiple right-hand sides.
+The preconditioner should already have been initialized with a set of Nystrom centers.
+If the Nystrom centers used for CG are different from the ones used for the preconditioner,
+the CG method could converge very slowly.</p></li>
+<li><p><strong>opt</strong> – Options passed to the CG solver and to the kernel for computations.</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="preconditioner.html#falkon.preconditioner.FalkonPreconditioner" title="falkon.preconditioner.FalkonPreconditioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.preconditioner.FalkonPreconditioner</span></code></a></dt><dd><p>for the preconditioner class which is responsible for computing matrices <cite>T</cite> and <cite>A</cite>.</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+</section>
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+<section id="module-falkon.options">
+<span id="falkon-options"></span><span id="api-options"></span><h1>falkon.options<a class="headerlink" href="#module-falkon.options" title="Permalink to this headline">¶</a></h1>
+<section id="falkonoptions">
+<h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.options.FalkonOptions">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.options.</span></code><code class="sig-name descname"><span class="pre">FalkonOptions</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keops_acc_dtype</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_sum_scheme</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_active</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_in_core</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_ooc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lauum_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-13</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_preconditioner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_tolerance</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_full_gradient_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gpu_mem</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cpu_mem</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_arch_speed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_single_kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">30000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">300000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">900000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">never_store_kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_fmm_streams</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.options.FalkonOptions" title="Permalink to this definition">¶</a></dt>
+<dd><p>Global options for Falkon.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>debug</strong> (<em>bool</em>) – <cite>default False</cite> - When set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, the estimators will print extensive debugging information.
+Set it if you want to dig deeper.</p></li>
+<li><p><strong>use_cpu</strong> (<em>bool</em>) – <cite>default False</cite> - When set to <code class="docutils literal notranslate"><span class="pre">True</span></code> forces Falkon not to use the GPU. If this option is not set,
+and no GPU is available, Falkon will issue a warning.</p></li>
+<li><p><strong>max_gpu_mem</strong> (<em>float</em>) – The maximum GPU memory (in bytes) that Falkon may use. If not set, Falkon will
+use all available memory.</p></li>
+<li><p><strong>max_cpu_mem</strong> (<em>float</em>) – The maximum CPU RAM (in bytes) that Falkon may use. If not set, Falkon will
+use all available memory. This option is not a strict bound (due to the nature
+of memory management in Python).</p></li>
+<li><p><strong>compute_arch_speed</strong> (<em>bool</em>) – <cite>default False</cite> - When running Falkon on a machine with multiple GPUs which have a range of different
+performance characteristics, setting this option to <cite>True</cite> may help subdivide the
+workload better: the performance of each accelerator will be evaluated on startup,
+then the faster devices will receive more work than the slower ones.
+If this is not the case, do not set this option since evaluating accelerator performance
+increases startup times.</p></li>
+<li><p><strong>no_single_kernel</strong> (<em>bool</em>) – <cite>default True</cite> - Whether the kernel should always be evaluated in double precision.
+If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, kernel evaluations will be faster but less precise (note that this
+referes only to calculations involving the full kernel matrix, not to kernel-vector
+products).</p></li>
+<li><p><strong>min_cuda_pc_size_32</strong> (<em>int</em>) – <cite>default 10000</cite> - If M (the number of Nystroem centers) is lower than <code class="docutils literal notranslate"><span class="pre">min_cuda_pc_size_32</span></code>, falkon will
+run the preconditioner on the CPU. Otherwise, if CUDA is available, falkon will try
+to run the preconditioner on the GPU. This setting is valid for data in single
+(float32) precision.
+Along with the <code class="docutils literal notranslate"><span class="pre">min_cuda_iter_size_32</span></code> setting, this determines a cutoff for running
+Falkon on the CPU or the GPU. Such cutoff is useful since for small-data problems
+running on the CPU may be faster than running on the GPU. If your data is close to the
+cutoff, it may be worth experimenting with running on the CPU and on the GPU to check
+which side is faster. This will depend on the exact hardware.</p></li>
+<li><p><strong>min_cuda_pc_size_64</strong> (<em>int</em>) – <cite>default 30000</cite> - If M (the number of Nystroem centers) is lower than <code class="docutils literal notranslate"><span class="pre">min_cuda_pc_size_64</span></code>,
+falkon will run the preconditioner on the CPU. Otherwise, if CUDA is available, falkon will try
+to run the preconditioner on the GPU. This setting is valid for data in double
+(float64) precision.
+Along with the <code class="docutils literal notranslate"><span class="pre">min_cuda_iter_size_64</span></code> setting, this determines a cutoff for running
+Falkon on the CPU or the GPU. Such cutoff is useful since for small-data problems
+running on the CPU may be faster than running on the GPU. If your data is close to the
+cutoff, it may be worth experimenting with running on the CPU and on the GPU to check
+which side is faster. This will depend on the exact hardware.</p></li>
+<li><p><strong>min_cuda_iter_size_32</strong> (<em>int</em>) – <cite>default 300_000_000</cite> - If the data size (measured as the product of M, and the dimensions of X) is lower than
+<code class="docutils literal notranslate"><span class="pre">min_cuda_iter_size_32</span></code>, falkon will run the conjugate gradient iterations on the CPU.
+For example, with the default setting, the CPU-GPU threshold is set at a dataset
+with 10k points, 10 dimensions, and 3k Nystroem centers. A larger dataset, or the use
+of more centers, will cause the conjugate gradient iterations to run on the GPU.
+This setting is valid for data in single (float32) precision.</p></li>
+<li><p><strong>min_cuda_iter_size_64</strong> (<em>int</em>) – <cite>default 900_000_000</cite> - If the data size (measured as the product of M, and the dimensions of X) is lower than
+<code class="docutils literal notranslate"><span class="pre">min_cuda_iter_size_64</span></code>, falkon will run the conjugate gradient iterations on the CPU.
+For example, with the default setting, the CPU-GPU threshold is set at a dataset
+with 30k points, 10 dimensions, and 3k Nystroem centers. A larger dataset, or the use
+of more centers, will cause the conjugate gradient iterations to run on the GPU.
+This setting is valid for data in double (float64) precision.</p></li>
+<li><p><strong>never_store_kernel</strong> (<em>bool</em>) – <cite>default False</cite> - If set to True, the kernel between the data and the Nystroem centers will not
+be stored - even if there is sufficient RAM to do so. Setting this option to
+True may (in case there would be enough RAM to store the kernel), increase the
+training time for Falkon since the K_NM matrix must be recomputed at every
+conjugate gradient iteration.</p></li>
+<li><p><strong>num_fmm_streams</strong> (<em>int</em>) – <cite>default 2</cite> - The number of CUDA streams to use for evaluating kernels when CUDA is available.
+This number should be increased from its default value when the number of Nystroem centers is
+higher than around 5000.</p></li>
+<li><p><strong>keops_acc_dtype</strong> (<em>str</em>) – <cite>default “auto”</cite> - A string describing the accumulator data-type for KeOps.
+For more information refer to the
+<a class="reference external" href="https://www.kernel-operations.io/keops/python/api/pytorch/Genred_torch.html?highlight=genred#pykeops.torch.Genred">KeOps documentation</a></p></li>
+<li><p><strong>keops_sum_scheme</strong> (<em>str</em>) – <cite>default “auto”</cite> - Accumulation scheme for KeOps.
+For more information refer to the <a class="reference external" href="https://www.kernel-operations.io/keops/python/api/pytorch/Genred_torch.html?highlight=genred#pykeops.torch.Genred">KeOps documentation</a></p></li>
+<li><p><strong>keops_active</strong> (<em>str</em>) – <cite>default “auto”</cite> - Whether to use or not to use KeOps. Three settings are allowed, specified by strings:
+‘auto’ (the default setting) means that KeOps will be used if it is installed correctly,
+‘no’ means keops will not be used, nor will it be imported, and ‘force’ means that if KeOps is
+not installed an error will be raised.</p></li>
+<li><p><strong>cg_epsilon_32</strong> (<em>float</em>) – <cite>default 1e-7</cite> - Small added epsilon to prevent divide-by-zero errors in the conjugate
+gradient algorithm. Used for single precision data-types</p></li>
+<li><p><strong>cg_epsilon_64</strong> (<em>float</em>) – <cite>default 1e-15</cite> - Small added epsilon to prevent divide-by-zero errors in the conjugate
+gradient algorithm. Used for double precision data-types</p></li>
+<li><p><strong>cg_tolerance</strong> (<em>float</em>) – <cite>default 1e-7</cite> - Maximum change in model parameters between iterations. If less change than
+<code class="docutils literal notranslate"><span class="pre">cg_tolerance</span></code> is detected, then we regard the optimization as converged.</p></li>
+<li><p><strong>cg_full_gradient_every</strong> (<em>int</em>) – <cite>default 10</cite> - How often to calculate the full gradient in the conjugate gradient algorithm.
+Full-gradient iterations take roughly twice the time as normal iterations, but they reset
+the error introduced by the other iterations.</p></li>
+<li><p><strong>pc_epsilon_32</strong> (<em>float</em>) – <cite>default 1e-5</cite> - Epsilon used to increase the diagonal dominance of a matrix before its
+Cholesky decomposition (for single-precision data types).</p></li>
+<li><p><strong>pc_epsilon_64</strong> (<em>float</em>) – <cite>default 1e-13</cite> - Epsilon used to increase the diagonal dominance of a matrix before its
+Cholesky decomposition (for double-precision data types).</p></li>
+<li><p><strong>cpu_preconditioner</strong> (<em>bool</em>) – <cite>default False</cite> - Whether the preconditioner should be computed on the CPU. This setting
+overrides the <code class="xref py py-attr docutils literal notranslate"><span class="pre">FalkonOptions.use_cpu</span></code> option.</p></li>
+<li><p><strong>lauum_par_blk_multiplier</strong> (<em>int</em>) – <cite>default 8</cite> - Minimum number of tiles per-GPU for the LAUUM algorithm. This can be set
+quite high (e.g. 8) without too much performance degradation. Optimal settings will depend
+on the number of GPUs.</p></li>
+<li><p><strong>chol_force_in_core</strong> (<em>bool</em>) – <cite>default False</cite> - Whether to force in-core execution of the Cholesky decomposition. This will
+not work with matrices bigger than GPU memory.</p></li>
+<li><p><strong>chol_force_ooc</strong> (<em>bool</em>) – <cite>default False</cite> - Whether to force out-of-core (parallel) execution for the POTRF algorithm,
+even on matrices which fit in-GPU-core.</p></li>
+<li><p><strong>chol_par_blk_multiplier</strong> () – <cite>default 2</cite> - Minimum number of tiles per-GPU in the out-of-core, GPU-parallel POTRF algorithm.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../install.html">Install</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#supported-platforms">Supported Platforms</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
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+<section id="falkon-ooc-ops">
+<h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink to this headline">¶</a></h1>
+<p>The out-of-core algorithms for the Cholesky decomposition and the LAUUM operation are crucial for speeding up our
+library. To find out more about how they work, check the source code:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><a class="reference external" href="https://github.com/FalkonML/falkon/blob/master/falkon/ooc_ops/multigpu/cuda/multigpu_potrf.cu">Out of core Cholesky</a> (CUDA code)</p></li>
+<li><p><a class="reference external" href="https://github.com/FalkonML/falkon/blob/master/falkon/ooc_ops/parallel_lauumm.py">Out of core LAUUM</a> (Python code)</p></li>
+</ul>
+</div></blockquote>
+<p>The following functions provide a higher-level interface to the two operations.</p>
+<span class="target" id="module-falkon.ooc_ops"></span><section id="gpu-cholesky">
+<h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="falkon.ooc_ops.gpu_cholesky">
+<code class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></code><code class="sig-name descname"><span class="pre">gpu_cholesky</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.ooc_ops.gpu_cholesky" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<em>torch.Tensor</em>) – 2D positive-definite matrix of size (n x n) that will be factorized as
+A = U.T &#64; U (if <cite>upper</cite> is True) or A = L &#64; L.T if <cite>upper</cite>
+is False.</p></li>
+<li><p><strong>upper</strong> (<em>bool</em>) – Whether the triangle which should be factorized is the upper or lower of <cite>A</cite>.</p></li>
+<li><p><strong>clean</strong> (<em>bool</em>) – Whether the “other” triangle of the output matrix (the one that
+does not contain the factorization) will be filled with zeros or
+not.</p></li>
+<li><p><strong>overwrite</strong> (<em>bool</em>) – Whether to overwrite matrix A or to output the result in a new
+buffer.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Options forwarded for block calculation, and other knobs in the out-of-core
+parallel POTRF implementation. Useful options are the ones defined in
+<code class="xref py py-class docutils literal notranslate"><span class="pre">CholeskyOptions</span></code> .</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The factorization will always be the ‘lower’ version of the factorization
+which could however end up on the upper-triangular part of the matrix
+in case A is not Fortran contiguous to begin with.</p>
+</dd></dl>
+
+</section>
+<section id="gpu-lauum">
+<h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="falkon.ooc_ops.gpu_lauum">
+<code class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></code><code class="sig-name descname"><span class="pre">gpu_lauum</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_opposite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.ooc_ops.gpu_lauum" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<em>torch.Tensor</em>) – (N x N) positive-definite matrix that will be factorized as
+A = U.T &#64; U (if <cite>upper</cite> is True) or A = L &#64; L.T if <cite>upper</cite>
+is False.</p></li>
+<li><p><strong>overwrite</strong> (<em>bool</em>) – Whether to overwrite matrix A or to output the result in a new
+buffer.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – A (N x N) tensor. This will share the same memory as the input tensor <cite>A</cite> if <cite>overwrite</cite>
+is set to True, otherwise it will be a newly allocated tensor.</p>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
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+<section id="module-falkon.preconditioner">
+<span id="falkon-preconditioner"></span><h1>falkon.preconditioner<a class="headerlink" href="#module-falkon.preconditioner" title="Permalink to this headline">¶</a></h1>
+<section id="preconditioner">
+<h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.preconditioner.preconditioner.Preconditioner">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.preconditioner.preconditioner.</span></code><code class="sig-name descname"><span class="pre">Preconditioner</span></code><a class="headerlink" href="#falkon.preconditioner.preconditioner.Preconditioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generic preconditioner class, used to accelerate solutions to linear systems.</p>
+<p>Given a system of equations <span class="math notranslate nohighlight">\(H\beta = Y\)</span>, where <span class="math notranslate nohighlight">\(H\)</span> typically contains in some
+form our data matrix <cite>X</cite> and <cite>Y</cite> contains the targets. We can use matrix <span class="math notranslate nohighlight">\(B\)</span> to
+create an equivalent linear system which will have lower condition number:</p>
+<div class="math notranslate nohighlight">
+\[BB^\top H \beta = Y\]</div>
+<p>where <span class="math notranslate nohighlight">\(BB^\top \approx H^{-1}\)</span> in order to make the preconditioner effective, but not
+too expensive to compute. Then, in order to use the preconditioner in an algorithm based
+on matrix-vector products (such as conjugate gradient descent), we must be able to “apply” the
+matrix <span class="math notranslate nohighlight">\(B\)</span> and its transpose <span class="math notranslate nohighlight">\(B^        op\)</span> to any vector.</p>
+<p>For this reason, this class exposes abstract methods <cite>apply</cite> and <cite>apply_t</cite> which should
+be overridden in concrete preconditioner implementations</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#falkon.preconditioner.FalkonPreconditioner" title="falkon.preconditioner.FalkonPreconditioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.preconditioner.FalkonPreconditioner</span></code></a></dt><dd><p>for an actual preconditioner implementation</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+</section>
+<section id="cholesky-preconditioners">
+<h2>Cholesky preconditioners<a class="headerlink" href="#cholesky-preconditioners" title="Permalink to this headline">¶</a></h2>
+<section id="falkonpreconditioner">
+<h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.preconditioner.FalkonPreconditioner">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></code><code class="sig-name descname"><span class="pre">FalkonPreconditioner</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>Approximated Cholesky Preconditioner for FALKON.</p>
+<p>The preconditioner is based on the <span class="math notranslate nohighlight">\(K_{MM}\)</span> kernel between the
+inducing points. A two step approximation of the inverse matrix
+via two Cholesky decompositions is performed.</p>
+<p>Starting with <span class="math notranslate nohighlight">\(K_{MM}\)</span> we obtain <span class="math notranslate nohighlight">\(T = \mathrm{chol}(K_{MM})\)</span>.
+Then we can obtain <span class="math notranslate nohighlight">\(A = \mathrm{chol}(\frac{1}{M} T T^\top + \lambda)\)</span> via another Cholesky
+decomposition. Both <cite>T</cite> and <cite>A</cite> are upper triangular: the first gets stored in the upper
+triangle of the <span class="math notranslate nohighlight">\(K_{MM}\)</span> matrix (called <cite>fC</cite> in the code), while the second is stored
+in the lower triangle.</p>
+<p>Whenever we want to use one of the two triangles we must reset the matrix diagonal, since
+it is shared between the two matrices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>penalty</strong> (<em>float</em>) – The regularization parameter for KRR. Must be greater than 0.</p></li>
+<li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel object. This is used to compute the M*M kernel
+between inducing points. The kernel matrix is then overwritten by
+the preconditioner itself.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – <p>Additional options to be used in computing the preconditioner.
+Relevant options are:</p>
+<ul>
+<li><dl class="simple">
+<dt>pc_epsilon<span class="classifier">the jitter to add to the kernel matrix to make</span></dt><dd><p>it positive-definite and allow Cholesky decomposition.
+This can be either a float, or a dictionary mapping from
+torch datatypes (e.g. float32, float64) to an appropriate
+float. Typically float32 requires more jitter than float64.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>cpu_preconditioner<span class="classifier">a boolean value which overrides CPU/GPU</span></dt><dd><p>settings and forces the function to compute the whole
+preconditioner on the CPU. If set to False, we fall back to
+the usual CPU/GPU settings (i.e. ‘use_cpu’ option and the
+availability of a GPU).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.preconditioner.FalkonPreconditioner.apply">
+<code class="sig-name descname"><span class="pre">apply</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(ATx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.FalkonPreconditioner.apply_t">
+<code class="sig-name descname"><span class="pre">apply_t</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply_t" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(A^\top T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.FalkonPreconditioner.init">
+<code class="sig-name descname"><span class="pre">init</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_vec</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.init" title="Permalink to this definition">¶</a></dt>
+<dd><p>Initialize the preconditioner matrix.</p>
+<p>This method must be called before the preconditioner can be used.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>torch.Tensor</em>) – The (M x D) matrix of Nystroem centers</p></li>
+<li><p><strong>weight_vec</strong> – An optional vector of size (M x 1) which is used for reweighted least-squares.
+This vector should contain the weights corresponding to the Nystrom centers.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.FalkonPreconditioner.invA">
+<code class="sig-name descname"><span class="pre">invA</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invA" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Ax = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.FalkonPreconditioner.invAt">
+<code class="sig-name descname"><span class="pre">invAt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invAt" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(A^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.FalkonPreconditioner.invT">
+<code class="sig-name descname"><span class="pre">invT</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invT" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Tx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.FalkonPreconditioner.invTt">
+<code class="sig-name descname"><span class="pre">invTt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invTt" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="logisticpreconditioner">
+<h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="falkon.preconditioner.LogisticPreconditioner">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></code><code class="sig-name descname"><span class="pre">LogisticPreconditioner</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner" title="Permalink to this definition">¶</a></dt>
+<dd><p>Approximate Cholesky Preconditioner for Logistic-FALKON.</p>
+<p>The preconditioner is based on the K_MM kernel between the
+inducing points. A two step approximation of the inverse matrix
+via two cholesky decompositions is performed.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">T</span> <span class="o">=</span> <span class="n">chol</span><span class="p">(</span><span class="n">K_MM</span><span class="p">)</span>    <span class="o">=&gt;</span> <span class="n">T</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">T</span> <span class="o">=</span> <span class="n">K_MM</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">chol</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">M</span> <span class="o">*</span> <span class="p">(</span><span class="n">T</span> <span class="o">@</span> <span class="p">(</span><span class="n">T</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">W</span><span class="p">))</span> <span class="o">+</span> <span class="k">lambda</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>So <cite>T</cite> and <cite>A</cite> are both upper triangular.
+<cite>W</cite> is a diagonal matrix of weights derived from the 2nd derivative of the loss function.</p>
+<p>Here we store <cite>T</cite> in the upper triangular part of the <cite>fC</cite> matrix,
+and <cite>A</cite> in the upper triangular part of the matrix.
+Whenever we need to use one or the other we need to reset the
+diagonal of <cite>fC</cite> since it is shared between the two matrices.
+<cite>W</cite> is of size <cite>M</cite> and is the only difference with respect to the normal FALKON preconditioner
+(<a class="reference internal" href="#falkon.preconditioner.FalkonPreconditioner" title="falkon.preconditioner.FalkonPreconditioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.preconditioner.FalkonPreconditioner</span></code></a>).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel object. This is used to compute the M*M kernel
+between inducing points. This kernel is then overwritten by
+the preconditioner itself.</p></li>
+<li><p><strong>loss</strong> (<a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><em>falkon.gsc_losses.Loss</em></a>) – The loss-function used for defining kernel weights.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – <p>Additional options to be used in computing the preconditioner.
+Relevant options are:</p>
+<ul>
+<li><dl class="simple">
+<dt>pc_epsilon<span class="classifier">the jitter to add to the kernel matrix to make</span></dt><dd><p>it positive-definite and allow Cholesky decomposition.
+This can be either a float, or a dictionary mapping from
+torch datatypes (e.g. float32, float64) to an appropriate
+float. Typically float32 requires more jitter than float64.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>cpu_preconditioner<span class="classifier">a boolean value which overrides CPU/GPU</span></dt><dd><p>settings and forces the function to compute the whole
+preconditioner on the CPU. If set to False, we fall back to
+the usual CPU/GPU settings (i.e. ‘use_cpu’ option and the
+availability of a GPU).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p></li>
+</ul>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss" title="falkon.gsc_losses.LogisticLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.gsc_losses.LogisticLoss</span></code></a></dt><dd><p>for an example of loss function used for kernel reweighting.</p>
+</dd>
+<dt><a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.LogisticFalkon</span></code></a></dt><dd><p>for the logistic kernel estimator which uses this preconditioner.</p>
+</dd>
+</dl>
+</div>
+<dl class="py method">
+<dt id="falkon.preconditioner.LogisticPreconditioner.apply">
+<code class="sig-name descname"><span class="pre">apply</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.apply" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(ATx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.LogisticPreconditioner.apply_t">
+<code class="sig-name descname"><span class="pre">apply_t</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.apply_t" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(A^\top T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.LogisticPreconditioner.init">
+<code class="sig-name descname"><span class="pre">init</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.init" title="Permalink to this definition">¶</a></dt>
+<dd><p>Initialize the preconditioner matrix.</p>
+<p>This method must be called before the preconditioner becomes usable.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>torch.Tensor</em>) – (M x D) matrix of Nystroem centers</p></li>
+<li><p><strong>Y</strong> (<em>torch.Tensor</em>) – (M x 1) vector of targets corresponding to the Nystroem centers <cite>X</cite></p></li>
+<li><p><strong>alpha</strong> (<em>torch.Tensor</em>) – (M x 1) parameter vector (of the same dimension as <cite>Y</cite>) which gives the current
+solution to the optimization problem.</p></li>
+<li><p><strong>penalty</strong> (<em>float</em>) – Regularization amount</p></li>
+<li><p><strong>N</strong> (<em>int</em>) – Number of points in the full data-set.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If <cite>debug=True</cite> is present in the options, this method will print a lot of extra
+information pertaining timings of the various preconditioner operations. This can be
+useful to help understand how the preconditioner works.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.LogisticPreconditioner.invA">
+<code class="sig-name descname"><span class="pre">invA</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invA" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Ax = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.LogisticPreconditioner.invAt">
+<code class="sig-name descname"><span class="pre">invAt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invAt" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(A^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.LogisticPreconditioner.invT">
+<code class="sig-name descname"><span class="pre">invT</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invT" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Tx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt id="falkon.preconditioner.LogisticPreconditioner.invTt">
+<code class="sig-name descname"><span class="pre">invTt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invTt" title="Permalink to this definition">¶</a></dt>
+<dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
+<p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.preconditioner.pc_utils.trsm()</span></code></dt><dd><p>the function used to solve the system of equations</p>
+</dd>
+</dl>
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+<section id="module-falkon.sparse">
+<span id="falkon-sparse"></span><h1>falkon.sparse<a class="headerlink" href="#module-falkon.sparse" title="Permalink to this headline">¶</a></h1>
+<section id="sparsetensor">
+<h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt id="falkon.sparse.sparse_tensor.SparseTensor">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></code><code class="sig-name descname"><span class="pre">SparseTensor</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexptr</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_type</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><span class="pre">falkon.sparse.sparse_tensor.SparseType</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">csr</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor" title="Permalink to this definition">¶</a></dt>
+<dd><p>Wrapper class to represent sparse 2D matrices in CSR or CSC format.</p>
+<p>The wrapper holds three 1D torch tensors which give the sparse representation
+(an index pointer, an index and the non-zero values of the matrix).
+It supports some of the common torch tensor management functions (e.g. <cite>pin_memory</cite>, <cite>device</cite>,
+<cite>size</cite>) and conversion to and from the corresponding scipy sparse matrix representation.
+It does <strong>not</strong> define any mathematical function on sparse matrices, which are
+instead defined separately (see <a class="reference internal" href="#falkon.sparse.sparse_matmul" title="falkon.sparse.sparse_matmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a> for example).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>indexptr</strong> (<em>torch.Tensor</em>) – Array of row (or column for CSC data) pointers into the
+<cite>index</cite> and <cite>data</cite> arrays. Should be either of type long or int.</p></li>
+<li><p><strong>index</strong> (<em>torch.Tensor</em>) – Array of column (or row for CSC data) indices for non-zero elements.
+Should be either of type long or int.</p></li>
+<li><p><strong>data</strong> (<em>torch.Tensor</em>) – Array of the non-zero elements for the sparse matrix.</p></li>
+<li><p><strong>size</strong> (<em>Tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – Shape of the 2D tensor (rows, columns).</p></li>
+<li><p><strong>sparse_type</strong> (<em>str</em><em> or </em><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><em>falkon.sparse.sparse_tensor.SparseType</em></a>) – Whether the matrix should be interpreted as CSR or CSC format.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="falkon.sparse.sparse_tensor.SparseTensor.narrow_rows">
+<code class="sig-name descname"><span class="pre">narrow_rows</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows" title="Permalink to this definition">¶</a></dt>
+<dd><p>Select a subset of contiguous rows from the sparse matrix.
+If this is a CSC sparse matrix, instead of taking contiguous rows we take contiguous
+columns.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>start</strong> (<em>int</em><em> or </em><em>None</em>) – The index of the first row to select. If None will be assumed to be 0.</p></li>
+<li><p><strong>length</strong> (<em>int</em><em> or </em><em>None</em>) – The number of rows to select. If None will be assumed to be all rows after <cite>start</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>SparseTensor</em> – A new <a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparseTensor</span></code></a> object with <cite>length</cite> rows.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The output matrix will share storage with the original matrix whenever possible.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt id="falkon.sparse.sparse_tensor.SparseType">
+<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></code><code class="sig-name descname"><span class="pre">SparseType</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseType" title="Permalink to this definition">¶</a></dt>
+<dd><p>Whether a <cite>SparseTensor</cite> is in CSC or CSR format.</p>
+</dd></dl>
+
+</section>
+<section id="sparse-operations">
+<h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt id="falkon.sparse.sparse_matmul">
+<code class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></code><code class="sig-name descname"><span class="pre">sparse_matmul</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.sparse.sparse_matmul" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sparse*Sparse matrix multiplication. Output will be copied into dense <cite>out</cite> matrix.</p>
+<p>This function can be applied to CPU or CUDA tensors (but all tensors must
+be  on the same device).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – N x D, sparse matrix.</p></li>
+<li><p><strong>B</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – D x M, sparse matrix</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em>) – Dense N x M tensor, it will hold the output of the multiplication.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The same tensor as the input <cite>out</cite> parameter.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt id="falkon.sparse.sparse_square_norm">
+<code class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></code><code class="sig-name descname"><span class="pre">sparse_square_norm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.sparse.sparse_square_norm" title="Permalink to this definition">¶</a></dt>
+<dd><p>Row-wise squared l2 norm of a sparse 2D matrix.</p>
+<p>The operation is equivalent to squaring all elements of the matrix, and summing up the rows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The 2D matrix. Since we compute row-wise norms, the matrix must be in CSR format (for
+efficiency).</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em>) – A dense tensor with the same number of rows as matrix <cite>A</cite>. Will contain the output
+of the squared-norm operation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The same tensor as the input <cite>out</cite> parameter.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This function is currently limited to CPU input tensors.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt id="falkon.sparse.sparse_norm">
+<code class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></code><code class="sig-name descname"><span class="pre">sparse_norm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.sparse.sparse_norm" title="Permalink to this definition">¶</a></dt>
+<dd><p>Row-wise l2 norm of a sparse 2D matrix</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The 2D matrix. Since we compute row-wise norms, the matrix must be in CSR format (for
+efficiency).</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em>) – A dense tensor with the same number of rows as matrix <cite>A</cite>. Will contain the output
+of the norm operation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The same tensor as the input <cite>out</cite> parameter.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This function is currently limited to CPU input tensors.</p>
+</dd></dl>
+
+</section>
+</section>
+
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="simple_regression.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
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+</ul>
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+</ul>
+</li>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
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+<section id="examples">
+<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1>
+<p>Starting with simple kernel ridge regression, via classification, hyperparameter tuning, to large-scale GPU experiments,
+these notebooks cover all there is to know about Falkon.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><a class="reference internal" href="simple_regression.html"><span class="doc">Kernel ridge regression</span></a> goes through the basic notions of the library with a simple example;</p></li>
+<li><p><a class="reference internal" href="logistic_falkon.html"><span class="doc">Logistic Falkon tutorial</span></a> shows how to use the Logistic Falkon estimator, comparing the results with normal Falkon;</p></li>
+<li><p><a class="reference internal" href="hyperparameter_tuning.html"><span class="doc">Hyperparameter tuning</span></a> is a fully worked out example of parameter optimization with Falkon for a real-world multi-class problem;</p></li>
+<li><p><a class="reference internal" href="performance_tuning.html"><span class="doc">GPU training</span></a> details the options to maximize performance on multiple GPU systems.</p></li>
+</ul>
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diff --git a/examples/hyperparameter_tuning.html b/examples/hyperparameter_tuning.html
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+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Training-the-model">Training the model</a></li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="#Plot-grid-search-results">Plot grid-search results</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
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+<section id="Hyperparameter-Tuning-with-Falkon">
+<h1>Hyperparameter Tuning with Falkon<a class="headerlink" href="#Hyperparameter-Tuning-with-Falkon" title="Permalink to this headline">¶</a></h1>
+<section id="Introduction">
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline">¶</a></h2>
+<p>We use Falkon for a multi-class problem (on the <a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">digits</a> dataset), showing how to integrate it into scikit-learn for hyperparameter optimization.</p>
+<p>Since both <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> and <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> are estimators, and follow scikit-learn’s API, integration is seamless.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span><span class="p">,</span> <span class="n">metrics</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">falkon</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Load-the-data">
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline">¶</a></h2>
+<p>We use the <strong>digits</strong> dataset, which is distributed alongside scikit-learn.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">Y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Label: 1
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/examples_hyperparameter_tuning_4_1.png" src="../_images/examples_hyperparameter_tuning_4_1.png" />
+</div>
+</div>
+</section>
+<section id="Split-into-training-and-test-sets">
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline">¶</a></h2>
+<p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Data-Preprocessing">
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline">¶</a></h2>
+<p>As always with Falkon we must: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Normalizing the data is always a good idea. Here we use the global mean and standard deviation of the training set for z-score normalization.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+<span class="n">X_train</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_train</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">X_test</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_test</span> <span class="o">/=</span> <span class="n">train_std</span>
+</pre></div>
+</div>
+</div>
+<p>Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># Convert labels to 1-hot</span>
+<span class="n">eye</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_train</span><span class="p">]</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_test</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label vector: &quot;</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])
+</pre></div></div>
+</div>
+</section>
+<section id="Search-for-the-optimal-parameters">
+<h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the-optimal-parameters" title="Permalink to this headline">¶</a></h2>
+<p>Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.</p>
+<p>Scikit-learn has great support for this, with the <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.</p>
+<p>Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<span class="n">mclass_scorer</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">mclass_loss</span><span class="p">,</span> <span class="n">greater_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>The parameter settings which will be explored by the grid-search are: - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data) - four different regularization values - two different values for M: the number of inducing points. As we will see, a larger <code class="docutils literal notranslate"><span class="pre">M</span></code> is almost always better than a smaller one (but it leads to longer training times). Of course this is not the case if the dataset is easy to overfit, since reducing <code class="docutils literal notranslate"><span class="pre">M</span></code> may
+also provide additional regularization.</p>
+<p>When we create the estimator we pass it additional parameters via the <code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code> class. In our case we want to ensure that the model runs on the CPU by setting <code class="docutils literal notranslate"><span class="pre">use_cpu=True</span></code>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">parameter_grid</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;kernel&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
+               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
+               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">15</span><span class="p">),],</span>
+    <span class="s1">&#39;penalty&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-9</span><span class="p">],</span>
+    <span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">],</span>
+<span class="p">}</span>
+<span class="n">estimator</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+<span class="n">grid_search</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">parameter_grid</span><span class="p">,</span> <span class="n">mclass_scorer</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/helpers.py:31: UserWarning: Failed to initialize cuBLAS library; falling back to CPU. Set &#39;use_cpu&#39; to True to avoid this warning.
+Error encountered was cublas library not found
+  warnings.warn(get_error_str(&#34;cuBLAS&#34;, e))
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%%time</span>
+<span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The best parameters are: &quot;</span><span class="p">,</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+The best parameters are:  {&#39;M&#39;: 1000, &#39;kernel&#39;: GaussianKernel(sigma=5.0), &#39;penalty&#39;: 1e-07}
+CPU times: user 2min 5s, sys: 17 s, total: 2min 22s
+Wall time: 35.6 s
+</pre></div></div>
+</div>
+<section id="Evaluating-the-model">
+<h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title="Permalink to this headline">¶</a></h3>
+<p>We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%%time</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator_</span>
+<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+CPU times: user 1.14 s, sys: 143 ms, total: 1.29 s
+Wall time: 323 ms
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">train_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">test_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Training error: 0.00%
+Test error: 1.11%
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Plot-grid-search-results">
+<h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-results" title="Permalink to this headline">¶</a></h2>
+<p>Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful. If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.</p>
+<p>In the plot red indicates a high error, while darker blue indicates a low error.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">res_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
+<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;M&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;M&quot;</span><span class="p">))</span>
+<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;penalty&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;penalty&quot;</span><span class="p">))</span>
+<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;sigma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;kernel&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+<span class="n">res_df</span> <span class="o">=</span> <span class="n">res_df</span><span class="p">[[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="s2">&quot;M&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;sigma&quot;</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">def</span> <span class="nf">plot_heatmap</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
+    <span class="n">piv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">ylabel</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
+    <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;coolwarm_r&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">piv</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vmax</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">c</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">scale</span> <span class="o">=</span> <span class="p">(</span><span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+
+<span class="n">c</span> <span class="o">=</span> <span class="n">plot_heatmap</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">res_df</span><span class="p">[</span><span class="n">res_df</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">500</span><span class="p">],</span> <span class="s2">&quot;sigma&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;M = 500&quot;</span><span class="p">)</span>
+<span class="n">c</span> <span class="o">=</span> <span class="n">plot_heatmap</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">res_df</span><span class="p">[</span><span class="n">res_df</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">1000</span><span class="p">],</span> <span class="s2">&quot;sigma&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;M = 1000&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/examples_hyperparameter_tuning_24_0.png" src="../_images/examples_hyperparameter_tuning_24_0.png" />
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
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+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+        <a href="performance_tuning.html" class="btn btn-neutral float-right" title="Training on the GPU" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="logistic_falkon.html" class="btn btn-neutral float-left" title="Introducing Logistic Falkon" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+
+  <div role="contentinfo">
+    <p>
+        &#169; Copyright 2020, Giacomo Meanti.
+
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+  </div>
+    
+    
+    
+    Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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+  <script type="text/javascript">
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+          SphinxRtdTheme.Navigation.enable(true);
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diff --git a/examples/hyperparameter_tuning.ipynb b/examples/hyperparameter_tuning.ipynb
new file mode 100644
index 00000000..53bb0f90
--- /dev/null
+++ b/examples/hyperparameter_tuning.ipynb
@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Hyperparameter Tuning with Falkon\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "We use Falkon for a multi-class problem (on the [digits](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits) dataset), showing how to integrate it into scikit-learn\n",
+    "for hyperparameter optimization.\n",
+    "\n",
+    "Since both `Falkon` and `LogisticFalkon` are estimators, and follow scikit-learn's API, integration is seamless."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection, metrics\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use the **digits** dataset, which is distributed alongside scikit-learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_digits(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kDENNJAxDTSQMQ00kDENNJAxDTSQMQ00kDENNJIxfEzp6e3tRXV2N27dvw2KxYNu2bZg+fbrRayMiHfwK9YkTJ5CdnY1PPvkEbrcbg4ODRq+LiHTyefnd19eH5ubmkYfRR0ZGIi4uzvCFEZE+Ps/ULpcLEydORFVVFW7dugWHw4H8/HxER0ebsT4iUuRz7E5bWxs+//xzHDp0CBkZGThx4gRiYmKwbt26Ua/jLK2n4yyt0ThLK/BeY83S8nmmttvtsNvtyMjIAADk5ubi1KlT//c6p9M5anCe6nygcJhfxFlawaFnJhZnaY0W0CythIQE2O32kf8lm5qakJKSorwIIjKHX7/93rx5MyoqKuB2uzFlyhRs377d6HURkU5+hfqll15CSUmJ0WshoiDgjjIiYRhqImEYaiJhGGoiYRhqImEYaiJhGGoiYRhqImH82nxCj3V3d+uq83g8umpPnz6tXLNo0SJcvHhRuW7t2rXKNXotXrxYuWbChAm66vTs/Q53PFMTCcNQEwnDUBMJw1ATCcNQEwnDUBMJw1ATCcNQEwnDUBMJ43NHWUdHB44fPz7ytcvlQl5eHlatWmXowohIH5+hnjZtGo4dOwYA8Hq92LJlC9544w3DF0ZE+ihdfjc1NWHq1KmYPHmyUeshogAphfry5ctYsGCBUWshoiDwOXZnmNvtxpYtW1BWVvavkyqehbE7ZvdLT09XromPj8fDhw+V6/ROH9Hj7t27yjU2mw09PT3KdTdv3lSuCYdjcayxO36Hura2FmfPnsUXX3zhV1OLxeLf6v7x/uN91InZ/f5tvJEv4XDr5ffff69cs2rVKvz888/Kdfn5+co14XAsBjR2ZxgvvYnCg1+hHhwcRGNjI+bNm2f0eogoQH49+SQqKgrfffed0WshoiDgjjIiYRhqImEYaiJhGGoiYRhqImEYaiJhGGoiYRhqImH83vtNROFh3Jypi4qKRPYyux97sde4CTURBQdDTSSM9cCBAwdCvYhhDodDZC+z+7HXs92LvygjEoaX30TCMNREwjDURMIw1ETCMNREwvwHb/d5uqvbRS4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "_ = ax.matshow(X[1].reshape((8, 8)), cmap='gray')\n",
+    "print(\"Label: %d\" % Y[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "As always with Falkon we must:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train)\n",
+    "Y_test = torch.from_numpy(Y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalizing the data is always a good idea. Here we use the global mean and standard deviation of the training set for z-score normalization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = X_train.mean()\n",
+    "train_std = X_train.std()\n",
+    "X_train -= train_mean\n",
+    "X_train /= train_std\n",
+    "X_test -= train_mean\n",
+    "X_test /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert labels to 1-hot\n",
+    "eye = torch.eye(10, dtype=torch.float32)\n",
+    "Y_train = eye[Y_train]\n",
+    "Y_test = eye[Y_test]\n",
+    "print(\"First label vector: \", Y_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search for the optimal parameters\n",
+    "\n",
+    "Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.\n",
+    "\n",
+    "Scikit-learn has great support for this, with the `GridSearchCV` class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.\n",
+    "\n",
+    "Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mclass_loss(true, pred):\n",
+    "    true = torch.argmax(true, dim=1)\n",
+    "    pred = torch.argmax(pred, dim=1)\n",
+    "    return torch.mean((true != pred).to(torch.float32))\n",
+    "mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter settings which will be explored by the grid-search are:\n",
+    " - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data)\n",
+    " - four different regularization values\n",
+    " - two different values for M: the number of inducing points. As we will see, a larger `M` is almost always better than a smaller one (but it leads to longer training times). \n",
+    "   Of course this is not the case if the dataset is easy to overfit, since reducing `M` may also provide additional regularization.\n",
+    "\n",
+    "When we create the estimator we pass it additional parameters via the `FalkonOptions` class.\n",
+    "In our case we want to ensure that the model runs on the CPU by setting `use_cpu=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/helpers.py:31: UserWarning: Failed to initialize cuBLAS library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "Error encountered was cublas library not found\n",
+      "  warnings.warn(get_error_str(\"cuBLAS\", e))\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameter_grid = {\n",
+    "    'kernel': [falkon.kernels.GaussianKernel(sigma=1), \n",
+    "               falkon.kernels.GaussianKernel(sigma=5), \n",
+    "               falkon.kernels.GaussianKernel(sigma=10),\n",
+    "               falkon.kernels.GaussianKernel(sigma=15),],\n",
+    "    'penalty': [1e-3, 1e-5, 1e-7, 1e-9],\n",
+    "    'M': [500, 1000],\n",
+    "}\n",
+    "estimator = falkon.Falkon(\n",
+    "    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000, maxiter=10,\n",
+    "    options=falkon.FalkonOptions(use_cpu=True))\n",
+    "\n",
+    "grid_search = model_selection.GridSearchCV(estimator, parameter_grid, mclass_scorer, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The best parameters are:  {'M': 1000, 'kernel': GaussianKernel(sigma=5.0), 'penalty': 1e-07}\n",
+      "CPU times: user 2min 5s, sys: 17 s, total: 2min 22s\n",
+      "Wall time: 35.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "grid_search.fit(X_train, Y_train)\n",
+    "print(\"The best parameters are: \", grid_search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluating the model\n",
+    "We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.14 s, sys: 143 ms, total: 1.29 s\n",
+      "Wall time: 323 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "flk = grid_search.best_estimator_\n",
+    "flk.fit(X_train, Y_train)\n",
+    "test_pred = flk.predict(X_test)\n",
+    "train_pred = flk.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training error: 0.00%\n",
+      "Test error: 1.11%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training error: %.2f%%\" % (mclass_loss(Y_train, train_pred) * 100))\n",
+    "print(\"Test error: %.2f%%\" % (mclass_loss(Y_test, test_pred) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot grid-search results\n",
+    "\n",
+    "Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful.\n",
+    "If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.\n",
+    "\n",
+    "In the plot red indicates a high error, while darker blue indicates a low error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_df = pd.DataFrame.from_dict(grid_search.cv_results_)\n",
+    "res_df[\"M\"] = res_df.params.apply(lambda x: x.get(\"M\"))\n",
+    "res_df[\"penalty\"] = res_df.params.apply(lambda x: x.get(\"penalty\"))\n",
+    "res_df[\"sigma\"] = res_df.params.apply(lambda x: x.get(\"kernel\").sigma.item())\n",
+    "res_df = res_df[[\"mean_test_score\", \"M\", \"penalty\", \"sigma\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_heatmap(ax, df, xlabel, ylabel, value, scale):\n",
+    "    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)\n",
+    "    cmap = plt.cm.get_cmap('coolwarm_r', 20)\n",
+    "    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])\n",
+    "    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)\n",
+    "    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)\n",
+    "    ax.set_xticklabels(piv.columns, minor=False)\n",
+    "    ax.set_yticklabels(piv.index, minor=False)\n",
+    "    ax.set_xlabel(xlabel)\n",
+    "    ax.set_ylabel(ylabel)\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
+    "scale = (res_df[\"mean_test_score\"].min(), res_df[\"mean_test_score\"].max())\n",
+    "\n",
+    "c = plot_heatmap(axs[0], res_df[res_df.M == 500], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "axs[0].set_title(\"M = 500\")\n",
+    "c = plot_heatmap(axs[1], res_df[res_df.M == 1000], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "_ = axs[1].set_title(\"M = 1000\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/logistic_falkon.html b/examples/logistic_falkon.html
new file mode 100644
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--- /dev/null
+++ b/examples/logistic_falkon.html
@@ -0,0 +1,953 @@
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">Introducing Logistic Falkon</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="#Load-the-data">Load the data</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Introduction">Introduction</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
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+</ul>
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+            
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+      <li>Introducing Logistic Falkon</li>
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+<section id="Introducing-Logistic-Falkon">
+<h1>Introducing Logistic Falkon<a class="headerlink" href="#Introducing-Logistic-Falkon" title="Permalink to this headline">¶</a></h1>
+<section id="Introduction">
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook we use a synthetic dataset to show how the <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> estimator works.</p>
+<p>We compare Falkon – which is trained on the squared loss – to its logistic loss version, and verify that on a binary classification problem logistic falkon achieves better accuracy.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">falkon</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected
+</pre></div></div>
+</div>
+</section>
+<section id="Load-the-data">
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline">¶</a></h2>
+<p>We use a synthetic dataset in two dimensions, which consists of two concentric circles labeled with different classes.</p>
+<p>We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 100000 samples to demonstrate the speed of the <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> library.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_circles</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">100_000</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">122</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/examples_logistic_falkon_4_0.png" src="../_images/examples_logistic_falkon_4_0.png" />
+</div>
+</div>
+</section>
+<section id="Split-into-training-and-test-sets">
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline">¶</a></h2>
+<p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Data-Preprocessing">
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline">¶</a></h2>
+<p>The minimal preprocessing steps are: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32) 3. Reshape the labels so that they have two dimensions. This is not strictly necessary, but Falkon internally works with 2D tensors, so the output of the <code class="docutils literal notranslate"><span class="pre">Falkon.predict</span></code> method will always be 2D. 4. Change the labels from 0, 1 to -1, 1. Note that Logistic Falkon uses the following formula for the logistic loss:</p>
+<div class="math notranslate nohighlight">
+\[\log(1 + e^{-y_1 y_2})\]</div>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>where $y_1$ and $y_2$ are labels and predictions respectively, which only makes sense if the labels are -1, 1.
+</pre></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">Y_train</span><span class="p">[</span><span class="n">Y_train</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+<span class="n">Y_test</span><span class="p">[</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Define-the-Falkon-model">
+<h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model" title="Permalink to this headline">¶</a></h2>
+<p>We use the same base parameters for both models: a small amount of regularization (<span class="math notranslate nohighlight">\(10^{-7}\)</span>) and a Gaussian kernel with <span class="math notranslate nohighlight">\(\sigma = 5\)</span>. The number of inducing points is set to 1000 which is adequate for the problem which is very easy.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">def</span> <span class="nf">binary_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">flk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">flk_kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set &#39;use_cpu&#39; to True to avoid this warning.
+  warnings.warn(get_error_str(&#34;CUDA&#34;, None))
+</pre></div></div>
+</div>
+</section>
+<section id="Define-Logistic-Falkon-model">
+<h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Falkon-model" title="Permalink to this headline">¶</a></h2>
+<p>The logistic Falkon estimator uses the same base parameters as Falkon.</p>
+<p>However, instead of specifying a single value for regularization, we need to specify a <em>regularization path</em>: a series of decreasing amounts of regularization. For each regularization value we also need to specify the <em>number of iterations</em> of conjugate gradient descent, which will be performed for that specific regularization value.</p>
+<p>We validated empirically on a wide number of binary classification problems that a good scheme to set the regularization path is to use three short (i.e. 3 iterations) runs with increasing regularization, and then a few longer (here we used 8 iterations) runs with the final regularization value (here <code class="docutils literal notranslate"><span class="pre">1e-7</span></code>, the same as for Falkon).</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> estimator also accepts a mandatory <code class="docutils literal notranslate"><span class="pre">loss</span></code> parameter, which should be set to an instance of the <code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code> class. While the <code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code> is the only implemented loss at the moment, the learning algorithm is defined for any <em>generalized self-concordant</em> loss, and we plan to extend the library to support more functions.</p>
+<p>An additional feature we show here is <strong>error monitoring</strong>: By passing an error function to the estimator (see parameter <code class="docutils literal notranslate"><span class="pre">error_fn</span></code>), the estimator will print the training error at every iteration (how often such prints occur is governed by the <code class="docutils literal notranslate"><span class="pre">error_every</span></code> parameter). This can be very useful to determine if it is possible to stop training early, and in general to monitor the training process.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">logflk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">logloss</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">gsc_losses</span><span class="o">.</span><span class="n">LogisticLoss</span><span class="p">(</span><span class="n">logflk_kernel</span><span class="p">)</span>
+
+<span class="n">penalty_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">]</span>
+<span class="n">iter_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
+
+<span class="n">logflk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">LogisticFalkon</span><span class="p">(</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">logflk_kernel</span><span class="p">,</span> <span class="n">penalty_list</span><span class="o">=</span><span class="n">penalty_list</span><span class="p">,</span> <span class="n">iter_list</span><span class="o">=</span><span class="n">iter_list</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">logloss</span><span class="p">,</span>
+    <span class="n">error_fn</span><span class="o">=</span><span class="n">binary_loss</span><span class="p">,</span> <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Train-both-models">
+<h2>Train both models<a class="headerlink" href="#Train-both-models" title="Permalink to this headline">¶</a></h2>
+<p>Training Falkon for 20 iterations (default value) takes about 20s on a laptop.</p>
+<p>Clearly, since the logistic falkon runs about 33 iterations (the sum of values in <code class="docutils literal notranslate"><span class="pre">iter_list</span></code>), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns. On the same laptop, the logistic falkon algorithm takes around 50s.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%%time</span>
+<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+CPU times: user 1min 41s, sys: 66.6 ms, total: 1min 41s
+Wall time: 25.4 s
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Falkon(M=1000, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f22222fc650&gt;, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;auto&#39;, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-07, seed=None, weight_fn=None)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%%time</span>
+<span class="n">logflk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Iteration 0 - penalty 1.000000e-03 - sub-iterations 3
+Iteration   0 - Elapsed 4.02s - training loss 0.6910 - training error 0.2550
+Iteration 1 - penalty 1.000000e-05 - sub-iterations 3
+Iteration   1 - Elapsed 9.15s - training loss 0.5639 - training error 0.1623
+Iteration 2 - penalty 1.000000e-07 - sub-iterations 3
+Iteration   2 - Elapsed 14.68s - training loss 0.4032 - training error 0.1627
+Iteration 3 - penalty 1.000000e-07 - sub-iterations 8
+Iteration   3 - Elapsed 27.14s - training loss 0.3696 - training error 0.1607
+Iteration 4 - penalty 1.000000e-07 - sub-iterations 8
+Iteration   4 - Elapsed 39.60s - training loss 0.3655 - training error 0.1605
+Iteration 5 - penalty 1.000000e-07 - sub-iterations 8
+Iteration   5 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605
+Iteration   6 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605
+CPU times: user 3min 46s, sys: 48 ms, total: 3min 46s
+Wall time: 56.7 s
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+LogisticFalkon(M=1000, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f2222305590&gt;, error_every=1, error_fn=&lt;function binary_loss at 0x7f22a05ec950&gt;, iter_list=[3, 3, 3, 8, 8, 8], kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;auto&#39;, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07, 1e-07], seed=None)
+</pre></div></div>
+</div>
+</section>
+<section id="Testing">
+<h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headline">¶</a></h2>
+<p>However, the price paid for with a higher training time, leads to lower training error.</p>
+<p>We found that, on a variety of binary classification datasets, logistic falkon obtains a slightly lower error than the vanilla Falkon algorithm.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">flk_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">flk_err</span> <span class="o">=</span> <span class="n">binary_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">flk_pred</span><span class="p">)</span>
+
+<span class="n">logflk_pred</span> <span class="o">=</span> <span class="n">logflk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">logflk_err</span> <span class="o">=</span> <span class="n">binary_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">logflk_pred</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Falkon model          -- Error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">flk_err</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon model -- Error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">logflk_err</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Falkon model          -- Error: 15.81%
+Logistic Falkon model -- Error: 15.63%
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Plot-predictions">
+<h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permalink to this headline">¶</a></h1>
+<p>In the plot we have the outer and inner circles which are correct predictions, and the circles in the middle which are the mispredicted points. Since we added lots of noise to the dataset, perfect predictions are not possible (there is no clear boundary between the two classes).</p>
+<p>Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where a slightly lower classification error is desired.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">def</span> <span class="nf">plot_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plot_predictions</span><span class="p">(</span><span class="n">flk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Falkon&quot;</span><span class="p">)</span>
+<span class="n">plot_predictions</span><span class="p">(</span><span class="n">logflk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/examples_logistic_falkon_22_0.png" src="../_images/examples_logistic_falkon_22_0.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span>
+</pre></div>
+</div>
+</div>
+</section>
+
+
+           </div>
+           
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+          <footer>
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+        <a href="hyperparameter_tuning.html" class="btn btn-neutral float-right" title="Hyperparameter Tuning with Falkon" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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diff --git a/examples/logistic_falkon.ipynb b/examples/logistic_falkon.ipynb
new file mode 100644
index 00000000..c157bfb6
--- /dev/null
+++ b/examples/logistic_falkon.ipynb
@@ -0,0 +1,436 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introducing Logistic Falkon\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this notebook we use a synthetic dataset to show how the `LogisticFalkon` estimator works.\n",
+    "\n",
+    "We compare Falkon -- which is trained on the squared loss -- to its logistic loss version, and verify that on a binary classification problem logistic falkon achieves better accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use a synthetic dataset in two dimensions, which consists of two concentric circles labeled with different classes.\n",
+    "\n",
+    "We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 100000 samples to demonstrate the speed of the `Falkon` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.make_circles(\n",
+    "    n_samples=100_000, shuffle=False, noise=0.1, random_state=122)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 7))\n",
+    "ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.1, marker='.')\n",
+    "_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.1, marker='.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "The minimal preprocessing steps are:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)\n",
+    " 3. Reshape the labels so that they have two dimensions. This is not strictly necessary, but Falkon internally works with 2D tensors, so the output of the `Falkon.predict` method will always be 2D.\n",
+    " 4. Change the labels from 0, 1 to -1, 1. Note that Logistic Falkon uses the following formula for the logistic loss:\n",
+    "     $$\\log(1 + e^{-y_1 y_2})$$\n",
+    "     \n",
+    "     where $y_1$ and $y_2$ are labels and predictions respectively, which only makes sense if the labels are -1, 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train).to(dtype=torch.float32).reshape(-1, 1)\n",
+    "Y_test = torch.from_numpy(Y_test).to(dtype=torch.float32).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y_train[Y_train == 0] = -1\n",
+    "Y_test[Y_test == 0] = -1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the Falkon model\n",
+    "\n",
+    "We use the same base parameters for both models: a small amount of regularization ($10^{-7}$) and a Gaussian kernel with $\\sigma = 5$.\n",
+    "The number of inducing points is set to 1000 which is adequate for the problem which is very easy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binary_loss(true, pred):\n",
+    "    return torch.mean((true != torch.sign(pred)).to(torch.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
+     ]
+    }
+   ],
+   "source": [
+    "flk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "\n",
+    "flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Logistic Falkon model\n",
+    "\n",
+    "The logistic Falkon estimator uses the same base parameters as Falkon.\n",
+    "\n",
+    "However, instead of specifying a single value for regularization, we need to specify a *regularization path*: a series of decreasing amounts of regularization. \n",
+    "For each regularization value we also need to specify the *number of iterations* of conjugate gradient descent, which will be performed for that specific regularization value.\n",
+    "\n",
+    "We validated empirically on a wide number of binary classification problems that a good scheme to set the regularization path is to use three short (i.e. 3 iterations) runs with increasing regularization, and then a few longer (here we used 8 iterations) runs with the final regularization value (here `1e-7 `, the same as for Falkon).\n",
+    "\n",
+    "The `LogisticFalkon` estimator also accepts a mandatory `loss` parameter, which should be set to an instance of the `LogisticLoss` class. While the `LogisticLoss` is the only implemented loss at the moment, the learning algorithm is defined for any *generalized self-concordant* loss, and we plan to extend the library to support more functions.\n",
+    "\n",
+    "An additional feature we show here is **error monitoring**: By passing an error function to the estimator (see parameter `error_fn`), the estimator will print the training error at every iteration (how often such prints occur is governed by the `error_every` parameter).\n",
+    "This can be very useful to determine if it is possible to stop training early, and in general to monitor the training process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logflk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "logloss = falkon.gsc_losses.LogisticLoss(logflk_kernel)\n",
+    "\n",
+    "penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7, 1e-7]\n",
+    "iter_list = [3, 3, 3, 8, 8, 8]\n",
+    "\n",
+    "logflk = falkon.LogisticFalkon(\n",
+    "    kernel=logflk_kernel, penalty_list=penalty_list, iter_list=iter_list, M=1000, loss=logloss,\n",
+    "    error_fn=binary_loss, error_every=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train both models\n",
+    "\n",
+    "Training Falkon for 20 iterations (default value) takes about 20s on a laptop.\n",
+    "\n",
+    "Clearly, since the logistic falkon runs about 33 iterations (the sum of values in `iter_list`), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns.\n",
+    "On the same laptop, the logistic falkon algorithm takes around 50s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 41s, sys: 66.6 ms, total: 1min 41s\n",
+      "Wall time: 25.4 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Falkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f22222fc650>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-07, seed=None, weight_fn=None)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "flk.fit(X_train, Y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0 - penalty 1.000000e-03 - sub-iterations 3\n",
+      "Iteration   0 - Elapsed 4.02s - training loss 0.6910 - training error 0.2550 \n",
+      "Iteration 1 - penalty 1.000000e-05 - sub-iterations 3\n",
+      "Iteration   1 - Elapsed 9.15s - training loss 0.5639 - training error 0.1623 \n",
+      "Iteration 2 - penalty 1.000000e-07 - sub-iterations 3\n",
+      "Iteration   2 - Elapsed 14.68s - training loss 0.4032 - training error 0.1627 \n",
+      "Iteration 3 - penalty 1.000000e-07 - sub-iterations 8\n",
+      "Iteration   3 - Elapsed 27.14s - training loss 0.3696 - training error 0.1607 \n",
+      "Iteration 4 - penalty 1.000000e-07 - sub-iterations 8\n",
+      "Iteration   4 - Elapsed 39.60s - training loss 0.3655 - training error 0.1605 \n",
+      "Iteration 5 - penalty 1.000000e-07 - sub-iterations 8\n",
+      "Iteration   5 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
+      "Iteration   6 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
+      "CPU times: user 3min 46s, sys: 48 ms, total: 3min 46s\n",
+      "Wall time: 56.7 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LogisticFalkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f2222305590>, error_every=1, error_fn=<function binary_loss at 0x7f22a05ec950>, iter_list=[3, 3, 3, 8, 8, 8], kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07, 1e-07], seed=None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "logflk.fit(X_train, Y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing\n",
+    "\n",
+    "However, the price paid for with a higher training time, leads to lower training error.\n",
+    "\n",
+    "We found that, on a variety of binary classification datasets, logistic falkon obtains a slightly lower error than\n",
+    "the vanilla Falkon algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Falkon model          -- Error: 15.81%\n",
+      "Logistic Falkon model -- Error: 15.63%\n"
+     ]
+    }
+   ],
+   "source": [
+    "flk_pred = flk.predict(X_test)\n",
+    "flk_err = binary_loss(Y_test, flk_pred)\n",
+    "\n",
+    "logflk_pred = logflk.predict(X_test)\n",
+    "logflk_err = binary_loss(Y_test, logflk_pred)\n",
+    "\n",
+    "print(\"Falkon model          -- Error: %.2f%%\" % (flk_err * 100))\n",
+    "print(\"Logistic Falkon model -- Error: %.2f%%\" % (logflk_err * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot predictions\n",
+    "\n",
+    "In the plot we have the outer and inner circles which are correct predictions, and the circles in the middle which are the mispredicted points.\n",
+    "Since we added lots of noise to the dataset, perfect predictions are not possible (there is no clear boundary between the two classes).\n",
+    "\n",
+    "Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where a slightly lower classification error is desired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_predictions(preds, ax):\n",
+    "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 0], \n",
+    "               X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 1], \n",
+    "               alpha=0.1, marker='.', color='b')\n",
+    "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),0], \n",
+    "               X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),1], \n",
+    "               alpha=0.1, marker='.', color='r')\n",
+    "\n",
+    "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 0], \n",
+    "               X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 1], \n",
+    "               alpha=0.2, marker='x', color='b')\n",
+    "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),0], \n",
+    "               X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),1], \n",
+    "               alpha=0.2, marker='x', color='r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LKqoK2azKhg06Hs/ptz/bua42zJmMMIYXEq3p9cAwIJEwaTZFtOi1MqoyBS2RSF5pzqfM50zbvtJa0d/uUtKLS0krQOrF5Yh0BiSvOOeSurUs8WOzie1sNtF+rdPhjNH5l8I0RZcGTTu7wPQNV7MJuZxKKGRimiudHLpdsV2nI7Y9Wzq6LxKtlojONJviWnq9F2/3UlETu128t9kEl0s87nZfm0jLa1GLK5FIrizOtcxntV707bJprtjn86Xfgahv25zOM9u5i6kXpgnlsjhOImHSaFyYXqzev8228vdrFZWXenH5IZ0BySvO2VK3fSOcz6sYhjBksZhJLqeya5eG3S4G1WNj5xdZWN3H2W4X+2w0VgbVq+kbLq9XpHq9XpFi7YvN0pJoGTc0BF6vyuSkiaquXJOmrRjv5WWVhQUR5YnFxHmPjq7UkwLoC2lKeYtWJDGImtiWk2CzYY6MnPZ6+gJkGK9NpOXVrsWVSCRXHi9V5nOqXqiqiIjn88JGT0yYJBLnZxv75T79tqCKAkNDJm736e3cS+nF4qJKMqkyMgJOp8qaNafXC5tNaFSzCc2myqZNIrPQn3/Q386RT5NPn10vFEW8b3FRdIvfs0cjHDYHc99ebYdA6sXlh3QGJOfMy6kBPF3qtj/AbbWgWFQZHzcHqdGlJZVyWaRgvV6TQEAY37NN0lp9fv0+zh4PVCoiUm+znf7c+oar1xOORyRiomnCeUilVA4c0FhasuHzwdCQhmXpJBJiPoHdvpIONU0hKqWSiqaJ8w6HT3YEUikVY8mivVBkWIFmOIE5n8RRLWCEwyddQz/16/WKFnSw0pf61Y60XIxaXIlEcuVwoXpxpjKf0+lFsyki8KWSOojKBwImfr94z9mcitV2tt0WzxuG+NH1M5/f6fTCZjOpVMR5PP64nWpVYWgIpqaEgzE29mK96HTE3w6H2JffbzI8zMARSKfFdva0hb2Ux6tCI3RmvdD1lYnJ3a743V+j4NWOyku9uPyQzoDknDBNBlGHixVt6EdYPB4olcQA3u0WhrC/cEs2q9LtatTr5qDLgqZBKGQOojP9tmyruzGEQmL72VkRQXI4YGLCpNUShtLpXDmPflSl0RDX1jfYCwtiIZlKRaXZtLDZIBwWRtqyxLmGw8LgqerKWgR9oXA6xX5UVTzn94tjOBIJ2mUVI5UlUMzjCFu0gmF6oQTpOXXwuU5MmIPoisslnrtYkZYLEeqXU4srkUiuHF4tvejbxVYLGg2VfF7Fboc1a8Tx+tnm1XqhKCfXs8fjYuJvMrmSnZ6aEts3GivvWU0oJOy+1yseP/+8RqMBmYyKqioYhkK5LPaVz6uY5ov1ol/q1M8SaNpK9tcwxPX5fNCJJbAscGbz+PN5lKBFMxzGCCcoLIvrsKwVvegP/k1zRc9eDhfbqZNcmkhnQPIiTv3ntywGA2OXC4aHzYsSbVgdYRkdFRGW/kDcZgOn02TNGhOfb8U4KooQmZ07NRwO8b7rrtPR9ZWswdKSiB71oyJer6jt7Btdp5OTUsl9R6KfJo5EzEFkSFWh3bZQFCFshsHAEegb4XxeHTgyExMmnY45yEJkMipOpzh301SZndUIhUxGRxNErAwKkMspHO1Okt0vju90gs8nzmHtWhPDWDHop96XCzHSp5vcJZFIJOfL6WzQq60XyaSK3y+OMTwsjlOrid9utwgIPfechs0mgj5bt+qDevZOR5xrJiMG7E6nGETPz69E7N1uBmWqp5YUjY2ZBAIm7bbYn6YJx8QwxEDeNFf2cTq92L5dp9EQjkwuJ7LhIyMmu3Zp5HIqdrvF1VcbXHfjKMquHPm8wsyMjeOFtQTS5iAwVK+Lz2jNGqEbExMvbnV6IXohJwJfOUhnQHISp/vn7/WE8XC7haGD8482nM4Q9SPyqxde6XZXJh71tzVN8ZrHs5JarVYVXC6FZnPl/aYpIjn9iET/eGLwLoyj2y2MuctlDiI+vZ64rn4bU10XBvvIEY1CQcHtttiwwWBqCkolkV1YXBRzAjodBoP1Tkc4Ev3ryOdFlP/oUfXEZGgVy7IwDBiqLVJoK/R6UKmoaM0k7d7E4L2GIZwP0xQG3rJOfC75NEbXgIkEi4ti/55CkuGIgm1C1I++lME/39WVJRKJ5FTONFB8uXpxpkHr6fSi0xGZ2mBQOABer7CV/TKh+Xkx0G+1FKJRi2ZzJRLfn6xrGMIJ0DQxqJ6cNHG5TBoNkRUuFkVgyuvlhL0W+xaBIuGALCxoNBoWjYbK9HSPdltl40bI54WNPZNeRKOiNCidFsfvdODQIZVjx2w4nRY2m4LdbmLOJynllRMTl1Xs3SQd5yjtNlSrKm43ZLMiY9HPelsWGItp7KpBO5oYzLNw589dL+RE4CsH6QxITuJ0C5f0U41DQybBoIg6vNxItGUJY1ypCANVKIj99kWj3RaDZ7vdxG432bTJxOUS7ykUTOp1jXbbwukUopNOi4FxPyUsMgnimLmcyqZNwrh7PCbJZ3KUX9CpNTXCQzrujaN4i0kcqlAGj6kQiYzQ6+kYhg3TVJidFRH+Y8fsTE4aJJMq3a7OwoJKvS4yGbouzqPviBw+rFGpiPrWRMKkWrVwOBSMuSSNYJGkfQTv5lFq5RT13QVqqoa2NsHUlI6qQr2uUiio5PPCwIfDJs3DClGrSGq/RiOUYLiepJMv0m6HUG0r2YvV4ny2hXlstpV0tUQikZwrfX3ol0j2B4ovRy/OlLXsdlei6p2OKA8aGxOv9fftcIj5Ana7cBoMg0HEfu9ejVbLIhIR2latrti9fjagX2+fTqtEIlCrCf1ZrRehoE4rksBTSBKOKQSUHlgKa9aMYxgwMyN0a2FBDNDPRS80DY4e1chmFTweC4/HJBo1T2SzLYy5FEVHkawVIzU2Qf1gCn1/Hq2k4Fw/RiQi9CGbVdF17aQgmjuv4K6VcU6oZBwTTKqLlI+cWS/69/VMi7j1/5Z68fpDOgOSAWdauORCl0ZfPUFrdXSh04EDB0SNZa2mEo+bJ8poRFQmHhdp11xOZWbGjsdj0ukY7Nih4/VyoiZU1PFce61Oq7VyrPl5dVCXGQyatNsqum7hcIDLJSZ6eV06ZrqAWTJolFRGrWWCYZWQv4fdpUEszEybE86FgqpaeDwKgYDIMLTbUC5bZDLqYOJWKGQSjzMQILtdOC7JpI16HapVG+vWGQSDBqFhiMRCHE8nsHdNcq4EnjisG1JgTGfTJp1crj8pWcw56NePVgIJAphoR/MMlfLoBuihCP61ozSb4rP3eFYmUDscIhPSn0C9uhNSP3uRyayUJ8kUsEQiORc0DQoFdWBnJiZWBucXqheDDKhjpdVnPq+yvCyOU62qRKNi3+GwmJQbDotsrWmqHDrkoNOxqNcNbrpJx+2GYhHWrtUZHTWZnjYHelGrqdTrYs7Ahg063a6GZakkkwojI2K/p+pFM6AybS3jG1KJeXu4VY2WEh44RA6HgqZZuN0iO2GzmSfmM1ik08JB6HZF6VRfLzod4TilUhqLiwq9nu2EQ2OIktY1Cp1mhJKRIOQyKY+O4nWaBEMqypCJzyd0enhY2PD+XAe7HYzhBJ0OTNWyeHNF2j6TdiCG74Re9O1+ryfOq1AQ5bSmKe6h03lyF6T+pOZ+SZXUi9cP0hmQDDjbwiXnMxno1OjOyIg5SMu6XCuGJxgUGYG5ORWfD3K5leiMWHjFxOMR5UDd7koJUChkYllCHPbs0YhGTQoFETHqOzMej0k6rdJsKui6ysKCxfbtOuPjJj9Niom6HiNPpJbFnczjsAWxxSL0hsPsTk0OJh3v2NHlwAGNel1lYQHKZZWjR0XWYWnJZOtWi6Eh0T5U01YMa6HQ71BhoaoqhmGt1LxGR0ieuMbFRTHYrw9P0NZMHBXhKLlcnNifENhGQyWdhlZLJbQ2QbNZxuMRxwxeO0qvJz5byxJzJhRFPA6FTObnVapVkYFRFJ3JyZX72m6vZIFUVaaAJRLJuaHrYsCsqmJw2B/Ew4Xrhc22snZA/7V2W9im/vOZjIho9zvx9Bs0+Hwm+fyKXlQqwv4tLKj4/cKRWFoS55ZOixr9Wk1kXrtdHdOEfF6h13tpvXASxDYaYVmPMpudGARYEokue/dq9HoKe/bAsWMqx44pWBa0WhYjIxZr1ujEYkIT+2sT1GpiblqjYcPvt3A4RIvUYlFlpjEmBt7tlbkMlcAEPYdJY05ldFTH5xPBu/l5kcHudFQqFbH/8GiCwvE86on5csPbRgcNLjIZdZDRicWE7tdqojVqOi3m7PU7IXU6J1cNSL14fSGdAcmA1RO03O6X/kc/U23nS9WkezzitWZzJUINKyVKQ0Ni3+vWmbRaKqZpnSglEoJRLKqDust+FCMQMAf19/PzCh6PRr0OpZJCMCie709MDgZN6leN4j+SxT8agmKeLHEOPqWyPLKWXE5hasrC6zUZHzcJBk0aDQVFEUa7WlXx+awTqVhhQAsFleFhYbyXl1VmZmyDVLDfLyZBF4sKdrtKsLFMwNuj6pxgcVHD5bLwl5NE4hbHqglKJQ273cLttggGxeczNNR3CsBaSOI+kUo2DAj3FrHGRQ/qRkN8hn2nZHFRRP4bDYVwWNTM1utiUvXqLNDVV8te0BKJ5NzplwP1J6qezX6cbfLqqXoRi5mDss9CQR2U8fQnAHc6KwswdjoiqARiblWtJgIvuZxKsagOev/DyfoSDpsn9EFlbs5Gu91f7EtBVYVeLC8LpyMcXtEL30iI+kKRxfIoylOQWzM5uH6n02RyUuhFqaRQqUC9bsPhMHE4FEZH9UEb03JZxes1OXBAzEvL51V8PhOXy8LlslBVhUJBFZnwXBpDN0hZExSLCj6fQry7iMOmUNETZDIayaRFpyM+237mwbLEYD9YXcTQV7Q23l3ETCQG8/P6cy9SKeEgZbPqQPubTaEpHo/Ui9c70hmQDDhdevdMRvx00f9+t4S+U9GP7pimMID9CWW9HmzZog+i2Y884qBaFVFvTdOp1cTEqtFRk1isy09/6iCfV9m/386OHcKzEClc2LdPGzgvHo95ImWtkMko2GzC0NVqIipfKokOFwsLNkKNJToFlfX+NMWCit7L022oNA7sZtZ7E4cPQyJh0T2WwlRs9MyxExEXUBQx6Pd6LXbvtjE8rJDfu8wbburwbGaSQkFEVzZ7F7A5VOraCKapoCgKLpeBUjeY+b8KxxsOFpngjul57L08iZEhZudEWVKppGCzWYTDImPSbArRc+eTKPYiGTPG4WaCDa5FHMey+IGUc4JeD44f1/D7RcQuGhURpqUlkUk5flyj0RCOhKKsZIFiMeHonMqZuoX0n4MLX3tCIpFcvpyrXpxpHkC/LKi/0no/cwwiQ93Xi7ExMcDub/fQQw7abZib05ie1qlWT9aLH//YQamk8NOfOtm6tYemiWBKpyMGurWa0Kt43OToUYt6XeHIEY2REfNEKSl0u0InlpdV0mkbCXORQkFlrSdNalklWD9M0x0im0uxpExQKMDPjS9TnlSxj8cGa+Q0myZOp0KtZjE7K5yOYlElUE8zEu2xN7+GdlsRZVYsE1RUSsYImqac+AxMKkmLwsESaVOjEpjgxvg8cVcWdzzEsTkFXbfodBR0HaJRMZG42YRAAFhcxhoqkrFi5OyjhLpJlMNZKKm0IgkKBXXQ8chuF6Ve3a7QykJBZJTtdn1wL8+mF2cbK0i9uPSRzsAVxLm0Flud3j1bW7HV0Zx+e7Z+f2SXSxjbhQURWTlwQBtE9sNhk3JZHbSFAxO/38QwVKpVC10X+1heFlEKp1Ms9e7zwcKCRbUKcSPNmuEOj89OksuplEoKvtIyIwmLcmucjRt19u+3UaspLCxoNJsmbrdCPK7Q6SgM15dwO/NYis5sY5SGFcOqa8R6y4SKs9zkhj3eG/GXlygWCkSuCjMxIZyUWGwlPR6LWSSTKoGARW/JoHasSDtlp6SM4yksoPhz+Pwma9foFDwJZmdtHD5sI1jV8PdMXNU80XKRqmXgn46wpIwSCIiITiAgHJi5OZXFRdFZot02WRNSqHYjdIYSmA1oRxMkc+BdVii61MGCacGgmHCdywknIBo1CYVEpqXf8aPPmbJAZ0ZCkQAAACAASURBVBLx1Sl9ePGEZYlEcvlzsfTi1Mh/fx5Au73SyWf1MfN5EdXP51+sFz6fsEGifaiF329SKqmDdp+BgHjd71dYWrLo9WDSvkx9zuDAwlqcTguv10JNJjEVGzbbOPG4Sa2mMDtrwzAUqlWIRJQTc8EsjLkk7ngey2ZwrD5KEpNYrUi8lWOdbx/eOBycV1FyeY51ouzYZuL1WoyMQCZj4XIJ/XE6hWNgWQrthoGhF9DyNtKdCdZqC4S8B1CGh/BvilMswvy8jdaRZewuGy1fFOVYgXCxQKdr0r0pQt2VIBAQQbhYzMLvF3qxsCD0YnjYJDSs0g1GqJoJfLpJ2j5Bpw5qysbIqHA2ej3hRGQyKrOzYkJzJGIyMiImMfc7LvU143R6caZ7f2oJGEi9uFSRzsAVwoX0Cz5bW7HV0f9OR6Q9bbZ+K0yxTb9Xf38wWqtBuLNMrWyhREdptcSkJ385SSNnx+sdG0xm7bf9dLlgcdGGqpp0uyLaT9fg2JNlciknWWOCUCOJrZHD7jCI2CyczjjbtsGzz9qItudxeDUKzlFMU0Tca1k7RXeU8UkV1akTumuM4p4UeVcUU0kx1j6Kp1mkFYzRDkTJu0fxzSapl5qUmkO43RZXXWVQq4mJuaWSwrqwDb+7y5ZIlrFyFitq4XOZaE6F3P4izglwuycYt5bplLM8m4mj5Eo0Wya6YYPQGhqPKFx1lY7TCdu2iYnR/bIkRRGOkntTTHSZKIvIT7sN+nCC8KSJuSTmUCwu2lhctNiwQR/cA6dTfJaVirhH3a7oZ21Zoq7WNEX5UL9l3+nuf/870K83fa1XRZZIJK8MF1MvTu1e1umItQH6ehGPr2SOKxWxfSxmUq0KvaiWLPSh0cFxApUkumVjsZkglRLdefqLbGUyopTFNC0cDjGwPXZEgWyZZnqZ5kQCNblI1VbE7TKZcFnY1o6eKCtSiLaX8Fp2sMfpdOD4cYVgQ2N3L8ra9QrYTIbuHiezN4nPF8JdK7Ap+zOMTgg9tIWqd5yFhR625STpZT+BQBCbDW68sccTT9hpt8UE45vXK3gKPa6OZZmoZBkasnBUdJRmmYXnUnRiCUKNJaK2HJlmjJn2OKRLdLoWnY6NwtQkZsZCURRGR03WrtVPZMtFO1RFsUTWZTJGSQftRKYglYKh8QS6BXNzQreLRYVAQJS9ejzmYGJwv8Sq2RSZgnj8zHpxunvfb6Ih9eLyQDoDVwjn2i/YNBn0cT5TC8p+P/7V0f9sVh2s5GhZ4vELL2iDSVLuUprGcYV5n0I0e5hOJU07NEJ7NsM1oxDTDVoRnaQ1hqaJzICun3AW/CY2m2i5Fo+b9HqjYiXfcoFQvojbYxHYEGV/HryNPJoGa2+LM+1I4XKU2J8O042LzkBxI4UVNCj7x4lu16nXVYJzi5RrdurKCEObRxmfz3OVPU+mZ1KORbEdeo6OoaF5fCwvK0TbSRaqCr6Ncd74RlHuNO3o0Fl04CjlqNXjrPVkyLVjbLppSEy6qmdpzhRZyCr0hmMistW28LXKNGc7NIhQ9o8zOamgpZY5VFToRUcIBk1MU9SQOhwWmgbr15vMza200fN4xL0dGhJ1sBs2CHGtVGB5WcPptAiFLMLhlTpSh2Ol80S3C888AzabhtMpSrj6k7j7Dp9pCgFXVZHh6Wce+jWqF2NVZIlEcmlwLnphWQzme51u0L9aL07tXtbXC8sS2ct0WmQD+hNr7bk0jVmFjJFDyRcAYa/WRlNcZytStEfwbogRjwtbWCqJjGelohCPm1QqsH59j5ERk/nOKMWsgreRxzaTZXydxVInRmZWYUzL8fNbdK67e5TGoQJGs8DR5TBun7ieUDuF32+Qtq/hjTd1qNdVlKVFFlsOZhLXs6a7m2BQ5xq1SJoMIaeJfhw89QIluwdySRo9jceacYJBi40bDYpFhfVrOxTaGkY2R7oVJ06GxtAI69Z0MfNZepUsizkb+9Q47egY/nKSqmXhqZfpLrY5+j8R7OvGGBuD1pFl5vIq+Y0xDIOBXjidFvG4GNwvLXFCP0VgbvXEbMtScbvNE3MCNEzTYtMmY3AP9+8X7Ur7k537eqGq4vlt2/TBOg39e28YQi/68z6kXlz6XBRn4Mtf/jK7du0iGAzy13/91y963bIsvva1r7F7926cTicf+tCHWLdu3cU4tOQl6Kd6V/cIPtM/ommKZdX7reKuvVZnZMQcrAeQyagnRYl0XfxzNxrCIWi1YOtWccw9e7RBRPvqq3u4iz3WB4o0fRF8Th/ho8/iyUJjdC2mf4jYmEZvuEf+xDLzdrswWsJgKcRiFqnnMsz/rIcxlmA2NUW4s5vhbpapcahu2kozr+IuWhipPMn/ykNToe6J4QuqxLyLlHwJOmmdGAWGMmmCjRG22pYxsjmW1Ws4flxjzJgnaqkYqJhWmfYLjzIUsTHTXc/C6ATO7FGi/hw9PUK7rVCvK7RaFkeDE1zb24/amEdvldB0hWIvzMGDNiJxFZ8KW7fqWJYNPWTROF5g2R2nE4ySKB4gWttH08rifkEnnFAx1AjFwxkC5hLrp0cZ2hLD6wVjLonhtmGzjVGpiEG912sSi4nOQrWaSiolIj4+H7hcIi3u8YhaT5ttpS63XObEJGeVZBKGh1UKBWg0tMGqm/G4yfHjQryLRZFN6HcO6Yv8qStdSiRnQmrFpcvqsqBT+8ufqhenW4l3bMw8q170NaNeX9GL9ethZkZE8zMZhVDoxOTXco8pTxGzq+MJGYwuPIZqt6HkewRCYVybwqh2MRfK5RJ65XCYlMsaimIRqGfQj+scbIi6+LnqBFsaBwlFwO8LYU+MYnPbUHWL8pE0tmKecYedWV1E0LuWQqNh4W4bjDb3Mz6axbKu5gbrWRbTJY4qV5N5NoXDqYKiEehB5/gRUI9RdwyTiW2hVhABK/xhbDaLTKa/UKZKfTiBr3GQUGueTrWM0oFcK4QVHcNfOMzopmEsIGWMsdmdpKDnKUdipJQYGzv72djZh1LIEDYN/MMqXT1C8pkcsc4SV20exbshhtMJtuUkubKdijaGoog5AbGYCCKJrIq4n7WaemJdBYtIxCIQEPex33oUTtaLpSWwLI1Ox6Ldhulpc+DwLS+r7N6tUShIvbicuCjOwB133MFb3vIW/u7v/u60r+/evZt0Os2XvvQlZmZm+OpXv8pnP/vZi3FoyVk40yTfM/0j9nvTe70ipdfvttDricFlImG+aHEZEJHj8XHR7zgeh+Vl8bzTKXorV6sqmd4kzJUxM4fRxhVCxSq6I4Bj/jg12zQ9X5C2ZccwV6IHiYTo0Wy367TbEA31GLNnabVMsKu4yGFSwqYEWe9cxJmYYFdmgkQjj6tWIGgzaISuJaIs46rmucGVZmFyjAl6qMdmCR7PYXig1xPddqLtBcbrB+h54RHbrWzu7adaqeJydzEcGteZuzjuaHO8EaOkJBhLWzSbFvG4xZGfpjCbDqardYbXDrHUCTPlKzDSyxEo91jOOckYMQC89RzOzTHsrjFsmoUxYrDGOEBUPYjZVXF6J/n3Q2uIz+/C3pwj2IGhLTEWn8hgpsukzRjR68WE61hMOEz9e3LttTqJhBDjWk3l+HFRR+t0inIj4aCJbIumCQewUBCt9/rCYLeLiFGrBbOzos1cqyUEu5816i93v7qdoETyUkituDQ5XVnQ2dYK6HfF6Q/yxSKRK60qz0cvDh3q23yVVEqUq5S7kzQOlfHUq6wfruBtHUQfTVAvQSN8FVZZRdHT9Hpj2O2iQ1wkYuL36zQaYDh0Rm1ZynUTVyKBc88ehimhMizWKdAXSZpTZJ0JplMHUIweNtsoVizGZK6IWklT9iWIXWXhX4Bhe5aR1B7apQLdnkqkm6FZsWMN66QdMWZbYUK1YzhUA3+whG7mSITdPNcIM99cg3bYIpEQNfiNhsL//HOOTek6o80qrFlPy4AJT54tc09QUiPsPxQVWkwSZVLFOREh5BjD2YBmUmfUcwBP6zAjqsl8ayM/q0ziP/AsPtccFuDbGGPpyQz12QoVZxT/lpUOTCDs9siISSBgsnGjuG+FgsrSkmjLXa2KLHCxKL4T/ax/f+6fxwOplIXXq2C3C63odkVb6+VldaA/Ui8uHy6KM7Blyxay2ewZX3/22We57bbbUBSFjRs30mg0KJVKDA8PX4zDS87AqaneM/0j9iNCbrcwGOWy+Luf0tU0sU2zudLLvu/tj4+bg1RwP5Xo9YpIkaj/tJicNFlYgFokwnB6Bt9SDkdQgVIFE5XmczM473oDRtegrfcHrQq7dmms9ywzEewxa5ug1bXxwgHYaP0foWKDrG+KZWU9a0zwJ4vsuNFEG9XwBgyWd1s0slXc5i70HTcwnE4x2TuKo1NEG4vgXj+EZ20IRQXDF2Hivw+yRc8zr0fIj26mehSKLYVgu0i7G2CNP0NdnyRKnk5Hx+FWyGTGKJUgvuunTJSLzLg24G0H2Lq4n3GPhRnwUyxN4ChXGJuYIj26jXAzia2Qp+EyufNOnaUlBYdjFHMuSy8UgUwOxa6xqb6bXsDG/soGRhdVWv9yANVmUXHEeKE8ztpDFi4XJ9YOEPfDe3gvmtWjfc0N1Goimj+29BzGsp3UxDW43aK0aHjYPNEJQmVqSnQdWr8e0mmdSkVM3BOL2IjUcaWicuyYDZ/PZGhIZGxarZXvy/lwLpMSJa9fpFZcmpypLOhMerF65d7++1IpdbCi7mq9ME3hAPQXJlutFz6feK7Xg/FxYVv6UWniMYbrR/C2c7jNBpVChUbTRtOXxa4rGEMRmk2ValVkpxVFJ1BPE3X2mNWcJOeg1ioTTj3BaL1OUhun6r4aW03n59enBgtn+Y/rJF+oU67kSVtxtoW7REqztPQizVaUoVs3EyHL0JBJ0YxhdmAkeQScEapWnJoaxqilGWuWKStDhKIw7Ukz21zLUCePs5XlePgGSiWV0v4Um1t78PhtZNUAwbbK1LH/xTsWwNesoPv99OJr8Y1FaVQt1hazKLYIW98yytpmj1RK5bg7QbeVw4pFUfwpuhUb1+i7OKrYOWRM4zmqYfzXfro9haPVGIebE0wiBvP9ScH6Qpr6kSz+9WHUyQTVqkrrSArfQo5GbITayAgTE+JeeL0isxMIiPupqjA9DS+8YJykF4oiSoMWFsRqz6pqsXmzOXAWzlcvpFa8urwqcwaKxSKRSGTwOBwOUywWX2TgH374YR5++GEAPv/5z5/0ntcTmqa9KtfWH7T3eqK38ujoi/+pLAuSyZVSos2bxaTVfn1fPi+Wbk8k4JprxONuV7wnEhHv37JFDErF6sAasViEO++Ew4dFm7P+8uXVag2tqDGsGmC0QbNRaLmpNSyc/zuP/S3rabWGSKchFIJoexHKdRpFFawa46NuIrseJ148xKHJu+k1XPiCPibLzzPVdmJlvAS7BlltHdkbb2f4yE5+rnQMW63BhnviOEpjTI/FsVIZjjcn6fag3YBSO4BiOQhF3FghB7aNHrZkH8XwmOQ3/jyh0mFC1hKF+STD1XmmzQAvKHez2JrmttoPuXbhx4BCu6GxsP6NvKH2/7GudowqCapKjJbiYrY8iTUSoOwPsH1yEXsxQ1Gps6yOMVRbxF1vEnCbOLZOYew9iK8RotWGgns7W8NpfPUM2FQe0TeRz4v7etddsG2bWGHz0CEwnnCzVl9mTJkhtv1mho7sJOpMsas6Ta8XQFFE14h9+8T9aDRgclLcV8vSUJQQrRZs3Ci+L8Eg7N27Ysg3bBDHHR6G7dvPfwXK1d810xTfqVfLyL9a/3OSl8e5agVIvbiYnItW9Lfr/w+HQmIw3882g2iDvFovcjlYXBSvJRLC/mzcyGDOkt2usX17iI0bYefOFSehWoVcrkawbMfdLlPHSaNiUOy46bVzhFx+2q4hcrkAlQqEwxDrLGIV6piqSrTdJFDdh3b8GJXYNK2uQiMwzQ25x/FHxmlsv4pJV4ZcXqW98Q0sqRBb2sfdlWcJjQwzdsMI9kScXA5Cm32k9rRot4Wm+d09UD1s2OAkg5NQoIRWeYGWP4jdN8KacA5HO43vhacY6/mZDd+EYs/jLmaYyj5GpJfBark4PPQGnMN+EsVDDJWXMDx+9mnX0tCHqe4vUVh/MxvWehibsBF31Cg0bWTUMcatRRzVBuEhE//6afzfP0iPEENtSI9uZzyUxl3N0CyrPFPZiGHAxIS4H+vWwf79cPxAg/Yz84zOLeH/+SGmotDT51g0IGe56eoBej2hEZmMuN/xOFx/vbD9pqkRiw3R68HUFINFQwsFkWHetEl8V/x+8dr56oXUilefV8UZsCzrRc8pp7mzd955J3feeefgcT6ff0XP67UiEom8atfmdK6s6FsovPj1bpfBIiOrZ/tXKuI1w1AJBsHlMslkRH99RemnFUU0qL90eSxmcsMNIYrFPJ0OzM9rKIroYDDSWwStTGPzFFazTRENa3GZp11vwK/m8WR1rAee5lCvSamqoSpJQsNFDnaHsU2M4J55Fh2F4Mwx9E4KW+05et7rGEnuwR5tUbU2Uex5MEIW3WyL9rHDPNK4hl/I7WfElmTugSW6wxF8c8fZ5prBZa6jeP3dFPekGJp9FqcXSpYLWyWN++kkra6F26vgri3g8vQI1FPoeHB2i/ibJdbyKM7uAbYGZ8HnxOezWFc8ylBpBkczR8Nlo5DXMZb2s7D1TpqdLtebj3D4eRt5sliqncjtThLJGSiWqbmHsNlUNlYO0agvc4vjIGVXkJBZZ7hcwchVcI4EmCo9hhK/CUdmidRzFv5yD0+zQG4hzrJ3G818G+WxfcSffBSra2PhqmtJjm8hHKjS6UChYPLCCw5GrRTthsHSNRGiUfD5QmSfO0ilYmNfdQSnU7SdW1iws26dQaulYlk6waBYXKdYNAeLvp1r/+j+d81uF1FDRTFPaiv4SvJq/s+9moyNjb3Wp3BROVetAKkXF5uX0go4s140GqfXi0xGlCaCCAwVCitzDLZt00kkxLXNz4uuQGKegkmokSIWL9Jpe+i6whwxp1GXkhxujRDpVckeatHNzVE6OEfFFUf3W8xxgLY7hGmCq13EtucQk/nn6S6nOBC7m0T6Jyj+Nq1RH1mnF3vIQ6dnofQaLC4qlBZ6RI0mDq3JnlwY3+w8o7YM/obFsaHbOO5IkFczjLQP0LM10HNt/J00pmpSDcawN6us11+gZwwTai3isLKoeon54iibmkuMFg6KeV3DMUaCDW6t/RhHQ6Ve7aAFPLSXqhj1YySnp9FcXdaXH0XNZsgctMO2EIH1IRJLh6mkqzScQzg8Ku1jhxhzLTPdOUg+HuS4UWekXkEtVfC6AmwsPc7C6I30ZheZf9KExR6toyWWluLktGmax/azuf4TTFeBnmeYZOhW2sFhvPYqzabJ8eM2GjNZvC4DyxphaUln3TqTztEWxYMt0p1RlpYUhoZEaZGmidIh09Rxu1cWPysUzIGjB1IrXivOphevijMQDodP+nALhYJM+74Mzid99lLLwq9e8KXvufcXC1u71hy8tz8Bub+qrqqK6E2/jVu1qlGvi2XRQ6GVlqO1msqRIzaOl9zYVQf+ANhDeaibNBsKxlyZg8NbCdl1ehVo5wusMXJEbEU6di+qT6N5NE2gUSLUymNMrGE+68NptLk28zBen0LbexUHpt9KpaJy/LiN9cVnuMoqs7X9GE5HkbH8Mt1Cg9kt99A4ViLiq1GxV8hlXqDkiOFcKhGLGsyM38q64H58vTIxWwmfXmR8OU/FEaOsOrChY/oDuHpVthceY5Ihau61zF77HjaFs1z/k39hqFHBcPtIXvNm2jMpnNkF2vvnmI3FGN31P2zP76M1NsnMde/G34FNxaepVGGn7x10kmmG62noWlTNIAGjwjbfPio5nSXnOhzBAOPJY/hns5S9Y3gKPbpOFcMyiFpZCll4wXsjm5afJRpbxnR58L1rO8FGj1oNHJkkhbIdTZuguGwwZs/hyvWwIqOU9i5SPCQmeDtPrKMwPCxqcW02k6uvFguVre4ydL79o/vftWRSDAhcLjEokClgSR+pFReXi6kVwKAEqNNZaURwql70y4Dc7pWy0m5XDOrEYlwWL7yg4XCYqzraMOgid/y4HZadBH0xNq+z0AImvaQNPVWjWtfojq2jUovSsUVxVg6xrnAUZzxILhBmQi9wfE4h6M6TD0wRay9i6Da2p36K062Q0rZge/udzMxo9Hrj1A+nWONMc71WYIjncbQrGPMKeMc4MnIzofKzFJchG8mT1pyUywrtZoHhIOjBIE6AUpnJcBH7/hfAptE+nKTkH8KpOnC74YbG07jKJUqEyAXW097xc4R7T+LfvR9vsUhqeCPtiRiFfArH4jy5do5uOM49c1/H3a6yOHEzqS1vxzG7xLr888z1hjigbKZ4OI2nnSHosWiaQTytCpuVfZhVnWxwHa5ogDULRxnPZ8g5xmCxQzKv4XX0iJElY8ZImXGuKs4SDCwTXe+jd2uMdrsr2nsXk5R0FzMdk04ujzNmUa/HKTyfxEz3WDjuxRpVBsFAn09kBSIRg4kJc1BGpmmi5NgwpFZcyrwqzsCNN97Ij3/8Y2655RZmZmbweDzSwF8gpgmLiyLSYrdf3IU7FIVB9xi7XXjm/XrOfF7Ug3a7Yp6AqorJpb2ehWUpFIug69qJ8xK/l5eFAfD7TeyhOOHmElOeNBMTPo7Nh9mXhUWfHYehY2k2zGAEK1cko0SJR03c6yMMl9MY88exqQq1rh1b12DWdS1bqk+jKCqubpUjnRjtR/ZiXHstVzefJh4oUZvJEUsdwMCGs5vFLDaZ+Nl/0DbsLGzYgsNdIpZ+iuBNP8/wBh9he4lo63+p+6IYaQNbapmGoWPvmfj1LAFbDRQXitolb0HbEcRmKgSaWZRsjvLROTbWcmi2Ng6vhrtd4sDUG1m3/F2uazyBu6kSKc3i7FYxChkKMxXis09jtao4rtuKtlCgbVNplgzGh2ukW0PkzCGYLdENRkj5N6A0Cmx1zIDDzi5TI1+J4nbr2KYS+OM9NppZPLOP08zXONL1Ex+D8P89RH37L1LZl8JeKnIoN4LdbpH3Jrj5agN9OYutkyUyHGRxcwi8o8TjIupvLqVZZ7cIrYkTDJ5YbXQ2ictuo9MbES1TPULk4aX7RyuKWMym3RbvWz25UCIBqRUXk37Hn3b74gymLAvSaXXw9+ioiPrOzq7oxfi4yeKiGMAtLIhuMqGQSTotJqfm8wo+HzSbCseOaRw+DMPDGt2uiqaJtQWCQRNXfAR3fpENG3WCviEOl90cdERIexyMaF0cLgWcUHDE0DSLKXeRaiSGvWvh7xTxOWuYpSWS0W14knM0VA/uVg7LrlH6730kh69jnX2Jsc4BQu0CuaJCx+FluHgcb24elzKH/dgRcrExMjU3k4X/xghdz8YNEXyNACP2AvXlWRrxdZRqJtb8fjqaHW+jhMfmJJh7HpvHRdsAo26npzrp2AO4zQaje39Mgybx3CJGIEDAqlIypyn5FMY7L3BL+SF69hECjSx1NQC1Gq0fP0HLmqft9WG/fh32nUUMu0arqjNk1THaCkvGEHbNRHNqtN1DDHUKbIxUcbpK7KnbKR8zMcIRAm+6gSF/kpvK+1Dqc1RzNeaifsaWagQOPcei9ybc+SRGrUgpP4I+mqBlwXQgjflCGjNsEr5qPT6CBAI67bb4ToyxjOUy8CRGcTqFrddSScBG0hobNKVQFKkVlyK2T3/6059+uTv54he/yL/9279RKBR4+OGH8Xg8zMzMcOzYMaanpxkZGeHIkSN8/etf5/nnn+eDH/wgoVDoJfdbO3W969cJHo+HZn8EtYp+FEdVz1yvubAgBuW6LvrOezzWwNs+G2fad68H1aqCpomIf6GgUCio9HoKrmIKny5Wt6pUFGZmNGoHUtTTLZSAl15PIZu1reo+YFGtOkmldA4etNFuq6iqQjhs0WqpNLItVJ+HpXoIw+liv7UFu2LQ9QQI26uESnM4w178fpi62kmifICwp4WaK7AY2EzdPozDZ2dL6Sn8foueZcfXKxOsJEnWAzRnshgLaWr+BFahhKeep+IdJapnCFfmGG4mURwaRS1GvDZHbyiCMhanog6h9HTMVA53q4TV6dHLlem1LdqKk1BpHm+vQqBdwDek4nIYLAU2UVBDRDtJNi38DyPZg5RNH6jgNJpYThflyDRKt8tY/Rhj5cO03UHq7jBus0GicghXt8EeYwtZ/xS1ssFVymGChTnsRgOrXMVv1ui2LFS9y6J3A1v/f/be7EmO7Lzy/Pn1PTz2NVcAibWAAmplkZRINtVSqymNrLtlY/3QJrN5GDON2Tzq/5nXmbF+mKVnoVottbYhxaX2ArEjM4HMjIx9D4/w3e88eCWqWKoqFtlUSyzivOQakTDYvX7u/b5zzpc7oqyvUKYz+oVL5BKXSVRAK+bQ9jbZvPO35Cdt3PIuH/zW/4jlLxB37tH5QYeH7QL3xy3uLnYJQyWL/vOLtAZ3kYs1RrXCoXEBIWA7bfPq1QWOFVHwhhSdhMAq4j3qkPbGuEmOSVz6cFKoQrWaTUs+SyIpleRnHjpUFXxf+czf/Vl74BfFZ+25X3UUCoV/7H/Cz4V/KK6AXy+++CL7JAxhf18lCBQWC+XD6eRf7G9+2vufccWZydhx5LNJ8Wd8Yfkz1lqJ2SwLHYgOO+CuWYk8oDCdCgwjZbkUH06uN/G8iEeP1A/9BwIpFaJIkiw89JLFvU4NN7XZN25QyEVETonzlQW53lOMqoMoOlyrDtgUfSqWx3Shk6g6sWrSSrvkFB8jp2HEHmVvwGwiWZ/MCI7HrFYKUz8HnS6qpqDYJlvTBxTDKZVgSGQ6WMsJtq3gN7ZRywVSw8bzBapM0ccDcicHeIEgWUVEkUJ1eYQWBVSSGTknQaYKAEejgAAAIABJREFU98pfY6UWeGH9Pq3eXexRm5V00AhRVRCqoF+5RknzuTB4l3I6Zl7YYZA7x8XVPQrjY2I07gd7xOUqR0GTF1fvUBo/pegPMIMlTjBjHRmMtAZFzeOSdkK5obJcqcxFGdubcWC9QG6ziB0t2Dh8E5lCv3GDxxd/B2PSZ/HeMfPDOUfHGr2kSVfbYTYTdFdloqM+tjtm2fHxXvwavh9g25KNqM2tvQWFXETOHWEbCSeLMuL0FAZj9KLF00mF6VTB9xXKZfmsM/CcK/7r4vP44pfSGfiTP/mTz/25oij88R//8S/jT31p8UViQKPoo/ar52Xf+zx3/sdnDGQXiOzzVit9luWf6UPFs2Eh9XrW3js6EkhFYk0nNDWIol3E6SmVaMRkVOfxSsN1FWw7i0zLtKEKUsKO6BA/HpI26iilLb761RCjf8pgvI9QazzMv4ZRTxGnWdqQ6ykUSil0FgirzoPCV9h9+KeUJ11QYyxL5Xx+ROAvKfUOs4c2kuDyNbT9LrXRE173v0u3dYs4X+RwcZlL/aeowRLKO+SUNXZOwtpHlxFXOj+ArRbOdom6M2aSqxClLfAWRN6M4uSI9XSMGa+RpolwdErTpxhEaMMpwZU3UOMCnlkhXj6m6A3w1TxTKpiKIAlDwm7AC4v/wMSok2OJjkfOtFiWt6h1Tzle1On7ebqVOv/68H9HGYwQeZt+5SqMTsmfPGKhV5FOna1GzPbyf6NxbQuvHZCGPpfGb7No7FFUUxZJnZMf9CnEGtR3UItVdnrv8vDa76O9M2MyVImLCm11l+UyexAbBkSHHbpCok4XKEqf2kWTYhFKwYDIr7Io7TJMNXIPRhQLI1QVonqdA3+X5ASCQGCaKUmSxcB+kfzos6zpT5Mu/CJTT5/jVwvPueK/HD9PZPTZ119kH30aX6hqJhk8gxAfSTfOilAf5wtzOkEtC5bxLuXlKTYjHo6ajHMaq5VgczMlCGAwkCwWCmEIpVWPcH9AYteJG1v8wR8EiHffZ76YsDZfwi1uIAtQODhB0VRmuU3y9gmL+YIwbdChxe4crphdVBGxpVegUSZ3cpcGY44rN9E2KjiP3sae9rgy+0vsUget4jDde4WDVYuvzP+W6nyMsV1Bz5tIxUdfu2yOHxCqJsPtr1G63qJcipjNBD5VcgUF8/Yx6qpPslzja3kqYkHODCn39zFKDpGnMdj7Qza0iMKj9ylHY+I4YaZUsCIXTQvx3Ag/0imOn1KbPMRRfaxkhrKxQ3XyGCsZ46/ytLlCVEr42tv/E9d9i3l5l8ixmAx0qvOnKKZDs56ykZPktBSRJohBj/JCcK0giUoVLphdDg52WJ9O0EUFZyulqMAMOLn8bcwffo/hWGVlKfTL288kYBtRm+FUYC8kLWMGf/ZjjK9c46J+QhqMGPbqxJu7LFONWntAfj4hv5HydN3ECjefHf7jOJMIbW5mkrLnXPFPB88nEP8Twcej3YIgkwIpyk8v9LPDe7mcUiplkWyftQE+vlnSD7P7TTPLg37nHY3BQHyYBBRTrWaynyDIXud52WsGuV1SD5S7A662xiS6ysSu83S5SzOXEgQKtp0RUaGQXSQ8D+SxxF4O0f0hBWMAb/osT2f4xzNmHXAq76G/2OKKJdkp3edErVKsaYSHEToTfrv9v1JUFgTCRCsXWKsF4omLPh7iJEsK2znSao1zT/+GjjSIzSKlcESt81dMyhcQ0xlW5KKkKS8f/t/YRowCpJZNed1FSSXTmU5aqxAFkvWdQ4zTI05FmXOFOZNOSBxbGKsRtekROWVFpAGmxjwocOdxjidSJR89JUmgZ57H0QJK8Zyp1WQuqpyLO6iDAZvBIb5qsKxsYFdznN//HqtAZ2VcQNME3xn/ewzXpxTNkVhEgWSjf5sUBelUsC6XaOYmJAuf5d0uD+yXyZeGpIpKKR4yNUpYP/weerXGnY1vsLcXcy28i270eP/xuxxYL7IoQRxKvKdtFvo2s5ngpcoxBTHivnaTI1Xy0vEBG70fcUqV01sv8KSatXrF5jaTH0/I51M6Jxrr/C7LZTZR0nV5phH+efKjP0ub/EWnZD/Hc/w644twBWQ/39xMn8mEPm8vfRpfGEaWG390lMULl0rZEMJyOX0mDzyTfiQJ9OxdlAi2ZwOuMWIqBWO1zlG6w3ZJ4roS00wJQ0GjkQUUJAmkRykFb0D4zpDC3oCpGrI5f0z8YMUsbOBGCterXcotlVYz4t44u/nIOGbHfciV8C3UepF5foPNuI2tQW8KVqwQ5XM06gn1xhS9u2I8BEMG1JdP2fH69MMOUXINhELeHdJq76OoCsIQ6O6aNEnxFZuBu6ae9Hl6skHx9o9wlnP8YhlvplB2ffKrIdvJQ3Q1RSgSEMjpnMAuo3dOSJYxJb+LakjG+U003yMvfXQlZr92i+JyyaXum5SSMcHWeVbnr1EaP8E6vs+SPKpdZoNT8nGMWI7ZDWe0lgc8Ma/S8B6jqgnj2g71Fuhph2im8Fi7hiIUokaJmjYjNGMa00OWj0e4tW0OzCu88lLKV7ciGu0eJ08kg/p1PA+GfVjMOwzMXS4ZJxSDEV2zzt/Yr/KS/yZff3JA1GvzQaUFzQ0iZZtdM8UvbyMORmgq3L+vMTq/y8YyuwBGUZYsBB/Np/hZeM4V//XwS5EJ/UPh16ntK0Smo4yijwxYppkt8jMpUKa1k8+mBArx2X/j4y3ds2EfZ5WfyUSh01HxvKy6UyxKul1BGCrkchJNkxgGPHggoFQkv+hTLkvSBI7sq8xmCqtVNszq8uWYclnS76uMx3DunInV0hgscjirAeemd2iNHtKfGjza/jYkCZvuAfPjFYulYHbsYqYBvjRRhODc4G2a/im2NyN0KlAqkVy/RjmdUNA84khBjUOq3fuovQ6qvyLJFTBij3w4x/SmiJWLa1So+x1a3jFSEYzDErEXk8YpVrQiigTTMYz6sNF+n8J6gI/F0+ZXuB9cwpZrdoKnFOMxrnQYpE0eGi+zis1swM46QQvW9PVd5rkN0koVkpR1YpPzp0R2gVI0IpImvbDKQfk1Lk3fg5VH397hu7v/AzUm3AhvU2AJhkZ52aE1e4QWBpyqO/StczgVk6Y6hskM3y4TjZf0W7fQFhP0ZoUAg8iLOB3nCNpjZGdCp36TnBEj5jOkblC8voFaMGAwplKUaDWHur0iNW066i6XN1wqwYRAqEznKrOLr7B+2CGerykzwx97WFZ2yGjWIjqrMqoqefRIQ4hsoui5cwma9verN1nCyEdVxM9r6358D/ysFvLPiy9r6/dXTSb0D4lfF774IlxxBtOUFIuScvnz99Kn8cXZ95KED30AmVm0WJScnn7EF6oqcV3B0ZGCVi2ypXQplSVSwolzlYMDjTBU2NiQvPpqxGql0OtlfrKXXjIJTI3xykEbD7i8ukOl9whXK/DQeol8MME52Sf0JEdDh8gHuQ5Q4ohUCm6c/jWGDBCmTtEM0Aom1vUdpJ1D0Q3SwYyye0ppcoTePSUIYdU4T345pOx1yc065P0Zfq5MMzql6vcgiIjWMXOljBmt0ZIAxffojCyi9pC96XsUFx2WosQgd4GFVWfDPaAeD9CSkKGxzSrWEQoYSoASxfiRSioFE2ODRX6TULUpBmOEqVNNhjjBFC3wWOg1Tqs3qMZDCof3SKRCp3CZk+ottoNjboiHSMfBmfcou11q7jELpUJH3cLPVajkQyrJmGTh43mSB/k3iHSLbWuCj8VCr7HuL/CWKeuJzzKyedp6g2bQRo7nmAUD/cIG89CmIceYWsLuBYXYsOkb59A0KBQVLsRHeJrFcJ1nuX2V2U86JIs1NXWG6q0xzaybJKOUnlfC9xXmc4UgUFDVbJ7E2ZkGPjqbKMpHHHG2Lj+NL55zxS+Gz+OL55eBfwR82mJTFMjnMw9ApSLxvL+/0M8MXKuVwnqtkM9/9gY42yxhmE0RdJwssm93N+WHPzTY3xcEgcKlSymmKVkuFUwTXFdhtVJ4+FBjPhdUVx1KmksQwJNDjaf7sD+oPNvYcZw92Eejs1gxk9Uq5GBYom4sqZkr3IWkO8+z33FAEWgioU8LRZF07T1erHeZdUKqyZDc0WPKyhxLl2zWPIpf3aP+tfOEhz0KoyMsG1qTB4R6Hm/sE4cgvYhYN9EDlySUmIHL0tdI9Rwja5PYcDB7bWZJgTbbDK1t0A3M5RR/HhFGgrlRZ9+6SWdV5S+1f8l1/zab8TGmEjKKKvSSFsOozI+Mb1JWXYa5C4ypoSkp1dyaxnmT+/ar1IcPUWyDgh7SD+usV+BpBS7kB6wSm8N4hx/lfpudusdy+woNMcGPdKpeB3s1Q4lDYsVAqjo1y8VOPHJun0QKtMinPnyE6rkc119GdLus+yuO/QbVRZt6MiBGMCnsMlfKXJjdxuy3saTPzNygPS0QpYJiOqeZ9jDqRXKtPFei+1S8IW5i8GjeRB61SVchJTlDrgLeOWlyO3wBdy5xVkPCdcpSyTwDWcSgwHFSouijNSllVlW8c0dlNBIfZlYrLJc/vXY/qfs82wOlUrZef1ma0C/rA/75ZeAj/LrwxRflim5XsFxmz+lC4fMPS5/FF2Go8OCBSrstyOehXpcYxk/zxXyucPeuRpIomIMODiukhP3HGsdP4EG3zHKpPBuS+OMf6wSBguNAtWowGoUcDMs0rSUV3WU5l5yMCzyYbNDwT3H1MiO1xVRvUMv5pGGCF6mcG35Abd3GNFKqjk/TWGBf3qD42kX84Zpa+zZ2OKepjHDr55HHbRQ/YOHpCJFSCKbooUcxnOCHKku7hghDzPUMxQ85FrvM1BqikMPVqmiTEetpCLrG1GgxoMFhr8AD8xUuGW10bwlxiogCFkqZlXTo5C9j5DSO2WGobSNIKeYT8g7cVl9BJBEtdYLmLhnGFZ4oF9kSA0rhkLlnMpVV+tY5dl6wKdoBlXiYebziFaa/gBRi1SSxLC6n++iRh9R1xNqlMjshH4x5tPktisOniKNjekuHflrH9qds+Cf07T0MEdPzKuzNbmP12yiBz1Bs0HdzCF0lZybklgPKZYlWcXjDuY+2XDNc6hxP8tijNqb0YbpA8X3eOmrx1LpG/1RSjYcQJzzsVTKZaSTI57OLgu9nPADZWp1Os7WWptkl9PP4QlWhUHjOFT8vnl8G/onhsxabovDstvzxQ9GnGbg+rQr0yffK57OqTRxnPoMswjv7ulaTFAqSZjP50PypsFhkr2s2U4ZDhYt6m7Q3YmE1OLKuMehIotMR4Toht1kg/nBa8MmJYPW4C7MFK7WOPT3mSvyQnfkDqmJON6xSnT5BSSKK4YhAzbFIHDbUEctpyjLN0Xz4IxrmEn+wxM+VmWl1YmGwGESs5wmbwztUvA7500OWC5WjRZVTv05JTtFFTH49xo1semmdFQ5xIphqdb5f+A5K4KOtl0zTMj9Qv8WfOf8tVTnGCFcM0gZzpYTrtHC1GisXXo3fouqeYKUeE61Fh02KLGgxBF1naO8y1ZvUjQXn7QHahS3ahWvsRIeoSYQmY07nZe77F4gUjWvaPsV8wkKW+AvrX+PNAuxoga0E3LVepyqn2WRif0WYqChxhBWv8BObWOiEadb/tPSEWVKgND2meXKbeB0ykWWkolGat2n4bdJ1SL6iUe/eozZ4xDIwSGt1NtUhjZs1XpLvs3HwJr1Zjp90W2irGRc4RuQchmoNdTjEmfWICyUK1zfZ7+R47O+SJIJhWCTyU1a+xkJmhsCs3Su5di1LFjlbk0EA3/++TrudTaRU1ezQUqtl6y+Xy9b1yYnAdX/6gX+2prvdzIdy9rOzPfCLPPC/rA/455eBj/DrxBe/TK44e79P8kV26JIUCpkMtFRK2dj46CB3lgzTbKY8fSrYkadEp0OCUoN++Rr9jsQ/HqEpCWm+gKJI2m2Nfl/BGHUJB3N8s44zP+ac+5BryX2KLBjEFYrDJ9SSPtIPCZwy88TBilfMJwmMplwf/h06EfHCY2xuMxJN/LUklirhoxP23Ls47pDC+AhP5OisSky8HEV/SF66FKMxMkxYyxyBsLKDZgITpYav2PiYrNMcj7jK9wq/jxZ5zF0DN8nhm2XMmsN4YaLNZ5xPjxGaQoctcvECLY0oiwWhUWCm1YgSQaqaGI6gnAuY6E2U7RaqruGEc8JFwFF6DlcUETJmKznJLhDKOY7V8+jLGXYwRy9YiPUaqQpywYxEKhAnWOGSXLQgTDSk4zCvXQBFoez1SFLY6v8E2R2hLmasYp1FboP6qs3Gch+xWhIW61QfvU1z/Ag3MtC36lyvDTDP1XlZfkDtwZucDHL8pNfE8JZcFMfk6w6PlzXsxYj8soPRKDKytxm4OR5551AUWGlF3FmCFIKBV6JWg/U6UzVsbHzEA2kKs5nC/fsajx8L+n0NkLju5/NFoSCfGeI/yRWfVmj6L9lvXxb8gxuIn+OXj0/Typ3lu4dh9vHj5uHPypNeLASzmWA6zaLlisXsteVySpoKVivBe+9phKGk2ZRsbKQfpsSoSE8Fu87eyxskQxjWtihqUFsqzNNMMyqlgpQSQ0iqvYdsVmYowuPV4V+ABHnzRSq3VxSPB+T8DlGuSGpsMs7X0YZdXui+R86MYTqCyRwriRhJB9fQkEmIEy+QP7rNIpixHq9RQoduWOFwvUFl3aad1KkoE1Slhkgj5qKGJQL0OCSde/QDgW+/xLJokQsnGEGEsxgwxCHUL6AIDenHDMc675m72OGCc0oXxwroWXtMoyKrECZ2g53gGE1GPFpuowjYVuZ4m0UO51tsLbuEwynrNMcH7i26fpVE6Gwppxz5mwyHO3S08xQWjxCawsps8bZ3ixee/pDC/H0CK2WinUfEaxCSqpgBECQ6x9XrVLwOtdUES0/IywV2GFKIJghSTnSBn6/iC4Vb89vkf/Iuh8Y1DuxNeqXL7KwSDEPhlff/F9Rel/vxFlGhwkrf5Jvhn5EMZry98bvEnROUXIt4ojBwt3nrzU0sSyJ6fTBjFuUdxrUdymXJTnqCklNxixsUClnFL0mytee6mcxgPs+qO+Oxws2b2eFlvc4OGpqWmQ7bbYHjQLGYEoY8GyzzSU1oGGbxtmfStvPn08+VyT3Hc/y64OflCvhifHFm8pxOswOVaWZdg/v3NYJAsrUluX49/jCzX9AbCVo7DeLNTbwl5K9vkqZQcBVcmXWRp1NQVYXcrM+GGNBKc6xPT/nq7C8opHNGL30LeUfQenyITCTLjT3KRp5wkbLlHrD2FTYnDwgiFdtIiGMfVj1cG+JWiZY/RZ8MCP0RiqmjI4gUk3S+Zr1IGCQ1NqIeGBqhouKKEooA258R+yl3zcsc1f6Am6u3qUcdZJDijTwGqY2m5QhVk7LbZ37s8DC6zE78lGbYJbDr5DSXyMwTy5AollhyDUjuBFepJUOur95lYrYIWxdYdD3q86cEy4S/S36Htn6B34n/nGZ0Si+tMPCu0fZq1LxTDGEwFmXkcEI6CKmuOoSWyszaISal6nfJsyIxCnhb27i1XYTn4dbPUel2qIdHrAKDpV5lmbaY5mPWUZ1i/IBzi79CfvABd8MrPCls0HYu85JIUKZTfmv1P7MerDgNCvhOhRySm9xlNFJ4oL9E+7hNvd5kNUh5crjLqdzmRrlNVbTpdrep1STi/BbFtM0VcYows4hq2844QtM+8pz4PvT7ClGk0Oko7O1lP/8kX5ycCCwru4Ce+QQ+zT+g61ms7nO++OJ43hn4R8AvevP8rCpQmmaRo8tl1rItFuUz7d1ymUV5qaqkUsnyng1DMp8rmKZkf1/j8FAwnyskiaAZd1Dmc4o7DrFdoKiusA4foQiBqxQJrSIlOWOn7GI1CgwG2d80GwVqTNhLjog6A9JU4sUm4dAl3z8gkYL85QZxscrKV7l59Bdsrp9iz4YUVwMKco4jfHRdoV15EdUQ7LiPiBYhxmQAQcAqtukaO3xPfoPi/JQoVknjFFcpoBNSUD1S2+Zt5Q1mskiQqJTlAt8o8P36v6Ib1Ch7Xa5E9xmnVX7g/AtyuYR5YDILc6xEiUFSR2hwbFzmIDjPX3tfZ5hWmVb2OKx9jSOvzg+CNyBNGGst2vkXiFYxh6smj0d1/r/5K/zYe4WfGK9xnXukqPxd+hv8P/LfMNYbFOUcYWjcs14jtxzSWuyTCpW74lUmep1aMiKnemiKZJ7fwLdLrLQi/ahOpJpUlSlW5EIsEUiacYeWtWS/cAt9PuPi4jaeNFjmWjxwXqPY288uesWAK+YxUjc56RksF1DzO1SUGXeCK8TTNepshKfkWKQOTt1mmpa4elWytXzMLecQo2jxZFwmN25zS7lLuZhgXWxx7lzC0VEWM/rDHxpEUZZQpShZ9SYIFDY2spSqjY2EWk2yXsPRkcpiIXj6VMU0M5/KmZzhk5pQ25YcHKh0Oir9vsA05c+lE/2yVnuedwY+wnO++AifxRXwxfhCCEmhkEn/4lgynQpyOcnduxonJ4L1WqANetS1CcOwzN5eQjRZsxGf4gUqajnPciloWnOKJQWlmEdKheEQHCfF0hN2tCFiNGExWlNanLKKbZJFgNE5wXIUtN0NVvXzqO1jLg7foxmcsjG4Qz51sZWAMnMiYaBoKtVoRC6ckbprKoujTOvvr5nY2wzMLYxemziQqHGAUBXCVEWogpVWZEiDRAqWaY5AzWFJn0elV+kGVZphh43wmJms8Hbum+zleswXGqOozL56ldByyBkxkZegrWbcSa7zuPI6q/ImsTQJA8l/4vdYKgVEHLHKb3DXv8RJ0CLsLrnrX+K78veJ8yU2vSNKcsqhcpHvq99mplTYStoYFqimSjRyma8MTvJXWKgVAiVHNRigEiOEZFXeIrRLiJM2rlnFVn103yUXzJEoOMqaupiwrfRwtQLB1Gdn9ZhVkmNut+iwhTNuE5Vq7OkdduURqaLxcL5FMnfJjduInM37yQ3q4ZB4kHX553Ees2yT5ktcih/xcvGQxo5FaBVJnnZwDu+ztRGTu9zi2rUE180O/ItFFle+WCh4nkKaZos0CBS2tlK2t1OKxfQZXzx9qnJ8rNHrKeRy2byjT+OKUkk+i9X9Rfjiy8oV8Lwz8KXCJ6tAUmats243G+eez2cb5Ny59NkUP9fNqqpZMoRGrZaSptltuteDyQQcR/DkieSKJRncmbFyNLraDn906YRacsBai1F3m7j3u+S1EZFZZ6KkaJpCM+4THUecXvkKr2tr8rFHNFdYhhqLdZEXNhLCUKNUCbF2LlJ89z2a8RFrX6egRszXOn37PNgmgVHgVvevEI7NsrpN6CvMZwqhmadYSHjPu0HPLVGM8mxHY54oF+mKHfKKx9fM98mVVbrueXpBgbyc4JiSp+qLnLLN3bDKSBrsiUOO1IscJ+e5M7vCtt7HlivWaYkBdXrxFsnaYMuZsbYLHC320DwFax2zCAWKmjKqGPSCFGYq2+YQNU14nNxiRIlUkdT0kPvBDT5QXmGRb6BYFgvV5DZfI9ZNlmGNi4HHO8kr/Gf5z7maPOacPKJnX0DSJ1BjUtNEiWP2xBHdlY4ah0QsiXWV2FCZpBWaypByNOTb0d9CPME1y/QqLxLnCtzo/jXTMM9m8JjydIH7SpNgsOCC2+aScoeJbPIg/3UKqxHnRvd4Wn6BN41XKKw7vN6/Qy26Qz7fpFBOqDuQDu6S08dcj+5QDOEgvcG8LTg9FRwfq8znmcazVkufmcuDIKswtlop9+5peJ6G5wnOn4/p9wW1WpY0sb2dJYucVXs+GSsXhh8ZG88i6sKQZylbz2PlnuM5fhqf1jH4PL7QtOyi4HlZEkyxmNLpaJw/H3NyouL7WfjEapW9d2+ZIjcn7H9gEDS3OdftcOPqY05FirJXo/3DPlv2gHbYpFBIWSxUdkSPYBCzL3Yo1OGV+TtsiFPmap22cZ5tOaFqz3E3blEppzRkij1fYk+7mOs5goBD7RpDtcUGXTaiDlocsy7WUewE2Z3QdlqYsYtFwKni4IYp1aVGM27jpg4/UW6w1Kq8rN7BUkICadGTV1kLG5FCviCYU+ZPlW/ySrrF6/pb9MQWR8oFem6DkrkiSAQTv8yJtsfJepeLRhvEFe7arzJUd0DTecP7jxTSCW17j5F5nh8sfxPHD7haOOXt9S3KygYrxSJvBFTjEX+e/gtui5ts2xMMIjzV4al+hQ1zQRQZ5AKJlxqEfo6mOmbbO8DVHdaFOrVCgOaF5E4PUbUchfUIPVpjJTPSnIMeSmSaYERTiqqBpiis1BVueYuxc4GytsTpfh/PKFN76yHyokOkr7AWE26uRtzwfB5aL/G+cY3dwV22n95jYb7AD8RrbJrHvLK8y/b4Lls7kmo8wRzcQVFGbKp98rkxZrHCvW7mHbt7V2c8VgBJsQjf+lYmMb18OWY4FLz4YkKhkNLtCtJUsFxmfNHpCAqFTN1QqaTPugGfFUH6cb7IPGvZsLPnXPHpeN4Z+EfAL/PmGUWZicvzFAYDhUolM3hl+s9MN3p8LHj4UGM8FqxWWcVoPM6GhOm68uHsgQTTVNBreUb9lJ3FQ5z2Af11CVEtk44XyO+/zejYp2ueB1WlOX7IaUdlvUyxFmNeG/5nduoxjEfIkw4yTniYXMQpK+imytU9n9aP/xNm95g0jBkXz2Ovp2jBithwmNrbtPr3iP2UMEpxrTr+POYg2OW98CZBroRA4T3xCt5KoiURj9UX+J78Jg/FC/zI/DbzxOEo2WU/PMeJuMC7ylcZW1ssZoIoVoisPNPcNiunzowyi8hhmNQI9DyL0MILdUI/pSJmFHSfzaqf+QSmOkmQUNB9rjUG7J2PyWkhs6RAmOoIIYlSFVSVghFStn08YZGYDlrks5cfECcKS4r04hqKoTIv73JnfZV1qLGVtllQ5FTscKTucTe5gfQ8jpVdLOlT9seIMKCXNmh0ZM3rAAAgAElEQVT7LYxoTc6KCawCtj9FX8xIwoT/WP8j+vXrnLskaLlPaKyecG75gJw/RfOWCENHHfQpeX0s6bMo72L7YyIslFYF1bHY20u4ntyjJXsUSwpxvozqGMhOn73uWxQWPQ62vsFdeZ35XKDrknZbsFgIoiiTFliWwu5u+uxSulrBcCioViXjcZZQ4nkKQkgaDflhStZPp0J8XBd99jEMs/V7Zpz8pMHskziTQ+TzOTzvy1fted4Z+AjP+eJn47P4wnEkw2EWLfruu1n1NQxVgiDzlvV66oecku21ajWFYgldxKyeTLmwuEu08Bjo2wS9JfH9Jyz3x8yiHAf6da4u3iNt95j4DmlvRBpKXuYn3PLeJgwk67FHT2wyXOVo5NfkhUezHFB953toiwkaCTJNMaI1EgUKDoVoRmHVZyUdZuULlP0uIo059hv8pfq76ER4uTqhVFmuVcrJhIVS4bvyD3hbeYPEsMgrLnfSG3xX/h6x1BhrG/T0bR7ElxnGNaZ6g5G5jVpyuJ++wIG3xYAWS1FgEJRZrnWCWGOuV2mbl0gLRdahzmCkcaKc59S6SKWcUt01GUZlBsomXTYpWgEH4TmmRpOauiBXUHiqXcZXckyUKoqukkYJp2GLGWWiXJmHta/Rc4vMfBs7XJBXXOZmk3ca/5KVr+KsR0zTEgPRouZ3MVIflyLHchsj8bEVn1i1iKWKtZrirRO+3/pD7m39Fl+/OsBpP+GKf4eae4KzGoFlIKMEc9zF8edMjSapqrFDG6lbrJ0q1S2Da9ckV6J7bMgeuc08S1mgsTggXnjUe/dwjSp3eZFBkA2jyzpSAt/PZt5YViYd+8pX4mdd4DDMqv2WBaNRNnhsvVaezSio1X46IevjHAE/zReOI4mizN/268wV8Lwz8CuDz9Jxfh7OtKFSZp8Ph5kwzraz3P8gyG7PZ4kTUmZtWk2D6VSwWEC1KqlWJUIkmfZ7awf//XtUmSF8yZPm73Dxe/8eZ3JKQSiMWr+N1X2XXfY5JwQftN7gm8Hf8NLqLVpPVFQRclJ2EMqCdXBAPY6pr7vojxdE0xX5aYeVVsCRKwJpYKYu5VWX5uopyyjHo/gifWULa6rSSVu8a3yFc+uHvDfb4zjeRZMxf6V9h7eCl0kSlanWRFMVZorGcXoO4oRNu4sQAi2OiFMTS/WxDJ9vbT1lKcocjPMYq4ickOSER0OboBPSVjZZpibLJIfqCTY0l+3yik5XQ0libOmipRGzJ2v0aM1WNKGbbrKftCgXQvJSEmLRW5hYaoAWR2yWAjbzM8rxmnv9BmFcIAwE65FJFCQoaoX74hY6CXNZgXWEnZP8JL7KOi5zc/QurybvIEyTJ8kOihDowYpysCbxBK66SSUZ45sFXi4csKqrRKUa4/o1SqcjZkqZAj6M1jjDPjlN4OXLrM/dBKtIomh0Wjdoig6/a3yfXJIybdQZt66i2xppGKPOZlxa3EZP1igbdeRGEy3IjF/jsUq3q6Bpks1NuHIl4cUXs0q/ENlQpNVKoGlZwtBgID7MJheUyynVamZM/LwhNIqSeV5KpfTZ/uj3xU95Cj7ZJfhkdrppPq8KPceXA78IV8Bn88VZLHCSZFKhNBX0epJLlyTN5plUI5N27OwkABhGwmK9jZOfkC4TivGMZeEFHH+MdXLIZqpwW96gGZxQCIds69COmpQqKd8c/5+8ePgeOzsaua1t9LVLIXmHY/UCtinZWDzC8iR66KJNZ0hVZW420OcT6utj8m4fRQgWSZ778hqzfpNJyaakjDkKtykEfX5U+QbnV4dcDQ/p5Rr8X9EfoiU+iapRyYe8r36V95Wv0qXGQi8RpkXM2GfCOQJpslWYc8U+YT++yJFnI5SUiuZSURc09DGDtETfr9CPqxjJlLyS8pXdE/783kWsNCWOJUKNybHGf+JxJXrMe+vr9NIa67JFa8unPdJ5ql/hdO1TUFaMqZA6eXLGiKLps39c49E6T0GV+B60k5fZEU+wlIicnnCovcCb7j/j26lgmpokqUAJYrZMm0laZkGBmbWNulyy0nRSRSNRchTSJUZO51bxmGKlSf/qNyi/8xjpQqLohIlJ/kkHU8TYlsph82XSvRfBLeOtYFK7QSPscFP+HbmpJNmpMdev4XoazrqDulqxs+hSNVfoezc5vbBJcqrQbsNyCb4vkVKwu5vy6qvxs3k1u7spR0cCVYXlUpAkKYOBoNcT2Lbk4sWEF16IKRR+9rpvNFLq9fTDOHWBaf60p+Dj++c5VzzvDPyj4LPGy3+aI/7T8Mn8dtOURFG2+IMg09JJmVVQw1BhOs3mAmxspGxtJVy5EuP7WWb0ZJJJNZZLwYULCY8fa1zUjwlDQbUUkwvm+D+8Q3eg0VsVUXWNVtThZF7kdF2l0zeoRz0aypjz+QnVTZuSHIPjoCiSkrKkYc4pHj3AmI8JEgPDd0mXa6QfETpFVs4G+WWfNFGYm3V+or2C50E3aDAN8kgnz9xsMEzrzK0mqdBx7IRBWEGzNRr5FcWiZCnzJMJgqzDndecet/KHdPw6sW5RMjy+81qXujFnLXOcjh3yRsgFu0fTWhIJC6kINpw5RhIQRRIz8Ri5OdqrSvaEQBArKrFhU1bmDOMqdjAjTSAIFfxAxwt1okAhr7jcqHRomWMS0yHCYLFU0ZSUQBoohs7K10Cm5IwIJ6+QOAV6QQVPcUA3mNlbeNLES0w0TfJYv86T8qvU1QndqEkSSox4zbFygbdq38FIfaz5hKORwwP9JnXbxS9tkAYRCI3SsoOZesRotP/Vf0/+Up3C7JQN95CNcw5c2ORrlUfY0y7q+U3uNf4ZpdUpl/o/5sX5j9ByBovGBYzdBhyd0g8ruKKElFnlptmEW7cSrl1LkDI7YGiafBY9WypJhkMF14UnT1TiGKrVbN0Wix8lQ5yt8Y/nTitKFqu7XmdRicWi/NDncmY0+/tdgo8nqqiqjaKsPzdR5VcRzzsDH+HXhS9+Hq44+/2fxRdnv5ck0OtpNJsp+bzkjTdCSiX5ocFTEMeZtts0JbkcyOMOBbFGFByKTkKl84CjxxHrUYDvQ7UQo6oK9/097gxaxJ0p5aDPb/JjyjWF2uuXMYwQMZ9j+Avq+pK87lOanKCN+gSpjrWeEK0i/EjFz5WwvRlKmhAbNvesl0m8GOF5qMsF47iIIzwqxZBZ5QJq3qahjgmMPN+Nfo9JfofrtQG5msFfyn/OQbyHbaa85tylVox4KK8yE1Va5oyvnO9hipClWmEwy+HoAa9VDzlfmjLwimzm5nixTl71smnsBDwdOIRmnoWrIVRBoggajRi59lmGNmGk4sUqRhoympukUoEgZtfss1eeZBVtXZA3QgJfoRs16MpNAqtMf11CppLQyGMakkHhIvvhOVpJj9vpLe5pN9HjNVe1J/SUFse5FyhbPo3olLmo4IUqizjPPXGLfv06jj8hPz5hMpL0q9fYvGxhLCcQJdjeDDtckKoao73XGP2bP2Kr9z4bcYdSIWX7SoHilSKvRm9h+zMe519mv/w6FyYf8Pryb6kywXfqGBUbkTNYjFMerzbJ5xWEUND1jC+uXk0ol1McJ+sIeB6cnKhEkYKuyw87ygoHByq6Drlc9prPMsSfdQP6/Y9idavV7CLreVlhtFTKOOnj++fXgSvgebToPzl82mXgi0bBSZm55Pf3M2NMFEG5LDk6UhmPFVxXUK+nzxa9ZWUJQZVKdsM+ONB4+FBHH/YIB0s6qyJxnD3kt9MT5MNDGs4aXZUYL+wi949wjh7iJC5d5yLNqI2xWuAMjpiVdrngP2THHrGhjpDVEpWKTnJyTKH9CFNNmJd3qR7dxpr0MLWYVX2HyMwhZ0s8NU+Ur1HdVIjHS7rKJnNRRToO73s3OOYcc6WIIwJ0S2CZCTe2Z6RSsHQVGvaS7fwUvZKn4CQ0W5LNVsy54oxmcc14bVNrCAwlplkJ2dF7uLHDfttBJCE3KydotsnJqspazXPhikL1fA7NX1GMxoSpxjAq48ca2+aIDWdFmGqMoxLSj9iRxxSSKYppkig6y9RBiJRte8TXy3f5Ru0eL+cf0kk2MfG4ZJ0w9hzcOI+b2DiaT9OYE4cK1VJMrNuU9DWmCZslFy81uWh1CFYxrlriuPgir50fkL9Y5emsgua5vCdf5f8w/h2RZrEwG8RhygfJTdzAZFVocXH+PjKIyM0H5NMlmkgJhEVx9JRjdklSQdyZkFNDRBJiz3rY3SOWnTWn9h5m4PLG6Z+iLmaM8ue5I19kNfSRKdTPWazLLVxXwTSzYTIvvRTTamUP10ePNCYThdNTlWJRMh5Dr6cyHqvMZmDbCnt7CeVy1pn6ZEV/Pld48kQlSbJkorOKTRhme+Xs9xuNFNf9+3vn48ayYtHGMNZfumrP88vAR/h14YufJzb00/iiVJIcHqoslwrLZVYMyg5nKaWSpNFIyeVSTk81Hj1SefddA9HL+GIui0gJhqFQO3gbZ3yKenkXpVSguOigPznEc1PsvKAUjtFlCAuXzshkwztmVz2lZrtc1o8wmkVSPyZ+coLtz1EDHzXwaJzeRYl89KKBTD+cd+DBzGqRT5aoukogLCZqA1sJWZtl5qFFlx320z1MW2HHHOGIFZHUeTN+nZ8sL6IKyF+sExfLUCwiygU0Ei42F7y+28GNbTzhYCke15pDKuqSqe8wHIGtR5RtD6FrHLtVqnWIyxWaBZ9trYcSBAzSGmOviEJCU5tQzcdEqcLQdTD8BZtJB0OE5B0YxjXiWMHRQ36zepevlu9xVT+kYS1RdYFuQOJFLGKHWBiEscBR1og0pKS55Bo2nWQTWw0QOZNF6Rxj1+KcOEWEAb5TRS/l2K26BHaJKRWSSLIvL/GX4jtZDKoVITwPP9GZ+nnqUQ99MsBxe5jBEk2mxLqJ1A1ak4ck0zWrtYJLAV0VVAcPUKdjgs6caOoxL2xS793n8uQdxkubZW2X/WWT1voE3ygQ7V3GslJ8P5sxkaYKm5spW1sp47Gg3xcfzhvIzOqLRRZE0elojEYSx1G4di1hZyf9e4Wjj/NFEGQdr0rlLCY3uwyc+QcsK5uT8fH9o+tffq6A5zKhXwn8rCi4M0RRFsOlKNlG8f2PorVcV5Cm2XtUKln2c5pmmrxiMcUwUgqFTBpkagkb1oBaLcWtbBMedNBnI8JQ0EsauInC1/u3ebQUrNMN6vMuX/f/X6LmFsJPqa1GbD0+ZlK6QPHJEdZ6yrvVb3I8t3np5C22J0MY+yT1OjPPpr5OWIcp0loRxRK/ucdqIbCnA9yJ4J71G/xY/w0uubcx3BU6EROtxZN0l92gw+XChH+79yYzs0F/foNUcbjYHPHfXLjNQ+Uabz5qYCcpl/JT5mmBSbKJn3PZoMd/t/Mf+PPVtzgY1bL/xPWanbpC6jicrLcIHQtHeFy7KVCWLuc3TkjWJ7wzfYF1YpFqIVV1wUZdUFnMOVn7qKqCnbOZuUVkEPF6bZ+T1Zxc1cBbxmznptT+f/bepMnS67zz+73zfOe8N+fMyqwRAAEUCHAQp6ZtkR0O2pYjHFbY0Tuv/IXsr+BFO1rRi3a7W4oWLYkiSJBAASgUasqsHO6Qd77vPL9eXFQBVAOK9sI2o1TPJjNyuKtzzv885/kPxZi5uMFr23Mas6fcKh8wK3/OsjIIKpWaErOhuwSKiqDI1BsVepaThilXS40oLbnQexR1haVcUqsL+FqLZ2fQtc/5IPlnPBFvMvc1LlfbHIjnOPW3qOHSzabYnz4mEEJqmUdh2iSJQ46BFi6QLkM2sr/jvP0mWaRyMDhBjFye3v4BebhHe/yQH334v1IZFiEGiqMzz2s0po+ZSx1O5GNuGpv0euusiueuDm+9lbNcilT9EdW5gPbWFpeX6zVoL/rUA4WZuI0si7RaBZIEvV75H63xPF9/Xpquv5Zf/Ema/mHq6nN60Nftna8Ky7a2YDb7f2nzvqpX9f9h/adiBXw9XvT7a2qQ54nU6+vpQBBAkqzpGTs7680mCCWiKJIkFaZd0KlN2bJK0t4OdbdPNVsxpUkg7qFOLtlKXSaFxVXe5t3h39KV50ytO1Dk/GT+v1NVMDd3ac0GDASJxcxmt56wdXmOIlbIaU5sbOF5FWrmIrkeeaOLKCks6z3U1Qojm+BbPT48+Bn18Sk7Qp+rpOAD8Xs8Vu8QlDpj8RxqJ3y39ilPlzf4OLiOh8G3u6f82Z885v/sv8lvPzG4vTtlq1tQygpPpFvMy5TXjBMa2oSyNAhji5FnI5cFWgM0Q2YSNwk0lZ4543gvYic+QRldcM9tkcQSQbVLTco4MOe8thfy28cOsaCj6zKBuE1RSNTTBe+1QqaBiWXBpjDi2BoiyRJoIKQLGsEQN0sR5IQoliloIwsZ3VqArUQ4moCgZgRZj0SuKFceYiHwMd/isXmHay2P640J72sHCEqMlY7opzeYtLaYTNv8PtjjNNrkO8bH3BCfcmfw1+hnV5RKRW7WqQqIZRmlzGicfQIDmWHjHQYbt6gN+typPqVfdvjl0X9LVU658+zf85Pf/y9kpk2gtgmqGuV0AXHJU+2Ak1XvBT15Y6N4YQrx+us5tjti8qQi39r5Yj2XlGd9bEHiLNhZB8Q5Io1G8eKs/4dr/Kt48Zxu+tyWFNYTL8ta/x7+4/3zCiteTQb+f6lvCpGxrApZXosjv84T9/kYN0nW3u2uK6zFl6sh/qVLKNdRFBiPRYa/GfHp+ym+4HB6uu6az84kLi5E4lgkVus0nRw7nKJMr3DEEGmrjVCvocol87lAejkhXsSsEgsl87GFkDKr8FclrWJG04hpvdZioF8jjSocb4DhzSkFASmJEKKQhjeAKCKRLUKlgSYkSGHAxNrn/uZPGY8q+lGbftHlRnPGr4K3OYs3cY0O6cY2olAxj22KJGewNImxcOySHWPOXesBthwjJAlquKRdy3i78ZSqhMATaIcX/Mz+W+SaSRoVnMzbqEXIsXpOZdpcLurU5Iij1oyeuaJUDX54/YI9oU+6imnHF0jkRJJDgk6JzHFnxo+Pz5n5GpkfkYWQZCKGmnPsjNhvrghjmUS2KJwGhiWwaS54z/8r/mL4Q64Cm5qZYYg5LWmJUqUYRkXNKXHEgIODkqt+hpxnSGlMHIFGSqXIeKXNfJSTLiK83MDScjYbAdPIYZUbTLI2WuyyJw0JrC6xoJNkEmKaYpkVJ73voM6vkPIMUaqIzBbKYkZcKKBq9LfeJOwdcuJvIF1coLgLOvPHxM0ui+YR8zkEVxGr3MKPFeJ6F6nhcHhYoKrrdbu5WfLBBwr9+z7LR3OePqoYJw2SJ0NuNMY8eGbjizUkCb773RxN44WYWNPW634dZid8QW8QMIz1y83OTvm1qauNRvUHaZRffdF5LiyzrJfTLu7VZODL+qeCF89tQw2jejEV+CZBZFGsA8LG47VoeLMcIrorEq2G7wvU6xVP//qK/qOIzy6aFEXF6anIbCby6acy47G41hd0HHQlh8mMA2VAMo+ov7VLWasj+0smE5F0FjC6EmkVU4QsQ5JF3FDGWg7p5UMahw7i9X0mQpckyLCDCWqwopYvKKKUNAM59IlFAyddgCqjyhUrocbYOCCRTCaRw/3yNfxIIrabzJYqv8vf5m/sn+N0NPzSYhw3eDzv8MTbJDPrtM2AG/aA790YEUcQzmKyqc9RZ86/uP0+jy4cpnOF6zzmz3f+A5Owzjw0GPgN2oaPJYZkaBQVHNXHmGKCJmRcvwU/3X1A+OSKQ7WPFAWs1A6VIKGIBYZecrt+TseKIE1JU4Glr5IXEi15yXc7TyhEFTc1aLYFEr2GYFtsC0P2Zh/yG/c1hmGTmllgCCGv28+YBzq2I9LV5uwcCcSFynIl0qvGxEHBNF1j1ajsclIc4LkCwyuZ03yHudgmcrpcFRtMYgclidY0J0tAUQrUPCIuVPraIYvb7+KKDZqzU5QsoZAUTtVb1JSE5vIMsdXkM+NNhptvMR6VGIsRneUJLfeCSes6Y7rMQwPB9ZgmNc7STaLGFlkmcnxcoCgV3/rW+nL/+LcB088WPH1Y8elFE/VqQHk1p7Wj8fsnzRcX/Zs3yxfhk7Va9YIW9HV4Ua+vsWhtq/6H1qPfhBcvO1bAK5rQH139Y5qB1Uogjv8wPv455/M5D04Q1qOv56+l1cJFXU5JwhK1YyP2B9TiKScjk989afHggcSTJxJZtg7sePfdDMMAZ9cmvxhTVWsx8cfpbdTQRZxOcAchj4sjtMEZveSSWKtxrl6nK05pJhNm7RssaLIsHBrBgEfpIfXFBVqZIiYBiaAxqboo5MhZgiHnpIpFlgoE2PSNI/zmDv+b998QZiqiWKGrOaO8x2/l7xLrLcyGyL45oQojklzGkUJu73v8fP9Tbu66uJGCN4nZ9J7ws+Rf86Z6n1/3r9EPWtTdC66JZ9xJf8/5qk2k1Qmw6agrvNLmKqpR5NC0UtrKinf3BuzUXKyOxvQiZjBW6Egr7hoPaWdD8kIgLnW6jYj9rYi40Hg2a1IWa23BhjDmRm2IIwS8VnzEMq+RJhX1rsKhNuTk0uAk2uKmdMKdxgW6IdCvdslEjZrg8pP2J1xTz6lLAepqwumig5QlKHJJIDlklUK9mrNvjmmXYyrDJBJtEskkzDSiUqdCxpBiUtkgrG0gWCYra5OJp/Fb7UeMXYO5uEErGVHXYuQsIs9Kwkzl4/ZPObNv44RjDmb3qLKcLKtwpTat5RlONkeTcrxEoahEYrPF8WFBzcrZa3mIrsuzRYP+r0eMTyM0FVqKSzyP6RUj3GFMWm8z0Xf51rdKXPf5q6TIzk7JYCCSpmsv6aurtTbAsipu3iywrPWhLYrfnLr6fDLxTYmTL6t39Ktm4Mv6p4QXsMaD+Xx9yf8mvHBdgclkTQUSReiqC7ynC2QhJ1LrbOXn+CcLZMfk/c9rPHyo8P77MoOBhK6vzQCOjtbZICvqKJMRsizg+wK/ce+ghiuy/pxwHPIwO8AeP+OgOGMkbvFZ8/u84X+ALmWs7G3G1j7pKsVxh/TjDq3VGXkhoccz0krFleqklUwjm6GXEaFo4+UWkdFksXsbT6rxL5M/Y5Y4zKsWmDr3o2PeL7+DZq91DkKSIkY+YlViSgnH3QX/4p2P2ax5JIXM5CTg2uoefxL9JQfSJX/59JhZ6HCtOuE2D2iXV4SxxOfJEbls0imuSCsFt7TZqoeIZYFSpRy2Xe7eXFIXXAZnOc+8Lpac8PPuB2wk50SZwpVfY2e/4tbmAq+wGLkOy1hDzSLeVe9xoz6iqgSa4SVpJmLUZKp6A11M6F8IJLnEdfkEyyho2hmDcgsBMJSY/2njL2jqCToxWuJzNrHJkgpbjokkmzBRaBdjbrQmWFWAj80sbRDJdZJSIRU0tCqmJvpoYoyjpSiOTi7K3DO+y7/W/3us8TlymWFXLoolY/tjdH/OjBbPDn/KQm5yHNyn8+weoVyjQKKZTaiHQ0pJxaWGnCXrIMrGDnvbBVXNZl8ewnJFMvGZX0Tcm+2xlfdp+eeUCx+9CLiIu6jHWywWIq+9ttY0GkaFpgk0myWuu16DJydfjxeNRvVCFP//FC9eVqyAV83AH1193WJLU3j6VCJJvvRqL8svhZPewytmTz2cXYc4Xv/PdnFJ5QUszB10ucCJJ9ypDxDjiPuTLk/TffJ8PR5brdaAIUnrF9jRSKLh9UnmEaoK8mKCtFwgvnGD4UVFMEnY6f+OjXhAKpnYlU/Hv6Ct+XSyMTUpILab6PMhwuCKLfcpRaOF2TDY8h5RyTJlUiAUOZqQo8sZSavLf0h/wDSyWfoqJ9khqWbjiCs+8Y95GB2gkvKdjadElY5kabze6JOvAsJCZ8eY872finzytMZgKCKnMXq04j3x90hRxMfVt7hfvoFt5NyV7/Fe8ff83erbPPAPWGibdIUrJoHNVdZGlCXe7Z5y3JrxYLGNUre4kXyKNB6RJiKlYdIqJrSkBYfiOZZeoiuQ6QbTuEa9VqL5cw6UPlvmHK1Kue5cYSRLEGAibGELAW9vXFAsPD5fbJFlAjvGnFXZQGkaNGsJO9aCpuIxWJh08ituyU9pFjMu4x5+ZSFWJZqQocglb3b6/HT7M1qNgsysU9u3WeQ1Qq2OaYLllLixzjSyyZOCO/srWjfq/OX564jTOb1sgFV5KEKBl6gsqhaVrCIaKkKrycLZ5Vb4EZuGyyIxcSMNIQ3ZT55i4+HLDVLNQbIMamZGq5mzuw/F1OXT32ZIH95j8shFHo9wn0yItq4RzyLMYEYvPKX57gGpVmOxEKi7fQ47PqVlUZYVZbl+pZxOhReXfk1bv+B8ncvQ81cc+EOB8TeJKl/WA/5VM/BlvcKLL+hAn4xZnblcuA0cZy2076WXmGWAcWNzzen3ptTDEdc2fMZ0+TzYYToVkaR1UvDzprvZXGPGYCBhzfuki5iqqjD8KfV8QXx0k8WkJJzEbJ79jlY0IFAb6HnAG+EH1ASXtrQCQyNOJczRGbvTT2iVM3Krzr40RBcSpCpDjiOMzKOmxIi6wjP1Jg/TQ8JAYBHp/Kr6E5ZVg1IQ+dC/xTypkUkqsi5hiinbOyVGvGDpKjRlj29vX9DeVvhouIleRGSiSnXW5zt8QD2d82+VXzAqtrAEnz+3/oJeMeKXy3f4jfctRFVBzUPiVGJcdLm+Mef6xoKOsuA02kTWRW76H2LNL1mEBjNxg4P6gjfsJ3SKKywT0lyhVCRi0cTUK8Q4YkcYcSQ/w1QzmqpPS1xQViLzrI7lCNzhM4JYZbTQUfIISVHIFZN6Aw47CxrM0VU4nztsVQNuqM/oMeKRu01eyeQoWEqKrha81zvhbvOE3QMB19iku5Ezyjr4SoNaU2K7umQ7PKFIcu4J38a4vU2QqgynBkejX7PpPUVTC1yhQZQIGCQIIvTtW6TNTbaWDziozpAMhcTPuEy7tHv4gw0AACAASURBVKMBm+klVu6y3LzFTNpEurZDLzrngDOM7QaDvgiffI7/m4ecXVlET69IB1PSBALBplXMcMyM4+slA6+B60Ld7XN71yPTrRc21KK4Fhf/p+LFVy//8M0i/JcVK+BVM/BHV1+32Ipi/ZLz3OaqqiAIBJbLtWNKOPAITuckYUFt18GY9pk9mBNUJrO8gdBwSE4nGGbF1UhitXWDRmP92esNUNFqwc7O2p6r8eQj2qsznlbXGNZvEJwv2en/jt7yCcnWPtef/hWt+SmR2USQBN5Z/ZKtfEAiqlTNJpvuE+zFgCfRLmmlcC19hB1PqZAo3IAqypiIPf5G/S8oEFHKhDO3zd/k3+d+ehOhqhglLeSmzWFtxr3lEYushoVPpWps2CENIyEpJYSqJJJsItEm1y2yicuk2KDXCEkVk1/N7vA03mZe1NmtrTjuupxE2zyadolLFS0Peb1xTg2XldgmSQQkU0WvIurFjCqviMYeHcZE85y2GXDnwOeqfoOwsc3x8Fcc+fdR8xhlu0kZJjRaIltln/duT0lzCWSFViOFsmKROezVF9xpDsgljeVcYJLW2c1OucPnrCqHVK/z/Z9p/Pn+32DOLrkcaQziDfp5l756yIE15iLqsarqpGh01QV12eNaY47eq6M0LW4op8w9jUrRwLY47rlcM4Z4q4o0l5BDn8ArKaYrOtmIMT1ySWEjGyCUJXKZkKAzzZusyjqfxYccq5fUwxHGTpOLpEMjnbGVXmDLKZolEu7ewPcqdGIis83WoczcVdB/+2tql58zCUwKzaAoRdqNjHZH4Fg4ZY8+vU5G78fXMGeXmP6Mx32L01kTUVw3qopSURTCi7Fwo7GmQuT5l04o3zQt+6rA+OtElS/rAf+qGfiyXuHFGi+E1Yrkcs7sqiJUajjLPtlghpuZUKuxok70dEIQrF9T3e0bJImAqgp4HqRpRbsNd+/mX6S8CtQefUh9dsalcsiocZPwcsm1qw9ojp8S9fbZ//yv2Fidohgiuajx9upv6OV9UquO2TFoTR8juUtWhY0qZezFp9TjK7JKpaJCEitiQWNU9ohKjZnc42m0zcflGwSJjBsonAjXaTkxkiZx5m8Q5zJVWdFVl9hqgipVSDWDJJeZxA0ER8dom3ieSJDpLMsGauZxnu/y18u7pIrNkXHO8V7C5+MuZ9kW524HTcp5t/mIa/I5Z9EmYWVg6zldw6NyfUovhjCmXU6YLHXadsQ7tzwmzZtMxS2OL37Jkf8pjhAiNU2iQqPuFGy2In547ZTMT4iNFq16CllBXMjsccG144o8iIn1JrOLiE4yYF8ZUSoSzs0et+/K/A+9vySZh0ymMqOwxrNoi361w7Ez4HF8gFfa5IJKT13QVZfc3RzgblxDNUSuG5d4voRbOqiGxF51yXWrz6Nonwthj0WoMfAbtGYnbGZDRsoun0t3sMMJtWKJXbhEhUoRFUiLBWe1O1zjFFUuEWYL9oozJEvHEBP0IsJuiNzb/lOMwqPmD6l3FczSZ74QqD+9hzBZMI5ryElIu5ihNk02jkwOy1PuyE/pXjcR97YwZwOq8ZTLmcWyqtNorJPq43hNmX5+uf8mvPiHWBGG6wfRfygifo4XLytWwKtm4I+uvkkzEEXr7ta215SI53zqPIdFUadu5zTSMduM8K8iZtIG4sEOnicQPVrzOHVtzY8z1YJRVEPXSzRN4PAQ2u2SnZ2CjY0S6WoMYciJ32VZ1XBXFe1kREtc8ob8EHXUZ1k4ePYWbyQfUovG6HlAUUgkGz0a/hBkGbd0KAwDM3Px5BZX5SaP4gMiySK0Nvjd0Z/xZNrhYbjPebHNTNvi1HwdNI0MBcVS+GS8RZLLmHiIooBEiV5TcJoCGileYSGZCr7oME6aOGrMG/UzPp3vsbD3CCuLKs8xLZE/PXrIhhPxKDnCXZR0qxG/6PyKVFDJVwEb+YjrjRFhquEGIkksstvy2NA8/Nwk8TPubC/Y1SeszC3CWEL0PIJC57rwDNGUicMKeTSglQzRDRFBkmjVUu7ULykbTapGi9cPV5hdi8hocRF3MTOXH1kfcFt5Qre5Hr3uRY+oz56xHT/lWO8To/Ms2eHE3+Is7K19+qnQ1JK6nmBZsMhriE2bbnzBzNVJw5JlbFIIIkrosxc84EbxGXklch726HsNmsICxdFw1BjHG6DmMX3pgN9bP2RQ9OgwIctFxJqNp7Zw4gnqakbuZWx5jzCkFMWSyept6uEIc9bHKx0eeVt8et5k56N/B7MVsWjxeef7YFsYDZUb1QmOlcPRPrFoIoce0nCIZghM2GDp7NJsVmgawHqMm6YCtVr5xRQLTk4k7t+XGY/Xr5W1WkVVwdmZyHgsMBqJtNtrylxVrYXGzyPpX00G/mnVPxW8qCpe+KE/D5j8Kl4kWo1gVbLBhFY8ZLfpc5l2KXd2CEMBadBndBIhigIVUGUFi7KJ71c0GnDnToGmwdFR8UUeQUUjvGLZD3nidYmUOt6qYk8e0VWX3CgeIvb7eDikWo3r3j0a5QKxrJAlEE0dI1khCjCXOpQbXWrxlFiyWdHkSXlAs3LxN3b5nfw9nrkdfpO8TV/co3IcPpS/Q1DZpLKBaZU8mXUQshRLWlNQclS0ukqrkZGXMrZT4VUO/WSTqNK4W3vCOLA5izexuwYj1yYQaxx1l/yPt95nIXa4tzikjDM2pSHfaTykxhJ7OeC28IBMdXCFOoLnkhUyHWVBpycSrCqSTODO9pLt7QI3kImmEYLvEQo2x+ololSQ+CWiu6DJAkPKEaqSlpNx+05BZdlUuskbvRH6UY9YNLnItrEXl/zY/h3vyPdob8vImsje9CPqo8fcTD7msLVglZo8y/Y4iXd4uNymQsLRIpyaCGUOqk4oWtTrBU44ZbZSma40okxBkEUE30dJPLakKwLB5tzfYFj2kIqcxKhxIRzS9c/QSZipWzxU3iQXZDbzS2rVinYnJ0h1mv3PaUUDFAnICww5BcuEoqATX2JHMya+yafLHVbPfLbPP0BYLhna15g6R5iVj942cZyK9oGOZZao8tphKLoK0YsQT+8StrfZ2akQxQpJqpjPxS+mVyXXrxdEkcDjxxIffigTBMILetBwKDIaCQyHIrXaGitsuyJJvtQRfBUvXlasgFfNwB9VrQ9Xkzj+0rqqLOHePfmFL+7t2wVx/OVC7XbXKneXOtJ4zHyx5siNW7coy4qGd4nuztZe/O2bRF5F2p/w6Uclo7DFeLx2kYjjdQS9aVbk3R7v37OIz+fEz64o3Zjp5h0sreC13hRXrBNdf53W8HPaqk9UmSydXeTEx45mpKJO1e2wjEyMcM5Q2meY96gEgYtsk78Xfsiv47fYV4bcc6/zy+JHfC7coTQdOo0Mpy3hVg5PFhuEmc6mMee2eUq7K1CKCldZC9Md05OntI2AQlSw4xlZUKDFLpdRh0LWsJsir9fOaRVTklBgkjY4TXfZMWZsyxMcOVyHi4RL1NijGY2w/Svq1RLbEfAyg3p0xcpXSDOBzVpANz6jlk3ZCZ/gJxrCZIwcJ9hyQpiItIyQO8UnbDRSLtNNmk7GUWuBcvGMm8Ij9GiBJAvsxE8YDyo2dJ+2uuLmXgBxjGXkbKtjnGiMkOeIWcZK7WBsN/AauzybN5hFDpqY872tE3acFaOozspTMIMx1nKIMz+jvLzCDq9oyS57xyL5KmSVWdRqIkbu4WUWSDKGVtBtZyyucpQqo19ssqjtc194Y50FkEYs5S5/l79LllQc6QNasxPsbMmGMMGWElAVJtYhSb1LffAQOXQRsoxr84+pV0uSXGSu9jDLgNDsYFpQswvY7CJLFfdaP8FZDfBCifaeRnx4Yz3OHV4RDT0SrYaur51OzFmfeBqRahZxLHD/vsxisbZB3NwsuLgQ+ewzmbMzielUQpLW1qS7u+XXiojh5T3gXzUDX9bLhhdfl4j63Cr06VPpBb1hf798IaiXpPXlKLdqFBdjsnyd9npm3CJJBJzlJfJiykzssejdoFXLCM/mfPQBPJ3WmM/X2OL7IlkmvBAgPw62eXSuw2SOdHWFkkas9m6jyzm75gxfbDDdfI3N+Bn1bE4pqgzVPWwhQJVLKCsCZ5PQLRE8j6myiS5mhKnEKlL5bf5tHgSHPHTe4Vf+27hlnQt28fUOatchqbW5KjZ4OusQFjrvNh+yYQdolkwgWAhFwbH4jFIz1gJdbYW2mjJemki+S2q3aEtLuu0UOfDZbfr4LjyMD9di56bNvtLHlAtiP+cw/AwnGBOnEvvpY9BUrNIjzhX0aM7KV0klnU1tQTc+o+EP2K76eFcRku8jZBlO4RJi0JIW3N6cs6GtuEx6NGWX68VDxMjnZm2IHs6R3AV76QmjqEm7ntD1n3G9NaZSFKy9BjvFOc7iHAEQg4BF+xpWWyfQO5wuW0zCOpZT8rOtj6jLAXNfZ+obmKsRjeU5ynTIZFhRDwZ8S3vEzTcEBguTBW1ECQ60ISfhNioZsqOwEluoiYeYxaSCxu+V73OhHtFWPcwyQJBEhChGiCPq0grF1teuheEYK12w2LrNWfsujWBErf8ILZqjFClNr0+jWlAKInO5S5qvBeradpPKqbHRKSkMi/Pa6wizOaFg4jgl4f4Nqkqk5g+JRy6hXMN1RdJUgIs+hRuSqDb37ikMBmus6PUKiqLi4kJiOJT45BOZKBJemFF8k+nEy4oV8KoZ+KOp5yLhJDGYzeIXPLUgWP/cttfjrFarpNVaj7yCYO2d63kC6lWfTdvHddcLWhVzWtds6nLAaGVR7uxQr5f0bpqsZhXThYJHjSSpmM0ExmOJqj+knHsUls3kNKExOyWexhCFiJsbvOn9mp2dnH7cZewZSHlCuy2gpS5KleLkK2rlCk9pUUkqZrJASUMeJ3v8y+q/4yprspVdopUhT7jBaXmNINMw5QRLiChkhWuNCdQanC1aBIkGVUmKhpLHfNf5FNcViSudHXHAViNCywP0PECOAraFIafhFnNhg8RL+UHzPq3wknf2R+R+zGfREQOvwTuNJ/yodY+5q/C0vE5NCXhP+T1kJafFPitpA0EWyHWLK+uY144Cbjp9DnYSHoy7eFWNcr4i1Nuga1R5TqR3aJVDbjaGNAUPu62gtm1qmzrbyhinWCKWJY3ZKU6xZODWCeU6FSDoOrkg4zQlxDhGDEOkIADg3LjJRNsla2+i6+CKdfxZTlOY0y6nvPNWzGJaoYgZP6v9PU01YFnWKEqB7qZAQ1rR3NK4thkSuiWrZcVl0KZRTjErHyWLOMkPiAuVk+oaE3GTs3yXHJlcUOhb1/m09WMEWeDJYoPD4hQ7mtLr5KSFzLjs4FcmzeQKrYgQJRE5idhOz9Hj1XpdiztoeUCotenUEt4oP+XgoKBotbm8lBh/MODpuEEqm1SVwOFuirVlU68WxOdzltOSB4Mm5dkAdTll7NkobYfhUOTZMxFdF5jP1wmR/b70BS1ozZXe2irodks07csk1X9YL+sB/6oZ+LJeJrz4arBYWRpIUvgiSG8+Xz8are111zzp55kyp6drQSUXA5qqj21XuK5Aw84pbYdN2yeVTLzGDgC7r1kEq4LLoQKOQ1WtL/+DgcjZmYDtjjBTl2nm4F/FNKbPKN0AtYio2QXH7ieYe3Uu5w5RpeP4V7R7EnKwYqMLpnuFRUhaSAiKhB1MkdKYJ1xnGNTISxExTZlWTX5f3iXGxBNr6HKCV5hcFR3+86MnlO02T2dt3EQjzSWKtOK6+AxTDEHTqMs+bclFE1KqJOV29RnH5WMe+zvM0gZ+aXIsneDkSzalK95tPWbgOjzIbjKfS/xX3xvQnXzGVdlBkFU69Zj9+DFVKXKa75HqdRAFQrXBVeM2r23PuNmZclCf8WC+jetJlHFGJNcpTZMqTYnkBi0r4I74kNpujZqVo5oSDdFluxngKDFCHNOYnVCTQvpLh0CwEWQZajWyrKJWFxB8HykIEIIAQRS5UI+ZNG6QOy0UW8HPDcJFRpsZRuHz7WsTlr6MKSX8zPkVRl1lGekEosnxVsA1e0LeaHG7t+C0r1PEOeOkiZDntJgj5TGX+TaVIHMqHHFR7WMQUiUFI2WHpbXDeO89GvOnxJlMr5pQNpuIhsIsrTGnheyt0CIPuyWhL8cYeUDTu6QsBAK9joSI5Y2pTBO/s8/365+xrcyQNts8PZEYPAg4n9fIMgFNh6P9hI1ji2K2Inw24+QxDPwGyZMBujvDK9brZjxeB6wmCcxmEmUJT57IKEqFKApsbxe02yW6vtYWfB1evKxYAa+agT+aeh4W02gYuG70gqcmy+uQjDhee+Tu7ZUvvNWfH/zWvI8wmbJQNriwbqFJOTvqmP3tjJWzw8ms8cXoeA0SF6smibo+3Lvdil4Pokgkny4phgvSqMB+8gk3Lv+Wa5zgqAn72ohmPKRTTFDSkJ3gCR3/kngeYQsRtWRMVVSIAthiTOFFPFFuMwhajMouH1dv8GH2NqJYoAoFF8I+46JNISvc3Ruvw6OEFqWsMnU1ZGVNZzLFiLFvk1YKLd1HLhLyQqIMEpKwYBXItPMrfmT8lgerHYJUIcwV6qLHbsPlu8Y9PvUOAdBMEVkuMcsAV24h+yvq9YqdjRjHyFmVDk/CfeqNkv1uxNLeZZh0sIsV3WyA50uElYGX6UhFTntL4mZnyqV8wLLaoNUuqBsZ5e4uZ9d/ilS36e3LiKMR0mgEwyEkCZfqMdPYoaMuudlbkEkqM98kV03qyRghTSkNAyHP8aQa7gLmxi6CovDWwRTdn1GoOnlcMctrbFRjvtt+iChWlCuXy7DDfvaYn9f/noWnMEo3uJQOifQmg8naoegN9SGSoXIS7xCWFvUGtPSIK88gKTUUEuyWylW1QRhLUBTY4RgrmVPTE/LOBn6ish2doJCxEJosr1KSWKSbD3D0lMRqEOptDvPHzPM6ntJAXUyos2LZusbv7B9jTi8x+ydMizoXm99e2+55Ez75SOS+u8d4WLFRTWE4YtPxGZXrkXCvV6Fpa+qQoqwvPhsbFVUlfEGLWNvHPV/z/1ga68t6wL9qBr6slwkvvikRVRTXNryuu17kzebaOUUQ1nkCo5FIO+zDeErRbnNu3iJYVVjhhM1OxvZ3Nrn0GsxmArK8phVN0wbTpEaeC3Q662RhSVo3BaK7QpjMiP2SxumnfH/6bziQLsl1k9eCD7CCKQ1xRYXI/uITNgcfocQ+GDp1f4iShJiZTyKZyGXKLK8RZhpXWYt/W/4pv+Q/wxJ9ArnOVdllWPRwOgrb3YxB3GFJAzfWCCuTup6iSDlFVDBOmoTofKt+Tk32SQJYrESCVYFSJhzkj4ljAS/RSQqZBJOWvOK91iOOrBGfhsdYUkIuqKQpSEKJGIYc2BM27ABxfwcnvGIlNnmcXKPRKNnv+Kz0TYbVNna+pFuO8OYFYSzilyaiptJupNhthaW+hW4baEaJ3ZI5O/5n+PVttm7Z1Iolgu8jXFwgBAEsFvSrXSZxjXZP4Jb8hDyImWVNct3GUUIoS6qiQEgSfKvLKlSZ1Y8QPY+79aeoUkYhqqwyi5UrctM4461uH2E+ZeLqLHOb7+2e85P033MxNfncv8ZD5TWaZsLKl2lJS9QqJZYd4lylVHUamzLjlck8cagzR5IFMqPJKjGwF5dsJufkpYgpJdTNhFh1UJIQK5gg5jlJDOnUQ0kCLDFC1ypKTUdUJOxsSZlXBFKNJF/j+9LY4ffOj2j6l+gXa7w4732byCsQJjNOHgk8K3Zx5yXaakozGiElASt1g6izi+OUFMUaG7a3181xs7mmBVnWOoh1bUn6j+PFy4oV8KoZ+KOp54mokmSQ59EfjKdarZJ2u2R/v3whiHn+9/O5gH8VIzs6F+whCLCq6vTaGYou8tlF/YvcARFYd8CCsAaJGzdy9vdLDGPtKFRY9bUd5PI+Xe8ZarRCoMIRfHakK+pyhObNWAxS5p5KOnVJc5mkUqlVHrmkULkRYhpxXu7xf1T/nH8j/JeoeUxRivTLHVytQ185QGmYyCpUik4q6NzdHWNpGV5mIagyG+0MzZZQFVDKBEkGP1zHuzeqOXfUxxyLZ6hChlxl3A+PwTSpbZn8sH2f3VZAOAmJ0BENnQ3G/LzzAVvthEerbbJ6Czue8U7vHFtNqLa3eVxdp9BMNhoJny+3odniht3HqYGvdbi/2qd2u8eb74porx2yY80RPY9SVFhUPdKtLbaKCy6Mm0yzJvqGRUPyEfv99QRA06hsG/f4TfS2yfZrDuXGBvZ+neFcJ7xcsnv5mzXpV9O4sO8gC9DIZpRxirrVRLY13nEesc85biAxTeoEpcVOK+SfW7/k9NLCzUyuiyfk9QbRwW2SSsEtbCYrHScYc6he4kYK58kWlaLSbFbc+HEHOQuJI4Eok9kxXQpVI7dsZFlBkOAmj9gsR5xVB/y78EfMpU3uLP6eXnDOLDBQkoh2foVaxowat5luvUF7/phGNieSbcpmk0XVJOjs8aR+FxybMBKJMoWzcpdUs3k4ajC8LDm7lPGFGquqxqE6QNcE2q2KSfsGui4Qx+uLz2uvrRNRNU34Yv+sw2reeSen3S5fBJB9nXD466gWL1O9aga+rJcJL74pPfs5D7peL9naKmk2v8QQWV5nzETTEC+3CFo7TCYipVNDk3LsukhlWzx6JJIkAsulyHQqvhBbvvlmxnvv5VjW2oFIEATUjkPdyrke3ac5O8Xyx0gUSGXOjuPRWp5SSxacXDkEixTdW1OGKkVHFyNkz0WII4pK5lI64H3xB/w6eZugMPiYNxmIezzSXqfVLPGtDS7YI9FqlKrOL+48QqRg6DcpTYutVohgmWyaK6KgQqkKvNJk15iRJwWvSZ9zJ/8UTYXPg30uOeDu/hU3D0I0KUOUBOrVgm49ZhLV6Pon/NcHvycw2szzGoWi8471OXU1wm6rVJubPKquU8oqPcvnQXAAzSY3mmPsGgRinfvJTWr7Dm++J6MddtnTJ6yEJolfUHV6GNdarKo6k6SGlXvU7ZJKURCSBKoKUVHAtlm98T0MrWT7lomg6zgHDYZPIuJJRG9xHzHLqBSFi+abyFmKg0sVJei2jCzD3cMZ+8UpI89mkrfIM3h9d8Gh9xGDbAstWPB6e8BSbhFvHfFs0WKRWTTCITtVn7RUCCWHqW/SsUJu7Mdku/vYgs9iJZFKFu81HyIoInq4oGbkFM0GEhVZWpK4GblsYJQ+xuIKMQoIE4WNuI+dzQnUOme975BWMlvBCVbhE/d2GVsHRHKNTHcYbL1NqlmEkUSQKFwKe7iVzcfPGiynBYMrmVip4Yl1thlh2yWOLeDv3iQM165Xe3vrFO2jo4LLS5kgWAeP/fCHGcfHBa3WN+PFy44V8KoZ+KOp5wf5xoaJogQvlO7D4VrlnudrVxX40gLL9wVEsSLVbLrXbR48kBCGI9TQI9vcJpRs/tW/0lh9OuDiQYi16TAYCGizEe65xyhqMh5L7IpDfir9XzQll2VVwzr7nLjQQZexLJE9Y8pu8AhBFrjYuMsqMfDMLpFXoEkZbqpzwS5pJnPCIXNhg/fF77PKHEZllzktxkWHXNYoBAVVh1+88YQ7uyvKOCVKZDLF4N3bC8JMoRZPcIoVGimOHNKQ1nxMxZBIFJu3Gk/50eEz3m48oRP1WSpthtI+vV7J//yjD2hOHmFv15hHFkWrzY69oCeMqSkhbmbipRqLlYwQR6i9BrWGwIV1m8uhRkv1eWfzgv7M5Kro0ogGSLpCVa4FujeOEo7fq+GoEUKeI0gS9XKBo5a4y5SxZxN0DmlvVOwVzxBOThAXC4QgoDIMaDQw3z7GudFDkGWEJEF68oQiyYm9glLSqKkxl9p1JmUHPXPZqvtsGwvY7GEoOcXrr5PMI+4eTjmUL0EQyFHoZz22vMfsSGN2kidIVOhXz9gRh8SxAJKCHY1p92TOxWvg2LzzRsJP7445m9bQxJx8GaKKOQ0rIbGabPSgQEEmY7+xomFGnFd7zLRtUBTa6YhaNEbMEurlAkXIWVjbXF7/AbvpKWa6QlNy5tIGo6xDrur4lYNQlTT9PvU9G61tUAkierxCLwIW1g6+UKNWq7hlXnJrdy0eb7VKglVF6di47lrfYhgVW1slkrTeP5pWcetWgWGsJ2lBIBBF6++fv5L+QweJr1ItXqZ61Qx8WS8TXnzVG317+w81A6PRmtefJOuXza96pZclZLpFZtTQNBgMJNJnQ3QNgsY2v/ylyvvvK4x/NyRfheA4eB5U/RFK4HG+arBaSfSKAT+u/pbO6pQxXcrxDImSRLXYkif05Bl71QV5JRP29hlJO4giTKVNVBKCXIMwJZEtvMLikfE6j7nB03iXD4pv84wD4krDo4aqVhh1lV98b0gW5mQZVJVAt5OzvxVRxAVFkNCoFqi2wkY+QhMz8hyEeh2/tPhJ+x7vbpxyt3ifjupxj7dQHJVfvH3KDxqfcK3rchrvgGNxuBlQxildbckqUhFZi6jnnkKWwuZ7O5DnXLBH/0KkaUa8U39MP+1ylbSo51NkVaBKMuZih1vbK659r4njVIhxTN3MyCcLCmuD1SwhTGU2jhy2rTniyQniaoVwdYVgmlRlCdvbmLd3cG5vIa1WkCSIZ2fkkkok2ZRZhd0QGKY9Jp6BJiXs7EtsiiOoOxg1mfzoiFiy+ZPeI2q4xIVGLDuEk4TvKB9yKJ0jJiFKEmGcPaDLlEVsIasCjlUg6CrBxiHHGwvefCOj28k4mzfZclxYeUgy7FozLuRjai0Re6vOrHHI29pnPEwOeSR/C6OtUFo1hNkcI3XRigihyskFFa91wPLuD7EXAxrFggSNRDI4Y4+a4HOuXkeJPG5bl9TlAHY2WRhb1N0+QhDiOjuEks2tWwUHYp/bey55LmLbFctZibphUxQiUbSelh0dFRTF+rJv22v80PWvxwv4p4EVHUk31QAAIABJREFU8KoZ+KMqQQDH+fJw/+o4OMvWYWLj8ZorulyuU/Usa/1KJIoVWSZi5yt0d4JQ5Mgth7O/HdHKZ/QXOnLLZjwWYbli9XiGvyzIjDpviA+45t1n03LZMRcI/zd7b/YkSXad+f2u7xHuHvuee2XW1tU7GhsJcEwUKI7MNDYvepoX/Vn6N/QgyWxGM6LIAYYACKCB3qprr9wjY1883MO3e/XgVdUARVAySpQBzT5mZRYZGZkZFe73fvd853zfGY6x5zfIKMaO11haTml6TRYrHia3eJ7sYoxu2I+egOPwsfdnbJaK802LC7nLBpe53eUz9RZxqnGW75GbNg86E2rWmrIek8032KsJzeyGWC/jRmN21AVvbX6Bto043fZoVbaYeYJZtTlsrSnnAZ4WEiU675W+pCpWeNEQz9dobC/p1SPyRcCeN+dybGM6Bh9WnuBtbpisS1zIHUKvg92p0r7tY5cNQqtKrlnomiKNcswkJNMtPjI+Zrxy+DR9QGDUEEoyqIccHORU5bwQlk0miPkctVjQ0tacpy2EYcJoxD3zGarRQFsuUUGAtlqhjo7IPvqI7MEDcBxYr9GfPIEgoGqGpI7LJGsw3NbZ6BXa9ZTdXYWybVSlUrB++YyNXqWsxeyNf0XfmvDOzpgL5xjdNimHE943PqZqRDgfnFAXK7xOiYtRmUhaVMSapapSYc1mqxPNYjy5ZrxxEbM523VGLgU5FrlhEVGm5Du0qwFvfagznJcY7CqSIGYgLyjFS9bKR1MSaZjMnR7B4ITDycdkmc61tc9G96gmM/rxGY18Rlap0dUmNGYvmJ5vGU8NsumSW/Pf4OdLvpS3cV3FbeeCH3U/oX3gMD9+D03mzB/NePZI8vCyjuMIBgPJ0VFe3Nevot+Xb8bSF4l0wYx6nnqzsc9mgtFIo15XGMZXrRZfp/gmGfgqvm548Q9NRP1tvEiSotq72XxllxgEAt//6sCjFFTkkloyYjnLmWVV4qeXaNMpqeFgNH0WC0F8s2b1dMxmKanueZwkjziJPqMpxxzYI9JFiFosqS4uUHFOOVsiVgG5FLzQbvEs6rM7/YzyZorZqTGRTcQmIFknPBZ3SW2fc22fF9sBs6zKtehhlEzudhZU9AAzC5meJ7Tjcyr6hjyMsTcTjo0zqtENlp4yXHk4lsQi5fZeyInxgiAx0fMMZVn8SPvfEHlOhSVOw6GWz+hkV9S3I1Z6nVwY7Isz7pbOkNMFny/2mIguB72I1v0Gq/o+kdPEbHnUoiGB0yIbrzCiABnnfFB7yli1+Sy8XbQFCei3E45aKypGiNB1tOkU8fw51WTCZFsn9Tz0zYbbfIkWhqBpiDCE5bIgjvb2SL//ffKjI7AsjM8+QwQBZBmVjkOqWUzSOjdZhyA2aLckO/sCfB+kpMKaSkWxsRq48yv2559w7F9j9NukThnNgPfFr/HMkKoVU/rghPr6ElWpMAwrDLc1mm5MIk12GxsWNwmbyzUiz0mlznYcIoI129xkpPWZWx2yvQPSziEVOcY+6jCv7GNbKWdhjyRIaaQjRJ6xMhqsjAbj+gmmLmlNnyJzxVX3XeJMo5bO2Ft/iZeuiGstmqWIW5c/4eJ5zsPogGS84oPlT/CzBc/0wir9TumS77mf0NxzmN16j+ElTB9OGQ8lN9sKg0Ghn2m1JDc32pt269+HF66rODvTWCyK4XxfZ6yAb5KBP7j47Z40TePVZv4VG7RaFX1vr/2jg6Dw1d3dlYWLUFzlxROFHM5Qv3lIaTniVDvgYbAHCLz5NcLQuZj5MBqzO/2Ulruh/SeH1J9/gvHJp6SLCJIUW8V0g1PMLCLEpZUM6a2fUNK2NJMbGvqasWoTBjkiCHksj/m8+UMcW1FOFozDCk/1O7TcDY6KGZgTOmKIk21YRyYlQhpyynfsX3HMC6IgZzqz2EbgseLtynPa+oxSvqFVz3h7d4pLwDrQEb5L6f3brAODnfKMg/lnZBk8vGryIt3D39ywL16y587wbzXJSj4XaZ+126OqlrTKG+7trth++3twuMdO9Iwdf0m2CpnEVYbuCWGlR6j5HPU2HLQCDvtbxkOJ3Gzxd32U75P1egjLYhTUWS0zpOeBbZMdHOOLVbF5ex7CspCVCmowKDbr7Rb99BQuL9G3W6QQuH/ygMvdjxAKVLXKXfsFqtVCRBGqUkGcnYEQVPIFpfcOMV68gO2Wq00DtbODfdhGdToke0dUdn1UpYIKAoRlMb3JeKkOuU7b9KsbHl9XmGzKyFTiseLd2in5bMXLZYtV7rGVFovco+Rq9JopJ84pp49zVJTQNae85b7gelpGzxNKIiIVFpFdo2yk1DdXaEIRbTWWWoM019DSBFtLcT1o2Sv06YRNANp4Qh6mJKmGEW9w22UOqzN++K9yjt0hNWvDtXPEOK4xSavMJ5IwNthoFarVgtWxLDg91Xn+vNihq9VCOBmGxfpxHAhD3swcGI81Go3icGQYilrtq1aLr1N8kwx8Ff/S8OJ1e49lFbbUnlfc79tt8fjkJOfsTOd6U+XFU4UXTuCTL3DXE071Ay7EPkpBaXJJKk2uFi6tmy8pnT/F6XjsH+s4V2fYn/4KazkjVwbVbIIXjtF1DSOLaOcjnO0Ky4BO8IKKHpDoJbxggloEXOR9nvofMLIGJJucbWKw0Fs4rk5DW+DLBW01pGykrCOLHXHJLufsJs9oZhPWk4TtVmDpGd30Es+JaTPmjn+Fb4bcv5uhFmsizWPldLDvH1G1Ywbb55Snlzwa93i2bKFsm8P0MffLp4g8x9utsLI7hE4Lbb0ilCUOuyHVH72LOj7E7ZSoLC/YWT0kTyTj8iFXvQ/ZVHqEyuWoueCgPOKwPmc80ci8GpVyRr67iyqVuMz6pMuMPIpQ9TppvY17t1ckBGGIVqmA7yMbDYTnFb0sQYD+xRcgJSIIyN5/H/+kzeX+dwttgW1zx3oO9fqbg4G2WKAMg9LtHrXgCi2OubyxWDf2cJsO9r09smoT99t3EI6DbLdRkwkrUefFos612MdIAlSpzPBFzGTjkm0z9vwZd0svub4xudi22SQ2kfBIlE63kdC1V2yDlMejKrfmH/PAeMLzeI9FXuFO9gUGGVrJJhE2rdIGM9lgZjFZnDOqnnBaukNv/gibhKoZ0S5v0GZTwkgwHgvyWGLMJjh5QHXgcr835Qc/zGlnQxrOhrF3xPmyxpNRFZUViUtk+OztSSqVgu1//lzn5UsdwyjOT46jyPMCI17jxXxeYIWU4pUt9dcXK+Afxwvj/8f38S86XvejmebvPpckxaZ+eanhOK9Fw9qbzPU1y2lZEIYZvi8plyHp7hCfThhPoO/MOS0pul3FvrigZo+J7BZzb4+j8Asa6YhqpuHfPaF86uH+4ieURYnPsj2yJMeKVmSazwsOQUgO02fcDX5Nlgn+i/4DvkzfQjM0ZrbLKO+j1gFf1B7QyKtkwsA2ClQ66i1IY4flukRLjamqnK4RoJkmt8vnCBTieoWhqnxLf4a0y3TShKzRZFh/gPhwj7vqc37U/CW/cjssohJXZxJDtWE0Yz/PEcsl89CgLlYouWbffIEcWgjPY9fPGOt9VEuQtd5mUHmKTHQOosdkx8dwaqOCgL32lsm8jCqXmV+aHA1C2rd91NZEfP5XdE0TbWPAbae4YLUaV09jNppBs39D/y2NSw6Y/OYasog9O0OZJqrZhN1dZLMJrw8mhYkxKkkQvs+5d5989w5akiJOT7m4tthJzsFxEEGAFgSo3V2kUYiSZZ5zpXYZpzU6q6fsrS+4COpMVg56o0XvVgWt34erKwbzkHxbI7zROZ1WmMYVylnAXe0ZxtIk0BRPpwNKWcA9/Ya1ViO2AtZpF5MSly9yauk1jozYzBQfb9/GXI44lFNi0+ZX3l9wue3wP6z+RzpMWdUGXLmHHC4/x0nWpMJi0jgmUy61l6dkqccXpXd5Z/PzQpyon/Llrb/kqJvRunqO/umEodNlc/yAy2SP/X3J+bmG3Bmw2GoMqhLPU3S7kjwv1stiIdB1jWfPtDfDycZjjSQpvm9ZhW3cbFYckPp9SaslGQxgOv2/rsWv44b/Tfzxx/8TvLBt3ugIhID5vDChmM00Oh3JYgGWJanVNJbdHWYvp5T1nIq1pFOVhLlikJ9TtyecRh0uxB7fzv6Gg3RG38xwv38P/9GPycU1jlqyXkI9uiZJFJelEwytji+2NMNzytma+dbh3D3G2AjOyu8yXUkWuU8Qu6RGlSE1NlJD6gaNUsBuPeL5mQ9awj39GbslqFoxq6TKn/t/xXjjcil3aegz3jZOGZp1emWNK3OPrHlAb0ejZc75b6y/5e/SD3j2osoXFzpa7LMTXwMG08CkEQeI0YZd7zFsBcq20RyHD7vn/Grjkg+Okfv79CtPkOICUgtttQKlEI0GuzXJ5CxHGQaLucPRfkynUYJHW7QXZ7SVQgsewKAgiS7lDrNwwv7ehoYz5qx5zGQo4XLDXhQVpJFlFbjQ7Rb6MYAkQZXLiM0G+SrLOz36M/LIR1uv0X7xC65mioExRvV6aACGgSj8yhHLJeerKhOzQkeM2c0ecvmozCQoo1+MGPRNVLUK3/oW+dCEqEkZjWBisszLjAKHA/2Cu+Ixk2EfY7fEzaqEEYf8hfVTnpn3kbUm00mHhoxIbkI8tcITU/569RaXocsPw//CRjOZVB7wJfc5Sn5De/IrpG5y07iNrSLuv/wP7G1NcrvM0qjhqwTt+XMC7ZjHztuY8YK3bv6Gjebz8Hv/jjv+Ne35M9Y/nrJyemTvPyD2dvjorYyXL22m7i5RBO/fl1QqkmpVMhxqzOcaiwW02/D8eeEwVCoVa+XlSw0peWVMUYjvu13JYPC7WPH31+LXGS++qQz8M8brm0iIos/z9ejrTqdEGIZcX2tMJoJf/9ogywTDoU6WFVntzo5kMhFcXmqkqcbFRcF4XlwYmCYsPr1ChBGRXsbUJNZiDKsAb3qO3zRR9+/ijK+obW84dm5olUNa6ZDScoS8GlMOJtSiER4BY73DtlQj021cc0ttO8IgY5tZ/K/8t/z79Ec8jfc5Ld3GJCXJTdbSYyg7TM0eZWOLW5I0rRW6klSTIUITeKWcO+9ZeHUDsVyw0XzKckOzqRBZTttekDseA2uMbLcptV2OPyghNht2uMTONlQ3l5SrJtOlzZABL+UhdqNEp7REj7akUlA5qKNMk8uZS5iYqHqdvNlE3UypNIqMSpbLBSsDXAZ1AqfJOG0QGT673YR3P1LI6ZLpzMCtmfROHLLjAiSElISzmKqV0Xqvgez18D1JnuQYmxXuwEeVy8hmE9Xtol9dIeZz9NWq0BFUqxCGXOx+n8nSptnMub/9NflwwnRuILcZftNErNdvJgfld+8id3bQg4BgGlMqKfZqa1itqC3PSbwaRp6wdnusNibu7S5Pp00cV2M6grqb4KQrPCum5Bso0yDeCqaqwV55TMnJ6XobWmJMxdmilhu0yRA93uLnczLb52pTYxMKambEyqjymXqHdWgihU7g1Bl371NTK/Y2jyhvF8TK4rH3Pq31GbVoiL0N8NMJhm3hOik76gpLy7iYlHDykJvyIUIofr29z3qtkZ3dUGNJ747LrVtFIrAjLyirEKvp8vChwWymsVwWG7OuQ7NZ2OZut1Cp8GqDL1wkej1JraZelYMLdvW3LRv/MfehP5b4pjLwVfxLw4ubGx3LkigFg4HkyROdICiMJEYjwcuXOsOhgRBgDq9YXUcYVRdTy6nFY7LZmvL4Aqdisjm4Q219SVWsOeEFAzGkki+wZmPU1Qg7XNIKzzFFyrVzhNMqYziF9qu0XaCbGgunxzSv8R/X3+Wnmw8InBaJWWac1RjR5VTuc6HtkRolGqUQPd6wJ66o6iuUZWE0q/Qe1PCsLWQpZhSSKo12JaFcM/FLGaky8K0Y/7BO46BM776Hfn3Nzuoh1XwCcUxU2+Fm5fFCHVGyclodiUCQb2KqeoB8Vem9mHtERhXVaKAMHZkq/KqAMERZFvp6jZKSC/uEwG4w0gdsY8GgL3nvzhJenDM2BrgutH50D7lXDEkLI53yZsLxLZPYMHBPOuRKR7cNKtkcdXiI7HRQhoGQEv3mBrFcIkqlAqN0vSCO3HtMlhatasz99d8hh2OmY0EuDPx+GbUu2nlUqUT2p3+K0DTCVY5j5extnyBcl0pwSaY0tLJNJV9wtW2wyjxuGvcRWU6vvCCcFjaGusgpOzle06Ccr3m+bLGSPve9cw5qc0wT9rwpch3BZEYlumEdGsS5zqPkNhYJNabMjQ7/IftzPpbvk8UKCUxaJ5x1P6Iu5+yOPsFLZgRGnUfWW/S3L6itLjGyhCB3aJRj6nKCKzcYKmUZ6KS6zVXewzIV5+49ts+HhFdr3L7H3bsZ3a6ivb1ACyPq+2WCQHB6qrNaFTqAAgMKB7o4LqoDrlusuWq1EB3v70tM83fb8v4l4cU3ycA/U/z2TTSfC5KkaCFPU/D9EotFSBAU3ukXFxphqJGmimazmMiqaQopBSAYDnnV+yzIMsWH7VPe7d2wctp8ktzjcuqwHz2l5md02zmtI4/O5a85KI3Yuk361Q29/JrWx/8Hi2dLRg/+lHy+wlpMsOOAuFwj8No0ohtOVh+TSYMv8nv8Wr6HqWJmNHio7gIaVqPMSqtSqUjkNqXiptyujoisKkroHO8knKiHGJZG2w/oHVl8u/uSh8MWQ2OfurOh0QC/qpFaZfRWjYWsorUb9OaPqM1eoi+XiMUCv+tQDW+oiDXDuMHI3GU2eJsPb02405mT1VqMzR3Sg2NW0mMqm3TUDbdbE2QGsxcb5GhGbXVe9HOuVlyoXcb2Hh19SsdbYekKre6TNLvsHGio2RItT/AbZtG+k+doV1dUohHtfEaaZQXzrxR+26J2+SWUSoiLC4SUaKsVstOBSqWoFngeqlRC03WCRUbZzBjcdiCKqG1HZOM5ZhLhpTPEalX8vTgm73bRp1O0mxs8J6XSNMGyEKaJmk6pri6ojZ6xlBUmdEh6O1TaNp+e1gnXElvLGNgTsmqTi7jNjbHH+apOkupEIVTEgr8w/4oj+5wr7y4iyZhvTLQ8pS+GZG4FKXViabEutcmsMuOkCjInqTZ5tPvnhIlDe/WMUjBHCShnIe3kEitaE0udktzQy29wnRzHVlTzKVqW4M2HzJweL9Zt8hyCZc48r9I2F7jhlJtLSVKqcGScs2OOWSYlundclCrGz08mAt8Xr1okCmtR14U4Lgb0DQbF8LHXPddCfNVqkSSFXa/jFITcP+Qm8VqM+ccQ3yQDX8XXDS9su6hutVol5vOQ9forvAgCjTBU7O8XfulCKIKgqCDPZjCZaBiGwHUl3+695LtHQ3553uWFfYfhwuGD0mMMkdNp51R2PQY3n7DvjEj8BruDnN3Z55R/80vWz2dM73yP2UxQWV3hJzM8I0GvV7i1+Jj24iVhZvNQ3mOh1yFO+VXyNv+ZH9L0Y+rliJnRZe20SVJBv7rBt7aEwqPpx7zjn/Jh7SETvU+1Y9Kvbfh2/TEPV/tcZh0cU1IaVNkfxJSqBmdyn1DzOfQn7GqX6GdniOkUbbvF9yUDc8wwqjOmy8zb54MPUu40RqSGzdQ/Jml18Q7qXGZ9pnmTNiNOjmKYr5meRqirEfXNNWK1QluvOW++zzj06JTWdGshlg6iXSdtdNi3R+TbDD3d4t5qIwBVqVC9fkxtcYo1HpN4HqrXw29b+NGocAWyLPTnzxHbLdpsRn7nDlgWstVCC8M3SUKQ2ZRtSf+DFmIyoXb5BfkqRNPBM7eI+bwQI2cZOA5aFFEZPqHSthFKoXwfsd3iqwWV8XO00YhVUmac1lG1GuZRn6tzwSTyqNoJu7UVM63F42Wfq7TLadTDExt2slOyDE7EY1odwcpsYyUR862NnsWsc4+SmeG4cKl2eKLfZYPPLKlgOAZfeN/h0/Z/RRam9JJTWutTDBmTSY1++AwtlyAlnghoZmN2uGJV26FqR/RXT8mDiCfehwSBIAggjXLyDErrMeSK03mVfnbJnj3CrjlQ9XFdxc2NRrlctFn3esXMJtcthMRKiTd4sbNTEE9S/q5GR6liBlQQiH/UfejrghffJAP/TPH60GHbRZtPEAjWa/HKI9rh4iIhCAohi+MIfD8nCHSEgDwXvPNOhhDQbEq6XflqY1eMxzqtUkBqlpE7Az75REfO1/hqjVvRiKsdnj+RaNdDWvGQzoHFLeuS9rNfIpOscE8INujDa4w0RilJSUsoVwz21g+xkpBH4g7/k/q3PJT36KgbHJHy0r6HQYqO5FZ9wXRtkeY6PT+gVtpyE9WI1op9e0g2nXOQP8OJV4xnJlkQ46VL6n7CW3cS7r5nkuwesK10uardI80EB4OU/eHfFX7KpRJsNojhEBYLLtQum/4xm8YOxskuzff6VO0Yr1sib3bI332AEQaU0hW74gLVblMRK9LBPpql4zYtKJdRlkW4NbAbDjuDnIoV0/eXZG4V9e0PqWzHVMSKih4im02EpqGUQn/5EoIAq1JhWy6jqtWil7/wISNrNNC3W0SrhdpsUPV6geRKFdUC10Xu7eFbMe69PqQpyvNA06jPXuJZMVqSIF0XUdwAYNuIR48QyyWaUqhmk/TePfLBoKDAPQ+h63i7Pplb5aZ6wsc/hauFzzv6Z/y7xv9CFAl+Fr3Pk/gIl4B3nS9ZhjYz2cDSM3qNhKuVz2Rp48s1QW4TZg4ro844qVFxYqo9G1+tmZf7bESNld0iKjdpGXPuTH5KfXNBjE0mTPr5OWUiNAGJV8dKtwhNI3XKWOsFqe2jRVvK2ZJL5xYXrXcYlBaocEsvvWBj1lDzJbXlJfZ2Rd3e8Pmoy9DcxzSh3ZbM54L5XHvlnCLodnNAvCoPF+zOeFxMaH0tOPZ9heuW2WxCbm40RqNCXFyrqd9xH/pjZIC+SQa+iq8TXqzXBbMJsFo5bLdbRqOiNc6yioRBiOLx8XHOzk4xSKlWk+zuSrKsqBCEoeCgFaAch0/mByyXAjNa07LW6JbG+bbL6EriTIbcLl+zb1yyK8+wVjPkOiJZJejLGWEoKKkN1nZNWQXYSUAtGqFnW4Zih8/lPS7iNsvUZWztMjR3wLKxVIxd0hitythahu1IlGYQpwZ5EHLXOiNbbTjWXyCXAZOJThrElG1Jo57z0eCc5p0aN0ffJWgdsNEqtK0Fdxo36ONx4dcfBIj5HNZrLthjUx2waR9g9Rs03ung79eoNHQyYSLunlC+OyBcZgVelKeofp+KXJB2d9DKNq6noF5HpCkbWabk6QyOLfx4Qm9XkPl1tFYdt6pR21zjth2E66JKJZQQGI8fQxhiOQ7xYFAMC9ts0JZL5GCANM1ikrDvw2KBarUK8kjTQNOQe3vgOJTv9PF75TfPIyXV6QsqTorSNITrImRxkCbLEJ9/jpbnqM0GeXxMtrOD6nRQe3sITYNWC2/gkbkVnmsnPPrCIB6v+UH5F3zL+4KXYYcLsc/LZYeOGFJnjkXCNX0022TfGPJ00SbeZJTUlpukQaJMVqKKa2xpNhQrs4GjJYRWjdRyuCkdYO020VYBd8Z/y070nAoLbC2llG1wCRGOReS2cEyJnW3INJOR6qDLDC9dIpRkk1jMBvfwumW8cIIRrtAnY+xghhassNOIyG/zKNwnywSdTqEzWywE06lGqVQc9Hs9yXKp4fu/Hy+63a8qcet1YUJh2wrT5I0d/NcRL75JBv4Z4rX92/W1xnxeMJh7exJQmGahVo/jLbWaxHWLjPS11VWhei+eU6rIOF0XnNmQxcsV5Z5Hbc/F7Xs8/k8j6g//Dm8z5qz6AD58l5qf0tmcUbcjzuWAdvCS+tkXOJcvuYmrXKUtdk9/hpWFbKo9IlmircZ0gufo5DzhNv+x9G+RuSK1Pa5kjwvrmLjSRCqB4Ri0ygEtN2Se+oyTBqvEYa8yo2GsUUrDS8YoCfvWkNRwiMMcx8j4y1tf4n73Hqpcxus63GwqeGWJG045aY55VaNDRBFEEfpoxEXaYyy6NA9KvPtDF3OvXfgoNzp4NUEtHlE7/Qw/uMa904V6Hdnvo89meFWosEabzQrrT9umdNLFu9MFKZG+j+r1cN/ew22YaFdXxeE7CBBCoAyjEITV62hxjFUuE1tWwdyEIZRKKMdBxDHaaASrFRwdkX74IbJWA9NEG40QaYoqlRBCINZr9NPTIkmoVIq+VODVRJSiZ9S2wbLQoghcF+X7RbNjs4kIQzQpi5+Zz1GuS2WvwlX1HqvPrvDEmv++9b8jOx125RlfznrMwjLlMsTSpm6usPQMO94w3TjMU4+GueZ+7Yaau+Vl0GYUN9imJpvUQi8V5dmk0SXSPdyqomms2Ft8Ti24JHHrPDXusavOQAkqcommciwSNkaVWHewRY6VhYwGD1g3D1CNBpZrcNwL8fd92skVvpNhiYTR1KRmBKykx85OxrB2l25XYRiKnZ2cMCwmD+d5MUujwFONSqUo8ZbLivn8qwmtul5YkVarZZbLkOWy2LyTpEgSXicDf9+lRdeLdr3XlYU/1PgmGfgqvk540e9LoqjoY47jEo5TDKl0HMVmA+Ox4OhIYduF09Zyqb26b8FfD1HzJTfbKjs7ErPhcvM4ZHYeUrl+yi3xkpf+A5bH76GrjLv2KbaMWHp9+k//Fv/yKcFNxI21i5hNaU+eIDPFsnML0oS6nONHY8glc3eHZ/67/Ib3eKTuccY+F5X7tNoCqemM8ja5WaLuJczzCqPIx7VyPti7Rg83XG6a6FlM15hyy7tmmfskUZHY/OX+53jvHOC1LYYMwNDx0wXv9y/R4rgwaYiiYvEuFlxt24z0Pq2DEu++k+A0XW7sPdJOH3/Ho2Jt8YNrjPNzKiLAPWwUh/harcCLmlbsX9Mp2mIBtRqlW2383eKgrup18rfewq/rVNbaBFhfAAAgAElEQVRXhdB3PEZUKoU7UKNBfnICQhR4oevEpVIhEs4ylOchRiOEYaBfXKAADg5I33sP2W4jsgztdYV5uy2m+k6nxf/RcRBSItZrVLOJ8DxUo1GUQ30f2m20yaSoBmga8v794vWrFZphFGXQOAZdp7JX4YU8hLNLBs6UP9t9it+2yKKUs5sSep4iDRtLxpRrRuH2tFpwtakR62VqTsRbOyuiSHEZNVklZSy2fDHdwfFN9HqFqNplnlXR3BIdMeHW6hMG6SkBHqlTpq4WlLIAUyUIKclLZUpWTpIZIBXSsFF7u1zvf4jVdPGthHfa11h39nHiFbvRMyJRYjEDo+phWZCdHGPbxTGiXpdYlmK91qhWBctlAbHVasG1mab6vXhRrxfJQOHoqBGGxet+ewbUa7wwzeLylMvFme0PvUrwjYD4/0X8U8QjSVJs7qZZPG61JFn2xg2MIChuSMMoylTX1xq1Gvz85zpBUNywhlH8i+Pi51dzxebFjO1U49LoY4/OaWQLdD8FpdFsFuWu0gS8eEq+WKN7DbztnPHSZKUdYoQB97JfUVYBgd4kMitMewO6swlamLLS2/zU+FPO9T0MPaCR3fAb8S6/zj+kGifsdTaYWYKTb6hZa7zGiifLHnmc8xf6f2JuVZgkLfxsQUvdkKUp91svGWdNNMfmPO7QjyK0MORyXUMslxAEqEqFC92n/30X7ewMMR6jbTaIOMZKQzr1Nd2jPrJUYmdfh+scfbRBSwO0x4/RNhuUEKhuF9ntIhsN0PVChKVp0OlAmiLr9eJCmiby4KDYIIUonjMMtNWqSESWS+ThIbLZRB4fw2xG0mpBtUoWRWgvXhS9mobxeicg/853YD4ne//9IntbLtGur9HCosogfR9Zq2H9/OeI4RAzCMgPD8nv30dbr8kPDlDlMsn3v4/x+DHG6SnStkHXyZtNqBWfl3j1ntPbtzFcl+zuXa4vBKQZd7xLFIqzqMegXuJiaHKyu+FE/YKJ1We1gKuJh1kyuNdesllIjngOUmKXughsSiW42WpUtTWhrJHKGl7boNHZ0ohzNomNthKsvR6PjAqma/K9zV/hJRrj9BgzFDjpGocU34pYa3UcsUIKg/p2RBQJkoMdjFXMoLbAUXPSjiA6m/F01WEVHvPxokOnK7i+NnC9azZOn0qlMNE4PJQ8eWKgaeqNFVwYarRaGq80f9g2b5Lp6VR7s5ZMsxAbX18X5WOQpGkBEq9uAeK4aLF4zRIVgjL5B73BfxN/OPH/FV5AsY1oWnFfvt6qBgPJ5aWG5wk+/VTj6Ki4X2ezwkZxu4XSRJEPZ0TPDKb6gPL0Gm0yod/pMJ9J6mVJVFdU9yTzOZSePAcUi1t3iUyf6cRBRCGN+BGOjJGGhaXnhEnCttQgUxMMcjKtzEjrc8EOTTnhkja/FN9lHvU4rATs+1NmK4MOc1SmeN8d8lQNaGRz/mvnxzzxKrwcNVFpgmGuSV2dt+oX3Gx8NLfJOXsMoojrdQ19c1FUU6dTLmoH7NwqXHkEoG23aICRxXTya3q+Rt47om8IMk1DVxK224KI+fLLYuBXrUa+u4sslQpc0LR/GC9eOfDwSqT7RnVqGGjLJVoUIU2zMH0YDIrX7+2RNJtQqZAFQYEDyyWyUnnTSpS//z5S05AnJ8Xf1HW0mxuEYaAMg2xvD6REf/IEXQhUu03W6aB3uygpUb7P9t/8G0gSjOfPMV68QPV6KMcpcLDZxLi+BsNAbTbk77yDNp8jez2uz6E+sOh6p4jVmouFz+BBhXxW50Ql/KX4MVOjw6/O+4yyOh3P4D3rE86WTQ7sZ6iSz0rcQmhQLimSVQS5JMVinesc3dbIZpJQM9gsCxHMzNtBr5Zoh6cchteg2aAsoryMoVLq0TWq1MSwBFKzaCeXLBc2Z50PScsd6tsbOtacweX/TOTWCCczlhvJs+BtSi8V3Q70Lq4Jq7voeqETOzyUvHgBoxGMxzq1muTp0yLRNs1Ce/bbeFHklBrjMaxW2huhvmHAYgFpKrHtYs2+1qxdXBQ4sVhotFoFIdXv/3HixTeVgX8k/qmloDznjcetpsHxcY7vF/63m43A90vMZls8T/H8eVGeiqKiH7RcVmjaV5nrdgug+PSszmSoaA4fYjx/ztm5wc9edHlW/4COMeft8d+Qf/oEOwt58G2TaPeInSc/gesh8VajVoppbC6xZLFxbp0qsV6mmU2wXJNrevwme5up1ibUiorARHSYGV0SzUFTGXFmUDdWNJIbyskK04SPmk+phjc4qwl/ov2EnhvRcDfYFYvujk7zXoNvOZ9jRQtmSZXcq7HKXSZDSacccDf5hNywGTv7ZMKgNn2O8egRLBaIJKF8q43fK6EGA5TnkZ+cUNUC/OlL9OGwmAEwnxc9l7u7yNu3i2RACKhUEHkOcYzsdJC7u+T376NcF1Wtosrloqe/VitYntWqYFs8j+zwENXvo+r1YoOtVinv7hKNx6h2G6Vp5P1+Ucl4ZfEhd3bI795FPz1FrFbow2HxXtK0KP1qWpFIJMmbvlDpumiA0DTEZIIxGhXgk2Xk772HCALk4WHBFq3XKCEQUpLdvYtIUy6yPuOsTv2gxD35BTJKmSQ14nc/xJyOyO0Spc2E9v0G0Uax1itFP7JIUFFM3d7QcZZ8Uv4BD9Mj1luLQ/uKd+wveRjfIZEmh+6Y99/a8M74r3h07jOc2ti3+mzNKnd3llQmZ8ztHqaRk8QasXCobW/w1Aa9UiKxPUQUUcrWpFJHmiW8eMa+fYN4cIf65ZfE10vWF2u+FPe5zntM28fcMi/oxufcbs84OSr6lMfjQki/Iy/JFhvMhsd2WzAzlUoxp6NWkzQaBbMaRQXrU6875HlhF1ccsApr0kZD/c5kV8NQZBkkScEUGUYxtOYP1XP6m8rAV/F1wotKpbDUXa8FzWaJs7OYclnx5InOfF60/yyXgnZbvRrSVRxO1muIzAqjK0V6Oacz/JTtywkfL27xs6tdtN0+A2dB94sCL2rGhm47p/qgj/HrT/BvXrKNdWr6mlIeUpZrNN0gtl30NKGZjTAsjdSrE0iHINT5NLvPU3GHsqez0urM8ypRIlCp5MS9xIhWNKyAtpjw7u6Y6uqS8mLIB+oXHBhD+v4c1enh7VRo3GvwkfkbrDRgGvlcxh1C5dBRN5wcbpFnV4xlCzWeUWGFPhoV1s5pimdu8W53YDAoKrNC4HccPDtBzGbo0yn69TUyTVGGgbp1qyB9Xr329+LFKxZeOc6bx6QpbLfI42NUuUx+eFi0olarv4sX0ymq2SzwYjAoKrqv3IPyd95Bm82KNqIoQr0if8iyohqcpsX3DQP1uoKsaWjTKapexzw7e3MqlbUaqtcrzCfeeqtILqZTVJYhNI3s3XfRFoui0p7Vqe+XeGvxU7JUMdG6pIN94uWWqpfT81aoVgulm0xClyxVVPIFYaLT8BOmeodz4w6zyEIZJneNJywSl1R3cIwcxzX4i7fPGH4REI63iCwmqO9hH3W5LZ5hblZYeYwo2aSGgxdNsLKI0KqRtXsIleMmS6wkYGU0ybwKO5vH7FgTzLqPvx6yfXTB+djll9q3ufTv4Dop7+uf46YrDg8zjptzsnKF2azQBlRXlzh5AL6H4xR7vOsWeFGtSpKk0N2EoWBvzyEIIhqNYv29HgRbtGsXa7aYYqxeTQgvJnZXq0V14Ld1BX9o8U1l4J8YacqrHs2CsXnNIv7fhWUVDM52W4iGX2eTm03x+6rV4mspC6bSMGA+f71HCM7Pi7aHzz4zkFJRq2lkmSCoD5DTz9hOFqQOTCrf4o46wywZlNYpbS4o5wumg38NP36IPhozTz2kzGmPH1HSY6Rtk6UaXr5CRiZRbvJx4wf859a/phld4CUrxEqi1Sv8cnYb28jo6xPyRJHEJc4in4ZrUnUEb3svuV254NnI4HJb58ebb/F2ZUnLTxkvIHV89uMnqO2WPX2GkDrj0zrpyR163gUD86b4gCoVcsPEnFygDYeIyQQ1GJA3m8R//udor/qltJsbjJ/8BPPxY6TjIIZDsu99DzEckt++jTw5KRj/NAXbRptMCkb+4KBgbXyf1z0h2ukpr7IuZL9fJBM7OwXTbxZiXZRCu74uLoxS0O2+oRDkwQGy1UKaZjEx8uKiaAu6uipulloN2WggDaOgtG0b2euRTyaoly+L32/bxeuiqPCe7nRQWVa0HJkmrNeoTgc6HeR8/sYRSXvxAvPnP0e4Lrqf0HIydrZDtKsrdnWJqHSRUUDnowHpssL0S0GaKmpWQGMToyU2Ekm9rqGcBpdbj3PjFk9GFSxCjrwhlTCgV5ryKPCJlE19+YT5dQRxyo55w2F4RrSVjF7kPJE/ZLA95aP0iu3hMT+77PCDJKcnp9hhRG7abEWZVHcZqj7Vmxtq0SVJbYD/1/8eLQxxnSaG49OX1yztFoeTj9H0GXY/wzRh/mhGPtHQWzs442s2wwma1sGywDCKcq7nSSaTQmA5Hhe6gu1Wo1SSVCqwv198rJ1Owbzu7haVgdfM66suNUqlgglSqrg9f9ve8Zv4Jn5f/HPgBRT3p5SFicR0WjCRr43HLi70N0OVLi81xuOijS4r7VIuTwknhUuatq8olzX2OGW2MmhpKS0ucNSCxyf/HZWf/TWtF5+hSKnIBa5aguPgRiMyqeOVKoSLDXm0Zej3edz7M/KOSfnhb9hPnvGJ+y2GcsBxa8po1CTPBNfzEpZq84E+5C3/hhZj0tThHJt5XuVRcoteLaUlrxmZXbIkZV+eIqOIvWwBrs2X0QB/0GDHu0aNRuy2twzDnMlGZze+geEQPI/8Bz9AOg7Ze++hJQlkGdrVFfqnn6IpVezDmkb2ne+gwpD83j2y73+/+MBfqbW18RhZLhc40umA5/1evJAHBwUmjccFrgCy0ynwYrstLk6z+eZw/xovkrt30V68KC7sa59Yz4MgQLZaxdfVakEepWmBDa8utmy30aZTZJYVOoPJpGhniqICa8pltKMjcN2i4jEaFdXu1Qrj88/BcdCztMCLzQViPGbfArGVkNd55y87XP3sipH9DnIdUTXW7LNlKctMVIO6N0e1O0xWXZ5ud5lnGR4bcrtMJ54zUT3CCPbsG15+kVDbXrEttemnp+TbKeLM4FfZXe4mEzQi0t4OiV6CF1DejCHaImVOqpeI/QHLvIS4vqK9vsJyArQkoDSdkmUSxyxhO+A4Cj8+pRaP0WdTLCUwtTqTL2ZUElgsjrBurtHXE9xWA+HBel2w/pZVDCY7P9d4+lTHsgoNZ71enNEsC/b3JXH81ZpU6qu1bpoFVrxe56+LRn+sePFNZeAfCU0rJv++PjC8Fo/8/fj7qvJiynDBKL4ed319XYiIp1ONWs1hudwihGK5LJKB121Dr9mkW7dyTk81TLOwF61UJM74CssSiOmEVnyNtZ5TKkOzlpFbZfRtQCkNqE5Pkcs1v+BbPOWE97Z/R1NOsCsWkV0lMVwsGXNj7fJL/SM+yx+w0ht0HtS4njk8T3aQTolYWmxSG5XBwBohdJDCRJM57+0M6TZj4mqbj+pPYDZnRYW2m7DTXNGsZ2wTjcxwqC5O0YBKPCKrNAhLLVw2VNMJFwufUfmQ1q7J/vO/KTbTNC0Y/nfeIfvOd9Dmc/QvvsA4OyvEYldX0OtBlpG++y7y3XfJ33672CBfb9B5jpjNIMsK67Y4Lvrzp9Oi9Hp2BpaFsixUuVyUU30fZVlokwkiyxCvmBUtDBGLBc5sRmQYIESROLxe9XFcVAcKmwLEdls8F8dFadv3iwnD5XLR16nrBYNUrcJrx6FXPWT6+Xmxq7Tb5J1OsQu9couQ7TZstwXYCYGq1/HjCdXtCH00KoTLpRJ+Ni20EvM54ThkNckxwyXldMWHtado5GiWyW3rBWdxl2lc5bC6xNoWn5eUglK0IMZinZaxbBi0Y17euMTSomKGuMaWKHcI4uJzEzJnY1Z50foQZdnMkio3qk0jHmIlIWu7wWPvAw4Xn9IPnhE6DdbCgxyiyxVn+gEb4XEn/pT74jGOljLelPlMe4fr6n1cJyO/nhI9G6FFIfr/yd57/EiWZleev6eFaS1chhYZESkqs0QmySoyEz0kG9NNNHrRwAyGwHA1G+4JEOCW/wCXHA5AcDazG2JmQHaxWGQXi6zKrMxIFdq1m7lp+Uw8PYvPzCNSVVWXaGZW5wcEwsVz8+fP3rvnu/eee061iLRdx3FA0yQuXgwxzYjRSOLBA4VuVxGGSpaQGLVtk9lseU7Ny+WE2sR4LHFwoBCG4llPpUTHoFgUg/vrmYLP6/qyM/B0/SrjRa+3xPMARKXT88QeUlFirlyJMM2IZlOm15PxfYl6eIIZzhkHSbRRj+LkECUOyBtzUnZAsMILO3TIDA6ZT0J6cpkGG9xw30U3Veyshh9qeGjEwFJNcyZv0NS2eDv3Ot2N2zzsFhhJWQZynnvcpuGWScUT4jhElSNUXaFudHntcgMrq+PlK1wyTzGHLZa+TDKjUy+MKW5oeIMFvqyR8bpIoxEZJugpg1ntItHIIRsNOXXLzBNFStN9gSuTCfHWFuHuLsGrrxJduIDcbqN8+CHqwQFyryd4uZUKUTqN/9xzRDduEL744vkOT+50hEHk4SGxYaB0u2J2ayIMJZVHj4R55MfxwrLA8wRWOA4sFoLm6vsoBwcYiQSLMBQyotmsuCl8X0hIJxIA51iBrouZgpWBZWyaQgJ1PBZdAUURHQZdR1IUomIRZTSCbhdZkkQnI51mJS0lzm11g0lALElE9TrpwREZv4eyLjgtFqTkqcCLfp95aDJpLlCWUxLxjBfrTZTZFCXwuJJscuJWCQMJUwmJfR/VX6BEPpY/xlUTEMtkrBmukWE+DkjLU2IvYHsjIHRDRq6FG2vIYcSyWCenzRgl6syUFMp0greIWLoy7dQFjoJNnh9+n7TbZarlCJcxM6vA9GxBJywQhTEvhG+zqzSwfYe9SZEn5nNEG5vEfojU6+EedknJM9RaAXmnhuNIuK7M7m6I44iE++Bg3W0Ts50XLxp43oLFQpi/VioRi4UQgJnNxP/Tqej+Vati7rNeF/9/1jP/eVlfDhD/jGtNHbDtz36TP6s1/Kys4XrYRMyFxmxvW3je/HxAWJaFpKj4FyJJwiDj9FSl1ZI5Ppap+g0uLO6Rr2qMtp5jd/mQ7eSA56wj9GjJHJPxC69RmR2izGfMPJ297V8ns+ywMX2Mo2eZJSv085cIrCSd9AUmUoZ7xkscOiVmgcl7rSpzLUVsWShEWPKSOJLoLxLMYhtbcnll45ivbhyzHR8wXeqYky533siSjCZcqU/ZMCdEukL9+QKRs0Te2SQtOcTzOWoQkDI9fCvF4NSjPTJZ+Dq5O1VqN5KorZYIZIUCwSuvED7/vAi8YShmCFot5OlUVO+DAOp1wqtXxcZ87cDj+2IIuNtFvXdPfOx5RJkMUruNNBqJwVxFIY5j4eCby4k3adXbkzsdiGPUx4+RRiOU42OiSgVTVVlYlnjfbVu0bM/OhFlYrydUH3SdcHdXOBGvOKIsl2LDr+vinGQZaToVw8mJhBg8u3ZNDJdNp8iui9ztiiHmbJbg+nUR5GVZzDTEsVAYimMhL7dcio5EMimSl6MjlNmMOI5JJCIUd4kVTLm65ZAq6VzenJPdSZOVJ8z0LNloyJX8mFeLH+BNXCzV5+GwQo0OVb3LpWsw9zT6ao1cOsTazIIzJ0Dj3mCTjSs6G5c1HiZeZOqZuEsZ/85tGuMUSuiz5e2RMj1UJSITjLD9CXLgEaHi+zHyfIYSR/iFMte1PUJUJp5Jt3CVxe5VhkNQcmnmex0sOyYKJeJrl3BdoR89nYpNf7EY8957Go2Gwngssb0dUasJypCiWCSTi9XsjnCiHAxEsO/1hLLQusW7njFY5X2f6/VlMvB0/SrjRRDMmc0kNC0mkYipVmNMM6JcFsnDYiHxwx+qdDoKcuOUq9kuhh4jVcpEisGd4C5XtpdczrSRR2MWmExfeo2Kc4gdOiwHCx5s/RZlv0FKXiBFIdNEhUlui1GyhkqElyvytvZV/jr4HaTegIetPG6+SiN1jbmVpz818AKZiatjahI5dcy3bjW5qu5RCU7pjgyKyTkv/JpB0h+yWVqwYU2IM0mqN9KEkYSUz5DSlsjdLkoUkWKCXyjTH2p0jgPmJMjdKLCVc5DCkMgwCO/cIfjKV4guXBBZUhAIvGi3RbxcCh39uFYTVNLNzadDHZ6H8uQJyt4e8v6+qPKvHAylXg9pNBLKbp73SbzQNFGcWh2jPHmCfHKC0u8TGwbGlSss5nNR6FFVEbfbbSEqMZkIetHVq6J7Xa8jLRZCHWiNF4aBev/+OYU03NkhLhbF37qiKqkHByL5aLVEwiHLosOgaeKc19yzZFIMO/f7KJ6HNJmIgtWqK42mwWRCsmyhdNpYSYXrmTNSO1ku7njkgi6ZdMQ8TpCu23zd+BDdH5OSZ0zjFKY7psCQUnJKrqiwiCwqdQkzp5M3Fzzolej6WRaJIpfqC0zJxZsH3FNu09u6g5ZUSfRPMcMFir9EsQzyswb5eAC+RyKa4scKi0DHHHWJVRU5m2SXI5axydKVmW1d4di+JnKrUhqj32Y8ltA1GFWvYNsxy6WELMcrSpBMt6twfKxg2yI/29wMqVRMfH9xXoczDEHvliSRV0bR046Aba+lfT//YhPwZTLwc62f9CY/q0LycR3a9Xq2YqRpQrqq01lgWU9vtCgSDpKFQsRwKON50rm1vG3H4MzJWD5WWuFicYiaNNjyD8lOTzhqW3xP/SZxo03aaWP4c1J+n2vWMYmtDKdBlYI5J+12sVMynpbgreXz/K32O5iKjxyF9JdJDuY18AJ2El1GU53eMsUiEkFMVyNUS+N3bz7mlv2EzOCA/szCXo64bJ+SKhpkGvdYLHTCKMC+XCFjeSSSsVDRWS4JUymkxYJ01aIVVYglmbha4eLrm2LzfXAgKuj1Ov7rryNNp0KpZzgkDkMxjFsuE60kNoOXXhIb9zB8WoVfBU5pMBDBVZIEbzIIkMLwnLMZZTJi2Hh7Wyg4OA7SfC6q9SAcg11XRIjBgDiTwSwUWKze0ziXE3Juk4nQ/1/Zwssrfom0Ei2ODYM4lSLa3gZAOToSnQvPE3zVzU0RxBcLUdHyfTEgJklEN2+KWYRcTtjRW5YwN8tmRddAllFGI2FsVqng3boFgNbpwFpur1Ag6Q9IZyTinR2CW7eINjdJ5lWmD9vIsxnJ/jFqHNOLs1TTc04WJdxAQdXgZf1dBgOJAgOWWpoX6k0M12Eqp7nb26YbFHB9mQIjuj2Zk8cxg5lB5ewDXpLe4YwarpLkivsB+eUZSzPD1NdJ+mN8JOZKmrGURZlOiMYOAQaOb6DMZ7zrXqVn1jFNmW3phHCyYDaXcZdg6QGumWE4lNjfF9JxYbjWiRYBe3c3XAX5GEWxiOMFmiY4np2OGBI+OFDwfWllPhN/7jsBH19fJgNP1686XhQKQo1uuZRotQR1NJUS/OdeT6bZVFDVGCucU94xUDIJkvMuuUxIMetSnJ+QHDXZO1D5Z+ObJMYtMvMzsp09MtqM9JUC+vNX0I4OKQct7GCKUsoxMso8iS7iRRpy6HGm7/BgdoHmPI2tLLicbvG4k2Me2ziugaX46GrItXKf5zaHvGF8l71phdFI4qb+mMyyQ5IpTtfFsHSKlRB0nYw0JVlNiMJHqyVip66TvFKhNUsjSRLR1hYXX0kh+b6I+ek04c2bhLduIR8eCu3+NV4sFkLK89o1wq0twuvXkdvtp3iRSIhOa7OJ5HlCiEKY/CDN5wIzVr4AcSJBVCp9FC9Wr08UEQPyaIQsy8STCSSTGLu7LIJAFI4U5bwrEGezSIOBKLqdnAiTMdcFWf4oXsQx2rvvwmSCpGn4L78saKOr15LPzpARWBWrKvGFC+C6xLoOpklsWYQXLhBbFlEmI1T1RBWEaGeHcHdXfK3XO//9UaFAKhyRvJCHQgHv618H0yQ3OGQ806DXJ+kN6I8M1HKK0UIUx6QoZttsIy/m5OI+sqqynRvxW9X3aXlFToI6d7vbjBYmJXqEC48PenXeGewyaXm8IX+HxGLAyKqiBD6bk4fkwy4DewM/Atsdg+vhKTZH8RaJRR93GoAfsAh0lq7EfrzDJL1BLieRc04Zni6JIuFNUM77nEzyHB7KjEYytVp03gk2TcjnI7a2wpWSl0mz6aLrYm4zmxWznfv7CqORjKLwCanRL8r6Mhn4Ja514F4VE8jlRKXm423gZytGqZRNr7c4d5tMJMQAymwmZOWGQ6ERrarCXTWK4GSUIvNclavWMdXJHvOex3Qq0U5d5GSQQpsOKXfuI6WTLF/5OlJvQKKxT//AYTaVkWWJoVxgbJV4v12n01FQpZAT4yoFe8l0KnHml/FDlYmex3XF4KUXaGSNBYXkEi1a0BtoXPbuk/QGFMeHJGsJ/GyBRNng7ChiGBUxlkNSZUNststlojt3RIAWTw+n5hUcOYVkGMTFknBULOlIQUB4+TJxuSyq2/v7gvM4nYrgGwTEhcI5/18ajZAHAzAMEURNU1TIQWzsCwWiXE64+ZZKyOMxrNV9ajUAlIODc7OvddCPs1kRsB1HDPwmEkS5HOarr7LodkXrdbEQ9JjJRChVuC5xLifaupmMkC4tl0WFJ5k8LzXHqio2+9vb4nelUuJGcV1xfr4vyg6lkrhekiQMZbpdpHZbDK+tShaSID4it1pCek5RUPp9waO1beKNDeJSSUiVqqo4n0wGLItGz2LwcEB5fsTV+D5n5hUe+bs0Ms+RsgIKhkjEnkSXSCQVDNklPTqh2VIJJ0scPcMCGy+dI/Dh4ZHNySDDPLaoxmdUvBOcOIFz9Q6Kt+TC9EMS8RRTCfHNJCMljxOn0D0H0imsyKEcdpCIYOkiVUtQyLRU0GEAACAASURBVPDylSHFZZPFYMGjUZlB+SqpUZNacMLMN+j5GdptBceR8X3B1CqVQpJJ4U7c6QiVlu1tk0RiRj4vNk/TqajKLpdw+XKIYQg6kfoFm6L6Mhl4un7V8WKtKNTvS+ddMUWJGY1k2m0JzxPzAj03yUzNULma5HriCB4f4vky85nEJFPn/kmGhDui2LpHZiuJv7HFtOeTXXbZPvsRgePSzF7nJH+bstShGHZpDiy+n/ldBm6SjDxhFug88C/TdbM4iRK6N6e/SBJHQkZ6JzNAjXzK+oig2QPPJTVpU7xgk9aXnEYbDLqQTKkkGBNubQkp5ytXiDY3kZdLUUzJZDjN3mYei06sBAR2ilRJFzz5y5eJ0mmU+/dRDg6ECdfH8WJjQwg9PIsXqwKONJ2eV9CjrS3CCxcI7txB2dsTb0gc43/lK+cffype5PMCCxwH6ewMyTQJd3Ywb95ksf4dqw7BeUXe94kzGVFE+iy8WM2nxZnM+UDz+defxYtEAgoFoW6nKKLodXZ2jg/I8lMesiSdDyAroxHSckksKo7Em5uC0prLIbGagRgOiW2bZtekO9Ipzw9JMGPg57kvX4dMmlIxQlosaM4y2GZMreTBbMb8pM+0J1SGPCPBOEjR8zM0FzkeOduchWXcyOSS9wG5eIxX2+Bh6RtsO48ouU3MeEGQzjELLbxAJYhVUuqciVUipbnU53sQg4+GtFMjv2FwpTwiPW4y7y/Yd8pM6tewug3KyxMOmiaDMINgDMtYVoznwdZWSCYjzF57PZlEwiCRmFMsRud44bri+RVy1BHlcnQuPvFFWl8mA7/Etd7MTyaijTSff5RTlkjE5wyUVUJPajpCn5+RYEZOGvGkm2P+8IxwssDrTNDcOZM4STYbo+sSphmzXIqM1PVkGE+JpgtSyx6jwgUGDZdi7yGOXUF5/jqGEjCNEihnZ8zGEoslTKQMf1P8T/zdyXP8cHILTQnQ1Yh7k22kbIpEMiTyIjTZpzOyCCMIkVHikIw84k52n5I55sDfoePYvHhlTFVqMtx4DncR0wrKLAZLYknDlqekUyD1eshRBGFInM8jRRGn5hV6jk3+Wo7LX8/iZYt03QyhapK2AyJdRzk+RvvhD0Xbdb25NwxiRIAK7twRrVAQuz/HOQ/G8tmZ4M/rOlGhQHjtGnEmg3J8jHx4iBxFotK+WJz7A6yVG86VIoSMjDAeWyyIqlVQFMxKhcV4LDbjniekUVfovQ7okucJvqamEWcyYhP/DBjEmYyoANm2OCaVQn33XbGhVxT8F14gvHiR8No1cU6rOQN5OBTt6JVeNYYhgASETF69TqwoIoDHMWGpJBSYEgnBNc1mRQLkecj9PouTAXbrgK3ZA5Q4Rosj2lqFKJa4mOvzfO6ID3sbRChcUffYSXZ5fJZmb7lDZt5go+ghGwrZmsXpMIUzlTCUkA2txW+kf0TXy9KUNvm6eZda2GAqp8jaPvLCwZwPGQdJuqltUu6AzckD4iDE1VMEuk1PrzFSCijTIa2BybTvM14axMkk6dYesqqQyIn5l76XotUSXTRFgevXQ6pVYbw0nUoEgaBWlMsmicScIBCYOJ9LhCEEgYRtx6tZ7l+t4P7f2/pVxws7nrFsTTga5lCaTTRPfK77c0I7TTYbMR5DOi2fu2urmozTmKG5c5LzLm03T78dk5i2kfMZir9xleUk5GySJD87xZr3WebrfC/7b/mX+r8nGY4Z9mOGUZbvuK/x3vgCZtbgK+kHOKQ4m6TpT20mS4OE5mNLc7bMHmVtALJK0y1STYzYvqRguwMOEreZjSLmvk5R6aMlEgx9nVRaQj49RV7x76NaDVSVk+orAi/qKpdfMPHNJB1ji0A1SeUVlMNDlPv30e7dE3G22URy3ad4USoR3LwpEoIoErHbcYQr/HAoOgK9noifuRzRzo44j15PxHfDEMeORsLj5afBi3odJAmzVmOxSj5+JrzIZM75/3E+LzrIcYx69+5n44Vpor73HlIci+uYzYriUhCIrsdyCfm8SKQsSxSR0mmiRIKoXhddiUuXhDqRaQpVvE6HWWeOPWiwvXzMbBoTJzI4Wppa0ef3Lv2IHz7MMwgy3MoecKdwjDbu84/952mOM1xKiHkROyUzcFOM5yZhAJcyXW6bD9iQGuy7m0xIc015QjIYo+SSGJ5D6CzR5g5+rNOxt8kuztga3ycMJJZaipFZZqrlmEUWYaTQa8dMez7yZELfrGN7Y8x4SWlD5cNGjrmSYjSSUNUY25ao10NqNTEbNpkIvEgkDHR9QbEYn+PFcimezziGfF5Qur9oWAFfqgn90te6YqPrTxUg1jbYjx+LTbxpnlMUmYxCSnEf2QtwY5WNVgdL1jlqghvK5K8XKSZjksmIxLhFoIWMMsJZz/Njkss+vbMZ8ziJFw64Ux8Rxw7Niynq2x76O+9gPHnAE+M6UlXGJU+nKdF9f0A7ruKlc5yZ1ziNIxJKCEsXWY343Z27/D+tV0j5C3QNSuoQJ9DYtKdIvsetUpe5XkBd6rSy12hoO6iGDs6M8qBHy77EbhXC2CRKOMieJ6rbjiOCaiqFMvApbCSpXRS6qRtbID/sobQ81Ok+EYiqzGwmAmIySSxJMB4jZTKQTIrqjiwjTyZPKx5wHpSZzcQbsDL9IghEoCwWBYdy7Tu++hflckJNaGvro/190xQ+A4uFqMKMhSFNVCg8/X2rQB+VSmKweGvrqTTNZ8iLnCseaZo4V88T1R3HERQiwxDyUrou/jWb4hquOKFys8lahzDa2YHB4HwqUT47E69TLBLU60Qr0wq53T5/bbnfp6YukO/kiI52ieZz6CvUrBGl+pTN5JDThkyhEJBJJrlzM09rtInS9CjKDgsvQ+zMeMl+i7dOA8LB84CKjku95PJkeYNytYtGTMmcEmlpxnqSu32NG96bFOMxNRq040sEqES6SdKWQJ8xUQ0K2RApKaEZGSbZMviw1XhAStvDtbP4l27gZ0DxRHv3pZcE7z+VEjrQiYQY+up2ZWxbaD5Xq/DokZjFMU2hvuL74rj1s/vsW/+z6MV/ub5cP836OF6stc5dF548ERKjpinC1HgMsRViO30iJ0AaalwddpipKk4vQpdUJmGZZD7CtiNotvEWEQ15C88DNwpJLXtMGw5OnCTp7/PViszZIov26iUKfhP7yd+TbcYcpG8yyGwSpnK4+y223/0/+YFRguxlTG1A2TllrtQZezbTVI5Xb/ZpzHIMPRVJUchoA14oHZCQF6hxwHG0iYYD+Txx0eRM2+F4mEayJCqzA8jl6BZuYCfnYE2R1i68sxlks4JGI0UUMy613aTAiwrQPUbrjtAXe9BqIVmWoIKOx2KjuyJ3S8kksucJ6ub6wR8Onw4EgXjI13RQQF4PHWuaoGRWqyLWrmP6f0u8CAKRWDwbiH4SXvi+6IjousDGOEZuNETnoVgUeNHvizm8Xk90EhyH6Pp1MbuQz4NlITcayJ2OmEVYLqltpMXMxTst6kqfccricmbEV66MkEZLvlF5zPeXX0XevEC0mYZFj63FkGGU5mRawPc9sjyiHhn0vU0UXWKBRb0Wcta5zGypsas/oqSOGG9fg+4hkd8l5fexwhAUGVeeEKsaiqphJSSS0oyZrbGpDWlpCbztFE+k22z13ifbOyIZKGh2EbVepK+ITnEiH7GxIebLNI3VXI7AgU5HKM/FMWxsCP+ntcJXtRqdG1uuxxOfXb8KePFlZ+AXsJ7leOq62GM2mzKHhwqPH6u4rsR0KobB3n5bZ79XYu+hB4sl7XcHTO51cMYRPTeNUitxKm1QqUSUgxZZ5xT9rEHcH9LvRlw8/Eek6ZRhegujXiDXeUIgq8gSpNQliUkb7n6IOhmT9Xqk7IiSNmJ6OkP2FpxJm6RykKgmGYxUEkmJKIhI5HVkx4EwIiHNyaYCLCtmIz9HsTTyhsMT+QZfKzzkP92+S2eSYOpZjFtLwt6EwaHDchpQyElceC0n3HVVlViWkYNAGMQoCgkrIHltFWB9H8lxSFseSd0V8pqeh3RygtTriaB+5QrR9ragHK3chVekPeEKqWlCX3nlMhVrGvJ4LKoay6XghVqWSCxWij7Rzo6gAmka0caGqIjk85zbC66XJIlq/urpt3I55mEo3IOLRTF8tZIOOVeLeJY0vOKenh+zqvR85BhVFQF9sRAdlGxWRJ9VICeOxXxCFInESlWJKhWhXrQynwkvXhRVJscRLWxNE92PlcmMPBpBGKI0m6LKZJqCOzqZIEURcSbD3KrhDFxsd0CqpDPN7eB4BnZaAVWjN9TIyQMsPSQlzxhPFfYHBX40vMo8NKknx1SjBt2eTMdJ0FnmiIslHDVD0XCYT0P21GtIS5eMNkOOQlLBmKVi49lpUuoSWVeR4pCFXaCduEA7e5XhRKNKh+3FYywtQJLBSOlkjDmxaaMvplSnexSLMUstBQj6RKkUnRuHnZ3JqKrBBx/4KAo4jkShENHvC8dXzxPc0XUQ/yJZzX/ZGXi6voh4oWmc34cnJwrttjAQm8/h7bc1zs5kGtMSlu7QPfZpvt3HO2yznEVMpRSzRJkztc4Fo0lZFy5KyeMHGM4AqdnCfvAe84XE1C4R5wtk2vsk1AWmDslEiPRgH7PbwHR6FJctVF0iPW2w1b6L4kzZi3Z5pD+HL2vo0zEp08XNlgn9mNgLSOCQ0eagqkiKTKUu8Y3n+jQWOWRV5oXsHt+6fsrET3L/JEM4dFAaJwy7MdNRxOWvlSi8tk2UTCJ5nlDTEUYfxIUCdk4ltbG6x5dL0DTS8pQUU0GVabeRul0RYwsFIatZrxNtbBBsbwucKBbFxnrlJxOvTMTiVXdVWsXaNbdfiiKh+JZKiWTANMXg8L8WXjw7hKKqosA1nZ7TqDCMp3ghy0jLJdHq7w4vXz5PEKTJRHj1XLiA3GiI81mp6EXFInKnQ1SpCNfjMEQ5PRVdhURCqPIdHxNHERSLSJVNptOAMIhJGS6RbhFoBpJl0Y4rtEYJbpmPKCQWdOdpnvQKdB2bkZ9C1RSy0hDJD3hyYtF3U3iSQd5eMtczlDIu45nG3NdIBGNxmYhJx2MCM0lQqqLMHWRDJfIDwt2L9IMMPanEzJHIGzMy4RDbEkXXVEHFYoGnJChoY/L9Per1iNYsQyYTEQTSOV4MBjKzmYHnLRkMxOzmZCKeVceR8P2PYgX86uDFL6QzcPfuXf7iL/6CKIp4/fXX+b3f+72PfP+73/0uf/mXf0k+nwfgt3/7t3n99dd/Eb/6c7Ek6Wmlce0iOZ1CsSj4/+22TCKx/npMqQQn1KlOWvh2mWA6pm9V6A8ktCt15jMZSQppNWI2dJW06VIc7lPY/w4JK6K/c4dZts7MCzBVm8v6GVM1QUY+JR5HOFmdaZghVjSK4yOedIuEbomeUiJEYjjWGC4knJmNloq4WZ6Qlcc8aueoW13+t9s/5P8e/ybNcQbDnaKnDcjWKBkyhYxGR6pQTkyIvICCMURqNyjpPTrGLpqd5iSoUbtqIff7aG+/LYJQoSA2+ckksiQRXLwo2rSDAXGpdO4OHI9GSNUq0YULYvO/MgHDsp62XRRFfLxut6y1/OJYGMGk00/bwqvBjKheFxWadUkOfrpUXvT1YTUbAIiugyyLxOTHvYYk/eRjZFloY+/vi0A/HBKt9ayrVVHt7/XE3+P7Qjqv0Xj6N0vSUyAwDEiliFQVudkU6kX7++dOxlE2K/S2LUskZ5mM8E6Yz9na1FkEIzrFr9HtTQh2d8lf8mhnrxIfv0nZ6xAqM+4uLoNlkg1Oac9zKARczHd5ZbPB6YMFk3gDDY+iOqGwiGk5Jj+ItvGaYyQFVP02oSdzgw/A9cmrPUw9xqzmiGwLr+NiTHssKmlqX61QPOlyPeuTfuLTbY3JDprMBkcMvvYtZKnF7mCPOJ/Br+bIF0OiiFWAF5f3yROV/X1Bm1gshESvoohiYLMpqrAC3566S/6sevFfrp9ufYkXT/EijlnR1cS9dnSkEAQSkwn4fkwqJT4+KW2Q9gZEpTLz1phhqsLZoczixiZyAJoc4jd6FMtFTpM5tu69iXJ2RlCucLr1NSRNpecE5AtVtuJHXFB6TPsTgtmMsFRBU2QURUIfHKBMJnScBPeT36RVvs10FHO4vEg1MthOOVysOnQaKTpNhZczD/nqhSP+Zvzr3D0tc9Iy6F8ssXPbgIlDMZA4W+boLSzSxoIbhQ8xOvc40zYZRWWwLKGJf+UK8mAg6JyyTDwaEQ8GKJaF//LLoqARBEgnJ4SXLolNr+8LCuTurvAHqNWE0+/GhrjQ6+r4mvBt209lYNZ63mFIVK0KzKhWkdtt0U1da/2H4VMJ6c8DXkgSUaUiNhgrwQw87yN4EazxcUUnPXcvXePFah6DVEr4G5TLIqkajz+KF4WCUBpad1EuXkSaTAhNky0jYmYl6IabdJd1ulqW3CVQpRBGLWTV42Fwk2Y/QUqZUDF6NIMKKW2BZUpcS5/xqJWn4RnUzSHX7VMUQj4cbvJuo0p2sMdV3cdMuZTG+2ihix/4SFpActmD3SLuyCXljBn32oSvvUFpNiKXDdk2fax5Evf+EXHPIWgfcnD9dYpGk/zZHlolA+kcF0oBtVq0toJYPX+ie3x4qFOpRFSr0UrKVz7v1j2LFevb4lcBL37uZCCKIv78z/+cP/7jP6ZQKPBHf/RHvPzyy2xubn7kuFdffZU/+IM/+Hl/3ed2PbvH1HVIp8Xzatsx/b7Yc4lnV+L0FNwnTfoFGXPcZe5I2FYX0yxzyTpmXNnCtuFwpGLFEQNnE7XzA+ylQ37RwbIlkuoBs1nMoHaFcRBjHz9G7+zRzlzF1gOMhEa/GzNYFJgEFo3MTRyphj536Xs6/lIl8CI6pyH2TOd6esn1xBmvlPd5f7jFdWufi2mJma8xjEtMyXK9Nkd6dIpMG6ZTXrxo0ZxIaOkFUWxToo0/SJJsN1EVhLJBFAm1ndNTMahUqxE7DvLREVIQIHe7hCstfu93fgf1rbeIWy2UdlsE0VXrUh4OxUVdt2xXFz2q1c5NweRWS8hyjkbiOMM4b9FGtdonKzk/7RMrSSLQOg4AcqslXu/ZN/2nuTE+a4Xheb8yWik6kEg87SKEoUh8VjvXaHPznP4kDwZisExRII5FK9iykAuF8y6LEobCzG09jJbJiMEww0Dq90XLulqlmsvRuY/o0CzmhNMF48EQrRvB1GBwEhNJS7LSmGQmJptMYtoS2d0ku1kNtXFMyuvT8zJcSo9xrBqPjnKcqpfJxwfkogmPlptk/QapoEKBPhlphiIZSMMhvuMxlOv4is18IVN8/z3s0xOCq2km2S2c/ozBKI3WaWEtv4uiKVgvVHlSuIVk1Zh1ZHK5CNMUly+XizCMiEpFgOpyCa2WSMzX5k3rt+jZtbaaX+ebX1QTmc/j+hIvxFqHhTh+en/pOriuBMQkk2KIuN0W8rftt7pMpjLqoIu7lLCnXXStzKZ5jLK7Qb+lkRxGxOMB04WKtpRQFyHF3j6WLWMaETMHnJ0rDM5GZAbvYB21GMs5lpHDxEiRCgak3AlES05zr3CkXkcNPWTJwHVlHgebjPoz7pz2qdKkZk/ZtVs8HNd4Of0B21WTH01v8OCxQeWFAl/X3kUfNojaC1R7h98wniCHIV3bpsSAOFJov5/k8o0k8jvviO6oYYhCxWTylFYzGiG324K7f3pKuLFBlM/jffOb6H//98JdeDoVXHfTJMpmBYU0lyOy7XMajtxuizgeBBBFqO+/Lzxbzs7EzwwG58Fg7Rj/EamnzwNe+L7YkeZygm608tY5Tx4kSdxU2exTqtHH8WJjQyQnlgXpNMGVK+JapFKfxItkklhRkCQJeW+PaGMDKZGAf/fv2Pz2t+k+XAqVP7vEsDElJqYaT+l3De6Pc1TNAXlzxk4pYhpcp2LLyNUE6Z7MtfEB1YTPltoiY8KJfZP3jupYyzHXg2MeS7ssJxo3gjl5r41KgOFOkYw8RrvJPEix0DMEAYTNLtJ0jLm/h/pchoWdoWGozGd9ct0Wqd53yOZlrGs19nK3UNQa86lMufyUWlouR2hajGWJwXzXhUZDCFCo6lNK6cfXug7pul9s07GfOxl48uQJ1WqVSqUCiCD+5ptvfiK4//e0BEc5YjYTG4rJRAUx6slXv+oRHZ3gRB0szcMrFHCMPLKpsi35XAvu0++3eNd5BUsNmU40Nvf/iY6ir+aATEqDJzh9Gy2SsJwIKTMj33uMvJxjmGOiQpVC0GKkZIh9H1dP8mJuD3LbOKM6+iAkmgVspiYQBAROwGOvxP9a+C55f8CBvEshteRF+Xt87+QCF7MHRIUR0VRiKBdBM6g692jMC6hZj/xlm029zalxmdmlbxAOHiANzmg/nhEPXeoVjyiXQzk+5uStNnK5QPF3NgWfci3aq+vIJyeCDlSpEDWbomKxaoeiqqJis77A61bLOiVfUWpYLkUrd72LWztMro/7Wdea6LvifP5C0/91tFlVeM4TgY9/T1XPuaXRSlqUMBTux5IkVJKKRbBt5PfeI5rNkLNZkVxEkTCfCUOhhFGtEsWxaK2vKF0nszyx6iApCv23ToijmKsbU7TOEx52ixyPi+xeGJE3JuwZz+EvbZZWljCTohfniew2cqRRimKyNYN45lBkQGNeQtZUukGJenjCD8OXeEt5id+Q/wuv6W9hhEu0yQIsj3nxNj+69h/Z/OA/szs5Y2BU4fqLyMMu6t0GbpBFjiQKXpu+VKYjlzmVtrhqBYxGYNui4gpw4UJEqyWSA9ddJweCQuS6UChE5y7DX8RKzhdxfYkXH13rLoHrir2wLMfIshhw/MY3PA4OZNLjE3r3OrhagLZbYJnIkywo7Ex8Ntz7DB+2OPJrlHMqYbNDsTVAnk0hmyWIR+RaD/C9mGQkk3QHaG4f2ZuDG5BKTFGVBGo4Yzl2wZvgShb14oKypfDurMDUlyirfWI1psiAh6cF5ITJ/1L4G7p+jomeI+ud8bz3Q6rBfWJ7h55zi97SQqZKJTeiFI1oJa+ienOKr87ZnD/ktL7DLH2ZxpHPbvMurSaEE9jYSSKpKkqnw/GZiiTNqGwJ4YVoc/M8PsqdDsHly8jDIeFqACPa3BRFlVWVHOCc6P0sDqy6BVGhcL6plld+AZ9rvPg4Hnx82OmnwYtGQ3QXfP/8epFIEMGn4oU8mwns3dggvHJF4OzBAUfGZcguod9H6RzSHWkU/TNk74Sx8zwJfc7VypjYXfKI20iSjJOrk7B1BsVL6I132KjMyEoS+W2bQXNIQTJwfBiqZdSwTRBHfC/6Bll9yh3pfS7rDbRpHzOcYbkLjjdvsb/7G1w++j4SEZPMJoocoM6HSIFKjxJJv03JPWCRuoBTvYF1tXbO8V83PuJYGI7dvi3MyBoNCUkSHgLVasRiIbBl7Uy8XnEsCkzrj6vVT08Yvgjr504GBoMBhfWADFAoFHj8+PEnjvvBD37A/fv3qdVq/P7v/z7FtRrMM+vb3/423/72twH40z/900895ouwVs/K2uuES5dER+7CBfHcjqcBjcYunUDIexZ+d4vk8ITdSwp622L+X/awZvdp73yNr3f+dzaWJ2y6I/pyhkb+JrNxi8gLqS4PqQb/jNWPmWolzqwC7XiLjW6XqDGlJk34l8JvspBMTGfAVe+fmRkSXuoautSnYo7wpi6hksDzNL4T/Bues0949UKPr2Ue8Fb/FpUgpJJwaHc6oKhsWWO2OCGkxz1nl6y25NpvXoZ+jmuVCmdxRDyRSPk+I82ke/kWg+UxW+EpJ9EGTu05Stsm2SAQm9BsFmo10QVY8eBZmXfh+yKbun1btFbKZXFcsym+t/7Z9dNXKMDhofh4nZ4HwSePW79J/zUTP3GM2mqRh09/vZ93FYuffj5xLK4TPA38cfyUArV2rtN1OD5+2rP8rd+C0UgMizWb4tqthfglSbxmoSC+f/8+Z2Ed16qwu+GxJbd5y0nS9XNctIZ0MxmqyQzO/TnPKydUsiGnhd+mOIoppjySnScMBx6WpHE1NyLQLQ7tWywmU66WuyhjncfhbWR3wcTPYNoRWX+IJ2c5lC6zaQ0hn0Objqn0HvJa6q+J8zIBOR5nfw3eTlCUXObGFVKMmE+zRNM+tuJT1B1cfYhlbeG6Yo6u1xNvkWnC88+LAt1goNLtpul0xC2VyYih4krlk3jqeeJnikXxfzrNR9rCX66ffX2JF59ca7xwXcjnxb1ZLot70zCg915AT96lFyhsJEKSt7fY0U+IJIXM5ITg7/bIGDbHI52vDe+RjNt0vRH97FXeT/4PPNf5LrNlyIZ3SH35iFDWOSldpx2m0DWZnfH7JGYt5EhnnNxkIVmk5z2+svh79pcJJuo2mXDB14uPeHJqoVoSzUWJ72m/zU6qw69VO2zNHnNibrM1HkBNwujcpxsVuBk3ebnU4KSpcC8qkE0bXHt9Ex5LXLtY4OxkSOzKJAD98jW6+w4DW2FLesSJdhGneoNSMCOdTIrYePOmuGBrvFh3e0UmJR7kXA42N58KSnwaXqxj6HwuXmtj47NxZf0mfV7w4sdhxXp2Yj2U/JPwYj4Xn/84vACRgGWz4mvTKWfvDXHDPLtl2PK7vNWoYcwXoNmMkjeomRK/Fv8TW2mJEyfH/cw2aWfJcjjA9iBrLdnZHTPpBQz8GkNth8Cacq02432lStuzyboOd/Vv0ggq/K70/6HrFgurhht7uMVdgv6IjeFDMkaAmk7Q83Ic6bdo7PnUlkd09G20iow++xB94aPaOko0x11M8SpbHB8L7Y1ORzx79Tq88AIkkypvvplkOhVYslgIrKhWxR7uWYLBGivWQk1r87IvYkLwcycDcRx/4mvSx67EV77yFV577TU0TeNv//Zv+bM/+zP+5E/+5BM/98Ybb/DGG2+cf97r9X7e0/ulrJ8UF1wXb/1KTgAAIABJREFUOh0Z35fZ21OwrBjDiHCcSDxLyTKTzIRmUyExPmP4Dw/Z/EaFhBbR1q6w7B+TO/4+ydP3CNUp8xC8RIloGdHxU+AGbM72CH2XOJgxsOuoRHyQeYWN3ofok1PCOOYD40Uahatk5h1+MHkRdekS2g5b6QZd3+TAr1GkwYv5I570i0wWJi3N5nb2IQ+dPG4YcPHCjO3jf2I5ukxb2yD7iklK0lBKGS72jpFNjenDh4TXr4OqUrtg4TT6zMZz8sqEGWmOoiJHahXJaFFIL8kpA0baVVHpyefFhVy3MLe3Bb+qUEAej0VV4p13iHZ2iFIp8cStjGFW03cfvfgr2bdP8DyfPS6OherOSjcs+ikDdbFapXd29um/95exftx5rq+BqgqZucUCeTQiSiZFdDo7E5KknY6Qjbt1S1SHVl0U+fQUAHUyQTYMYgwsuU/qosXUSXGjdkq61eHoOIvsLvFDg83CgtTlFIdTm7qyj5XLIU2OkIZDUn5EKTHi+vaUfxy/yOMjE9Nx+IZ9lwfuN5E0uLfYYVdWqQdtIGB/WaEu77OMPUapDM3S17h5+h0qZ+/gKzrvXPsP9GoXodtHjWfowZySt4+XlJGtNKqh0my5KNMfst9xaEWbtFoyhULMe+8FlMsR2azY/Pd6eU5PHebzmGIxxDAiul1Q1egTBbs4hslEptmUVxTbiHr981nxqdfr/9qn8F+1vsSLT35vNhO0tU5HZTiUCIKYVCri8WPBZzYvlmm863DmyXjHTeruffrfqLBYyDRSV+iPjtno/r8MwjROymO4zCBrLmHkE/kz7gZXuTP9B4gX+FrIIFvCVxO0tQq5WQvNnyDJMfup2wyjDH2pgE1Mb2aTUYb4ZpKBl+TN0UWuJB6RUV0a0SYtx6CSkLmdeUBjHBPEAbmsx2bwA5a9DMvcNVKbGpPNHYrz++yqR6hRyGhqwq1bsFhQ25VxTk6Yjcfklu8ytyscSlc4NEvIdouCNSUvNRlldmC5JCgWkVdDtud4saJSqs0m3L9PNBgQrP1b4LPxwjTFZlfTxK7ws477IuDFTzrHXwRehCH6P/yDGFxeOBg7ZVLmgqmT4Ub7LnNtm9EQSNhYzMleMMjUAvYOVIpqA80/49DdoBr3KegRNwqHtFJF3nx0k1kzwVW5wU3/HX60/LeMXZv/HH6LpBGRYshhtMGF5WPych8pDmjMEpzu/jo3Tv+O/OAR0kzn3u5/4Kx4kajdxwjOKDofUg6ayDmZSfEyXmSi7Z9iew7dxoRWf5tWSyaTiXGcCE0LVgITeT780AFiLCsmnQ5IpYSyV6sVfaIzMJ3KDAZrIQChOFSrffHw4udOBgqFAv1nbvR+v08ul/vIMc9OML/xxhv81V/91c/7a//V1npyPAhEHCkWo49UFuMYul2ZblcmDIVDXbkcnc8xGYZI0nVd3FyT04hC2KNajfj2w036d5uMHm1yiwnVYEI0PuEJRdrGBrsX4NLxMUNZJBdG6KFaEql5l8i2eX7yfZa6gSdn8ewKDet5nui3uBa+w0KxaQWb3NBOeSn1Dg+Mi9wb1ElKPr4bIikSoaQiSzFvHteoXzbwAwMprxLPi7y4G3DSaUHiOvG1F4n/6Z/Y1FpEpSq8+wj56EhUJG7dOnf4lXI5qvUc3ScZEWyrVaov60Seh3p0JIaRLl0iWP0Mq8HXtR4+y6UY/E2nn3Ij4cfzKj/+vU877med+PlpOJ+/yPXjzvMZ0nFUKHDOd1m3xoNAmKENh8grR6Noa4tzzsxaWnU1Z1BP6tgreT1pzUE1DGQtQf52meen92ieSnywv4uUTnMrdYiU6fHuXQVH0ylaUwr1JK0giRQHaMGcYZDh/zr5dZayyekwgRLHmHrAjVyL5dBlic078YuE4Yd4bQlpfMJSsUkuJiSVCfmjD7ir/0/s9DtU9u5SlNosMwlml26gn37AdDinG8f0a2Xu7unMszKqKrG7GzKdynieuO3K5QjbhmRStHkdR+b4WFRwtraiT1x2SRLP9brS4/tf3KGwz9v6Ei+ijzT4zs7EfdpsClnDalXGMCI8T9xv8/kaL2JUFY4fR+j9PoYB393bweyeMDvY5LX4EGU6pj9cMI0TuJWvkkqE7DgnTJQATQtJBHOUhEElbLNo9YnMMbq0ZKFlcOQsjfQ1RkubxKTFA/8KB9E2LxSbXMq/x/+h/I/MBgYjJctNbY+5VWfu6wzdJG8e19io+vjzAlLaIF4seeFlOOmfIKXKULKJ791jW2sRTafwg+FKbrQIV68SSxJxqURcKFBLJuk8jkGWiDY2qN7xiZugPnyItFxCMnnu+vtxvIgsS8wJlEofpfl8Vtz++Nc/67gvAl78pHNcx3Tff0qh+jS8GAyE4Zuui0RrrX9rGAIrVsW7ehCQlBtE6SKxZXGq7KKmTa4XR1RvGpx9MGUwNmifhKhZj29m3qajJgmGE9SlS1yoEmldwqGKJIHj6jSiAnutr9FwS5y5JdA1vmY94kKmQzA0OXVrRJ5GXh7Sn6aR/BNc1caYd9CWEzL7H3DP+J8ptLvstE+5HHyAU9rBv3md2dGIxEmDHhmmRoUPH5l0VBmQ+Na3QiYTmcePZWwbLl4UlNLVaAnzuUyjAfV69Il5gDXNb61t8stgEf+3Wj93MnDp0iXOzs7odDrk83m+//3v84d/+IcfOWY4HJ4H/LfeeusLyQ9dV3fi+GkHrtGQWSzEx1tbIhN0HM4rimH4NPivXewU5WkbSZZj7jkbXNJh9Nc9wmiE7fTJzj2eFG6SW3yfblcmpzXZM18CK8vXzQ8opuakFh16qV3aRh5D8rjgP2Jn8iGLfJUPucXcyhLOXJLqkIZ+AaUQkp96JKMxZbnNRDLYuOjw8DBBb2LwvPwj8tKcbvIO++4W3X9pUtfPOPPryIkrVEoBG/mQk81NGmczNl54gbjXg0aD1iSDZBWoij9eBJsoQur3aT9aIM1k4kyGsFjgRDPZsltE6bSYGVhrP9u24DGu+Zu9nmjxBsH5TMEvbH0ar/LzuNbnuVbD+Lg97roatLoxz9UvZBn1Rz8SlZ5+n3i1E5HP/n/23uRJkuw69/tdnyLCw8NjjsjIyKHmrqqeGw2KJAiRfI/GJ8pMWkjPjGbciUsu9F/w73gm7biT6ZmMNEqchweQABqNrh6qa8isHGOeI3y+V4ubmdUNosEG0AS6wTqbTMuMysr0cD/fPed85/vOtanOaKRt6AcDrV7kedDpkG5vY737LgowTk8x50Wa9pztRMv+bW8Z9KwE/ISudc53P9pmlWxRrCoa3TIn5jWCB89Ix2P204y/mr2Mk9SZizIWKdu5AS9vDUlDQd5KGBkNRv51evlX+Mb0T7kdvI/I2fS8a3jzM3ZG7/J73v/J2cu/hXpk0F4+Zrqq4WKQxQGTpMl3wpc5Wr/FRkDTAduW1OspBwc2SlmkqXYhbqVnjB8sKd3v4Hmwtydx+qeoUwG7W//i0jvOp4VHvqy3yFct/j3jxcmJwXKph5fb2/rAv9nozuKlIFilondcFguNJYWCHoZqdoaixw5qDif/dUyymFG3RtibmGf+HZrB9wkjj2K2JPQVYaXD6/Y/kRY31OMVkdNl0qjgBWNK/Wd4s/eI/Bors0iPFtFKcZrf59VOSDD1IBI0ZB8Rxnxz5xEfp0WMRHA28/hV+RdggHT2ebpsMxyPMXMRRqnDtntO1mhgCoukuQVOiLp2jcyyOH+4wZxJOs5KT3odB1WpYOTzEIac9R39wBkCWa5wbBfpfr2LcXam98c2G13hfxZeXG5kf5EP7FcBLz75O140ea4oQfDpyYFpaqGJS/GNS7wYDjGePUNub2MUCiDlFb4Yw6EW6FgutUTrN75BOplgHR5inpxgnc1oWDm6XoAahuyqHqK0wzhXoV6OUVHEeOBR27NoCMnILfA3vddpnrzDXfEhw/l9/nF+k3IyBARlptRyEbvtDc14iRj26KsG8+Ie5bbDnbO/pbJ4jCo4TFQZV65pR8f858X/wd/v/AeuHz3CC4cMRBPz3Y9wYsmh0eUgus9H529dCS15nqLRSJnPbc7PLeZzmL5/RrBasqxsU6/rheJmeEw7EwjxL7HikwJSX+Zb5F+Ln7kYME2TP/zDP+SP//iPkVLy27/92+zu7vInf/In3Lx5k7fffps//dM/5Tvf+Q6maeJ5Hn/0R3/0RfzuP7f44e6OaeqcdDkiiqLn3f7NBj7+2KJa1R3H/f0U24aDA4sg0P/2d38Xsiyl13P44AOLY3a5JcdYpqK4HmImAnuu6LXuUDPnNIM+X0/+Cnl+H1HxqZgr1oU2Y3GNv7N+i87BP5Gw5I6YcZTucCI6vDt8iSQBX00YF7oMpnksEn67NuGd9X327WPSTUTZqFK0p9wp9UkNG2VNGIguggUyPCP30ftkNzxEq0B26xaZ4zP5eAmU6W47nGbbTA4e015MwA6gUkFGEbJQ4DxsMjJ9WpVzOjfgdBwwiLcwNhu6/hzz5AQzCFDtNrLZ1Ekml0OCVs25dg02m09PBX7SN+5Hzec/j4TblyEuVCmMoyO9MPdJVQq4alkbs5kmNoJ+/dmZvn7b21fyeVcV6Xqt5fRaLS1neuH4yWCAISXWxx8jxmPEakXXWCITiZruas8GIXir/Ah54wbisM9OuIZCitHZ5lxssz5f44sV99yPOV7+GrbICOwSvthgOoKbW0tyeeg4Sz4Km0wLHoWizYF/i5eC97gZPQFlcObe4sT9TX5j9qe8fvxnFNIlSThHximFaMZk3uBUtPlB7m3CdQZnp8TuDpuN5Otfl1y7JhkMFOu1puut15J8LsOZDSnNJMrrwvEpajoiq9cw1PPL+UlRjk/KBX9Zb5GvWvx7xQtNVdNNIs2ClBfUIINHj0yaTYXrSioVycGBdeWl+MorKb4P3/xmwvl5jiQxeBjv87oaY1pQWAwRiUEpTPiw8CY3sifshY/wxhkqPyMr2DSqKdNoi757nZNNm3I24201JefEJKbL4+IbfK/fpR91yAuDf+RtFspibeTJ7DzvbG4jE7hTOGStTFQ+pZZfY4mMLD5lJFocxdvc5Ijt9/8SrluIWo3oja8xfTRHrFZs373L8abO7PFHtMIjmE4R3S5cu0ZaqWD0+5zOSoykoFFL2c0NOFYlBgstBLHrupjvv/8CLz4rLn/HC0dlo9//NF3ocnJg21qa+qKLKXd2NAVISmSrpaW+y+Xn/LUk0de4UND0LNtGjMdw8X+YDx7AZkMnPsTITOSkoL0aTJNu8JRupYxQivNljXv1CLlTYhSWYbZk2TdpzRf8zrVnfPv8OgE5HLtEy5hilgyKZZOiCNnNnnFglXhm7GJU2oRmjfu5f8BdLpm6u/S9PWTRpz15yPWjv8MX7xNJm7O0iVqFmEnA3Gnybf/XmM6bFNIeG6NDPi957bWM27clvZ5iNtPPaN3NKCwHuBlY7Q7zB+d0GyPieo00fL4/9suGF1+Iz8Bbb73FW2+99amv/f7v//7V53/wB3/AH/zBH3wR/9UvJD45gbtUJbmkJPb7BoWCPm8Fgb4JCgWJ50mOjy1yOa1lHgQwGllEkWJvTz9rrZbk5ZczjNNjWq5ESkES17kuzsBRpH6Z+e03OTgvcTt7iDH5kNBwkd37LKXLR/Fr3B98wNoGsQj5rv0W3zd/k2yxoc05U1FGUSCKYLY0yOwK/3X8G1zfDngUW8Shw13/Ife99xiuXDq5AePwGq3ciMnGZWx63KtO6d6wdQd/NqO7O8BoC3qtV+hlGewKthyHrc1DrU8cBJq2kiSYJZdWPKLbiJH5Et1dkNEaYVVId2q62xMEKCmRuZzmKF5qIVuWvvA/7VTgX+NQ/rwpPz9tXHRufuT417b1jXQhyXdpXmasVnoEvFrpv9uyMI6OtLxcoaB/3mKhk/tqhXFxQrHmc8TpKSKKQAgur5a4MDwzFgtUsajBotmkk1+yFb/P98wdtqwR1uCctupxPnEIUodMCiqFABAoE4bLIgUz4fG0STGeECWSZdRh3z5lWd9hNO6QT0O2hg8I7m0zuvu73P3g/6Yc/yMzMySwPFaqhBVt6BtVUiS5bI7rd6hez5BSsLOTIaVBtyv5u7+zWa8Fs5nB1N3FjDa0RgN28gMWQ+jJLYL3u7xxwRW9PIBd8j2/KrfIVy3+PeJFLqcLgE/W8Wmqla2OjjStVClxkfKklvJ8ZJEkel/08rCRpgJ1dErdkBRDgV2q0wrOcSs5VJIRJ9c5nSbcSB4hVz3IuUS7e4xyHjN7i9bZAdFoziL1CN0aj9y3CKchkVOmJueM0g4fb66xiQTVXMBfzn4FTAOp4GuNJf+x/R79E4kdL6lYKx7P6iyQGKbkbrNHJ5fC3g1UltFVRxiNEsNFh3NxA9X2aP9unnbgIo+OdL6/dM61bUzHpLFt0K3HMFR0qyuk3UJUq6S+rXPYZvMCLz4rPilF/cN4cTk5uOCdGUtt2nalYFcowGKBarWQ9bqeAozHemfvwmDNmM9hucQwTTg8xEhTbfhmGLoh1e3qRlOthnjyBDPLyCoVFNB2bMR6TmadMzIqbIXPMJKYljrn//rwbaaBS9leUzFDlLQRlkCZBoOphbNqINOIjeORZR5VI2DlVKlbeXKbOQVryXz7LhXzY0rrM+rxiI/dXR5yk5fiDwjjPFPhUQpHrEOTYr2BW1W8+WbCa69JajVNB33yxGC5FHw/2GU72+BPR7ze6SEEHEctTj7cxXkCr76aXgoz/VLhxQsH4s8Rl46Rcawrx9HIYDw28H2F6+olE8fR33v82GS1MigWJbOZoFjUDnbDocF0qilEjXiEuRySb5eork7Izcd0mjFhvUPByTDmc5JQcuJcw6p42K5JJR6ST1ZYVZ9G2mPlbZEcDYgnIaV4hBvOOJdN/jn7Gk7OoGiE5AuSXv4avrmmIhYESQ5pWpDLkdk5Kn7G//zr5xR3qwglObX28bcKnIRNlmkR03X4jdoDbfAyGiEAczDA9zIGE221rIpFbm8vELMZRpriAIHvQz5PsevjtfKk9+7p5JplVJYnlOcniM0GczxGXRw65daWPmTatnYHLpVQrqu/BjqpXapFfJ5IEq1VfaFKdOku+bOE67psNpuf6Wf8xPFZrpQAQminZylRloWIIu10OZkgPU8b8lQqmE+eIKZTRD6PvHCT5FJL+mKhzM7lSA4PEfX6VceN9RojipBpigCEYWgH43odoRRiteLkGMKVQgyGFNMZA6PDxN7i3UGXnIrw5IL96pSx0cIyJK22pC7GnAd1ArPEXmnK6/K7dJwpT7IbeF7G7fB9vma9S7ZJMNIEd9ajsj5nkm9zlr+Om8uoqzH1/Jqz+j28ThFXrjlblmi1dJu/09EJu91WF3tzOcxqnvK6z95eRr9v8l5yl+lUYJqKXE7hurquKhbVFePgJ73tft7xwoH4eXyZ8WI2002hSkVSrSqqVcWTJyZPnphMJgZZpigWBVkG5+cm0ylEkaHNjiYjxHxIec8jfnJGQw7Y207IeyZBmiPFxtgsmVWv4YkNYbGKFW0oZxPctk9p1SeUedR4ogeIcUptc0ZguJymWmbRMzYsa3vMih3MeIMrAuLUROZyrGWBVi3hrd0e19/yMcslHJEgyh6P1R1WoYPt2ty5B+VCjDg9xVgsEKUSlfkxvU0FI01RhTx3c4dIz9O5v1olFwQEvo9qtyn6guJr15CtFsqyUIUCtfOHlLIZxnyOSBKIIoRSL/Dis+LHuN4rz0MVi6AUYj7XzsqOA4aBuriOstPBfPQIsVhgxDHZ9rZuME0miPkcYduwXOLM5yTTKVSrZO02YjhEOI4ee/k+Vq+n5Uhtm2xvT/sUnJ5yNioQnC8Q8zlFK+TD6DbvbO4yS31+Lf9dKsmQodlhSpWbO2tuu2c8DbYZyiaFao4btSk3Z++QW07BNmmKAXvRU/aNU8RsDpnCXY8QQhJYJRZGmWIhwxdravaKkb9P80aRVX+D3/VYrQzq9QylFJWKotFQKJVDVAo00x4lT2IY8K3Z3Ss6n+teLPbnf7nw4kUx8DlCCM0tsyx14VQnLvZxFN2upFTSBcHJicFmI/B9RauVEcfGJYWb3d3swi9LcKe1QQxPKRcT+iOb5P1DFv2QD4YtsrxLNzqklM4o1PJsx4c0xo9ZrU0+zL+Bm5fYtqTx+HvUsjFn7nWSxEDECQ+y+2xMn4Nom2VSYKWKFH0Tz1xzNKvRKc1YBxZ2ElAQIf/r159wpz1lOZU8nVRZjVOWC/CMAE8tsHyXoLlH8+UKxmqFcByUUjxrvMk6diDnIJZLslRpP7DZDEcpItfVdusXZlqqdjEFODnRZjBKoTwP6ftk+/tkt2+Tvfzy867zZZl9kYyN83PEYoHYbFCe9/metB93iP4p4xeS3C+T+CXQ/fDfIASqXNZJPk0x+n1t0KYUqlbTi9zzOcLzNEhPJrrDY9uINNXF2GyGs7VFslohXRe1vU126xbWkycow0CUSqhmUxcQm42mDLkup4McY7NDwxhx137CyblFb5xn7O5iRBEte0LbHhNZHhNZJU1Sts0hTWfKwXKLam6Nr+bc5SN9KPHL5EsW17PHiN6A0vgIL5pSEDFGHKH8Eqe3fwOvpLCExIvGiGKBlfL46FmBXlDh5EQwGBiMxyaDgUWxqEgSg2rVwTw7oO2tqdUlx0cm83HGyihTrerLenRkXt12nqfo9bTF/HotyOXUlSrrlyleFAPP48uOF5WKdjSt1xVJwhVeVCrqgk6km0rb2xnNpi5qNxu4UQtYPDmnXIw5PrOpDA8Yn8T0ZnkwLWrGlLwRsZsfUpz1sZdjpkuHo+JdSl6GbUq2jr6DlQaMG3cohWNkmHGeNnlHvMko8LCiDUPVwClaFEVAf1mkaMWEgaKs5ry21eO3/vuQ7cqaswdzHm+2OTj3UUnC3cJTqlXFQLUwb3Yp2xtUsYiIIo6Kd1mVtjQVURjIdUjx7rbuOq9WOIZB5DioQkFjRbmssWI4RAQBYj5H1WqQptpsrNMhu3HjBV58Vvw4vLiYoCjf18XPZIKIY53P83nI5zVeLBaYcQzj8ZWBp7Feg+ui1muM6RSn2SRZr8n295Hb25izmZ405PO6gLhUDYsihOMgFgtOph4j9xqt+SNeco846TscJrsEdoVGdk7LWWCoFLwi88zDFLCdPmOysJllJRr1jMJmynWespQlbD9PxdpQDc7I9Y5wohUOEZgWhsxw2hX63VfxfYVtSdzNmDRX4Mm5x2DlcjitMp8LlkuDDz6wGQy0uVq16uCOn9JyN+TziuXSYDGV9IIyaSpotxVpKpjPBVEkPoUX87n+uu+rLx1WwIti4AuJy2X8IBBMJtoVslrV1eSls/loZBDHurvT7cqLf6colxW7u5JyWZIkgmHcYv3oCOvoGNOA6ckGIQQqSSmKDcZ4jIgTVnaFAiGlwQEyVYxoMr7+BmUnwJxOYLVhFuRIVzF/mftP/EC+wiwr8Szu0k9rNIoBr3fOOFk3mIYFRJzgRguUVBgotu7kcV6+weRwQ1GtGAQelmNSSYf8L+2/JTA95lPIzwZUZkcYUcTJosx4U6Tph9yp9kmrDUarAqk08NWc3L17hElCtrenk3W1esVLF1LqjoRSKN9H7u4i9/a0qsSl2+MPP0E/bcfmXztE/xTxC0nu8BzofszfYAyHEMeI6VQXYratuz3VKsZshooiRJYh9/cRcYy66PKbx8eY/T72aETc7ZL86q+Svv46oBU8SBI9Sm61tPvko0dXLs9Lb4tCOGEveszxskKYr1IuxuzdFFTXJySLkCixaXhrnoS7iCjiNedDprLMYbLDadBgoTzWaZ4T8xo97ybfbH2AOznDGg9xgymZFKhGjfn1+4SRiReMmfm7+CVJUQU0rxc4Vl3mWYkwV2Y0MrVe+/qUjr+k0HLpdiW19ZD5ozPSWot+5Q45M6WajNhqJNSue+zsaGOZblcbkVmWYrXSh7hezyBJIAwFnvflSvIvioHn8WXHi1pNUa+rKybHJV5ICS+/nJKm2rF+uTTY28vodHQH8uNem+jpEaXhkb4n1xs2G0idAvZ0SHN5hJ2GbKpdni2qNGdPyQczsmqN8fU3IIiwNgtYBYSxBYs1/y3/Tf5M/SeGcYWn6gZRZlJ3ltSrGf20znolSBNFNRuhTJOyuaTy+jbKzhGdjnk074BpcLM85vesPycnIkZZHTGdsjP7AKGU3gFIKrTVgNv1IZldYLQskLg+ZWuNKhbJtdtsfB9Zq+mGwyVWZJnOZ3Gsu9e5HNn9+3oKUK+/wIsfF58DL8RCd+cvZb1VraYnzKWSlh5dr8H3tTJTLodYLmG9xjw+Rsxm2EoR3rhB/Du/cyVsIaZTvbOw2UCphNzbwzg9RazXmMsla9OnMD9nNz3g2LpOIHO09x1eeVNR633IR9NtnHhNyU0JnTLRMoEwJMosRlaH81WZaeKzxmOU36GwU+KafEph2sdZL1GZZJ2roW7fIjRLRNjIRKEqZTxP4hHgNl0eR7sYJY/zTZn12mC1EnTSE0rGEqta5G5xgD09o69aRNdvc3qk2DKGFJyUW2+53LkjMU2FbSuaTXWxG6SYzQTzucFsJq6UJL9MWAEvioEvNJZLgWEofF+xsyOvpIx11x/KZXnlQrdaCTYbA9NUbDaCMBQsl4LhMIehQiof/jO+WjBNS5AmVIM+r+U+hqJLtxpgGlqWdBbleZrssA4t7oqHlHIxE6qEkYEKQtiE/L38NU7TNl4upWSsKDgZdl6wkD7rLMe1Qo+GMWZbPeOuf8Yk9jicNZDrmPvuU4rRmGgc4PgO7UpI0uxwp9ojR4QlE0rBAFWpsLz9JvmSRed+CbHZULzVIs17cG2XUgnyUUS8Wj3nnQcByvf1R8dBNhqaNrS9rQuFS87nZ8VndWw+z0zucyTFnyR+Ycn9X4tLAMzn9bW6cL/bk6PyAAAgAElEQVRUxeJVkhfzuab2XCzYyU4HlcthHB6iPA/HNAmbTZ3Et7YwP/jgasLAzg7ZvXtI29aLac0mxDH5r93B77iISoXNLCFvxty7k7JVmLFMXY6Wdbb9JTvFMSpOEELxbNWiL9ssUxcyhaFSxqLJ1GrRiY9wDg+JNxmlbIZjZhhphlKSXvM1Ms/HXQ2Rnk9SqjEs3+BkUuJkXSWehyShZJL5VFdHGKMJa5Xn5V/zSBIYHGT0FjazUpdSSdG86VFyE/ZvQG2/eHE7CWxbd2erVcXBgclwKJhODXZ35dVh7WdkD3yh8aIYeB5fZbwwDF1sLhYGYagFKbJM0OsZ9Ps5kjBi5+SfaAfHLKwaY+rsJ0/Zt065Ix9iNGuM23dIHJeaHLGaSWZWnWJeskWf87CGsVlTsQPOFiX+LPhtAqNEKRfhsyDN+0yMOrFwMLOUujHmmnPKfesjKrmA07DOZJ7DUgmFYEoxnlFRE5pdk7TSoNuKyKUrfDnDC4dg26xa13FeuUm3vIBaDa/hEHf3EVtNis08SEleSpLh8AovVLl8hRWqVCJ9801kqYTc33++jf0CL362SJKr5tylcZu62CsQQYBYrxHrtVYFzOX0sna5DHGMOZ0iwhDb9wnu3EFeu4ZYrzXl9GInTb38MtnWFlmphDEaIfJ5VJJQuLeD9/IOxmLBeiPIV3Lc/i2d95/2PCZRiX2vTzkXYMYxji052jTpJU2kNLBFSiIsTqI2eTPm1uo98nM9QXKyAGU72MEG6ftMrr1KkhlYyzk51yIo1hj5NziZehzOKyTLEFtkKN9DHZ2Q9SaofIH27RJWmNJf2hxmO/p2KfvsdSNaHYPtl1zg+ZLwhcItYSg4O9O7P7WaZop82bACXhQDX1jEMTx9apKmgs1G0GjIK65Yr2dcmedubUkWCz1COjzUWrarleYmB4FgtcqxOJ6wJc9phKd05QnNUsiWPMMs5THWG0ZTk2QRkhv1EIU80/3XeFO9w57do5QtaDUV42WO3HqG2gQUsxUhBaZGHU+sKNoRq41N3smopQO+1nyKvVnwivuUW/4pbtkiqbYRhuL2fsgwrbLXDnlj+5w0TBkfbshGM/Yn7+LFY92h2d+n2PXxy+gOjeuiLItKOsaPx8hymUK3y7peRywWOhFLqV/n+5rXnqaIMNT89lLpX0+8P6pjc7Hs9ROPgn/G+NIm90sATFMt4Xr9ur4pDUN3f5IEoRSqWCRrt8lu3NBj/DjGAFgsyHU6BDdvIut1zPffx/re9zRP1LJQ7TbZzZvIGzewTk9hOKS38plXr1OdPkMFAaVmntm111nU9/HNDf1pgcL0jK8b32GrHGAEa1ZGmf6mDE6OgivI51LCLM/RpkmlCtvOmM0yZX/5AXU1RmEQ2wWsaINIUn0PKpiGLmq6ZJxr4+ZTbqunbDKbrjNkPzkkCgXVUkrslgksj/HYZJrWGSd5QOA4gnpdkhV9Xv7VAuWyIp9XV/KOtg35vEJKqFTUxdcUudyXr9vzohh4Hr8MePH4sUGaCkxTsNmoi0lzjtnRmF2rR2f9hE74jFLFoJJN6CbHRIuUk7jJIsrRXD/DkRFOOce9yhlezaZr9KilA+R0yTp2mIcFttQZ86yEtBy2rT7D0CNMHSrWiqYz42b+mDesB9wpn1IqZoSWj7dVQCQJzY7J/l7GG61jPeFYuaTjOXuz96icPUSkKZRKuK/sU7YD7Q1w0WX2fShPjxBBgCyVKHS7BBcXQlmW5v6Xy3o3yfP0YTKKdIHwefL8C7z41+MCL5Tj6Gt1gdOXBZNIEj25932yO3f0e1IoYCyXeoE7y3C+9jU2+/uoYhH7H/4B83vfwwhDhGlq6tCNG8i9PawHD+j1TWZRgdLtNub5OcpxmBe7iFvXyV9v0DtImc4t7sff4/es/5fULjLcFFmmLomysRyTzCvhFmAaFZmEHve9Z9wKHxCsJPVkgFIGKlWYQuKsJywLbbIow54NWWYucrokKLcwlGQvfYo0cuwX+lQnz1DKwC8k2E0f5Xk8HTZ4MtSHfsdRtFoKp1nCKHvcu5fhurq5e1lbtlqS1UpQqyniWAsG5PNfPqyAF8XAFxZZptWDLhsN7ba8kvadTAR53exguRRMJgarFUgp6HYl5+cmo5Hg4MCkOOmTm/eIbt0jSQV2HNAYPCQeb3DmY2yVcFJ7mVxO0QxO8Flye/TPeAVJJRlRnB6TLiMMAwaFPc7NLrnVEt9YsMg1sIRiKT0SLFqlkL3CgGlQIE5NnGqB2t0WLXlOeXqI6A1YGT7XK1M6Nx2EYeC1C8j5Cjtn4QcDxIUucfb66/rjtWuoQkEf6lcrvSwWBLpbbNsEpqkPboWC7s4UChiDAWI+xxgOr6hDP8kI91Mdm3+DZa/PE1/a5P7DABjH+v3I5a58B8R6rV9Xr2ue6HqNMZmQbW8jOx0Kr7/ORkqMkxPMx4/1XoFSqL090r093R3a3tYa1LMZy4lk8uGYZB1SSUacjgqMRwaFWgGfBcbJCU1zwo444eF8i+OwzVTUWYkSlu8yWTjEKk/OkeRreZYbC2O14Fb2MeV4jC0yzp1dLC9HToZ40Rh3McAAwswhMIuEqYW37BOdjpFhip/NCQsVnGRN6voMV3kWVJjNBKORTa+XopTgjdYpdXNGWvSJY33gH71zzuQoJM6VyOUUnqeIIk3hKJcVW1uSSuWrldz/vcVXAS8uZUZXK3H1eC4WegIQRWAYgnIZhkPBcmlycGCSG/RwVz3W1+6xkgX8aELr6PvYw3OsOGS4dZ9Z/Sb7g++yb57jWBl1OcSbnLG1eYY5n1LaDAgdn/f8b/A02mU7OcJJN4ytJoEoMs9KOHlB219Tza2JlMNGuuSbHvWapBL2ENMZRhpSKabcKT5D+D7eXpkUE8tQ+GKFuVpp869Gg/SNN1CFAtnurs5LhoFxeqqXgYMA0pS8ZRFe+KhcNpgwDL0zcOGF8gIvvuD4JF4Ui4jZTMvpXBRln8QKVano92K9RoQh6fXrKN8n/8orBFIiej3M99/XjSPTRG5vk9y/r3c6bBsjilhkJSanCXIdUTn/iNO5z3ieoyBCSg2H6MEB9bTHPecxar3mLKpztKpz7uxjlxyk5bCI8ozDEmUvxataFBYDWAd0k2dsZI6pVSdySlh5QS4LKI2OKayG5BxBGBvEdpF0tsZNZqyGMbl0g5csCAoVSmLNJC0zj/OM0yphaHN8nLJagVIGv7Jzgi/nRDmNF6WS4uzbPbJ5wFyWaLclUSRIUz1R7nS+nFgBPx4vvhBp0X8vYdtaCu6TPlhKae7nZGLQ60GppEe+hgGzmZ4Jh6H2G9hsIIoEVdfkPKwTDmEy7eAnhwwDH5GTnO+8SuvsPbaiI5JAkmYmVpLQSs+Qykd6HtHaRY5m2HWPp8ZNvmW/wh3/HZKCS11sOJ37JKmkbk9pqSFn6zKRcHHsFGVtmA4Sfj054pXyOR8uKmSnGcbwIdYMvTB66xZdpw9ZprvC1SqUy1rTPgi0rnOSaLOXxeK51Xq7DVtbSNPU7oVJojWPLxZXZberE/Rmoy/gT+vM8VUwgfl5xwVJ+cokZjzWEq+2jdza0mpOF2H0+3r8W61eyiHAdIpxaUkfBCjXRdg2yjSxplMYDEjHY039Mk2280NQBqNpk3F0Ddlo0iws6cpniNGI7eAJypAYwFY55EOnzNPeNoOkgTNOiIIM6eZAGeTTDfPIZSPzDO1tcuacczrsqB62cDDsmFo6xDQSDMtgoQosRYlK7zG+r3jPuYPnGghTcaMyRvqCQbnC0NqhWoVeT3f5czkQQjAZKrbUCLU2GIkOucEpheWIgtNiGuprZNtQq2nd6GKRK3rHi3gRnzd+GC9sW/sPJIl2qXddvVc2GBhsNgbrNezs6N2yQkE7Ey8WFvWiyWlY5/QhTFSHYnzITnyIUXI5qd5hbVVoLA5wF0NUPo8drtjKjVDmhpyCeVwknaSc+h3eXd/m0GzR6qxgkSMKHc7XNTxrw697H7ISZQ6jLiqIUdYOKj7ilnWCoSbcNz4klhXMXoHz0zE75nuInR12b91CyA1ybZFd7BbheRiTCRQKyAvnWmM61Y5rn8SLToe029W5azzWuUlKnc9cV0uPvsCLLz4u8eLsTL8vk4lu9nQ6z7Hioni61MmVvq8PPMWi9qVZrWCz0Q73joNMU6hUsPp96PeRpRLGbMZ2LUZYE8a9OuPNHrLdplEL2ckvkE9DtuYfIoTQ6nW+T0tkzLMuo3WZeCUIybOUJjiC1cagYa+ZqSrP3LuEG4eG7JFJwdftH3CeKyLtHMVwgp2G4CimokUSKLzlMbSqzCqvUlZznJzkTn0CKCyrwtHWLrZ9aR6mKT6Oo0jCjGwyQi00Xjj9U3LzCUm1cbW7vrWlXYhtmyuH8a9avCgGPmdcjnYvJY23tuSnHCZNEx4+NJnNdKLxfc0bKxY1/WA203bXH31k0GhsI/qPuF0eEmUpH8rbdO2MbLFhcCRZqn2uyQmbos+0cY9rj/6acBMjNwsSzySjyFnWZrHuMLz2Ena1zbtnv8VoUyBYpGSZYMvoca94xMFyFyyTgp1R8TTNYp54DMYmphDcX38LYZUxHRP5+h3dOSkWkffvo0yTzPeRlQrGZoOs1bAODrAurJRlva4TyGuvfdoIy/OeczO1FpdO6kGgk02j8bM9MeIrYALzi4jLxJ3L6femXn9ud33plKK3YyGKtO+A9lvX37/o5EjHQW1tkb76qnaenEwwx2O9pGwYiCDAGI3YETGD0g3IFzCCgN34AWKiNa7VBQ/1VHSRrQ61mcCY2ThCcj7zsNOIptknyXIssyquGfIo3GeR5Lmj8iTFInfTj/gfN/8Pca7Apr5LTA7bUmxPzrDcMovIY5grcWzfpsqM8m6Zey8rNocjci0DIznmzNyhGZ6w3JQ4o6qlgO90CNMU1RvRMgcUSpK40sBsdGibkp0dyfm5ceUk3ulItrfli9vsRXzu+FF4cUllt209MdhsDE5OzAvFIeh0FGkqaTQkh4cWf/EXNv0+lMvbbGcPKQUTsFKOVlVS8zoqtYimilJlgSkyokYHLEml9wFrM2OVudQ2MzZWianY4X3jNc5r95GzlD+X/wPDxGSdmFgqombNWUcmK9tDZYrAcGkaYxZhjoNliVvpKbXskL2kz8l0B3O7fWX+pRwHbt2CTofM0ZLTWJbO8VJivf8+cqj3CGSrpfECNDaAzlEXcsdX5gygG04v8OLfLpJE+wJ0u8/N2gzjOVaAvl6mqY3J0lTf2JfvcRhqR+JSSd8LrZZ+7XKpRVAGA03RWi7ZcceMe3mtZGQY7KYHiIczzEIBlWVaxSgIOCm9xHmyRTkx8OKIdWbxZOJhGSmvNx+xytU4nVQpOSaPo+vU1R63zI+o5Vd83/b479K/JymViRpljCgky+XZWT+jV7rOQOwj3RqLtI5jWNAtc+slRXIyIvEN8uKYvrNDfnjMcpkR59pkGaSdLvEY1InGC7ckMa81WFY6bJXkVZH/VceLF8XAj4lPGhLGsW6iuq7+/Pj4ucOkUjCb6YUv01R4HliWZDrV3Z3h0CKXk1QqWve80YDpSGDIhFVoE5tlZo1v4NnnVI05wiix2Uxws3MmY0U/KHGWb+FHQ3ILhWHBsPkSx7mb7Mw/5Gid53y5RZoZRAl4Yk03N+AsaTBIqghpUs6lEBa4kT/mujqhI8/JXJfd0gzZKWJMh2SDCqrd1qYkUYQslRCLBemdO1phwLa1Xn2a6gmB7+tkcuMGKIX13e9CEGAdHZG+8cbzjswXldQ/GV91h49/i/hkB8y2nxcCn4xLYFyvdaK+dC8ulTQtKMtQOzvIalU7el7Y0psHBxjLJSpJ9NhfSk6XFWRa08VfPs9x4LCz/EDrgYchol4n8bfo3f51xLsDbpiHbIIu6/gGOQQyXeI6EUXRZ7xxKQiDTZrnce4+VqvO19cfYPU3uGnIpnWL+LXXaDz4Fo3VEfIgZOXcZpDboZOf4DiSGzdSlnODKDRxRj3a99o0k0Nmoxm9ksv5AAxDMZ0a7L/RJepPWK+gUgG100VEz42aw/D5NCAMP+319iJexA/HD5vXfhIvoggODgxMU0+RXVcSx/rMpZSiWITx2KBWkzx+bOF5kvFYFxBSCmwbfNNELmKM6YyFVeX01W/gzvu8uvw2tdURcWLSd3bgbMomquPmIYwgLHikqcG63Cbvgjc7YxxscTaro9KEmjVnzz5CoHhn9RJ5YWPaAlAsIhfP2PC29ZiWMWTXnSHr23TjELV5DIs2Ym+PzPexxmPEfI7M5cheeklrzf/gB6g4BtfFKBS0x0mWIa9f11PI42Odgz5hjniZu+TW1pVj7gu8+DeKT+DzZ5q1CaFxOwyhUNDuxRdLx4bmQevG0c2byK0tPQ06OcH86CNNEb4QpDhnF1kxkKYBuTzHrsdO6QMta5ok+mOxSFpvMVZ3uZaNWPZC/mr+OqG0qRgxKk1pOgPmqUEYCoqeRRC5HLgvkzZW3F8cYK9W2CIkdutsXvsa3oPv0FgcoCYzjv3fJFtLtrwpnpthFRW9U3Aji7ulU2p7TTrTQ/pnEQO3RD/VkwEhwLreJTl9jhfsdnGi5+bOvwx48aIY+Iz4pKW8Yej7v983sG3tJDkaGUyneuR761ZKpyNZryXvvmuSZSCESRwrwtC4MD0yODrSi2RBAJ2vdSid9sjlEqbeDpOh5LTQ5jfivyKtN/l4+yVuD79F8+wMQ0jOcrs0l4c4BhzkXsOo+qSByaBn4KtTMrWDIyJcP6FubwgDl+G6Qs6O2CpHGFlKELkczktcbz1FCYEhJc+s6+xZUu8B+D7Z7duYH36IUgrDsjT/87LTnM+T3bqF0euhNhuMxQJpmly1trJMO3HM57rb7HkvOjI/z/i8HTAhdKGwWGiqULUKb7xB+uyZ/r5p6mTf613ZLGZ7e1cdI2M45GxSZJSVaHUUnVsx5987ZdRXiDDHjrtCNJtk+/t09ro8Mzq8c5DRX9i0GHCz4OidFmWSBiYiZ6GcAm0vQvlljia7XF8/Iy5Wmfi72JtneMMjeH+DCDdgGZhCkDpFNsuMVSSxbIOjI4v53j2UNcA/eYJcvg/tJo8XLRblXVw3oNnUyTr4+BQ3f/FsR2CenVLc6xLH+hJc7v8IoT9/wSx4EZ8Vn8QKy9ILhc+eaaOx8Vh/P8tgvdaFQKGg9weGQ4MoEiSJIkkUy6XGhtNTiyAwOD0VLBawswPtV1oYRg97mPLd5DVm7FC3BapSYaNgnFUxlKI6fMjY3cIMzvDjMefl11g7ZdreGmc0oJycE2dbKAWlQkLZt/BWKevIIkxMzMzgbvWU42GBWEE/ykNDGw6eqB12CLQEaBShtrY0TgwGMBiA6yIuTK2k6+om0nSKOD7GiGM9EbAsXSVF0XOe7cX+wL/IXV+109RXLT4vXjiOLhbSVDMB6nUol0kt64rWZcznl9Utstkku1AZUr7P+WHGaJLRKC7p7sKxWWf0zyOIbPaskRYXyeVQ3S6dWoGTvuKv/95nsHJRKuNaoU8tt2QRuzyLyliOienY3Lq24SRo0lsUcbMDAssncX3iKKQwGVN89AOUBIMM0xJ4uYT35mXykWK5ENTLgunOfTjrEz98RHL2PpHb5slyn3WtgRNDoyE5PTW46RyTL/xy48WLYuAz4pJxYdu6qzMYGOTzeiegXpf0+zr5z2YGDx5YtNuS69dTrl1LkRIePrRwRn2CVcbS36FYlGSZxd3iMeV8id3GAldEpDgMRYzc2uV/WvwZ6xOYvf4STQvib1XIO+dMVJuVUeak/gr5ApyWX0XGCmM84zTY4ijrMI0tqrkI25Akps3MqGF5FtK06Bt1RBySKkE3H1AtBrzpHfKsdJ9R+DqqnLDTldDvYz5+rLu6vo9Eg4AxGoHrIm/dQq5WiONjzCRBjUYY/f6V7JuxXGoOYZbp5AEvkvrPO37U9f7htuXF6z4FBNo5RSf05RJyOQ3yQYAxnSK3t2G9Ru7swJ07yMaSymxJN3qMOk/ZccYIx2FYfAnO32Onttadv3qdg4OI3tThnviQih/SMFP+evoGi8ij7oacr4sU85J8QbA2LWpexE5wyvmqzH/x/nf+s/ovvKHewz1/BgLe9X+FD9VdplaNQVanLqccF+4wLzQoJQZHtbfZO50Q9wV+OMGvN7HrMBopHj82eVv+E+b+mMm1uzRe66DiY5zpiFSA1e1eGD9pugZ8dTmgL+LnE5+gVROG8O1vW4zHBoUCdLsptg3zuYG2AtFYUi5Lbt5MuX491S7FPxiQRpKgvMP5uaBWk9x0TlEtg8LubWrhGVs3I+ZbLV5aS9ROwvY7D7DWgs2tt9kzRsQfHDLIbTGXHpndwakWWdV26RdvsTl8D3O1ZBzZzKVBJiVWTjKY5sDoUCss2SrFnEUVvrO8S5zEdAtj3vQPadRS6qZiaL+MrMRsd1KtJT+bYU6nZC+9hFCKrFzWSkLFojaw6nQwHz9G2LbWqb9wspW7uzq/DIeaJ14sIi8lpl9gxc83Pg9e/KiioVjUU+WL18qtLf1+zueaQnwx2dE0spjKTkTbnyPXa3YGjzFKKZbhcFJ+DfXhx+x0UsThIbLb5ekjwWDu0hY9XD+g7+zQC2pEIk+SGiQbyXZtxtm8hSgX2HfXXJ+f8Gh9jVWpxFvuP/PK5rvYkwGQ8f32f6C38BhHDT427rHnjFm7dRZeA5Ymk+bX8E7GzI8NXHdCpbLPk4GeCDx4YPF7tW8jclPkK3dIb3RR0S8nXrwoBj4jLidol2IAF4XxFf16d1cSRZoXGseC9dpif18n/k5HUi6DtUlpyxG5dsaD+S6d9JhiPMbrumyWClM6iDRl2xvgPvkQMX1GWt6jTZ/hu1OOghaJV6FyzcJ7dMAj/2t0X3bZtUP+v3e2mA7mPA67HLNLJiDLGVh5g6NpBZUp8pZuVZVMSdmGirXh2r5AvvorPIsMtu0ejGyMUhlOvqf3AqIIbtxA1uucr8tkqkTHtq8e+tNzCyeo0slvUK6rO8tpCkB65w74PulioVthP6zY8KMOpS/i3zYuZPUu25ay0/lUQfApILhcgrngwlkPH2oVjsUCYzZD1mq6IKhU2M6OwReIfz5E5POI6ZSujECajNMaGA128iOOV1UWJxu28jN8ETP1ryFkgTsM6M9dIllAYGAaMXFoMw0EppEyM2o8C5tkwLC0TxZ9RLzKkKbFtLTF+PY3GBl1ivM+znBGPi8Y5nfYKZ9S+W/fYbE0WAeCKJDcCf6WSbbkkXEff36CKadMRY1NqUsxkMi9LuWmRFgCs/Oc63m5P/fitn0RPy4+yc67xIhiUTPxLEs/TrYtCUONFycnJr5vYNsp7bbE88BsZFjTEblaxmG2izc9pbCcYG03OB/BdlWxyhy6rZD4eADvvk/u/Bl5MyFdVflYthnntpD1Pq+O/5FNovho65u0XmuQf9ZnsAh5NqxxYHSYUcDNZwSxRawcPl40KFse7eKSjcihohTHSLlenbL3K1tkrQay/x71LMOo1BBGDzEea1dhy0I4DrLZ5IxtlMzo5HL6QFgocDYqIMwCXblAep6eQmYZstkkvXUL2m3kZPK8mrqMFw/dLyY+Cy9+GCvSVC+Fj0aaIvzggVZ/2mxgPNY+Np0O6dtvs330d5BLYJNh9PuI+ZzdZIBSEacrR+OF7bFjnXO8qjIbSgpWTDUXs9uak80qHAbbFIwIZecpqIBZ5JJsLIpuTNGN6cc13pp+i1Z+infbRfYKmKsllkxom894/Pr/xtm0SiNUGKGW/6zUDRwzYfTBKU+fWYQx7NgDXtv7G06TX2Hu75AdnqGYM9mqYfld/BiiVpftbUmGwPglwosXxcBnxOUiyCW38/L52N2V5HLaqbRUksznejF4PjeQ0uLWrZQwhG98IyYMm/jzmOnHA9aTCd5yQpCrILu7uPUZnIH6m39EnR+hKtdY2z6PNts0//wDksmKuLbLJsmT9SJk5f9n781+LLvOLL/f3me8w7nzFHPkzDE5iCqJUg1dVeju6m603Q+2Hw34zQ/2v+M3+9WADRi2YaDQbbhLVarSTIkiM5nMOTLmG3cez7ln2NsPOyKTpEiVukpdIqlYAJGZwYgk7737fGt/01rXSIYhD/cqvFU+4aS3w4/TPwKRUPRinHSKjAWzMMVCYDka30kZJzmIBeUK3OoMeGunS7OWwzpYYQ27bI3H6MI2cj5HN5tGPqzZhGKRpGHciVU/x0Zpwskvh4yPU1pRghgcI5XCLhaJr13jfPX+xTr9Z/tkv+5Seon/fPhk2TKOf/NhRqVQ+TwiSRCeh3Jd0xUIQ9MHBbMktligL5b/1tdZCxx0ENAfWvSiNt2nAdedp9SvHDGZWIxWivkMSoywSzGSMa5oGIMWO2I2d4li2M9KbNhn3BIfUxEzplmRUgHc2YAb3R+igcmtt/HXIUoTSsUuevUzdodnWCf7jKhyaL1ESR1hL2c0ex9xK0moqTNWpQbP2rd5Yz2j0TBqLyf2FrYNaxgH4jg2891JYsYD222F65qY8Nlj+1UN/pf47eCCK5LEhMHpVCKEolSCa9eMulySmPGhv/xLl9HIdAqyzKLZNOpBu/+iSXkaM354Rq7XZ+/UolbVTDR0OmCtbXDwkST73vdpjvYZlXdZ2kUOBhbtw/cJt95iIduUphP21RqeXpD0F0zuJlyZ3+Vvlrf5kXiLgWxRFnPsLMXWGtuKyLmaeZqjGwnICfBdtjsWrVzKejChmU0ROZeNgx8j5g7a98Gy0JubZtRQSiiVyNwmw/0I9TBi7c0VJ4eCaV/S8CYQ97EfP4Zul3hry7xReaPljuN8mi8uueJ3h9+ULy6C3bnYviqXkVGEms2QhQK62TQ7hbOZmZ9RCtHrGd+b+dz8NzY2WCu6aCHoRyV6boPuLzOu57tc340hiRmHBSMx5zkAACAASURBVBZTyIkV7dKCQHaZLFwSK6afeMh5zORkijOPKFtzXsru0jyN0Z6PPR3izgeszSNeLv6/fLT7P/It/w7x0ZCN2ilnR2fk8hDvjzjOmriW4QKVKm4lHxHGfWx6qFqVk+23+O52Qr8vyTI49raec8EFX/R6xjBwMpE0Gup5YfiTR/fLzBWXycCvwUUXYGND0WyaS8Ini6pGqtDolM9mmvlcs79vMxopHj82C2D1+hZ+OqRWV8TFDLdkUYkOGIzKeDNYP9tj0V8wOBvxi8K73IzvUm9OGS/GlCeHzHUBzxOMixvEqUI+HfKLuYutR3h2TMGLsC1F3o8ZLPM07RF1b44lFHvLDiUxoS2m/Hn7iOutGUoWaA/v0Un3jXYwkJ6r/JBlsLVF+sYbkM+bjoCUDB/PGCaAf0ZtJ8faSYiq3UAEgWn9PnyIunXL/B3lMmoy+Vyb+H/QpfQS/zj8Olm9T0ami+81Vqhm6S9JTPD2PKjVni/84ThGgWI6RZ5b0RNFZDdvghA0393h7K/nJmnIqryy/Bnb937M+4t1vlvs87+7/w0nT2Iq3oKCG1PTcDRpsxn3sZIco3mVsqsolDXX9SHV3kO0XjCwPHxZI5fMeO3se6TZRzhrFQ5232XhZ+SOf4rWFYKCIlSCq4N7tJkxaGzTWg+42u+y5g9ZfOMmUVMDJnDX64osM5x1kQRMpzCbGXWIbtcsfzab6leO7WfnxT8vWbjE1x+fLJy++Wb6XA3z3ObjQlgH2zZKc+OxCYePH5uRonJZsba2ww5DymVFKZCsNhvIJ2dYYZ6DgyoVNMHZHoteyOH+mPe8d3nLuUsunXHj/r9nbaZJ/IAT1WZavcKrw59iHYfMMos+DUZOg63SmNEyz0r7uCx4vbKHs5rz0WSXYVKh7Z7x1tqU1+rHOMKc983wDmK5NJ2AUsmYgillkoIrV0hffx3KZTYGA2R/Tr/r0f+7CMSK1o0ya9YCFW6bNyQMkU+fmpHTL+KLS6743eGL+OLzRoc2N80o6bnkuAxD4yR9rgYlRyOzSyClUSI8OTFn6NwPJHvlFVSpRPPdgLO/mkIuhxjZvDL6PrvRPd6LbsLWNxkNGzjLGZ3shO4kIAY8OcWiQHdaxBM+TWHxYP2PeHWxT3D8N9i+Q6hcYruK0oLd4x/x30Yj3Je36F+9SrujSZ8M8XpjtF5wZrXIL4bka5Lk2i3ag1NU0mX92gh1+zpWTnN6KpnPJc2men4sbRv2903R6NEjm2rV7JNeSAp/8uh+2bniMhn4DL5gvBrX/fQHeSEt2m4rlJIMhxrLEiwWmgtPkySB7s9OWO7ZZm+q1MLSKc6Dj3h78QHluM+zWoPD1Q7O0RH/dvw/M7dLWF6BaX2HVyc/obic4y9DctGYpS6wH3UYqCpPxCadTYVr2yAF/WObvIwgVdgyZJk5VMUYT0Rs5UOYLUitMWuzx7huH8GEo66LUIKOPzNbammKqlaxT06I//iPwbJoV6sMH90xfe9ejx3nEGYzoxyxtYUUwigI7O+b3YEvGpi71Hr+3eCLlsQ+U32j0TCf/4W83HxOurUFu7vPW8L2gwdmVnQwQOVyyMXCGMsVCuhG43nAP3magu+Tbm/D0znH0yJiHCCEQAyHTLKMvmiR94e09TM+SnfJ2QlxZpFG5iKgHM39aIcta4em9RhnFVG0VoQrC0dlqCTG1mdYrKhwB4It/DzUNhJcv0Z5kjBuOZQKBY5ii01/wPp6SGkn4KiqSJum0l+tKj74wCbLzMpEqxVzcmJkIU5PJZWKcZMMwxfuxOdv36ekhS/vLb+f+Dy+kNKEyy/ii35fEkUaxxHMZpos0zgOqGdH3N2z0UCaCFIf0lqL1/rf5/XRAVIrnskqp6UdWkcf8+/G/wv9/CZTv0wr0LQXh/irQ4pZj2h0SC7uQao5iddY2TavvRzTX9TJiwXpLMWVEcOJh6cSitmIYj6mko2wlE9jvoeyXPz9PkKeQhhyMC1jzRSd0hxaLfA8NCCzjPRcLa5dOKT/13PkwuwMbTeeGZWyRgNxdmaMqIZD1EXL/fP44pIrfnf4PL74vE4NmHGvVstc9isVpOvCrVsmWMYxMoqw9/dRlQpMJqaLLASiWDTJYBAgw5CjcQD5vOGb7h6ncR0rbCDjhCcPFEpY5HKKY9XBYs6CAp6cYichy2URYSsO3XXmixZv3vxTXh79EJnGaO3Sz62TW41xlktqyQNSNyLfsZisv0HLfYaV0wgZ8FYpJRtIVltXKSVDNjpnSAH+epl9y3jO2LYpFM1mNkEAGxsxBweS01NTLLpwsAeTDxUKn+aLxcK8rZ735eSKSwfiT0BrOD6WDIeCMBQEwQsXuSSByUQgpXkmJhNBFJnvE0Jzdmad208LcjnNZCIJH5xw8N6Iw7jFUXCLRiVhfBqTPz2gfPwxpf37DAqb/Kjxr3j1yV/SUSe4ekV27Sqv2I+or05Q85C5LNPQPXqrgHmW4z+KP+fQ3sFyLW6/GvOsVyBKbOrelJY+ZZ55SAVNulzRTygxJsZj5QasOz1uNvocWrv0sjr+Ro3Cn71j7MmlRFQqaMtCWxZiteJwUWP1rH9+0hWpdCmvF9BCPLePF5aFqtXQhQL5IPh818XPuuR+mVLi3xBfWkfJvw+fdeSEX3HlzDUaLJMEsVxy4dBlHR8bKVlANZvIMDTnYLXiZBYwseqU5cwoTtVqPMvfYu/OitAq0cjNuLYbMfvBfd57z6F3kPCq/pBcAZ6WXmcw9Uljxf6khrAli9RnunQp2hG38/epbAXsTysczGoIxyJgTj4eE6gxlWRAgRDl+uhyCbfo4IuIvK8oTk7Yrs3MBb66Rm9ZoNx7QtzcZrW2hr6yQ3QwJFspknzAcGjcX7WW2Lam3c7o9eRzw5lWS128XdTrmlLJRPuTE8l0amLARUyw7X96C/pLB+IX+CrwxXwuSBJYrSSua2So01Si948Y3BvxaNrkoXyJop9QjvsMwgLi8QGtw1+QP3zMaOMlfhG9xOtnf0Mxm1FyI+xGid3KGLd3Qhjb1MWIkpghFiEfije4m94Cy8Iq5cnqdZaRgyU1DWdEslT0sioveU941bpLIZsxm9ssUp+NdsK2tU9pq8zhokZfNfCvb5D7k7eQyyUazAjhuRmViCKOBnmio7Hxq0lT0kKZUkWSttvmDSuXEXFMtrsLtv35MfVrwBXwNeKLz3FwzgcByyj6NF+cniKGQ0SaoisVhGXBcom2bU4nBaaRT9lZos9HTve96zz9KCVKXRr+1PDFe0957xc+Z13Jy+IuWblGmguYhi6H0ypR4mA7cDIvEqcWa+6I3aBPru7zaFDnxuBnXHee4oUTZJbiphHFdIIjUlK/gHQkuZKN44EfzyjKkGZlRV4tYHONeZYjP+kx1GWizjbp9g7hsyGrhWLlBfR6AiHkucaGed6VEqxW5s9gHIp9X7O1ZUYELzoC87lgMDDPveP803OF+X/7Yr64TAY+gTiGx48tVivBbCZoNEw2COa5ePrUYjwWLBaCYlHj+yCEBjS5nMB1odPJWF/PuHIlY7AfcjrN8UxtkySafKfIjjjEyeU4i/LIyZDK4pS3oh8xysosVw4ISU5ErJcX5KouYWSzcAIqw2eAZuy2GNauMVdFopWkP7Spl1M27C5NZ4iIYwJnRbZK+FbhQ/558+cQBATukoUIWFk5UjvHMnZo7ObZ2FBkuZxxfzSpL7pSAd/n+GFEf+JRu17iRu2MNKgyPIxJZyGVVc8oQihFdm5brms18oXCFwfAz7uUfoXwlQ3unwcpTSA/73Xm1tZYhuELEs7nEdMpOpczXyuVjKa0ZaGrVSaiymA/IskHFG7v8Kz+Jv2+QAxHtMUZG9Yxcjhk8+jHjAaaKJaEiceJvUHLHlGzJnSjCgU35lSvIYRgubJ5p/Qxb5SfUL9ZpTe02Zu3WcgAryC4lXtGc3FAXi2ZiRKZX0BqRXV5jLWcM/Q3CTOH0UigTofstb+BFc7peGO8tRaenqBWKatqmziz6K8CrONjlr2QNFfA96FY1KxWgoMDSaWiCQIj+ZjLwWJhYoJSMJ2K554ErZYiCPSXLrj/vuHLzheeB0pp0tR0oYIArl1LaTYzctmC3iLP3ek2SgniXInt9RWl8Azd3GLem+KM+lQnh1xbfsSxs80i89hUB5RKCqucg2IRaznDX07IrcYc2Fd47L/KT913aesu47nDvthhtzqiHA+pO1MK2YREuFQY8T+s/29slafEuCyVh1fPU8hrTlgnTD0a9YyNbYEqFs3FT2sYjVD1OgI4fprSi8s0yiuu70YwWdCf+WRRSllPkKsV2veN8di5r8AXxtSvOFfA14gvPsMVulw2PP9FfHFuLKYdx4iMbGwwXboMuprEL1D41svs+zfoD23kwQFt2WPD7yH7fbaefJ/hIs8q1EQUyJC0vSGJnWOZ+mhpMdMBy8zH1Su+Xf6QbzYfY2202d37Plfmd8C2Oc3v0kxOKSd9XLViZlVASEK3TGP8ED2acVq8wTC/wbCrKOopQ6dDdXlEkJcs1zaopD2yKKVnrYElmajATE2MlliVAN/XzOfmfGaZGSMNQ4Hvw3wuznNfTZIYvjCNME29rqjV/um5Ai6Tgd8YWWbmgy+WQtrtF8H93KyPclmTz5sMME2NlfxiIUgSQbWqyOd5XgFaWgFTUSFJNM0mNMJjsnnEonmDO9Vv4q9mtPZ+hhMumCQ5fuT+IRYJFTmlqBdIz8GPprjhlCiEga5z7F6hmIwhSzlatcjpBVdaE/7l7QOs+ZwkgQ15yM3gmEZpRVWOeTn/mMXSoZUc4ouE3EYZIeBG4RAxGqE6Hay9PbTrIgBdqSD39ggnKX7ZpfN2EyyLYKdKXKkjijmCskR6HjqOjU5ws4kulX59MvAVx9cmuMOvVN+ef24XJJxlZh70vI+ZXbuGOnccxXUJAkWmBL20RnfsE44TmrkZL+eeESQDBIBtI+/d49bkZyRRxqzQYWI12MqdMslKxInFo+UWKtVYWUzNmpD6RXIeiGaVd16ekSHJ5isCNaOUjNjR+/hiRc5NsVwLkcRYWuP4kqVXRqYp8dGQ4dJjOrOYNq5yZq0xW3uFnFzQGD9G+jaLrRsE42Oykz7jOI/TKHKhbjgemxnQalXjeZowFNj2i5jgurBciuczo5WKfv6z/9S4TAZe4MvMFxfVwYskMpfTFIsapQxX9FclQqdMFAmqVU27rfGiGckkIqmu8/3cH9PufUy5+wgnWnAkN3mau0WdAZVsDLZZ7G3GxzBbMFIlurQh77OKLfb0Nr2kSr5ksVmd8c21Z/jJlLoYUGRCoQDrfo8/yH6AFiAcj5vWA0QQGF7Qmpv+nhkPXV/HGo/RWYaYzZBaI7pdlhTxmkXWdy10s0nheou4UEYWfIK6+3yOQlerRl5UiK9XTP0Mvjav7XM6Nc9f2+fxhRCkt2+jGg3UxoYpUBYzwxeqQXfkEY5T2uEzXpYPCKy5SSDyeeRPf8qt8U9IM8Gk2GGcW2db7rOyA46XNY4WFVQCMktYy08ol6DWgCsvSXLzIdEi40yusUx8KvmYWnqGq1dGzStXRaHJZUukJVgGbVahYnk6g9mcUVokLjU58a/zrPE6DYbUho9JhIN7+zr2yTG1tMfZLI9dKzKZSDodzdGRxPfNmLjp+pkRoVzOPPuO84IvHIffWSIAl8nAb4wLJUzb1lSrmkrFXPovSD8MxXMH9c1NRb8v+OADh/ncuES++WbCeCzR2myWB4Gxpe50zD/JOKTQ9KnevkZp8pid3i/ITc7IOzGJsthZPWBot6GYp1ONSCcRs6FiToFn7g3ez3+HySoHSrOMLQZWk13viAJLuocZBRYIrWh5U/7dlZ9jb3WYRDlEMaAUnuF4glIJM+e9islSKCcDdKeDmM9NFn/1KkQRVq9HUcwplQXZ9euIKIIso1S1yL+8iQhDVC6HkJLstdeMCU2hQL5U+noEwM/B1ya4X+AT1bdfeW3n1toa0OedH9nrIRYLZK+HlpKyG9JdlCCMQAhulY7h7MyMEJTLiMkEOZ1yYO2yjCwKrTxJocQHx21GSYluGLDTXGCnKzbsE1jFOCpiSR6/IEkKJdbzI5qLZ6z17rIVPUBagnzZRiUZSoGrIsi56K1NVKmEniw4W1XYL75EWO5QW7OZvfw2N95ucESdTnlBejbFmk85eRQRV1p03U3qdf18YThNjdCJ65qxoGpV4zifjgmeZy5zlcrvLrDDZTLwSXyZ+aLVUnz4oc3ZmWQ4tLh5M2V3N6PfN/sph4eSdjujUtF84xuJqTBmS/yaz40/u0b90d9R6T3BDccUnRStYTM7YOBvUMhlVO053nKIXoTseS/xoXqNbvUG+XRGPp5yP7tO5JW4FnTpTz2syZgWXYZZldeLT7m+PiXLJFO7juXYvFQ5ophXzBs7CKVgNjMV/mJmLoRKoet1CAKEUsjplKK1JGifV4YBPI/ia1sUPTMgLZRCbW6ea88bxbnL4tFXBJ/p1Py9fFGtIodDkyx+Hl9oxS3rEYzHpgB5LlttHR1xlK0zt8sUmjlSLD4cbLG36LBQeba9UzrugCxVBGIOccxOMOTj5DqT/Dqj/Drl2QkvrX5JcdWnUPdwiUm1xcrOk9MRys+TvH4bNZ5hjQYMkxInO++ggzKVawHzl/+QTmfJkdig4oUwnOAspyx7EVm9wb35Nq2WYjCwzkUlTAJ/sRNUqWhyOZMElEqmE1gqmcTgd9E9/iR+HV9cLhB/AkIY84hPCqx8cgms3VYcHprg/fSpJI5N21dKQRia6s9gIFks4N49i1dfheFQcvVqyscfWyxX61wVx9w6/DGlx78kPzll0LzBvOBQ/OXPKKdDmmLA4da/5KxQor14j5P8LrO0wL6zQ5TkeWxtojXYVsSfFH5ERc6ZzMo81VcZRkW+U/oAKSS/1G/y6q5mrDZh/oS/7b1E2xnx7fwB67WIw0mJ/olCZJKN+lP05ibatiE1hjLadY3zcLmMPDx84RapNbLfR21soEol5GRilosurPfW13+Hn+AlfqsQ4sUSWRybxTDHMbedZpOjRytYnMumxDEHoyI7tlmAFFmGqtc5ytYZOAH15lM2t3s0hks+yP0z+ss8VT+knY15PX/Kz8NXsB0PpQVlL+L4rMxiUUDHPi+zT9Fe0o8bJLFLMnNYkxkoTU5GpALkckpRWBzWbpF6BWZ2G79VwbnSIu/DeGzGNvbEu/gff8CtWxmeB95rbQqPTeD2PE0+r87H/cyv29tmZ2C5fKGEeHJiOgdgpIa/wpMMl/hH4Dfhi4MD8+fDQ8lqBVIKlNLM53yKLy4USYQQjEaSfl9wtNpgTR/x7nv/N87DnzK1FB9t/wt2lx+xdfAhJWYMrTMevfpfkJvdoTHf47R0jbuz24zKBWIrz8PVDsiQDdkllz/hlj7mvdkt7mbrDByXN+t7jHWZoOaQ6pSz2jat+3/LB6st5rHLt5sHbDZCjgYJ/WMN05S1bxTQxSLKdbEPD808+fn+GFH03K1TtdvGodZ1DV9sbMCF4ISUMJ0a4YJLfD3wn8IXUcjxvmbTOw+ejoOq19m3dhnkXJrFAetrXY4HMz4c7jBZ+hS8Jbecx8bgzrtCmHkIKRiKOseHcLSs4jhV3q78NauszHI2ZjxYEueb5KwVUmV4VkwsfOx0hQiKLFcW+ULGs8IGW2+XiVwL14W7dx1OT+Fh7lu8nrzPus4oFDTh9hp+z+z6RJEilzMSoo2G2Q/Y2nrBF7mcEaFIU5MrXZiSfVlx2Rn4DD6ZAF/Mel2YSTiOGRmYTCTjseD01GI+lzx+LI1xjGVmxRYLMz6Uzwv6fTg9tQhDGI0kLXeEf9Ylm03I5QTeaoKnQ+Y338CbD8lbCaHyCENNHNR55t3ir0dvkq0ylLSQjmRaXKfgJvyp/wPW/AHTpEDolijmNN+tf0hQFnyYvsLDtT+iZk/Z7wfszVuUKvDdd+aoapVSGdR0gXjtZfJNn2xz04z7FIuodtuMDdk21nKJ9n2E1iZZuHAXPl8S0vm8WRJqNiHLzCLqubTY1w1fq0rPZ/Arry1JjGtoLmd+P58/r/Rg2xzOKvSyGk1vwo3OGHFwQv/ZEjWaUS6DCgKshw8JQ8nsdElhq0qwUWTqtRgNYBTmyRcEr5f2sG3NnBKzyOaqd8Q1dx/t+dyfrHMyzDGfS9r5MbpUIp6nNNIunpWhigG5tSLSc+C0T6RcZjMQvssydrkjXuVQb5KmEildTk8T4scn9J+FzGbCLAOnGSuvzGpl2rfFoiaXMy1ezzPLYKenZhRwuRR4nmY6NRe28Vg8HwX5MrZ9f9/wZeML29YcHFhEkeDw0EJro6o4Gkls2ywdX/DF6anEcQRhCAcHFouFKSRtBSOCkwOWkyWZnaMY9amtOVi1Ena6IBZ50kmEmy2xO3V+Jt7hh72XsKTiCvtM3Aa/9P6AljPkv6v+H0TKJ0o9lrKA5yi+2/yIoOXzd6tv0fe3aLdSng3LPOQWm+Uxb75r6oXlbEhWayI21/Bf2YJSCVWvm27y+jrWwQGy38caDtFra2akxHEQi4WRT9Ea1emgpTSJgO+/EC645IuvHP5RfNEeIe58TG9okwmboOmipcR+7z3Cfsh05ZG/vkZwa43p8YLJ2OZYtSnaK15rHtNwJsxyLaJFxro/Zl0dMJQtHg0bzJY2RTHnJfshrBKyRCEFaNfDciV+wUEUfOLTERNVZj6ICQLJWVLhXvEb3N2vUCi4HB3FlMuCk5+cMO+GxLFkfSNjOtRMdJksg05HPxe+KhQ029umMHTBF9OpMN1rF46OTLIfRWZ/6MvIF5edgV+Dzyqc5fPQ75uE17LA88z4TxharK1pVivjp7FYmOWRMNQ0m4JHj2A4tOj1NKenV9jURappQDXp8dpsgZ+FhLMlunIdd3DCdG6RjCN+0f4Odzp/Sv7gR+g0YRBVqNRXbLceIRczjocNJrM8PbuJKOTZWevzJLrKNXFMpizu3HGIqte5Gv8t73YOWC8tOJA7bMoQkaZslCawesqRfptkWmbt7Sb2T3+KyuU4XNQQzS3WskPDdGEIpdILDUXbBstCHh8jzYs2S2GXMnBfDziOOeSLhSFvIYyMXByjNjcRPYdaU7Mm56jIZ611AqmF7KYo20bV69hZRruekSYO3bXbnHQnOOMBNTngj+p9lOuio4ROS/Nkf8VOoc9GccZLlTNKO1c5/eiEp6pF6Lb5XvoXvCrvUa+4LKNTIgdy1YBR3qV0+IBQBqTFKtnSIhtOmUkYWwJVEEynmu1tCMaHxNM+40qHD5w1fL1P56TPO99SdN0NqlVFo2Gqu55n8t7l8tPSoReIIsN7F5fAL5NE3CV+N/g8RUytzTlKU9jdTfF9SaGgKRRMSJ1MzAXBsi74Au7cgdXK4vQUxuMdHlhFnMUO1ZN7vJ4cczqS5Nfq9Nvfxn32iCyJGC8Fj+U2860dNj96SKBiVDznjfUHjNM2dmzzKNxkkObpqgYi77HbmfJEX6OUpejZjNN8h6cpCDdhU55wu/QEnBvI6RThumwkJ+iGx37cQI8i1l4vIe/fN3wxLSPqFTasLoxGUKsZwpxOzRtiWcatNoqQk4mRMP6s2dglvrr4T+GLqWS9GYHMI2cRKmigKhWcyYROxyVNPE7ar3N67wQnrVGxRrxbvkOQjXCDJmU9Z3t6RtfdYrM84jvb+yT2TfpznzPdYlbZZu9onSvOlK61Q0U8xE0WxF6ZZTGPnE3JYhs3XjCjzSrKMcws7JNj+sttul3ToROHh6zbQ2g1+KnaxBk9o7gc8KfvKI7kuQtxZiZFlDLPOLzgi9XKPP8XqxX5vOGKLytfXCYDn8DnaUbX66a1cyGHvL1t1EQ++MDm6VOLLDNFjuUyIwyNKcXpqaRaNfNhzWbG6alDGCocx2I0goP5Fm+pI1KlOWldp6JGFAqK8qMPGFBFI0hsj27f5tbT/xWPhG4WkNo2D7MdXl0dUghXHKotnqgNytaKmh7CdMY49XhvdZ2zyAZvSNA04rb/evuX/Kx3leFQsrnhwXxuHGWFIGm06U885H+4x6accDIp0Xc2aGiJcBxUuQyFAmp7+8Xtx7aR+/tmRMiyUJWKCfCXMxNfP9j2c3MZHAe1uUln81wzbV+bh8P32VCPEC2BtnxUo4HO5ZDDIVveCG0N+Ui0IIW15oB6LWUje8hJ1wXH5b9uP+Ugd5PBzOVB/m0G2SZVPaDjH/B4tc1yZfP/hW/zF+oU6jXSUoEwJ3GHfQbOGl2rw6F6jfp6hpRgJTUcYSNzxvcjl4PWmqQvG+i0gxgJVq0tDo4E8kAwKdgUCilHRy8Mohzn0/cZ2zb/bmvrhQHh5X3m9xef9exLEjNedj4lAxhjofEYfv5zh5MTQRxLrl7NmE5hNLKJY02vJ9nZUeRymnJZ8fixS5Jo0lTS7QqeLLZ4IztkvmjyxnqFYRaw5R4w3Z8x9Dr40x6xdigf3ac0PqTsFmmMHzO1ynxof5MKc6aZxy+i2xzEHUBQdeboVcwo3+D+vkOWxgTbY9RyRdOacI3HDIaS9dkMVS6j19cRvR66VkOvUvpj99N84W3RYok67yyrrS2jP3+hWa8U9ocfmodmtUJVq0ZO6ZIvvn74PL7YEJCk0LeNV02SsGEdIzo+ydYWyvPQWiPmczadZyTNb/LogY+Sa6xtn/LqRsT26R0OFzWkBRtXQqq5JTO/wfezP0T4FtvZU9a8PtVZnyNrm0fxNn9ufw83b6OUj1dxmOoiK7dIGGvuuu/gr2mW21eo9DUHyiaXM3s/t29nyFPBx2mdrrtBuoDglQ2e/a1kfFegOjb1ekqWmWfd91888xcFAcd54S9y3gj7UttmXCYD5/isO1yno57Pe124xSllEt84Nllfu63pdo0hRaGg2N5WPH5sM50a7dkLQ9fr1zNOTmyaySFhVnvBnwAAIABJREFUBKXMI040RSY093/BqlAlDmGufNx0Qeh0yG9W+VcP/i8WS9grvkyhmaNRyPPwwOc9cY0dy2chJOvFKa97H2N7Lvf6GyxXOYKCYpHZBOkZ8x/u4a7m/PveOp2thFpugSrWsfp9hOMgooiNwhDldjg7bNNPGxCF1F71aV/3SGcFcwOy7Rcp7SfKpHKxeJ76qmvXXnhzfxn9ti/xm+NCDiWfh/HYaIl7HmcfjUl+csLG1RdOlEddF3HjT9gsFpGTCWI8Rk4mZK+8QrZaIcdjROyjBzbt/AwVZmyKQw5yN7GuC9anH3MyTFhXD7EKO6jdMu/fsRksG6TziFIQchw28OMpS5kjCqp0sxT/4JQkDiikYybVMqtII5tVbDLWOg3kVp00zZ4bwuSvt7n+iiJ3lDEamWe4uLuOfzUjmRkzqHL5xcL/RQGg01EsFi+CuJSws6N+pXBwid8ffJIvLhaJs+wFV4A5XxfqIr6v2NjQ3L2rAUWxaGaIl0vJdKrRWjwfTSuVNCcngmZyTL2QcTLwqdCnEz4kOh5DXpPJGYXxMcNWm2fWbb49+yuc5ZRu0iHv5QjyE/blS/xg8Bp5e0W7ETPsCvKe4g3rA6xCmXv9Nks8ZJaxXDkEewdYZz32xwLl1fjj2j5aNxFKodMUUauhXZcNv4dqtj7NF69VaV5zySYTowpzcGCKRxf2yxeutIOB2UsbDFBBcMkXXxf8Or748TFbxZEpFto2B0kH5/ZfsHn2vtkhGY9R6+uod95B+T5yMMAZ9VGRou2NUfhseCfsb30H59F92uUF3eMVtysr7loBj7zrPLgnEHqBm6SMZIknq2vMRMC76scsyLNwy8QDGFDAUgn9whZCKyrXKsReQuWf3YawSTtOsSxTBB7QYbMErTij1zOje+61dbyyxnHM/fD69ZR8/oWbsNbmZy+KRUKYKblP7hZ9WY/5ZTJwjot9l4tWzmfHA1Yr+Ogjm8UCJhMzRtDraYpFQbFopKSmU0m9rsjnBVprgsAsLf7iFxbdruCqo7kl75AXSx75W/iriCAekuYDotYuoyzBm/TJZwvs04ggGbGiwJF/jSj1GM5dVJyhlxn35TbNYAnJnGVs80b4Ew7n7xCJNn4uo1CV5IignzJMy0yWDllhxO2rB2RRgezqVWMNLiVyMmHj1U3Gjyao8xfT/jdXYW8PZjMzBjQem9kIxzFVn/NTrQoFYynfbJo37ujIVAUunAq/rCf/Er8e521feXRkIpyUqCBAd/sMXR85mbD2UoGjccDwyZTa9vnSYLGI9H1zJiwLeXKCWC45vTNgsnCRwqfRcPjZ4TqWvYLhCOVknDnb3FteQfplht0Sy4WmG1YYz8sEaco89ijaERVrzt2DGrvuAXfiV2hkJ7yaHJGfnnKv81+y2mjxcnqHW9VTasVn3F/ukMvBYGBeVqEgqVahUMgYDASNhma5lAwGEtDYtkRK9TyQX1z6jo+N0tDamlEIu3Alv8TvJz7JFxej04WC+XocG6fSZ8/MuWo2FSAYjTSua/jC8y4uC4pyWZLPKzwP/uqvXI6OJMulou0rbqzucosFOXtOYE9RSjDIbzELUsq5Pvl5D5l6zFOfNW9Cc3rC1G0xsao8SndRUcJQ+ExzLepeF71KWCzgrfTHHMzeIvK2qLtTcpUGZIJJ6KOTOdOl4qRYZ308JvnWtyCfRzx6ZJxlZzM2bq1/IV/YT56g8nnjJH716vOEQDWbhkOCwHwtji/54uuCX8cXloOtZ7T/uMHRM8VwMqeVTM1EQatlfr5aRdVqMJ0iwpDTO2eMj4vIokVj2+O93lWcvUcQWSSRS0+ucW/xOqKQ46jns8DhaF7FTiMiu4TWmnf5AaDYD1vMVZ5MSWpZj5bocZy/zn946b/nv9r4EWuLJ4jkgGG1w2Ih6fdhb09SKJjn23FAKXXuRi85OzNc4TjmWb5QoHOcF+ISg4G5CzrOi0Thy84Xl8kA5uz2+5LhUDIamQ/vs+MBcWy6AsUivP++CfCNhiYIMmYzyWwmuXUrJQyh1ZIsFqbi0+1K5OkpxbGkt73JlfwpO9lTNvo/xcrmPLvyh6wym53RAWOvzqi6w2vWA6ZTSJWkV7pGwJT/s/evmSc+dXdMFAtC2+XRIMdWTjFxq8gkpZiMiOwcw3mdWtWhXIlYnmW8mb/Lk6SNTWwWe6ZTlOtymK4hWkZu8eRBiCoG5gHe2eHoKGHLdV/MSUiJnM2eD8KpnR2TFHxyXgJedA++jH7bl/jNIYSp5ESROQOrFSwWbNQWMD/jbNak/+MUwiNa3pS1XBkiUwZRxaKZD16twLI4HAdYtuKaf4hlac66ecarTa5lH7Mue/SmBbq5EkejgEHYIcKhl5YolgWHszLLFBCKYk7QVevcWt3hl7ObdK11dmWeHXufidsiUZI7ky06q3skdx/xdHuDg4pFs5nh9w5YpSHd8jrTqaDZzGjEp3x3O+L/+eUuaarRWlKa7OH0BM6OIak4Nkuf0oyGEkW/3WP9eaOJl/hy47N80emo5wIqFz4DYWh+nUwE3a5Fo6HY2krxPJjNJJMJ/NmfxezvS9ptxWQief99GI81hckJ3kqT7qwj1Ak3qlP8s2PScMpx7RWEhsXSZrr7XcTJMW17QhZJVJJCEDAZSf5Wv4Wvx5RzfZ6Kq6zObKaixpVij4ndQKiEIB4RySJDXaFWTiAEkUS8XXmIXIYMRRPRvY/9+DGqXudQb6I711jPDr6YLxwHFgukkUYyl/ydHRNPtrfNm3LJF18//Fq+GNAf5Tj7ayMF2upINjaUGTFVCnzfJITwnC/sdMn13BjL9+geVJgsXK4lKWv5Gb20zb30Coczj6FTY5SkLJ0SaSlgOEjRic2GPqAcrEizPOMYTlZVAiei6s/QyiLFpjA84n59m9rsAYPvPeNx5QrT4hrt+IBiPaQXrjMeC+JY0+nAq9VjTo80R8k2vi8ATfLkiFLVwtlqPd8HyDLOlcNMkvDbOtb/ubniUk0I8wbPZkYr1rY1nY7J6IpFow1bKmn6fcnBganwCyG4eTNjNDLqIsOhhWUZadFiUTOdSjxPI6UiTSVqOCU/PyPIpxyXblJd9lmLHiOF4l7jOySnI7KdXWpqwC3xEG8+wFnMGblt7GxFtkhwohnDrMJSFHCtjJkuIrWi5s64Jp9wf3WF9fSAb5bu4wUWk9ZVToc+rxT2uCKe4vkCYTt4akmJKUfZOv2ohF92WeyPGRyuqK87XL/tkjg+g1mObLkiqFnoIEAHAWIygVzOuAzm88/dCJ+bkVgWea0JL6TmvsJW8p+H3yt1CDDOk7OZCdjwfEQssJecsIbV7yFWMbfEfVMhLJVQrRZZp4M2fykijglPZuSyOS81zjiN6wRqQhYl3MzuseGeoRLFQ+slenGFbtwgDlPKTZeDqME8LeA6ivVgSmx53Fm9RIxNxx3gONAMFiT5Eu5mnVGwTbWUkXvyMb5MGPsdfnSwweyjY+KjMSEuY1U6n+EUuMsJctAnnGnsegm9f0Qp6qP9HJXtIpZlVMEGA0mvJ8nlXujJ/zaO9SeNqJbLf5jKxKWa0Av8rvhibc1oi1/oiEsJe3sWJyemSNRuG06ZTIyC0GhkHK1933yv8abRRJHAtgXZYEpDdXFkxkPrJle8PoXJITgOZ6qB5QhmzR22cn3W+x/hsKI6OUQq40ljRSFh4vLAeRVfhzxOdwlTD99KuFbq0sy63F9ssy5OeKf8AK+R5yi4xSzyeKPylCurj/F8KPkxqeVSyiUczav04zI5J2WxN2SwH1HfyXH9dYfE/gRfNBzjQeD7UCi84IpzyaVLvvh64Fde2/mmvIhjcwPWmos56SCXcOzuYnVPEUnCS9H7JonsdFDVKtnFXonnYXW7LAcrfCvmJechXdWhkIxRWnLDfcqG2yN2c3wY3uTJqM48yVEvRcRuwEHYJE5tCl7KVr5HkEt5GO+SxRk4NqWaxbX6iMgJoFxmEqyTj6f4x0/Rts+wtM2jn8+Jj0YcjzxWXoksA88TTCZQUmOykyE6VXSjMpXZEUHYw6/5BBsBUppJkMlEMJtJajVzj/xtqM39NrgCLk3H/l5IaRzi0tS0by/I/kI27kIyrtVSlMuKYlFzfCyZTASrlcVqpSmV4MqV7LxaJHj61MK878JkrVHKlfBj3g5/ROBm6GKOqj3jlvqYRFnkvYyrxz+gNXvMWNbpl67gkpCMl3jxEmyLzHLp6jaLxCdgzo1ql5c7fcJJjG1pXrfv0morjtQG2vEJeyFv2He4Et9Hl0rMZUB/lmOxtFkuBfWdHNvRQ8LjCYV7P2fbPsGaTinuVMjCGNFpUmjnjYlIuQxZZgzLHMcYy6xWphLg+88VBPLtNgv42gV2+P0L7vLkxFTspERtbSHCEDGZcDTIsxAF5HCE7PfJpgtKgSa7cgVcF5EkyLMzZK+HqlQoMaN4vcnxqMi8vA5hRDA7xY9GlLMhhabLsdylN3A4nJbZ8ro8iTYZZBVqxZB/fu0h5WzILMnzbFJlWx7yZvY+19pTko1t9Ftv0B7cY3vwPrtnPyfMXJ62v8njZw7usEt19AxZDHhoXydJNIOBYDqVxLkSpXxKcdln+aSLEy+RnSZPkk3CUFzYalCtaopFxfp6RrOpn+/Qnx/5fzA+K0WZz+vn8+e/KS6TgRf4XfPFJyVGs8zsBPi+MSA6OZFkmWC5lESREdtpNhXdrmS5FOztWSSJYLUCUQ6ohsdcYY/t1RO2CjPmIseN9D41PSSsdHglvUvl4fs46RLSDMfSxMrmOG1jpxHSgjFlfqHfZK6LlJ0F72waz4DxSGCnIa/nHtAuLbgj30BmGfmwz5/p/0gSKyb169isWEWCs6HPMvOpr1lszz4ifHJG8dGHbFnHCCkJ2jmy5Qu+uOgYP+eKctm8cZd88bXBp17bOVeI6RSyDNVuo6tVrL09SBKOpmXmlQ2sBw/MLtk8pLhdQXU6yMnETB0Mh8/baUFJUGx5HKVrLLwqRBGF0QG+rwjUmFLLY9xTPJqtcRaWaTljHsfbCFvSCkLerj9GeA6TMSTzFYnl8we5D7jSmlG/VmRZaqGlRW3wmCuTD1Gp5rG8zqNHEr0I8aRmlSsQe0V6PXHuMi7ItQOihSY36xHvneGrJadZi0F+E9/XFAoapczlv1o1d8V63ZiOfRm4Ai6lRf9eXMwCf1ELRkqem425Lty+nZKmNq4rOTuDRkNTqymyzNydXFcRx+b7w9DMhbK1SeOjvyE/PmLZuMrTG3/Mt3/4P1EaP6LhX6dXfI1pmsNZ+mRVBy+aoIczkkhynyt86L3N2KkTRgUyAb4c82/Xf8ReukMU5FlL95Eqx49mN6FdZdc5YNc5ZTBxuJte5eXWhKaz5H58HXxAa9a2gPfP2BidIewpmSqjpFGJ2WiEqEb8or910eY9d9OQx8fYH3wASqHW1kjfeuvFib9s9X71EcfI42PzmZ5/xqphZj77xRKNwoKtQkT3e2f0VQM1C2ieD1Dajx4hx2NIEpTjkG5u0v1wzOAooZ3+mM3CkCPPol9+A6IqqrmOmgUcyi1Wyuej6Bozx8XyNTU/5MmkhZUtGcUFXFY0dA+LBCdccWP2Pr29CdWappScMcnypFaBnidwXU01PkNmE/adt8nnFZZlFHKXS1PFPRRbfKt1huNIHAd+yTpFS+D7L2bCVyujDe845i4Dn14W/YcG+c+TorzElx+/KV8oZc5PEBjxifnc7AYUi+Zrx8fy/BwpbFuwvq75wQ9sNjcV3Wcd3lU/YTqFxdorjKXD/Cwkby9Aj4jEHB35OK5PedVjOVcoLPKrMe9Z32IlXM7Y4Ak3UHHKN8pPuVk+Y0qFajmlLMbg+vwwfh1RLfJyep+m/5R7gw41a0yFEZutmK7uQLhEK8VGeQr3u2xOT6HqkMYByrLAsn6FL55zxblTnzw6uuSLryvO52PkaPQ8QKq1NWM62fPpWxb1pubKaytO/n/23ixGlvM803xizch9XyqXqjr7yp2UScoSPLY8bo0b0za6jQG6LwfwpeF7w7eG7+27wVhAA220gbmYwXjasq320qZkyqQoLmevc6rq1Jb7nhkRGcsfc/FX1TmUSFkiJa7nAwqnMjOYrIj443v/b3vfH44YuFmCaYZqKgW2jToaobbbiEQCYVmwWtG5OWPQV6gEt6lvCg4jlUHiKogihJp0mDGLmZ/mrXmehaFTyNjk84LeNE97mcVfpHiGEVeNLSw1QPRmmOGc+Av/E0YAhfvbKLMlUeU8mhZhWVAKe/SjFqYWYqakVshwKH20bavE19eojzuEIShKxKHVJKeFBMEpgy6uK+dKFUXOHvzo7MBHsU8CK54EA8f2QT4piuSNfestKSPvOCqtVsD9+3KAuFoVDAY6+Xx4Cgo//KGBbcPduxAEssUgFouw7x6y51bRcxEFppTe/i/MSOMlLtAzmyiTOV5xja6v4S8UhvOIrOdxO/VV9s1z/IvzMo6jE4mQrDanqI/4fzuvkkorlGMDtMmMbaVOLjrgwuJddCWkS5pdWpSsCa3ygP0+lFaHRL6Koqp035vQXC6JokiWdTWN04mYKJKvHxuaUDudR8i3XMp/TVP+PplAPv/J3rQn9ou1k3t98q9poiUtSoZDowFB5ivUd3Zg4qMsZ+hbW+A4DF7bJsjkablbaA8eQBDQ2ymiaBrNYAfaNq1eF0Uf0UmeJUpsMPIbXMx3GSxS3Heq6JFOxpzRHxqsQgvfSyEUk6vGXVwjw5u8SkUseLn/XV6c/SXplA/FIsswTrSC9Nvf54ynUvEOWZXWOPsr8IOeLLXGB/sU4zqLRZV6uE9QgEpFBvPnggOWqQb9vgzmLUtSRYahdPDJpGyZisV+nDP6Z+3p/Nc2lU/ss2sfhBdywy+JJra2NDRNodUKiMflzKymCXxfpVQSp+713XcN7t5VcF04e1YK2g2HKpNJxCibQ2fCxugH5PcGvFv4GimWiCBkpmZIZiLmts5kpVH09gkVnYeZq7yhfJ3uMk0nLONFKjnNxg9ga1YjyJdoqlMU12XXrZCNHvJS5pZkIooqTJUS+VTAS5tt9kdJlHGPSFFQEknaN1c0CRC+j2JZKMmkPDG5M3qEF6qKurMjF7VpIopFOXgD8qLNZhIznlS1vhh2sjN9XHzl+H1NEZTKKmsXLALtGvX2d2CloSym6Ldv07u3IDpo08zN0RYLOTMwTjHsQznrUd+9gTqJaE0mUHDp5i6hxiz66SLl5ZSHTplDt04UGuihh/AESyfLoZ1HJ8lvJP+RM+I+QZRgoWWYzW0aN/+OSNEIdZOdwlmEH6IebPPK4p/QY9B+5X+j/G8r/O3fgt4+JGYbqM0qth3RjA4pVQW6Ltv7muEBYbjGZCLx4gQjhHg0I38i0+R5EjdO7GfBi08CK560CX2IRREcHam8957G1pZGEKjYdsRioWHbCvO5Qq0WkstFtFoC04xYLmFnR8f3Zf/n1auC4VDF6ByScYfY1Raxa5vkj+6jeUsGQ422qJBTZ9TcXXQdpnqRvDplNQ+YiAwrI4uFQ7TyOVQa1Mo+/379+3QWGXpellhS42tnH9Ky76EEIef1B3wlfYuukyftDjF1wdOpLcZmhYEoUcp5XMx38dfP0PPzhEIhG00RFy4QPPcc/quvynIfoG1vQxii7eygHhyg7e2BpkklYsOQNHGehxIEcqbAcYhXKtgn03NfMPvSlH3hlC8x0nWifJ4olwNFIVlNkC7HZFlfCLR2m3QGUkmBYttowyGzh1P6fp7Q8ckkQw7UDfZHaWLRiqa9hWJZUCoxzW2wEialZoyFVWKnlyUdTXloV5iFSXyhE65CPMVi4iVRNbhU6qPlklDIkwjniFDlnLJNGGnMp+DOAha2QsHrUrQcMmnBxfMBKWEzUVIk/BlNs09OnaEeHRIuVnSjCrP6BYzuEYX5AUbKIEikSSZhNFJwXdnj7bpyJmgyUY8HyxTqdRkceB50uyrz+c/W0/l4e8lHsSdtQo/s08QLIeDtt3V2dlTu3dPQdYXRSDnu8VVJJCLKZcHamiCfj7CsiMFAtge5rkKhEFEshvi+Qqx3SMoeIi5fImaGxPsdVkuPm+IizkrlorhHcXWEk63SDiu03G3wQ0ZamXmUoKs3mXlxVlaezdaKa9YWe04FEelUWzqXkg9pze9CGHBRu89Lmdv0ohrJ6SGmCc8m7jLKn2FIhZI142JrSVCs0p/FCM0EWdMhePppwuvXCa5eRTn29yeBv/H666jdLup8jkgmiTIZlDBE7XRktdE0pU9Jp0kkk18en/oFsvedm6LItuHH24hzOaJ0mmQpRroh9SSU6RR1MiFdMkkpC9TDQ+ZOjEEvIjDiZN0eB16VvrZGNBiS1Ffk/QFUKlAoME2u4cSylBome+0Yh6sKFW3EnldnGVrYfgzHVVn4Fk5oUkot+ebmTWreAWEqy43sq2SCMaXFQ/TZmOnS5MAu0jfrrM9uU8l7VJQBTz2t0dVK+IMFFQaoqRiKvaTcv0ewWNEOqyhXzmG0jzhn7BGZBgezHJoG/b5KsylYLmVl2velyrjjSGHBdFrqGHwUvPi4WAFP2oQ+kvm+jOZ0HRxHRdMEUaRgmuLYuctNQL0uTgbi6fdVhIhwHJVSCSYTBduG0NOIMiWiWoMm3ycqltBiLtpkSSkck7EHKNkEVkywqRwRTScYapYZaSyWBKuIsjLkUmKXRsMkZ3msLyeYK42zqTkV+lQTMzw/xSQq89ZMZxDmKVuC0pkMvnWBbvIsynRKM92BpaDlbsHBfQxFoMQXRMchq/7WW6izmQw/V6v3j8NrGur+PlQqiFYL71d+BXVnB3U6lQxFuZw89ol9uvZzoh0QpZL85YRwHx6lRKNIVopUFSwLkcmgdjpEyyWNDRW/UqF/z2Sw00VhSa5eh+Ia+9tTmvNbHO5DPxmjPH9AczqkMwvRJy2EZ3HG3OddL8vYNikbNpHnkVIFWhihpy2msSrl3i02gy0q/iE72jqZ1QLPB8O3sRwHXYeZo6BWLPZneSp1wVr/DolxRDcqMTKqFMZ38LI52kGLV0cPmcwNBm6e/r0Yflnj6AiefTYkDGVbR6Egq4Gadto9dUoj5ziylajREJ9plckn9osx2z7WrUhJimnZKqqQzSpomlSnbzYF47HUJojHZQeNoki8iMcBZHZx6eoYuSJRFJG0ImaV8yS8m1yZvoejpjCEyyC1QTkfURq+QYIph1aVYVQmE854Zv49xolv4p/JUIr6tOI2E3/JRnpEw4hR0hcQi5HRA8arEm+M4xhJKZFaOpdnT3kZNZahrHdoiQnCU2it3kHpW+hjFdYtORfU7aL7PqrjIMpl6Q8cRy7+eBzl8BD1mJFOrK0R2DZqPC4ryGH4BCs+K/bzwAtFkdSw8H68OHGCq5VMHqZSUvC0WERfraj7baiX6GTOM3hH7jXK9THiV6/RdtLwbkBreZ+DvsWglKZs36SpC/LOJbIrk2mQJK+MWIg4K18FXSVAJaE4NBMDRCLJMl8nmiy5bH8P03Bx1CSDeIvZKCQZton6I+xYGs8PSWcypPtDytZdMm6V+TJghk5MDxCDGSs1y96qwbnpQ/YnOjM1T2ceo6er2Las+vm+3BeWSoJmU3B4qJ5S1nveZxcvngQDH2K6Lks+/b7M6mxuhgBsbeksFtBoqOg6ZDLitPSzWkGjIVDViEYjIhaTswOd2BoJ94gr4RsYlk58LcvywCZtzanbO0xTJfKxJeZwiJYwudF4mQe9HMlhm7gz5ZZynmHjEi+l2hx5Vb43uECuFKFNNHamRRLqGXJBnEU2x5repaMKpkuTQvJtxCrknyaXKDTiXL2sErpFlPV1ItOkad9FdRxQNdSdHRnVnwxyTSYSrYQ4DQREJgMgqeJOeidiMYlsjiP/2yeNz5+unQz+HqvlfSTu7g/6jsc+w/dPKWVFrQaOgyiVUGczqUlhGDTKLj39Kno6DULw3Hqfw57HcKTT155DTU0pXimy3ntIaJo0sjOaVp/786fxQoOkZuMFKdpuEUMLqSUmZAybd/dKXCnsc1G5T1M9YKznOKJAJWxjrKbUgglZXMZmHc9Kk8wmGIg8naMyw5FLbDkhPH8VJdtgIKBCn2vuDxGdCDtV4jYtRiOFqhFRLstnOJeTw6AnztoweJ/KbBDIR2U0Oh2pefIYfMksHueYYhqKxYjr1322tnT29jRsW8Mw5EagUJB9w0LAeAxnzwpisYi1NZkxtG2Njqhy2XmL/GiA/9xlhltLzhUO2ey/g5axmKfWSMUht38TTdjslq7z2vQZ1rw9xCpEVwTZlE8pdcB4BO9657HyPnO9yNt7FntGjDPJJKEQZLI2/7IssF5c8J/Ef+a/TZ9i32/wfCugXh4RRucRqoq2WNCIj1GPDomUTYkXJ5oBti1bf0xTRsmGgYjHUYtFxPnzEiccR36eycgS2wnbzBP7dO2TxAuQCcN4HFGvo9y4AYsFjXqCw/Vzcs5M01hrdBDVGMrrOwxWWXrxr6IUlhQvF1jfv0NopnjWvMFYt9iyX8JTTFThI4gz9ZIYqiBjTsgv26zaU95KvsiFxHs0xrfJ+xO6axe4b1xgNl7yVfc7mFGCZULjfu458ukAQ8S5f0+BeY8wVqVSCHDLTbaXCtcTfS7O32FyT7DvVljkG/R6CmfOgOcJzp8PjjtFHnVbx+OP1Ifhs4sXT4KBD7EgkBuAYlHw7rs6g4HKYiEjOc+TN/nuXZ3BQJZ+q1XB7ds6sztdVv2IvrlGsSgVTfOLI6ygSzU2QlFz+Feepnjn+1Tcd9Eij4F1lnGok/LnWIGKUz/HLLnGcJUhGM15Q3mBdGgxGx6S0nqMVmXCIE+psmDUCbndLiNWWVrlJWnDQUQK+eSE27mvopoaYS1NMXafpniIsruLslxKsY9CATEaybYNXUcJAunkUykARDwOuk6dJLuJAAAgAElEQVTw/PPSgeu6nPo/lt1Up1M5NASIWk0OjT1pfP50zfffr5b3UVIOH/Ydjzt9VZWZHt+X7QCFAp2OSlh9kVa4S/uBh9G9Ta8Hmq5xFPg0KgvGvo8yGoG9pLUaE545Q3uZJVQA08A1cwSqSSxSUQMwCBCRih4JFm6McZggHMxZq/QZ2wnSqy4HsSomCTKxkMAJ0FWwIhvdmLE7rRH2J4SHHuWEyyqRwxx0KFoa0/MNztGlGeuTSQbcPXedwS2BqmpUvQOaukKrVSCdfuTYfV/yyj8eDJyIc59kgx5PjH2QPdEW+OJZGMLFiwG+D6+/brC1pbNcqly6FCKEfGRu3JBwWygI5nOVGzd05ne75NMBUbSGqkoyisz0kLWgjxlXGLkqybNVNnbvktb3GdklOpmL+PMlBWFixBQOzn+Ng+0Nuu08MTHiyDpD5FiYD2YU4kvm+lkcM4GmrZj3bCZksdUGr9S2eDgvMwssMEO+nf/fmeoVGsqcRvguaruNOh6jnkzOp1JEOckCo7TbKL6P9v3vE16/jrpYyEqiaeL9+q9LsUrbfjRpH49L7Dim4RKt1pPF/1mwTxAvUBSpJ2AYHB2BYpyldWbOwZGGttpicOCjOmPaswXVWIx6zWf0toc2GBCpKi0cwo0N2ocRopQi8jO4fh7dNxCeThSpJFgRKBpaFHK0yPFacJkLboe5YpD0pgRqRHeZ4G7mPNfdv8QPFeKagxeDwmyPt42vw7s3iaUCqmnBg1XEwapIUz0ierrB+XSbnD/EMgL2K8+SFLBYKJyPPcSIaayvF06Tw583vHgyM/AhdkIfd7Lxz2Qe9Xpls4JEQkrIHx5qbG/L3q/FQiHfe0Cqt4ctLFwzS0MccKnzGrVKgJsuYz8ck9q/z9rtvyMXDFAQxBSf0Bckl0OiWAxfjaGmE3SUBk4k+6XdMEYoVNKJgDBUScYCrhfbbLKNPQvwfI1WrIsfzxI3QxqpMZ4dkg96nDH3eb64i7K3h6IoKLqOqFQIXnxR/r6+LiXnq1XZ66dppzzQYbMpKSVdF8XzEGtrkic6kUBZLGTvp64jGg0wjC9Pn+Rn1VQVxbZPUxE/LWXf+87tw77D9+U8iWmC4xDFYkTHA4RKGLLYHjFohxyMk7hWjiCWJJ1VMd0Jq4FN5+0h6rAv9SqyOcLpksSlGrMwzbuJl+msSqTiK7RUnO1BHtfXMTRBKq0wD+NkTJdIAWdl0I/KGMGKsjqk4e+SFjOK+oxBZgNRLiFUnYTiUlgcUtAXuCuNRRTHyZS5mOnQ3IRaJeBsZY66XBKNpuiBS1Svc9bYp6r0ufK8QbqRRtOkD9jbk8/5dKqQTEbourwsJ3okudyj9z7Mfl580Sf2ZGbgkX3aeOE4Co4jWUdaLVn+z2blPFkqFTGfq9y6pfPwoXoqRBbbvU/N3mHmJ7CNLFVvn2v916htGjyYV8ls/ZCzu/+DUvsGhh4hdIOyvU9+tkcgNI5yl5m4SXKFiA41xn6KblTFVpIYmkDXBCslzvXCAYW4Td3bwfYMrMgmFYzxtTjnKyN0DVYO5GcP+ebmDTLDXRRVRZnNEBsbRKmU3OyvryMMQ2qKtFqSSnJ9HWW1IjrWGVGWSxTfP1WYjXK500pylM0SJZNEyaTUGfg8+NSPaJ+Lc/sk8eK4ahSlUsz6PqPtBYfbAW68gJ/Mkk6EmMJl5WtE93ex39nBOZygBAHkcvihQrKZZjbXuGm9RJs66aJBYCbY6aXxhY4Ri8imIlDANRI4vkne66NrEWgqWkwl5U64MH+HmKUyjVeY185jKAGV5Z4knFg7g7tYEfcXBJrB02fmWK08Z4oTEpFNzFtgzKfovktYW+Op3D4Xcx3q52PEa+nTweFOR2U4VE41SU7Isz6rePEkGPgQe/ymRREcHmqnlc2NjZDVSm4KXBcMQznmjNbw3RBvv099chdr2KZh9CiYC2wlhdtb0Oi9Q321j297OHoac7Wg0r2NFTks0jWWtU3C/gx3vGJx+VmyiyOYzrFdlZFWItR0zpfGfOV8n/7MYneQIastyForDF1wJj8mHsyxwgWrRciG8pDBzGJUvEhjtS2d9nLJQeYaNikyyGusrlZyYxeLyZ/jKF+USpw2SYehdOLHbUOKbXNCtn0yYPq5cIAf0T4X56YoRKnUI2Gfn9Jz/NhA2Ad9x+NO/0RtFOR7QpA6W8I34hwEDZa2RtryqLh7XDEfcDBMMg7SrGdGXF7+gMBMMJhZhMUqrcPXebClsD/L0Tpvcnt1nqWjkVYWbCYHmJaCT4zAMClkBW2/QGI5xAoWNFMjOdDoR5jCJbBSLAstZvWLuJUG+e5dsvM2hgbxgsEs08Q8s4ZQdCaHKx7Mq2xlXqBRsIkfbnM+dsD5C4Lmc3km6SbLpcJyKYWi2m2VTkc71RiR5FvRadbnp7nUPy++6BN7Egw8ss8CXkjRSYVeT7aRptMR1arAtuVA8WKhksvJebLVChZTQXw+oDq+S9k9pGl2SatLhm4KfzBns/cD6vMHzJUU8+I66c421eEdFFWhc/Fl3jOep9n5ISndITp/jsXAw5+5eL6KEjfxFItXLw2w9BWjocbQSbFptYkMk4wyZ80aUdCnuEJmg5PukNFMp5EYExkGiutyOM+xNAtk1KXsaRACRVWJDANV0xCFgkwmmaZkpYsiOUR3TCyBrj/yHUI80h54ghefvn2SeBGLnb7OZBX8QpmDQYJZuk46q1KdbnFZu0s4s+kFRWw1SSloczm5j+8LBnaK0LBY3/0nvn+0QU9UaLxc4a17WXQEZuhyvjjAiGussHCiOAVjyVPcwHTnbJnXUHSdS+YOheUBYuXxTu7riFqN/pkXyM33Scz6JBSXTDgmVbNYGjmC5jq+mWK6NeL2sMpw83lKSZv11X3W1QOuXhckNoqMkk0WC6kx5bpw547OZKLS6aiYpqSyPo6R/9Ug4MQ+Sbx40ib0r9hwqLJayQHCWk2KxIzHkkO2XBaUy3IQBODy5YDUixV2cxFrr/8lxtEtYrbF8Hf+I8XVEfX3foi6PEKNxXDSJbLagliooM4hHU4ZpJPo5QLjgzn7SpX2gxUKLfTMlJit0oviHCwsrhT7nD8f8P3tBMQjKqaLEgruextMRJycPiE33+e8+oCKMiBcBdy/XYGNr/PCxY4c3jQalO/dR1MOiFSV8Nw5RLOJKJdlC1C3K/s743HUfv/0gRatlrwwJwNDT/odPnv28+Du/qDvOLnnnidfG4bUEqhU5PBgGNLYnNDLV8G2EW2fenYGFGjYK+rRIU1zCuk0jZIjwWJlowDP5rYZ2TVuHjTpj3XOJY5IWVOGYQ4R18mnI/KGTWeaJDIsEDqX2EF3FxRpM7CqpLWQlDmkO9FYLiNS2oxUUsMpnyMI83QTZygqE5bZy7R7OitXMFRbJMYhr1de4leKIxRNQQEO1RbdjooUUpVZXiFka/RJrOw4sL8vfcFPqznwRFvgi22jkWwN9X1YX5daAqORXCPVqjidN7l4Ec6cCTg6X4V9Qf57/x/mrVskyxa9f/sfKXtHxA/fJqPM8fQ4iZiH8MYkdQ8Ri4OiEiTzXFrtMPIT7ParDJQQJZ9Gn3somOyvKlS0BZngHvNck7Drk0qHxIAotDg0r7Dnh2REwPPWTSrmiPZEY3uZRvE2eP5llb3CM/TGBhVliNLpSJKAUong0iVErUZQr6P2enKeTNfBsmQryHiMqNcfLfAnePHZtU8QL0SpJGnKh0MaosvgWkVqCwz61NMTWNukXpihHdpEwwnN5AgRmTRKIFwfbT5BURSuZg+ZscEb76bwVxFnYvuY1ZDQtLBVk5imk1VXTL013Fmc8/p9qnaPvOJiOQPiqkdFHfOUeI97g2sYw3t4iRzZ4pR4QWMvKNNOPUvanzC1yvT2dabTCh2jRfUwoPTMC9RjQ7xQJQwkXnQ6Kpr2aA7AdeXGPxaTz/zPihXHl+wTw4snlYGfYJ4HDx5IZcjxWCWVErTbGouFAihsbITE4xHJpCCTicjnJWvE8mhJyh6gu3PWYz3UwGflqWi7D3FKTRJ4TEQCY38P11EYqUUSkUM6nJCPhtyJLvO36r+h29fpTS1cM4OVUlm4BnM/wWSpszPMo0cBl3JdjuwcqfMVwnQaZb6kpA7ImjZNq4c7dqlUI/RKjmHlMjOy2HqWsjGmpR3J+YAwRFy4IEu4uZx04kIQVSqwWsl2kFxO0kymUo8mYUDukB7ju/pcZEM+on2hzy0exz5Wg/zXvJTa66HM57IdwLZRjoeGRa3GYd/CngYAKIFPFEAqmhK/0iRdNFBWK5T5nKjRIH2+wqR1lfnBAmZzbFvlobvGaGGirhwq4oiWNSRp+TSLLtlowshJMrMNnvXf4KvB/6CwahOhUI+PyKoLzMWIlBmQWY1pug/ops7ipguYqoIfRHjVBv0+/GBxiWC4YLhnM4vS6O02iaRCYCYIQ9jbjjBLGZZLcF2FTkc7zdCUywJVhWRSlmxPNAd+mqzN4xXHn4dM/ZPKwCP7LOHFZCIZ5x7Hi83NkFRKkEoJkkm5XlxXIRgusOYD0swphT3U0MdxVVK9XVaVJp4N9kKQPtrCUZMcJC+SjBbUlvfJqEv2jLP81+n/Qn9ksPIVioWIrl9m6WoIRef+oMA8THAltU+AzjjMEGsWqeZcFG8FqxXX1gZU1D66a+MRwys3mKYaLI0clZRNPTNFPa4GRLEYUb1OlMnIbHAuJ39MU+JGoUAURe8PBj6k8fkL7VO/yOf2UfHCcSQVra5zMMuyjCTNjuJLrEgLOUdmvXCRDFOUKELxPKJikdTFGguRZN73SWgOPTvL/VGFwdwi7oz4RuEHFBgwU/LEjYDL1REBBtuzMr+0+Due896k6HVIKA6aEmLqEclFl7w6J6nYpPwJTr6OJiJsxaKn14isOJ027A1SOJ7K0TzDaqUQHxwRChXfSOB54MxDgmSWgwNZPV4spHBlPi9OK4Y/K1bAJ4sXP5fKwNtvv823vvUthBD82q/9Gr/1W7/1vs993+dP//RP2d7eJp1O8/u///tUKpWfx//6F25RJNc7yFmok42A78sI8OFDnfFYIQwVcjmBc++I1PYdjtQC4eVL6FvfxnrvLrl4RFCrk6mkWP5gSLZ7n7gzQQ08OlYLx59ScCco04iW8pDNRIfeBPaCEg/mNSoZh0Z8QEXMmHpxDjsmFzMDMJakYxrTkUUs5fPMxh5Px27T6ymsxR2EZaIR8mL8Pf5msU7PylDaqLBmzRGzdQ4GcbSGRfH6NYjFENWqPFnLkid5XPZVO53T0XhRrwN8fBaCJ/bZMNkHJzN6ui4Zgj6s9+VkWMwwJOOUYUi6OM/jsK0zGhuUcgsaLWjfiOjdz8FqTr3bRbtxQ9LWRhGq6yJMkzCR5rb6DCRcSsYh/27jLtF7CrvjPEstR6vhsB7vkovv8Tf3LpBTJ1zVv0/RH3IQrJEjRV5Mqa0eEvOmaFoM33MxI8BQSbEgRGNo1eksi+TaXVJZlV8y3mSiaIhkhTO5A7KrAXtumX29Tufv22TcIe7bOte/WT3FO1WVzv3KlYBYTJ56p6N+YNbmJw19fdlFV7+MeOF5Uo10f19DOU2cCMTDNon7d5gkC2yVL/PswV+RunWXUkzg1RokjBB7a05isYceBqw0gRPFcIVOcj4hKaBaOMtmostyFnEwLnHgVlnPjtlIdDhaZOg7OczBnMyZGSKw2Z6e51xhwZnWkOvrO3R3VhieT7Pmovga1XCPru/TXb1IkK7w1FVB1BeIVIqDvoVaK1O61JSDwJubUjtACJkOjSL5OopgOHyCFV9E+1nx4nhoHMeRnyeTsjtBq1AudGmUA7qvj+lE6zB2qFoW5nvvoezuoj18CEEgh9EXC4LiBe4kixCGXCz1EOYO4kHEZJBkHi+j6jq/lrvH3jzHbJng6+tbPDiaMo1VcZ076KjYKBRNG8V1MaMQPVhRcrfxS2tgghsk6C4yRP4Qt1KgZg4oaAPuGNcoZgXPlQ4wxgMeGGWCVBPn9UOK4QAn3SVxaQ3DkJv2TEbw1FMfDyvgk8OLj10ZEELwR3/0R/zBH/wBv/3bv823vvUtrl69SuaYhhLgO9/5DrZt84d/+IdYlsW3v/1tXnnllX/1uz/tTM9J5BZFMoqr1SIWC7npd10FVY3Y2pICM54X4XlQGj9AOTxiJ2ig1ask/Bn1h28QjaZMRAYunYODHpnhLoHjM9EKVLwOeuQR5fIIL0BxHHyhM1i7wp5fZzbVEX5ITptxpdzBV2KUE3OM5YzDfhw9WBGicU7f5d+b/zfZuMda2WfqWmimxpo44GhZoBeUmPYCQmeFldKZlM/RzV7AqmZIl0zJHNHtovZ6RKaJqNXkgPBxYBAVi7IP9LgGpiwWp6FulEg8GQj7vJrvk/A8HCGkiNxkgjKdojiO7Pl93EOpKspyiXp0hOJ5cgbleDBs6ZskvCmNTanJnjxbJBAqaipOdnaIMhgQLZcQRYhKhbBeJ6vOmY8FtoiTSkUM1Bpn5++RXnbwfZV0TuXZCzOS8ZDejovwI9KrIbar040qZLUZZWNEMlgQ6TrJWIAufLzqGmY1y9zWOVxm+XvzN6mG+zTyNnoUsAo0vPo62mYD1V7iEGeea5FKRbz3ME9rzSdE5dKLcXxfzgdZluwBbzQknfCHZW1Ohr4mE4XBQCGXi043iD9v+7xVBr6seDGfy+Hidls/Fai0LMh2HxDtHdHRGwCUxIDY3n30lcNKjRPHxdzfx4sMDs0NlDDgzOwGYaTj15rooYs3XJJMRBwVrnLPbmEvFWKhw3lrDy0ZRzUUyuaEvU6czjAGQchmfsJ/yvw/ZIIRTX+HQDFZKinqRo+pE8MJLRaeyWRhspr51C4l2Y+adIuXiWcMstEUxfMkVhzvYkSz+TNhBXyBfSpf4HP7WfBCUdB2duQxnidnDoXA9kxiKZVmYgiGQaqexIun0GI6qZyGsr2NOpsROY7Ei2OmqnTcZ2YbLJIV7FgRf77ixdVr2EtQRMhaTfD1a21Y+SwHHkeDGN9I/jOMxqTCBWbkkk6BFdoI08RQI1RCokwWq55lz63yL8rLDIcR5/R9YlmThUgQFktw+SLlMngjm1kQR91o4roRHTtHoxYQolA9n2A81kilJCb8tFgxm0kCGkWJfqFddL/QysD9+/ep1WpUjzPKr776Km+88QbNZvP0mDfffJPf+Z3fAeDll1/mz/7sz4ii6DRD8lk2SS8Kw6EMfut1QTotmExUgkDFMBRAUK1K/zYe1rBTEdn5GOdfRuzEcmTDIqnVAH9iE//uD9DDEL9Sxc1pZJdDit0+s8wa3uZZhmOd2VjQ4IBWrMsbQYswinADFVeY3J40SWAT+Q4N45D31Muo2oqk6nAttsXhskAz7HOwyDMcBZSTSw5XcQahwvW1HyKEy62Dq7w+WCOzLrj4DYOGukSoCRm5a5qcETj+Xayvy7D0hCxX01AHAxmtD4dSat4wnjQ/f57NMGRm75g6Sx0MZCbnxAk/rqGuKPK9Y+l59eAAlkvUfp+meog6HcFboVw7xSKtrAqGi3JvDvM5yt4eKnD00Cfc9mlZfZ6/do13ugY3nXMs/JAXtAH5pMk00pik69wI1ijFxgQZAwKdG/5FktGY/1n8FVf9d8j7M+xUGlXMUBwHPXIwlZBFto4TqnhejtbsBvvGGpESYjTL4EYYZxu0kiHpqyWSSdjZkcOfENG3GqRKCun0imJRUK8LwlA+BseseafZnB/N2pyIy2xt6TgOjEaCl14KfmEBwefJvqx4MR6rzOcqYQiaFtFqyUduklhjVYiwliPCB0O6OkRegjAGwV6fyJgQlio4yTwJVSd395CY7TPPZHGqGxxOz+L5M84Y+9xSumhsEIQR3WWaRTLGQotj+g5V9ZBekMIxchT1MRe0BxwMEzRLDnvqBqOtKcXYIYdCMDCKVDIDnjG3eWP+NNudNUbDFJU1l9JLKo1wApopMWB/H9bXZZtgvf4EK74M9rPgRRDI+64ocg7xuFJQy4Pa3UW/fx+RSkEsRuvCBfTpALE0pf7R/j7q3h4EAb0j8M04retZnm/U+ZsHGbZ7GfLLAyJFMs5FuRy9tQ324wdoyRkPlyW8QGUw8XhRuYMp5iTFHHW0IrJChA5RGGKpU/xKHdVQMcwQXTOYGCUeRCqOdpFSWTDdfI5STlKBKpdKjEYqUSTY3dUwzYiHokGtBZubHul0QD4vSCQeYcOHZf9PCu26LhXM02nZQvjss588XnzsYGA0GlEsFk9fF4tFtra2PvQY7Th7PJ/P35cN+jTsJ5VnTiK2kxu1tiZOBROP9ZaOn4eIZlMKE9Vqgm6xxLuZGpXb/53wxn0CvcR7qZdJxCZszm+TUHqo1TXm5YtYoy7x5SFLI4evJggdhX1/jU55g9pqDwZjnJWKGkVoQvLmaoGOH5TIxVxcGxLGEuH5ECp87/AMv5wYM+wUYbGgpg5o2IccbLxCSYloKAeoRsAoKBCZGRQ1RaPswvyRWIx++zaMx6iNBl6rJR/4E3GpIJAPf7crW4qKRfmgJ5NPyr6fZ1MUaDQQx7K6ar8v3z8WivkxOwF820bt91GjCKXdJlpbk4OE87lsHVIUOXE7nRK2WqhBgDabyfe7JkNRQlkEiL2IrpNhEa/gO3PuLdZQNJXnkrdYnckzi7I82E8yHAZ0pyYaEXl9SjGckI7mKIQoROS0GaFmESQyxDUHEY/ov/CrOG/tkNR0aoku5rWLTCbyz+vfPyT+1Bq6rqIogvV16YCvXg0xTcFTT4n37V1OfAX8uG94fPkfz1XjOPLREEK2FB7Ld3yp7cuKF6oKxaLAshSKRYGiqJw5EzCtFemcKxP9t78nJsYEaKyMDLt+iro4xMZkef4lMrMHpBdtMBWOkmdR9AThZIlr1Nldu0Z+doA2nmAoHm4EKiH/1L+OEouhC4coOkfS6xO3lsxFkq1enup6icHWFEV4VOYPWF91OTA2KNYjGvoIZjZfKdxiMo8RCQtUS+LFTPZ56zdvosznqEFAcOHC6bUQj93fJ1jxBbSfBS9OHOhxhl/f3ZX4oKqISgVhmohkUh4bBJK2drEgrNXQ222i1Qp9NkMEMYZRCWUaRyg6Qz8DlsVgkmVnVWYz9pDzZyza51q89U4OMYzIej1uLM8xDVIMlArn9DGm5yEiBU/omOGKhVXCyhqoaQu1UcYcZynPxpgZm2nrCgkjgkgl2DnEu7ZGEKhYliSTqVSkDlW5LHUELlwQ6DqcOSPe1zX1Qb7h5BE4GRCeTiVOZLMSKz4NvPjYwUD0AQvgRzM4P80xIMvD3/nOdwD44z/+Y0ql0sf9837k73g/oB8ecsoS0mi830d5HpxUNlcrSaxzdCQ/Mwy4fBm++93T4BjTlIpyt27J44arF3lGP8JdaDi5OmvuAlPEmcXymKVN1vb+BXUypq9XCRvrpFWHjDejknAxGjrvBd9k1tO4Ptvhpt/C8eMohsD2TZRI0FklUI2QXy7d55n8mO+Or9AXOWKxGL9svQaTCWfNfVA0zlkj+PVfh+V59ts6sYcOm5eLcP0yy9IarZd02NqSiDWbwaVLj1bjiWLOyUV6/ELmcj924XRd/7nft8+KfeHPrV6X9zaK5P1PJKBe/2DwLpXkA7JaQa8nXy+X8mc0kmtDVWF3Vx7vOPIYXQfb5hxdzKDIPzpfYTy+wHnrPv8h9d/pmnn++WGLsw2Xpy9B/5vP8L3/so27NPjqpT5/dyfBdj9HM3zIA+Uc6aRHJTgiRYBbaDFNrRHGUmh6hFmocbVss/srz5L4wXdJRHP01tP8VeKXMDr7pOw+G+qUVK1FrSbbNt58Uy71eh1aLT4wO/OjviGb/fHqQKHw6NGJx2Xy9Ell4AleCCFnCUwT2m24d08+ar75Itf1I+xFSDGdpywWRHqJh7EaV7p3SLfvsopnaeefxinEqUQ9cm6fKFUkrBr8Q+ffYBRVzt7e5139HAuhY8UUfKETBQZ3Z3FeLS+pZF0cEeP+fAOnX+WXEu9QVkacM7sQhpxJ9CFfg5d+Ffp99idpWjELrtVhfZ1luUrrJQ3efluWP0CWxM+cgbU1edInF+3En3wIVsCXwKd+kc/tp8WLYwYhdnbkullbk3hwcCDnzvp9yOflwzCdSuepKNJxBgEEAefEQ8x4jH+cvMLYa3A+3eU3Mm9xY25xN36RWjVF+pXnODI2WYzv8MrZFbXlCPsgyT3nIgnVwTR8EumA+uIBig5KPsle9RVSGY0IlTUzQelSHfoRUdRlWFjj9tnfZHm0T6LXp7CcoqotLl6UWyPHkdAnhHzdbH40vCiV5PumKf+tVD4dvPjYwUCxWGR44hSA4XBIPp//wGOKxSJhGGLbNqkPCHu+8Y1v8I1vfOP09WAw+Lh/3qn9aHRWLAp6PfVUNE9VxftuUBRJefnxWPq6wUAOgBiGLAWn04J4XKdQ0Nnfj4jHQ3wfJhOdYlEhnNsMsg2i0YSN9muURAe9mGBlJdk8/D5R7xBnbZPRvorjWbj+klhdRUsbKDffZi9xgafMmzyVGpJ0nuHm6gx77joxXVCw5iiJOK5rse1WmB6p1Go2kbvC11Sccg1lafPAW2etHiGqVYJ4nPYkzXA0odQwqLd67M/us/03XSatGBv9t8AwUHs9gs1NiMcJkkkZ9Z9oDqjqKV/wSSWBx+49QKlU+rnet8+SfWnOzbLkojcMufA/LB0qBLrjyKFgQGQyqIuFLPECwjDQjn9Xd3cRYYjiunL91GrkzyVZvaPhjce4MZtCvo2zXNJsFMjkAtIv1bj/4AGmsiRyAhLmECPVIjF1CUKDPesyDzKv8h/G/wdCmWBEgolVxD1/hURapRR0ibVvsaFuk321yPjmlNVgn/KOyzSzhhLOcfdtomaay5cDdnZURiOdKJLDnQ8fBiSTj8q4j6tInvgGmdX5YG2uUOYAACAASURBVJq4CxceYeRo9Au5bdSPBzQ/L/YEL3SOjqTgkAwMNDIZQS90OLKabC7epDLfQVcFq0KForNL0dmmlygzbl5j+s4epj9iGoNcWSNc2Xhvvs0kfZFnYu/xauot/s/5/8rd4CI9d416doazUtE12HbXWDLhYvoAJxVn4qjYtQJOXOfe9CLr0R6i1UKkUgjX5VA0GC5DiusGjeQd9uc+999wmdxastH9IdrhIcIwCC9cIMjnodORQ6WP48VPwAr4EvnUL5h9IF7ouoxwP6zpPZfDXK1kwkgIiRexGOrNm1K48phcgsEAZTaTrYGrFUoySRSPU0gFeIc2njvCdfsUnppzrjrHoUamEDI1TSbbByjOlHl3RKaqEAzirBv3yek2rxm/QdGcUhVtYjHBUoszy9fxn9og3XnAJFIIgwXrSZeoPsMwj+gsbtNfqdT9Xeg7jPJZ5vMA25bBwHCo47qygnx4GJzCJrw/kfDT4MWZM58uXnzsYODcuXO02216vR6FQoHvfe97/N7v/d77jnnhhRf4h3/4By5evMjrr7/OtWvXPvH+zx9VzIafzN+qKI9KvVEER0cqs5mK68rMTrN5Qg8niCLJKS0E7O+D1d8niobs5a/SiG6SdiGvRLhPX0F/cIgxmTDWMxysf4VwcIdMLEK1TZTVnKJ5iK/o/LvxfwbfZ+gkMZTrqKpAEQARQjX4ytk+qq4xGiZpTw0u+u9yobXg6saUesrmqHmZ7rs5AstF8wr4Dzy0wT7l5YRGbMnBrfNoz+uUwgnakYe6GKPM54THi0XU66gnyOZ5nMqwfljryBP74tgJfcHjcvI/ygJynA0SpRKiVEJ/913U+Rz95k05L6AoaPv7qOMxwraJMrKsq2mapCCcTNjvmlyN79CJlkzHCn+b+ipV9yFXL4xp5ub84M0ao2DIlU0fZQF/3XmF2/0c/sqhYOUop4a8vPy/8PQE7XSVMOqjKwGLhcpsFhHE4mjjEX6uwHTmEsTWGL/ZZzQ1cSyFYkUjuZknUxBsbal0uyp37mhYVsTurkY+L7BtlUJBMBqpFIvS6a+tiVPf8JOGvVT1SWvQj9oTvJB48cILAe22iuMoxHpHNIwB43ieMJMj0IskC2Blk+g3buEqEVvNV/ATBeKpMTEFVGdBzF9Ssl0ikvxa57+SFSM8O6Sqj9kOfQwtYLyMc22tj54yKMfnjBZJxqsUlcySb2ze4blXdY52NHrWr0LnPRoJm844Tig8NLVPeTqkkVU42D2DdiGgZIwxekPU6VSe2HGCSO12pX/QdZnePLlgT+yLb4oiE4k/iTEqisB1Ca5ehTBEv3MHbX8fbX9f6hetVij37qFYFsIwiMplcBy0ZJIoCMBxOLAucDm9z2DcZeTm+OvDZ6hqPa6tjSGKuPXOEaVMDj2/4N3ZWWZKnr6vclVMyJkOG8oeG6uHLMwcbjZFTA/JTQ+Y76foTg0OllnUAWzODgkTBg+DHMtRn1h7yNxUiYwiGw3Bzo5MCug63L+vEUUqQijoupwROln2YfioLejzgBcfm01IVVVqtRp/8id/wre//W2+9rWv8fLLL/MXf/EXuK5LvV5nfX2d1157jT//8z9nd3eX3/3d3/3ATM+P2kdlhzipTD5OgauqYNvKiWI2uVxEOv2T+VsV5ZH47oMHGuOx5BlfXw+JxSLKZcGDBxqWJT+v1aT0fDxc4JDE8xTM6Qg9aRI7W6ekj/GXPvGkwS3raQaJdd5SXiC32KcaDTCVgGhpExCjNX4Pw1nwrnuJLX+TA/UMtfgYP1RxQ5NIRBSyHmrgE/kCzZnzm5XXaRodlDBk6WhM5zpZZYpYuox3Fsz2FxQObjDveQxGOlYhTv2cSSYYw2KBslgQXrokFQWPp85FpUKUSkk++ROu4Plc/ti21B14whv9ubcPPLfH5eQfZwE5DhKUxQJ1MCByHNT5HNFqoSyXhMUiaq+Hur9PlMnQ0VqMrv8yGdNBGY/B8/jr0avcWzS4Yj3g+eIOh7Ms980rLFYmG1dM7rXzDGJNFG9FazPi4SDFd7bOQRASpDKk4yHPuf9Mc3aXVMxjq/xL7Ksb5MIRWr+L3qyiJmPs20WCqcNc5JlPPMZzEzu0CBNpRKmEXWywtaXjugoHBxr9vsJqJRUjLUtmb9PpiPlcIZuNjh38/8/emzxHkt13np/33D3CY98jEAuQQCL3rIWsKhYpcRUl9XQfxmZs5qB/YS5z0b8zpzGbw1zabMa6R1KL1i2KEimy9qrcM5HIxBqB2DcP3/3NwRPIrGIVJbZIsSoL3xMygATMl/e+77d9v+qMDP7Q7c9fNTWhc754zhebmyGmCXm55GSRwRq4ZFniXr7KdmGIbi3wSDCu3uSD9FtM/QwLrURa92l6B+j4+C7guWzOPiFlTfkoeIWfiD8jmxdkEz4zJ81wZdKqejSzSxL+isXQZTtxwP90/R5RuUwhnNAdJrHHLmvGgMWJy+jpivkwpHR0h8UUBt2A5NUNWldSFKYHsRjAbEZ08SIql0MJEfvUFAqI2QyEeM4ZX8AV8DXcU18S/Nq1fRFXwK/zhesi53Oi7W3E6TzZ7i5ysUAVi3Qzl5l0XqEQTWAwQAQBf7P6EQ+XG1zPPOWbqbscyQ12rSZLo0QyLdn32hTLkDccStqM/7p3jZ1Bibxc8nr6EWUxRS4tOtYDHLOEV2tyJ/kNtOWM1HKIt9Yh4S6YuGksL8W+V8Vf+ajhBEemWWxeR9tssb8vOTjQGQ41goBnhoKxmqQQsL4e4XlxTGSap3NjCl3/lB3THwy/d5+BN954gzfeeONTn/3FX/zF2deJRIK//Mu//F38qX8WXzSs8WLm5sXo7F+i3xoEsV60UvHDHQxipznXPRuix3UFvZ7k3j2NgqUhu8cUQ53D0jWCN3/Mje7/SXTQY06bk40/ZneWJkInsVllln+T7PwE1VtSWPQIU2CGNlNyXBcPuatuohmgNJ1kIiJQGk96WYS9opVdsVol2Avq/PTxJj9IdumvWgwmSYzFgLXNEh3vMd2owZNhhrGnaFQllStFaq+XCa5ejbO5mUycvVUK/eCAaD4najZjx+EgeN7oalnxTUmn45qW531aPeAcLw++yP7wBa8BORhAMom0LCLHQTxbeGK5hHQaadsEZope7SaRqLOezdL9yX3GpS0iNyBqNHjfzjO4/BbKz6BKcDwe83iao6QvqHYMfnZ8nXe7SVSoWMstCNICty8YqTJDvUGVOTd7P+VJ7U0cmSalWxjdp9itLUAQ5Cto0xE7tTfIzx4xTjSQjqJPG2NHkEzCbKaRSERIKTDNiMlE8N57Oum0IJn0MYx4CYzHcSPnfP4vd5E8x6dxzhfQ70uOjyWTOwMypmI6lWRzisfp7/Pj9EeMTjR0o8BB4zUW7ddwn4bItQo0aojhkLSeRsxWpK0RXpSjEEywjSYyUFTElD4blE2bvuMx9bLcfxpys/0Ru6M2w1WVynzG3zy6wiuVJN3BFqOlSxPFkVNj3dila7zCrt1i7ITUynWqBZfKGzWiYpGo24V8PuYLQH/0iKhSIcjlYgf703bSU67IZOKLt6zzAeKXFb/JKvfz+GK5BKUQYRhLzwoBhQLSsggzCXr1V4hyOTYSCbrvdJmKDaIAovV13u+2GeRuEgVporUkw5HFUCSpVlaE5QZ/+0GHkZ3F8yVeY438ZEnWHpMiYGQ0aIdDgpMVyUKJodmmo46pWfvMFhqRWWZpVhj5KdLOlCgUTNM1+nSoDCLGYw3HgURC0WhAuay4dUs+E4zQ6HQitrYiAI6OJErFQUGr9eXnipeujvfZ8q7vP9/Af1vzhtOM0em7fWo8dPp7ogiy8x6+E6In1hmN4rKv8/4x5dEuR+ltxtttfvj4b9AXPaaNy/Sv/jkaKTI/f4/VSuGvN9DfvM7i/gNqe48xxn0qpoUXSUy1Ipew6SRmBBasIoNi2kGTBvO5xthKkcImzxwvmeZX9jfB2WJipyjnXK4YUwaiwSDMYiSgkPWpmz5RrkZrU8NfW0MuFmf+AlG1Cr4fK0MoRVQoPG+UPi3/Sgm6HktKBkGsFPE5g2HneAlwaif/2RORrseL47RVoFolSCYJqlXk2hp4HiqRQJVKEEU0fvgtnGqN4TsW46cVtLDC94sfoxIJ7hhvszvKECVzvF3fYU0dce+4SuSZKCPDySrLuzs5nPmKtxtPaCd6/KJ3BS9QhNLgRG9R8Oc0RI/y4h+YJOtESZNISfRalvYbTZz3H2DMF7xp/ZxZrUIpd8yBt0b/UZf7YYdCQVEuK8plycZG7Bh565aObQuyWUUqFXH1aqwQceoi+dm95RxfTfwh+GK1CHGcCywWkqP9iEb/PsZI42n5GpfoUnXuMCu2cL//AxYPhqTSBsXVPtO+QP7xqzjmNqvgCeX9HbKTfVKRxIs0jCTcyBxy29rnvn0NXxhslBdoSw0ZwU8PrpMzLNLS5cBr8uE8Sfcox2op2M4fUElpDOw2QzuHnpIUEgG1YgIlJe0bOTxAPnwYzwgFAVG9Dr4fBwCn04/w/FCYTMY317Zj+Uml4FmS6ZwvXjJ8EVfAr/NFpUJwOnS8vg6OE/sZVSrgedT+l+/jjRTDhzmm3RYy9Phu6Rbk89xKfoenvgLN5O3CfdaSNneNFsL26S9S7OxqfLxf5XKhy8aFGf2u4MCu01x8jG5oBEJD+SEF75gL3GFs1PGzJpq1pPbWNfzyRYKP9sjtfoCsFFgWy7RqXX46qrI769DvS/L5iFIpvpRsNqLVEgwGsdSo48QzQkLEyyGdfu679mXnipcuGPhNAepvg88bIHOcOMrb35dYVvz51Usu+njI3AzYcdeZ3+nijZOM1Db1gk5l+RG6PmGSa/ML/U8Yddtcz465ei1gpDUw+8e0RidYzS3WSwbRMIFpTyFMYBoJTlKb6HmTV9RjdtRFVkGSbCFEc13a+oCVI0maCSpZB2lmuT3OsvRNWt4J3/1hg+EqC/MZwx6gTRhqbSqRw2HQpBGG8WZeqcRMJUTcCzqbEXU6Z+7DANHaWqz5G4bxZ882e9nrxWRwXh14OfHC/MDpopK9Xvy9ZDKWnLUspGUhSyW027fjbI/vE2xsELVaRBcu0K5K5j+ZIVYrUIrmhkAENncHSy7mV0RZD/3kiJHnoRyLqxciNHtAv2dSPBbMZBlNOTycVblQnlE2Dyn3BxgpyW54kw0xoCmPKfszutXXuF9+jVRijUbgkfujy6x+oaNWU8JsjpnZYNEPoT+knhV4osX6uqJUimcCtrYC7t/XkFIwnYoz05h0Oq4I/Gv3lnN8efD75Avbjn9fvy9ZreI2gauXXKLeECFg5rXp9STJmSQKFBfSAxqJKeO16+z5HQbv6szyb/En3Ob1b2kcuz7i+F1UQseutjDwMSIXQg1byxNoBlKD9VSfdf+YXtimYiqu5cbMJ4rZ3CCKkryae8oyVWPhp3h0a4213IJvfTuJ8taJpglkLkt/kUGMxwxVgYoQ7JmXacaT00TlcnyQM81P88UzDoieJQRktwsQOxI/45aoWPxqnIzO8dvjxej5hcj4U3zRaMTeFGEYJyH391FhiNrcJOh0YgWhZJJ2K2T6sRMPEqdStLcC0D3u7va4mIkQgG4vGI1WkKtzLfMUuRQ8PWqwFugkgpCjaY6WekQ5PGGZLHNZ7rKvXUCtQtAla8ERJh5+co390nfIv/YdpC/JZfcIsopxc4vl5g3sd+5T6t/lEImWaVMqxfKh9XrMF7OZ4OQEDENgWfJMev6Z/95XhiteumDgi8q7vy0+mzE6zQju7Ul0Pc741OsRJ6rNeCyx7o0ZPJwjh5KJUaWrb/DD8AOkUPx8cpPusELOGXH1ypC1fIpJ5wbzhUHp8BbZLMj3bmEFKZKGiSFCfJlDT5mY7oqlpVM0bVrhiF23w3Bisl2d0DJsMsGMW7NNCAW56RhrGCGiJZ3afe7fzxNlDaLiJZ46DkU/4keVT4jSafpzk2Bf0V4Xz/VRhYBcLtb+LRaBZzrRuk5ULsfZndOQ97SWHkW/u4d3ji8nXhwkPh0gf5YeD155JfYbmE6Rh4eo0wNBtUqUy8WHBMfh6LFHVC4z3rcRqzFHg7iqVG0ZDAYSfTrmnd5FcmrKjcQjKsdLHh6W+HB1k43EXfxCifdnV+n7DbZll7bhUGSKCEKy9oCFmSanPCLTJJivyBQmHEevYoeSyzUX92oO//AJzvbrrPoRUUtn3Bc0qxBWwbIElqXRbMaukRcvKiwrIpmMB8NOTuJ1v7b2aQ3pc3y18fviC4DZLA4CUqn4bwQBTGWbp4911MmQ8a8mFD24n7hJWQ7I6oqhqPJfTt4gMz6g6Md8MVMp+oXr5MM+pd4O9sYlFkcWLFYE6QyObxBWmrAQTN0UmqZoZOcoleNwvoaP5EZjSCE5Y2FpzMmjGxr7xwlm0udPsj+hWRty171CcOkSMhiya5uU3IAfrN1F5er0BhD1DNaTcVsH6fRZY3SUz3+aLzQNfP+MO84OibYd6yp+FU5G5/jvx2/ii6tXkc9Mxoy/+Rsiw0BkswTlctyJUCqB73PUj92qB3oT5Y45nMa/o7phMjgKEcM+7zgXyUVTboQPqAXH3Os3mK+KvGJ+yGjV4vbiBp5dYkOLyJkOdpihtnxK2pvhpVKIXIUgSBIuXVwrpKuv4x32ufKNG8w1EzffYFVpc1KTWIsReJJ6XXHhQshqJdnfh2pVce1aSBhCJiNYW4vo9eK2QE2DRiNWHfsqcMVLFwzAb1/e/Tx8NmOk6/FedqobPhzG6iNKgZtqUzKHlErwaAf21AaX5VPefNPj+FgyHAqqw/tkrSGT2lUsCwrfbnLxv/0tmeqS426ejKuTnkXoSmLpWVS5AjOLjLDoWXnuiG18D0QiJK15jKcSOwN1fY6ufG4fttC1EoVowp+nf8b1+S+Z7FwE00SvVyg6BjVjBsMh65kxIpEgcqrIUSx3EVWrRJUKcjyOLz4I4k0/m31ePz8NGHQ9bik6bYg7z/K83HjxpOO6n6oSkEzGaiKPHyNHI0QyiXj6FA2Qh4dEoxHHiyLT4hWqRY/6xoL7+2n+YbxBRQ0omQG1ms5xdIHxJMnBtMbEzXJFHfJokUIGDqmkxZ59mZXK4Nohw0SeiysXhxSdcJeUvkR3VxyKMoVGAdML2Tj6Fcm05GBwmYmr6PXzLAuvIUYRjq9jl9aI2oLijYhGIyAIYn3nXk9weCiRUtFoKHRdnfmInC6Dz4pk/GsPkuf4w+L3wRfxHFn8dRjC7dv6me+Ev9ZkK+yzsxO72DtuRHM94uaNgAcPdTZOPmA8lhhRn/G4SqEAhUJEJzyBLCxuPSK9/5AJBbKGhtIlui7IaCv0SPChd52hkyGZWRJ6PqEyOOwl+GZpgbnyeTC/wHBUpCimvJ56yM3EB8jHPlKZ6MslotmguBhQE0PU8TGdZB/SaUKzA/4zzxkpiU4lYT/LF5b1nBts+zlfCBHPoJ0vlJcbv4kvslnI55F7e4hkEuk4qMGAxHSKMgxIpznUtxhe/A71DZP6sMe9E42fDV+jmllREiG1NcGxeZHxXpmDeY2xn+WqnuLxJAVGSFpa7OtFJlaK0kqg8CllV6wcDVSSsggQrsM0TOLU10nPT7g4eJ/+zys8ntd5VMwwWrURuSrFyRHzrMGDtW+iqYjLlyO2t0NcV2FZktVK4XmSjQ1FGCrK5ehsZMZ1P10E+7JzxUsZDPwu8GLGSNfj1qDhUOK6Eoiz4aeKIqsHXSYTGQ+BZQU/Cn5FOzNBJ8t469v0Hp/w5vznXOMRoTUiHb5G46//iZFlokpZQt0gPzkk5UxYGQX2L32fmjGjpD2iulryinuLf/S/gx9ptNIW7ZrH3nGCnWkNUh5RIoEyJEllk5AhBW3BE2eDrcQhRq5MpEnKF3PU/RliVoaiSetiAi8zQ+6fxG/ndEpw40ac4T05QZ46BJ6ynGHEPYGuG2eDmk3OU6RfE7x40jGM527Up8/edYlSKaSuE5ZKaNMpqt2G8Rjl+8jQox52aaYUsjBHrg34+ZMUk3SNatnErTaoNar8uLzL5E6fj++n+Kv+KyjPIx1ZZBMrmsYxu9pFhqscUz9DxFV+lJzgiQR512JpZNnXrqLntykMdqmpHoXHH2OkDllN2wza/560WJF3Bhi5KloxYrXSkRIsK27v2NlR5POCfJ5ncwTR2Rp/1iFBtytPlfTOskBf5ER8jq8PPssXe3syPtAbYJrxe5HJxC1DqeERvaEknRbUoh7V8IRatYxoVPH7OvLOA16dP6CRXpCe5WlYdbjlEKyVWNYvUn3vP5G2xwRGil66iZEUCCWolXo0pwPeXL3L/8P/zNyt0s6N8dN5xn3J/iTHjdQT3plnQWqYRsQ3U3d5Z3GTbe+Q61f26OdMEEOqmy41zUGbVKGeoN0K8ZzduOVD02CxILhy5Wze7FN8kYyzulGxCKVSXB0Mw3Ou+LrgN/EFEOVykM8TVSqo2Qyp60SNRqwc5LoIXOrWE9plC61qQ2PFuF9lLLOU36pil6rUEho/vvuQyYMxH9/R+Y/jtwm8gLoxYT3lkU6F+CMdy9VZRXDopglSaarBPpGSKAFdY53H4atc1SBtKCq7H8HMwCpf5HHt2xQiEIMh+UyVVis+8wmhsO14+N9x4Pr1+EhUrcbfX1+PmEwkrhsnjH3/eWXwy84V58HAF+DFKO40G5hOA8QW1LNZ/EJkxkdkV0OWlSpP7XWmx0dcCh+C77HyEtA/4vJlyEZFDOr4kSDxwTsI/xC2vsHhG/8rF/7f/wM9dDiJagyzF1ALgVapgByidJ+pXqHMghO/hJQaLXXAWtHn7rTNk1UTV5o0jCnNik3SmvAguEZRvoMhFcqykFLSn5mI1Yq31C3CVQrpeejZLNqdO5BIIIDg2rU42xPrYcXKEeUycj6PS75KxcPCp03U5xWBrwc+bzjs2RxBb89H3N1h4/hXyNkMVS5zaJXR705Z8w+QQtD2+kQVF7VjoR0esqEWjLOxfG3otlkP99hPbdC4XuBbyY+Yn1RYjgPuuVt0En0OUw0UkpOZyTwwET44yST7rJMwPN7L/RGb+T6JdpVc1sftGczSawxVlVQ1ixfqJO7fZrDw2WlfZ5JtIw4Vvq/IZiV7ezFX5XJx4mowkCSTnPWENhpxi8dwKOn3JaYJpVKEZcVL5as0JHaO3z1e5IrnpmSxt8BiESuJPHqk0+2COeiyUenzMFtnr7pB6uRDitGIyFf4R2NSqTpqq0D5WJBRipQzI/FwgMgZzLa2kQJS2CyTJVZRhZlWR4U6G9oRUSKBmdXRbI1RUGPpZdgUI0qJMeOMhqdn+LuTVxECLmWPaJUdxosSxZwOohfLQ47HDBZphOtTl0e0gydEByXkeIyuaWjvvosoFFC6Dt/9LlE+H19wKvWcL04HRXU9rgRI+Vxm8hwvPz6PLwyD3kGIGoxZD5+i37mDGI856ieRIayZM8RshvB9OuaCMDWBiUCbTNjQRoxKJVS5TDTRWC85HGZu0mwq3jIPmD8yOY5S7EaXQU9zW9ZYLPMs3AQHqoMX6ERSsQzS6NkWgcphpiHTKWFYMC10mAG5hCRo5nEsHW7dZ4Lgaec6VrnNYiEQIqLTEXz4oUYqpVitBK4bkUzGxbLTA36jETGdxq2ls1lsNpbLRV96rvhX+wz8PvHfqxv9r8XpMNh8LlitBPm8wrYFiYQim1W0WhEnJ5J0WmEGFjJjct/a4OBAQj6PljJIXVojTGRQJ0Ny4wM8LcXDrT+jxghRqYG3Qn/lMt7fvUfh6S1cs8Dh9nfpyB5Ve5+sN2G0yvJ3o2/SS2xQzLr4mSJ2mGDup7hUnZLQQw6dCpFm8urGiP/t+k9IGx5dv8442aJRdElUMhCFrGQOZ+ySzBkU9NgEShSLsSb0M9lQoRRyOET2+2jTKeH2dvxz8zlyMkFMp/H9KRR+XZj7Gb5W2sovEf7ZazsVUX/BdEx2u8x3x0xu9QgKFfJywcE0xyC3jbFZJ7dZIrx+HaUUSim00QgxGHAgNrBsGb9voxEpZZGfHjL9uMtH7yu8uYdjwVhViIglWe47l+g7BSIFmg4pZZEIXbrGOsZGg9xr62SWJ+w5DcKpRTbhk9NW8L1voe/skA3meCufVWuLIJVlfV2xWklsW7G3J3HdeGC40wnZ3g5pNGJpyNFIcv++zmwmcJx4mDjuhlD4fqwisVjEe0Sx+Pn6879PfNV8Bn6f+EPwxWe5IptVaFrsUZBIKPJ5RbMZMZ8L0mkF1oqxnaZndJjNBHapSWgkya3nWKkMxmRINJwwl0WcSguZMTFSKcyShu7b+L/8BJVIMd18DSNn0on2yUy7sWNxqHN73OaX2vfIpANEJo2nmbihwVZxzIPpGg4pWiWL//3771BNLThZFgiTKdYaIQOjzbLYoWEMSUYO3tQlzJUopDxUKoXI5ZCjUTwXpBTy2ayQdnICQNRuo3I5tMePEZ6HsKxYXOLZDME5X7w8+K344pQr9ueM740II0k+6XI8MOn7RcztFqnXN1GtFqrZRIxGCN9HDIeQTHLg1LBDE1YrhO+TtgYUHnzA5P6Yj96L8Kc2YSQZRRVWIs04KnMUNMnaA5qqSzoRkFAOc61MPuWh3biE1qzRd/Pcml/A9wRrUY9K28B95XWie0+pzh/jiwR7G390OsaApgmWS8HRkUDXBaYJFy4EvPJKyHIpWCwEo1HcPh5FcHiokcmos9sxGv1huQL+DXwGXjZ8dhgsCD49ZOa6z6ueqrNGPh9R/CSiXBaEoSC/3WL9ashwKEkcDVGVMrmswlj2mE4g8CEoXST57g6MVgyp0Wu9Ref4PQLLIeNM8ZMVHsjr2JHBdviA48a3eTw3CJc6syjH+/NLFEyPRsHFiGYUwgn/eHyFRORwqTHF0CL6hRuMXYuiHHKjfIRY7qOhx2W6Wg0Vhoh8OnAZCAAAIABJREFUHqIIVSwiJhNUsYhqNglTqXigB87cA0mlAGJVoWcSo+cycV8jnPZ+nsL3Wc9NSTCgv5tibBXBcajLD2jnc4j+InaYlBIxHCI8j6MjyTihqGZmrLdCDndc7h1+k1J3Sn+sc+dkjXX3ERvGBA+N+8Y3OKHCkiyur5HVXUo5n5JlYQUpDBcWqSoyOkboGol+n6oxJz0+onk1Q2Xw1xwXNKyViVAeRIcUZEDYk+h6C8cRBIFEyujMKEbKeM2fXvKp0WoQQLkcUSzG2Z/jY0m7HWd8TuXkzvH1whdJk36WL6Io3j7l5XrsQzBTHB1BsQhBuUXl1QDblujLPl6zTHk1YLmA+UpAvkKBQ0bvHpJ0FnzU/hFrnQLJ+yMS3QMyjkW3+AZ3nSZeMOWbvMPu1p/i+YqTQZKlk8SWGXzDZCM74mb+gN0DE6Xp+HqSTmXFJN1haqxxMdqhUg7o5PY4ckJkIIjS6ZgvpERlsyjTRJTLiOkUVa0SlcsEm5tn0tRneOEg+IXOtOd4OfF5XJGeoDNiuGMw1uvIw4c01AHtQ4uw/kY8c9brIcbjOCDQNPaTlxhmqlQbHuvBPgddnfsHm5TkisEJ3JlskHUHrJsnpLMf8V/cP2NJjtnSZC2EZmKAkTVxvBoZe4TtCOxVCqNUYR7qlLwBG+xSCIZURUhr9gvG5ohxJmRhhlyQh5QFaIHOMLFGvy9iJbBZRK0WV5JPY6JkMuYKx4FaLfYayWQiTJOzioHnfXm54jwY+Bx8ntzciwqLo1GsLOL7cY/Y2lqEEAFra3HFoFSKyGZheqtLJMBxBFure3j9OYfttzi6/kN+MPu/yfR6BFGKJ8lLCCugerVE8qNdlNToZrfIm5KV5TEJizw6KVFuBhRzHoSKwTiFHaW4VOryneYTfrm/zu5snZuVLm9cnVO9kOSdBxUK/oi6Pqa1nkQrFDnwGnRVQFO6iCgivHEjPvT7PnKxiBtbHQdKpTM9aVw3zvC8aC//ecLc53j5cLqpn1aNer24XazZjOXhjo9pXs/Se1IC5wjN91nXnhAttlCTCcJ1kUFA5DhxpqhaolzP0xZzolqN9fEDbk8VO49CqpzQsaZUVZ/3xLeQKYMwW0KRQy0FmgyJFDjLAMtP4KkMSqT4TvQxF7IhrsgSnCwxEopZ6QqbpRHcfkDWTTFfexuzmOEbnT7m8h4P9RtM9RaJRKwetLUVPlOGCGJ1I/35vPypq2ynE1Grxa1D3a5kMpGMx/HB7/RWPTv/fKkHxc7xu8MXSZN+EV9sbMTvynweUa3GctWjkSSTgeBJFwUkpwPW1WP27QInN3+AVFNkdIDpz3ESOayZz9LRuZ6ZYyQgEFlG+Qv0ljnKaZtsGMB0Qa4Jjh8iPY+hm6OYC7jWtFkTDvcHG6wcwaX2iguXTUalm+Tmc6KoTbs1gFGVTsriIGjSJaSJjQhD/P/wH87k9fSnT1GmeRbpyOHwTGUjqlbjngghvtjI4RwvH6Iolpp+Nnt4xhVRBLZNuxXSVzWiRIKEYdDJLKE/iB2Kp9OzuQHCEJVKIRtVyiWTlhwR+QU2Vl3uzBQ7Jyb11T4dz4Mw4G7wFmZKJ5HJ4E6TGASc6E3m0T435ndJs6BHk4q+4NVEj5PiRZbZBuv7/5WstmK49Q1a4oDEu++QHkseXfh3BPVr/I/rH+MdjUkkb7BMNshmBZubEYYRcfNmiJQwmcizgeFnM9AoFbcHVqsRug4ff6zjeadzptHpOMWXii/Og4EvQKXyfEj4xQfk+3H01+nEPcP5fBwAZOc9MuMehbkkMGocP5RctO5CUbHv1ikqwdEyj3mxwfLogMdaBz+tc8tt488DvjV9F+f4AWboMQyL7EZbDII8TT5mJZJUvBMGosXcNsjMutS1iKpakAsD7h2XkdaCakHjzfwDRvrr7NyRkE2hKejpbbxVA9FIYTx8QKWpwfwe0fY2hCHBq6/Gh7zFIu73KxaJ1teRvR76gwdxMNBsErz2Wqwc0+udi61/HRBFyP392DXyVCYukYhPxo4TvyelEkfjDJgCYRgI1+VwqrFu30VlMvFJ2vOgXieSkuZyiXR3iCYTemOToPYGN2s29+eK/nKdhQo4cqpI6XIYNHENiZFX1JSNDFxMd0nJ6ROWSliUGYsqg/4tbiZ3aF8u4t+sMCtssT14j97QQFcbpNwBqYOHPC1+k9zJU+y1Et1SgydPJJmMIp+H7e2QYjE+mK1WsUpYOh3RbEa89lpwphAhninxhiG02/EeEASflh79sg+KneN3ByHiZ75aPT/7vohTvmi3I2azOCu4eNAnvwrJaxrDx31INJDv9mhYU8JSkZWlyJo5SFS5YHQZL/K8b36HvbqO8/SEkpxj/O1PmJQtqkaGJ9o6J16BeZimIRWr0KQixxx4IaGvEc5tOtkVN/LHGKuAR4sSk6liozxj0xzxcHkZrVhgs7Fiup/jr56+yjc2BoSZkPHOnGrRA/2ZuVgYErz9NnJ/nyCVgigiWlsjajTQP/rouSFZtfq8CvC7MHI4x5cbz+Sz9Dt3zqSmo7W1mCtcl6jVAtuOJUNTqfg9sG0Ox7DuL5CPHqESCYRhxIaVrRZ4Hi11hHImdHuSCI12vcxNptxTee51v4HpjojCgJmX4s54HScbcTF7QpgS9L0ipdCi7g4YFrfYUd9gJ5/jqvzPdIafkCxcZHV1HSOaYhQEB8sKJadMSiwwu/vY7hq96YxQaUxqDU5OtGfxrOJb3wrRNJjPJVLGMwHlckQqFSeIX5yX97z4PCllvBccHsqzZfFl4ovzYOAz+Dx7+hdhGPH5xrJgOo2nxRcLyTohq70R2tGEpTEi5yl8a0I6rcgrye3m9zmwBMmBZCs9pJDy+eTb/x7nn/oUx7dYX91lrGmo69dIPbjNdx/9X/wq/UOSoU1FjMn7A+a7O4y1a2ybQ663xmyHD3g/epNBX5ALpnxf/RQ1SLJ/0MXSSlxPf8Il/TEPg21uRR1aDY8f6X1a9TJiCXI4RGlavIA1LS7jlkrx0Bcgj44Qx8eIbJYoCJ7rSn+R0+A5vpp4Ifv/qZmA/X3kwQFytYqN6Vz3udnQdApRRHcvYujrVNegfaXMyX8+YORsoJwBG+aCqFIhqtcJrl2L/8ZySeK999BMkyAUDJZpat4hJT3gZ7OrIELWzQPycsSCGiv3mHpSp2Xsc1y8wJ1HKRqJMWWOOc57TA0HoSRhKDBqeSrtMrdP1pDTIsnuEju5QcLJszm/z7b7Dt3KNR5UvodXa9MIItrtCBAUixFRJEmnYTyOE1mNRhwAaNqnk5kvZoNPZbRPE5+r1Xki9OsEpTgj8/n818ncMOLz0N5erD2+vw8pKyTvDDnej4h6U8zDR0hdYJsFMozRNcG72R9ha5CcjagUI+x8A9nqMOze5tvDnyJ9j5mexrv0RzQ/+hk1a59M9g3y0Rx/mWQ4SxJGM6KgxKVMjytti+/mPuCvj99AzsZUbYfXoscMjxo8HYRsH3/E61vv8cmjLHfCm8yOMlxvjqkbE9pZD0KQlgVSon/4YXzh2WzsJVKtIns9ZL+PWC5Rz2RETxfCOV+8ZPgsX5y2gs1myG43Pj/MZvFmmEjECcTRiKNxhunxgmp7Sbvu0n2jw/AfLETaoOX1oV6PW9HyeYL1deRkArqOfvs2yk0wsJIo06RTGHOLEo/tDg1NYzv5kBvqHhNRpyi7XEn1eL1xyF91v0XRcfFlngwWlytDxrkU1tomicUBhQtZZLrByUcnlD/8FTOZYafyPQrzPWqTx2y4EsfcZH/re3iFNgU3olKBRiMkm42w7fhQb9uQz8ctf6fB/2f54lSI5nQZnLrYf5n44jwY+Ax+kz39Z39uMIgHRXZ2dJZr6+gVnXXtNtqdXdLBEpHPcJzeppu6wXuHHVptGNuKmxtJZk8tLh39LeYyZH36jxS0CcfFa6xZM0pun6Q9Z0M85ihxgUZiiTG/hRJJDtOXyKdCSmrEg1mbla7BYsbANfmH+QU6lSXfuHRApJ2w6K14YmeYG9AxHlJJ5GmXVkRWknB9HaEUqlhEHh3FzW+nza2adiaSrZpN1GlNK5ZTOlcSeplwupEvl8jF4rkM3AsmdMzn8fsg5VlfsJxOkb6PFnqULxdotiOC4joN24YPhxh7NlEulpHz3347biOYTuMgAoiWSy6E9xBKMYyqPCxdQg3TdPJzCvOAy+KAcbhOK+Nxseawd2SSyYbISpl+Ms831j6kOblH2v8nksLliXaZ7nGV5VKQXdxmqMqYwRzXkkSqTDlZw5uF+DnIZgUXrgZYloHjgFKC42Od0Uhy/XrAK68EZxv66Ub+Ik6zwQcHsXTkeCypVKKzJXLuUvz1wb+EL04PAYuFpNeTSHmBdiRJJ4cUixFBb4oUIPOKp/MK942b7PQ7vPKKYmq1WC9OWdza58LqLpen71JxjljoJUbtm7y1+9fkV4c4vqSda6ClFMHKZzN6gloK6s0LlFlRSUz5h/1tbFfAyma8yvCro00ul3r86R/vMd+f81e3tthy7tExHtFQE0S9SLswJ0qVULVafDFKoT94QFSvw2AQN0c/ayGM1teR+/uxwtBp9hfO+eJlwufxxWoVv+CFQhz5zmZEtVqcQHp24pWzGcY8QTVt0bhRIdJLNDIZxGgEfhbhekSFQhxcNhrxGUQpsG2UUrTth0ivxsnwIgPvItP2VWpji01zRTTJcFEbMNP2yaZ9LtWmCNvlT9fv83De4n6izdXakGtP7mBO3seKGjwwv4l5UiU1G5KyZsyiNGO/yHwgmBsXSQZHLG0NzZM0mxHJTMRoFCedwlDy4IGOYcTB/5Urcdb/9Jj0VeWL82DgM9D1+Dx82v/12YfjeZ82YFwuYx3pTifixGgyWvZx1RxnbLEyKgzSDczLLeRAEOwfY08ippUsiTCkMnzMpb1fEALTTIe2dkJ95xaB5zFO1qnqE0q6jWXrWPoWj8VVEpFHpqTzkfMaR8sEhXzI9wq/5M7iIvdXV1nYE97iXTTHpo/JKDSRRsh1eRdhrXFIlsabF4muXMH4+79H29+PU6GlEuH2NvLp0zg4SKXikvCVK8/NYk5vwHmG5+XB6WkmmYTx+Plw+IuVonw+DgBGI/T9/ZgIJhOiZpO1KgR1F4x07F597RoNfQc9NQRLQ06nGD//OarRiKdzNS3WJweYTmmHh9zrlpj5Lu1cwNuX+zzstfmFe40yE+S4S3U84FHwGnd7VSrahKu1MdXkAiuYkbSnlLwe6bTik6dV0osT7PGCgjvgsVOlIqbU3EO6hSYPKq/ww+0hLT4hcK9RKGzieYKHDwVPnhikUhGlkqTVijd3pXg2D/TFty2djkvAlUpEJvO7c7Q9x1cD/xK+CMP41R8M4qVVqUSQbuJ/MsTJ1FmxQACqr5gW6iS22pi7gt4HXQI3Yv5mFoKAC4/+nuriFuNEHafS5PLjn1CZ3cOJEljZBq3pI6KEgZYsMnMLeC6EQqdQ0flwcpnpCgwt4PvpX/Lf/G9zGLZpSJ96eIw1dckZJhM7xU3jNirQUDM4KG6y9tZl8Dy0e/eQto2YTEDTULaN9H3kagW2TVSrEbz+elwJOF1A5wvh5cLn8YUQyNGIqFIhePXVOFUO6A8fxs/dcUBK1lotpPQIIK4YXb/O2nyOPD4mWlxEWRba8TFyby9uK63XibJZZK0Gnkez16MnLzE48JkmI94s71LdTPGrjzd5bNR4rbBLYjEkmKZ4ajeY2TWyaZtyWSPlzTATIcneCWo2x2pdJtwZ4C+naJN9em6REWXK9gTTX/Je6juEhSpvXwhoOre460Ozuc7urmAygSDQWV9XZ/4CYRjfnq8yX5wHAy8giojlQZ/hsw9Wqbga0O/LUzEdZjOJZcnYYXLvmOTDMfX5FNeDaDDFXw1x010qlQ6FRUhmPsLdmUBGw3SnJEMbM1qQ8AU1IyJAECqDGUVSoYe+WKJ7koPKRdIZHc2eceeghEqYhMKl4B4RJNIowDRChBTszOpcMg8gmUSXWTY7groP0VaWYT9LsKrQPjlBpVJEjoPY3gbHQfT7ceY2kUCVSrHq0LOIX/b7Zwv+zFHyswMVn9duco4vN057Xlw3/vdpFs/z4uzfs345eXISf97tQjIZ9wnrOtH2dtwP+ux5R5UKLBZEh4f0705h6dPSp6jlEoDDqI1MJmi5LlgWHw0u8GjWYKElyekWgW6SC8bccjdRaYPiepb/2H+bVZQgUgJhCNbnD7jg3MfWNTxTkQ9mFPsf44+W7NXfIIFLtqQjnTyhAnc8hVyRuVHjPy1u8EeHdyimRyRSWwgR+w1IKQBBEMDRkSSbfT4Elkx++pYpFXsOnA4Qt1rPN/YXb+E5Xm78c3wRRc8HzZWKz0/DoWB/36DhHVBwNdKLATlrjG0LvGwZZQ+J5BG5XIeSCNFGI+yHE9xIJ+Eu0ZVHmSnV4C5ZwyISGmNKBNIk9AXp+ZQUIYX6RRJ6iUO3TPeujswk0X2L7fQJ/WiDjOUSiRVWlOK93Rrb6cfgL5lW6gRiwps3FhyEAb3qDcK+pF3yY06IIkSng1qtkJ4X7weHh4RXr8ZJo1MX+yCI+aJcfp5Mks/v1VmgcNpnd46vBj6PL5LJuB20XAZARlH8+amyVCpFlM0SmSbRzZvx7IkQIATBzZvoYQhBQP+DHsrxaaeG0OshWi262esIp0zb3uFoVWE0XfGRc4m5lFzoZHjD+Ii7pe9zb9UhV8hzybzDx0dNjOWYcnKI277B2uBnNFe7NLQhvjnFcSXR/v/HUekGjp7BzRQhmWeavkJ78k9k3SUro8T7+f+ByNrjx7NP8FdDtM0OhYJiNIqryZOJOpsXe6a2+5Xmi3OfgWeIInj8ONaIDUNBKqVIp9Wp8zoQVwGePNEwjLgy1myGGMYzBa2DIyrH98nYQ4Zmm6d2A6kiapkVFyozqh2D0UjijlYomaQ1/oTq6oiZl6ZidSkGQwzXxiKLCkJsLYvyQyxZIC8WrDU8/HoLyzWYWknsVUTFWJAN5uzLLbSsyY3NObWKz0nfwNeSZKXNNwsPCNM5DmYFqtkV6bRCbK6TVXPk0VE8FyBEXJozTcQzZaEonoqBMDyTHBWzGSqfjweIfR/hOKhs9lN9gynXxel2Ufn8SxcQvJS60UKgslnStRpWMomw7VNRZYRtI1YrxLPMjnqW2VeZTPzsczlUqYRwHMR8jra7i9zfx3j/feRgwPzEZbAwCbQkuY0iB/mbDIIyKWtAMZpyNMmyN68y87NouTSzccTH403yuoWoVng8LnFklZl7GfKZEDMJWWVxzdihVFEksglCJyQMFLlgSmCkGEcF9pwGvVUWJTVkLs37lT/FTZYpMSAoV/FLdWQxz9jPYZqKVEpSLMbDX9euhWdKQmEYZ230z6RM4pYPQbGo0HXF2tqv/8y/Fc59Bp7jy8QXSsXf7/djpZF0OqJYjN8TeXRMajnEXoSYaoXfGzMMC3Tzl7m5tWQj2WMjO2J3XKK/TJGMQi4sPkHNlsjAY8PeIasWBL5gIbIkIwc7V8MPJYHSKTCn2E7y5p9neNgrMRwbWLZGiRm6ChlQJ5EWXL8RkNE9rIlPteTRVsc4uSqBHZDApVNa4l27iciYFPqP4/mhwYBoczMudSSTYBgoKYk6HeR0igLkZILK5RDP1MTEfB6rwxQKn+IKMZ+TCkNWp62p53zx5ccX8YWux8/asmIvCtOM24yTyfiMYBioXA4MI+aV2QzjvfcQgwH6/ftg2yyOVwxHGqGWILddZb/4KoOBIKN5LKYRQztHajFiJOs8WHYY2xncVUS2U+DQKrM7reLZEVaU5Ya4x2v6PcrJJWZaUHMOKfsDViqNFgUY7hKZ0PhEvsbxvMCu3CIhQ+zmJn2/gkhq6BmdsNVGy6RZGVmWIk8qBclk7BVQq8Xqc3Hr0HMJ+q8qX5xXBog37v19yXAYZ/kh1hIfDiVh+Nw80bbjfq9qNdaOjaI4izibCTr2GE2GzNpXWSQbTJKC9eHf4/mCsV6nGg6oozO4WOOqv0sJSW69iZafkfEUhhegdIOsFhAowebqEb1khx19ExEpKt6Qa7NfIYwruLlrsFgy9YpklU4kAxABmibI6w6HdpJ9yvyg/nNEYQ1NhBQuVxB1j7WOhMWD+ODW68U9erpOcPUq+sEBQTqNPp8jXRdu3UJeuBBXCI6Onk+9nNoxv2ild/r1eBwHC0IQbWy8dBv8S4nT1ISUz4f9lHpeDfC8OFg8faaWFT/jZDLOEJ3+/2dEII6OkKMR67qPqHToizon3iUis0A9d8h6OERNXaTQud7oE50IPlhWWWpFBqLOOz2wFiXGUZnWRZ0L6SkXMxN+cT+P4SswDIZOjs4bFbL5fQ4P0izHkNdtMs6EXKbMnt3klcoJM7PK2/Ue0+QmvipSL2p4dkhu0aOdgWSzzuXLEaYZr2nTjGXghkPJ2lrEcBh7CXx2KPRUKjKV+nJkdc7xb4d/CV94Hp/6fj4Px8dxcKBOxmSKAX6lTbQSPF1Wac8f4DkjDks3WDu5TS45o1ENodVh++hndBoQpNbQ+x6JB7fRpI9KKaQfIFRAcvWUPg0WWpGcYaNNp7j3dvmR4TDJ/DHKmdJ1KzS0PpYqUi9MiRwNgwDfh/lcxxWw1gaVMuHCBqoRsu48Qh4MYDJB2HbcI27bRJubsQCF4yClRLtzB2nbZzq8UakUR0W+/1xm6UWuCIJ4IR0eIqWMs8fnHgRfDfwmvjitElQqMVf4PngecjSK3w3HOasKsFqhn5wgDg6QYch6zkeoAv2wzslqg7C+wVrhAa2szcmdKTUdtNSQdXHC06jDA+ciw0EVc+RgmWnSLZNIa9BMWFjLy0yiJIXRIcV8SDKykVstjInkeFEgcfiUZORSi/r8bfrPuGgc0ExNSBUKzDfeIny6Q2tdY714xMlQsrURkIuOWf92lfk8Th6tVrEkcGxaGXPHV5kvzisDxA9puYwNw4SI+8Da7YjlUpBInEknx1rQAaRSEXt7GoOBZDQSJBKCfNqn3oAbf1IkfbWJWFikwiVBIoVWyDKZariFKsmkoGPOSDgTOqsdCr0djLTOpLRFwllirBak1QI3MuIXynZw/3/23vRHkis99/vFiSUjI/e1qrL2pXeuMxyORsPRSII80rVsw/fCG2QYsK/hD/6zDFzYgGED1zYg6V6Nrq5mFanhTjZ7q669ct8zIjIyIs7xh6iqJqkmhyNpZsiZfgB+6M6sJirzxHnOed/3eR49jTs3qIdNZqkKbXMNIRSn02IyTpQ1cI0CqeUi3/xGgEAxlQ5DrUxGzKnbE27sLhgZVTwX8rMm4vQUvdVCa7XAtlF7e6h0GgYDtPEYfTBIWiHzOfHNm8h6nXhvL6nuXFhLYllJB+DCNUAbjUjPZsw1DZVOoxznNyqG/jey0nOBq9/t0lhf12kfhYyHinxRS773OObsVMOLbAqth0kwzGKBKhYhipJKYK+XbPxCoFIpsteXaeZvIIslZDbLtcYk6UAZBlnDJ79VInVrmwN9j9aiQmfsMFuk6MxLpPQQ3dIx0jracp1wLvE8xZo9YGPyIcWghYpiHG/IfC7RZIwl56SDCdJfcG6skZ72yY3O2VQnZEo6maU0y927qJmHiGPMlCB2MhQKCttOxjqSmPnEIs621VWq7CU0LUkgdhxFofDrSZK8xLPOwBN8mfhCSoDk9WJRcnoqePRIp9/XyaVDNpdcrr9osvKHO7SbgkpqhmlBPq8YyRxH2jZBfZUt/YQVc0LFO6DinVDpPoR0mnltFW04xNF8Qgw0b44R+MhYMRM5/EBgd86ZxBm8xjbH/QLtoMRc2dhZDaplNl7IsrIUEbgxnahC6BS4vdzjVvaYXGrBibWL35ySdWL0/f0kQHA0QkulkNvbiXZgNkvsROdzxGwGQhA3GsR7e8jNzSTQ0rYBkn3i4uCozefgeaSjCD+baCKe8cVXB5/PFwJ1Edt7dibw5jqF3sFV8KQqFCAIECcnySUzjpPOkW2TvbFCS18nXm0gSyX2tgO0xYK8NiOXXjDafhltc52jwvMMhzrNSY6en0UJE+YBdkanXvARaZugsYFz9phsPKbh7iM0xUzLsRjNCaWOEcxw9AXazKUjlqhP9hHTGUpYVESPO4VTDEvAcJJcJsZToliHXIZMJkkRHgySM+ClTahlfXX54reuM/A0TdOl8KtYlBQKSTDMx+2RL/YywjD5wvf3Ba2WxmQiLgRkkuxLKxSIsEZdarMBKiv4oH6HjNthNhDouqS2oShHbUytSN9xGN87IL3Q6KXWmGQb2LYk/ehD5rHNwKoTY7AVHDDUVhnFOQ7CDR5aNzEDH1sLyJoBupNi6uksjDS1/pheG26n9yFV4aPZHqN0g//1xQ84a1v03DlSt8B0WJUy8YkXArW1BZMJ4qL6I1qtJF1ybw8tDMH3EYMBcm3tkzoBKTHeeeeqTShLpSeWcr9uafwzfD5+nrhP0wirS/TPI+R0znp8RvP9EaNximrWQyzOEi/pUglZLiOaTfSDA8SjR0mMfL0OQnA2K8F8grBSyHqN1vsj1v1HyaxxowG6jnQcrpX7vLO/R3uWxY0q5J0FIogxA0F3mGK79UNuz7o01RLT0ZQwJZGHLfRsFt+oktc6BJGOZSq8OM2mdoI1VViNMm1rl1p8yO7BfyC/yGA/t8WDQQ3LEJy3FMoQ5HJJGAxcFr00pEye/act4y/LnOcz/PLwWY/IF+UL05TkcklH4P59nePjxE2ksLVGZiukRhf9sMvNss6kcYPo0ZBORzGZCKIXG7xQOKKmOkS5IkfuHhvth4QLG7d0jfKyID33Ud1zfJFh5DSwgymVeYu+vspMz9E28pymdugPDLK5mEo8oZKZczKt4hqwOvW4k+kw1SOGLHPX2+a1ZYXyJ5wEde7tO1TsiEZ0jDJN4vX1pPhTKkGvh350lBRMB0qqAAAgAElEQVSBjo6g0UCZJuqCCy51Y3JjIzn0KZVwRXJLSjqNFzNV4vw8qTA/44svL74IX5Rr9A89ZJRmvXlO890Bk+M55SUNER1cmVJchlVqnpeMi81myRhRtcpZ30GppOOF59Echmw8/iB5j6bR2F5wHteYHln0Zil6cxvbCAhcUMong8X8dJ/t2c+o2B7SmyB6LaJ4iubOMeppPHONknHKaJEi7Q7IaRNenv2IuFwldrKksibZVoda8xTdGlB97hajXoTIGZyeK5ycuDDLSoS/uVzSIA/DrzZf/FZdBp6WIXDZsXqaovvjfwdXNumYpqBQ0Gg2YXNTUa0qymXJh4eb5B+OcF2NUilGCEVh0WfQU2SvVcnM2ozyNdYtn+5DaOX/BasIyhnBPNB4pF1D7u6SOXuE4/bIxS6GFhOHitBM8XbmW3TCKo4cMQt0Xlk/47x8m9Z9hZkzmUmHQZinn/4G40yIk8lyPk7zf7x+g5vrE6QBZvsMvEMYHyF0PTnMVatXt3ttMkGuraGqVaK9PYRSkMshOh2Mt99+kkRsmjAcJl2BQgHx+HESWra1hUylkn/zWcv3y4lLe7iLB0GurHzitctFv7oSYXxwj95dyWQ6RVarVDMz1qcfJa4PnQ7aZZs4DBGzWVIRimNUo8HpIENXVakuR6zr73M0DOmfBHTdGnXfZfVahhN9i5a5yv2ejT8XCE0SScF4nmYYp1loPq/cHDM5i6mPDthy2mTraQKzQtAfILM10uUcp5l1JiNFXriUpEuaKV404qD2GvrWGkJkSZ22iYczvNMxqeoKhWtFerMVTDM5sF27Jllfl9Trkps3k+c/lXq2jH8b8VlcAb84XzSbCV/oenJwqFYlfWedeH/A8bGOk1EoK6kemuM+s66i6p8S5HSCcpX2ewGn0zrHfJNXrL9jLMq4Kkv0/C0KRx9i7z8iUjoLPUVBi5FSYWghP7L/mP7275J6cIopUvzJ9Yf8bHwTERpIUzCLoZnaYN9K4SqLbCrgh/cb3E55dK11mA1Yjh9D9yRJh72w0JOrq4l2yDASg/XVVcLdXUQcJ9qB4RDj3XfBcYj29p4cIieTK8tRcXBwGeudFKWe8cWXF1+EL3Sdze67GGOd3o9tBrk8YmxSd3qsdx8mF0bTTAqP02miLbgYOxOjEcpxOA3qdOMy9eyUhvYWp/42vYlN73SZembG2nLIcWqPh6cVumcRhtIwNINZ5BB4giA2uDW6zzjUsftN6t5HLAplVCGHOytgR3M0XSNddri3/D2sw8dYKuB6cMzMFzzUt3mw9S/4o9J9VPkW2cWQrm+SO32AV7mJvlzGHTYomInofWlJYllPsqiWl7/afPFbdRn4PE/op93cPh4pv1gkTkK+D7OZYGMjIpMR3LwZkckko0PTuyfEOgSBwh53yBwMeBRX6I0NVk9CuraJrnqcqBzTdJ3ysuDfrf6PvDj9MSuzR4S5Iof6FmVMXtL+DmfRJqXmGDKgZMxo2H2MeUzYm2GaWXrjBqR8SrZPTW+R1UN++tES87nG9o7iD7/e5WG7xN33Njl+HPK9m4+p6H3WrTbafI5cWkKrVJCallRpsllUKpW4QOTzkEoRbWwk8+GGkbSDLw59KJVs5kIgL3O2i8WkR57JfPmvwb/NeNqDAMmmf36ejIDZNjKfZ7UwoTdbTk41vs96+hjhuihNQ5om0raTfyOVQoUhmqYlVUIhMPodltxj1gcTZLXK6soYc/SAwWOXwWSM7rqYL6Rpbr3Gw5Mp/jjgpWKH02iFUZihOcnR9fK8c5JGz/wJ6WjC7+g/YyvsMpIl7lW+hXQqXJOHFAbHvJf5I5BgpQdsBe9RS89I3ZSsrh7C+22O3Spr4YxSMGQ320S+eIuVx8kh7/xcXC5n1tbk1bP/RTb2Zw6Kv3n4efkBn8UXl+FChpHMD3veJ/nixo0kzdrunjEJIY4VzqSDNW/xk+kLdCZ1csN7iJ/eY/H1W5wcWmi9EDdb56i6ijXVMadDqpOY/vpz3GtX2TPnbE8/wopdTEKyckLsgCkiNk9eJz+4T7t8g3unmyg7YCntU89FtA6y/OWjGllHcuu5gGI5xU/vNjjtZdlcdnltfcjauIU+HCJ9H8pl4q2t5BKQSqHpOrGmoadSiX4oDJG5XDImmM8jjo8xptMkrOxCOMw0CSK8Gp5+xhdffnwRvohjCAJWN7P03r0IahUa6/5+cpG07cRO9MKQX7NtlOehOQ4qm0Xs72NMI5aCY1Zrc1B5VuttjA/26R3MGIYLtM00VnnK/nmGdlenqI1oVPv04iL7/SpeZHP3pMxWAbrOBqvREfl4hHQ9usU9pBJkwznZeIw9H/J6409pHL5OJhqRszz09RX+bPfvwCjhPu7w0aDGeqaHVSmy4zTp3bzDS36M6yZLeTRKOn2Vym8GX/xWaQaEAM/TrjbrLzK/dVkh6vc17t41MAywbcX16xHPPx/jOIpyWZEdnjF9POCj3hKH9g0q+oSc7nJm7+BpWZjPKfhtvFHIsdjlo9kSczOHNpmQiSakmaNPxtSHD2mEJ9SMIWbogm7gSws0Qd0aUk8NCXyNt/zb3PM3kbpJpRhhpHX6M4fOGaybTa7X+kTr64STgM4oRUpEZLKSV9wfJGNArRaabXNGg0mco6hNUPM56DrN/QVe1yfvd4g3Nq7mxTXfTxxkXDfRBJTLqMvxINtGViqkl5Zwa7VP2sj9huArOQN6ufMI8WTnEQLtMijGMFCFAk4mgzcaoT96dOUAIpeXaX40wzsfoUpl4p0dQjNNbi2HfnBw5TSlD4fJ5XJjg+BP/5Tozh30/X3yckzBa6EsC4ZDRLtNof2QVXWG9EM6+T0m6RrnLYP84ISX7A/JpiIiy6G5qDIPdQxDw3EU2bSElMW10c9IOQLddTnYfg1LBjgPPyTVPqPiHmOvVbgZvEtmPiCwsmQWY5aCU/RsnrZRQ+YKFOWAXDikVIZ4eYUwTPQC1arC95M94vK/bPaz94jLIkG7LZhOf/77fxl4phl4gn9OvvincMVkojEaabTbgsVCI5X6JF+sRKfEzT5drcab7i1Mb4wd+1CvchhvMFnY5Nwe2UWffg/OwgYP/GXq1hBpO2TlhOx8wODUJz9vszG9R1kbkApmiWZXmvhOkfqiRXV6yDDK8+b8eQ6CVeJUhttbM7qLAkdHAm3mcSu9zx/+dznudescHQlymZii5bKR6VE8eh/abfTZDGXbNK0tJv2YYjxE8zzi9XVa7w3xxxE5t0W8unp1yNd8H5XNouIYpeuoSiXhi6WlhDtSqWd88WXC07gCvhhf+D5oGmeHMZ4Hcm8PlcuxyJUoxsOkswCIXi95fxCw+IM/IHz5ZaRto7Va5FMLCvMOmuvCbIbe6ZDvH9CwesggolO7zf64RjhXVL0jlowBqbSgr9UIFhqmLvGtApmsIh8NyWuJ26LQNcZGhfOtb5Abt4nOugQ9j4p3xq52QCnqExTrWLFP1Rhj6RphpUwwC3CyOpYekTUCMsJHW1/BshT5vMIwkjuS62q47lefL36rLgP/GCFH4rSQtHhbLUE6rRACVlclrVYiNpxMNPK6y2k/w8P5WhIs8UKN5S2T3tAgWAiKvUOask7XzXKWu0Nhckjq+DHmYs7DaJtuXGSj8w43hm+SNX2MjMGksoWyLIQGllig+z4rtZBmaovWOMc8NPCkw1a6xckwT3vssG6d872bj7nXqfFo1qDV1MhrMzaNUxZuRDDwULHikXadeGWFaHOHHnXkeZuc4dEcZukNdDL4FHT3yYznxTiR+eab6PfuYTSbKNtGFQqoWi3JLKhWcba28C7txj69qXzF8VXc3C8t/DTPe2IDe2EPpxznyu7PcRy86RTR6SQ/G0WcRCuMf3CP+vyYm/lzFrUVBsdz1EmTwrwN8/nV3LAyDNA09MEA86230D/4ILGMG4+TmPp8PukueR7adErBb9Gc5dF6PfSzM77ND7DLDg/8TVZWYpqzAu4iRaAshGkQS8Urg++zoZ0xx0JGitLwEBlGHGkbqIlHeXLM+uB9UimNB2vfYTYT1GeHhLHAteuMVYZpbZOl7+6wXAmIvZDpIoXK51kskud4OtWYzzVKJXXBfeqpFRwpE0eZ0Uij1xMUi+oiVEb9SjWQzy4DT/DPyRf/FK6wrKRYOptpV2uhXpcMBiI5OLR9lJ2maawxnWoYG8tU1yzSWcH03GUyAqkUM+kwnWpo2QKFzn2saM6BW+WR2iXrdllrvcWN3usYloZIW/jZGlE6h4ZCRCEV1aecD/l/xH/J4bSGHoesFiYopXjYKkEQ0EgPuVlu82ZrncNpjWAwZ007Y1U7pzu2SUUzHo6Waed3qd8uM6zu0ZvYyGaXAhNOtQ36pwvSaUUx7CWCYcdJPOdNE/P730c/OkL3/SS3JJtF2TZyeRmVz+OsreFNJr9xXAFfMb74LK6AL8YXYchJ3GD4xgFLss0N55gwW2Rw4KI6AwqLLrguxsFBoiebJkF75nvvYb7xBsbDh0nY2GwG2SxCKVQcJ6F2oxG5eYfWNId/0qM8OuS5zCOOxA47WwukpnM2KeGGBpFuU3cm7M7ex5kPUJrAiy2y8wH5aZOeqPLYX8GZttmefEBBTTha+x1a1Vssnb5LtqjTDOqEwxHTbANRzpP9zh1WNgSWP2YyTyFKeR49MggC8H2NTEZdGS1+lfnit+oyAE/E719039E0GI81lEp+rlSSlEqKyUSj2RS0WjpBAFOy9KMCrZbBbJYUR17+rkN+NUPq8QMWB02EkrjCYV01yZsupiGJyjU6qVXSluSVyQ9YoosqFaFYZI6NbxWoMGAR6XTDAm/IbzCkRC4eIiydblzC1bJEsU4uHpP1e4hel3A6p9/XkX7IynLEujzClAHNSZYDdwm1tk5lJ8fy1+vEi5h+F5qlO7hmgcqynrTqhEDMZhgHB4m4eDCA8Thp7WUyhDdvokmJNp2iLRbI9XWcTIb5/v7TN5XPqjx8RfCV2twhyYKYTK7s/D7h1vGpB8FxHLwgACnROh2wLLyuR6Z7xNquher1yKw4RJU6euCRj4bo5+ewWKAubQIrlWQtHB+jnZ8jXDfpGjlO4i2tFDKX453gNj+TX6fktRkEORSCdlDmrryFUS9xFK3RmuWRSqAsk1LY4fd6/y+bwUPm5ToP17/L0mSf1eCQKJ3nzcEOQ1lkN3xAvmIQp7OcWDvEfoiRd1jsXqdys0FQKDCvrZJby+I29vjocZrztoVRypLPS3Q96fKNxxqmqRiNkmrlpys4SiVhU62WQMrEY9owEkIoFNTlR/8rWebPLgNP8MsoHv0iXCFEUiX0/WQqolBIfMUXi2SNdDpJp6Dt5egtChgGHBwYhCHIbJ5v/lEKMRmTO/wI3XeZxFmKQZuGMcDTTMJSFW1jjZQ/QmZyfKP3V+SMBSqdRq+XiGKDsLpEetJmHqfwsHnX+gYn2hbpcIKwdEIEw6iElLAZPaQQ9XnUzDMZQnA24kalgx4uyIkpYjLm7uIaI71CfrvE0gsVsqtZpBfQ83M0nR1muWWq6ynW7C7aaIQ2GqFPp0n2SK+XPPfZLPHSEnGjgRZFiZPQZYV5NGLebv/GcQV8xfji87gCfj5fCIHfnJBdjGmsRDCbka2niEo1jEGXbDREn89R4zGXzgyabSfuVAcHMJuhBUHy/73IKBDA2bzMw3CHs8IdjGGfRZSiG5V4e/4c0smQvVbnYFBhdXqfWtziVN/ktclfcH32NgU5YGKUOap9jR33Q2zpMdCqvOE9x23/XZShY15fx80tYcwmuGTR6iWMvesYBR1zrUa3doPsisMk1+DxaZpmx0JmshiGolaTGIa6SiHv939xvsjn1a90mT9zE/pHQqmkG3CJl16KiOMnf2+aychjsZg8J6ORoNvVPm7Dy4Z2wvueyYG+i8TkuWqLjayPb1v8bPE8XmqNJaUoPTrHiSZIqdBNQTzyyLotonQON1VEi30yKmK9/TYz8zlymTR72gF/P7lFNyiwV2rznHGP83mOn0xeIuPE7GbOiXWH3WyImqeZPewxHukUzTm360NWKzragwes2TaDIHXlCV37s+8y7/ex3ngD/fAQRiPU6moSOx4EaM1mIioOw0T4ZdvJhxJFnz1s+zQR0ld0k//K4NLgOClZ/Hy3DqWuouOxLJa3U1jnczjoIOZzjFaL9WUQuSlSq6M1m6jNTZrzMovtPdbtLuLuXbRej7NxHn0S01A9lBDoUnLaT3FWvcWHXoMWZbB8lmnxf41eoxuXeSnbIx/2scwG1XJEPhWwGLb5L9z/nR21z/3sSzyyb/Odo3/PdryPGUyJ5gs2Fo8oBl1O07uonEbe7/C8/x+5l/8aBxvfprZVxl+6Rb8/oXtxeT8/1ykWN/F9jRcnIcVikib+4EGiHSiXJeWyvKr4fHxm/HJk1raTj2tpSdJoyKvXP0t4+gy/PbgUGXte8thZFnS7ietINpsUQFstwXAIIDCMGCkTEeKphPlcw8hrNBqwme1Tr9m8Za6hxhpydY2v//R/w4wD5kaKNAsWXkzoh+TDY4SukWNKK65jdNqsFx6gpyV/P73N+9ObWPkU3658wLo84AfDF4mkIDUzeKF6yk5+jhZ4TMeCwamJsHy+vtbh5RsCbaqht1qsRRF9bTuxCs1kqf3n11m872Dq+pVdJGtrV2OEWhCg6zqLP/3TJK8mn094wPOSPecZV/z68YtyBXySLxyH5fUA3Z0gzofJgej0lPWlELFhoFQp0Z387u9yRoNwZY2t058mFxDP43RSwHQ1lr0WStfB8zjL3+TdTp19dQ0zVLxi9vA0h7/q/w6aZfCd9RbTZg7iiFft9yjNHvCfhf+WVOxTsmaYsSStXL519H/iODGeKOPJFN8Z/wWBnoJcAbO+Su3wHpq9hl/b4ix/DV2rEa7tEccwmwp+8hOTyURQrW6i64otLwmb/eij5Phcq0lu344AfiG+MM1kD/iycMWzy8Dn4PJce/kFx/ETQbFpJq4Ql5eDwUBQLks2NiRhqFgsktvizNWZ56tMb2+Qufcus2yd5dsTulqab0ctBoYkk5EUe29QuV2FoSLlDpktdBy3T+ROiewMZ5k9ZgoK4Qlb4T1Owm3MQolCNkQZYBqKs2mB86DCNHLYirrEQchmuYOml1GOw2hq8cBb43qpzZr/18jFLtpoxF+Ofo9JbLCXySBXV2n/8JDVzAAxHhPv7aEbBiqTQUynxOvriHye6NatREzc7SbCoEbjiefe0zaVy1Cyi7CRS9HpM/wSoWlPQmE+3bv8tIpJKcTxMaLbTSr6up50p/7lv0S8/TbGZJL8nOsSbW8nlqC1GkQRi0mBzjwPvmKzVuP8R0cMhibVOEZzHM7iJWSkIJ2mN06Rmk+xQ5P39a/xE71Et1TGjlzKGw6671KPz1nR9vnpcJt0tMCJXXJiSkOds82Yl4d/S06bcZbdY6BVubG4S0oLeLD6++irY+TZlFRvSCEzImW30G/fYTZLKjfNJriuzvl5cvgHRRwnYxxHR4JuV2BZin5f0GjIq2V8uaQvl/iltWQYJtaSl0W0xeLzhafP8JuLMEw4IpN5sg4ymcREJwyT3IrFIlmHQiTvXV1VaJoin08OB/5Cp7dyG93oY8yBpRrxSoO16Axn5RC3vMr6O39JvtjFqS8xya1Q+ODviEZjSsNzdAMizeRx4SW8jk8h6PCtxQHvWq+SSks0YZJJRTSyY/6+dYtBmMdhxrpq05+YTCLBzgZM357xwN2AwOR/2X0f3OSXenO/Sr+wRXXFINrYBMuk/YMD1oNWYjW8s4MWRVehYnJrC7W8jCwUEOfnifnEdJrwheMkH9RnccWF2BTXTT7IZxeCXx4+jyvgC/GFXFkhunEDce8exmCQ/JzrEu3uwrVryUVR0wh7aXpdDSNcZi3tcR7UGSwsKtEYYSlOojpxFIBuYfoeunTpGXW+X/0zpjOBphzKjkeq5DB1PW6Ke8RTl9y8x7JsY+oSlMnCtCkujinGfUJR5rR0m5XT++S1DofZ25xf+z1enf0M6Y8wohx2RZKN20S3X2M6nXByIhiNQNeTULHRSJDPK7a2YopFyfGxQCmBlFCpiKvn/ovyxZeNK55dBj4Hn3dZrlSe2EmdnSW+s48fJ4eL2SxJnXznHQMh1uilTW7LY5xbMY1GzKK0REAK24bs4x6lSRdZKXKY2iSzeJ/1zo8xXReFhjJMNKGoDR+TiS30LNTCMR214J7b4L+9+VPupr/G2aRAy99EWiE3Kz1WSj6P/VvIaEB+1uXo3OZRsEN7kUGNTf6i+03+5HmPf/P9DR5EVX5n/YiXS21O/IjexMYIXVbdKRQKBH/8xyAlotdDTKfIdDqZFTRN5PIyMpNJHCIu5tGfuqkYRvLzrRYIQWTbiUXdsw3+l4un2Z48rfJ2mSI5Hl+VL+XaGqLdRuh6kjNRrSbznKMRraZGHKZY2c6w2X6A8UaTj87KPCqUqYclNFxEFKE0E2mk6E4M5DBmSdxlYL1MoGfYn29QLUm2Kzq744cUfQ8xmxE6Gn9+9BzGfIJtw93SN8n7c14Y/4iUG5MyfE7WvoFbXCE/nhPbaY7zt2msaWTkDD9fZarVEAaI8ZjgB28zvfYHxLFisdCBmPlcx3Ek6bR2Ma4h2N836HTExahPzMqKRIjk2W82xaXJEo2GZHlZcnIisKxEEHZZ1fnHFNie4TcDT/vuNS3hCM97Etp+OXL66JGO7ycalSiK6XbFRd5MGyunWFmRLC/H5G9s4rpZsqMe2bMe1nzGYOdrnObr5H/2Q4JIQ7gT4pSNoUeIWKPin2IYNkVtiKtsJpFD2gr5n3f/I4+Kr/Bu53mmVsCqGHCz2mUY5TgVGzidHoWi4vHQ5HSxjFpo/JsPv8l//9oD3t6v8v2H6+RWHW5vtVjKPeBsUqA3MDAWKVY1DeU4xJeCYNtGQMId0ymMRkm+gO8nfJGI7xKHmU9zRb+fBFNNp0QAk8mzDsEvG59lhv9F+WJ9PeELpT7BF53Hc2IvYNUJIJ1m8/x1+n8nua8K9DIVRLRAhWMMpYgtCzme0XczxCOXihzQS6/ycF5iGjSolVx+/0Ybo3VO1Idq3OHRdIVHi+eJtIAVq4uphRSCDpHS0UyDUXaFw1f+FeXWfQIrRUvb5Hj7NV4y9jGiBV4mR99ax00VKY/GVPZf50F8hyRTTeD7CikvR3uS4lG7Lej1dDoduH5dXBWHL/NGvghffNm44tll4Ofg8tB/aRulVGJDePlFLy/LC+eJpP1zciI4PtbwPMF778GLL8b8/u4hqt3hKL/MT4INMm8PWLOOqD9XIp+XBHEFh5jZSEAuyyS7RFa1mccVpG6g5guUgpQW0nU2SBsh2sQgrUd86G6zVThjqAyOgxJpEXCzeJe95THyDA6bZQ5bWyxlJuyu+3wjPOBgssRPTnd59/+WeDLN3vqU/3T7A+LFBuvpM0Roo7fP4XojWaW+j9FqISYTlOsm6ZIrKxjHxxgffogsFIguUik/0zcripCFwpNAssuUjmdl0189Pj7KFQRJ5a1cTro8F6lJstFISppRhNzaIlIqEQXaduJG9fiU8eMZxls+6/IILSozkgUKiwCVyVA3BrT6Dc4HOWrFgLBY4cBbpRT3OB0XuRcukS8uKBQMcmGHR+E2ZrggpyYMBhkW2Cyrx+yoJirtENg1bHeOGYW46RL35DV0V6cuzznbfJmFU8T1Z3hLy/iNEoOuznH+FiWvSWo0Qh6dYNhF6nXJ2prkIg6BVErS6YirwJjlZYnjSJaWnhzuF4sn1qNSPtn0L7nz41Wdz/Kgf4bfbFxue8vL8rIofsUXHz8YXPKF6ybiwX5f4fsaDx/qaBpU/TNe3e5xtqjSd9b54H6T9H4Xay3D7Srk8xI/U2CQXcabRGScLLGTxYoXTO1lIgKkP8eMQyrxDD9f4yjeYRovs54ZUtkqMp81eeDtcR7kebUyZC0/4mvOCX91Au8fNHh0nud2/pD/6fkf83Z/hx91bnP2/+Uo6mNy2YhvbLVpGE3iOMe6fo4IdfROE66vJ790GGL0+4nrXKmErFSQOzsY77+POD5O8gdSKeTW1tM/zChKLgtRMnqBrj8ZQX3GF796/BP5Im4NGf3Nh5iix2pmwKlXwRQCOVcow0AVitTjFk1vk/NZTNWZEuZKPA7WkXLBe5MdZrpDNhxRSEkGAw1hLiEsnZNTk56foy9sXhU/Iqu56FkbYxgTazoLZXJafoHh4wkGBsopcLf0bapySCe7RSaO8OsVZvMdBiu3mfZavDQZsWydMLU2SKUUOzsxlYqk2TSApGucxC9JbFtjaSm6euaF4GIM9YvxxZeJK37rBMRfFJeb+HSqEQQauVwiCFksYH9fJwgS95EwTPykJxPtylJuMNCoVBSplIbjSOb9OUedHD883GQ201D5CnvOIbneAYMgTxxpxO4c/cFDZp5gvHDIMcMQEieaMc+VGdSvk/LHeHaZ2Mmi8gXemL/A6+M7TDoLQndB18/R9bJMJoot90NELsNoniaMYMXq8YeVt7kl7mLZgrvjNbRUikZmyL++80O0gwOE6yKaTZxv3SFrh8TlMvpkgsrlEu3AYpE4C1y4BxEEaOMx4kJ8pFIp0kHAvNf7h4IwIRJR2YVJryqVkpnTX/cT8AvgKyUI+zxcWsUtFkkFDkgHAR4k369poi0WoOuIbjdxjapUkKurqEwG4+23KTTvE4+mdEo3aLcEh90c6ZSitpNFlcs443M8X+fefJdzt4JyPZz5iAfmc/zA/x2m5CkYHnd4n6ZXwp/GdNwsXVXFjnxu1ZusV13yTNkWx3yz9+/JMQNdIOIQLZa0szuM7RrZ3hGp2ZC2ucrcyDAearxX+T1S5QwPvVV6VGh1bWInTbEoefnl6EroK2VSsS2X1cUGrWg0JFGUmAZ4nkY6reh2xdVSvWBqcJsAACAASURBVAyb+SzryV9UePpPwTMB8RP8uvji45ainqd9Yi08jS+6XcHJSaJd6XYFpZIim9XI5SSa5zGcZ/jx8Sb37+sMZZEXax3q0weM4hy9nkDM55juGKPVZDAUBAtBTk2xcha2HuJm6ni+jjRThKUq1KtMfJsPvGt82G8wbQd0ByZDL01rliXrdXGYkbYlH402KIsxX68+5l+V/ppt8Zg3vBfxQwOLkP/qxlvcSu2jtVoYrRbadIr9x98iJ8cJX0ynyb5xcJDMfhcKyf5hmsnhUYirnBKUIj2f/0O+uNyfAG2RCKQxzSsnm68KfqP5Yj7Hu5ibVqaJFgSfyRfl934EDw/oaUu0wireVBIpnZwdoJZXQAicYIA3N7g/3eA0WELMXOw0/ET7Dm/5z2GKkB3jiHg848StUlk0OQ4axLOAFb3Df138SzbiQ9J6gOMPyccjLOUjRLJeRpSRuTw9WaY+3scNDCIncWRs2xvsZ59nNDV4aNxEWRl6Q0mUzuE4ktdei6hWE2OJyzHARkMShonttZTa1X6fy6krs4Avwhe/Sq6AZwLifxQ+L3TmImmdKHqinxkOBc2mhmkm+qilpSSKvtGQtEQdMy2ozCXdbnIAmY4V2/aYXldwUHme+ZHiVs9DLiLMlCAyLPSMidEfk7UVkTGFdIqC4dIOHbIpl9J2Hu+uxQfzXfL6jIbR4nRR5Wyxwofz6zTmczJFHX+eY4JHO65SM1r0ZjmymgtWibI15c3mBq86HZhMUEJg/vjHhK++iqzViHK5ZPVqWuImlEqh372L/L3fS1Ios9kkediyrsRjl8nGn051k41G0jqEL57Q8Qz/dDylWyMrlWRhQ6LdUArR6yXvvXhdXFR6CEPk+npSSgcwTbQoYl2d051s0a7cZpQy+XbubdacU95yN/nQ+jqV3D7GSCIXkrfEi8z0Eh1vmbLjsZlqU0sN+duja6SUR90e09WyxCmHrlbBnHUo5iIsd8yt6d/gxENmxQbD6h755n3KYYfs6JRhmGWxCJnaeY7mNXr3e0ilc+hrBPUVPhobSKlIpx22Ypd8XjEeC4pFyYMHOsWi4vhYp1CQlEqJaDibTQ53QiS//mW1/7K6+6wD8Awfx88LKPs0X0gJk4lgsVBYlsKyEsF6LgeZ5TrDE8FqKFksBJMJTEcxNTlmaFQZ5VbxHrYoT06Y+4oRBZZMhVI2lEuorktxdoqmZxlmGygnT5Ex15dN/t3pCofdJWpGlrwcsWVMOQ2Xed19jvWqy6G3TGM1wvbh0F3i73MvgR6yaveYeAZTs0LLK7KW6iB0Pdn3gwDrb/6G6PZtZKmU8IWU6I8eIXwf3nkH9vaQ9XqSNtvpoFz3CV+MxwhNQxaLn2ivXY6iyPV1PtFqeYZfLp6iDSAMkcvLyfgDcDHrklwMDCPhdNN8Ol8ohbZYsGo06fVt1PoG7dQGdXtEhS6i4NHsxHSdLeS0j2YJBosiB/o2xEXOp0Uq6Rm3zX2EKXh84lDK9RgpxUAoLFIsGQGZwTnloIUWBphqQSDSYOoo0yYbjSjMTvio9j2K3XdJ6VN8VeB0WCf2Smg9i6MsZG8t0X6seEuuM5+73FmWVCrJoX0+h9FIu+KLfF5SLEpqNUkQJCYz8/knK/5fNb541hn4DHxW6MylUNAwEhvCVAqGQ43xWENKQbms2N6O+drXQkolRS6XOEPMZkmlsVZT/MmfpLBSc7zQ4OxhSKnzgDhSpLZqTNtzcl6P0MpQKoI2D9AnA7QgZJpZQuoG0zBNYKQRjsVwkcH2x8SaST4dYhNQsmb4Ko3YXmOq8uhLJXa1x0g0/uPxdR7Im5Qqim+84GKmNJpRDb/vshofowwDTankkC8EcmUFJSUqlYJMBnFwgBiNEPN5IiIGtDhOLMaiiJRpsmi3kbUa6lJHcInLgbqLywXwlbKQ+0pWej7tH53JJGM+0ynapaDb90k7Dp4QqEwGlSxatNkMbBtlWcn7PY/2QcB4rFGwfE7MHabldU7jNZQQbJYn5AogxzN6bYXvC+yU5CPtDnfn15ibObbzbf6bO+9yy32D067Dg9kKjxdb5OwFrz4/5bHc5s3RdXDnVOfnvKDepbzo4GbrLApVCtcrdL/+hyh/gR26SN2il9mgtfISdkoReyH7Yg/XLDI3s1gWFAoahmFSqwVcvy4ZjRIHh4MDg0xGoRTcuBEjZTK/7fuJjiCdVhfCL0Uup8hmFcXir6cD8Fl41hl4gl8XX3xeQNnT+CIJJNMQQmN7W3L7dsjt2xGWlTgNzeca5+c6ug57e4r/5Hs2Uy/i+DyF9viQvJxQqus8Kr4MnR7r448I7BzVmkKdd7G9MS1rjZPd16hPH5NzQoaiRjNeAs/Hiac4aUk+G+MtDMqWx7lqYC8XSRVNlrZsskGPNw4bfDTboLxms7mrITRJkwaaUqwsDpL9pF5PLPUcJ+GL5WUUCSeoXA5xfIwmJXq3m/CFlGhCoF1YSaZsm/likfjYl8s89eH6+EP2jC9+efgsrrj8c6mUdAgmE9KahleroQC5vo42HqONx/+QLx7McPf7jCs7uFqO8M5tzidFQjPN15ZPGfsp5s0RcQxiMaer6jxmlxM2mFs5tlJn/A9Lf4E2HHK8WOHIrTHHQRkWuarNgXWdrpdnzT8kH/QomjOilE1/+QZoGubOKqO1m0ytMpX0nNCP0ByLu7XvkrYkD2arxEaKMUVqOxkWC431dZPZLOLaNUm9LvH9pON3/75BsZhU/qtVdeHCmqxBTYNS6Qk/fBX54tll4DPwWaEzn/6i83lFtSpxXe0yn4vbt2NWVhSepxGftKjoI4obWWo1SaUC19Nd+oOQB+Im845LVXapLVrIagVjNEBtbHCcuY6ZNVm0JxhhwCLSWezdgHyO041XuZd6Ae2sjR25dPRlBloZXzkUMiGOCBjLHMOpheFY/O7XZzSsPvcnq9ybrVOuavzr7+2zFJ8jDI32/hzL0tipTVCGgZjNErHv+TmaroNSdE4l46EiL0do6TRqMuF0XmNW28K5tozM5UDTsGs1fCDe20s27s/btD+++bhucuH4dT8tn4Ov3OYO/9A/2jCSUa2LP19Wb9L5PMHJCdpkgt5qIev1ZOOXEs11k+9H0xhPBF03Q/PBnKA3o9MWOMrF9of0uxp2/wwtCGi5eQ7ZQeo6k7mFlUuhxzEZI2DXfQcVhNydbXK6WGGhpVjYedTyEndbFbyJQmkau+oRe5kmZj7F4/o3yUza2GLBynMl7M0K7wS3iKZzBr7NSmqA7Y/pWys8YhvVaLCxoRBCY21N8sorFhsb/sVohk69nnQGlAIpBbu78QURSExTsbsbk8+rX1s794vi2WXgCX5dfPFZXHH52qf5olZLxtDCMFlT29uSej3hi+i4hRpO2H7JwXFUMtKmAs5cm/GZz0xlqNFFq5UxQp/89JxZdolFvko2HjEeQmjYPMw8z86uIlM0mG3e4K+nryL6fYpqQM9aoSVXUGkbOyNQSuGHJp2pw9o6vPRyyC1znx+c36Cv13lurcdrz/W4mT6g7eYZDKBuTcjZIZrnoUURequFplSSRKsU7Z7FeASFoIOwLFQQcJzaw01XcfaWIAhQpRJ2JoOXyyVmEh8vEj0Nz/jil4ufwxUqnb7ihLSmEXS7aEGA6PdRmUzyXimTi95kgrJtxgPJRw9s+kOTbe0Avzmldx6zCAVBZHI7eJuzYYaHco/H3jq2mmGakM7peAuLbDwmNzklY8ccTWocyk3m2Gh2miEFhm6KcZAmY8yxDEmtEBAsrXFevoMMQ/LZiOy37zBWBc6iJVKDDhNRJMOMqj7meFHnA+dVzHKWl1+OqVZhZ8cinw9oNBIL0dEoyQc5Pk46AOm0RqUiyWQSPcDGRkyjIb90B/+n4dmY0D8Sn2HE8g9aPUIkXrPJHFliU3jZYYuCGMvt4XcEcX0Vq31GeHhKyjPIzjV8BxZ6iRe19zGCHucbVcaFAvXqMrnDR8z0LG56l3kgGMab5PKCKNYRpkFzUaErajhpyQvRIx4G6yhhEOcL9Ps51EyhSoLIlOhaTGuSYU2csxWN6Lx+zKvm27zf+xa2Z5DPjDkLa6wsxcgoSnrZhQLi8BDW14n1Oq3qDkjJev8dzuQqPT9H+WJyhEIBmc9DJpO4DQ0GSctQKeTGxifj5i8/xMtsAtNEnJ4mfbZ0+plrxD8nPm1Z4DiJ0OtyROhykc/nyHQ6GYiUMvneLi542DapP/9z8Dx24hix810eyAIEISII4OSYujVhQJUPUw3qz9XQ2ueoKKIfZNAl/EH2dR6KNU7lKv+2811yakIcR9iOIK/FHKstpu+PyERtqvaCr5vvUx6fswimDApZrGDMzCmTbZ/CX/4HFrVNNgYtprogP3nENF7i/eLvM1q7yYuyQ1o22awtY75S58aNiL29LH/7t8kGXS5LJpNkhK9cVpTLIYVCsrlHEaTTV5NTF0LPTy7fZ3iGT+PnccXHR0zjOFl7a2uJ1WitllSRwhBkEGOOBqhTyGRWkUdniOE51vtjNLFKNgtOtsj18Zu4VoFJw+Dc3GAzc4B8GDOxaxxaS/QXRebhFo10n86kgh/oBKGGLJcohiH6uM0oWqGcnuFFeaaeDrFGqyepVWc8eLNKTk2oyTbT0xm6eoeN6IAPOhHH/hLn6SrLlQAVx8itLUSziSyXEefnEIbEeo1WZRPimPXpXc7kKoMOlAsGECbuM+UylEownSLa7adzxcc/yGd88cvF53GFcXFUvJyHM03khT24aLUgCJLvM4oQ4zHmm29CFLFjmgwyL9DzNLqyyuDIoxY8IOrBaF7hZ8Yu+nqO4QcWwhsziHLUzA67yy4P06vc66zQnbzGrnVII9XGFCbDRZZBmKYadKloLXb1I66ph1SiDrPQJDX1sbQBXmaJ4eCY0t/8hIbIYUwcepFOfjqh5bxAN8hQXdX5fV5n+bkVGqs1XnhhQamUZX9/wfl5YhcKiWPk5qYkk1HcuZMEBzgOV908eEKpX9UJ6GeXgV8Al0Kxj4dEQCIgHg7F1VyYaSahMufnAj2zzmgksMY91PGATlcjLhahf0reGzEuPsfCD5lOs8SHHunSEMeOUYf7LI47dMUSrl2kmhuxWRhxPCli9h6jmRpxdRl/VqA46ZDO6bx0I2DQ0tD9ADO2CdFZLATv/r1Ga5InMz6nJAdEvuBn4Q4PtCqGUOQW51i+j8xI4o0N/n/23uRHkiw79/vZtcnN5zF8Co+IjJwqsypr6urqJvux2eTrfoIgSt2CJJAL7qR/gCtyQ4AgNwS44I4rbSQIEKEBaGh4Dei13mOxH8lu1jzkPMToHj6PZm7mNmph4RFZVVk9VYuVReYBAuHhbuHDdbP73XPPd75Psu1YCi4M44t7OKRZDAlFgtFQMDkoEqVlNjZPqJYNwkL1XAc6myXsdOLKwmh05i0Qbm2d6xOvZcpkOf6xrPg4w3imGvGrjidIvYb1OmsjDOXmzbjMLwTKcAiAME3CUxdiZJlQksCyiDY2kA4PaQX7THwdpAmSM6UW3mMwySDckKm6gcQAqVYlF6wwHR9ZiyhGQ16vuoipyo/6F5gHl0l4c+obNiO7xHxlINtjvhX9mOvchnQSPbCYyTkKlok6PUTWQgLJwcPHd4ZECwfXSmD4K6IAiMD3JRo1n1w4puCERBMP266fJefJURslkFEaG4zHCqMRmKZyWhGIHcaTyVgR4uZN5exUfPll/1lC8Cx+7vgsrDg5iZWrxmNBqRSfa4oCDx8KhkNBItuCmUBpD9HdMR0LzMUKb2jiyz6Ty6/SPOhg9WwWyKSbGa4d/4hwuWQqZRiWWzhWQKkIiXxEb1EiurePYquE5SpTP81mdMS0XGAZCjLJANfy0JGJZBXXl/mf/5cM2bZCxTukqC+ZsM0bt+ts5CsceJvklDlV7xg8HxqNmA4ahvEcMxohNI3NYIyYdhgsDMa9CmGryobfoXK1TmgYMafc8+IGu8kEMZ3GPQhAuL39MVrQM7z4J4qfhhUQS74OBnEils2irFaEmQzqhx/GcrKpFGGhECtGeR5RLoe0WPBq+g7vDwpEiwUVr0Mt7NBb6PTDizwKsnjlNMW6RuS5tOdZvEDmO60PKEwczIXMj4ebHHlVdMllozChtyqwCg302ZCv8SN22aOSNJE0hYVSxBlPKPXeZpXIYqazZMI5QeCxcgT2XCMbzSloQ35c/Do3xDFaWqHSKJFzuoze8/BfKCNEvMgP9tvUyxJ+tUG7rWBZMb10bTJWLIaEIRwextcvxIl+o/HlM5t8RhP6BcLzYD6XzhgWcSc5LBYShUJsPV+thgwGgslE4uBARlUjvGSWWnSC44BtS/yN9evMDsdYfZua1McYdTmsfYV7syoL10Dbf4RkmfStLPd3v42a0ak0FLTpgETnAAmJRGiTiUxe9/+BtLTkSNoil/LRwhWdeZZsykNLxDpXTm/BwYnBRnjC64l3kaWQh2aDf5xcYRwWuK49QMXluv027a6Kuf08w+bzmIkSufYd5IMDpOGQTCakfwJRJos0m3HpORDTaVw+XK2INI2kZeEMBii3b8f29KcSY1EqFU/kj5cifZ+wXI75hpYV8xJzuadWZeipKfv+orzZJ9QsRa+HdHKCfHhIpGkkul1Wp5Sw6NT0p2vlmEgFskU5pgFMJhzPsjzqpJF7J0jEXZFGMGc5D0irK4aZbewwwclA45X8I15SPyCXDXlzdAnNkMgJk0fzKiMvyyJI4QqdXlQjjGAc5mn4xzzPB1TlEYYWkHWGhFGEL3QsvYCmBCRSMm4yT2p6wkwtcj/7In+T/E8oJy1eTtwnkQgJrl1jsMwS9kYcPIzQK2VWDx4ihkPMIIWjZYmieOIuleISb68nCAI4PJRZLGB/X6ZSiVit4iThaVxvPKMJncfThBefhRXzuYSux6pVpVJIoRBxeCg4PhYsFgIhIvLbafLLLqsVtNsyb86eZ+8IvFDj+uoDVGvKqHSR29Y29iKg9OhdhlKZt7K/zaK+y2WjQ0WdMF3qROaS/KqH5tu0gkMuWjdxIhVPSZHKgmt6mCIX05myMu7EorfvMZ9L/OeFN7gsHjIIS/xN7yXeX+ySTbi8XrhFZtElN97nWGwzffkbjMuXcD7cI+sMkPf2IAjIKhY9M33aJyZzJduJ+5Q0jSiRQIxGGL7Pan8/5qaHIYRhbGa43on+JF6USkijEZLnxVShbPYZXvys+EXw4rOwYjyO8Xw4RJycoPs+qygiqFaRj46I0mm6Q41xqkleNhGjEZJpcjzP8fC2j2Ka4NhIKxfDnWB7Oqmixr3oCp4boU0H/Hbmx/jIBJkCB9Mcz2XaDMwkR/M8JlkGfoG5lMfV0oRhRODBrn+fTfkENaWQDSYoizkeCr6ewhRp1IQgnZGYJeukzAELJcexss2Hma/RzM4pSHOy12pUfvMSg/smw9sTHt2T8IwEWq9NYj5kSZIglcX3Y8Mww4gpf4OBwHVjv5DhUGKxEKTT8TownY7O+oWepnjWM/A5Y30tKcqnG8Vi52zpzKk4mYxOJUYFBwcyJycSRatDI2eiJyLGY4G9ENjbu6wGFjnNZrbSOdJ38TwJWQrRlxNcNG6lXmMeJVlUdpAzBvXJPcKpiSL5LLUsW9Y9Lo/fQqukmagVjuc5zDk0tD7Vskfan7E3KdG30oCgqM4opZeMklt00lfoOUXyuRDsFbvyIcdWiWO3RueRizYdk7r1FvnxPseDBGaUZG6pHPYMLFcjLdsEepKs3aXtVrGHDunVEGM4xE4k4p2BdJpI0+IJu1iMBzMIkBznbKdHmk5Rbt2KJ3ZNi3sNvmj3jc+Ip2Jy/2ST11qO76dN+J98zHWRHzxAPj5GPHwImoYeBKxOFYXEwQHyYsFsEtFPbBEEkKmnOBTb3JpvY1kRu+FDLtdnBEuXm+IlrMDA1IvYnsZI3mARZEgKm9eTNwklwe3ka/y4e5leUCGtrbia6XDob/LIqmNHGoX0ilejdzGwUFSJTGRynY9IS0s0PN4p/xZ+KMjINlZlC1ukmJsCJfK5Xf0NSl/dolaXKBV8dEOgfv0Gh7Mc9iJkdn/E6KMhq8mc5HaJjrxJOh3hOBLNZoDvS2ha/HcuFzGZSASBxPGxYLmUKBYjtrbCxzcrn5oexmfJwHk8TXgRBJyaiX0aK9Ybr2s520ePZA4PFSYTiCKJHfkII1wiRJygOlKWe8plyu4JaWXJMjS4n7yB6wo2jSH2ZEVHbtHJP0cQRIjNGv4qJD89QMwXBF5Izh1QsNrUV/u46SKTIEtguQRuSLEquF484VanwHhhMHMN0rJDTrPYqVr8x+ibtFcVtLRKreKTsgao8wnHbpWjRZ6T/QDt3i3Se7fILbscmQWsIMH9bo5hD1JZmUhVCVWDxULCORqTMnxEt0uiUGB5Sv9ZS4eG1Wo8aKcDeYYXQsTz1sEB0nJJ0GjE+vbK00lweGrxAn76BPb4BOe6yA8fIjlO/Hs+R55M0HI5VqsVYjxG3LmDPJsxkwr0Ei2CSCZTTXCo7nKrt4E9WLKjtXlO38PNlbjpX8fUi5hSmpmXwtdSBG6EJTL8Tv5v2cu9xK3pFv+hf4NJVKKpnHBja8S708ucuCVkKaKYWtHwD8lGUww9ICmt2Ar3yDLHk3TeKv4WTpSgrMxZ5Jp0kheJJlN0yef+xe+wvRVQvFZBVwPyX9tlGmToLnM8vBvhd6as9juUEiYzfYNjaZNMJu4Jil2Ez/Eim43Y25PxPInhUJDLhZTL543DTxNWwLOegc8V63LvmrLYaoUEwcd7Bh6XjFqHZcWGZYlBm6rUZ5kuU/6NOsWox+bbA6yjR7hFhfRWEe14QEX6iINEmcGyyrHzEgiZgVmgqk1JGxHL/gAnXaK/vU11701eG/8AsXLw02nGbobshTT599v4XgS6Rj6nYjsqhu7jORK64hOl0/y/4XfwTB1dDvj13PtksLACnR91r1BbHbNSXeRalo2ZyZZ5m7ZVZDgKCWYeynROvlVC1A1W+jWG9+/STVWQB21KmxraqA/LJcrGBmGzeSYjGrZaZ/bl+H5sUlKtAsQLz3abyLYRsxn+a6/9U3/FP19EUVwuXesEflHxJB1DVf20Q+STyuzrx9bPAzGtS9fjMv9qBUFAWK8jaRr1dBq3mmb4MGJ8C0hrVPQZG5tTGt0ppKpE9TzRrEgkZKplj/DAJZ/q0Zvo3OzVmAXfQCiCKLPCNgoMfYOd5DGBIrOapSChs610ySsrkprHV9x3UXWZut8j6S9JYjHLbvJidh/vZIyj6IyCBjvlBQNRZ7aIK2ar9yXsXEj/QpVrzwVY4zblcovDZQNDHmEkQEgS9+wW/b7McikIgghVFRQK8WZYvR5f22tJ0Rs3AnQ9ZHc3PKMIPYn+8TRM8s/i6YjH8SIM4/ND15+MFev7giA2EzMM2IyOSCyGuPky5W/UyS4G1O4MwH5EuaHgijINuUeFj9hLlflweY1JUCC0JBaHPq30lFEqy8ws8gKHhKk0pf5d6tO7OA4M0y1sKUVJM2FoYal1rqWPuDtvktQD8H0UP0QxZD4Kb/DO9JtYpmBH7/CNwi0Gbo2DeRFzWEX4K1Y5BbmoEUwntPwHHA3zjHQFbxYyUWSiXILi9SJ4HrfedMDwuV47QVw0Yl754SFKoYB//Tpnzn+aBkGA2Ns7s2YNq9VY5/7gAHkwIJrPUQD30qUv7Lv+qfG04sXaK+BJWHH6vj+GF8XieXYrSbFk+FonN4oQoxHhlStIrku9Cm4Fhg9UxrMsyDKV5JTqJZvGqE+gp8AogJ0myOaoSz2iwQzJG9Cfydxc1NkPf4fCho+LwkitY5kSL5QDxrqGktVpWgMuqkcs5SLl9JKLwSFXtH3ywYiUN0VTIqT0ilflm6yCFVOlhC03yKVDDq/8JoujBYX+PWy7gqbB0KiyIQISiZjy877RpKnexfcjkOChu4k1iT1E8vnotMp3jheOE9+uVGJZ6hde8Fm32n3ZsOJZZeBnhOfBbBbv9E+nsTxosfhpxYh1ZS2mzUdruXb8qU2imCB5tUEqFRGks7xcHFDghNQLLT5wn2P4cEFk2vSWaaxUmZOwTsEdYJJhZBrsuA+IrCWSoWE4M8rTh6TdKSES/cwuvp5G9RwEAarv8JBL9KcawvfRlIB0OqJSCTA9HVvJUazATt3i5dQ9KuqM+Rw0FZZGiSRLxnaKtDvhwWoLiQhfUlErOdxMgcZFjURWZanmGJ6E2OUmW1WbegukyQS9WsVOpfBv3IgrA+UySBLi4ADl5s2YG+r7Z+6E0mQS71xks4T5PMHWVlxieZridII0Viuc0ejjZmr/1LE2gDktT0W53MdL6a5LtJZvPVULkhaLePVh2zFd65Tvoty6FfN1l0s0RWGVTiMePEDyfSTPI7hyhUwrR9cpIO8fgKbxFfUD0heKceNerYY9WlGe3qepj0i4MyQhOLJKlDdVFo7OeJXllnWBlWzgp/OsXIm9SZFHizq6AXpeQ3NM6pO7JLwFGdXh684bNGmTDWcohgyKQJUjlrZgUH8RK1EmaLU4WFZYLgIioZCVTczyFqOdl8mlPBpKn1rFI5yYWD0bz1NJJFyqJQ+tnCGKYr3oMJQwTZl6PVaEyGQiNjfXCX9EOh1LxsG5+NJi8XH6xxdZDn5WGTiPpxEvJOmnG9Ktb8f0+YiEv0TPJ4g2m6RSEUopw4uFAbmgx3Jji7vSVaYHCybHDqaUZqqX6UYN8u4AS0rhhBqZziOEvcSo5ygOH5K2+iR8k2WYwJGzqFmNVFHlhDq2KbjXy2N7Kk1jTLUGTqBRKfl0FxlC3SCXWPHf/dYtCvKcpD9jb5ijkHSYiDJpeRmLZNgW95XnkTwXL1tETWmopTSpnMxSSmEmiiyXgvJOkisbo1ipJgjQt7exVZWg2SQql2PKTxShvP028t4eYj4nPDUuI5lEBOli+gAAIABJREFUHB3FlJVikaBej/1Snjb+3tOMF8lkjAePKQQRBPHP4zTeNV5ks/H/yTLq++/HeG3baJUKK11HfvddpDBECkOCy5fJXCjRf7CMZakVmVcLj0hvF2Ks13WWQ5sSAzb9Q+amhJEAW8uRVh0WboJ77gU+mO2yUXARisShWeLAqTEyU9T1ITuJE+TFnPr8Hlq04jX5bS5bH7ARDUmxRMkmEXKEsC0m2gYHV36blS9IRks+0l/BjxSSoclMrzBovYSWS1DwB5TzHoXtDNMPu4QLjyDwqddCpCBAKaZZrQTjMdi2QFUlCoWQej0kl4spQRCxsRFRLp9XBCwLTDOmBT4NWAHPaEKfK4SIdaGnU4lEItYe/1lf6hoAPA/kfJJUI8PmZtxoYtsSCzPFRE4zyzZxHLgzb2L6Bv1VFtFq0jazHA0SBPMVfihR8IckEhGp7TLVD/8Geb7AXknoSgDJFEKV8Tdq1N0jahWXg16Ko1mBsZOiqo54vnhMSYwZmGlkJeK1KyPUlEre6TIJCqT8KQoew4XBeJUkmZLpiypSQqMrmmxsqXiqgZJQMQoJmrk5/XZAenJMkiVXN03CZhO510PTNJxqFTQt7gOYTolkGWmxQMxmsWuxZcWT0mpFuLmJ5LqEuh67FtbrT58m1+kEaeTz2PM50Xpr4JeJz1s3lKQ4yUomzx05hYhl3ZZLxGQCcMbblRwnTsY6nVj2LwzjvoxT0yARhpwcBSw7S1KzQyTXpd38KncWm0TNBoujBavbhwxPQpyFj+YvyZltpEwGplMyLMgHE5L1NDl3QKgZWPOI++EVek6eA7uKF8nMyVPJ2ixtmWOzQH+RIkJiY9Wm5PcphEOueLe4HNxlJ9pDx6GTuQyyip4QpGSHfuEKTrWJ9PxV6v+qRcocMspsM9eK+IUyN43X8DyJEytP6Aa4R0MSkc1IVOC5G4SuQ94d0j6IaC/yOI5EMikhSTENqFKJztQNl8vYbVJRYtnIbjfeHXIc6axy8ElN+S8iniUD5/FlxIu19GilEuNDmEoRZbMfcy21lilmaoq+vsl8Lri7aGBHOkoxy1HYpGPmOJkYhAsHNxDkvCGpZEguB7Xjd/CmNp4nocgg6wpqSkVu1WilhjAacjArMXGzpMUSVfLYTA4ZzjRE4FNIrnh1d4icUCisesylAsnVmIltsLBVXDQyqYiDxFUkVdBVW1QvZ/AiGUWByqaKPXIQoxHpRZeXd4ZEGxuxq/1shhYEuECUzSI5DtEpNUX0ekiSBLNZPGeqKpJtE1y5EuNGOn22IfHPFi9+FRyTT+KFLJ+5CXMqBSs/ehSrOUHc9GtZiOPjeEGvqoSNBlEQIO/tIaVSdPdWWE6CVOc20mpFe/N17ji7SKqMeaeLfTBkuMywnDgkohW52SGSriN1u6R1lzwzkiWNSKg4K0FCcjmUdvho0uLIrSOLEF9NUFdGtFdl+osUzBa84r/FxeVtROihKhHfs/6ay8F9koGFK3QmpYugyCi+i0iodCvXENtNjCsNdq9reHqKbu4KlpTmvnyVE6nBQuRQiBOh4QOTujxgojYZb1xAl33EcIQ1D3g0zJFMSqxWEqlURL0eG5LJMmceUqoaX8sQVwRMU2I0Emha7FfzRWMFPKMJfa6QJD7GF1bVJ1PaPyk56rpw755CEKxdTH0UJU7AM5cbyK0E+28JOh2Z+VzCTzUobEoUCgH5ZY+hFOEeB0SRwEsUuFB4SOvofVRvgpNIMFwWmCQTXLQfUgu7JO8OmW/s8Mguo+kSkS2heyYXlT02mfPO7DJhBDtNj5df9ri3KPNv979FLWeya1gouoc70yknLVLCpxPkmUc15IQKZorrmUPK8ojW8D5H5mUYj5AUhSgMORqnaD56hH/5MuRy+BcuxNzzxQLRbscSlYlELAXnOJDJxLU2349pKZub8cLUsuJJSVWfLrm4teTaanVWtv6l4kmUnV8yIXjibthaEvYUUBGCblfgZfK0Ck78fZyc0G4LpO1NNnWdyLIIhc5AqbCchWyKBd2fdNgPLjAIVzS8NuFwykY2gT816aerMPHYVCZIkwmYJpLnISYTTlYlepGKb3ss/YDpXKYbVvEDjzI2x20fTXeIVj5S4DOea2QViUt6H0VEeFKCatAhiYlrFMiFEyLN4Ci1xaDxAmo+TWiqbNDj8C1B1LrOkbaFW4BMJkTvhoDEwQFIOy0upxQ02WeSbvKi1GEiIuR6kVpKwkgFqN0TNCGRv7TB7u75Nb6uAKRS8TAulx+vtFer8bFPq5Pks/ji4pfFC4jxwvdhNBL82q+5pNMx1dRKNJCaCf72f5VZMzwW+U3KrYirSkhL7tCXQR6F+IFEqBe4qr9Nvd1BRBELvYhiW6SNiFwwIrtw8N49YJYo08+/grHwGfgSydkJ30p8xGGwyQP3FWqpGb/+ikNPafK+n+LteZ0NeUhWdZFSCsZqwbVij2O7hGnkmCUaKLYFgwXXU/uU00uk/R5YKSRdByE4dio053OEZeHv7sLWFr5lgaqivP9+vMhX1XjxPJvF85ksn/uihGGsNvTPHS9+VVgBn8KLsFY7o+yKTid+TAhwHLrtiMDbYDs4jOmnR0ccuVVkqcCOJBHN5wSSwsDYZnnvHi1Mum/12A9T9JQ6rfZbhChsRAf4aoK+XIODY5q1CUwmSIYB7TZyv89Q/iodKigri/YiwdxLMFNL6HSZzSRujoqomEhBkqp3jO86aPKEa2qXpBJQFFPS/pRAVnGTOSTXIZB89ssv4F66zrIPCWuCake0S89zxBYrTWKm1EkSYQ3iDdsft1v86+cC7JMeSb2MnpQp28c4203SClyuAHeOKKUFTrHG9es+29vh2TrvcaxYs2/XPaSlUnjmSfC0nJ6fFZ8rGTBNk7/8y79kMBhQqVT4gz/4A9LrBpXH4nd/93fZ2toCoFwu84d/+Ief52X/yUOIeIL/LCvpx7lhshwbUbhufEKYpmCxiDg6Ely5Ep5lkPfuxcfn8xGvvhowGklcyx2jioBoF8rtO4wUGVkOuGE8pLY6oOIfM1A0hn4RX5MRuoar5JEWEzxV4I0WJMIeOzMHxJxZmKEzSSFMk3LD4VKxS+LaJu88qhDMFrhegdBIslET3D3ZQEu7JE8lHVP2kqu5Lve8CwhD55Z9kW8Hb9DpeYydIyo1ma3wgONehX67iFzwaF7rw8svx/JjnQ6i30e4bjxgw2F81SST8aBNp5DLxbfDMNbxms3OCdtPk1zcWmItlyOczX75q/pUpQchzm9/ns/4uP52EMTjuVjEs5KmQRgSSAqjZRrhpmgFA072A8bWiPJoBBsKUblMU54wcWUO98oMjG2kWsROSjCdjum5BjVlSnnxkM3chOP8ywx7ZaIItuQ5ka4TyjLHQYOBn+P+eIM9Z4O5kiNXFARjHUMJGToZQjeJ5MtsZqbUVBfDnnHsN/hJcIP/Kvzf+Ffyf+SSdoBnZFmkG7gupFZT9lPXeMf5Km5HoTq4TRjOCQsK9nMvMLkjoesRw6EglYpOc02BrodIzSqXrvmUpz6lZUDBGaAZRR6sGqT6bRpan9rzeTZ/zf/Yxt3Pktv+supIf9HxDC/i+Gl4MZsJRqO4GVHX4wVHMgkffRT/XasJUql4cbGb6FDMeRwtFSrt2ywUgaQGXFcfsuGfkHHH9MMkK5FAMiKEMJkacc+QPu2TmS54Xl8wDFckhcXz9jtMT2CeENzYGvK1S12OSy9h9RQ6Ry6qSFJMKcgpnUm4wWulRzh6FiMMSAUzLlcHPBoUEZkUt0ZNrjuPECOf2uIdapsRHatI//0rCGNBKzUmOjqKewMMA7pdsG2EqsJkEm8gna6yxHgce9j8S8KLXzVWwMfxQoh4Bauq5+ooiQQBKqNxgDJJ05I7tLsq4+4J5aZKWKkgjo9pbAumYsKhsc1AuYgkSeykAyaRR99Ms5FbUraO2Cz6HEuXGIVFcGEr0SOMIiiXOU5cpturcLzIsb/YJVvWkMIseqAQBUlGIx8rKKJIAbs7LrmDFVl7Tsfb4Gvh2zy3uk1VG4EMVqbIorCNN7PxvSkPtevcWnyN3PwR+jIgN50S1GRGU8FgEJ8uMXNLolKJME0pxprLVXZ3fZQTi8HNPqmEz0mrxaLbphKNKBYKFF/02dk57yH7JFas8771farKlyIRgM+ZDHz/+9/nxo0bfO973+P73/8+3//+9/n93//9Tx2naRp/8Rd/8Xle6guPz9qMhfMeHVWFdlvgOOdZoWlCoxFvjFtWfJKUy/FjDx8qHB/HvNIwhJ4VoYwnyJEPvsRWokctOWNDmZLMZZiK67yf2GF6aEHk8px1Hy8RspDKOFaAE9ikvQOuqFkmyW3Kiz0cOUMUwK9lPuC2/zwPb0mkoykXEh2+V3mf9+bX+dveNhnJolma8VxrSXeUoDxc0O9BefUhg9QFFCnkSCi0coJSUaV+SSec1mg6DtFogewBtgyTSaxXn0zG/QGn3gNnA7Q2jlkT66IoRsT11msYfvZ2Gjx5S219//+frh/rE+DzPK+iIIbDeKs5mYwbq+GzP9OT4jFpK9HtnqlsEIbguoT1OmEuR7izA45DvZUkPIkYDDYZHoUo99+jZhwjxj5t/yrNjQ3CXI6WqvLmYQ7JCahkXb5avM/7oyzIIC0sWukeKEkaxpDw8i7DjoeYZWmqS94Z7zKc6bxgPKAbaXTFKwxmOdJJiVreprtIk1KnLAPB1dQ+J06JbWmPVDSgLI9p+1UykkkzOEQYCm61gYFEqCcYGk2SkxPGhsLKDknZgv4AIg9Gf99jMtkiiiRUNeLGjYh8PkBVA6rVkGo15OLFEEkKSSZbOPfHrI57FP0+iTLYmTKFF+uf8g9Y+4U8/pU8qfHzWfxi8Qwv4vhpeLFYwMaGOHXGji/3TAYmE4HvC/b342Q3kYi4dxu2jSnONCCXi9iQeiS9OXVjgqdXGYgqP+5cQF6aZFcDdB6iWjaOkmbo12i5exirPhcKPS75x1hBwJ5yFU/W+bXmEb1Jjr4vI9ptriccpjOZnp+JvW2SJvlWnkZqzHv7RSrLHuPbEkXvhOF4E0UK6C0kXix71IuCsFymGQSEkos6noOwiVw3XtAbBmI2iymO83ncLLzGiFO50V8KL34aVqwXwOsv4mnDC0WJG31Xq9jrZXMzvv8XwYrHj38SXqxWhJVKPN6nDY9NLQBk+oMqw06E3O1Ss95HHkgclTZolR1QVVqax1t3GghzQTm15KvVfd5daIjskmi+oKUcE2pbNPU+XKgwOHZB3mHTfcg78ysM92Se3z3i7qzBsdcg7MkYBY2SumIyyZFQ+kiRzQ3jLnfmLxMqOiDxKu9SCofoOISehJRMYJXrVKQx/WyBjvQCqVkXzx5yaBcoSRPcMGL1Zo+b7g6lUsh4LNFq+SQSES++6OH7gmbTp1iEZjMk93qL8c4YedTjOa9H35HQb5QZp+o0GuHH8OKzsOHLiBefKxl48803Wbcc/OZv/iZ/8id/8sTJ/Z97rLPD5fLU2CjJmbPkyYk4UyiBePJ3XRgMBOl0yOXLguk0wnEE/eUm9kKwa99G1yKyJYWVVOCWtkshEyIfHtANKlj5Kmm7T34+Yr6QGGW3WLgSdfcAyXfZkIfMpTqSYjKR8qyMAv9P91WGqS0IIlJZj3JuRsE5YXh4iZRrkWmGfPNqlzeCf4V09BDmc2xbIis8FBakcwJx/Tlc55BWdkbUGSHmcyLHYdOykBIJor0c7OzE6kCVSqwoZBgAKOMx0v4+UT4fy8Fls3GJcrWK1YVqtXhx/Pjk/MmJ77NKp1EUVyI6nbNdmbDR+Oyr8BedUH9VsZYYWYPY6W7P46Y6Yan0aSB50oS+7lDXtJiKlc3Gx+g6wrbjxrtyGWYzmqUSo44GYYSYTdmU9zk+CJn2BPKmRuOCwk+8VxmVFXJFiSA84c2JgZKcE+ayKHt7dNpLmsYhwjCo3/ARgymDoMTokclIUpBSCULDoO6MSLorhKQTZjRS8oqLOYflMESSIt7rb/Kyfpeq18Y1EmSWE77lf8jX+RGyHGK5Kt7CI6iV6EzzdJYNlku4NP97hpQZqUUm9Wu88kqA9cGQiiNx29xkczPmbj7/fMhXvuLT78fX3cmJoFKJd1KtRpPRe2NmM8F8AXa5RTSOF2BrpYcnucbCT1/cPYufL57hRRw/Cy9kOV5wCAHvvKNgGHHF4OLFgOVSplAI6XQEM6mJO5PYWd5CGJArCebzAvcyF0g5I5TxjL3k82ToEyRSaN13iCJYpKpMFYO0O6HgjnjFf5sok+ZRWOaulCadgR9NXsK2Bf4UntMWsApQnQB77pNNmlyouojLO3zwoMxssUDMZyihRyirKKsJqd0ylc0SgRTC8AT5+JhICLbkIZHrIq0EciIB7XbsYp7L4Z/SRZFl1HfeiRtci0X8F16AXA7Rbv/8eAGfjRUnJ+B5sRJOqfSzaUZfBF6cimyI4TB+r8fHhK3W+fyvKHG/xJqXssaLzxqDx/Hi+JjQMBCDAeH2dvycqnpGyWqWSoyKRTg+QhqN2Ir2OB4aTKUZSsah0YKfVH6HQUMmr1mEfps3FxGKOSOS4+Sx4yyph/uEly5RvRI3KY9vyoz7OYbpPCQ8ulKe6/oD7uWv4IUKQy9L05iSK4GsLJl2lthmQMu9SUIOqehTtrxDSsEATV7hIcAJ0KwZk41LHI+yHPoNmE25lH6fd6RXOKlcJZf2aGRlqp1DTgYtwjBkuRRUqzH3P5sNse14eNYFGHmnyfDu+Izdcei00EIYjfiYodhnnRpfRrz4XMnAbDajUCgAUCgUmM/nTzzO8zz+6I/+CFmW+e53v8vrr7/+eV72qYhPngT1elzqPZXYP4utrfC02hlP9LGxzLkF/cZGyHgscN2AO3dkphsN3OkQfdlFkiLMVJmi00dZSHS0LWyjgt3tcs2+Q8YZIANSkEKIDNlgTCQEKz3P1dUHjNUSveRlPuJFLkQHbI8/4NC4zMAt8I/W89CvsiEPyYfHtPuX+NtVhVLxFvvHLkVs0pEXO1AmLnBN+5BytomcyODvVpBv3Yobo6Iotpbf3kaaTuPJWlUJk0nCfD6esGyb6PAwHrcwJFzTWdY18FOXYoQ4v4KetPB/kqzmmkvqOOcZl+N8dln1V8nF/GVDUeKJFz62TSiOj2Mup6IQrjW0ecKEruvxOEdRvKIIQ5T9faSDA8RkQvDccyDi3TjR6XDydh95piMmEyJJ0DkIaZVtomzEIKrw4V4NTyTZueLzlRcs3n1D4/A+XBzf5nXlbdpHIWO5BmNBvTVEfe89Nh2HoRU7T1eKKmWOGbhV7lIjK5tU3BOCVRZUFa2UpJYLuP+Rz4FbYOwoXFeGLNw0ZcniNfnHFMSCI/0yKz1NyZ7guzITL0VSmrPI7ZAatJkrEbouUWlJyDt1nH/oo0zHLOwdJhOfWi06o1sMhzENYzIR7Oz4+D7c//c9Tt5XsCxBuRySzbbRmvWzKryqfrmk4L5s8S8VLz6JFT8PXmQyIYeHMV5EEeTzIalUyCuvgK6HyLJCKiXR6TSoKwMSdg9JgJkoUfP6JNwpfSXPQi9ROLnJ5dVPiIIAQwmor44ouiBCm0BRyfsTQnOJlt9CkTLcM17g9enf05sZ3Eu8zP3UNmEQUA0P2eSQxTzJ/V6eyB8xHcNi5HFBHSJ8hzBfYbqIuKbdo5LLInydcGcnXnCaJpEsEzUacUOrLMOp/0x4Sh8Nt7djSVFJItI0Ql2P8cK2fzG8KJU+GyvWu+OnfVU/lWb0ReHFutLh+7HTMny8eWm1QhwexslCFMV4Ua9/PFl4fAzWeGFZiMEAeTBADIdE+/tx74amxWPfbnPydh/1wAbfQ7KXHPd1WrkpYe0y/WWTjw4SrGZw4TmJr1x0eP/fwv4tn4uz23x1/AM60wx9uUZ036GZ7SNmMzZlmZFSQCqVqagWlbDLLe03uL+M2DZ6nEyThLrEaJ7i+gWT/I7Ke4cO8sqmaT+imPW44nxESYyRpQhXTTJVK2TEAs1e0rXSHEubFJUpE0WD0GeWqvJSqgsvvYDf6ZDzRuwHW4ShzN5eQD4f42mnI1AUuH9fcPWqj+PA4tYJRw8V5gsJ34uQ7RM2vlL/2KnyZZMO/VnxM5OBP/uzP2M6nX7q/t/7vd/7uV/kr/7qrygWi/R6Pf70T/+Ura0tarXap4774Q9/yA9/+EMA/vzP/5zyqU790xZRBO32+UZvs3melJfL52u2fv/8di533tsZBAq1WpGvfz2e4xIJePQo/j398IiL2SmFyKZ6rc69UZFs18TrDDCLTTbUEQmvw6VRF2+QxxEqqqJwabWHSPiYUhZHL9Nw9tEFvB0YyOYUEYxBEWxmhwRkuNNr4Abb/NfVf89vOz/hvx81+Lv2FV4297gu3WJAhUWyzPtyhQvyCdvqmFa5yFG0iWm6NNptGI9jQlwmE982DJQwJFWrwbVr8PWvx1fNRx+dl3Ynk3gSfvVVzrrhVBU+Ocm6Lpjm+cI3lzvfvViL/dZq8YCuOfPrBXaj8enne/x5F4vzUmku93On8IqifL5zcl37P6UJsU5wYoeimMioKPF70/UYBFT1fBwcJ34eSYJ8Pv6ccad6PK7rsZjNYHMTdJ0js8BKpNitzWjpbY7kFINbDUZbNZ6TjllOXLJZBSnh851XXHAjrjUj0tIDWpkphg2XRJujSCCrgtS1azCb8dZ4l3mkIFSXUC6hDya8dbyB6SnYAn4j/Y/shVe5a11EtQXlLZVvvvAT/mr+CjetVwkDjf8s+r/4Rvh3ZI0Vk/Qm041XEULCnTxAcXxywqWjtChJc8ydXV6YH+C7x/hBlavJBMHFDPKeR+tkSLvfQNNgZye+pkwzPm06HbhwATo/OSFlrbBSO9yXW6QyR1wIB6TGFtrF1tlX8clT7su2u/NFxzO8+Hh8FlasHyuVzqezTufJeBFFCt/8ZpZaLT5utYIf/CC+zFsc8c3CjNWJTelyA7lfpDg08bsWLjr/pf5jkuk7bMtdDvULrAzYmD+gEg5BBMwSNUZyDj3yqPldsokl251/JFj2wdgELUKKZE6sAmoy4teL9wmcMf/74F+z18vzXxR+xAviDuPUBd52X4Agz1fS77GTCWiVFI7GKebzHI1Hf39uJHa6G08uh7Jakd3agqtXYXc3/tBrXDk6Or8AX3wx7jv7efFiDcRPwopTSiWeF8+jT3q+x5/3i8KLUgn29+PbqgqVyjmOrgU41nQqXV9Ln8ULiU+OwRovTDPGivVnms3iz3iqynRkFlgFGjsbx7TocrQQDJwdRvUE1/IL3jEN0oZOJunynVYfqi2uVk9I3npAKzkiMQjYlTuoio6cLJK8epVOB44GCRZhSBj6iLnNyGpweG/KUN8l5Zl8r/ge/8foG8wDnZWjcOOqg7Pj8d7xJTQn4ob9I1ocowsXO7mBmW8gSwFWVGYWgiEFFOUlUzbIpYakigb/afgTVvIGNec25oVN9ITHcjzmw1GDbhdu3oTt7fin3Y5xYzAA0T5BDD0Wxg4PRYvS8ohrygBjuqBwqXV2qjxpifJlxoufmQz88R//8Wc+lsvlmEwmFAoFJpMJ2Wz2iccVT7P/arXK9evX2d/ff+Lk/u1vf5tvf/vbZ38Ph8Of+QG+iHBd6PfF2aaDJMWL0PVu5GgkKBbj+5rNkF5PsLcnGI8F2WzI88/n0fUh43F8Us3nMB4LmuEJxfQYtWBwpO1wtIL2gzGutUk2TNJwOqQVSCsDpldfxBNHVFI2ysEeYQQ9o8WhcYlKYoGZ3WTVd/nG4v+kFl7gTuIlJFUhNCPuDAtMI51y0qYvirytvkpamqMESebTFSdKGVkDK1fBV7IklrdpLwz2/t0M6fWLRL7KIn+DreRDolNazrG6i2hUuVKLmCYShKkU4XiM6PUQd+6gDAZIQUC0u4tvGPj9PqRSdI9DAqHSTIzOxrfdFsiBS0Oes9Z3PGvE0rQzGTRM83ynJpGA06ZDNC1ONJ4UYYjy4MFZk62fTvMp0vhnRLlcjs/Jz1M2TiTiCV1REB999DF6kEinEcfH8fuZTAj7/TjRWu9eRVFcGl9rXq6pUa6LMp/Hqj5hSOg4BIkEfi7HIgdG5x45rYdp2xQMk2UxwLZNbidbWIZBqneTZCbD7Tckmrk5acvihvI2YjpkNZ9zYuUIjJBaYobVbvPurST/MMxSzHlcLs4YdU74H++9zDQsoAUWOcPEDm1ezP2YIUnmK53RwOXhLMO30v/A3P0WtkjR8E5IqzaBbuBWa1QTI3J2D7+eYTaJSGUDdpf3UMpFxsk0i8QG5cUhyc6P8N7ZppApsy80krrFRtbGMGA6DXjjjZD5XEKSJExTYjr1qCcihrrOPJcnJVvY5RyiOiWZmSHrBqNR/LUuFoLJJB7e2ezp3+lpnFaPnpZ4hhcfj09ihRAhqsoZVfTkJJ57KpWQViuk1Yp3Gz+JF4YxxHHiRUe8jlPYVdvMU2O8gs5h4hXe6wvMRyOC1SYVNUkxGlOxD3EqSWbeBTKmTU61wdMZUUN3TI6NHaR8hrQRorYPeMH8D+y7LdpygwfiAgl3woNumWmgk9YMuuktSs5dVm5IaPt0FZXLWZO/ky7jFvNkNzTSvT7tRzp77Yhxa5tcOke+1YJCAQYDDvVLiHyVjWs58pbFVNcJl0vC4RDlgw8QN28iLIuoUiG6dIlwOsXv9X4xvDj1XXkiVpwqG7Gzc55cPK14kcnEUqvDYbxSleWY/qkoiKOjuM8iDGE4jPF2sYgrAsQytR9zvBsMEAcHCMdBmU5B12N/ANOMx3hri0UuwmjfJW/fwzJNijmBpapY6QI3tQ0sxSbdv4uhTrnzwxG1V8dkprd40biDNOmwP08TBWmqRpeomuPhO11+eGcH25HZrHrUozY/6l1mb1aBAAxtTkKx6Loh30n9O96IfhtppjI/tfPkAAAgAElEQVTqzKhm57yUnzGcaiTcWPbWTySZ52p46SzlaAhSwN3EiyTcOTllSlPpI7aLLKcmGi5ILim/BDOHebJB0logyw7x0ET0+x4HBwKICAKBqgZs1iKGqs4inyNlLlgaBZzsnGJ6QTo9PDtV/rnhxeeiCb322mu88cYbfO973+ONN97gq1/96qeOMU0TXddRVZX5fM7du3f57ne/+3le9guPxzvIZTlOAhwnXtBXqyGr1XlisK5oxo3D4ZlCRL8fl6ZqtVhfejoVzGcqcr6M26oTuTHFwUoKlrKMvFI5Nsp8U/4HnGKOhDVmWNql3vkbelKdlL5EVWSuaIfYlS3eNF+ikN0jK3W5aD3iZvgSR/4Wx26NbDQhYzgg4MSvcqeXoxke8fXqfcphnx85v04gSbxUGFLo30ezZvQlA0lOc9k5gkSCYVBCWk7Z1PucDBKMkhrFcgDmEuG6iAcPcMtl1B/8APnuXaRuN+4XWJvEGAY4DsHcY+TmAUGzEdDe8xnfnVAuBdCEcGPj4xPoeifkk+Xf9X2P19yfFL4fv4f1Ts+6jPrzxuctGz/+3h8vY6/fW7kcu4Dev4841c92v/UtRL8fS8GdnJzRf8ThIcr9+xBFRGFIlEwSlMuEW1uxU6QQbFzPo1gSoVc6Syg2m3OO9waMTZPKRYtmdcBUucB+30Acz2lkZ0SbmwTJJNg2QegwCIqEhk19PqeXfwWzq5M1TVRtyv3Vq0zCPDortrQTvEDB9BKUPZPfaNzib9ng5r0ks/QWcmCQTYe8PHuTBA4nSoN8SqI6f0Q4DbFydexsBROdlD9B9iaYPYk7l/8Nhd2IUCmiTx5RmB2QerWMv5HHajeYTsOznHA8jv0DcrmQSgVyuZBCs0HzqxrZPY9eT8TS5FtN5Ob5BP7JZjA4V4R42if5L0P8S8SLT6qNKEq82LftcwwYj8VZ0a9eD8/UNPP5kFwu7h9YH1uthgyHMX3In6iULxZZbtUJu4KhLSPyMJ7JGKqC5oXIwSFKJUe4CElaEyI/oK22kJWAgtShmlrQ37rKO/mv8bL5P5F3HWrKhP9b+W8Y+zm0pUlGX+Aj6K/y7PktPjALNBMDqvIhkyjP/yD/t+QLMq/kB3B0xMQyIDSQMCisuohMnqNJmk1pSscqMlQalE0TcbyIe5yiCHSdMJVC/+u/jrMd0yT4+teJTDOmEiUSsFgQLBVGtgYImnWf9oMV4wObct6Fxifw4mdhxboM89PiacALSYqTnMewYk0HCovFeDPozh3EdEqYz5+70h8dETab8bFRFKv7dbuI8ZgIYgPKXI6wWo3HTZbZuJZDmYUEq1ZMV1BVWvM9jvYDJlaK8sUMW6V9xorKYQ+i98ds2QvCixdBCLwIRmaSKDihmkhwbBZZ5up45gB1Pua2W/v/2HuPJkny9Mzv93cR7h5apopUVVm6qnWPwgAzu7DFEgBJszEceOCBZ5zxAWD8AjDccSYPNKORPHBhC84aMFCLwUyL6erqqurqSp2RoaWHh2t3Hv4RWdViMANgltvT7NeszCozPDKEu7/Pq57npR+vEUcJJdWhlMywYo+aOqKeTLlbOuNwts7JkUKtWCKnd9kunJKd+gShQZJAxXDQvAkRChc73yRN86SJgnVxQSEbcNwq0v7G77N/8WNKByWU8YjcvQrFgxJ6sI7+UczpqcCyZAKQpimFgiCXi6lWE8r3tojXTSpEV8XdSn0TkX1R+F2dmhVerHZ8/jpjxb8qGfjBD37An/7pn/KXf/mX1Ot1/uiP/giAw8NDfvjDH/KHf/iHtFot/uzP/gxFUUiShB/84Adsr5jxv6b28kWQptDtKmSzLyZgVmpg2eyLoCKOWVZ6ZIKfz8tjz8+VK3n4679ZZzqVnQLfV3jyRMEX2+QWLfpjeMBTZppDoOdYmDX2Tv8eLZ5jhIJLsUbBm1DNGThbm2we9dgw20y1AhNRYM895ml6n12jIxfjJAbhzOd9O88iqELW4d/tPqbnVpmnNUwz4cg1eVM/YaSvUXMvqWWmiGmA1mlRzxXoKzfpW6+idDo0kiOaYwUSSSJOTBPtr/4K9flzxOEhahQRCyH7arMZytkZ+l//NQe+T8a6RfvBbzN6PEa5uGAj7bK5UScJK9KRLR1hUi7LL/WzWl5LJSFlJV/6Waf7cmVm9W9VEfpFGtCfIWT9XN7CP9c+k1EqnY6sYpkmTCYo3S5psSh5GYsFSr8vl4f1+0R7e1KpKQwRwyE4DiKOiV99Vb5f35fHqyrJ+jra2RkMh/KYWo3EddHOLlhPFTZZEH/3u+xMjln0ElR7Qndjg8i8xvZ+ETEes9l+n8tBlSf2Dr22wlbnbxFik9Nonz8ffodqIaRaDnnDfMrRYpPrHPEouEtIjZslhXSucqCf8Ph8jY1ZxH8n/hd2M5d4Zo3/u/gDfkf5T9yavEuapHjOjFlBQy1lGVrbZKaPSBKodj9mo1Yim01QtDJzBXLjPuneG3zvIGQ8lon1yYnGYqEQRSmVSsK1a8mVGEm/r7C7m1yNGsfx50/fy3HCV2ke9Mtg/3/Ei88mmCv3sXJjqzHwQuGFuzIMea2ukoLVNen7cHSkMBzKopPWrKMbMlhptTR6PYEb7VCKz/HbfYzohLZZRDOLNAdPKE/PCY0cuYnHeWafiXbAVt7n2rZP+fg/omZhpNfRheANHvIwuYtnmeRFgK8lzKYhP/pwkzBM+e3Ku3x7+4z/o/2biPwaiRJxYE2oMuBIXaeWzKiWQurGkP7hiGFmm35xC2H4NGbP2ek/h2EeDg5kMJrNov3lX8LxMarvy/0z4zFUKvKx995DffSIgyRBW3uLbvImk787AsdhQxmwfu+OLJa8jBel0gtuwMtY8Vkxhl8VXnxRB+BXgRefzShXCYYQ8qIIQ9lJcRyJHyDxwXVfdJo1TeJur4fo9RBBQPzKKyiDAUmlgtLrkayvo7Tb6B9+SJqmiCgiXm4f1o4OWQtimnEI6+vsjB+ysLMMuIZS22ezHqFkMjTjx3Qew0eLfTrnGroy5O3xXzAix98Ov41lpeQ3stwpPOHSqWJ6Ia5apitSOtZN5pkqVibBTH0+fgjfm/0j+9o5qqHwLPsmnprjNf8j8FycxMIPOlzu3uBa/JyL2gOU4BDXy7A7e4ybq7DlDfAbVcxMRLDXxLqA73435O7dFWFf4+JCJYoSGo2Eb387wnWvmvfU68mVCusXKbwK8dXhmv2rkoFCocAf//Eff+73BwcHHBwcAHD79m3+5E/+5F/zMl9qW92nYSgvgnpdBiDn58rVhbKxIRdPrJKG5bLYK0efzcou5dmZrFiOxwrvvqsyHCpsRmfsZ/sM7YSdgo03N3i+8R2unfwdgQ9eqNBK19FJCLNlzG/uUA6mFCePWEwi3i9+l2pxyOuz97i++Ii/MP973jG/x9vau1SiNmfscBjv46YW//vkv0Ep55m4FurCRSQRz70dhKXSVxpU4whxmHLDOGMjJxj2UhrxIT23BEqW7nCTfW9OOT1Gy2S4mJUY9nZpegPWCjYiCFAcB549Qzk8RDk9hTRlpzylY+6iOHOUTofdwifEpw5JLie3FguB9vHHRDs7kM8TvfaadPgvKya4rtSj3t7+9F37BZWZKzLyz5Oc+znqRdRqL6TtPiss/M+15XjVKqNUOp2r6heaRtJsgm2TNBov5kPh6nMr47G8kDod+TmXcgjJ2poExckEZTQiWiykckSpdKXLrQwGbC+eg2GQnjjy+bkc29MBmm3zE6dEr3mHeHOT/cVPuDBuMDI2GIYl6oYDhsHre3N+9Owao6jOpZpl+86Md7sNgjSilPpsNOBZ+jqPZ3V21RZJ7FBOhlhGxI7epxhOmDcO2BNtdMelLxqoBKAZVM8+ZLZ2HUOxGURFJkmB03EZ70RwvTBiWqmj6wm8M0Ubvs+z2tvcvh1TLLLc3CrJmQ8eRBiGTNaXORYglVlWtI2dneQLT8+vKuf72l7Y13jxabzY2kqoVhM6HSkqsVKwajQSbt6MrkTH4GphLLou3cT5uUIuJ6/h0UjhvfcEoLAVn7GX7bMZP6dUTHhU/BbXw48ZBjnMxGTglJmQZd07Z3DnW2T+3TbJj/+B2tOPeBZd5z/s/A/8nve/8fvd/5V89G3+3PgDjs0m303+lqGf42NxG01R+di/Ti+8S7ZqM3SK5OwZHdUEa5dBskFfVVH8LKXDHt8v/JTH0z2EMyaNEtb3FjzsNfHjTb7fPkPJ5dCOj3nn4zKGd483xfvEpim343a78M47KP0+wrbBcdgNQ3peEaXTQfg+u5kPCQ91ku1tKcf5Ml7kckT378sMi5fEGFYVAsP49A3+y+LFL1K629j4dDvoX4MXL2PFasYsTV+8F1WViY/nSdxYbpfHND/dSfB9xIrAMp8DXHUElMtLtGV7KimVEEDi+7LL3+mwff6x5FUc2sSzGWxtsTP5kP5A4/HFFlF9g+aNNbothZERMQp1akkXYRg06y7PnDdIRhYfJdfZtUIm1iaKOiTOmCS6wZm1QzEXkqCx0fAZHLkUYzlCWkptpkodNtaI7YQwUHHiIgKBngZcv/g75rVtzPmAS6XOxDN5fLzORi1E0wRMYurRlN74A8b7bzGbCW7fjqlWIQwTNjelmtDbb0dXX+uKBzAaKYzHcj/Bzk7yhafvq4IVX28g/hfYZ6uGGxvJ5+SK9/Y+rTObz8v7M4okt3M1O9rpSKnRlW+yLNkZcJwliWykcuo2GMQ6f3/WY7tgc91sY5fW2c4rTPx1FvMs75e/z/ejHzEfBOSyPsNcmVGYoRz0+Vi9zW3lb9kQZ1wqT7jgJuVFm4o6xYoXmIrHR/YdukmAvshhGSG7xTGb2QX2pEEcJugFDZI5aRAwcu9QdhfUiiG9cYVRWiLsTFA1lbldZ78OYPL0TDrJRqlKen0dul25ZObwEJEkKKenpJkM57vfRanZMoAdTzjXG2yUyyRra9K5RxHp6g7zfRn4rlL11d6CVWtmsXhBvAV5d7ruC/0+x5HP/ezd+ovUi3wfTk5kxWVZcf+ndKQ7HSlt2dyKr5x461JFVeX1Any6DK1pcvtmsSg/86q6tbEhHbmqfrqiJQTR/fuI4ZB0KdOa5HLSsY9GiMkEkclIZadmE87OUAcDlGUpMglDtH6fll/j0jfYKTs01LGU7JuqHA0rjB4lTLx1+mGdfr5KI7wkyecRmsYPLx7gCYu+skFNFzydFQnjAC2ZUd7KcP11ldZHBq1xylO3wi1jynfFf2ZbPUMAGSVlq/OQG/wYt3mNp41vkrb73PSfUFwMCYVBy7pHx9wGEvI5QVqt0nah3alj336TreE73HVGrBcuqNXWSdMXYxamKautK2ly133x/xXlwvPkJWEYn8f6n7dM5mv72v459kUdps9qkO/vf/rnTOZF/r8ixXc6CZom/UoulwAKhYJUQhkMQFHkGFHiaTyfNZjF19honeEbIDbqmJ0EP6ly4t5kqJbQlZjXRs9I7QpBqBDqBUaZJptKh6GbZ8cf84b6AT/O/i6l4JRy0EVNcszVPIO0wZnT4PxM49Z2hnpuTLMWYHdSbLFPaig46wdE3SETF/5P5zdp6GMivYTuTbgceDAeceRnMcUet79d592HFiftgINilchag5U63WyGenkpi0jjMZTLXPRNlIyNGA0R8zkXu7s06nXJu+p2pSSp571QHlqtg3WcK/llfF+eoM92DVbHZDISS1YO4mW8+CWwQjk/l8o9ti0Tg1+wz+AX4sXLWpW6LsdJ41iOWa26IYbxoouxHGtS5nMp752mRA8eyK6AELLQVCpBPi+70suuQlIokFYqpEEgF4Y6Dtg2Yj5HnJ7ynnuHcFTkt6ZPUNKUtR2df3hY4fDH63zr9pCevc9In7NW7IJlkRQK/PDiu7T9GpGikxYKHM4z6FpKKW+RRCn3rjskImGe5Jn1YeZo3Cscc2f2I0zhYJOnFA3JXf5nMmrEfPsmx/N1snaP6/ZjokRBEfCT3G/hqjnqeZvNpMuicp+H8zXG+S0OnBb58YhipUWcb1KrJZ/DC9OUl4DnvRAkXOVyrvtCChg+jRdfFaxQ/+eV8POX0Gzb/q/9Fr7QwhBmM3E1op7Npp+T+V21mV6eR87nU7LZlGYzSxAsrn6naak87rKL05riGSUmE4WLC7D8KYnQKJcFPdZINJ1G1KExOaay6BDaPk5oYPo2BTMkE3ucuBs8zr5J285Rnp5zsPgIR2S51HbJ6iG7zjP0OCCXT/ifrv+Ivx68wifxTbzUoKzPuW61edBoMZhlmCZFspmQ7fyYIJNnFmTp601MJaRiuaw7RxQrUAkHZDIJ4WDBk/EGrUsdI6vywHjGdnEqv4NqFTIZxGyG4rpg25xlDujV7lLbMbiTPSM+v2S4yBNu71PMRgjXBd9HXSzkWEySEN+8KatCoxHC96/mslLTJGk0SKvVK+UIpduVrdHZTP4tIRCLBalcQSizsKX0jHCcq8gwXc54ieXrkqZYloW7nBtNCwV5gn+OTaeCYTsm6Q4oJRMuj0IGXhHTSikWPz+rKmyb1UKKdHkxpaUSottFzOcSAAoFknqdtFSSj1sW+gcfIPp9RBgSff/7pEtQE3FMqusklQrxgwfEGxso8zlKq4U4P0fJ50lUlVlS5CTYZmjsohQzTMt7nEW7JKrOdK7hzhKeXpYp6Q67hSGNgwIzvc7f2W/gZNdJEkFrlENRoTvQ2M33sWp57O1bVKop3feHjN0snVmeP+D/4rX4PbR6mU7uOnlvyKZ3BkJgNIpEmSyoipQgjFOGQZ55bo2h0sAv1blIt3DzdT4M79DrKXS1LRLdoFRVSPN5fF/OgTabCZWK/I7bbTmPbdsWquoynwsWC7HsDghMM8V1BbOZYDIRFIvp1Zju6n4tldIvbdu3UCj8134LXxr7MuLFF2GFpnG18foXYUWplJLPZ/H9F3ih63JrqjXuML+YkRSK2Lag3RZY/gTLGeFv7TEVRfajQ/yxT3NxSLRI8ENQFwsMLaKScVmMAjrTLO8ob5ONZ+z1f4aiCc6iDRI9w23xCXWvzUwpUVlTKSk2j53rjNMyGhF3yy3eKHzCVsPnUXcDUSpg4VG3Zti+ScepMDK32NT73FvrUbbPUStFNvxT9MTndFTl0WGe8cLkIHfJ28WnKEKQlsuyE5skUmrUNBGOw1n9DXqZbWpNnbviGVF3yMArEhzcoiRsxGIhff7KT5fLsmNwcSEfG42kf9U06UsNQ+KFECjtNmI+RxkMENMpwvNAVaWvX4F7EMBiIRMV05Sv8QVYgRBY5TLubCYf/wULyH5pvFh1kZcd6mRt7WpTcVooSLxYBfa5HEmtJvFCURC2jfbRRxIvKhWiN94gbjYl9ixHjuL9ffn7rS3JMfjwQ7R+n7RUIs5muYibPBe3CYrrNKouz+KbtAcZplqDZBHQuVTIzntYeszugyxTc53TaJu5WqYVbzELDHxPIUYjsH0yWZV8w+TGXYWL5wntjkYxHHNHfMwr3jtY4ZxZdQ9Rq1J2WlQXHWKhMrv2CgVho5Bi2CMWiUE/rvJe5d+SjWeQzzOt7vGYu5xPinyyaBJrGYSmkKnl0TR+Ll4oisX5uU+hIDfb+z54nkzo4xgWi0/jhaL8emAF/NN48XUy8C8wRZEXxCpzXF0AqyrQbCYDjlwuXd2nVwGGbAdn8bzF1e80DY6OVE4/cPDPB6RRilIpkJ61uNfoE6gmUZCSmQxpp+tstj/AWgwxEp+fBfewQpt99xkFZixCEy8Q9DLbjHI73E8esxZckok8nq5/l+2qw+70CVY8p6dv84/RWwz0LYS3oJyMEZrCmj7CTUwK4Zj5FMZTlX2jzfPgGkYw53a9j2nEGLd2yKc2b+kfQGeAb8cEToITZUBV2TlQuLk5lU5FVUkti3hzU44Lzecots1cL2NVZCsRx6GwWyasr6MoKYVkirJYSMWiapV4Z4dkfZ1U01CfPEFEEWIyId7fRziOfJ1l9yEtlSCKELYtKx1pKp2yZcm9B8sd4dpPf4r2+DFqqwVAuqyuXAXc+bzUxK5UyALubAaaRrpa8rU6uS9bmlK2W8S9IaNjh05QYzFPqTV1mrtfMJYUx9IhW9aVnqBwXZnwBAFqv3+1jEfEsUwONI24VkM9O0MYBsp8ftX+1o6OSBVFLvrJ5+VynUYDMZtJUF1+LtKUeWjQCWqMC9c5qbzGcfV15sUtmE6JexNMb8JsnFIspFTKCfOdW/SdHMOBgj/2sD0NI/GYTBW0NCYRGUauhRtleDjcJRO7FNw+lupxS3nOTnlKNehQERNEnJAaJmk+S1W3EQhGYZFZlEXRNdJcjq2axyJXZ/jqb+EoBdxMiTiWjrxaTYlzRbbvWmiazOOkihAUiymOA8fHKo4jmM1MymV3WSSU+wjqdRlYRZFUhZhMBEkin/tFQdqX0b5OBl7YlxEvflmsyOdlMPKyS1klDC/jBUiOwHwuOHzXITPt49opaTFPftziZqlP5EbkFI/n803yw3Nqk2O0wOXYvEU896kGHRpBh0jNkCw8Qi/mE+0OGyWXa8qp7CZbr8lijvqMRnjJR8abnKQ79I091oIWBcXGTU2u5QfMyeHMUspiwmLgMXYMTHfCmbvOutanUBYcND2sgw3eVt8jsV0GrYi98JgzZ40kiUk2N/md/Seyc7lYIJY+N8nn5ahQHMNsht24RtZM2dwVKL0exXtNwlwJUchS9AeycxsExNevE9dqxLduSdWdVgsRx1cFFeH7qIeHV1X11LKkDzYMUkUh1TTSZaCdLsvDyuUl6tOnaB9/LMc0o4i0VJKJy2ewQrgulqriLo/53Ml92X4ZvPgsVhgGJAlpPo8IQ4l/7TZisUAdjWQpW1EkRs7nctTIsiTueh4kiXzMtuWOmtlMFsl0XX4HmQxisUAkCWkQkNbrCE2jH1Zpz0s41eu8V/0+g8oN5nGOxF5QXrTxnZA1eigq9Kw9rIN1NAV6pz4nwxKZ2COr+MRBwmBmgaZTM2wGVpPzjkHqB7yuPKRohZRyISJrUvQGZMUCJY5BVUgMk1xFAz/CDgymooyiKhiWYKs456L5NvatN/AyRZIEXFchn4fQKqBX8+ztJVfq3V+EF2Fo0moFrK8n6LrEiNVyQE37Yrz4L7XI+ldtXycDv2L7eVXDl6tAQSD/7zifdvbttoLvWwyHHvm8fO5qhG+clKiWYrRxH2vcQXFdLsM1os0mpZ0cVibmfu9vWJsdk8Rw5G8xjgr0xDpNcclIW2OcWccNVQx/TjO5oFBIcchRToa8mnxIJvEoZFzUjMpFtM5T7RWGM50NfUxqWSyUPJEX4UcKF9EWYj7HiSzmkUFaKlFfS8lsVpi4JiOljmEIDEeSWN8f3eQy3cIoqFCrERk5rMsjym6XVNdJ9/dRplNErwejEUk2S7GiUTAD0s1NFN8nzecpZkNya5aUPptMSKtV6XzjWMpndruon3yCdngImQzpsgugtuWmSzGZkJqmdMqzpdycZV05dOG6MlEYDlGfP0fxPFJdJ15fJ9nYuKoUXdkyQM9WKjhRRFqpoHS70oE6jkwgVgSQNIX5HPXoiJKw6Z8EcnxH17lxX31xXJKgnJ/L9utqYdqyqpSsr5NUKqSmiTKZIE5OUJZcgtQ0ZUK0BC/9yRPEeMxyAFIC3PPniESuS1THY5RWS44OBQHqyYmcCR0OwTSZ6A36hQP6TgGnP2dw7HFxqeNPfdaCC+7mTgidmIlvYZYN3CDD7NSm6l5wOiww8yzwAxRVYTzPEKYKaTaH6rsYF6cEmsUb223uBI8oMyNMFCqTCyKhEik6we51MpFLKlSSFOw4C1FMJ7PL8dZ3KFQ01GqeelNHrxXwfYHrKnS7gtlMOujXX5djFovFSmY7ZTqV1ZvjY5VCISVJTGzbx3VlElEoyPvXMOQpm0zEslUsn/vyfftldvBfJwMv7MuIF78MVoQhWFZKr/f55OCzeBGGYNuyo9XzShSzMXG7T9ntogcLRloDZ+829XLAW9O/ob64IOPPGcRlHolXiPwUV2TZis84tu4yS7IsIpNK0MMoaGjlLLl5n1vOQyraDISg0sxQExM+VF4nntqUlDnCyFDXpyjBgrGfYxSV6McNgqHDMCqRxFBpqBTXdPp6k0FSpZhxMbUIplNO7QaPpntolkGczZJubuKSpdn6R9TFgkTXib7zHbTTU5SzM9IggFyO/E6ZktshuX0btdcjyecplgXZa3UZFE+nYFkyAFZVlF4P9ewMtd9HnJ9fEWwJQ9TLyxd4sWJ0rxb/LLfBKUvNYdHvSwW34RBlMiHe3JRLNZvNT3eIl6zvNJcjm83iWNaLrsO/FC9A4tbLnfClJHVqWaTLBZ9iNpOJU6+HWPEIlniSZrMonY7EzKW6ieh20T75RGKB76OMx4jBQCrUCSGThCRBrHbfRBEXYo+pXuO0X6QcdPnxowqTQcJm1OIN8xFG4jF2LUwzRZgGnQsIz3swneP7ClM3gxMY5FWXnNPHEi5DbR1r3EV3pvzu+rvcLV8SLUISoRC7EflpG+EF+MKkd/c3sNwpmcgl481piS0uzAM+Nl8jYyqU8hHZNw4w1/I0myndrrynhsOUIJA8mzt3JEdALoP9PF6srRmMRj6LBcSxoFaT969lfbXx4utk4F9oq8rNyyf+5SrQy0SUVXs4SWQ7MJu1sG13mXXKv+P7guFQYaEXqXhd+TtPsNi9ST4vs856/xPueh8wql6n27iH6ypcFyfU/RaL4jqlZEyxrjKu32BDdNlNzvHrm/gPXkGbTynO2ijFLOXfe4vpICa/GIDvMdDWCUp1vvXNgMj2eDTew8NirexzS3lOJ2owiko0Gx53Kh2CqY9ZzJApmzhaiXbP5Efzb3LGLtduZvj2zRY7uylPT3Jc9LNUMg6FbEQ72WCSWaOYixBRBPk859o1FtOYouZCoRl1P4gAACAASURBVEBcq0kHtGy/pvm8nKXXJUEsBcRkIqvloxFC00gVRVYxJhPpJEslGfA6jjwxaSqfuyRUiThGDAYI10WAZJZaFsnmJmmj8UJHejkbKqZT1KMjzNGI4PJSntDlLKmy/Fl4HmkuJ4/vdtEfP6Y1sFiEOvH+NUQQkAwmlNS5PO7oSFb+PY90yUEQto0yGMjuxqpt5DgySbIsKBRkIrMEMpIEJQhIl9uG1X4fZThEjMcSFB2HTl9n6lsUy8uKVBhykTTxf/acwqyFP/I59xtcjMs8dq9zMc7TmeUoen2+X3iHx5Mm48I25ZrGorGDmTg8O82B5zEMiviqRTEZEyYadprHDB3uG8/RgwViOuWVzMfcNU/4ff0/kulf4vqq7F7oGqXZGZnFDLt5g7DWIBh7sFgw18osdm6wdTtLsLWLl5qc2yWSXIEkEahqShgKbt6UldZGI+FlfM3lZCBl2wq2LY+/d8+k0/EwDOnIb96MKRZTymUZpCWJDMhWX/vL9+2qQvtltK+TgRf2ZcWLX4QVmiavM9v+9DiRVKATVCoWs5mLpslgZLEQVxrnC72I3u+h6ymFAtjNG9RqUO1/wmbnQ+bVJifXvoenF3hl8DcYekqqCOpiRE4PibZ3mWTq7HFBzbCJD26hZlTy4xaaknB5//uYew2sw6fsLj5hRoHT4gNe3R9xYLY4WmzyzL2GUATr2QmldMo4LjAMy1xvTKgYC+q5BcI06McVhkmVn53UOMzcRc1ovPZ2hn/7aouzSZmPz3KI0ZitHcFlvM5w6x7l6RlCVRGaJrHCVSkmU4RpkjSbJJaFEAJlOpWBcT5PvPThyfY2YjiUuDAaIVZkoWUlXSyr+5RKUqluFZzrusSC5cilMp0ihkNZkMpkwHFICwXSjQ3SSuVTBGIxmyHmc9TDQ8zxmODiQvKsHCnUoCwJvMLzZIB+doaYTtE//PAFXuztI1yXZLagpNikui7luX0f0e8THxzIkSDHkcWfVkt2jR1HFsyAJJ8nbTRkMuP7qO22LC4t8SbNZFBmM9ThUG5HXSxgPqcT1JlOoLBhwXSKSFMuZiW8syGl7nPU8ZB/HN6mnW7z1NnhbJAnmgf8TuavuK4d8zeZf89RuMPudszapuD5qcEnwwaq67BujZnGBTLhnEIyY110qYU9DOGxOT/kt/2/4KZySDUzI9c9I/IicByyuGi+g+7PcSvrOJVtjFEXOzBZqHnON96iWZ6Tu7+NmynjeynncZNcLsX3FYpFWdm/f19W+QHKZTmu12i8UP5Z4YVhGCiKFOQwTdkNaDaTrzxefE0g/hXaSkZuRfYfDpXP8ZNkixdaLQ2ImM3kc5rNhFotwHnawk8jdA1KRYUN7YTTZJft7ZQtZ8C1G2vM4jtkHz0lEec0c2POc028WpPo2KYw7rGfpBiWQrR7QGLWCDsTkmyBIFugwpjO8zlid4sKl3xn/pgD94Kf5X+byalOLSfZLxkRsEWLtrZDLptybW3B3qtFJt4+1+/ZbKwnDI7POXqcMAryOFqRrdtw+9qU9bf2aJ8m1OMuzLso6Qju7xNZaww7CYpj0dze5jxp0ncLrCunpKGLaLdRTFPKnu3syAqJptHpaaROyua67NXpjx7R+nCCEvlsuc/Rlj26aGsLZTknf1WtWTrvK+LwijmakwFtdO2aZOiBVNrpdF7IzL28un4lPwPS+fq+9BQrubkggPFYJgdRxEXPoFepU9+D9Qc+3b+/ZHBmoAzmbN5xUAYDKfnpuiS1GpGiXK2MV/p9+R5XBODlDZwUCjJpqdWg00Hp9WQLd39ffg5dR7NtKUmqqihJQjzXGTo56Ohs3re49NcYdFw2ekM00eU03uakeQ97ANWoi6HqpJqFECp/Hvx7BmmJekkhwUaZqpy1C3SDIqdBlbFaxMwqpGEefyHVOgwlwJz15XdiCHaiEw7mz5gsBOV4RMYb84/Z77MR93lF7aF7Nt48wd3axSgr+JogCArECXyU3iMbpmSCISgJYZgCcvzOMFJyuZS7d+W9MxrJxU7lcsL6esLFhdRybzYTstmE+VyqCF1eKhQKUsFlfz+5cua7uy/0olek/l9nMtjX9uW2lyVHV+pCL4uUrfYRjMeSWGrbskAxm0lCaRhCpRIweL9N/hshnY5CPpdQUc5oazvsF0dY9zaw7txAaydkwx5F06GeT3miP8CdDslO2mQvj9nY3KVsgNtoIqYjvEjFq6yhRz71+TnH+j1261V2FjN+V/yU+3mbYd8g1A10QyX0FGralNSPGGgbZPMK13YTyns1yjmXe68pdM6m/OwnMPYC5qFJeVNhO2/wrd/L8+R5jo3sGJQhmcglSRLiQpmBVyRjl9kpOJxHm/SNHdYWJ1CvS4LwbIZiWQjHucKLziRL3E/ZycvSr3ZxweU7XZSFzea2guI4KJUKSbUqCce2/QIvVtJ+L+MFyG7BkmOVlMsoy2LTp+xlAvFkIvGi0UA5PkYxTZTpVJJ+Qb6W48iC0HAoN/96DXpag/peyubaOe3xgsFpAUKN5vwcZTBAvbiQZGDTJHrlFZhOXxSO0vSKJ5cUCiSNBkk2C42G3EWwwoskId3bkwW2JU9AgEwUxmPiRNAPy8TnsJPTaZ2lDDsRa6cXCNPnncU9vGyVaJ6wbxzhKNtYVsSzzH0+nt/nYf4e1UyPkahx9GFCZ5rj1N9goloUVZ+4XKUyuyT2QkZhjq2Mx+vzf2BNGVEUPcI4y3SUYrkOdWXKKMzST8u4+hY76QWVsyfM1/aYNO8wCvL4WoXa4BMGGzeZcQ+znhK0huS5wKlsAjJoV1WFzU2pLlevJ1iWvNeq1YRcTkr2uq68J6NIPhbHCtOphGXLkupfX2W8+Loz8F/Aej2ZYSoKrK0llMsvxoHiGOp12Rkol2WGucomJx+2GT0d835rg7PsbTwn4U61SxIkJGMbRQiUV28z3bxJRkRUggGJppMi8AKN8403cYrrrNnHqHFEqmeYT2Oimc8kyDLKbZNVfIzWKdlBi2LGQ/cXWFpIMneZBlmCTB4/0hC6zsN+k2P1gH/zSo/f+A2f+rrC8xMTvz/nN7PvkvaGnM1rZHMKjVrIq5mnWKMe7dMAtztnXz/njdszbK3CNC6wExwSbmzTzV/nZ7zBsHida9oFzeIUYUvpUTwP5fQUAXJrseNgDyL6TpZgvUmhrNB+5jIMy1hZQTEccJnZxz6ZkLu3TZrPE92/z0W4gdt1KI7P5Fz9kjycNJtyXnLZRk6XG36V4VB2IzIZ2TJWVTlzKXuFiDDENE286RTt9FRWYuZz2alYLFA7HVLDQG21EK7LYuBirBfZfLNBWihQnp2Ttnto7py8udxEN52SFgokW1tS/Wc0Qn38WHYsDINU12VV6uwMJQiuFIxSy0J9+lS+byGI19ZIV4tx5nP5WTMZUtdlYdSYBFm8RUp3bGJv3yZUDaphnzw2g7jGcbqPozdwRJZU1zFqOaaLDFnhsVMYY+oJD3tbHNmbxBkLo2gwV8pMcxssYgvby+CHCggFT8uTiTz2xCk38h3WMyMcCrSt6wROhBYHbJYW5PUAt9RgWtmFNIGtDZ6HezzL3GNibbB9yyJrpTi7Nzk7Tun0M3ScAmkK9+4l3LqVcOtWyDe+ERHHgk5HZTwWVxKNQsgKarWa8OyZxnRqMByGGIZMHlajG6sqzsvV218XMhh83Rl42X7d8GLFIet0Po8Xci5ZUC5L4QIhFlfiOJaVMhwqhEeX9B5NOFms8TF3MLWI64UeZr/FLCky2ryHf+0m5UKEOWyj7dQJVIvq+JjT8iv0C9fQREjDPsVLDTyjgNVtMXENWhuvMTfrlLrP2bj4GVZOIVsQmO6YXDrHtWMuaaJkFAw95flsnefuLlNrjf/2rTN2bhmSa+vqFCbnrBtjhhchWT2krMx4sDOmPDqiP8pgn41opF2+kX/Mej2kndknNbMU0jk9dZOWeY2j3CtYu3XuxI8k72kZxIrhEOX4+AovZsOY0WVEpJnk9sq0LgRDr4iVU8gXQYljOkODWdcnf69JvLtLvL/PxaKGOwkpjk7/abywLMSqu2zbV9uAr7BiqVYkggAzjvGnUxTHkd2JpcynenIid+IsF54o7TaLhYJZNdm8XwRNo6Q6pEdnqCKhoLmkqoro9Uh3d2XHwvfR3nsP5eQEoetyZKlalTjW6aDOZQcaTZOdheNjAIkXjYYseC275+LyEqEopEGAvXmTWWDh2hFdt8JC5Amra1SYkMPmYtHgON7HqBQZKjXMoo6SJkwCE1MNKGYjsuGU96a3+NjeQVSKXN+Y01O2OBd7LCKDWGgkIUShoJaZ8Yr+jIIyY1bYIs3nsUtb2IFBuIjJBxNyDQvHqjG7/gB1YRPlS3QKN/iL9f+RcO5RrSmsvbVOmCtx6G3z/Cm4gYKnFymXU/b3E7a3E95+O+C112LGY7ndezXW47rSyTuOIAjg8tJkMglJU1lcOjhIiGO+8njxdTLwK7aXZ0HlMpn0qki9ag1blsV06mFZckyoVJKzoJMLl/NBnra2w+PHKqOoSORBsQIpguK6iX59C8cR9LQt3OomiziDHWTA9TjO3iWjgxtqdAoHmJsl7ChHVnEZl/cwd2sIZ4E67GMsbMqZOflsjJeaTD2TJ9k3GSkNNoJTVNfBC3VmapVSLuDBd0yeXJTpe2WuVUZYoc3jyxpOkKFaCbHGHbyOjWcnGPGMtFDgVlNWXSZrt+grG8Spyu7oAx6dlJnaOnoxw+u3bDkCk6bSmfo+XbfC8O53KCpS5aDktGkNs1xMCszSIv5Rl3rOYXsjJN3cZKrVGY51wtoaxbLCebjB6GiOJVwqF4+l0kMQSNm0YlGSZ8NQBtCeHE0RiiKJxZomR4WW2VtaLJJaFkm5TPbGDeYgqzmZDMonnwDIv53LSSJZECAmE/KbeQr1DPH16wBoDx9S6j+nWECCy3SKMptJNQtFQTs8RDk7k5J1hQJxswmuy+A/PWbW8STBbGuLZG2ND/9Dl+5pSDM+l+pJpsnlacJioVAoSgm61DBQLi6YTgVBf07XK1F0unTjOvmtAtlZl+L8kvPsbTphAxW4lTsiCWLOnAYhOk2tzeuVU44XG2gqzBYa7UkBV81i+wZpsUhrYDEPTfbqc0JFZxbnMTMh9/Tn7OV6pBHECQRuxCy/hRApVlag6irHe7/JWbDBXC2BpmHUckTrTd4rfY9YNziflpilRU7GRbqLIpqWUizCzZsxk4mKEIIwlPPTYShbv5VKskywZdBkWQm2LcjnTXzfo1ZL2NpKyGT4uc77i8Y6vqz2dTLwwr5KeLHCiiiCatUiitwredLVSJHqLTgb5DgMdnj2TGGSlJiNYLvhMLfq7PzGBp4nmCtF5lqF6Np1Lk4SfC1HEGn01u6QtQcEqU67eJv5zdfQFzMwMqTbTVKgPDxFjxfo5RzFzAJr2sNw54yzWxxaD2hHa9zyHuIv4JRdMiIhv6aTq5scztao7FpUgi5nsxpOZ049O8fAx+3auP0FJdUjCkNu3AEMg/jmTSZanX7+OrnROe7QY9CKmLomd+pdiiVIikWZCMxmEAR0kg1G+69T1BaU/T6x7fLkvMxxtIuYTlnzztluJqRLpbmpbzFQNgiVDIWtPJdnKcNWiCVcSoFUZvtCvKhUYDa7UhkiDGWXdin5mRoGaS4nZ/gbDbL7+zieJ9WNfB+xHCNC0+T+mDQlyedRxmOyB2sUzYD4xg2EbaN+8gml6TmFuiF5ZbMZar8vX6NQQH/3XdSTExiPSRoN4tu3UVotBj/8iFnHo+T3iDc2QAge/mhO9yxiyxrL785xaPVNFr5ObruE4vuSUzadMpsp+O0pAydPPp1zWb9P0fCxwimleMyT6DajuEw97/ON7M9oTXKcu2soqkJNnfBW7YjOLM+lX2fqWdhxnnIuIDU0zpw69lyXa21KgqbWpZwN2NVbKPUyVa+Hp+WJ3QAPk2wwY1rYIhEq/fX7xEJjLKqM9TrT4jab2wqXt7/HMK0wTkt8NG4yHgvOZ0UcpUC1miKFBeW40GoJrGGkeB7UanJsSAb6LFUdUxTFQgifSiVhd1fKgL9M/v+sfVXw4utk4FdsP089Al5kkfV6llxuTqGQXnUNFAXGUYGzSZmjI4VuF4pFhVFcZBQWGAZlum6RbBbu3o0wjBT7wuGnP1U5nK6hBw61ckwhHKFsb5Du75PdrzLXq+QbBkLX2MwM+SQ5QF3M8bQ8OSPGiQz8SLAIDerKgHrQZn1xAn5AtSEoqA6TpITTsrE9g/kkZoczxhOV570qbr7KwC1QM22en1vMJim6O8VdpEzMDZJsgY37RdLhhOFHff76SZOunWOz5FBvJCRRStEKUbtd2u93mc81kjhloDdJ3YB51+V5O4+W0XCGAeWH/wBhyI0dB/8P/oC02SS/bpKkKn0nS//Iw4kM1txTtitzlIsLlLEkR1/JrGUykojl+3KN+9IRiuXSlZV0m5jPrxyzcnyMeXyMZ1lyuZmioASBnGc9PUW9vESkqUwmisUrhQk8D3F0hP7hh3ScEtOuT26niFjyDtoXgsUkpDA5l0t2RiOSJEGZTokODpg/umCQ1IkOTykJm85JzGnbwh35mIML8o0MF+wwOvfJqgFF1UFttxG9Hup4TKFh0OqZdBYVBk4W30moeG1u785lN6Naoe3X0VQFy+nxtNfAXUA9uOT14iFH83XOxR6jtIqeN+gFRdrzMk21TTL36Po1DowLbqrPKaVTavqMa6UR39Z+wlrvGVrgME2LjDIbbPon7IpztHwGq2ZRGJ7jaAUWtW0m5V3OoiaJojKfxoQbTWyK5PMJmiaIY2g0ZHv2W98KEQLW16VT39yMEUKSuVYrJlaBUxwLLi5U0tSgVnP51rci8vlfjyrOL2NfJwMv7KuEFy9XHLe2siSJQz4v8UJV5XMWap7zaZnTU4XhUKBpgoVeIKhu8OSyiucJSqWUGzci9HqBn/w/C559rHIZNSjHQ/aMHloaEhzcRr2xB2t1utU75K2Y+uyEKGPh+BoXxXuozpy8N8RbCEZhHj/J0MyOudf9a3Tf5VS9xnbdIe8PGUYV/LFPEqdo4xFWZHPcL2EaCbZepT3KMpkIRl6W/LzPNLY4OtMxChlKhYTCukV62uJv3i1x0s5CklKxXKpNjXI0lBvZf/Yzuq2UqaOTaDqDtEG68LGnKT0nT3uUwxq3KXaec2ttRLS7S/g7v0Oaz1MoQRym9Bd5eu0UtzVlPe3SzI1kxT5NIUlIqtUXeOG6aB98IGVHw5A0TVFsW3YQgkBy1mxbVv1PTyVehCHukieQpinqZCLHlJbE3HRzk3htTXIZFEWODcQxdLvoDx/SWZSZHY3I7VeuyNGXfZPFOKQwvYDFAnUyITFNlPmc1DCwT6eMJhmiVocSU9rdDKf9PG7Hxpx0KBRTWkmT4VAlG80o5GPJR1jutimaAZcDi0u/wXCo4OVqVJwWtzfHqJ0O8/w6C0+lmo9xOjNORmWmM4WGGPCgcMyj2T6z7DqddIvEtBjMLQI1S8npENoBfqbI960f8x39J1zLD7jOIb5Rojw6or64IEJlqK9jTjqU/T6FjIdTbXLTPEOx57iFOufNb9LTt5g6Oq4d09G3UUp5hBAEgcCywDAEBwcJt25FbGzENBrpchFzSr0uA3zD+DRe6DrMZgphaOL7PnfvxuzsJL82Vf9fxr5OBv4/tF+mbTSbZen1PHxfUCi8kJQrl1OGQ0GrpTKbKahqSqOR0mym9PuCTEYwGskLPjq+xD0dMtbX8NQcZXXGdzLvo00ndNcfMLn5Bvdv+xyoJ2S9Eb6wUHyf3pFD2etTZoQVLQhmHkGk0oja3DTPENMZdpIjVRT8XIWbxQ7nTo3b8UfoIuRc2eediybDsIQIAg6dTS7tMp1JFi2NWM/M2LmtY2gRw6TO4GiOJXy2tlP+6vI+F6M8GWfCb6U/IpfYDHsQmTnsns/o4xmLyMAqqujffoUn52WeP1NIEzDViEI4RLlsowwHxL0RxUJK/MorpJkMhUaGwWNJFNPOzrilfiIrMu22HD1yHKlK1G5LctnGBmI5R6ksFqTLeX2RyUi1I98nLZVQnj2T8qPHx+itFqHnkdRqUp5usYDRSM7mX78uCWZJgohjKaE6nZL5u7+TAHF0xGTjDoNphjhVqTz7KZcXgmEPTCOmZPpS1SKSpCkRRahJQrGRIe6PGLkFOtX7zI0q+8oJa633GE8ydDqw0MusmTO2qo4cE4pj0nodfJ/WrIQbZ4gNi72GQ8/YRsxsrMEFxc4z5r2A81mZnrpNMJwxjkrU1DH1zJTDcJehukHbuE5YqBBmctiDENdTiWyPfGLz6nqbRs5hwztlR7TYr4z4hv+3qOMRE61KS9/H0GLK6ZiK2yHKV3i0/3tEVpG0NyB1XJ4VXiezXeeh8RYnzxNanQyBUSCOBYoi8H0IAsG1awlvvhlz44ac+fd9OUvtuoLLS5U4lsHU1pZ04Nms1GPf2kpYX7fY25uTyfz6VHF+Gfs6GXhhXzW8WBWJJpPP48WKM/PBBzqdjuwg7O0l5PMCqTEgEwHHkUon9uNL5kdDwsoakzDLPfOYe+kTZlGWixvfo9y0eHWjw05pTHbUZuRmcc4njBcGpXjCOl3M+Yipb+EmcpnlDe8xiuvwbuZbtIxr3NSOKSpzhLvgwLpkYq7/v+y9aZNcZ3bn97v7kvfe3DMrM2tDFVAFgARIgpTUVGtpyTO2wjERM+F5YfudI/wF/Cn8JfzKX8HhGGvGm9TdbkndbDZJgMRee2blvufdF7+4KJLdrZYdE6GRmo0TgSgEolBRQD33nuec8z//H4+varz2ttGFCKKYv+kdMvCLhJFA2fQ4coasDh8wncqsAwXz+hTHgefhIV+sDqnF1zSSa8qLU8KZTzpfU1Q8rl+HDGcaXmai326hfnSPp09lXp2ojBY6VWFKM7qE6Yz0eogjbxAEgeSdd0gLBcqjVwxHcu67H0YcR49BVfNuexAgJkm+4HuTL2wb6dUrMAzEy0tSXUeQ5bwYWK0QFgsyx0F6/Djv2p+eogyHBG+WmoXFIi8aooi01SI5PMypyr6fT4jfTKLVn/wEcTRCfP6cudFi5FokSJQun9A7jZksNHQloqiHuUx1s0HIMqTpFEGSKGZzorXPVNziuvIuGwrcCp/RGDxmOhYZzE3cWKPWkthJzkglCVEUc8Kp6+b5AoPUtNndyxg4t3PzDHeKff4F1wuLqW+xDAyWi5R5XKQgumzrI/pSmwv5Ds+FY6SChqgr+POI0VQmXfnY4oofvNuj475mJ3iNGq4I6h20q9c4qxEjpc6VdYfm4hWJYoAsMnTu0Fd3ED0PKfK4Mm6zlkvM3/8+02HCybnCy36ROM6nxIqSEUUipVLKnTsp9+8n6Hpu3JKvSAiMxyLd7t+fL7IMDg8NDGNDu51+bfDyu5Av3i4Q/yOEIPxmHHUUfQM4DMP812Qifk1Ir1TyBchWK0MQUh49inn6VCaOBQQhJQxza8XsckbopiwWGeJkwnP7PoeMMLNL9qwxkXGBaaZUszGT6y7CeMl6tGR3tUIuF4iEGrNpBqKHnnqQxISTEDOZs1WwccUyqhDz88UxteASLwmZqg6eJbMRTF7NJfyJjWFkONKauVKkaCx52AkoyHO25XN+PgyYjRP+8ofvMZTb1IQRO9EV9azH/3z+LzhcjPn+7gsGX+whrXwaxhJYMUjfI74MWDxbUMmmZGIZMQ2oKXP205/RG6kMZm346xOapkm6vc3149xJ6EZH2qvV2e79Xe64U6mQ3Dj/vAHFpG+gYaLnQa+HWK2SalpOuTRNxNUK8ZNPcg3mdJp7TY/HiEmSd+2PjkgdB1EUSd5MGbL87ZIvbn0rSVAsQqVCc1cgLVaYdCNmg22YTKhlp+wIEenhIeGf/Vlum/rVVwiOQ+r7JLu7tESRsaySuRsEYOtYRb2cMWm8C5sNaaFA8yiF8QYxy/JJiKbRrT1kEEK606AWbBi4JpNrk9nYoqam7EURX/CAzzdHTIIKmW/wsHzCgdnjR9OHnIT7rCmiFjSixERNI/b3Yh5fyLi+gRRPqfh99GhDmMnYVkzTf04lmzKRDeate7hKGcmMccILJvoO/u4h/XiLiZzhLItU/WskNV/sTf0up/EerpAxP4ednYQkAUkS6HRSisWUu3djPvtM/ho8vb8fM5+LN+6vBME3e3xvuDx4HrTbuePD23gb/9ziPzZfrNdg2ykPH4rMZhmPHkWEochnn0lvHOpuvA1ExMGMyE9YhhnGesLrxl1YJ5jimnvxE8zWERU9ovDk5zx95eEvLVhu2E6nxE6JUC8zSTOyaIMSBOjunFWoEqCz6yzQo19wktwi9DIK6YSBW8bciglSBc9XmC23ydYVTCVGC0I8o4hd6aO0d7kff8ZATpmPVH7itxn1dsiI2JGe8E74c34cfo/1Zpv3sy5Nq8eVV2Y6V6nrU1I5ZhC9Bz9+wfJMzmm0tsp955wd+ZreswFjP78Abe2NEE9OIEm4Pk8hy8imM7Jyhe5FRpvzfHetWiVVVcThMH+BxDHpgwd5vri+zicTN17Gb4waiOMcbjkYwGSSb5yenCC9MXjANHPXt9EolxvdGFMUi6Q3MLM4zoFgt25BpUKrk4KnM+zFTFc7iFevqUuf05Ey4j/9U9Lbt5FvpKWaRhYEpI0G7UrM+LSS5zLHobUnIsUCk2k1Z/1UKrSqC3i2QJYkBN8nabcZdTP6eotUUqlqEoO0wvzKYzWtUF+fsBfH/CR8xKv1NrYtsaJNrTChpLp0wwbjtM2VV2GtKBjFfNJRqyW86BaYhSYtbUxh0CVahwxjh6q2obTuEmoqQ6FDt/oeViHBrx8RSibTnTuEocBEaZC9AEyJQC/lP+Nuj7Nkhz4Si0XeMLp9O6FUAtNMqNUySqV8yXc4zPNDsZjzJOkLgwAAIABJREFUBabTvz9fpOk3lPobTtzvUrydDPwnDkGAMDRZrz0U5Zct5eI4/xzfFxGEjNu380XH4VBElgXSVCDL8uWxZjWiqG0oyWvM/Tq3zAHrtcAz9R28iU86mFG8eMqLVyrXbpHxWmezgrLXJ1RNajsK6lYZ2d+guzM0ISQ0HEzJZ1w5RFNTxr6NFGxohpekosRX4R38UgNFhau+ziZQ8CORj/cuEcoOpi0Tt/f58E9Fel6NqxchwnLNp9NbnHptTDXi3zZ/yF8P3uFFcAvCmG25z1DdwRBc7onPsbMF/aTB+DIkHK7YFS7wjApaQeFuK++82yyIVBNJVzA7DldejVFYZGvwmDuFS9LJgqFzm3S2xCkJ+Yu2WPwawEWS5Ato6zV4Xi7vCYKv3SPSYjFfDqtWc9/m1Qrmc9R6nXi1InNdpOEQymWkV6++JlDiODnAxrIQkgRxuUT48st8KXl3l/B738Mui0z+6nnuBT0ecdfqktVqX5OThTTNv8ebr2maXE9M1oGcS5LGI4QXJyyHPp4ngpzb7kV2maLi5QWPIJDZNuN7H3P1PMJbxeybfYJ6m/6mSJYK6N6UT+bHPHP3qYkTHDNFlDOujQN+5r6H2KghkiBZGvOlTHF+we93/1e4HhG5MR/pj9nZ8nhv/FfoQsiBdEY9HYKiILYb+JHEsthhWLvLSq/hq0VWYpFMlrgYWTTHT9n4En9V+ld8pX+AkMTY/gRvFbPISshyyr17uanHeCxgWbC1lZFlGb2ehCgKiGLuKhRFuUe0YWRUq99I7yBfwsy12Aay7H5nOjw38XYy8E181/JFluWqEUEwWa28X7MgzTIYj0VEMd+l+f3fj3nxQiYIRDYbgdu3E6JIYDaDshXTKm6whTVbzRRFzHi5aTOSW4QLn+rwJfFgzknfpLuymQUWm3mMJXqUpA3b79lEmoPgBTjTM2RVwDfKoBmEhoUhBtjeECdd4CYqA6HFdK1zuO2yHMecLap4vkjbnPHo1pBL9nDKEocfVFAkH89usHze5/lyh7NFhWla4c/KnzAOizxe3SZMZJx4iikGnCrHGHrKXf0UJ1sw6MHk3MdbRdSlCbocUy+HOFpA0R8SWSVkVcLcKpC223QnJpNzj4Z/wV2eEUsqQ3WbVNFx0jn4fr439sZW9CZfZG8qLEFVYTQC285vkFH0taOcIAgIgwGi76M4Donrgufl3X7Py8FehQLJnTv5tDrXriC+MX4Qnj/PLbC3t4kODihmc0bPlrlr0HLJsd0lc5ych/OGoyNcX+d21W+cgq6HGkt7C6l7hdQfkL48ZZlYuBtyuZEsEzllypvrPLc5DtE777CaplxcKniJxu6hQFhp0r8WSMMUf5Px2fo246BEmkBJ9YjNAl3lkL5zhF5UcQWLQNCohkOa/S85uPwx7fET0iTlI/0Jj7a63Jn8lJK0zFUE4phCwyCVFAalO0xKt7iy73JtHmCmG5wtnfNgi+B8hLyec8It/tL6rwgFhR1tSBIk9D0H1xXQNNjZyWWjaSqgaQK1Wsp6LbBciiSJQKGQIkng+7+eLyBf5E9T0HWDSmXztcP4dyneyoT+mUSW5QdOVQ1836PTSZHlXP950/HRtPxg7uwk2HZOSt1s8uJAUTIEIdeHnoxLVIsxq35Att6Q9seAwKJ2gCAKaOGaojfkp/4DTu13SRYua63M9jbUvSuqlZRYUNAzH2m5IJNkQtlAsnWMcIVdkSkoIboUceR/wUYrcyYeMPd11NE1R+kzNp7MKCoxX6k4ZsJJz2IZFpisNb44r/Dj021eb7apiyPKVsCFu8X/2b3PKK6yJ13yPemnrDciM1ejpq5IvJCVUuOMA+ZzgSSTqBlrdg9FvGVEEoNjhGSyTFELsJI5kuvi9hbYrx7T4RJps8Guq8RehJSE2LUcNx/+0R8hP38Ok0lOYJSknGR8fo5wepqPbMfjfNlYlnM9aL+PEAQkDx+S1WpohkHy/HleQLyBv4ijNwtnyyXJ7dskjQbJ7i7idIr8t3+bTwbeUCeVr76i/9kEdxHnnaU0JVJNikVydsAbyVJy7x6C75M2Glx/tWLkO9SFEcfSS657Ar+4anOpHuFoLo8eBvQuBa6iLWYTkM5OsVgh6DrLWw8YfTFCWC0RB9colxco15dIgctwbeFJNr5qc7fW59GRz5NZiy82d5j4Fkki8E61x/msxOtBCTuccS/8nIPkBVsMOKqN+fPg31P2RxRY0856VEopfqODaWY07Q1GSaOur1hNYo6l19izK8QHx+gatNwTdEdlYO7R94qshBKteoSsSZR3CzhObgEnywKVSt6gy51VYL0WuLwUWC4lisV8tKvreQFQKmU4zjfuXauV8IY3lzuy/HP2gP6PibfFwDfxXcoXN4Ti1UqgUDCw7c0v7QuEYd7hbLXy6dnRUcJyKTAYiNh2RhDAgwcJJycSq5XIs+syTiFmehWgL8fQvUZrlrhofUR9/hqdgCyBH0l/wikHSO6awK5wtBdQcMfoSkooqJjjK2Rvg2/ViK0ihhyiRS5FeU3ZCmmLPXayC/pBlcnKoCkMYbamKVwzncmsBJvAF1iGJv2pwdXcxtAT/pcnR3zebVFW1mRhTCibPJ7s8mrVQlHgHeMVLf+MpSuxCAuUtRVJJrIOdc42TeaBSZTATtNnv7Zk7NlEmomTTCkVYqxkTnpwgPzqFcFnrzBXA7bFLlmzSXF5SZJJSFmMXRRJd3ZyYvu3nHaEwYCsVEK6vEQ4Ockv99MpyHLOzFmv8yVhTSM9PCQtldA0jfgN3RffR3BdxOUypySbJvG9eySNRi5ZXa2Qf/KT3B0p95JFXK24/lmfjV7N/44gEAcJTklEgHx6/eYyj20jxDFX7DCeSFRtj/sX/zv9RYFPJ4ecFx9QLGY8al3RG2n0ThKmUxB9j0JNA9vGm4UMelnOL7juoT55jLyaIc/HnHstVjgU9IgPmpeMtH0ukxa9oErBypgFFpa44XpewJ96bEfnHATPOExf8b70BR/sT7i3+imV5QWGGLAtDah0ZLJOi7rjo3sLxNt7SKFHyz1hK7hCClyUjx9SWI1w4il+oUYoqlwJuyhCQntHBNuiUMglPvv7Kaoq0Gql+H7+cb3OLUMnE4EgEJFyzM2v5YubRX5NA1U1EMXvXq6At8XAP2ncUMRvALPLpUCpZLDZeJhm7hxhWRmSlDEa5frnyURkeztlNMqR14OBhKbllx7XhXIZFosMZ9tGGfcpFqG0bTIr79F6/mN2vZfM5yJTo8Wz+ABNyw/8HaOLaBs48y7hMkSczel5RfxqC0lIqIsLrGyFHLpkqoZ53Obe5ufsd39GbXPJ62iP67nFx/5fUYxG9IRtxlEJkJCUDD+RGG0srk5iHveaDFYWoixyW7/gnnXB0/gYP1EJJZ3j6AkLoQS6QakQoFcMLqQDnq530JdDtuMzBFFgXdkhiTOkwYDi/ALLTEg7HfpZi+UoohSOsFc9isNX9AYq7tjDMhOKZoR13CbVNJK9vfzCPhggpimp4yCdnSFdXSF1uzCZIIzHiOs1ZBnX8i5zpU7hqJn7/Fcq9M9TwkBBu/wKUdfJwpC0WMy7/0GQuxKZZj4qzrLcA/riIpcIbTaImw3dZZHxUqfaUTiKHhMrBiNxi8ip5Iu/wyHZGzoypgmNBpt5hIlHZztDfPyYxAu5mFgkqk5TGfP5dQtfKxEvXeLuiNrsJYvE4jTaZSLXKC+u2MxCnMEpX4Z3SBKBcVqnmo4xxYC92pLX8SEvtPc48xp4mUGYyCQxJKrBaiXhhyKz2GEsNNkWu9yXXvIo+BtqyRC2yoi6jp2tKDQNmh2BVfMQVIXVSkSb9HGGp0ikDM1d0kyieP4lwu09xD/8ED+SeDUoIggCZsvm9gcGppn/8+/ciSmV4OAgQdPggw9ivvhCZjjMuz0HB7nW0/d58xIHyM+6JOVdVd/PJwOOY6CqbycD3+X4LuWL/D6YTwBkOZ9q3Vjhfztf+L7AYiHSbKZcX+d2iaORiK4LpGnGYiHRaqWcnAgUdyzE/pCdexqKmGGIHu2zv0NNQ/pBhbG9B8slkekgNKq8K7+Agok1zfOF2u8y3BRYHD0iQ+DAf4YoJKj+krRcxblVonP9Ke3JUzr+KZOwyMtFnXeyLyklU86iXVbYRIkIukaQqixcjR99UeJ0UiENQzrOgiP5hJm6Rd8vk8kaTXWMGHgIcUJgVCg6CXrD4UI84Pm4jjm7piNdI6oK6+1jsvEMYTyiOL/Arut0K++y0LcoeX3E589xZhcsFxneYI3tjyDLcJoGhbKcMwcUJd8FkKR8/ypJEM/fWI6ORjnIzPO+9vjv1h6yiC0K97e/di7oejWiSYAxPUeQpFyS5LqImw2paea2n1H0TXEwneZEesOA+RxxueRKu814KlPdMzhOvyJWDSZrk7hcx0nnuQmF5yEsFrk7UbXKSi5SCOd0Oiniz35G6gach1skWgE5DnhxXqCouCz6AfHSJ1oFCFsNVnKZ3kinuOriTQJMf8zTWYdYUBmtC3SsGVmUUK0LPMkeciEdEiomXpJraeJYRNwsUFZLNnKRaWQjinDESx6oz9h3n+EkcwKzjFSyKBYz7I6F3S6w9mSSIMFdJqSDKaY7IzUtevYd7IvnOO6A4P0PSW/tE2cyA9fBUx2sVoFOJ6VWy+uh4+OYajW35zVNOD5O+OQThcUCPE/k/v0EyGWjv5ovFCWHUSbJdzdXwNti4J8sbro7i4XAYpF7RnuegCTlVnHF4jfLw6L4jbYtCAQMI2UwkCgU8qr1wYOYVitluRTZbPLFSXXQI174LBb5svFRc059cYLniZyGLbqV+zxSvsLxRuglHSea0VQmCJMZ3sRjLNRYrEV8xUYhwdIDIqeCvh6jWzKxZmEOr9C8GSgSxWjCsfcYMQq5Um7x2nmff2n+kAPtinlsMdvouIFGd27ho6ELIfdqAywr43+b/jGRqEKW4Ek289ThnvqaY/kFey2PS+s+I32P1LSIdAdKZcrhNW6oEPemHEQvaCsDKJWgXmd5vmDsO8RhQskf0BsbPPMPCVOZ1nvVnEycZVzPLNzBGmdyllMqRTFf2Hoj5clcNyceC0JOPG40mFvbjD2bdO1h3a5zbhwxDIqUtQQzmZFKElmlQnbnDkIcI1xc5LKjFy8QRTGnIwPi48dIkwmZLCOkKetpiLkast3Mfarthk60vY+cxRRXvXxkm6YIlpWDYDQN824HR1rnXtX9Pk7HomhHuLUdzpW7DCYKs6RE1dhQ0TZcXQqMNzbLpEDXusvg6YLJiceXgwbducPTzR5JKlArp9y1L5grVb7SPuQi3GEjmrSLK9IoRd/MEMKQLWlAmRlCmvBHwo85NK/ZjU+RpAwrySdK8nqGr5VJJAXPaTLJykSSybXUQfQ9bJYUmxrOrSL6qIckCQzvfZ/Sgy1GvsNslk/ELOsbRxVRhN/7vYTpVESWM2Q5fybOzmR0PXcJyqmr2ddsoPFY/NqWcb0Wfsm7vdMx8Tz3n+hN8I8Xb4uBb+K7kC96PZHpVCAI8rP7q4Xst5tLN/nC9/Ol4cVCZGsrh+x98EHumrXZCPT7AtOpSHHZZXblEUUCWq3AnzufIM9mXK9t/kPx31DZXPFx7RVrX8FXLLaTS5ryBGUxZT3wmcYOo6jM3NyiHV1iyTkYUcgylGYRNXSJpht0ISAVROxkyjveL9BSn0+N76HvVfgT/RMOV18wC0x66wrToMDC00gyiT+ovUDWZJ5HB/STJoIgsMoKeKKFKQUcZq/YkS7Z20+5rH3AUNuGMCJUbVLDpGIFuLOAaDDjIHzOtj4mq1ZZhCZj1yJ2A4rRhO5AZTKRWFotYtXAaVs548bKbUY364zi+jp3/HmzI4Yo5rLSSiX/mGu2yGo15lqTcVYjRsYuClyohwzFJmUlRtcDsjgmK5UQ6vX88j6f56T5Xg9pMsldiMbj3Kp6vc5lnrKM112gp2s6t2QEVcWpKsSKjqhKONE0Jw5HERQKCIJAurWFcX+H0vAVUreLOJlg3WlS2tJwG3u8mLfozm38ROawOuN8VmG+ENE3Uxb6FjO5xqc/FXC7S570GlxvSlz4DfxMo2qHfFB4yt/yh5xJB6TlJvWyi+sKbNYidjCm5F1zV3zOYfaSQND5Y/HH7JlDivGMNM7QgjWBbCIGPp5iIzaqzHsBA6nNUOkQrGMKVoYleYiP3qHjvkZbjInKNS4e/OccvG/SXRZJUwDhjVSUNxaiGQ8eJIzHIoqSIUkCspwzZprN3EjCcXLYmK7nz9uv5ossv45xdGTg+9+9XAFvi4F/soiifIt9Nss7/GmaE1EbDRNF2QC//PLfbPJFmGIx49atBNcVyPkiOTxptRLpdHIbxe/vniOOJ8jtKufGMfVyhP3yC5a+zhUdCuEChxWiCI6V4CgBwmbNMrHwZwHhOiJ1A4ruEF2KSBpb6D/4kKVSRXTdnE65GCBGAQPrgPOghRGv2cqu8aUC/5f1r7grvODj0lNS0+LL6A4uJptIJUwE4lTMQSlFm7Ff4toroZnQLIeYks/GlanYAWrdJr17F92UoN1Ea5UwNhPOLlR8L8MQfO7xBGU5Y+FpWPKG+Ac/wF3EvJTvM01KLCmyWSas1hKBqKEpKXZd5SrtMIpKWN1XFCcnNxvauQf/YIDw1VdIQZCj22u1/C2xv0/hoEosaYyiMqPzkOBsxFbvMw78l5zPDBaxRbGqkL2hJfd6Et4aivNLsihCvLrK9Z/Vak66rFTIGg3s7SK2lSL4PkKWL37bB1Ucb5i7E93QjKtVMkkiaTbzBecoYvB0wUKsUMwW2AdVXk9qPB9WGccV3jte83rVJBivudw0EESB7brH62uHJ+dFLtdVREVmJTjIFZtOecNOYcT/k3yfl4sO60gni1IsO6ZZDqmszym4E6Iow/BXyGnIH2Y/5j8Tf8ix+ykFwUcWQoxgRcGfEFAgbdRwnRpLc4u5b/Aku4voeTjJgmlhh20uqQRDguEat7nDpbRPoNkIgsB4LLwpAvJujWHkvtCNRoKi5HKhej0nQw4GIpuNSJblC2M7Oyl37yaUSimallGr5ZOCOM53+G682x3HxHV/+QX/7YvVb2sX6G0x8E38tueLMIRXrySCIC9k9/dzqWi7nReyv1os3OQLy8qo11N0PT/Pug6FQsZyKdJuJ4giPChdIM/GaDt15ltHlJaXGMsh45lC37XppD0S00aqOfiRSMu7wEzWTHwLbx7gLSLSJKOWDHFY4jX30D66S+hnKEKMvRlgeDOiAHrqHr2oSUsYsBVdsZJL/FD/C/7tnU956H/C2hV5rj7gbFNH10XWrkRZ37CSyvSTOsvYIkZmq+gi2gWyMEKNPXbqK9blHdLtbdRGCfZ3MI0MM1xwOrDxIxk9XHBXfo42GzNz1bxZ8HvHdAtHfNrtMHSLZElKwztnKRa5CpoojoajelxqtxmvDArzXp4vsox0by+Xi/b7cH6eN41qtZxRI8tke3sU9krEqsn0KmT42iN8dklz+gw9CRkPAuyikMuHFCX39/dlHNnLJT7DYT4RWK3Imk2yViu3wG63sdMFzk4xl5rqOsJmg1XXKK56pJ1OXqyYJkKjQVYqkVQqSP0+4mLB4LXHXKnjOBnWrSqfvCgznCisEovmnsKrZZvBwiAVJdrqCNkx+fRJgaeXJS7CFk19yUopo1RtVEPi2L7ih/Ef8+XmACkJ6JgrDD2kbnn4VzPq0TV+olIVJxTDKX+R/DvuC8+oBT30zEOOPMQ0Rg/XxLKOVC/hYjI09+gq+ywFm+kopaSHBKLBYfYaZTFhE2lc3/oei0kCjo0s513+G4VEscgbNgBUqylZlpOCa7UcFHZ5KeG6IooCDx/GlMsZx8e/OV+kaQ6FDYJfLwa+6/nirZvQP2LcoKl9Pz9ogvDN5vobEji9nogo5omgVktpNm80bXnh4PvQbOZOKvnIGAYDicjXmCQNLhcdXr6EdSozdXPLy5Pmh7x78e/ZLcwQ60XSap3wasyVdQ9tNkLU9lGdErXZKzJNpRFdIyPgaff5P+w/p5UIvL8a47DCs+tcaLcpOte4agV5nVGSAv776H8i8TNO9I8Y3X1E+ETFXA5px1OGYpOzsIMb6yhTMFSTWIqZhAZRZPOR/gu2zCWuL4GqMpnK1IoBg7HKWnYo1Y5YnAyJNz618S/YSz6lpx4yKt6hp7Zo/OQyp0gmMmH7fSJxl/Xp59zTXxCvAiaXTcauBfqcZvCY3eWXpLpOFseIl5cQRfmod7Mhq9UQw5CkUEBYLsl8H/H6mj3PYzo/zDs1RYdOOwG5QaRYjMci9E7oLF/Q7UlMxyKNzZhPg13ioMKHztN8OWw45Ofuu8ilAu93hiSFAgO3RBrEiLJAYrVp7u4yCBoIJZvBJgB/xaPwMoegiSLXX62QvDXpaMrIuU2mF0j1HD42X7S42hikX0qUtjTm+rtUWhPsZMHJrII4v2C8fpdloDKVHNpSn7o642hnzWlvm+eLOherGp12zIM9n8E84sVXMspYpC2v2Tb6dOUdPlz/LUfGa2ruAE2M8TIFVZYAhUyQcPUiy6yIa+6QukC4psiE2uKUtOogW0WYQDxbI4g2s7SIezZhNoNNZZtSCcrlfDnYsjJcN590zWb5xX8wyLWe9+7FPHyYMB4nqCocHeUFQpKAZeWDlZvLEPAPIuJvpnY3PIJWK/2tfcG/je9OfJtJc+MydPNnOR311/NFvmQMmpb7p29vp7wB6H6dL6SBipc2eDXdxv2bHmtFp1v8PcwP26SffEklnmIXE7wP/5zyp49xz+BT7tHMRqj6PrFZYtt9iWCo7MTnhJnA/P5/w9+e3uPQ+yEHo59RMDI2WYk5RUQjYxnXCVOZhrzgf3D/R5LPbdKdOpPbH+F+mVGJRijLDE3IGARNpnFGXZ2iySqeK5AmFjVhxq3CNdJqyTStUJEiXi7bHDoe8dzlMtqlYvhchRlJEHAvec4t4Quu1Fs8c75HN3P46OQE3ALdxRGT4iPu2jaNqIuSRpRnJ0x7baa+Tlab0ticsq30ETyPzDSRf/Sj/D/47AypUCAxjHyRWNNgPs/zxWTC3uQF0/k+guuSahqddsJMaTCcqKRth91X/ze9ocZkCPVkQb+7piftULc27JRWMJlwtSoxlBy22tBedXPJ6kAiEUvgbJHZAs33KnSlfYSixWC0hiDhkXmeu92JItevQ+S+TzxdMLEPyHSduHRIUrZ53WuRpQHC2CGxLITygo7ykkVgMXoq4E/mzONb+CE8S2+xZS4IM4U/eTji+cltPpnktrB/8eiKA3NK7/WGvxkfs/EKHJpLHqoXJKbN7fAFW9KEsj9EETP8VCFSHFQpRkkjMknG80VW9jZBILKMZTrea0xFZHTwEfUXf4d0foZsGKzu/AnTYcJ6NWM8FjCP25RKKbWaQBznkLAkEdG0lGfPZGQZZjPxzbOR8v3vx2+YTVCtfiPBe5svfj3eTgb+EUMQwHFywt23acOFQt6lTJJ81HtzqHQ91zorSr7YYtvZ16AZVc0XxxaLfBxc3jVZZA6LhUgYimSrNWKzxrJzzDvOBY0mmGpCnEqU2jpD6xb98jHLqzWem+GtY0rimrXVQFYl4nINX7GIn57QHj2htjoHQUSKI9qb11SzCaFq8FJ9l/3kNTvpGaVozFPukvgR9bTPncXPaQpjFkodIfQx0g0bwabFNZtQIU3AKQRMejEFwUWTIqo7KpTL/LvX7zL1TCJRo1KMefpEoDe10LKAVmHOVs2jyw4v4tt0zwE/QDt5xmhV4OplDGFEK7niI/FnDKRthDAgS1KOt+YwGCAsl4hZRlapgCDkMLLVCtH3yVwXLAux30eYzxkMJJ4/FxE0BTGOIUvpnqWoItRaKdmrM6YnawZuCX+VUlfn7Eg9Lqx7vFbfwdfLtI4LfPLU4WTTosictp1TmZfnC8ZBkWVUwGsfchm38DGYOHuMkzK+L7IYhaTjGetrl8nLBUY4Z5TUGEgdxmmNF70i7jKh0RLxIoXpRGSyzGVZU3uXohkz9S2eT7ZYqA2q5oasVGJV2cZsWJxttsgARZO4XFeQvHyxrRfVkQc9KtmEPyg/4+PWCbeilxSTGU40ZWG3WGk1fK2ELCRsRJtUN3D3jtBKBoq34lw+IFm66FoGnRb6UYv26U9Zuiozaxup7LAxKvjouGoRvV6g3U7pdBIWC4ksy7XQR0cpkC/NNxoZspx9LQsqFnP+xhtGHJVK3hm68Wu/eXa+7d1umr88Gfg2+TWKfhk1/9sUbycD38Rve764OX+ynFEuf+OKdXN2f1O+UNW8cVQoZF8/C4ryTb4Yj0X0usVSKLJcCoiui1LUubLvcVidUa1D2YkxbZGFb6JULaZSndnWMW5/zWSSkYQJdW1FUG1iqyFzymx8iei0z/b0Mc5qSCypiLpCZ/EUO14w0jqcNn+PY/cXtINTRM+nyzb1uEfsxrTiLlvSkErQoxNfsU51KgUPfyMQJ1BQQ4zViNAXcFgyL+1S21ZxU52/fHWXDRZSQSXwBE5OFVYrGQOPohnQrvs8TY55vugwvXAJxi6roYe3Aa+3Jl377Gld3sm+pC9u55JR4Njp5fag8zlZsfi1hFS+uMg1/mGYA8dcF+nNEnH/NOD5oIokZGSOg+i5dK9EalUJZTVgfOYyfO3iq2Ua/gW78jVzfYsz+YihuoPaLLKYpzyd77CJNWq3LJzNNUKWMe/7TMQtFloDt9jiyqvl+aK0x6yf4MYay75HtnJZX8yZvF6xTAqssckqFc5XNV5eFpgNU4yqznwm4boCBBGKbeCpNooqgKrww4tD7KpCJoiojRIn2j2sikIkqGgEDKMaq9gimy/p+GcsLxZU/S5ZpcR/vfMj7kVPOPC/QtMyQkEjlg0yVWFWPiCUNAJ0ErNAtt3Jd7qijPPq+ySjGVm7jW9evnG6AAAgAElEQVSWuSWdofYuiESDy/aHCLstlq6MqcWkjsOthwYgsLOTMJtJJEnGdCrSbudL89VqiuNkVCrp14vCzWb+PK1W+cX/5rn6Tfni5n727fhdyBdvi4F/5BCEvAD4+y4mN4dJkjLCUKBQyC84u7vp16CLm8+JolzusF4LnJ5KjMcSUZQvvGgarAWb0LCxZl0ajKncreC/+x7FhkK08JAcg1ejMtdCC92b8wFfIGkKs8otFoUW684h0XjNnasf4owvmCgNXhn30YWA5uYUW3YJrCrbiy9pcU0cp/iCgRyH3Ime8f3VX1IQAj43vofkbzhOnyKlIefZHutAQZZEOuIVLe8M1xMpJxMqypKVWud8USGUDeaU8H2R7nmG60kYgsdt/QpfLPBUe4Td0EjqTfTJFZ9dd4g3MZlhctc6o1SXsLWAi6CBZBqkYYBQqRDFGUXVI7t1i8y2c0s2w4DpFCSJ+IYWLEkIrktPP+AXk1sM5ialjsF777j84mqL55MmxXBCUx3hyBsGczP/Ac3n3CkPclvS/T2m1i6DbIsvL0rMVwq27HI3/QqHFUKaUlp3iXZv4doN+touz08LLEchTWfDof+U+vQZn140+Un3kPk44572EmGx4DTeYbLS6KdNWrWAYJOQOCUa5ZDRWEZLNpSZUK/FDJMGo7DEi1Gd/qqAZSQYRYXB3GK+ELiYl9CTDf/y+JR5VmayNmj5l/jLAKFc4r8s/YhHtXMqizOcbElFWbGoHzAq30EqWVTDAfgBKhFPjA8Y7n2AOw4Q4piNWmJS2KFseBRqOvd2l4iBx6T1Dlf2XQY771ONR6SFAl3riN3dlEIBsizjxQsZy8odt+r1mFotP9thCOu1SKORMpmIFAoZ/X7utOL7OUzGtrNfwsL/KiL+V4uBf4gU/tsUb4uBb+K3PV/cnOObBtCvnt3/r3xxc95v5Aw3+eLsTOLiIs8Xtg3zJJ/O7QqXlMIRaa2G9AcPUSyVtjIkUXXmdoerK4m5uYUVzfmAz0klmWB7n4Gxj6jLGFenlCcnxG7EzGwx0dtY4YySP8ASfJJKldvuY0rugCxO8dGpJ33254+5Fb/E1iJMf8pudIaZrDkXdsnCDCX22DWHNOIe8mrOLDSpx9fsbvkMtD1GqwKruMBgYbJeC0z7CVIaYkkudWmKr5f5SnyXra2EZWIzWFo8Gba4xSn79TUl3WWmNNhVr5mLJVzBAkNH0HXiKMNpmaDrpHfuIOh6TpVfrRAti7hYzKEl4zFiHNNNt/hsss+ANva+w4M/d/jiic7TWYvS8ISDvQ3DhYEgCLBacZx+RaZpbApNQqfKprrNy2Wbq5nNRq2wn5xwJDzL7aVlmZKwIjo8xhVM+jR5fmKwdGWa8piD6c9pRF0+Pa3zk/Fd5sOEe9JT1q7C61WLcOGyKLRJNwFRuUa5lBAkCpnr42RzWs4a364hCBmfTQ54NSozc3VulSbMpDrLjcL1okC28iisB/y3f/iSaVLicl6kMjunEM44qk/479p/iVaQqC5O0bLcontoH5AZOjg2xWxBGApEQcKldki/9RCx1WCWlUj9iAgF2dKpVjOO0+dMKoecHf6AS+cuVdPDNhLGRpus1abdTsmyfCrw7JlMqZRPy7a2Ygwjh/LlkzMBzxNYrwVMM2M8Flkuc2Dr1lb6azni27//1VwBvxv54m0x8J8gftPF5Oblf+MU9Pq1zGCQH9paLX/BZxlcXopv/HLzy3+hwNcAplIpo9lMqVZzjWi2ctlkJkNthySBaVzEVGOurlWuX2yQ10vq5oYtcUy/fh+zqnMp7VFUPCZRgXoxYCmVmbbvMXX2aUR99GRNoJcQowDJdyGOOdHfYVXssBee0A7PUNKIhVjEk2yOvc+QxZQTDrimTZJKdPQhO+IVXqIhqxJerJGaFplRoCTMWaxVLvoG47nCTmnJljmnY83xMoMT9S5DqYVRNZB8l/l1yLNpi3WgcKBcsltZYnxwxNPgDlO9Q2VLpqnMMGoGT69rzEODdDBmIxexbtWId3eRBwOoVMC2uabNInVw/CHzqMBiAnO5ikTMK2+H9SCgri14V3mN2nG4vhLYREouWCwWSewyjrRmevAI9nZ4selgbibMhhHvSM/RtASnKOSEYknCzuYMvDL2esDFoog9OicJYh6pj1kFGt2ZRbQOmYQ2kiwiCAK6ljLxLMZLg00gEbgpeskAU6cw76FPe0hhgO8JLBMDkQxTDlmEBYZhCVERUdSM3swkRuW+fkrDXDNIaniuQMGfchR9yaF2zvFhSLn7FF2KEMnwD++ilCwUItrr53hGmUt2mcQWbmpQTcZcmXdY+xLzrMQX2iOUO7sYekbF7TJZa1xIB6RZhl41Sap1nG2LrTsmjx5FFIsZup7x+ec5FbLfz/WggiDw8ccRhpH+kuOD6wp0uyJxLOA4+dSgUMi+Bsn8fS/pX33B//8hhf82xNti4Jv4LuSLX80V8A/ni35fZDbLoXzCG6TKYJAXyjf5Imd0pGxtpXQ6+bPUbKaoocdwbTI2dlgsRNaSQxKkKLrIs26J4PQaK16yU91gbsaMt+6jlwx66m7utuN6+J1bJHaRk8MfUFXXON6IAANXd7DmfQrrEVmc8NP6X7AubrHrv6Doj5BkASNzaYY9bBa8ku7gpgZkIKgiPyh/xl74CikJOU0OCNUC5abCIrYpxyPOJxaXYxNJEqgbCw6KYxJJpVLwuNCPuPIaeGqJ9uQJpxOHaegQZjLvO6+w9Zhh8yEnbgv37vtoWcB79zd0/TrdZZFJP0IMAxwzIXnnndxSdJNPV7EsrtMtVuMYZ9XNJ55ZmbnaQK6VeLVus3nWp17Y8G70BRN9C3ec252lnQ6xVaKoB8yEMl77gNX+OwT9JbNBjBXOuVvtU3SynIK8XIJpYksbBisLezPgYlzAHl+QBDEfxX/HUmvQHRlEbswksBE1haLsEWQKX062mU0EquoSxVS5DLdQkoBb3lNsf8TSlclUnfFSpy6M2MQGvmhwtamRvVkM3wQq9/zP+APlF0zVFuUKDF+HfBz8NVa25rA+w8g2yP0Bqi7gOXWUo20Ub4VfbaGGLn6mM1gYjMUGsiIgaTLj9n1ej4u4a3iRHCDeO6Qa9ikqLs83u4iOSaxZiLaJrxcp3W/wp38a0mymGEbGfC7w+rXIei2y2WSUy7l058GDGNtO3xTEAvO5yHyeS4e2tvLpQbmck4V/k/7/7ysGfhfyxdti4J8gvn3YBCEf6U4mAtfXIoXCDTo77+rMZgJXVzk048YbN3ckgmo14/bthGo1RVEyej2ZSWgzDOw3o+L8a74albic2wiLBS1phLUeoHdKJInAnj1jIZUZWPtcXggsG3foRnUUXWS9hBSJQLHwAwk/FokUg2V1H1VK2ZQ77E2/wFBTFomFhs9xoUs7OuUFd3gaHVEQXALZopKO8UKVLAFJSsgKBS6lfaYrFWG14uVyi/HGoii7mOmaB1t9ZpV9vlztg2kQrBOuVhW+uq5x6jXxEh1JEdHKGme3/wWr8g6ioYEfUImHnE8shn0QN2vWsc4srRB0btEqbciqVUTf50K/w+lmi3lWwlcdYtVkx5rxbFRjEleYpmXSdpuKMOe/uPuKquxxti4z6cXULI+j7TVxbYvxpkCSCLQ/qPH5c5vulUS3J7GSqiSqhlHRSK0iL1YtupUHdOVbvFx3GF/5BMMV3jLJR8s9kVFYwrWbdLQRni8z8002ic5kKiNFAZa4xqzpuJKDrsN8Ar2pTjD18FONJ7M9lmaT7fKazTpj4amM3SKsNtSkGbIEtzih5Z4SDecsfIN4FfC+9AW3khdEooE5H1Bed3ExmO+9i7TdJCsUKPl9hN0Wiwcf8x+cf83z+Dat+BIv1RhXbjMV6wiaTDduMjZ2iPyE0y9c3NTkaXKbux+o1LMRsWZg39siTXM3iPk8L4DPzmSSBJJEZGcnB2iuVjlw7MZxaLnMXR9EMe/SaFoujfC8fOnSdQUs69df1r/pBf/bjpp/Wwx8E79r+aLXEwkCkTDMpwXLpcBolDsHlcv/L3vv0S3LeZ1pPuFdRqR3x19vAFyAICSIYlFUUSVTxepRL62a1eq/0P+jxz3r1X+ga9RqUy2JEqWiaASQhLneHpMnvY0M73oQOPeCEEFJa1V3UeDdk5MrIjLPJL7v/fbe737f4uUsQRCUA/oHBzn9fvZSrevRsDQCDMNyfek6HK9qjH2bhw8l7GxNPZnSF8bQrCPLAl1lwSStM4qbnEdNRrUb4Nhkhcgi0PEyg5XcQopjMj8BWcSt7SLvNFF1icb6BJmMHIlArtAvRhSSTJjIOOKWRFBZa11Ez6eWzFhkVSqyT6DUeBzusvUVhhOZY7dFHEFX3+CoIYmkI3UaLKUWupzxbOLwYtngsbsDosRcaJIaDmfmTZLL17Df6DFfKoSpjLRa8PTUoLo8Zr0SGMm7yJZOrx1TNBrkvR5n4iHbUGYr1RisHQKrRZ4W7JpzHmwPmYtdZkUTmk2a6+f80ZsvCDyFc79CW5xxrbMgr1SZhg4pEru7OafqVe49UJmNcuZxlY1URe530Aqfmh7xgX+bnzX/DU/k28SriO3QZTUICHwQlwsGmyqTqIbf2GHXWhJ4IousihcIhJuMs02NrjSndyBhmAIPpy2SMMcOx7ixyU/mV5luNA6vitjxkjAW+WS2i5uY2NGMS/YMJIEbwc/Z9Z4QLiPOBjJ3gh9zoIzQC5dJ3mHfe4jgh2yVOs/f/i6tngS6ikxKenDI6PL7/F/pdwjRUKSC3LYp2k2ebLpMshZztUMcgqYL/OR0l0VikSZwfddFbxpErdJrQpZLFbnzc5FHj8rB4DzPabehWhXw/bJYVKqzioRheZbqdgvCsDRsNYxypmY0KjHnl+HFL8OKi7X3VcaL18nAf4P4ZVXKarV0loTSUVWWYbEQODmRKAqB1Uqg08k5PCwn5TudnCAoqz8nJxJZJuD7JTBUKgWVSqkzPZ2KmGZRGtXoVbrRGcZyRKEZBNUOQqOCXpHxzrdsPJmN3ua0+zUOOeba6mc0ijkN94wok4nkCqftt2lpPjuOS2vzgsBqMRa66MmWveSEipaT16qM6dFLB9h6iCxm6KnPoqjRdkJ2aj5j8zKzrMaJ12aSNvETnU1qoekFTdPj2G3zdNlhtREQ05y5bzGMW6yo8mBzgC1s+Lb9Qal0pN1gudXorh9xO/+E5c9OeTGpcryookkpl+wZSuIiS5BNFzg9g/MzganUJ1MtREtjPIL5xuDpsslabLCNVATTJPJSrtfHpEFC62qbmVzFOn/C3m4OQYBx54hiNGVBi5/ds3ATA1sJuCU84JPlHufpDl61x/QkYe6Z/PXZbf5m8y4EMVWWGNEKzRR44XaYehW8wuSN/QWhUmWm7nA+VvEyje1W4FA+I5Z05nKXhpOwGkUIywW5H7MKDEb0WRcOgiwzi2xO5zU2iYUo5ih5TDcf0rE99GTL9ew+l7d3EbMIU/D5w8pPaGob9PWUmndOIqsM3/k3pDt7OG/t0K7HzLYWD7vf4qPm71NUbMzlkCTKuev8Dh/VfpfhwkCYznDCGZmioegSy0mO1HTwCwP7dp/IKxhNZeaxQxSVxjCiWDCdCihKSX2Q5ZI2lKalCsSFUkSWleskCErOaLebc+1ahmW9cmX9Mj7nl23w/9LjdTLwKn7T8GKxKM3HDKN06j4+lgiCsivgOKW60N7eK7woJUgFHj2S0fWC+VzCsnKyTKBeL/XXp1ORRgM2GxFXtKl7A1rxCMEwSBotPKVKW5jjTkJeVG6z6FzDqKscbB7Q2R7jyD6dyX1IEwK9xqR/h565oZmMyRWFMNfIRAk7mFPBQzYV3MJEEQpUDXBsbiWfYAYLzqUddpoRjpWyNPo8zq4xCFvMPJO4KM0hLT1lGVqcrWpsUpPCi5i5BovIZhjWcYUaj/19DtQRzVrKiXSVjdKgIvvo/hL1+WPGz0LcSYKbmjhqQL8eIK3nFG5AlTWDbIcJHczMpajWCN2U0UJlttZ4IlxnU1Rx9RZCkZGOVlxrTsmR0d+4ibQZc5C9QNB17JpIYtdZzAQGK4f5VmOZ13hDuMfU1ZmkHQbSAZFfEI9W/Gh1m++tf4fjoI+TLakkSwovQpMijldNxkUHH4s3jzYESpWZ3OZ8buLFCucrm31tRorARmyQSzKqt0ZIE4zNhCiTuRddIxQtqlrI2LX5eHHIJtSRVQEnW9EWZ1QVjwyFt/OP2F/fpZrMOVDPuXKUEkUF3dVj4lQisBsEe1dodgSs//jviBKRxXnKXeu3eLT/bWwzQwo81maHj9vf4fGiiTuK8Tcplhhh5VuEXpvHI4e6HuJZLRr7OicDlZHnsN2KXLuWs9mUB/7VSqJez9nZKT6TbIeiEF8mwK1W2UG4oABduZJxdJRxcFDiyK/i/39VsQJeJwO/dvFlnLR+P6fRKC20x+OyG1CqYebIcimJdaEolOflCz2biYxGIo5ToOvl95vNkl60WIhMJmWltdGglNGKBaLplrnSwXUFvNtfYzYu2M4TXnhNklzmnc0P6MpLxNDHNkvNeT1aE2QaUa6gyAW11TFFlDArmriFRVNxS66p3OIsbVOJ1lxXntFSVlySTyhECVVMOAwekmdgySFSlrPMaihChirESKqEpoKqCdwftXmxrLNTWVMVPfxE5sxvshWqRIWGZgjIDYeO5XKkjzAmx6RujLYcsXEVVDFhmHZoFFM68oJ+M2RVv8TZczh5koMgkpk2u8UJZrphFlX5aHMFL1LZpjqSobLSO3SKEc1oSDzbwjYkDUKU4Rm26CEYBtmlS9SMmKSQePYwJ85V3hQ+Zds6ZCjuMcvrOJKPsNkw9qs8Ti7h5hWcGnSZct0550P/DSaBgymE7CgTGk7CadDm8XaHwC8oENAiDw8Lraqx2wrwlzHpOmC1FHiv+YR6V2YhtjiLOnipzsy1QJW4og2osWagHpGEBfk24jB/we32iFo656A4YU+d0qjEQIEWbhCAF9U7CO+8wfDot2hlE0Zri48Hbca+zUTewX94TtM7Y6zswm+/y3otkNsOm9jEcXKkKKCluSSZxFptwv4eX/96SqA65Jb9UkM9CGA0kghDgSwrqzTf/nZCr5dTq5UHljguFSDq9bKyYxjFZ3KjJXXowpX1V/E5v6ob/Otk4FX8puFF6WBf+tAsFiJBUGqs1+s5+/sZ7XbZAfgiXpTV03LtdLs5lUpJL5pMxM8UVko1L1EUEEQRLXSZSh3SRGB26WtEE5fH5zarwqElr9mVx/RWTwjdjLbu4vhj5DxiTpO52KK7eoq2HJMWEp4vIksgkROoNttQQchydDWlrbpck59SMyOU2EMUBHRvia2GbPUmS7mLpcRkSYFYZKSiiiBL7FQ33B22WAQmtplSL2asIotl7LAQWoRopJUqbcvnqnXKoXiKt4FkvCaa+1w1z3kR7iDEKU4+52pjxlptc5rs8eJMhTQj1Szq6Yzd5Sdka49Z7Tofra/g6U28bYEnOgRYvBF9iJ4HRMMVLjUsb4AzekzRbCIoCubtXfI4Z/A0ZrbW2XPW5JUKkVbDs9rkgkSyDvh42GObmWSmTb67g5AmqKHHdf0Z39+8xzx12FUm1KUNjWrKx/51BhsH0fNYxA5a5mHIMZ12wRaLSjBnMsw5Ek7YORTpdTPOgybjqEZ1cUK+DYkLhR19Tm5YHMZPkQIXJfTZM+dcqi3orx9RV1yqZkxc72F5I4q0YCDvc3b5m1gNFaHbZpvpfLC8yvAkZxI4PAv7NOZPMHKfxe6bnIu7bEUH20jZiHXMpoYcB7TNLZkbENpNos4e3WsWgWzTbpd7fBCA5wmcnpYeQnku8Nu/nfDee6WnhqoWzGa/iBetVik80e+XRmSC8I/z/7+qWAGvk4Ffm7gY7KpUvtwAKU1fScIpSsmFLopykFJRXg1KiiIvzcyKomyBrdciriuyXotUKgVXrmRIUqnJXqnkKIqAFmxIEgEBSLOCZjVnKO8S1Los0ho9fcX1/CE3lOdlpyKNET0X04K42gZDp55MCdGZaLvc197m6urnKCRsrB5eajAJqyhZgKTLONGcy9lzrHTDLfUJXqQzV7o0izmL2GKVOgjkNFWXy40567TCadxhGymomoChpqhiyiDq4CYGharT7ecIqkqUKTSSKVfnP+JN9yes1wWP4sssM5tNZuNrDQy1YGt0WGxkzicqj9Z9utKMF8o1BCGnWc/Y60Q8O9EIFhHn2xrjogvdLt95b4ro+wxWNuOhQDhYUgyHbHOLs6RH/vZbWHtVCEM2roA7z0lRmI+zsh3ZiVlvFbTpgMVC4nxj4xgJ1/e3tK453H2g8jPvNoIic7iXcke+x4nb4IPtG2ydPRKjQr0SY/grUEtVh69fW7C1d3ji7zJ2bXJJQTMFbr2n8vTU4HRV5Zr7EW9mH6HrAqvcoVYs+dPqf+YgfoKTrWmkE+SKTl3f0i+G1KUVxClCHDOkz2nrbZ51vk4iqgzTNoO0y/31LhutjTcNkKYjtDRgZh9wt7iFZeX4/mcyhoGNuNfnZm1IswG2nfNYusHhYU4YlsO/aVrqo4tiWZUZDCRMs2C5FNjby2g0Cq5eLR1VL6Tfjo5Kf42sNJGk1SrXQZKUQPFF9aAvxld1g3+dDLyK3zS8KKVHS3wwzVKq+sJYSVFKetAFBeKLeCFJBcNh6WkQBCKHh+WeBSJQKnQJAuSLFQUCplHQbObIQs6kcY1zdmiIS5rZnBu1IXa6QltOUL0FIjl5s0HktHCyFRXBZZvqPO/9Fr5b0I7PmSh9NkUFJctJJIWz6g30cEPHe0ErHKKqOWbhISURQ+mAXFE4TXrUhDVaHqDLMYUkk0sai8RBVQoKUcAyCpJcZuDVWWYOSkXBrAgopoK83bBbnHE0/gnZ0iWIRLaZxTBsMCh2kWyNnWbE0HP4ZLrPydKmlw/ZqnV8u0vuBexYa2rFiifnFkEgcpbtMBF6RFqV929Oaa2fsimqfPikSTacsp88w3Nz7le/Abt9jDuXqC5e8PCZziy0qUkbJKHAqopUWbPYqMRnM2aeRRGn3GyNOfxml+FY5fmZykTaY5432GklqMEGXc95GuxxKl/GbimoeYyUhPS0OaohQbvFIrY5TnYwLIksh/qeQdHpcHossNoafG37A347/QGOsCXQavzr4i/5A+n7NNI5tWxOJVvTFqY0FRe5oqEHKyTXxUt1Rsouf73zp7jYhPUeXmOXT5e7fDDaZ5vqCEGAuR4jyyIj4xLH+d5nRc6CQKniYmL2q+yJQ2y7nIUc129w/XrGdCpRr5fJbqPxakj+6VOJZrPsdB0eZsSxwNFRzvn5P8SLizmyev2Vwes/hhdfVayA18nAr0Vc6NRuNgJ5biBJv2h3fXF/uy05n81mjm2XB/o4hna75IBetLQuZEvzvKRQGEb5OcvKiXpJgvNzibOzcoG0WjD/+Tn6Zs5Ttwk3rxG4GfVkilhkHN4xcF2J2qGNFIcY/hxlPSfLJFLDIVYMVr2brF2RgXqJQLYxCBAXM2RDYXN0m/nROyiDE1r+KZoQYUkx1WyBLW7RUhdbCjgvehxEz/BjiXv6uxSCQCcf8V7yYwK/4GP/BnEsUTVDurWIVDF4OG4y8WxUIiQxJ/RyNr6C50uIUchsrbOILBZpjaG4R9rooNQMru5s0OMNT8NdPvWvcRL0sOWAcVBlUOyz2435+qUZP/3zDfdeOAjLBRscEKCQZJralrYT8VcP9jiN2rhqhygpmAZVni/r2JKPEET88OSAJ8807OUp/johrziEjT79/Rw3t/nB/Q7DtMsmNdFUAbmIEeKE0dri2aaDrUf823fP+Di4zsfLQ2Zxg7VU59qbMsp+i58/qiILCbeM52SawYt4j5Ffp1FLcX0JwxQ5OTd4Fu4xXyrY4ZRvFH9HJz3nRL/GQfKM96O/5ZJ3l7ru81y4zGXhOfvOmiSXUDMfY7skVk0+PPj3rDqXabYFxGadjVhl4+wx8hw8yUabj8t2fgHL7g2q1YLr13M8T0IUCyRJ5FbllLrqIwgFnicikRKqVarVgqOjDMcpO1iaVm7U83nZsg1DMM1y6DFJyopN6cNWbtr7+/ln8zRlZcd1X80J2HZJL/oyPudXdYN/nQy8it9EvPC8ckiy0SiN+fb3M2SZlwZkX4YXplkOIieJQLVaqhMtlyLPnomfUVRF5OEAcTYjqbeJL18n8jJa+YQ0yskrNpFeo9tKaGRT0skaNdiSSAaJ08AXDB6bdzBnQ+Zan8nBu5jRCmmzIrdszvbf51g4oJbM0FKfquyhSxmVYE4uCJjJmrq0ocgKlmmF87iN3rLY4xQrWdMMhlSiFWfZDoqU0a4EOC2FQK7weOAw8h2kIiPPIEvACyS2noQcbjndNJjnTcgzVuYOfqVNc1fjSB3iBQIfeG/xqX8NrQio6yH3vSNcpc57u+c40YQPPpD40ekBir9ElkUWSRVTjTDkBCUN+PH4Co/jI2pGQhLGfLS5guuKyKaKOBzzZw9vsXi6wc7X2MWGcfcOhwcxU9/i2arJi4nDWdpDEmEbq1TTBYUf8MnskLFr8FtHE0Rb59PFAS+iXVLZwLzcxmnJLFcyzyc2B40NjpEgWyp3/SuYckw9myGmCX61izSfMRzJrNYiUhLylnCX68UDdtUxV6MH9OMXKHnMzDxgL33OQfacsLfHWqxT80do4QrX6vK9o/9IWGlg7VSpNBWG0g5ra5f1WsCTbSruGEkqHYHDo2v0evlnGv9ld8o04a3aKekmQBDL7rCQpTQv2QRB6S3TbOZcuZKxXpeFTdcV0LSycJqmpfb/f028+KpiBbxOBn4t4vM6tZJkIAj+L/DULu5rWlntabVyWq2S879YlKBQrf6i5NwFd9SyCprNUj5rvRZI05Izd/HMYlFO3PvTgCtvaYzVPep18OTSuY2xh+EAACAASURBVDcXZCLVQVVLWTtFFTCkGKo1Fq5CZtewaxL7nBKKFhuryxPxKrImYIsRRa/DunkZzVsyk/cRE59udIZduChiCoKITEYkWmhCzH7xgkgwSVWDnjznP2z/VzrpgL9Lf4cT4YCr+jEH2QnDhc5Z2MJPNPxYxZIi4kJhsTUpipya5uMmJne9y4ySFrvKmDfsY6ZFi8jp0LJDXhSHHG87BIUOokwoVtjoXcJam1Y14nSs8aPTQzS14IgXLPMaqWmzWks8W7eYDQvSXKKaLpilLRYrkTCVUcQcz+7wt6MbPBo36IhTtIZFkKqs5xmjs5zVKOXesM3xuo4pBnQNl1yQmHsGrq8gBAG5pBCnEtOlxtaX6NgeqWbhZxp76piasuXRA5GB32abaGwzkyCU0CoCm0BjHlRYbSQ0d0G0CkhQMKQIW/JoZFNu5g+oJVPSuCAwG5i2yFzv04+OsdYjYrOKnkeEmcpSqPGw/Q2Ur7/BZWtCpFZ4rt5AkmB/v6C6GWCLHpoqomlQsxPqRw6yXDCblUPtneiMq86UZiPDNbu4uYk4m6HJKXv2hr6zZbBxyPPy+UajbN+KYoFlCS839YsOmGWV7+TBQWkwtl4L6HrZMk5TXn62rPLQ889RiPgqxOtk4FX8JuNFOVNTOhMvFiKq+srX5lfhxWZTrrkL99blsuwspyl0LA/Z0Ym7e9RqOWLdZjnLWaxLoYrDwwKqNt3tc3y9TlapclK5SaNYoAsJre0JuW7w0HiTx9f+GFsO6SQjHCvl1LnFk8rbJE6XtveUHe85chKQSjo5IkqRkCGRqTpIEqaac1t5RCOe8Ib3AfV4xIlyCVNLsM0c/BB/HXO2qrLwDeJUxlYDVnEFL5QRydGFiEVscy+8hiTm/JHxfVLdZJL3MB2BwrL48fIWk6hG03DpGBteaDd4GF5GNmR22wHna5vvnd2GAnrKglS1SEWNZ2Ob9UrEjVT8VEM3BGr5ko9GXeZZg4bmIzRq/KePbvJi1eZAP6fXjJl4FTYna05PFZ6dmTyYdgkzlQN9SIpCkKh4vgBJxioyiBMJ15cQ8wxdTem1Ux6ve1REn0NthOhteT6tcR62KHKBh8seNXmLYUuwdjHcOfJkQu6H1ObHVIsVtuRR03x24xMqqYuZukzooNkKx/YNKsUWJ1/hUyERdaQ8IclVhsouzw6/Rasr0WkkhO1dlsbOS4GTK+opHWuLbZeCKI6Z0rlq4/vle+Y4YK8G3GxOyNMCz2qzLSwa6QxDSbnZX1GVPCaBw2olflbhL4tIZTJbzsz8MrzY3y+N94KgXCP/HLz4qmIFvE4Gfi3i8zw1xzFQ1V+s9Hz+vqKUbdo0LbPgWq2UT+z1cuQveEa/4sCV1aLShKbkio5GEstl+ZtHR7AVKmxwCAKBdjKkLqzxGrtYvQr37kmY8zPipc/Nayn5cEbmhthyyG5+hnTrMplh8WxoUoxnjIoOUrdJvVrSS+qrE8JE4kHvO8SKxvXgE5rROWoeMVd7qEqOSsyhcIKjRsg1g0N5wO+4f8mt9FNIUn4ufZ131Y/575zvc9X9GevYZBbaaFlAnQVSErHMHIocFCGhrvpEiVR2LTKbo9qKprpEFxPscMazaI8n8WVCo4pe1dimBidLh+nWpO0+JZ1u+GSyw8YVaKYTutqSm9dCxGYD15c4XtaZrRQayQhDTvBSg/36EouA87jF307fIpBtBKGgl50xXppMoird5JR1XOHD+RXG4i6WkfFHB3dJkfBimVnkkCHTbiTcubZh5ckcex2GrkMc5hSyCmHENlF5+lynJ48R8xQ3MXji7SOkEe+bd3nPechqGLOKTMx8yzWe8H7+A96QHrGy9wlkm4awpJOeEzotHvW/iabm7GsTjHiLLsZQrfLYfJNx/2soqUdd3mL87m3e/E6NwbZOW3cxojWXmkuC4wWjvEMoGdzc29DQA6ajnHFQIwjK9zTb+Bze1BBsiyNzTCBWkByD686Ew7bLeGOxzGrEsUC/n9Hr5Z91BcrhYdsu6HTyf2CkJAilZGLJa75YE6XGdBiWqkTb7ZcrCn1VN/jXycCr+E3GC9suXlZML5KDi3Xz+fhVeDEYSKxWAppWVmwD2WbgVmm1YFcYcqu/4NP1PsNtlcVCoLIc8Lb5BKVmka19Mi+kV9liZVvMmsxQ3GU8lpinNYJc4/AgxxF9NHeOkXss1C73nG9yffMTdsJnSKpELsol/YkERUyo1CQqR1Xq8pqGe8JV9yPayZCg0KmpPr/X/ISv9c7YO/sA15OZ+aVRZa1YIoQxblGhKAQsOaSmh2xTE5cKthpypTlDIyYLE4Q04X56k0Vex5ccag0Y+VXuz/r4gcBV92esziP+6vQ6m9jgtviAtrKkfsnGauqsZvB8XsP1FGQSjowRas2hnx8T5Ro/96/zX6a3KZCQFYF31Y8ZBTXcecIisDgL2wzzHiEmdSviG3svmIU2Yhozjx180ebOGxFxIiLmGcdBFyHPWbgaw62DGAXIqshiDjXJJU8LjoMeo7BOX5ry3e6PEMIAbTXDjNfUWdLIZ3wt+5C3lPsImkoi6SjEWIWHW91j4eyiGyKGJZIVICgSoizyg8afEOzcwEjX1PWA/tfb3P6TDou5RFNa4cs2/fQMbT1nu8oR93todZM9dcrguOB0XSUMS6OwhubRu6SSaDZ72oRNarF3Q2dHGnPU2TJYWEyj2mf+ABmiWJqGJYnwsgvWbv8iXtTrBeNxybKYz8vEuEyq/2l48VXFCvjVeCF/6Z3X8V81BKEcEE4S6PdhPv+Hz1xUZy5eygs5rSQpFSNUtbx+wSW9sM2+4FZPJiJ5DrVaaU0/GORcuVJy7Bwn5/33IU1zokhEm6Q44RQ7gyfLPYzZgEsHc4okQbo3Y70VOa5/k29Yn5AdLykenzLKGyyyKjU2XPM+xi3e4fS973J7/H3kc4HNpMKN4iH7s+8hKwIZMqKYYysBNXlLluTgVIiCgp3khIo/I0vzUgZVl/gf0v+FtKhgrSNCQeZt5S6XoycsixqeVON5sU9FimhLk5Lb7vcxCNCklLrlcy+/zZYGXTPAMWMKuUawNMgLkclUJ4tyCiWDJOVpeMAwDVFVaChJ6WTZPuDdd6c8nPVgKaNEAm5cgTjhzDVo6Au8AnK5wUA6ZJR1Uf0VbWHD3yzuYCkhZrjg3mwHT6lBllOVXQ6bG463bQ5aC8aejW7J6FLIOGsynDqsEoE8zVlsFSJZoOFO6dtbTl50KGQB3TS4fBBRjOecL5f4eZ294DHGYs0fFA9wRQtPrbO7vIdVeEytQ67YI3Ql5XB0F8MIeNbbZbBTIxUv0Rv/DU17guD7jJpv4sn7+HKH1TKlkWwQp3NGo3rpVlpkmNsZth+TNnaxtgXCdI6822IwkGkzpSKC3tulXs+J6l1arWO6OwK+2+BKNmJTiAiywFToMNH2EUXYbEpaUKkcBEdH5dzB7m7+0kG15ESX7/jFUNjeXvlcu112vqKo5Etf7N2WVT6bJK+++zpex7+0+FV4cbH/93rlfXiFFXFcfrasVzjyz8GLo6OC+Vzk4CCjKEq3+52dHHOe0hUmtAKZZ94++5zztnAXfZMTFQ2GeRej2aAtPEFcJni+QhgKnAh7VKMFxulPyY72Ef7ttxDvWcjHLp3JMw6CYzrJkECqoKURmhiiyyFpxSSTdQRDpDF7jpVIVIJz1koLJNjJZuxH52RRG/38KXUhRxJCjopHqGLOffEmp8UuO5yjyTnzossw6kKe0TR9TEfmB/H71GWPRJPw5BaFrqOlOSkSH5zuI8ggyyFK4vPIPyL0DRQdLlXGNBsit/ZFPql0ESOVvFJBC4Ekw46XPBo4NKYhtyo5ua7zfH2ZJDap2QvscMSfJX+MvJiQeiEHwilerGBZAZgaphCxiEyOqlOmmKwyAzSZj86bLH2FPMqQpBw/klHikDz0SYuApw8EaqLPnfY5j8Ueu96QT/3L7MUvKE6GHEYxueRhCR7dfEEcp0SShlKEKMKIpFJBzsASZQ71Ec+NJnVjQzVbUhF83FTHq/bIzSYPK3foLBNarkuYKAhZRnoyQlEFGrKIaIhQpFTqMlGcIV/dYTaEajLlzWrBtLuH4+Q4hx1s5RTTLjC1GvmnEzpiznYrMaHJQNyHolTL8v3S2scwoNPJaTZzjo7KjvHn8SKOyy6AppVnqmYzf4kLr/Hiy+N1Z+D/x7jQqf2i3fXn+aEnJxJ5zkte2xcHXYqizG4XC+GzFtgrWUVJKp81jDIzTlMB1xXpdks+6ZUrGZuNxHotEGlV6nbKu3tD+sUQSwjYGi30noMq5wy0S4giGJaAXDdQHJNzegy0S9SiMamsYx1UcY0uVtsg3sTk4zkHk59RZBlircLG6SPLIkawRIs90oqD6/Q5M69yafpTrGyNlMbM1D6xVaWmhxjRhlzWmNOgl4+4WdynKaxZinUu50/4w/zPMfGY0eZM2Odbwf/DXnHGVN8lqdRZxA5uKPN03sTLDOJCxg1VglRFFWPesp8TbAuWscUyqaLIBS1hRi1f8Ub1mA863+WD5x0eDutkYULLicnSnHXR4IYzQBUTPgzeZEmTAlhvRJauQpZAxUhItQp3NwcEcpXLO1ve/xOLJ5M6948dTrwuUpbQauX0LJcPlzd4PrZJjQoVWyBJBNaBRi2b04wGHBoTHDtnkrbI3JA33J9wo3hE0/CwawJ64nEpekhd89hzHyFHAXOli6Um3DRPENIUV3BYmH2awpJbylPalQAr9zBtCUFVSMwq46jG9c1HBJLJ5I1vIckiWuazCC2WlV0iP6eh+6yHIWbuE9da6Nf6jMMqk/OC5aMFgZsRaVVawQDLmzHdWiS9XY5/OCdJC6JQ4OCPLlMU5YzA6anEalXyHhyneOmkfVHx/2JF86ISerHJ12plmzcMy2sXKltp+pulEPG6M/AqfhPw4vNY4XnCSy6075fOql8civxleLHZlJVRwyjN/j6PF3EslvtTL0cQSsoRCOy/YVHRU7rCFMebUFc9zLZO7d0DFnmNmWeCbdNcv0Dq1tnefoczaR9/K9D2T0qX42uHpM0OJCnBPMQZPKbunuGpVdz+NWrZApIEJQlBVgn3L7NW2jjuOc56gCKm5IpGqFWQbQNRFZGyFDXY4GPQyGe8md/FIMTG5c3sYy6LJ8hihltUaCVDrqYPsI2EMX3mYocg15m4BouggpAXRLLF3DeJcxlTjWiLS/Row2nUxc1MmnbMrjzECFesqLGuHfHT6RF3z5pUjJS3rYdM4jqrrMaetSQTRT6I3saXqmV3dy2w3oosfIsEiYoFZ3Eb2bFQ95q02gVbFx5NWrwI+2SSxtXdLb7k8HDUxA1VcsciFxTU7ZKud4wtbTgqjrmtPaah+IijEUbiccv/Ge8LP0YVY3bNFZoucJg/o5JtqLgjxCRiIbXJdJUdaUImaZyoVxk5VzgoTqjrAZ1ugeYtyGotaDVY5Q5mvGZ//YCt3WZx/T2yXMCRfU6iHqFUoeJPcYQtfiixNVrE3T00rWCeOMxGBdO7C4JtTmFXuaqdsX48J6A0v+N8zGJRUoJa/+oK06mI58HZmYQgCKzXIrVa2QloNMqO1he1/38Zy+JiLf1T8OKrihXwujPwax9JUr6UpY02L2UXL7LVz2escVyCwUVi0GyWU/ZnZxcqEPlLHpwkwXZbuvOZJtRqpWvxrVuwXoNhdLGORySJyI3rKef9Lvv7OY8edfHOJNrJgKLZIum0+fCxwCfZHkfBh6RXrqGKGXv6kvDJXc7evMXHjff419X/mTCpoNRE3GYNXUrYzk2awgOWSp915wpeLLP37O+Rk5CtXGdhNUgzCUmT0eKUmb2Dul0xNXY54hGJZFOPluzm53SFcy7lpeX534m/xx+n/wd3+ISfJ29hRivk1YZBsYtoOOhVlaWg4m0kTCVA9TZkyHy4PqBABKVAlkUu9z3Sqcyz4hKnpwecRVdJRZl+fYYobxGLjDPxANmIUds1no9UdA1yJeFS3eej5w3GYR1NgyINCFyR1BLpNkfQavH9v7OQ8pSa6hOEEovc5kgasQxN5GiLJWoErsUgsdkXX/DfV79Hdf6CTrZgE3bp7T5jdhzhBDMc0UXSZBrbBVqQYgpTQsFA3KzRiXlq3uKD/r/jm9VPqQQfshVh0LhJ1D+gPfkvVNwxrigRXr2B+ae/y+qv7pJ870PUZMNgU2FWaTKeyhxczslbLXo3uuxNBvhKwWE3JwxzohiSDOafTFnmOxSdXYQYrlsT9O2YSgUCu8Xa2sW+N6BSKQ8VqxXc+89j1Ct9NC2n3RYRxYLttlSQuFDL+rIB4M9XSj//3OevXaylX/U7r+N1/EuOC6xQ1bJaCq+qmxfXPx9fxItGI2c+F192EG7dShkMxJd44XkFjUZ5QLp8OeXNN1OCoOzYLZe7mMMF/Z0cVclp/OFbTCYi21DEa0g4xYDN/g3EGy0eh/v8/ETiqPshoXRA7UoTZTkl+dmED6p3sGWRmrWln89Y1Q7p6WsCeR/NXxOEddZGlyCyMMMVheuTGBUyZELVQRBlcttGz3wmaQNtMcLTW3Q5wy+qqHHE5eIJVWnDQmyxLFpcyx/QZM5WtPjh9jJH+j0iV+HEuklFjbDrIubqnHi5RchbyJkIWc4gbaBigKbQ0DxqToa71vjf4+8g+jJ9s0Kg19ltLulpa85Wh5wXDjXDRdRUBtk+oaDzzjsx0njI9x5eZpJaKLpMtx/zaHsFWQ45aMYIRc7pSKepeSxFiYWvIaopu9JzktGS/WjD2uyxTNrUsilv5p/ybekvqOQumWjylnTKKGyw8EWa0pKWvCJOFE7zI8J5yI44IqdAiVxSWWcjVEmbDVJNI8tCUtWB/iGJYrFdnqMmEUGmkP377+JV2phnz0j+3sV1RYKtwEm9RT2XqdZyhG6Ly5d7bB+N6I+HCL0OkgzFTh9zPmB1LHKy2sVq7ZJs4P3DMclqhKblLOweWb1PfP+cS1U4PS3fW+//HKNf63/2XgooCphmjuPknznU//I18mVY8cXrF+vpNV6U8ToZ+DWIz9OBVLWUwyqn4n/xuaIoF8kFlejib7OZs15DFImcn4tIUlk9LeUeBWS5ePl/FKWkWzSbIA8HL5OIKBLhdMiPz/c/m/Iv0Jsd1BbUD3Jqusjtn5+iFyLFzg2Um30GP/oY72zO4LFEY/Uhi6LFttvFrqXUjIh+9IBNAYOD3yJSbMzCxTp9jpglbEWbh71vcrD4FLvYEHkh4+o1FFWjHq75evJDlonNUmxwKDzm96W/YSzvMo87NPIF/2P6PzGnwV/Lv88063En/oDYaLMRm3iRRGU9YzdeMBY6GKpE3x7w6fYS88AiKWTqmkevFvOj+XWqyZy67KEJGTNfp1ErEJOcHXvDedgCVeHwKOfUO2DjiHT0mHGqkokCsiYSZQaJovFkbaHmMZfbK/qdnA8XfWYziYYZ8s1bYx48kghc+PiJg60EvCv9lPPqDYL1hOXSwpFGVKUJd6xn6PEGpDHmI5dLQYCQZUyMfR5rb3OpuA9xjBMOUIUMSYas3uBYfZtNanGyqVOp30KWRdQC6uExS3uPeeWA1HQYfO0/sPpAxD/XaU6GpJnISrxCXwu51RjRbDbQ3+0Txznq+BR5/AJVrXLrVpvgxRRnOOHj4k0ypWCxENFru2zmM3r9jEolx7/exxoMIJhxpneYmztUOEc4npUKKI09JKls+dp2eZgRxX98nXyeNvRl137TW72v46sdn6cD6Xp5LY4vpKh/8dlfhhdpWiYEaVoKS5yciGw2pblfFJWu9bJcvMSKcugYKhUIHw8YbsHbCuhNked/M8at7TKdShhGQWT02PlXLbIMnJOcO/VzdElEeOd3CQ/7+D/5mGw4J7z3CSouD613WHZ09rxPsauQBBZ+r8t2K6I1LYyHd5E2C7Ziha3VxyDCcc/Z2Duo4YZYd9B1iZof0ErvM080AuroUkBXmuKrDk7gcSf7KUJRMJZ2+N+Eb3M5fcpN/z55xcaOQ56od7gz+wsa/hknHCFwjZ29nNN5hflaJCwUIrPCQdfnrNghimxqesAidVA9Fd8X+calOcORzCCsozsFzZqCl7VZpxZ1S8SpghCr5JJEXOhkssqDrUwexDhKwW5jxd++OGI3P0NnTaupYIhTtM2M8P4JR2LMjcojPlXeZbt9TG/7jL42xxE2XDFOqSkBZAqHmyE7cYZQ5Dyz3iJVNHy9QT18iuZNUMUEqchZVXf5ufF7mOmGWrZl7FxhU93FzlcUUcFU3yOs1kj3j7in/DGrsYD9WMMZfchSqCNpXTrSgkvVhL2rLZKjXSpBzp73U5xkQJgVVG60ueT9mBeRzrnbAUrDsLiyx3o95eCwnIOcqn2amwFhPmeUt/hYOeCGeUb4eMZ2KxK0dvB9kTTNPitq/uOH91+GFb/s+mu8eBWvk4Ffg/h8xrq//4scuIu4aA9fVP2r1Vd60sNhqQCxWIj0++X3t1teWtArSk6ew3xefr/bzaluBoynCzyrw7y5z+qTc7T1DMEQUW73WS5FikLi5EREFBMsC1aqRFxrsdD2+Fot5gf2u8gHA7SzKTvGkrh7Cfft3yde3Uf5wZ8xW26ZW3sUb93BNxpc+vF/ohWck+UFM+eQLIgZaJc48D4isgyC9g41PcTcCnSCE7SoRkea0RMHSHlKpfA5M47oeqdsFZsk04i0GleKc65xTBKPqOyqvJg67KTnNKU5t7O7PJke8JBLxEAuSLwv/D2OGPJUeA87XFLEMRvLQa9oiKKEvB4zWAh8lN6mbXr0OhmiIvP0XKfNkI1ssRJFnqQ9Ckmg2lEJw4LMTdDUhM1K4M/nuzSzM5xCYZn3mMVV3u084PkkRk5CroWPacorzqNPmWo7rBKN9/wfIospJ5U93tc/oO4/I08yQsFkUtnD619BW7k4gouazHGKNaHs4BothrU3UeOCP8j+gnay5Ey4g33YpDKd0189JLx8g0fzOkvnGtV7H5KsJTqnTzijzjPnFm8Wj+kHz6lPCmrDfYKTFuuZiHO+omqkaHrB2QoMEVxXZOIL3N9IJAl8vXNKo5FBAboG7oshR9cLFpUGfadLcV6wYIc4Bk2XaOxnHB6mXLqU02i8SgQ+z21+Xal5Ha/jH8Y/tbr5ZXhhmvD4cUm98LxyHuD5cxFVLcUp3nknxXXLglRRlAlCp5MjnQ8oJgtcvcNk/4DJ6Tl2OCGrCZjNPVarksLx13+t8sYbKZUKbDUJ12ihdXa4spPyf+tfZ0/7ACV8gXf5Mk+c9+gfLRCenBEfH3Oe7OFfeZs0FXhn8hfo0QqEjIXZw9NaxJsZnriDsEkRqzqBWcesCJibDHU6QYhlWowx9AwpCrGEBBMZS/YIdZsn8W36TLieP2ZPGJErPi3F57ejD1FJaFTWdJfn2OmYB6df45ABqtSkXwwIhAb1GJZbiwfSDW6o5xy0AraxjaYEfPS4Qn32jK+ZL1grXTyxgzN6zn4R0LO33P3bI06lSxxpI8adK+zlzwmWCdfjTzADj7/88A/YV5/TFMdcyx+RVhye2Fe5MvsRR8UTmsUcpVDoi08pRJklBrf8j9nmNlvDph3NiHMTPd4gCgqBZjNrXiJD4db67zHDORohnlwnkzXuNr9JXFQ4iAY0hQUj6zZpfx9Fzug9f4Bb32Xq17FiMO7+FKXZRT5/iq84PBVu8K36YzrBc3YGOXr9AL/SIn46IRx7qPu7CL0W8aMpVrRis7zB43CfR4/KGZVvHZ3QaObYlZwoFtlXT6ldEnDrdUZKHzuAobBLuwo7TYHOYUa7ndDvlx2B13jx/028nhn4bxC/jJN2wWn7/N/Px+el5DSt+GyA5pXiUKNREEVl1ce2S/k40ywz8QuZufNzifv3ZSYTEbPw2J77RIuAR/fhh2eHzCc5ymLCnjgkigR8ySZJBK5fT7lxI8OXbALFIQxLIJnNBAq7ChRYh3XmnVs0oxn/L3tv8iNZfl/7fe48xL035jmnyqyqzMqq6qqu6qZItkhCFP0GPPjBwoMBAd4ZXmvhraCNBUja6R8wJMAGDHjphWVLjw96lPU49dxd3V1jzlPMcSPiRsSdvbidXc1BJB/JblKtOkABEZExZFb87u/8vtM5tntIOl8QGRZPWr9Loamy1f8hcSTgLxMGoc1ML6OKER1thVFhDQuP8oYFrQaxbmCGMwRZpJSOsrJmku0AzeQMTQhw5TKCAJboMcm3iVWNatKj4u6zwikNaYAspwi6Qt07wJKWGLbI16X/Qlu8oC9VaZcXXE8/Yi3vovgeXXud+92/pTLaQ5pOyAseSavFN7eesf9hAJ0B/iLhPKmzjCV0XcRSfRQhwpoPyIdDwlBgMDeJJYXfVV5n2zpmGOc5pU140meXjyhLY1riBdf8D7gSPsGSl6zFh6ylB4zSIrPYYC06oBx1M41+o4BrNbHEJVU6BLGMEPjoYsxMLzGrX2EkljAXI1jMeRa0eaTe4rhv0u6+j9zrcHEu8IZ/m+mRi9jpYk27zOcQOCVkS+X6tkojPcTOwcG4yPkPLkjGHgtP4EDY4D/9oMBHD2WOhg7z+hreUsRNHFrxMep4SFSqcGReZ2sjxF70KLZVomabIMhMjmQZllqeKTayLFCrpWxuJj+ysV/2Qv80NaDP4pr7IuDFzMBz/Evhi09zxC/DF3GczdzEMQhCNqOzupogCFnlALLB4fFY5MkTGd8H9fiAxV6PrtDg735Q4GmviHR6Sis6Yo6BJzroukAcp1y9GuM4KZFhkToOacon8tiSLDIttJmtbaOqAvVcQNX9iGmS46K0w751i7vCu9TDY8Z2m331GvF0QX7eYVDa4olxk0C3EEWoV2KSUpkwl0ecz9HSgFw0RvKXhIIMUUxenlPQ5syNAkkqkhMXTI0agZajnlzQnD3DVAKceMw416JKl4IwTEULpQAAIABJREFUpSIMqIoDNnmGqgqUHZ81+ZRd4xmVVY2yNKGUdHlp+I8kXZe13ttck55x+5rHitZBeLhHaXZMvAywUo9oGWOrPtfsc9rLA8r+OcrMpRUf04zPyKkha8YFt9P3MfwxUxyKQYcrwgG6HJJLZrSX+7TCQyJJoRGdoKQhfqoSx1D3T1ADDy1e4upVIt3Ciic4dozgzRBDn0jSGZhtpsVVxLmHEAacR2WeCNc48uvI3pT8+TOC0ZzBWObpoMTwaM5oDLlJJjhhbZTRHJVXNmcYsy5JocCjA4PpB+fweI95vsm3jX/HO38z4MNDh/k0Zb6yyTC08DyBenCGNe8ztyoIN66Rz0XU6JJvaiyrbaJIIAxF0jRFKjksFRtJyoaGr1z5fPjii8oV8EJa9LcOv8xi+3EL7UtjMUl6PlhZLGa226VSZiIjipk75dZWQr8vMBgIn9jTSwWTcJnA/hGLwz6hpJHLCawEB6xXp8xzNR53Crhu5lD56qsRonjZyiSwXAIIDIcCnmiD49BqJYiTBUZvjwt5hYPaffKjI7RkQdh1OTrT+F7um4x9nfPSLulwxH6yQVdtkxgmxloJfaVI426NuN1iNpdQZi5+JDORSxixB0lCx9rkwcrvYy8HrCbHmOmczu7XOPWrrPpP2JBPuJLvEcg5ToI6UhJwU3jIK+LbfNV+n7zg8tS8jSqnbFun3Fi+za73Ji+P/4FSOmKyVAnVHKt6l3xFRJAlaqNHyNMJPhonrLKV61BWXFQlpSINeK3wLsVkyDIQEYlY0foY4ZQddY98OECbDCgtz2jnZ6xUFywWoMUL6kKHlfSUctLlQ+M+Q7nGrehdVsM9jGROoumoloIUh2jxHMXSoFElCCXeNV7hIH8bfa2cKSXJEmeV27yrf4k71XN6rsJMKRLEEpPYIhe76IGHpIrEN28h39kmrVZ5KXyHayshuaZOJy7RTytMQh3ZHXKirHNxknDekQl8gSSBM9fEtVcYj6Gd9/Alja3rAvJsQrrSxDZj1q4IXDyc0T9Ycja1uXYtYXMzYn09ZmcnxjQzm/jLrM6nddU/bZb0m7zm/jngRTDwHC/44jl+Hl8kSTY02Wolnyh6XYpPlEopZ2civs/H0qYpYhwy3RtBp8fpwGSj6FL1jsk7KbWbRaZinpMTkX5fZDYT+cY3QtL0sjVJYDYDSRLY6zksVJt8PptDUOcLktmEjn2VU2OT2sWHFPwux7MyP4jucySss4hkpnqF2cjne8lXCCQTVYXalolYtCn8N3cYTlXSKIHFkiAWWYo5VCFgoRcZrNxiJpeoLE8oKVM69iYnt7/F6tkbFBlTE/qITo7CssND+RY5NaAu9riaPuOG9JCm0me/ep+pUqEuD/nK+P+lOHjGmv8UpnP00KPAGENLiAolSkmfavchduJynjboSXVUA1q1CGfRpS2fc1U+QJJSHqXX0MWQUjLk5fhNzHRBJGu4voEYhmyUx/StDcTFjHw4oMKQQjrGSmf0zVUWokkrOqYY9tDxEWWBxLIJNYtC6qIXNSRL51Rc40PlFqfONl5rk0p4zlgo8k773+HaTa4UxixCidEyRxCKzAMFOxwxEQuEjRXq2xbK3W1kMeYa+2xtQJxGdO1NnsRb5JcdJv2Ig/XXOHh3RjJdMPMEzicWk8hgqRaYz1NscUZVm9C8miO1bYS8TbUUUYm7nJ5KHAyyZOPNmzFbWxHtdszKSoKiZP4Bl9LqnyVffFG5Al4EA791+GUWmyCAZWWb9acVJD6tOPRpJRbLyi4eVc0yQZd23N2uRLcrIssCoWFzNjSJzvo4Z08pTo7RCjrq/V2UzQa+D0EgMhpllYA7dyIkKcseffSRzHgMe3si+TwslyKzGUyEKoIuosYLbMkjl87p9eDpoMxkJuN6EvvWLUrhBaoYYyRz5mYZYWsDb30bSYjxHnVRNZDHQ4JIRAgjxkkePZqRCiKj0hXMhs2z/B1msU5BGjNPc1QMj2bVZz3ao+IeogohNf+EijHjZvgurfAId6GRBPA78X/B1CPqRZ819wGr3mNyiyHduMRjYZfSqk5rDdaNDtPzOWlvxE59zJvCy8glC5KEXfuIOBH4+spTbve+QzLz0IhQ5ZQ7yx9QWp5yHDS5vXyda8Iz1pMDlpKBtnAJJZV8NKaS9tGSBbGg4MoV8qbPVvwUkpiB0kAs5FDjACMYE8k648ZVhs4KA6XOgbGL75RZGEVkQyERRHzZZDFLOJdWkJIAPa+zH66gBBMOjR3kNIJGHXFzlc1v1NmsjNmUjonzDbx6gwu/RM49w8CnUEyxlIC5muc0rPJEuoFtBLTVAdeuhMwkB2wLT3ZoGi7XCj1WGgG5nRa5cIw06INhcDItkCRZi0Ktln6SKQQ+yepcHlIuDy8/zSb+877m/jngRTDwHC/44jl+Ub649CgwzUyf3f844M/mzlJcN/OqmQp5lqmONOihPHuCPTghlDWSm7som22KxZjl8kf54t69rMzQ6Yjs7UkMBgLn5wKlksDZmYBlJQzCGsUGLEfLjC+Yszcu8sFFFcGdMJlL9Oq3EGMfLY2oBadQKRGubiCv1UgFkd7BkmIF5ONDgkhmGmgsBAMjnrOsrdBr3SKdzRnKVUZpnunGDrv97+JIc7bVLOliiVOsxZBGckpFGbEePEVNfKzlkDiVuCY8RVhrUk0vaHfew1n2ELwFoyBHR2hgXGsgrjYonD0kHMzJSzOGq3c4YoUDZZtW3qOkTvHNPNcX71FOekizCRO5RE+o8zv+d4j9FHU5xUpn2KLHJvsEyBSmZ4hpgpNMcHARyERDplqZku5hRy6JIBPpOXy7jJYsSVNIVA2v0OSw9BLPkk0elr4MhoZoGUROCVER0WOPxSylo66g6DKhXSCc+iwimY7cZqKUUUo57G+8xNfvDMgrC1ZbIW55h6mfsvRFrOEpek7EyzfZWAlYDEN6QpV3l9s4ZsjNap9yIQLHIdBsLCPiRqVLvRxSumJhBFP0eEFvZqJUbAYDKTO5E7OOh25X/MTI7NJc7MeD3V8nX3xRuQJeqAl9YXBZFo7j53q6P01x6PK5qvrciVhVM212y0qYTkUODiRcV+Q0XuXurR7W8QjHjhFvlphsNmlVE4ZDkf19yOUEDg8lnj0Ts9mBMahqQr+fyc6NxwJRlFKpCJgmHEptGrM+ppHSmdeYhgLTRKC+HLCunbAijui0d6lejDFVj5vhuwiLFtNeil8UMV2XXOcRqSjjEaMoCVa8ZNLYwvBdiumI5ckJ+toqarOInMKt9AkLowDbt+l8EFJ+ckxjcYgj5yhMu+QWfeZakUjSUNSEVWVAbfg3jBdNCJZcyCuogUscCWxJj4jiDaZDkaGvUhw8wYlGfF/77zFLGooUIOcterM8da2HtfcBkR+QM2E9vKAaDVjxH1NIuwy9p5RlF71gEiYSondCLKmUoyFryQF5cUIo6OgErAnHBPkmWrMNE5tQdBg4NtHFE8wkxVPzuC//Lu+1/g3p998kd7KPFfn0J00WzetMJJm16Ye8lDtgsF7m8KxKGHa4OnyDeGODRCoTF0o45ZCy0qN0cA4XfTy9QDetw+N9zAD0FYfiTgVj0sOdyky6MVvbTV7K+dRqdbpvwmwhYNvZ4V6SUsxKg7IdUkn6LN/rI1ZTwmIVN2jTFhMKhexftZr8yLoNw+e3f5oCxAu8wAv8cvhF+eKSKy7V6SBrG/K8rO3UdbPD/EmwQikccHPXJUkSku0S8d06gpBd14OByNOn4DiZRPazZyKqmvGFKGbOr0kCFxcCYZhmohXAfrSK6Q2R5ZSjUZ1FCN1E5GrU44p6gsaIw/Yu9c4Yo99hffE2c/kl5l4BNU4pPfoh+bTDUpEY2xWC1Kew7BHjoMQL5M4Jc1+BUoEgLrAqXVC1p2iORpC7hfH+WxjykgU+6/4Z0iJEkgVC2WCmGahiRCE4YuXh/8pYKRHoNhdCmcboIywxT0Ga4tFkZXiMNBph+WMmuSZnzg2UOKKY+KRhhDHtYy/2KKljZpJO2VrwavA2YuDRSk7IpR5atMDXS5xXqkhTicLsDC2aIwsxeUYoYgSCjChFlPUpSbWCVG7iRSKBmJJaJvbJY4Q45lHtNZ5e+zf0pRry5EPWzAGxHyEqKqf6c764ZewT3S7w/odVmv0PyU9OmF+9yZGwTckJKdVSrk3eZnmUEC4Txrkq4wGkgUi+t4e9s4F5pYKp19CfPWJjRcbYqNMYh+h6C/coRUkE6vWE1VUBw2iSzweU4y7qQZc0Af9KmSVtpDg7s7TbyceH/GwdOs6PKiz+U4pBL/DL40Vl4DeAXyXy/FkR8eVAzWVf3fl55tbq+1lGSJYhjrN2oYsLiXY7xRqdUZ3uUzaW5AspRSdCL6g0ti22tmIePpSR5ZQgELl/P+LoSOTttxWOj0UuLkQMIyGKBFbEM1qmy0Kp0D7/PsWgz6I3Rz47RhahnpuSUwPWC1O0isUt6SGeUUWIQorylKo8JtQcTtU1tGGHWtonklQEWSaxHdxcjcXObULFoiH1EBcephbzjnSPmd1AChfk2nnSL93jif0yteiCkrtPaXGGHs0IBZWh0UYzJbScyEQrIuUMnN4+ShLQL2wxkkqUhQGr4inlsIOfqhTdI24Hb6EnC6bk8a0833ReZ9t7m0lqservkfg+OHnCRhtjMeLl6T+SSzIp06I0Rc6phF96FbOokvbHiL7PVvKUojIlKlR4Un4FX7UoyDMKhZiDG/8KwdApDA5xxAlncQNXKXLCCu6NV3k6b+EUUsr9JxRUj0FcYBQ7zNwYP19F86c4vT1kf85u/CHSfE6nuMUPjK9zcztCjRdI/R7JdIF3PGF+8w6D6l3coz7i0QnkTJymQX7FJFUUCrcblDdMvvrViMlExKhbhLqNpgk8fiyyXIqsrKQ0dyzGH/WYTAQUFSqvbf3IkFeplGmgDwZZO8FkIpDPZxnKn9X//OvAFzXb86Iy8Bwv+OIn8avwRRasP+eL24UTrN4hlrQgl4O8FVFoqAgFm83NzOPg5CRTFlJVgd3dmLfflnnwQOb8PBMbCAIQBIE1+Zy6NsYRAyrd93CnEulFD+XimFW1i6wIiI7JzWqXXD3H9eghrlrFjCdU5AlasmRst5mEJrXBExQVfMVC3NogCROCQpll6wpm5FJJhvioDEsb/ED4EnXDJZBNtJevETTbuFqD4vwMYznGjKaQQCrJ+LJFUGkQmgXSKMEQfexZl1m+SZBz0MM5tjxH12JWJg9puI8x4xm6EJCmKXGQYJckXgu/w2b0GPwAZemhyxHe1m1iSeVG5x+5GjwklRQGagNb8lDLNsrOOrHjIIzGiElILb7AUkKmpVU+sF9FlsEUl5iOwtMb/5ZQ0LHnXTQlYT4RGMkV3pReYXnjNsORhC5F5OUZejglUVXC8ZKxb3Bc2KUcXmB39sgNTrkS7+MJFo+LrzBU62yvLdAnXYa+jdjrM0kdwmKJQfUu0YcfkfTHhHaJQtugeVXDz1cob1mU1k3K5ewwH+cc9KpFr5dVmXI5KK7biJ3uJ34Z1a9tfWw2mXkH2Hb6scKV8Mm8yae5Aj47vviicgW8aBP6rcOvstguy78/zVzm0wM1nzYju+ypk+XstUmS6fYa/VNupe+zvR0R3H0ZYWOFxeEAOn3ORyae5JCmGTEYBkDKgwcyZ2dZBUDXU156KaZSATt2OX17SHJwhrXo0lZ7LAdLSEEN51Q3dAxbwfMV7M4z2vEJYk7jWet3WVplhCimai/QWWKkPidxnX7jBjNP4kxbZyTXCK9d56Byj3Go43oqkZUnrDTQ/8M3GYxVfKfCg32HatrFW99hdfoIfTogTGUWZolAtSGNCXMOytylIM3IzfsgwrF5nXFpg43ogLzsYUZTSvKUenhMKskERp6r8gGbuXMKwoQtu4s+H5HXF4S5IlGlisMU0+tTY4Aig2Co5GUPt3GNzuo9zhovoU2HNLQhDXtGWKoxybcZFDbpX7mPWrFIG3WifJHp0QRtPqKf32RsNBk5G1jTLoOJxlP7NpvLxwTdGX6scihuoAz7TFKbvlQnV1IpnD9GSBI6M4s9Z5eutEIaJbzRWaWyOGXSC1FVOLwwiCs13LTKdLxEXnr4+Sqz7TvIRJw9i+hMcoyTArqe4rrZ3InvC7TbMcViyvZ2xJUrMfXgmIMPAsJQYDwSqZcDfC2PpmWtCSsrmdLVZJK1BilKSrmcfJLp+SzxRd3gXwQDz/GCL34Svy6+4PiMwvGHNJsJ7f/uNsnqCtaiR+e9EaGkczAsfGxils2qlUopx8ci772XmWgaRpbJXVtLWFtLCXsug49G6EdP0UfniGFAEEnk3RMcPUDe3qS6qrIYhtidZ9T9E1JN49nW77OIZeT5lJXSDHU+xrcr7Om7dHJXWMwTznPXCHWH6c37jNICI9+kq7ZYFJsIt3eo3G9x7hVYJCoPz4p0N16mPDnEFH20+Zip1YBUwpcMekYbSRMwFmNy0pJ0ucRPFDpJFW/tGvllj1LUp+j3EGWBWNO5MDcx4ykb0imKFFFUPRy/T6zoKEWDeb5BrGikuRzm4JwiQ2aFFpKhMLUauHabk8Iup2tfQl9OKZlLCvKMRaXFhXOVvrFKZ/s18iUBr7ZOnC/iPnOxhDk9pcmpc52hVKE5fcY4NJnlqrTDY4YjkVgxMk+Knss74S7D3BoVa0mt95ho7nO0qPF2/neJRZVJZOGdT1DTkGmcQw4W7J/pLDd34LjLdBYxMpvMWlcRLR1l1OfdpwXOxTazmcj6esxslh32NQ2uXImpVhPu3o0pTE/QIo9+X8KbC0xHCRt3csznArlctk6r1YTZLAsCIJuHTNMseP0s+eKLyhXwok3oC4efpqH7aTOaIMgeu9Sj/rQG9aXj3quvRiyepaxqRdJalURsIwjQPxexvB6DsULnPfnjIR0B2054/FgmCGCxECiXUwqFbJOfzQSWhRZDWeSOsgcIyM0iubnLVC2TJCAKMUtkEtOi57xC0X0DP5QpX3zIsX2DojVmOhuQijHycsbZ+jcJghR7OkQLPRIzx9PzEnG9zjDaAqNCeTnCOxrR+/sOR9xnZyUh+OgCP19D/4//G/PzLqHaRE2nSHGAteyRRjGF+TkzpYjfWuGoeBP55IxrozdxJ88wSzKu0saWljQvPiRVZcZru8R6i7Z/gJY+RNJVkkqZoGnRiwu06jblg/fgg0cEfooejpFEETWvMQ2rBIZN6/QtRvlVivmEfOzjKqvoWzXm3ZR5kuPUvs6yvoO9VWL23Y8Q3SOCwi1EQSROBZZLgUP7VWbUKO69Q8eWaJRytOwpBe8hx1ORcVrigbtCnQ4lwUGtFklcKO/U8E8CSiUBeXSM58sEYZ4xLagnrC86aNox8kZK1Nyhb7QxhQRW29AV0WIB0Uw4OhIZjTJN8kuTIsvKvAIK01Nkf8DSabKsttF7p4Sn5ySySO5K+5P1KcuZxK3vZxKlipJJGTabyYtS7wu8wGeAXwdf3LmX4u8Xqb9UJl1pk1yIzIFZf0Q0V3jclT8xDyyVMmnrvT2JJMnELdbXY5rNlPE4qzQcxWtYhoCQTijZEVE6IRRS3CvXGNZL5JYDBF+gk9+Ech755C3E2Qzde8j+zjcoP/g/YDRBUAsMK5s8VG5RPXmX4PAQtSJxFBUpDkPmuassjQreXEDtRwzDHnvhy8xSkfuFU4aThFtH7+EfDVh6UySlQCrJuGYVZ3bO6tmbiKrMNFejd/M1zs5E7PM91sYP8PwKQrvIoisgmirWYghWgfx6lUVQ50rwkPzyCQkK8qsbjMMV5q0NDLdL5cH36M4MpGDBUjIx9ZhE0xnFRcZJGXXcJ19wKDgxVrhkWN5Cv1IjPAZBz3FgXEe7f51jv0Hu2ftIs2NGq5tM51nw1Y8KjM0NwkilGPaR5YS6OCBvy7gudJwC8UxgMICBIeHrNkurhOhBsyGw8AIMocMikXkk7SD7kHdiNEFE752i5fLYu0X6xipJAn4tISZB6QvknOzQfnycKVItlyKimLX9VKuQG56iz/tMSxXOk3XK81PEQZfJBxEUVtH1ywpSxjGnp1np6sMPM3dsRXnBF58FfqXKwPe+9z3+8i//kr/6q7/i3r17lEqln/q8d955hz//8z/nb/7mbwiCgJ2dnV/o/V9ken5xiGKW7V8ssg2+UHg+KPbpjNBl2TiKQKtYWNfrRKbDcikQx7BQ8kztBk87+SwjhICuJ1QqWZRuWQKeB+12jOMIrK3FqCqsrCS8s1fGSuakhs69r6sozQpuZBEoFsH6FtE8YtiJ8GcRcXOFiruHMbwgHYzwBZ00ZyG4IxRNZvbhGcrTJ4yXOmOxgltYw2RBrfeQYDTng3SHh7m7FOUp1wtdYkUjNG2UkkPr6f/H1rO/R1IljlZeRak62JMOogQzrQRhRGoYuM3rvLn1H/hO9BW2Ot/DCUbMtBLL1jorJ68j+j6eZHOw/hoVBpTLEgz66Mkcf5kyXdtmNBBpfvAP2MePiFOJKAA/kkjjCM8oMRBrzEMFY3xB3X2KGi85rtzho9rXiHSLsaeiLqeM0zxueQNz2qM8PaE/kHhQ+V32lWtsX1kiDIe8b7zCXjfPeCxiz7uYVxtEy4T9dJ3xMGGhOFyRj9nK9/G0EqP8KlM3ZS05YjYXEWtlBFmiyJhCVUEraHilFTw3Joh1hHYZrWqRz6foelb5ORoXef+gwPGxRBiKFAqZ0ohlpWxuxty+HdFoJORlD8nSebxYw/NALDrYRsh4qjAMbRwnW4tJwseZwkzmsFBIPx4u/vWqB/04vqjZnn9OlYEXfPHL4beBLwLNQlpt4KzYxDEslwKR6XAUtTgc2vi+SKmUYpoJopgiCAK+L6Io2XX90ksRsizQbCYfzzAI9PwiejAnt5HnRrWHsHuNhWRxsfoy6nyKMJ+z37OYhga+F9GMT2GxZC3YIxA0VCnCj2SOzesIr79N5fBdEnfG48IrDMw2N9MPSLoD3kju8iG7aOGUsr6g2NRILAejZnLl6LtUTh+QJikYOqGkI5dspCRENmXiZUAgm7hahfn2S/yw9K/4/vg6G9MPKIR9LOYMStcozM/xfZFQUDgo3aVdmmPWCpi9Q0RviidazO++ivruuzgP30EcDdDmU6Rowam2wVitYccjLvwSgySPM++yOv4AI56yl3+ZB5WvEZsWM19FXkyZiXlmtQ2Ebpfq/ATXFXk99zW6xavkNR91MuAt9VXOXIfBUMCIPYpmQC8usM86XWsDfdpn2zimpfZJi0VO5DWiIGElPsLzRATLwM9XqdehLvTR1sp0ZjncmczEWkcp61hWQrGYsrqaEJoObzwqcnws0utJ2WOhwGQiYBiZ5OzOTkwOD6umY263ODiQmIoOkyE4RYFB4KBpWVXqUt0qijJFK9fNqgSfNV98UbkCPsM2IUEQeO211zg6OuLOnTs/dXNPkoQ/+7M/44//+I/5gz/4A/76r/+a3d1dHMf5ue//YnP/r8N0KnySyfn01P1lX+hlX/Zl2dhxUi4usj5RUczMZdrthGIxG/Icj6Ws9zEWqVYT5nMJVU2IIpEN9Rxp6lJYt4giAdNMaRy+ybo1YXVHYk3roEceStGk3U4IJIPZ2nWuBg+5ETzAsAUeDaokgoA5vmAo1zjSr5FqBteqA8r9J3j5BjO1hKjJpK0W9asG+mwAooinFulWbpCoGkkMV2ozSiV4+esaxfERvVmOHyT3CFMZ1dIwGwZpoUjvystMEodQzbEUTY4LNxkOwY0cnFyEIy9Zu3iHSFAIBJVZdQNjMUbtnRFNFkyFHIK/IPKhM9FhPKHef4ggCAwqV5kbBS60dfzmGq5YIq3XEOOYhZLDLa8zs2oc3Ptv+c/efT64KDN1UxR/hpETKLYMJElAGPSZRyozq8Z8IdGlSk72CS5GHC8bCEJKs7DgSmGItlHNhqs0G+KQlj7CyqUE2zfQ7lxltNCpmlMEYGUVNqszimWBsFAhbKyQzyfodZv1WxUGAx9VzSo+pVKCYSQIQgpkqg5hmJLLJZ+U/A0j6+HUNMC2CIxsI6/Xs8GuUZJHKVk4TjaUOJ9nFY7LHuUgyEhCUX796kE/ji/qBv/PKRh4wRe/HD4vvgA+URL6dF/2P8UX9XrCxkbMcplpw7suaFrWVhpFAo6TslxCLpdS9juocxetZuO6EoqSstl/g/vrE1YLA4r6AsuIqW3qlPMRo/p1YkXjxuQtbgbvcjLLc1a8gbqcse4/4wPrHofOLezpBS+VjtDcHpIisZQterUbsNpm1zlhhkVUKBEoFhOjRhikBHqejcKYV3ZcrhYHuEqFD4qvMeqEjIrrGPEce6vA2F5loLUZmU0wLUxL5Ie5rzMagC/bFMUJVYaYsx6eUiBOUsRSkXQ0RhkP8PsebpxDizyS6RJ3IqL2L3DGpyycKiOtykgq8bj9e0jrLSJZRSVkbpTpmauoZZOgWOPtK/+e70f3ef+0zMJLED2PnC0gmCZxCupkQCgquHoNURQZyFXUOCA4H9NJqui6wDXtiHIhQri6juOAYJksRJOrygE5U0C9v4N/5Sq+oFPPZXMTre0cVdPDkeZQq5C02yhlm/xKjlpNYzLxP1GkunIlJkmyg7tlZYf3KErRtOzn7XbGF7adopQy/4koyhJOhULCIMqTWhaCACsrMUGQKWAtl5lHTZJ8fnzxReUK+AzbhFZWVn7uc54+fUqj0aBerwPw1a9+lddff/0Xeu0L/OIIw0w1Ipd7rhpx6U58ueE3m9mh7LIs/OOl4svgIZe7LC0n5HJQKMQfK8EEzGYQRSJ+LyHn9an5IY0bbeS33qRuHuE1bxFWLXrnF6gKuHaNjY2IW+ML9r0E99pdpkcu2p6L1bvAa61jILA0Shy37rM8eoc1r0dJt1iGCnEqIjkWpiORkOKu3KBo+dQOJOr1Q1xnhaIf0y51CfJsdg9gAAAgAElEQVRhpkLwP32dd+rfYvT2OfXud9n3HRLHZuKnyH2PWW4HuV4gjqF9+EOaEryz9Qr/59b/yP/8D/8DUhqQyDIXuU1EQWE5XBIFKY/0O0ycOg19zGrnbUonD+nEZd6Nd1HXmwRf+jLN8UPcQQW3UoLhmLFSYb37t/iaw3L3K0xcgZN9gfXeG4w8hYvKDYQBbI+eEp70yRUS6jcL+EsXZ9GnV7jJA2UFU4lQtT2qms/r4qtsmT8kL3Txkiof6K8QSyesb3RR5BJCu8rZdBXbTZmXV+kr0FVkGsYZggCvfinkjaSB5yVcXAhEkcB8DpoG5TIsl5kcoCiK9Hoix8cSo1G2LhYLkVotodfL3K4V5fkgoixna+ky2yjLfHwYyNbV5Tqr17MS7z/ltv0CX0y84IvfHvw4XwRB1r73aa4QhOza/ll8YRjZddzpJDhO9vxGI6FcztTDbFum25VxTxJaSp96ECFfX2X17HV6+hHVapX3hk2iVglRlSkKAU2pi11OOCisMnxc42wsIM+miLqEkZcZm5sMhQbn5fvo3pD1wRsY0ZIPjVvMo4SWNkTRJYb1XbrlBnkhYetpl7Ra4Vx+mS37iCu5Lu1GAfF3vkHhVCT5rsyRWmb96Ls8ML7MbHpEHKeMrCKB4VBIR0wjgd2z/8w0J3NQvMv3qi+z+fB/QRlOsCKXp84u0jwiWkYkxIzrO/SdOqvWmLVHf4/+5CGT2KCbe4lo+zbWlSL7wxLadESvWMa/vsPsh08wO/uMSldo3LvO2VhguXdBa95j329yWtxlMRfY7T1Fq3TZWomZqXmWD8cU/D7nzi4Ds42pRNjuPgUhoCe3GYtlmrWYiVYlVQVyJ32uXq0QSreRlJhH41Xy+YRldYWFntBRVBrGGaYJ6+shnZUG5+cwGokEQabuk80NZt/5xUXGFZ2OTL+ftQ43GpnhZLX6s/nics1d+l38uALWC774fPCZzwwMh0PK5fIn98vlMk+ePPmsP/ZfHBTlJ3s+LzdvRYH5PLOWHwxEzs9F0jTb8CXpR19z2es3GolEUSYZ+vSp/HGmGK5ejXCcBCHfohrFrGgdTLdHP3A5q2yS3P0dlN5Dpuu7qCoUBQHnZpOnfy8ydBXiIMaslql1Tzi281B0WHzty1inA3Ymb2CqQ5yigKo3qNgr0Jc5jaoUpl2K62VcV2R28z5ycIEy6pN0+uTFPm6SR7zWptPJZO4qi1MUsc/BuEB9tkc8PicapxxVb3K6rFCbZw7NiiIiSrC4+Qr/9un/jm/Y9ON1omodUJFMCS9M6NXu86H1GuJ6CdE4o+qf4scL5EBgrjcZ2lcppjBpX8+y7bmUR7MKXHSRRchJEzwzxDNbfOXRdzC1KX+3fJVja5V6fEFRStE77zB7LHDUbuO2t3GDOpLVwhzB6p065zKU1Aa3SRCFFh/MUxazNmYzZbnZYjGD86lIGrQoFhNUNeXu3YTDwxbq+IzJRKBYSpnNBDbzx7wVrlOvpwhCzPXrcH6esFxm0m6CAJOJSKmUEEUJlYpIFIl4XkqxmNBqZa1jvp8FnJBt4BsbWRuArj93M11ZSeh0xE/W2acHhn+8l/kF/mXjBV98PvhxvoDnB33fB8/LDnvn5z+bLyC7lj1PRJJSOp1sHiiXg9u3I3Q9SypJ11qslUPqQo/SpE/iufirm8x3t1m8M2F0vYE9PsXZSemcpfR6CpOTc6y1ElGQUAlO6acWizuvIG2WyL8+Iu++TqmUkhMFArNOWXG4kG6xNvwv6ElCpVLiWGySyyV0uyJ6r09xMaBU76Bec+ioqyz2skrImnDK8aLH/rDAFnssJ11CRedh6T5EAvl8hUbaJxZkkkIBpVLnbvf/YmhvEEYeqiHgJAk9Y5VwkXA4KzOof5WgViGfvsn4YgVvLmYCDbUW6u2rJBoodo04qbMiddg/kJHCGDOec50nzMwdcvMLviq/wblU4EKtcSKs0WxfUElT0qfvMdsHv9pm2NxhYdSIjDY7zZj8oslUhJLSxFhAR9glEVOUQGRRbBJpkE5F5LUGC0nAjjNdf9tOGShtVOn8kwSObkAjPOacda5ejeh0RHZ3M74wzQRdz/Z+SYLNzQjTzBKGQSAym/0kX5ydicRxtn4uD/i6HuH7GW/kcjCd8oIvPmf83GDgT//0TxmPxz/x+B/+4R/y6quv/twPSNP0Jx4T/omw7tvf/jbf/va3AfiLv/gLKpXKz33/f46QZfkz+dsqFX5EdzdNs4v05CT7ue9nmZxC4XlWZ2Mju335Gt9/niF2HBiNoNGAYjG7v7WVDYidn4Ms30R2fYpFKHylgFi8B8goys4nn6dp2TDZs6BC/9kx5qyHEGlMrn4ZYQqvtEZMnQnDUCGRPBbtVQ6TFg1zyOJghGZblGdDjHqOMLRxHLDEKY9zOxQu3mI6gSt1DUnLI51POfJWEU+P2U0OeBzDO/lXiDBZhlVK2glCtc10UKfohDz6aMzSXidJ4Pf6/4lte4h37S77V76JdfCAavcDLC3g7caXGQhlqhWZ9Ts6zYeHsHoVt55nejymFLgMplNOw1fIJ1AcPmDRg7NwCy93BbneYjN4RPvkAbZ8Tk738TZ2aLa/TvP4mCi1OPBvYqUjJGmAIeVQV6/w0s4q77wDgwF858kO935/h8kZ3FDh0aOrnOeuEk2hZUOzCcfLbQYJCGdw/z7k89n/f3F2TOp7HLKOc2cVu35MOe4xmbj8w/4qvg+LhczduwU8D2q157rjvV723YdhttmXStk6q1azrP/JCXS72W1Ny8q5X/4ytFo/uhZrNX6jmtCf1TX3Aj+KF3zx68fnwRfwvAown2e3Z7OfzReQBQ22DdevZ34CkpQ937KyPeT3fg9ef/1jl3HjNk3xrez2Rh6jdY8wlLnVjCiVQNcLLEMYuvDg28fokx4nygbtioEsTVk3ZdbaMz6KNmhVZ4iuR6WocRS/RqcD6mjEuviItNAkZ4s4Tp72xEWprvJDvUBz6JEkoCs6olpk8oFHT1/FcY+pXhwwC+GH+isYsslUqrKtn1DI65yEdZZxyMGRycyoYpjwr+3v07AC4mtfYd+6i/70Ae2j71NmztvOPSZGEXNrjVeLR9T2O0xaN9mfVVgJ9rFin9lcZ6y2UEY9gkXIfuiw71WZVTZ5pVqnNjuk/f63EcplZnadeHWXq9XrXDs5RhpYdKWbSIwwFwPiOIe6usFLv7PK669n38PJzKF97zq6B74LIxdGC6jkQJ5DWtum3wf5LOP3ajX7vms1GL9/jDDzOMlvcPdfr7LVOGZx2OPBA5cHR6ssFjCfy9h2gXw+OxdAtvdnyaSME1QVVld/lC+Oj+Hhw+zQb9sZt2xu/iRfVKu/Ob74l8oVPzcY+JM/+ZNf6QPK5TKDweCT+4PBgGKx+FOf+61vfYtvfetbn9zv9/u/0mf/tqJSqXxuf5sogiiKmGa2SaQpjMdZpkfXEyaT51P5aZpF7WdnIq6bZXgEIVN9CYKE4RByuSzdq6qZgsxo6JLEmfRoUXkX68YdXDf72y7Lfq4rMp+rLEOfRm6KU3fQdwosrFUmb/4d4x+8z6S5TdRewzASjKcPmTlVxi9tkD/4gILkIiYOc2sFSYL+6wc0R89YdEaovsSHcplqMkHqzanVDuhEUClrXFSbVJI5kWAjSXPkkUbcOSMfhkjjmI/E23jaCvXgmEVvwFmpSG/tFta6xiD/Ks4b5+wFMvNX7rE9ekCu8xj+44JJImDfWUVeKgTDgDRekhBweuFzTIPq0GaxSNkzyiQCzMQ8uZsOraP/G0Vc4AkmD7e+xmZrzA/e9bGXU6aeD6FPEFmYFx7p033Chs3RkYrrilxcpEhSjKoKmRpER2Q+h0Yj680sFEIePFBoNjMXUcsKOT6W6XYF/P2Agm0RlkvM52MulByTyZh33l/y5qMF9XqKopg8ezamVgPXTWg0EprN52unXs/af2w7CzAHg2yteJ7IYgHdrsDGRkq/n/D0aUSx+NtVyv08r7nPE61W6zf9K/wIXvDFrx+f19rVtKw6IEkinpclc/4pvriUJg1D6PVEJhOR5RLSVCSKEp48EYCY5TLBsrIgQT4/pS+OEYSMG6xRxhe23SeKLpMPGfeMJj71So6S5HFzbcpQvs/sZMDBXpepeIhjx1hrMtF4wlCuc3z7ZW48+X+onj1lmn+ZZ7k20tkhljSl+2BCYSpD5whFkHjslqjtucz9jC8WS1iKBoNWkxZzotBGLc5Zeiri+AwtzHwD3o1uE5ht1sVjJosEOVcmbmwhOxoj+1Wc6JyZLyPUW1w5e4Z93kPd7yAwwrr/Msm7Es+CXWrjZ+j7D3i9eQutu0AYDOljETXLeBG8Wfsm3wr+FiXcZx4s2DOv02vsIKYzInxMaUoYLpkMfc4WFk4wR+oecH5uc3KiEMciw2H6cVYe9vczydh+P/N6GQygUknRNIF2O8G2U8IQ9vclHj4EYxSytZFjmCsQx0MeejnOnk144+2AiTVHUaDfN4ExjpPQ72etPL8IX7iuyGyWzQMIQsLZWYSuZypUvy34onIF/Gy++MzbhLa2tjg/P6fb7VIqlfjud7/LH/3RH33WH/sCH0NVs4zOZZSdHfKeuxJ/+sB22VZUryfkcpfzBSLzeXbfcbKI3vfBHJzCuE+XGl6ziTk4pdnvoXaOQc+0jC+TfIYBN25EvB81yGsJpUqIutWkHCWM1m5iBA+JzIRDu03Te4tlKjDWq8wLbZyiQOf1Dv2uxOJogrFeohbFSPvPGE7LHNg7rOdTrly7YPSwi+tWSVKYXHuF6aNz5OEAJxeSOlXOrBLLSKI+7SKImQvm0ZGIm1/h3r2vIOl9nM0i7wzWaXHG6Vf/PWEIRijyxL9N059T87q4aYH0cMJYrmDe2yI8L2FUKqgDKISnvK3eBzVFBarLE2xJRO522R+VMt3uVGD17E36xsuIaUJVHHHhpUxufBVJguToQ1aiPtODc9J0DUFICAIRURTI5bK+y1ot5fhYYGsrpVpNqNUStrYSsmHf+OPvMxvunVYanEUCppeyXIqkacLIamNsyZT7mTpDqQTr61nJV1GytfHpVgLPEykWs3J7qZS5U1sWTCaZHrSmiVhWwmz2/GDwQv7tBf5r8YIvfnMQhKxFYzLJrvmfxRefnh/I5RLW17NDf6cjomkJhYLIxkYmJJCmoFycok369EtVFpU2Rv85Xwi68fGAaBY0rK4mHN5poJsJteiCzS+XCLwmwwAiTaCShAT1Jkwu8AORkVlDEATil1+iJ9U4Xaxw8l2YVkxMNaRS71HrjOjMBQ6dba6ZKTcbFww/6jIeV5nORLztl+m9dYa97FEuRSh2lYtlidlcor3scvD/t3dusXFd193/n33O3O/3Ky+6UbZkx46luM69jYug+PoBLfrQ9qFAX/pQBH1IgwKNURQJGrRvbQI0DZCibVAYLVD0IcD3FLQPsd0icWzFjiPbsmRdKPEyHM79xrmds8/3sLhnhhQpDkWJEsX1AwSRw5kz58wh93/vtdf6r9ubQY8CoGVzuJb+NM6Ey+jbMTSRg99ew8b/+b8orws0SwZaLQ2e0i/h0Oq4OYjBvWIDqSSi3iH0lsCtQQyJ4Qqk38Y1/RNoNgG9qeGM+w5O9NcxMDXcaUapZ0OwDIe1hJvDPDy6BV+vhqEESuc+g0pFw0L/QyQ6ZWwsF+BwzKJctiGEBocD8HgszM0JpNMWbtzQMTtrod0WSKVMlEoGsll7M9KPTaceDYvtFPotDUkP6UevB3TjOSDrhH/TCW5mBgDkKPdf7RSpucZuetFo0IJE06i5WKslUC4D2SxrxaPmQG5Cb731Fr71rW9hdXUVb731Fi5fvowvfOELqFar+M53voPPf/7zEEIgnU7j7//+7/GjH/0In//85/HSSy9NdXx2hzg425vOCDEu1tn+x6dpFB2o1zVISQ4yuk4OAPPzEoOBNqpByATbkC43zEyOFgiuILyuIXxBP64XHbh+XceVK9SnwOUiW7FPfMJEZsGDoSeIa9cMNJsCDTsEb9SJQa2HSLcIIQTsEzPwnk0jnZaoDAK4I3No+rMoFSyIchmyVMVQd2Mleh4yl4fpCyK2sQzDHCA664ZlauhvWKi3DNT7bjS0EITfjQ/sc/A4LLSDafQCcQTCOjZ0HwIBoKmF0PCm0XOGUKloKPeDaIsgtHAACPhxrRjCnXYaRrcOl+wiq5fR8GbQQgDV0Dw+tJ9Ct9CEq1GB2xjClw3CW1lGxCwj07yO1rUilt2ncOXElxFxbyDdugm33UXFjADSworrBLTZPPSoH7bbjZkZG8UuufF4PEAuZyMalXA6NSwuis1CXW3TmlMgErGRSpG13/y8hfV1Hbdu6VhcFHC5NJw4YeHUKRJ2r5ccGUolgeGQekZ89rNOnD7dgddLvytCUFGYlCT6Ph/ZvTWbGiIRG52OhlBIIpWSOHvWwsKChUBAwu8nW1LVuOhh2oXuhyfVIeIouQmxXtwfj6NeKGvS1VWBfp/0wuOh183OSghBaSMjJxnZgfC5UQ/k4XJt1YuP1xx4800HFhd12DYwO2vhk580kctZOP1JD5YaYVy7ZmC9F0G7biET3oDb7EDoGlqxechcBqdOmXAnfFjVcliqB9BEAO26RLvUg1WswRzYWImeg/tMDj1XCDP2HdjdIUJZN3pdDfbQRLGsozn0ou8JwQh4sB5/ChstiZIjjaYrjkRaYOgmm+SWFsbtQRp6JIDhUKDU86PrCJJRhCOIa50crPUWLIcTXrEBt1dAkzYK7SAuVU7gCs7Cb7aQFiUYMBHIB2EUlvDU8EPYt+9gveXDx5GLqARmcT5RgKNWhivkQrNjQLMt1CLz6CXyaCIEf9yJTEaiPAjBFffB6dRw4YIJv58sXtfWNFgWOfM4HBTgO3dOwu+XSCQsuFzA7dsGbt7UUCoJnD5tIx638cILdAxdp3tN95gWfufPuxCNduDxkP3n+vrYZSoclqMeATvpxXPPmZidteB20/uYJmvFYfHQ3IRefPFFvPjii3c9Ho1G8corr4y+f+GFF/DCCy8c5K2YA7BT05mdME2ylJRyM8dzOF7l6zoJQipFkenCWhZDH1Ct0MofALTZHIbhOHrLVUhJ24OlEqUb6TpGItHvk5iUyxrcbhvBhIa5ZgFNbwrrRYEVewbWjwuQmgbfmTSCQRvVKrCs5eHvl1B3xeFwaOjGc+i0gXl9GW3TgU7waeRPpRBsrMB5Zx2LgzSs7AyqVcDUadsUT2fgMDQANpYvOwDQxDgaJceD9XUqpIrFJOp1Gtx6PQ3BoAZnEhB1GyISRb/XRDAo0ZFAxZtBqwAkwgIoDSEqZUStEjRdQBcWqnUXCkjBOH0B3QbwTugihrrAbFBDOJbExkYSkZYOXZfIZm0YJzKoOVPorujIhCSiUeD8eRO6DqyvCwiho9fT4HKRx/OJExZKJTFq+qMKwdNp2jWgRR09ruu0uwMAZ86YCIUk5ufpPUxz7CiifgfU9q1t02NOJ/2fSFAqkcs1niREIhRF2l5gyDAA68VRYRq90DQyGlDOYaZJ0WWPB/joIzKcUM5CmgasG1kMXTS+bNeL1s0qADrO6qpAICAQDNJrNzZU6hIwGNhwzmQRa78Pt1Oi6kzhxkYe/dtA/XIRgbAGEU4D0NDrASvtHD4l1tHxxKkGIpSHswfk7WU0NSfqsaeRPJuCUy/AXirB7UiiE5pB3WGj1rHhkUBiIQOnBazd0FGpiM0xV0OnI1De3FX1+23EYhrKZYqaFwoaTjqW4ZpNQVTW0PfE4K1WgWcX4CrbKHSy0CygOxCQmoW0o4T2UgnGUEe3D9SHSZRj52DEc6j3bbwnJfKhElxeHc75JOrtFHI5G+vrEidP2rDtDAbhFHqLOqJ+G/G4hc99boBKRaBYFHA4SC+8XtKCUMjC0pJALEa7Oa0W4PFIfOITGno9C8mkRK+nQdPGWpFKScRiA0hJNSFS0tygUqEdk2pVIJ+nnQK1q6y+3kkv3G6qUdi+s8A8OrgDMTPCMOiPejDAyAvetoFymewkLYv+kE2T/lG3yfEAsLYmEI/TH7oQGD3f76djDQa0OKjVKCd1MNCQSNjQlgtwb1DRYc2RQvr2z3H1lhOrwzgKNxw4f97CiRMW5sQStBLNUqVlIxJYxhVtBrYQWO4n0AvmYaxZ2AjO4PQ5iUhPAB4LKysCfj8tPPx+inhlMhLBoI1mU6BS0dBoaAgG5eZ12Fhe1tFs2jhxQkeno+GMewnP6zdhPBtEIiFhXfXDvXwTlbqOkq+IjxqzaJg2MoYDJ71FeOMJ+NtF1N0JGLNnoA1m4PFYKBQETpwA3uxcRE1Y6HUoMh8MWpuNVsh5odcDVlYMrKwI+HwSXi993lSbSc2C3G6Bfp8cGpJJuVnfQR0bZ2dJcJtNAbebdhU++UlaUKhB3O+nAVnZuAFbHUXUPVPpAqZJzkBLSwKaRr8rKoUAoHudychHWijMMMzh4HDQ+NLv01gQidDkXZkN2PbYUexeeqGClZ0OPScYpGPcuUMiVC6LzeNqyJjLcDslHK06fE4g1FmGaQrUrldw1YqjGdIRjQIvvWSh81EBob6N4VBDJCIR9C3jej+HXlPHUj+JujOP8GAIM5ND1CcR+FBHzQRKJQ2pFGnY7KyFVksgkzGhgsXXr+tYX6dAi89HfViuXRNotylvPthYhluUkU0Z0GdP43T/A7Q/bMK8eQVvl5+Fo1vAR51ZuIISsbgDC6E16KEkwv01tLwJeM4mkEhk0O9bGAwEav48ljuz8HckHA4NXi8Qj1swTYHTp81RKufiIulFJCLRagl4POQIpWljvRgMJHw+C1LSz2/fFsjlJDY2BDY2bLTbpPWGATz/vAkhSE+8Xlrs6frYoVDdV6+XjEbUYtDpHOvAbnrBWvH4wYsBZoRpArGY3IyG08Td5aJBcTDAphUnPVcNQCqK5HLR96aJkY3YM89QDqlyn1BFZ80mLTg8HomcXIKtG/A+N4+VWy5o62UMFptodE7inf4JaJqFM2c0GIVVJBwllHIJiLkshjcL8HbWcdIBmJksBICFvIVQSKJSEfjInIPnFOAwLXzuczZaLQ0ul0YNukwSm1xOIp+XWFw0EAxKdLsCJ09a6HQMeDwWTFOHx2PDtoEz/iJyYcCVDUIKA1VPFFaxCtcHFiLDEhY8NkruPKJWAe5WHZG1KnRDw8LTQdxKZvGl6BDvvmtA1zVcuWIgmSQL1G5XwO2mXP1GQ+D2bR1LSwZCIboPHo9EMmmPvJkjEYmXXhqgWBTw+WjSfvKkiU5H4L33DHS7VNS3sGDi/HlzVMBtWbSr4HBQYdfNm8Zo8ZPLSeRyVGCm7qvDQdGgbpfu+2SfgMmvlbArpt2FYhjmaKP0QhX/qmiwro9ThKbRi1xOIh4fYDCgxYVaQADka7+0RAXMp5xLiJjr6C48jXBGYv3/XUP66k9xtRTBTcc5XOvOIWTRZHNOLGPeuw57Lg59PgP7TgH++joaPaCdysEMAnMxC9msxI0bAhXvDKLPAEFzgGJRh2GQzaYQtICp1wVmZmiS7HJR+ksiQYEuISTabQCgybbbJ5ALDvCJFzwwe030zLPYGEThcBtwVAZ4NlXEcFnCzudgNddgluqIOWtwOjVEsmG0zmUQDJr45S+p9qBU0hGLWQgGKSVHffYuF3D9ugFNo89bCMDvp+h/sUiLgkRCYmGhh1/+0oDXS3oxM2NiZcXA5cs6LEvDxobE+fPmyBGuWqV7UCwKGAYt2ppNgXic0kwTCYlsdqwXwyEFi0IhOepRBOytF6wVjxe8GGBGqMFbrfZnZynqnErRAKD+cNUfv2oWsrYmtqSGTKaXzM2NV/+quU2pJFCva3A6JTY8OhKnoyjNZGDd+ADBM3EMBoBPZJAoWOj3NRSLwNnTAmefDSPpT6JUklhs51B4DwhFdSSTNpJJ2oJU3ZYpsq7D6dTg81Haj6bZqNcFUikqaHrqKROFgoDHI5FOA+22hWTSxIkTAqZpwzTJV9vpBNrBDPoLbkTzDVRLNhrRGZjOAiotB3p1C86BgXP+JegbOrruMO54k8g7inB6DZwP3oZjNoP1dYFuV0OxaG925xSoVjVEowK5HEWe4nGBWo2uIRiklK1kks6jUKDFmW3T52qaNKm/dcvAjRsG2m0btZqGXM6GZVEh3vo6HY+8n+leqRSwREKO7tdwSPdLRWvUfR0OadBXKV479bPYD2pRw9EghjnaTE723W6aNFMHWTnykQem0wu3W9lNjsef27cFFhdpzOx0BHyGjn4qjsQLKRQrQNtXR+Y80LgGVGayWH6XIuemaeHUgo1YOIxVPQO3G3izM4voQIMvqCM2R8Eqv38cpBoMgNVVHQ6HBl0XEIJqqzodqs8SghY+lkWOORcuSADUWK1WE0ingX5fotkEZCqDsg0g6UYo6ER3xcad5y9A3i6gl9BhGxbSOQO50DJkV0exHYGIJZEzijjzNNBy30YvlMPcnESjocGy6Dw8Hhu9no2zZynopWnjAI/DMdaLSIQWL5TrT8G506dp1+bmTQNLSwZ+9jMHqlXahUkmsZkuSj2GLGurXqjeEio7QGmFiu4PBrR7U6sJNJuUPqaKzVkvjg68GGBGbN+6A+7uW7C6Sk4xbvfYAeBe232Tq3+nk15TKEi0Wjp8Pg3dSBq3TMD1sxUsfWwgHJHwZJP4ZHKIVsCCpmnIZi2cuRiH6QK81qYtnV+iNptHZI4GxhdeMEfRkTfecGJ5WaBW03D6tIZejxYLHg9GhW1S0oLF46FIVq8HhDfW4C4N4XTOwuu1cfbsEHl7CZ2eAzd7WSxaQayXAlRQpwEf9fPQckBsngqr3dVVONZMwJ+AwwA6/gRKaya8QQfioEGbtp7JXSkclojFxCg9q1DQUavR92fPDhGL0c6FELQQqFbFpmc/RWg2NoBymeJmNggAABoxSURBVH4+HNrIZm10OtTeXRX8zczIUVdH2x4XAqpGQ4ZBA/lggM3tcLklilOvU06optGC4CDbu2pnaHuXU4Zhjh6TY8H27rAqTeggepHJkFY4HBTQMLNpDFISly4BG1cLaN4wEIulEV4APh27Aynn4fNRfVTkfBKGE3Cv006o3y8ROpFFvw889ZSJU6fkKKJ+/bpqtEl60e1KZDIW/H7amSiXKcperYpN5zQgFjPRvbEOfc3ChpVHNmthYcHE8GYBmqFjcZDFigiivRaA2KxJe+t6DtFnqPYhJTQ466vQXSZEMAGnC6gZCVTWB0DegeFwPCn3+2lMTyZpbI7FaAFy65aORoPMI+bnTWSzlGI6GAA/+YkTlQod45ln6LOv1cZ64fFQI0nbtkcLt3vphdrJabUodUvtkGjauGOww0EpRb0e6aoa31kvjga8GGC2sH3rbvJrip6I0YQ6Hh8XBO213adW+LOzEoMB5a57PPQzo7ACb7eMQTiFwTNpRIcrOBcvIpmUWLRm4PXScxMJmuRWKhStiMUoYu73A1euGAiHKXLh80lcvCjxxhsO2DYtPDweC40G5UeurtLrV1fFKDfy2jUBt2OI3p0q/DBgZjIINlYQcpXhCsVxozv23O73KbffsjTMz0ssLQGJhA03LPTaAlYsgRWRw9P+JWj9MvKZIZoD4Fd+ZQCPx0AoRMfyeMZb5ZoGnD9vodu14HbLUcOwel1s7mqQoCrDFKdzvN3e6wE+n4Z4nOoOwmESjUpFIJuVo90ZJdCWRZG6RIK21MkOkPI+JwvAbBubuab0nupn97u9O2lHuFOKEcMwR4vtwZ5JDqoXKgChxvVcTqLTAZzFFfg3qmgkkmifSeFkZAkZo4h8XuLmcGaz6zFNbKtVsTn5JdMIt5ui4ZUKTVr7fYqof+pTEq+/TnrRbusIBEgvMhmaePf7NBbnchInT5rodgHdM0Tj4yqSUYHWMINQcwUBfxk3W0kMBmSj6XQCy8u0+2xZGtJpC6urArOzJqxFC1ZXoCwSWPHncD64BOFYRzQyRC8mkc2aCIcN+Hy0AwCMO/JGoxILC8BgYGEwIC1UuytqF6NUouBRsSgwNycRDo/1IhLR4PPRa2dm6BrbbZpw76YXoZAcaY7agZ+0oFbN6rzesfkI68XRgRcDzL5QK/ODrPBPn5ZwOs1R4erimgPVbhLrrhnMh4foIQcZM5GUGkxQ8Wy3S8eyLIx89dttOg+vF3j/fQOmOS6ALhY1xOP25pY1NTtpNATyeROhELCwIEcuCO+9Z6BSEfB656D1DbRvVjC7UYIZthH+chQbsQxaHwtUq8DqqoFz50yYpoDDYaPfBzIZG6WSwOLHDthmCu5sBrMZiacupNC+IrFc0LFeIoeNcJjEp16nnYJ4nFwWSiWBW7cElpYM+HwSlQrw3HPmlvxZn49yMhMJORokn3+e0otefJFyPtUiZ/uA7HDQPaAmdBQpEmIc/en3t27jTu4EAFtTAu6Xg24ZMwxztDioXug6cPKkRDBobholALdWHSh2kyg6c/hMyoQZy2E4lEg7NQxM2gHudulfPC6RTALp9AD9PhCL0aSVrDDFqI5hY4MCOjvpxdycifl5iatXDbz3HuXoRyISndAMlgcG8MsK5uZK6GsW4s9HUfh4Bt26hitXgFiMAk+WJUbuS6ap4f33DWgFJ1LxJBz5DGZcEuc/ncLi/wCdVQ31uoEzZ0z4/RTFT6UolcfhoDSs27cFSiXataDAmAmfb1ycnUjI0Y4MMNaBSb3obga4ajUxivzfSy+UQcVgQI5Ck1qh0oWUrfSDGN9ZLw4XXgwwU6NcAtQgM+0qffsK37KwpSGNfCkOywLC5eGmVz2wrs3ANIFGmbaIAYpIxOO0O2BZGBUDT7ofqcVCvy9GBWCRyLiJ2s2bBjY2NPT7AmfPUqoO7RSQF3L0VAbJzjo5+3gkrGwOpWXaTg0GAcuyEI9TxOncOfLWj8UkXnvNiXoug+KahlMOCaeTtl8D5zLY2ADCm507PR66bsvC5hYvPa4sWAcDG888I7GyItBojIu283k6T+XupNyCqGiMrt3vH+84TEZ2bJsm+83m+DNT9wOgKFI8ji3dqNWxZ2cfnOMDO0gwzPHhQemFELQrMNKLT8eRM4Fi0UQwSGOxFpuh1JXNYIht0+uVJbLPh1FQSWmFSmtSHXDDYfpZICBHXvlKLzodMaqJqFbFKJc+8UIG8u11ujabmnPV3yY9cbmAEydMDIdUBH3+vAVdl4hEJAoFA2t2GivSxoKfIu6dDuBZyCASkegX6L36/bE9p99Pn8fSElmGqh0B9dpodDzeqwCTaZK2zMzcrReBAD233R67xxnG+LGNDfrc1P1wOOj9ACCbpa7CCpVKlM2yXhxVeDHATI2m3d8f+71W+KrbrbKtjMe3pq6EQhQFDwYpH7HfJ2FJJsk1SOW/nz9votMZW5+GQjSoUzSGIijNpkA6Te3XnU6axF+5YqDR0OB0kg9zarCEii1QXLMxHOgIv1uAjOXgdpM9m8ejwe3GqJA3GKRBNRCgRUejQXmlqRT983qBX/zCQKkk0GxqmJ/HZmdI2q4FxhH8VApYW9NQrVLRtirCXl0VoyhOs0lWo0tLtPW7U87tZFFXsShGDcTW1igt6plnzC3uTsoNZKeW8A/a8YEdJBjmePAw9UIICigpRzQV2Y5EyPTC48HIZtMwaHK8tkarAKeTtMY06TiaRnrR7dJjbjdNoNWYGAjYcDhIS65dM1CtakinbQghMbhRQH1DR69Hk/9saQWadhLDoQafj3ShWh3rRTJJY/3Kio1kkjQgnZaj5mzNJo3J7bbA5ct0vmQPSpNzFQhzOmlS3++TRqbTdJ2FAu0YhMOU+w/Q+y0viy07vZP3KJ2WuHOHtLRQECNdKJdpNyObHR9b7e5ns7vfc9aLowkvBph9sZ/6ACUAOxUmTw4symlislBZiYGqK1BFrCr9xTRpUHS5aEBUA2QmQxH0YHAsAtWqRCIBLC9jFJUPBum4qRTVHSwvCwTqKzCrVXQDCdhn83BZS6hdXceGX6DZnMXJk4DbTbsJc3NytA3tdALPPmtidhZIJsmyU0Xlh0N6TjhM0f58nhx+/H5KGVIRG+ooDJw7Z2JhwYTTOXaKuHWLCsE6HYG1NXIfcrvp2pTDz+QgP1nU5XRSLUC3S1H+RgMjJwpVr+B0jnNAlT0gR2IYhjkoe+mFcvKZHG/2oxfAOHXFMCiSrdKENG0c2e52sVlPRnohJb2HSrMxTYxqqCY1plajQIxqgiaEiUaDAivOYgHDlTKGkSSME1m0Bssof7iG9NCJWiCHSITeMxolI4hul4JY2axENEo60GjQhD+VojE5Fhv3jbl9m8Z55eiXzcpR+k69TsdNJOSosHZtjRYCakK/vk67Bz4fnXungy3Wnwq1KFI7DEoLkknaUY/H5ZbdZJVOxE4/Txa8GGAeKLs5AEyKwuQkdPJrxU5ioLoWK+HweintZTCgn1ORGA1mqlBNNT1Tk91cjoqgVlbIBu3qVQO6TgW3GxtAwKHBkYqi0s3CJSRaeg5RIdFrOtBsaqM0G4+Hzlk1WNE0OnY4DHi9VAvx0UcGWi0DgQANxMA4jWeyGyNA5/bcc+bI019N1JU1HECDuJQSlmVACBvlshgVQO/ktDAZXQsEKKq0sTFOO1K+4Oo54TDuiv6wewPDMA8L2wZWVmjSun282Y9ebE8lyWTkVHrhdo8j48Mh7WIUi2I0NubzNNEmZzqBa9dIL0IhiuDHcxoanjhKnSz8bhuDSA4bhkRt2UB1oGFubryLsV0v5uYoYHTiBLn8LC4KrKyQjWkqJUcLl2CQFgDK2tm2Sd+UqcTkZ6Qm506nKhKWaDapUPqjjwwIYaLZvLdWqF3vdpsWTG73+HOd3K1hrXjy4MUA80CZxgFgmsKgyYHOtjFyhQDGEfHtnvhqMAMwKiYGaFBUEfyVFYq2dDp03FCIipEbDYFCOwsNVGSlnr+0lMHiFQNzc+NUoJkZuWXirs6XOjFS1ObGDR2nTlFU/swZEx7P3d0Y1da1irKkUpTeoyzhVPGWwzF2dHC7zVEerJT3bv41KZLZLEWGGg2xRXzVczIZoFBg9waGYQ6HSYODe403e+nFTrsP0+iFej/bJt1QehEK0RgvBO0YT+pFMEiBnUAAWK1kEZiTeDkzRDxOaasrKzn4FwS80h41a1S7ApN64XTSe66uUj1Bva4hm7Xh8UicOUOBIl2nINhO1t6qfq7fH+uFrtO/YFCOIvu6Tu9n2+PuwXtpBUDHLhTEaMdhUitULyLWiicLXgww+2KvrcFpJ/r7KQxSC4zJrpXbLcu2D2adDk2Wy2UxKqadmSH7NV0fT7Q9HjpuIkFbrsvLFBnqdCjV6NQpiUZDYjikItuTJyl6ZJpkHTfZYEV15AwEgPffB65dE3A6NVQqAidOyC12bIMBReqHQxKhlRWBy5fp9SoqtbFB16K+zuWouFqJgdpBmGZBpQq8dlpAPKgGMQzDMJPcSy8cDhqP9hpvHqZeqP4Ik3nywyG9die9UO+fz9Pro1E5sisNBGgSXirRe8zOUn+DYpGOWalgtMjQNNKOVgvI54G1NQcuX7aRSgmcPEkBKlX4PJmWo6xa19fFKN3TMMYaEYvJ0TifSsnRgqhaFXd1hd7+GU9O5oXAqOHmdq1Q94614smCFwPM1EzTBGTagXuaXFJ1jMmBZ9IhRx17J8HxeqkIbG1NwOMBbJsG/WyWdgnU6ycHt26Xvld5pmoAvHCB7NhmZ4G1tbGzwvLy1gYr6lypn4I12uqVcnx+kwOo10vdfZVbkhKJ4ZAWCpO2cGrRMlmQp3620+e802cyTYSN3RsYhnkQ7KUXlF4JCCH3HG/uRy/6/XH92U7Pm+yPICVNsPfSC4eDIuVKK8LhrVoxN0cRdICKbAsF+lmtRlqhuser6wkEaPxPp63RIkOlMm0fq9XjGxvjtFn1nEm9UA3AXC6MegbMzm6ty9vts2OtOL7wYoCZmmmbgBzUAWAnEdnukKMeB3YWHNOkbVJNG7v2qEFrJ9ece/kkKzs2FRkyjHGhlWFsFQOVr6pp5ug8PZ6t+ayTk3nDoPdUlnfqGhIJOVpYAOPIlvpasdPnfK+6jb0GcHZvYBjmQTCNXjyI8Wan8S6dHqdkqjQXYPfFiSogtixsmfjvpBf30orJ56tj2DYVOfv9YytstbOriomj0XExsbJh3T5Wqw726jnq69lZWrCo52yf9O/WFG63z4614njCiwFmag5ra3A3EZl0yFGPAzs/d3I7VBU83Qs1iO9lhacKzhYXqaisWiWhUa5AAG3Jkv3auFB4t8I4KamILJmkfy7X+OfbB+mDfn7b359hGOZh8Sj1AhiPdXtpBUDnV6uJ0Xnn87sXxE6rFQrDwMjeWRUSqyZeAL0vFe7erReTY7VpUmqREIBlyVGPAbVbcT/FvKwVjIIXA8zUHNbW4G4istvjOz2mcjJ7va3t0feyztxtAFQWeLZNg6eU4wYsqheCci2azFdVqT/3us7hkM4xEBg/d7uDxn4KtDifk2GYR82j1otpHwPG9V5C0NiuDBzuNzKu9EIFfPJ5iRs3aLfassbj+X71QkX+VZ8BVTfGWsEcFF4MMPviMKIFu4mIisori0z1+G6C43SOu/Hejx2ayqVU7hPtNtBqCaRS5D5ULotR8dZeC5b9XOd+j7Of4zIMwxwWj1IvVDrPXs8Dtk601dh7P1qhxmlqHEZNIoWg7/t9gfV1jPoFqPc9qF6wVjAPAl4MMI8lO4mIbY+3Qyf9kncTnO0D3bQ1D+q9lBhIOd4aVg284nHK6x8MKNd0csGyn8F12nPf6zg7NXrjLV6GYY4Du413lcrdE/qHqRWGQTsMKtpvWeOuyLncuFHlg9QL1grmQSAe9QkwzLRMDtDKL3kv1ECniqumjaAMBjTpnxxc+/2xC5B6rdc79qpWaUST7zktk6/f6dz3em2hQE4YqvskwzDMcWa/enG/WqFSUJUTHDB2M5rsiqzqBZQzkBqnH4ResFYwB4V3Bpgjg8Mx7sqrOiPuh2kjKLZNrkXVqkCtNnYzCoeBRkPu6Ap0kG6M01i27vY65T/NDWAYhmHGHEQv9hNtNwzagRgMaNydmSG9CIXu1guVcnqQzr33oxesFcxe8GKAOVZMsyU6HNL2rmrkohwcJiMv212BDjLA7mdLWjEpCKrzJBeBMQzDPBimTZ/ZXnysxvKd9OKgWgHsXy9YK5hp4MUAc2RQk/TJrowPI6ox6fLj8ez9Hgd1ZLif128XhFRq7DPNRWAMwxx3DlMvJouP7zV+Pwj3nv0eg7WCmQZeDDBHhsOyQbufoq6DODLcz+u3fxb7zTllGIZ5knkc9eJBuPfs9xisFcw08GKAOTIcpg3aXh7SO9meHiTqtN/XsyUcwzDM7jyuevEg3Hv2cwzWCmYaeDHAHCkO0wZtp0m/yr9UrhEzM3LUFv6wYUs4hmGY3WG9IFgrmL3gxQDD7MBOjg3A2EauVhPo9eh5c3P7d4RgGIZhngxYL5ijDvcZYJgd2M2jWuWd9npUXDzpLc0wDMMcP1gvmKMO7wwwzA7sVnymabTVq5qL7eUewTAMwzzZsF4wRx1eDDDMDtyr6EoI2urlgiyGYRiG9YI56vBigGF24V5FV1yQxTAMwyhYL5ijDNcMMAzDMAzDMMwxhRcDDMMwDMMwDHNM4cUAwzAMwzAMwxxTeDHAMAzDMAzDMMcUXgwwDMMwDMMwzDGFFwMMwzAMwzAMc0zhxQDDMAzDMAzDHFN4McAwDMMwDMMwxxReDDAMwzAMwzDMMUWzbdt+1CfBMAzDMAzDMMzhwzsDj4Cvf/3rj/oUHhp8bUeTJ/nagCf/+pgnlyf5d5ev7WjC1/bkwYsBhmEYhmEYhjmm8GKAYRiGYRiGYY4p+je/+c1vPuqTOI6cPHnyUZ/CQ4Ov7WjyJF8b8ORfH/Pk8iT/7vK1HU342p4suICYYRiGYRiGYY4pnCbEMAzDMAzDMMcU41GfwHHgpz/9Kf7zP/8TKysr+Ju/+RucOnVqx+f94he/wA9+8ANIKfHyyy/jt3/7tw/5TPdPu93Gt7/9bZRKJSQSCfzpn/4p/H7/Xc/7vd/7PczOzgIA4vE4/vzP//ywT3Vq9roPw+EQ3/3ud3Hz5k0EAgF89atfRTKZfERnuz/2urbXXnsNr776KqLRKADgN37jN/Dyyy8/ilPdN9/73vfwzjvvIBQK4W//9m/v+rlt2/jBD36Ad999Fy6XC1/5yleO5XYw83jDesF68bjwpOoFa8UO2MxDZ2lpyV5ZWbG/8Y1v2NevX9/xOZZl2X/yJ39ir62t2cPh0P6zP/sze2lp6ZDPdP+8+uqr9g9/+EPbtm37hz/8of3qq6/u+Lw/+IM/OMzTum+muQ8/+tGP7O9///u2bdv2//7v/9p/93d/9yhOdd9Mc20//vGP7X/6p396RGd4MD744AP7xo0b9te+9rUdf/7zn//c/uu//mtbSmlfvXrVfuWVVw75DBlmb1gvWC8eB55kvWCtuBtOEzoE8vk8stnsPZ9z/fp1pNNppFIpGIaBz3zmM3j77bcP6Qzvn7fffhtf/OIXAQBf/OIXj8Q534tp7sOlS5fwq7/6qwCAl156Ce+//z7sI1B6c1R/x6bl3LlzO0YZFZcuXcIXvvAFaJqGhYUFdDod1Gq1QzxDhtkb1oujA+vF0YS14m44TegxoVqtIhaLjb6PxWL4+OOPH+EZTUej0UAkEgEARCIRNJvNHZ83HA7x9a9/Hbqu47d+67fw4osvHuZpTs0092HyObquw+v1otVqIRgMHuq57pdpf8d+9rOf4cqVK8hkMvjDP/xDxOPxwzzNh0a1Wt1yLbFYDNVqdfT7yzBHBdaLxwPWiydTL46jVvBi4AHxrW99C/V6/a7Hf//3fx+f+tSn9nz9TpECTdMeyLkdlHtd27R873vfQzQaRbFYxF/91V9hdnYW6XT6QZ7mA2Ga+/A436t7Mc15X7hwAZ/97GfhcDjwX//1X/iHf/gHfOMb3zisU3yoHNX7xjx5sF7cG9aLR89x1oujes8OAi8GHhB/+Zd/eaDXx2IxVCqV0feVSuWxWYXe69pCoRBqtRoikQhqtdqu0Q5VYJRKpXDu3DksLi4+loP7NPdBPScWi8GyLGxsbNxzy/FxYZprCwQCo69//dd/Hf/2b/92aOf3sInFYiiXy6PvH6e/MeZ4wXrBevG4c5z14jhqBdcMPCacOnUKhUIB6+vrME0TP/nJT3Dx4sVHfVp7cvHiRbz++usAgNdff33HqFa73cZwOAQANJtNXL16Ffl8/lDPc1qmuQ8XLlzAa6+9BgB48803cf78+SMRNZjm2ibzIi9duvTY3qf74eLFi3jjjTdg2zauXbsGr9f7xA/wzJMJ68XjAevFk6kXx1EruOnYIfDWW2/hX/7lX9BsNuHz+TA/P4+/+Iu/QLVaxfe//3288sorAIB33nkH//qv/wopJX7t134Nv/M7v/OIz3xvWq0Wvv3tb6NcLiMej+NrX/sa/H4/bty4gf/+7//GH//xH+Pq1av4x3/8RwghIKXEb/7mb+JLX/rSoz71XdnpPvzHf/wHTp06hYsXL2IwGOC73/0ubt26Bb/fj69+9atIpVKP+rSnYq9r+/d//3dcunQJuq7D7/fjj/7oj5DL5R71aU/Fd77zHXz44YdotVoIhUL43d/9XZimCQD48pe/DNu28c///M9477334HQ68ZWvfGVX20aGeVSwXrBePC48qXrBWnE3vBhgGIZhGIZhmGMKpwkxDMMwDMMwzDGFFwMMwzAMwzAMc0zhxQDDMAzDMAzDHFN4McAwDMMwDMMwxxReDDAMwzAMwzDMMYUXAwzDMAzDMAxzTOHFAMMwDMMwDMMcU3gxwDAMwzAMwzDHlP8PmCPBRLnSzqUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 936x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(ncols=2, figsize=(13, 5))\n",
+    "plot_predictions(flk_pred, ax[0])\n",
+    "ax[0].set_title(\"Falkon\")\n",
+    "plot_predictions(logflk_pred, ax[1])\n",
+    "_ = ax[1].set_title(\"Logistic Falkon\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/performance_tuning.html b/examples/performance_tuning.html
new file mode 100644
index 00000000..5dc11457
--- /dev/null
+++ b/examples/performance_tuning.html
@@ -0,0 +1,380 @@
+
+
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+  <title>Training on the GPU &mdash; falkon 0.6.3 documentation</title>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
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+<section id="Training-on-the-GPU">
+<h1>Training on the GPU<a class="headerlink" href="#Training-on-the-GPU" title="Permalink to this headline">¶</a></h1>
+<p>This notebook will be available soon</p>
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diff --git a/examples/performance_tuning.ipynb b/examples/performance_tuning.ipynb
new file mode 100644
index 00000000..0be2e993
--- /dev/null
+++ b/examples/performance_tuning.ipynb
@@ -0,0 +1,49 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Training on the GPU\n",
+    "\n",
+    "This notebook will be available soon"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/simple_regression.html b/examples/simple_regression.html
new file mode 100644
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+<li class="toctree-l3"><a class="reference internal" href="#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Create-the-Falkon-model">Create the Falkon model</a></li>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
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+</li>
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+<li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
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+<section id="Falkon-Regression-Tutorial">
+<h1>Falkon Regression Tutorial<a class="headerlink" href="#Falkon-Regression-Tutorial" title="Permalink to this headline">¶</a></h1>
+<section id="Introduction">
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline">¶</a></h2>
+<p>This notebook introduces the main interface of the Falkon library, using a toy regression problem.</p>
+<p>We will be using the Boston housing dataset which is included in <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> to train a Falkon model. Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">falkon</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected
+</pre></div></div>
+</div>
+</section>
+<section id="Load-the-data">
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline">¶</a></h2>
+<p>The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions. The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.</p>
+<p>After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the <a class="reference external" href="https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation">model_selection module</a>)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_boston</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
+<span class="n">num_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">num_train</span>
+<span class="n">shuffle_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffle_idx</span><span class="p">)</span>
+<span class="n">train_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[:</span><span class="n">num_train</span><span class="p">]</span>
+<span class="n">test_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[</span><span class="n">num_train</span><span class="p">:]</span>
+
+<span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
+<span class="n">Xtest</span><span class="p">,</span> <span class="n">Ytest</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">test_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Pre-process-the-data">
+<h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title="Permalink to this headline">¶</a></h2>
+<p>We must convert the numpy arrays to PyTorch tensors before using them in Falkon. This is very easy and fast with the <code class="docutils literal notranslate"><span class="pre">torch.from_numpy</span></code> function.</p>
+<p>Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data: convert it to have zero-mean and unit standard deviation. We use the statistics of the training data to avoid leakage between the two sets.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># convert numpy -&gt; pytorch</span>
+<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span>
+<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span>
+<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span>
+<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">Xtrain</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtrain</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">Xtest</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtest</span> <span class="o">/=</span> <span class="n">train_std</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Create-the-Falkon-model">
+<h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model" title="Permalink to this headline">¶</a></h2>
+<p>The Falkon object is the main API of this library. It is similar in spirit to the fit-transform API of scikit-learn, while supporting some additional features such as monitoring of validation error.</p>
+<p>While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much larger datasets. Here we only showcase some of the more basic options.</p>
+<p>Mandatory parameters are: - the kernel function (here we use a linear kernel) - the amount of regularization, which we set to some small positive value - the number of inducing points M. Since the dataset is already very small, we use set <code class="docutils literal notranslate"><span class="pre">M=N</span></code> (every data-point is an inducing point).</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">()</span>
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="n">Xtrain</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set &#39;use_cpu&#39; to True to avoid this warning.
+  warnings.warn(get_error_str(&#34;CUDA&#34;, None))
+</pre></div></div>
+</div>
+</section>
+<section id="Training-the-model">
+<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Permalink to this headline">¶</a></h2>
+<p>The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed. To gain more insight in the various steps of the algorithm you can pass <code class="docutils literal notranslate"><span class="pre">debug=True</span></code> when creating the Falkon object.</p>
+<p>Model training will occur on the GPU, if it is available, and CUDA is properly installed, or on the CPU as a fallback.</p>
+<p>If KeOps has been installed alongside Falkon, the first run may take a bit longer due to the compilation step of KeOps (to avoid using KeOps you can pass the argument <code class="docutils literal notranslate"><span class="pre">keops_active=False</span></code> to the <code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code> object).</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Stopping conjugate gradient descent at iteration 1. Solution has converged.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Falkon(M=404, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f31c8279190&gt;, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;auto&#39;, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-05, seed=None, weight_fn=None)
+</pre></div></div>
+</div>
+<p>Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations.</p>
+</section>
+<section id="Evaluating-model-performance">
+<h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-performance" title="Permalink to this headline">¶</a></h2>
+<p>Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method to obtain predictions on new data.</p>
+<p>Here we print the error on both train and test sets.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training RMSE: </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">train_pred</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test RMSE: </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">Ytest</span><span class="p">)))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Training RMSE: 1.794
+Test RMSE: 3.534
+</pre></div></div>
+</div>
+<p>Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">Ytest</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;True&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Pred&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;best&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x7f3148f13a50&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/examples_simple_regression_17_1.png" src="../_images/examples_simple_regression_17_1.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+           
+          </div>
+          <footer>
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+        <a href="logistic_falkon.html" class="btn btn-neutral float-right" title="Introducing Logistic Falkon" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="examples.html" class="btn btn-neutral float-left" title="Examples" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+        &#169; Copyright 2020, Giacomo Meanti.
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+    
+    
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diff --git a/examples/simple_regression.ipynb b/examples/simple_regression.ipynb
new file mode 100644
index 00000000..68b25646
--- /dev/null
+++ b/examples/simple_regression.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Falkon Regression Tutorial\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook introduces the main interface of the Falkon library, \n",
+    "using a toy regression problem.\n",
+    "\n",
+    "We will be using the Boston housing dataset which is included in `scikit-learn` to train a Falkon model.\n",
+    "Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions.\n",
+    "The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.\n",
+    "\n",
+    "After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test \n",
+    "set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the [model_selection module](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_boston(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "num_train = int(X.shape[0] * 0.8)\n",
+    "num_test = X.shape[0] - num_train\n",
+    "shuffle_idx = np.arange(X.shape[0])\n",
+    "np.random.shuffle(shuffle_idx)\n",
+    "train_idx = shuffle_idx[:num_train]\n",
+    "test_idx = shuffle_idx[num_train:]\n",
+    "\n",
+    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
+    "Xtest, Ytest = X[test_idx], Y[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Pre-process the data\n",
+    "\n",
+    "We must convert the numpy arrays to PyTorch tensors before using them in Falkon.\n",
+    "This is very easy and fast with the `torch.from_numpy` function.\n",
+    "\n",
+    "Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data:\n",
+    "convert it to have zero-mean and unit standard deviation.\n",
+    "We use the statistics of the training data to avoid leakage between the two sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# convert numpy -> pytorch\n",
+    "Xtrain = torch.from_numpy(Xtrain)\n",
+    "Xtest = torch.from_numpy(Xtest)\n",
+    "Ytrain = torch.from_numpy(Ytrain)\n",
+    "Ytest = torch.from_numpy(Ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = Xtrain.mean(0, keepdim=True)\n",
+    "train_std = Xtrain.std(0, keepdim=True)\n",
+    "Xtrain -= train_mean\n",
+    "Xtrain /= train_std\n",
+    "Xtest -= train_mean\n",
+    "Xtest /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Create the Falkon model\n",
+    "\n",
+    "The Falkon object is the main API of this library. \n",
+    "It is similar in spirit to the fit-transform API of scikit-learn, while supporting some\n",
+    "additional features such as monitoring of validation error.\n",
+    "\n",
+    "While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much \n",
+    "larger datasets.\n",
+    "Here we only showcase some of the more basic options.\n",
+    "\n",
+    "Mandatory parameters are:\n",
+    " - the kernel function (here we use a linear kernel)\n",
+    " - the amount of regularization, which we set to some small positive value\n",
+    " - the number of inducing points M. \n",
+    "   Since the dataset is already very small, we use set `M=N` (every data-point is an inducing point). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
+     ]
+    }
+   ],
+   "source": [
+    "options = falkon.FalkonOptions()\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=5)\n",
+    "flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=Xtrain.shape[0], options=options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Training the model\n",
+    "\n",
+    "The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are\n",
+    "two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed.\n",
+    "To gain more insight in the various steps of the algorithm you can pass `debug=True` when creating the Falkon object. \n",
+    "\n",
+    "Model training will occur on the GPU, if it is available, and CUDA is properly installed, \n",
+    "or on the CPU as a fallback. \n",
+    "\n",
+    "If KeOps has been installed alongside Falkon, the first run may take\n",
+    "a bit longer due to the compilation step of KeOps (to avoid using KeOps you can pass the argument `keops_active=False` to the `FalkonOptions` object)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stopping conjugate gradient descent at iteration 1. Solution has converged.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Falkon(M=404, center_selection=<falkon.center_selection.UniformSelector object at 0x7f31c8279190>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-05, seed=None, weight_fn=None)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flk.fit(Xtrain, Ytrain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Evaluating model performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the `predict` method to obtain predictions on new data.\n",
+    "\n",
+    "Here we print the error on both train and test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training RMSE: 1.794\n",
+      "Test RMSE: 3.534\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_pred = flk.predict(Xtrain).reshape(-1, )\n",
+    "test_pred = flk.predict(Xtest).reshape(-1, )\n",
+    "\n",
+    "def rmse(true, pred):\n",
+    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
+    "\n",
+    "print(\"Training RMSE: %.3f\" % (rmse(train_pred, Ytrain)))\n",
+    "print(\"Test RMSE: %.3f\" % (rmse(test_pred, Ytest)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f3148f13a50>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(Ytest, alpha=0.7, label=\"True\")\n",
+    "ax.hist(test_pred, alpha=0.7, label=\"Pred\")\n",
+    "ax.legend(loc=\"best\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/genindex.html b/genindex.html
new file mode 100644
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--- /dev/null
+++ b/genindex.html
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+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+            
+          
+        </div>
+        
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+
+      
+      <nav class="wy-nav-top" aria-label="top navigation">
+        
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">falkon</a>
+        
+      </nav>
+
+
+      <div class="wy-nav-content">
+        
+        <div class="rst-content">
+        
+          
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+
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+<div role="navigation" aria-label="breadcrumbs navigation">
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+  <ul class="wy-breadcrumbs">
+    
+      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
+        
+      <li>Index</li>
+    
+    
+      <li class="wy-breadcrumbs-aside">
+        
+          
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+      </li>
+    
+  </ul>
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+  
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ 
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss.__call__">__call__() (falkon.gsc_losses.LogisticLoss method)</a>
+
+      <ul>
+        <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss.__call__">(falkon.gsc_losses.Loss method)</a>
+</li>
+        <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss.__call__">(falkon.gsc_losses.WeightedCrossEntropyLoss method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.GaussianKernel.__call__">(falkon.kernels.GaussianKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel.__call__">(falkon.kernels.kernel.Kernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.LaplacianKernel.__call__">(falkon.kernels.LaplacianKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.LinearKernel.__call__">(falkon.kernels.LinearKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.PolynomialKernel.__call__">(falkon.kernels.PolynomialKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.SigmoidKernel.__call__">(falkon.kernels.SigmoidKernel method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._apply">_apply() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._apply_sparse">_apply_sparse() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._decide_dmmv_impl">_decide_dmmv_impl() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._decide_mm_impl">_decide_mm_impl() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._decide_mmv_impl">_decide_mmv_impl() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._finalize">_finalize() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._prepare">_prepare() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._prepare_sparse">_prepare_sparse() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.apply">apply() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.apply">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.apply_t">apply_t() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.apply_t">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/center_selector.html#falkon.center_selection.CenterSelector">CenterSelector (class in falkon.center_selection)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/optimization.html#falkon.optim.ConjugateGradient">ConjugateGradient (class in falkon.optim)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss.ddf">ddf() (falkon.gsc_losses.LogisticLoss method)</a>
+
+      <ul>
+        <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss.ddf">(falkon.gsc_losses.Loss method)</a>
+</li>
+        <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss.ddf">(falkon.gsc_losses.WeightedCrossEntropyLoss method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss.df">df() (falkon.gsc_losses.LogisticLoss method)</a>
+
+      <ul>
+        <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss.df">(falkon.gsc_losses.Loss method)</a>
+</li>
+        <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss.df">(falkon.gsc_losses.WeightedCrossEntropyLoss method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.GaussianKernel.dmmv">dmmv() (falkon.kernels.GaussianKernel method)</a>
+
+      <ul>
+        <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel.dmmv">(falkon.kernels.kernel.Kernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.LaplacianKernel.dmmv">(falkon.kernels.LaplacianKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.LinearKernel.dmmv">(falkon.kernels.LinearKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.PolynomialKernel.dmmv">(falkon.kernels.PolynomialKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.SigmoidKernel.dmmv">(falkon.kernels.SigmoidKernel method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel.extra_mem">extra_mem() (falkon.kernels.kernel.Kernel method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    falkon
+
+      <ul>
+        <li><a href="api_reference/gsc_losses.html#module-falkon">module</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/models.html#falkon.models.Falkon">Falkon (class in falkon.models)</a>
+</li>
+      <li>
+    falkon.center_selection
+
+      <ul>
+        <li><a href="api_reference/center_selector.html#module-falkon.center_selection">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.kernels
+
+      <ul>
+        <li><a href="api_reference/kernels.html#module-falkon.kernels">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.mmv_ops
+
+      <ul>
+        <li><a href="api_reference/mmv_ops.html#module-falkon.mmv_ops">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.models
+
+      <ul>
+        <li><a href="api_reference/models.html#module-falkon.models">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.ooc_ops
+
+      <ul>
+        <li><a href="api_reference/outofcore.html#module-falkon.ooc_ops">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.optim
+
+      <ul>
+        <li><a href="api_reference/optimization.html#module-falkon.optim">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.options
+
+      <ul>
+        <li><a href="api_reference/options.html#module-falkon.options">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.preconditioner
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#module-falkon.preconditioner">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.sparse
+
+      <ul>
+        <li><a href="api_reference/sparse.html#module-falkon.sparse">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/optimization.html#falkon.optim.FalkonConjugateGradient">FalkonConjugateGradient (class in falkon.optim)</a>
+</li>
+      <li><a href="api_reference/options.html#falkon.options.FalkonOptions">FalkonOptions (class in falkon.options)</a>
+</li>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner">FalkonPreconditioner (class in falkon.preconditioner)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu">fdmmv_cpu() (in module falkon.mmv_ops.fmmv_cpu)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse">fdmmv_cpu_sparse() (in module falkon.mmv_ops.fmmv_cpu)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda">fdmmv_cuda() (in module falkon.mmv_ops.fmmv_cuda)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse">fdmmv_cuda_sparse() (in module falkon.mmv_ops.fmmv_cuda)</a>
+</li>
+      <li><a href="api_reference/models.html#falkon.models.Falkon.fit">fit() (falkon.models.Falkon method)</a>
+
+      <ul>
+        <li><a href="api_reference/models.html#falkon.models.InCoreFalkon.fit">(falkon.models.InCoreFalkon method)</a>
+</li>
+        <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.fit">(falkon.models.LogisticFalkon method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/center_selector.html#falkon.center_selection.FixedSelector">FixedSelector (class in falkon.center_selection)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cpu.fmm_cpu">fmm_cpu() (in module falkon.mmv_ops.fmm_cpu)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse">fmm_cpu_sparse() (in module falkon.mmv_ops.fmm_cpu)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cuda.fmm_cuda">fmm_cuda() (in module falkon.mmv_ops.fmm_cuda)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse">fmm_cuda_sparse() (in module falkon.mmv_ops.fmm_cuda)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fmmv_cpu">fmmv_cpu() (in module falkon.mmv_ops.fmmv_cpu)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse">fmmv_cpu_sparse() (in module falkon.mmv_ops.fmmv_cpu)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fmmv_cuda">fmmv_cuda() (in module falkon.mmv_ops.fmmv_cuda)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse">fmmv_cuda_sparse() (in module falkon.mmv_ops.fmmv_cuda)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.GaussianKernel">GaussianKernel (class in falkon.kernels)</a>
+</li>
+      <li><a href="api_reference/models.html#falkon.models.Falkon.get_params">get_params() (falkon.models.Falkon method)</a>
+
+      <ul>
+        <li><a href="api_reference/models.html#falkon.models.InCoreFalkon.get_params">(falkon.models.InCoreFalkon method)</a>
+</li>
+        <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.get_params">(falkon.models.LogisticFalkon method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky">gpu_cholesky() (in module falkon.ooc_ops)</a>
+</li>
+      <li><a href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum">gpu_lauum() (in module falkon.ooc_ops)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv">incore_fdmmv() (in module falkon.mmv_ops.fmmv_incore)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv">incore_fmmv() (in module falkon.mmv_ops.fmmv_incore)</a>
+</li>
+      <li><a href="api_reference/models.html#falkon.models.InCoreFalkon">InCoreFalkon (class in falkon.models)</a>
+</li>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.init">init() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.init">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.invA">invA() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.invA">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.invAt">invAt() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.invAt">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.invT">invT() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.invT">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.invTt">invTt() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.invTt">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_dmmv_helper">keops_dmmv_helper() (falkon.kernels.keops_helpers.KeopsKernelMixin method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin">KeopsKernelMixin (class in falkon.kernels.keops_helpers)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel">Kernel (class in falkon.kernels.kernel)</a>
+</li>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss.knmp_grad">knmp_grad() (falkon.gsc_losses.Loss method)</a>
+</li>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss.knmp_hess">knmp_hess() (falkon.gsc_losses.Loss method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.LaplacianKernel">LaplacianKernel (class in falkon.kernels)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.LinearKernel">LinearKernel (class in falkon.kernels)</a>
+</li>
+      <li><a href="api_reference/models.html#falkon.models.LogisticFalkon">LogisticFalkon (class in falkon.models)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss">LogisticLoss (class in falkon.gsc_losses)</a>
+</li>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner">LogisticPreconditioner (class in falkon.preconditioner)</a>
+</li>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss">Loss (class in falkon.gsc_losses)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.GaussianKernel.mmv">mmv() (falkon.kernels.GaussianKernel method)</a>
+
+      <ul>
+        <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel.mmv">(falkon.kernels.kernel.Kernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.LaplacianKernel.mmv">(falkon.kernels.LaplacianKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.LinearKernel.mmv">(falkon.kernels.LinearKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.PolynomialKernel.mmv">(falkon.kernels.PolynomialKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.SigmoidKernel.mmv">(falkon.kernels.SigmoidKernel method)</a>
+</li>
+      </ul></li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="api_reference/gsc_losses.html#module-falkon">falkon</a>
+</li>
+        <li><a href="api_reference/center_selector.html#module-falkon.center_selection">falkon.center_selection</a>
+</li>
+        <li><a href="api_reference/kernels.html#module-falkon.kernels">falkon.kernels</a>
+</li>
+        <li><a href="api_reference/mmv_ops.html#module-falkon.mmv_ops">falkon.mmv_ops</a>
+</li>
+        <li><a href="api_reference/models.html#module-falkon.models">falkon.models</a>
+</li>
+        <li><a href="api_reference/outofcore.html#module-falkon.ooc_ops">falkon.ooc_ops</a>
+</li>
+        <li><a href="api_reference/optimization.html#module-falkon.optim">falkon.optim</a>
+</li>
+        <li><a href="api_reference/options.html#module-falkon.options">falkon.options</a>
+</li>
+        <li><a href="api_reference/preconditioner.html#module-falkon.preconditioner">falkon.preconditioner</a>
+</li>
+        <li><a href="api_reference/sparse.html#module-falkon.sparse">falkon.sparse</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows">narrow_rows() (falkon.sparse.sparse_tensor.SparseTensor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/optimization.html#falkon.optim.Optimizer">Optimizer (class in falkon.optim)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.PolynomialKernel">PolynomialKernel (class in falkon.kernels)</a>
+</li>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner">Preconditioner (class in falkon.preconditioner.preconditioner)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/models.html#falkon.models.Falkon.predict">predict() (falkon.models.Falkon method)</a>
+
+      <ul>
+        <li><a href="api_reference/models.html#falkon.models.InCoreFalkon.predict">(falkon.models.InCoreFalkon method)</a>
+</li>
+        <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.predict">(falkon.models.LogisticFalkon method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv">run_keops_mmv() (in module falkon.mmv_ops.keops)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/center_selector.html#falkon.center_selection.CenterSelector.select">select() (falkon.center_selection.CenterSelector method)</a>
+
+      <ul>
+        <li><a href="api_reference/center_selector.html#falkon.center_selection.FixedSelector.select">(falkon.center_selection.FixedSelector method)</a>
+</li>
+        <li><a href="api_reference/center_selector.html#falkon.center_selection.UniformSelector.select">(falkon.center_selection.UniformSelector method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/center_selector.html#falkon.center_selection.CenterSelector.select_indices">select_indices() (falkon.center_selection.CenterSelector method)</a>
+
+      <ul>
+        <li><a href="api_reference/center_selector.html#falkon.center_selection.FixedSelector.select_indices">(falkon.center_selection.FixedSelector method)</a>
+</li>
+        <li><a href="api_reference/center_selector.html#falkon.center_selection.UniformSelector.select_indices">(falkon.center_selection.UniformSelector method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/models.html#falkon.models.Falkon.set_params">set_params() (falkon.models.Falkon method)</a>
+
+      <ul>
+        <li><a href="api_reference/models.html#falkon.models.InCoreFalkon.set_params">(falkon.models.InCoreFalkon method)</a>
+</li>
+        <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.set_params">(falkon.models.LogisticFalkon method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.SigmoidKernel">SigmoidKernel (class in falkon.kernels)</a>
+</li>
+      <li><a href="api_reference/sparse.html#falkon.sparse.sparse_matmul">sparse_matmul() (in module falkon.sparse)</a>
+</li>
+      <li><a href="api_reference/sparse.html#falkon.sparse.sparse_norm">sparse_norm() (in module falkon.sparse)</a>
+</li>
+      <li><a href="api_reference/sparse.html#falkon.sparse.sparse_square_norm">sparse_square_norm() (in module falkon.sparse)</a>
+</li>
+      <li><a href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor">SparseTensor (class in falkon.sparse.sparse_tensor)</a>
+</li>
+      <li><a href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType">SparseType (class in falkon.sparse.sparse_tensor)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/center_selector.html#falkon.center_selection.UniformSelector">UniformSelector (class in falkon.center_selection)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss">WeightedCrossEntropyLoss (class in falkon.gsc_losses)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
+           </div>
+           
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>
+        &#169; Copyright 2020, Giacomo Meanti.
+
+    </p>
+  </div>
+    
+    
+    
+    Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    
+    provided by <a href="https://readthedocs.org">Read the Docs</a>. 
+
+</footer>
+        </div>
+      </div>
+
+    </section>
+
+  </div>
+  
+
+  <script type="text/javascript">
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script>
+
+  
+  
+    
+   
+
+</body>
+</html>
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diff --git a/get_started.html b/get_started.html
new file mode 100644
index 00000000..9e40a84a
--- /dev/null
+++ b/get_started.html
@@ -0,0 +1,419 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="generator" content="Docutils 0.17: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  
+  <title>Getting Started &mdash; falkon 0.6.3 documentation</title>
+  
+
+  
+  <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+  
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+  
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
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+        <script src="_static/doctools.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    
+    <script type="text/javascript" src="_static/js/theme.js"></script>
+
+    
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Examples" href="examples/examples.html" />
+    <link rel="prev" title="Install" href="install.html" /> 
+</head>
+
+<body class="wy-body-for-nav">
+
+   
+  <div class="wy-grid-for-nav">
+    
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
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+          
+            <a href="index.html" class="icon icon-home"> falkon
+          
+
+          
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+
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+            
+            
+              <div class="version">
+                0.6.3
+              </div>
+            
+          
+
+          
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+<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
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+<section id="getting-started">
+<span id="get-started"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h1>
+<p>Once Falkon is installed, getting started is easy. The basic setup to use the <cite>Falkon</cite> estimator only requires
+few lines of code:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_boston</span>
+<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">Falkon</span><span class="p">,</span> <span class="n">kernels</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">load_boston</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="n">Y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Falkon</span><span class="p">(</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
+    <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span>
+    <span class="n">M</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="passing-options">
+<h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalink to this headline">¶</a></h2>
+<p>A number of different options exist for both the <cite>Falkon</cite> and <cite>LogisticFalkon</cite> estimators (see <a class="reference internal" href="api_reference/options.html#api-options"><span class="std std-ref">falkon.Options</span></a>).
+All options can be passed to the estimator through the <cite>FalkonOptions</cite> class, like so:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span><span class="p">,</span> <span class="n">Falkon</span><span class="p">,</span> <span class="n">kernels</span>
+
+<span class="c1"># Options to: increase the amount of output information; avoid using the KeOps library</span>
+<span class="n">options</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">no_keops</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
+               <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span>
+               <span class="n">M</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+               <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>   <span class="c1"># Set the maximum number of conjugate gradient iterations to 10</span>
+               <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="more-examples">
+<h2>More Examples<a class="headerlink" href="#more-examples" title="Permalink to this headline">¶</a></h2>
+<p>For more detailed examples, have a look at the <a class="reference internal" href="examples/examples.html#examples"><span class="std std-ref">example notebooks</span></a>.</p>
+</section>
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+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="install.html#intel-mkl">Intel MKL</a></li>
+<li class="toctree-l3"><a class="reference internal" href="install.html#keops">KeOps</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#testing-the-installation">Testing the installation</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
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+<section id="falkon">
+<h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline">¶</a></h1>
+<p>A Python library for large-scale kernel methods, with optional (multi-)GPU acceleration.</p>
+<p>The library currently includes two solvers:
+one for approximate kernel ridge regression <a class="footnote-reference brackets" href="#id6" id="id1">2</a> which is extremely fast, and one for kernel logistic
+regression <a class="footnote-reference brackets" href="#id7" id="id2">3</a> which trades off lower speed for better accuracy on binary classification problems.</p>
+<p>The main features of Falkon are:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>Full multi-GPU support</em> - All compute-intensive parts of the algorithms are multi-GPU capable.</p></li>
+<li><p><em>Extreme scalability</em> - Unlike other kernel solvers, we keep memory usage in check. We have tested the library with
+datasets of billions of points.</p></li>
+<li><p><em>Sparse data support</em></p></li>
+<li><p><em>Scikit-learn integration</em> - Our estimators follow the scikit-learn API</p></li>
+</ul>
+</div></blockquote>
+<p>For more details about the algorithms used, you can read our paper <a class="footnote-reference brackets" href="#id5" id="id3">1</a>, or look at the source code
+<a class="reference external" href="https://github.com/falkonml/falkon">github.com/FalkonML/falkon</a> and at the <a class="reference internal" href="api_reference/index.html#api-reference"><span class="std std-ref">documentation</span></a>.
+Also, make sure to follow <a class="reference internal" href="examples/examples.html#examples"><span class="std std-ref">the example notebooks</span></a> to find out about all of Falkon’s features.</p>
+<p>Falkon is built on top of <a class="reference external" href="https://pytorch.org/">PyTorch</a> which is used to support both CPU and GPU tensor
+calculations, and <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">KeOps</a> for fast kernel evaluations on the GPU.</p>
+<p>If you find this library useful for your research, please cite us <a class="footnote-reference brackets" href="#id5" id="id4">1</a>!</p>
+<section id="contents">
+<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="install.html">Install</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install.html#supported-platforms">Supported Platforms</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="install.html#intel-mkl">Intel MKL</a></li>
+<li class="toctree-l3"><a class="reference internal" href="install.html#keops">KeOps</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#testing-the-installation">Testing the installation</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
+<dl class="footnote brackets">
+<dt class="label" id="id5"><span class="brackets">1</span><span class="fn-backref">(<a href="#id3">1</a>,<a href="#id4">2</a>)</span></dt>
+<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+“Kernel methods through the roof: handling billions of points efficiently,”
+arXiv:2006.10350, 2020.</p>
+</dd>
+<dt class="label" id="id6"><span class="brackets"><a class="fn-backref" href="#id1">2</a></span></dt>
+<dd><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
+scale kernel method,” Advances in Neural Information Processing Systems, 2017.</p>
+</dd>
+<dt class="label" id="id7"><span class="brackets"><a class="fn-backref" href="#id2">3</a></span></dt>
+<dd><p>Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, “Globally Convergent
+Newton Methods for Ill-conditioned Generalized Self-concordant Losses,”
+Advances in Neural Information Processing Systems, 2019</p>
+</dd>
+</dl>
+</section>
+<section id="indices-and-tables">
+<h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
+<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
+<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
+</ul>
+</section>
+</section>
+
+
+           </div>
+           
+          </div>
+          <footer>
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+        <a href="install.html" class="btn btn-neutral float-right" title="Install" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+  <div role="contentinfo">
+    <p>
+        &#169; Copyright 2020, Giacomo Meanti.
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Install</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#supported-platforms">Supported Platforms</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#pytorch-and-cuda">PyTorch and CUDA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#intel-mkl">Intel MKL</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#keops">KeOps</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#testing-the-installation">Testing the installation</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+            
+          
+        </div>
+        
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+
+      
+      <nav class="wy-nav-top" aria-label="top navigation">
+        
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">falkon</a>
+        
+      </nav>
+
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+      <div class="wy-nav-content">
+        
+        <div class="rst-content">
+        
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+<div role="navigation" aria-label="breadcrumbs navigation">
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+  <ul class="wy-breadcrumbs">
+    
+      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
+        
+      <li>Install</li>
+    
+    
+      <li class="wy-breadcrumbs-aside">
+        
+          
+            <a href="_sources/install.rst.txt" rel="nofollow"> View page source</a>
+          
+        
+      </li>
+    
+  </ul>
+
+  
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  
+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
+.admonition > p:before {
+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<section id="install">
+<span id="id1"></span><h1>Install<a class="headerlink" href="#install" title="Permalink to this headline">¶</a></h1>
+<section id="supported-platforms">
+<h2>Supported Platforms<a class="headerlink" href="#supported-platforms" title="Permalink to this headline">¶</a></h2>
+<p>Falkon is only tested on Linux.</p>
+<p>GPU support is achieved via CUDA, so using Falkon from Windows will be tricky.</p>
+<p>Using the library from the CPU only is much more portable since there is no CUDA requirement. CPU-only Falkon
+is thus likely to work on Windows and Mac OS easily.</p>
+</section>
+<section id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
+<section id="pytorch-and-cuda">
+<h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permalink to this headline">¶</a></h3>
+<p>Falkon depends on PyTorch, and on the NVIDIA Toolkit (for NVIDIA GPU support) which is usually installed
+alongside PyTorch.
+PyTorch can be installed in several ways by following the instructions at <a class="reference external" href="https://pytorch.org/get-started/locally/">Install PyTorch</a>.
+If GPU support is desired, make sure that PyTorch CUDA bindings are working:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="intel-mkl">
+<h3>Intel MKL<a class="headerlink" href="#intel-mkl" title="Permalink to this headline">¶</a></h3>
+<p>If Falkon is not installed with GPU support, it will try to link to <a class="reference external" href="https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html">Intel MKL</a>
+to speed-up certain sparse operations. MKL shared libraries are usually distributed with numpy, so this should not be a problem.
+In case sparse matrix support is not needed, the MKL library will not be loaded.</p>
+</section>
+<section id="keops">
+<h3>KeOps<a class="headerlink" href="#keops" title="Permalink to this headline">¶</a></h3>
+<p>Falkon uses a patched version of <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">KeOps</a>, therefore until the
+patches are merged in the KeOps library, users will need to uninstall existing KeOps installations (if present), and
+install our patched version.</p>
+<p>The patched version is available as a submodule of the Falkon repository. To install it, you can do the following:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ git clone --recurse-submodules https://github.com/FalkonML/falkon.git
+$ <span class="nb">cd</span> falkon/
+$ pip install ./keops
+</pre></div>
+</div>
+</section>
+</section>
+<section id="installing">
+<h2>Installing<a class="headerlink" href="#installing" title="Permalink to this headline">¶</a></h2>
+<p>After having installed KeOps, you are ready to install Falkon with <cite>pip</cite> (from the root of the git tree):</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install .
+</pre></div>
+</div>
+<p>This will take a while since there are both Cython extensions which need to be compiled (if Cython is installed),
+and PyTorch extensions which take some time to compile.</p>
+</section>
+<section id="testing-the-installation">
+<h2>Testing the installation<a class="headerlink" href="#testing-the-installation" title="Permalink to this headline">¶</a></h2>
+<p>To check that everything works correctly you can follow the <a class="reference internal" href="examples/simple_regression.html"><span class="doc">Kernel ridge regression</span></a> notebook.</p>
+</section>
+</section>
+
+
+           </div>
+           
+          </div>
+          <footer>
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+        <a href="get_started.html" class="btn btn-neutral float-right" title="Getting Started" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="index.html" class="btn btn-neutral float-left" title="Falkon" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+    </div>
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+        &#169; Copyright 2020, Giacomo Meanti.
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+          SphinxRtdTheme.Navigation.enable(true);
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\ No newline at end of file
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new file mode 100644
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+++ b/py-modindex.html
@@ -0,0 +1,405 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  
+  <title>Python Module Index &mdash; falkon 0.6.3 documentation</title>
+  
+
+  
+  <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+    
+    <script type="text/javascript" src="_static/js/theme.js"></script>
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+ 
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+<body class="wy-body-for-nav">
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+<li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#testing-the-installation">Testing the installation</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
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Reference","falkon.kernels","falkon.mmv_ops","falkon.models","falkon.optim","falkon.options","falkon.ooc_ops","falkon.preconditioner","falkon.sparse","Examples","Hyperparameter Tuning with Falkon","Introducing Logistic Falkon","Training on the GPU","Falkon Regression Tutorial","Getting 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\ No newline at end of file

From 0042d1f5292516191796d6ba7d4dda28e0053a7f Mon Sep 17 00:00:00 2001
From: gmeanti <giacomo.meanti@gmail.com>
Date: Tue, 1 Feb 2022 20:09:59 +0100
Subject: [PATCH 2/7] [skip ci] Update documentation

---
 .buildinfo                                    |    2 +-
 _images/examples_falkon_cv_24_0.png           |  Bin 0 -> 6652 bytes
 _images/examples_falkon_cv_4_1.png            |  Bin 0 -> 3122 bytes
 _images/examples_falkon_mnist_6_1.png         |  Bin 0 -> 5360 bytes
 ...amples_falkon_regression_tutorial_17_0.png |  Bin 0 -> 6535 bytes
 _images/examples_hyperopt_4_0.png             |  Bin 0 -> 3012 bytes
 .../examples_hyperparameter_tuning_24_0.png   |  Bin 9469 -> 0 bytes
 .../examples_hyperparameter_tuning_4_1.png    |  Bin 4565 -> 0 bytes
 _images/examples_logistic_falkon_22_0.png     |  Bin 211967 -> 122933 bytes
 _images/examples_logistic_falkon_4_0.png      |  Bin 154507 -> 145956 bytes
 _images/examples_simple_regression_17_1.png   |  Bin 6082 -> 0 bytes
 _sources/api_reference/hopt.rst.txt           |   52 +
 _sources/api_reference/index.rst.txt          |    1 +
 _sources/api_reference/kernels.rst.txt        |   23 +-
 _sources/api_reference/mmv_ops.rst.txt        |  103 +-
 _sources/api_reference/models.rst.txt         |    4 +
 _sources/api_reference/sparse.rst.txt         |    1 +
 _sources/examples/custom_kernels.ipynb.txt    |  944 ++++++++++++
 _sources/examples/examples.rst.txt            |   43 +-
 _sources/examples/falkon_cv.ipynb.txt         |  433 ++++++
 _sources/examples/falkon_mnist.ipynb.txt      |  284 ++++
 .../falkon_regression_tutorial.ipynb.txt      |  378 +++++
 _sources/examples/hyperopt.ipynb.txt          |  496 +++++++
 .../examples/hyperparameter_tuning.ipynb.txt  |  430 ------
 _sources/examples/logistic_falkon.ipynb.txt   |  119 +-
 .../examples/performance_tuning.ipynb.txt     |   49 -
 _sources/examples/simple_regression.ipynb.txt |  403 ------
 _sources/get_started.rst.txt                  |    9 +-
 _sources/index.rst.txt                        |   33 +-
 _sources/install.rst.txt                      |   24 +-
 _static/basic.css                             |  114 +-
 _static/css/theme.css                         |    4 +-
 _static/doctools.js                           |    7 +-
 _static/documentation_options.js              |    2 +-
 _static/js/theme.js                           |    2 +-
 _static/language_data.js                      |    2 +-
 _static/pygments.css                          |    7 +-
 _static/searchtools.js                        |   17 +-
 ...erscore-1.12.0.js => underscore-1.13.1.js} |  151 +-
 _static/underscore.js                         |    8 +-
 api_reference/center_selector.html            |  291 ++--
 api_reference/gsc_losses.html                 |  315 ++--
 api_reference/hopt.html                       |  403 ++++++
 api_reference/index.html                      |  282 ++--
 api_reference/kernels.html                    |  866 ++++++-----
 api_reference/mmv_ops.html                    |  497 +++----
 api_reference/models.html                     |  391 +++--
 api_reference/optimization.html               |  312 ++--
 api_reference/options.html                    |  272 ++--
 api_reference/outofcore.html                  |  261 ++--
 api_reference/preconditioner.html             |  329 ++---
 api_reference/sparse.html                     |  277 ++--
 examples/custom_kernels.html                  | 1282 +++++++++++++++++
 examples/custom_kernels.ipynb                 |  944 ++++++++++++
 examples/examples.html                        |  262 ++--
 ...erparameter_tuning.html => falkon_cv.html} |  347 ++---
 examples/falkon_cv.ipynb                      |  433 ++++++
 examples/falkon_mnist.html                    |  756 ++++++++++
 examples/falkon_mnist.ipynb                   |  284 ++++
 ...n.html => falkon_regression_tutorial.html} |  340 ++---
 examples/falkon_regression_tutorial.ipynb     |  378 +++++
 examples/hyperopt.html                        |  872 +++++++++++
 examples/hyperopt.ipynb                       |  498 +++++++
 examples/hyperparameter_tuning.ipynb          |  430 ------
 examples/logistic_falkon.html                 |  428 +++---
 examples/logistic_falkon.ipynb                |  117 +-
 examples/performance_tuning.html              |  380 -----
 examples/performance_tuning.ipynb             |   49 -
 examples/simple_regression.ipynb              |  403 ------
 genindex.html                                 |  342 ++---
 get_started.html                              |  259 ++--
 index.html                                    |  366 ++---
 install.html                                  |  280 ++--
 objects.inv                                   |  Bin 4378 -> 5566 bytes
 py-modindex.html                              |  241 ++--
 search.html                                   |  242 ++--
 searchindex.js                                |    2 +-
 77 files changed, 11818 insertions(+), 6758 deletions(-)
 create mode 100644 _images/examples_falkon_cv_24_0.png
 create mode 100644 _images/examples_falkon_cv_4_1.png
 create mode 100644 _images/examples_falkon_mnist_6_1.png
 create mode 100644 _images/examples_falkon_regression_tutorial_17_0.png
 create mode 100644 _images/examples_hyperopt_4_0.png
 delete mode 100644 _images/examples_hyperparameter_tuning_24_0.png
 delete mode 100644 _images/examples_hyperparameter_tuning_4_1.png
 delete mode 100644 _images/examples_simple_regression_17_1.png
 create mode 100644 _sources/api_reference/hopt.rst.txt
 create mode 100644 _sources/examples/custom_kernels.ipynb.txt
 create mode 100644 _sources/examples/falkon_cv.ipynb.txt
 create mode 100644 _sources/examples/falkon_mnist.ipynb.txt
 create mode 100644 _sources/examples/falkon_regression_tutorial.ipynb.txt
 create mode 100644 _sources/examples/hyperopt.ipynb.txt
 delete mode 100644 _sources/examples/hyperparameter_tuning.ipynb.txt
 delete mode 100644 _sources/examples/performance_tuning.ipynb.txt
 delete mode 100644 _sources/examples/simple_regression.ipynb.txt
 rename _static/{underscore-1.12.0.js => underscore-1.13.1.js} (94%)
 create mode 100644 api_reference/hopt.html
 create mode 100644 examples/custom_kernels.html
 create mode 100644 examples/custom_kernels.ipynb
 rename examples/{hyperparameter_tuning.html => falkon_cv.html} (78%)
 create mode 100644 examples/falkon_cv.ipynb
 create mode 100644 examples/falkon_mnist.html
 create mode 100644 examples/falkon_mnist.ipynb
 rename examples/{simple_regression.html => falkon_regression_tutorial.html} (71%)
 create mode 100644 examples/falkon_regression_tutorial.ipynb
 create mode 100644 examples/hyperopt.html
 create mode 100644 examples/hyperopt.ipynb
 delete mode 100644 examples/hyperparameter_tuning.ipynb
 delete mode 100644 examples/performance_tuning.html
 delete mode 100644 examples/performance_tuning.ipynb
 delete mode 100644 examples/simple_regression.ipynb

diff --git a/.buildinfo b/.buildinfo
index b65534f0..4d019a0b 100644
--- a/.buildinfo
+++ b/.buildinfo
@@ -1,4 +1,4 @@
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diff --git a/_sources/api_reference/hopt.rst.txt b/_sources/api_reference/hopt.rst.txt
new file mode 100644
index 00000000..05341189
--- /dev/null
+++ b/_sources/api_reference/hopt.rst.txt
@@ -0,0 +1,52 @@
+
+
+falkon.hopt
+==============
+
+.. automodule:: falkon.hopt
+.. py:currentmodule:: falkon.hopt
+
+
+Objectives
+----------
+
+.. autoclass:: falkon.hopt.objectives.objectives.HyperoptObjective
+
+
+Nystrom Complexity Regularization
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: falkon.hopt.objectives.NystromCompReg
+
+
+Stochastic Nystrom Computational Regularization
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: falkon.hopt.objectives.StochasticNystromCompReg
+
+
+Complexity Regularization
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: falkon.hopt.objectives.CompReg
+
+Generalized Cross Validation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: falkon.hopt.objectives.GCV
+
+Hold Out Cross Validation
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: falkon.hopt.objectives.HoldOut
+
+
+Leave One Out Cross Validation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: falkon.hopt.objectives.LOOCV
+
+SGPR
+~~~~
+
+.. autoclass:: falkon.hopt.objectives.SGPR
diff --git a/_sources/api_reference/index.rst.txt b/_sources/api_reference/index.rst.txt
index 7e410b4d..d7b85f68 100644
--- a/_sources/api_reference/index.rst.txt
+++ b/_sources/api_reference/index.rst.txt
@@ -16,3 +16,4 @@ API Reference
     mmv_ops
     sparse
     center_selector
+    hopt
diff --git a/_sources/api_reference/kernels.rst.txt b/_sources/api_reference/kernels.rst.txt
index 2c1ca868..bf0ef716 100644
--- a/_sources/api_reference/kernels.rst.txt
+++ b/_sources/api_reference/kernels.rst.txt
@@ -1,4 +1,5 @@
 
+
 falkon.kernels
 ==============
 
@@ -11,11 +12,20 @@ Kernel
 
 .. autoclass:: falkon.kernels.kernel.Kernel
     :members:
-    :private-members: _prepare, _apply, _finalize, _prepare_sparse, _apply_sparse, _decide_mm_impl, _decide_mmv_impl, _decide_dmmv_impl
+    :private-members: _decide_mm_impl, _decide_mmv_impl, _decide_dmmv_impl
     :special-members: __call__
 
+DiffKernel
+----------
+
+.. autoclass:: falkon.kernels.diff_kernel.DiffKernel
+    :members: compute_diff, detach, diff_params
+
+KeopsKernelMixin
+----------------
+
 .. autoclass:: falkon.kernels.keops_helpers.KeopsKernelMixin
-    :members:
+    :members: keops_mmv, keops_mmv_impl
 
 
 Radial kernels
@@ -35,6 +45,13 @@ Laplacian kernel
     :members: mmv, dmmv
     :special-members: __call__
 
+Matern kernel
+~~~~~~~~~~~~~
+
+.. autoclass:: MaternKernel
+    :members: mmv, dmmv
+    :special-members: __call__
+
 
 Dot-Product kernels
 -------------------
@@ -58,4 +75,4 @@ Sigmoid kernel
 
 .. autoclass:: SigmoidKernel
     :members: mmv, dmmv
-    :special-members: __call__
\ No newline at end of file
+    :special-members: __call__
diff --git a/_sources/api_reference/mmv_ops.rst.txt b/_sources/api_reference/mmv_ops.rst.txt
index 18b7bb00..dea952cb 100644
--- a/_sources/api_reference/mmv_ops.rst.txt
+++ b/_sources/api_reference/mmv_ops.rst.txt
@@ -24,100 +24,31 @@ kernel-vector products.
 .. autofunction:: falkon.mmv_ops.keops.run_keops_mmv
 
 
-fmm_cpu
--------
+fmm
+---
 
-Blocked kernel calculation on the CPU.
+Block-wise kernel calculation. If the inputs require gradient, this function uses a differentiable implementation.
 
-.. autofunction:: falkon.mmv_ops.fmm_cpu.fmm_cpu
+.. autofunction:: falkon.mmv_ops.fmm.fmm
 
 
-fmm_cpu_sparse
---------------
 
-Blocked kernel calculation on the CPU for sparse datasets.
+fmmv
+----
 
-.. autofunction:: falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse
+Block-wise kernel-vector products.
 
+.. autofunction:: falkon.mmv_ops.fmmv.fmmv
 
-fmm_cuda
---------
 
-Blocked kernel calculation on GPUs.
 
-.. autofunction:: falkon.mmv_ops.fmm_cuda.fmm_cuda
+fdmmv
+-----
 
+Block-wise double kernel-vector products.
 
-fmm_cuda_sparse
----------------
+.. autofunction:: falkon.mmv_ops.fmmv.fdmmv
 
-Blocked kernel calculation on GPUs for sparse datasets.
-
-.. autofunction:: falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse
-
-
-fmmv_cpu
----------------
-
-Blocked kernel-vector products on CPU.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cpu.fmmv_cpu
-
-
-fmmv_cpu_sparse
----------------
-
-Blocked kernel-vector products on CPU for sparse datasets.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse
-
-
-fdmmv_cpu
----------------
-
-Blocked double kernel-vector products on CPU.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cpu.fdmmv_cpu
-
-
-fdmmv_cpu_sparse
-----------------
-
-Blocked double kernel-vector products on CPU for sparse datasets.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse
-
-
-fmmv_cuda
-----------------
-
-Blocked kernel-vector products on GPUs.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cuda.fmmv_cuda
-
-
-fmmv_cuda_sparse
-----------------
-
-Blocked kernel-vector products on GPUs for sparse datasets.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse
-
-
-fdmmv_cuda
-----------------
-
-Blocked double kernel-vector products on GPUs.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cuda.fdmmv_cuda
-
-
-fdmmv_cuda_sparse
------------------
-
-Blocked double kernel-vector products on GPUs for sparse datasets.
-
-.. autofunction:: falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse
 
 
 incore_fmmv
@@ -131,3 +62,13 @@ incore_fdmmv
 
 .. autofunction:: falkon.mmv_ops.fmmv_incore.incore_fdmmv
 
+
+Low-level functions
+-------------------
+
+The following are some of the low-level functions which help compute kernels and kernel-vector products block-wise.
+They are specialized for different input types.
+
+.. autofunction:: falkon.mmv_ops.fmm.sparse_mm_run_thread
+
+.. autofunction:: falkon.mmv_ops.fmmv.sparse_mmv_run_thread
diff --git a/_sources/api_reference/models.rst.txt b/_sources/api_reference/models.rst.txt
index 05aecc37..3b9fa81e 100644
--- a/_sources/api_reference/models.rst.txt
+++ b/_sources/api_reference/models.rst.txt
@@ -5,6 +5,8 @@ falkon.models
 .. currentmodule:: falkon.models
 
 
+.. _falkon_model:
+
 Falkon
 ------
 
@@ -12,6 +14,7 @@ Falkon
     :members:
     :inherited-members:
 
+.. _log_falkon_model:
 
 LogisticFalkon
 --------------
@@ -20,6 +23,7 @@ LogisticFalkon
     :members:
     :inherited-members:
 
+.. _ic_falkon_model:
 
 InCoreFalkon
 ------------
diff --git a/_sources/api_reference/sparse.rst.txt b/_sources/api_reference/sparse.rst.txt
index 13e13b26..ca276325 100644
--- a/_sources/api_reference/sparse.rst.txt
+++ b/_sources/api_reference/sparse.rst.txt
@@ -1,3 +1,4 @@
+
 falkon.sparse
 =============
 
diff --git a/_sources/examples/custom_kernels.ipynb.txt b/_sources/examples/custom_kernels.ipynb.txt
new file mode 100644
index 00000000..28f2b2f8
--- /dev/null
+++ b/_sources/examples/custom_kernels.ipynb.txt
@@ -0,0 +1,944 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "be9efd3e",
+   "metadata": {},
+   "source": [
+    "# Implementing A Custom Kernel\n",
+    "\n",
+    "In this notebook we will show how to implement a custom kernel in Falkon.\n",
+    "\n",
+    "There are several complementary parts to a kernel, which can be added to support different operations.\n",
+    "We will go through them one-by-one in this notebook:\n",
+    "\n",
+    " - Basic support: supports learning with Falkon!\n",
+    " - Autodiff support: supports automatic hyperparameter tuning (in the `hopt` module)\n",
+    " - KeOps support: faster kernel-vector products in low dimension\n",
+    " - Sparse support: support learning on sparse data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cc7a4428",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "\n",
+    "import falkon\n",
+    "from falkon import FalkonOptions\n",
+    "from falkon.kernels import Kernel, DiffKernel, KeopsKernelMixin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b37d1d0",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Setup a simple problem for testing\n",
+    "\n",
+    "Load and preprocess the *California housing* dataset. The `learn_with_kernel` function sets up Falkon for learning on the California housing datase with a given kernel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "be73c315",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.fetch_california_housing(return_X_y=True)\n",
+    "num_train = int(X.shape[0] * 0.8)\n",
+    "num_test = X.shape[0] - num_train\n",
+    "shuffle_idx = np.arange(X.shape[0])\n",
+    "np.random.shuffle(shuffle_idx)\n",
+    "train_idx = shuffle_idx[:num_train]\n",
+    "test_idx = shuffle_idx[num_train:]\n",
+    "\n",
+    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
+    "Xtest, Ytest = X[test_idx], Y[test_idx]\n",
+    "# convert numpy -> pytorch\n",
+    "Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)\n",
+    "Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)\n",
+    "Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)\n",
+    "Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)\n",
+    "# z-score normalization\n",
+    "train_mean = Xtrain.mean(0, keepdim=True)\n",
+    "train_std = Xtrain.std(0, keepdim=True)\n",
+    "Xtrain -= train_mean\n",
+    "Xtrain /= train_std\n",
+    "Xtest -= train_mean\n",
+    "Xtest /= train_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a58a7c95",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "def rmse(true, pred):\n",
+    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
+    "\n",
+    "def learn_with_kernel(kernel):\n",
+    "    flk_opt = FalkonOptions(use_cpu=True)\n",
+    "    model = falkon.Falkon(\n",
+    "        kernel=kernel, penalty=1e-5, M=1000, options=flk_opt,\n",
+    "        error_every=1, error_fn=rmse)\n",
+    "    model.fit(Xtrain, Ytrain)\n",
+    "    ts_err = rmse(Ytest, model.predict(Xtest))\n",
+    "    print(\"Test RMSE: %.2f\" % (ts_err))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d046959",
+   "metadata": {},
+   "source": [
+    "## Basic Kernel Implementation\n",
+    "\n",
+    "We must inherit from the `falkon.kernels.Kernel` class, and implement:\n",
+    " - `compute` method: the core of the kernel implementation. \n",
+    "     Given two input matrices (of size $n\\times d$ and $m\\times d$), and an output matrix (of size $n\\times m$), compute the kernel function between the two inputs and store it in the output.\n",
+    "     \n",
+    "     The additional `diag` parameter is a boolean flag. It indicates that a) $n$ is equal to $m$, b) only the diagonal of the kernel matrix should be computed.\n",
+    " - `compute_sparse` method: this should be used if you want your kernel to support sparse data. \n",
+    "     We will implement it in a later section.\n",
+    "     \n",
+    "We will implement a **linear** kernel:\n",
+    "$$k(x, x') = \\sigma (x^\\top x')$$\n",
+    "the parameter $\\sigma$ is the *variance* of the kernel. It is the only hyperparameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bfd90b30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BasicLinearKernel(Kernel):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        # The base class takes as inputs a name for the kernel, and\n",
+    "        # an instance of `FalkonOptions`.\n",
+    "        super().__init__(\"basic_linear\", options)\n",
+    "        \n",
+    "        self.lengthscale = lengthscale\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -> torch.Tensor:\n",
+    "        # To support different devices/data types, you must make sure\n",
+    "        # the lengthscale is compatible with the data.\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            # The dot-product row-by-row on `X1` and `X2` can be computed\n",
+    "            # on many rows at a time with matrix multiplication.\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_sparse(self, X1, X2, out, diag, **kwargs) -> torch.Tensor:\n",
+    "        raise NotImplementedError(\"Sparse not implemented\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f19f3e95",
+   "metadata": {},
+   "source": [
+    "### Test the basic kernel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7bfcd1be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the kernel\n",
+    "lengthscale_init = torch.tensor([1.0])\n",
+    "k = BasicLinearKernel(lengthscale_init, options=falkon.FalkonOptions())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "32b86480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.3538,  4.0383, -0.5058, -3.1306, -0.3159],\n",
+       "        [-0.9498, -2.0581,  0.4684,  0.8994,  0.7577],\n",
+       "        [ 0.3122, -0.1038, -0.5039,  2.5076, -0.4032],\n",
+       "        [ 0.8383,  3.8545, -1.4094,  1.0497, -1.4979],\n",
+       "        [ 0.8344, -4.5258,  2.9362, -7.7300,  2.0740]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The kernel matrix\n",
+    "k(torch.randn(5, 3), torch.randn(5, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a3134217",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[6.1084],\n",
+       "        [3.6743],\n",
+       "        [1.2653],\n",
+       "        [1.2448]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Kernel-vector product\n",
+    "k.mmv(torch.randn(4, 3), torch.randn(4, 3), v=torch.randn(4, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "53521733",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ -3.6467],\n",
+       "        [ -9.8628],\n",
+       "        [  1.4857],\n",
+       "        [-12.8557]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Double kernel-vector product\n",
+    "k.dmmv(torch.randn(3, 3), torch.randn(4, 3), v=torch.randn(4, 1), w=torch.randn(3, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "31837bb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.07s - training error: 2.36367178\n",
+      "Iteration   2 - Elapsed 0.11s - training error: 2.19508219\n",
+      "Iteration   3 - Elapsed 0.14s - training error: 2.19265079\n",
+      "Iteration   4 - Elapsed 0.17s - training error: 2.19265032\n",
+      "Iteration   5 - Elapsed 0.20s - training error: 2.19262338\n",
+      "Iteration   6 - Elapsed 0.24s - training error: 2.19262123\n",
+      "Iteration   7 - Elapsed 0.27s - training error: 2.19261861\n",
+      "Iteration   8 - Elapsed 0.30s - training error: 2.19261885\n",
+      "Iteration   9 - Elapsed 0.33s - training error: 2.19261789\n",
+      "Iteration  10 - Elapsed 0.39s - training error: 2.19261765\n",
+      "Iteration  11 - Elapsed 0.42s - training error: 2.19261956\n",
+      "Iteration  12 - Elapsed 0.45s - training error: 2.19261932\n",
+      "Iteration  13 - Elapsed 0.48s - training error: 2.19261909\n",
+      "Iteration  14 - Elapsed 0.51s - training error: 2.19261813\n",
+      "Iteration  15 - Elapsed 0.55s - training error: 2.19261885\n",
+      "Iteration  16 - Elapsed 0.57s - training error: 2.19261742\n",
+      "Iteration  17 - Elapsed 0.61s - training error: 2.19261813\n",
+      "Iteration  18 - Elapsed 0.63s - training error: 2.19261980\n",
+      "Iteration  19 - Elapsed 0.66s - training error: 2.19261956\n",
+      "Iteration  20 - Elapsed 0.73s - training error: 2.19262052\n",
+      "Test RMSE: 2.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Learning on the california housing dataset\n",
+    "learn_with_kernel(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1a4b946",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Differentiable Kernel\n",
+    "\n",
+    "A differentiable kernel is needed for automatic hyperparameter optimization (see the [notebook](hyperopt.ipynb)).\n",
+    "\n",
+    "It requires inheriting from `falkon.kernels.DiffKernel`. In addition to the methods already discussed, we must implement:\n",
+    " - `compute_diff`, which works similarly to the `compute` method but it does not have an `out` parameter. The implementation should be fully differentiable with respect to its inputs, and to the kernel hyperparameters.\n",
+    " - `detach`, which essentially clones the kernel with the parameters *detached* from the computational graph.\n",
+    " \n",
+    "Another important difference from the basic kernel is the call to the *constructor*, which must include\n",
+    " - All kernel hyperparameters as keyword arguments. These will be available as attributes on the class. Hyperparameters do not need to be tensors.\n",
+    "\n",
+    "**`core_fn` parameter (optional)**\n",
+    "\n",
+    "The constructor can also *optionally* contain a `core_fn` parameter which can simplify implementation by uniting the `compute` and `compute_diff` implementations. Have a look at the implementation of kernels in `falkon.kernels.dot_prod_kernel.py` and `falkon.kernels.distance_kernel.py` for how to use the `core_fn` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13c002e2",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "class DiffLinearKernel(DiffKernel):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        # Super-class constructor call. We do not specify core_fn\n",
+    "        # but we must specify the hyperparameter of this kernel (lengthscale)\n",
+    "        super().__init__(\"diff_linear\", \n",
+    "                         options, \n",
+    "                         core_fn=None, \n",
+    "                         lengthscale=lengthscale)\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):\n",
+    "        # The implementation here is similar to `compute` without in-place operations.\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            return torch.sum(scaled_X1 * X2, dim=-1)\n",
+    "        \n",
+    "        return torch.matmul(scaled_X1, X2.T)\n",
+    "\n",
+    "    def detach(self):\n",
+    "        # Clones the class with detached hyperparameters\n",
+    "        return DiffLinearKernel(\n",
+    "            lengthscale=self.lengthscale.detach(), \n",
+    "            options=self.params\n",
+    "        )\n",
+    "    \n",
+    "    def compute_sparse(self, X1, X2, out, diag, **kwargs) -> torch.Tensor:\n",
+    "        raise NotImplementedError(\"Sparse not implemented\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7dd73bf",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### Test the differentiable kernel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0f223c67",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize the kernel, with a lengthscale which requires grad.\n",
+    "lengthscale_init = torch.tensor([1.0]).requires_grad_()\n",
+    "k = DiffLinearKernel(lengthscale_init, options=falkon.FalkonOptions())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cc862c47",
+   "metadata": {
+    "hidden": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 2.7480,  1.6149, -1.2979, -2.3070, -1.1852],\n",
+       "        [ 4.2437,  2.8397, -2.6248, -3.1610, -1.1940],\n",
+       "        [ 2.6474,  0.9644, -0.4447, -1.1742, -1.0197],\n",
+       "        [-3.4735,  0.4214, -1.9773,  0.3380,  2.2361],\n",
+       "        [-1.8094, -0.2183, -0.5620,  1.8260,  1.8644]],\n",
+       "       grad_fn=<KernelMmFnFullBackward>)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Kernel matrix. Notice how the outputs has a `grad_fn`\n",
+    "k_mat = k(torch.randn(5, 3), torch.randn(5, 3))\n",
+    "k_mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a22671f4",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([-0.7049]),)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Gradient of the kernel with respect to the lengthscale.\n",
+    "torch.autograd.grad(k_mat.sum(), k.lengthscale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d508d0d7",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kernel-vector product\n",
+      "tensor([[ 0.0198],\n",
+      "        [-1.6055],\n",
+      "        [ 2.3654],\n",
+      "        [-0.6039]], grad_fn=<KernelMmvFnFullBackward>)\n",
+      "Gradients:\n",
+      "(tensor([0.1758]), tensor([[ 0.6192,  1.2183, -0.2544],\n",
+      "        [ 0.6192,  1.2183, -0.2544],\n",
+      "        [ 0.6192,  1.2183, -0.2544],\n",
+      "        [ 0.6192,  1.2183, -0.2544]]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# kernel-vector product + gradient\n",
+    "m1 = torch.randn(4, 3).requires_grad_()\n",
+    "m2 = torch.randn(2, 3)\n",
+    "v = torch.randn(2, 1)\n",
+    "k_mmv = k.mmv(m1, m2, v)\n",
+    "print(\"Kernel-vector product\")\n",
+    "print(k_mmv)\n",
+    "print(\"Gradients:\")\n",
+    "print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c56f9a2a",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.06s - training error: 2.20815659\n",
+      "Iteration   2 - Elapsed 0.10s - training error: 2.19324374\n",
+      "Iteration   3 - Elapsed 0.12s - training error: 2.19264197\n",
+      "Iteration   4 - Elapsed 0.15s - training error: 2.19263649\n",
+      "Iteration   5 - Elapsed 0.18s - training error: 2.19262934\n",
+      "Iteration   6 - Elapsed 0.21s - training error: 2.19261909\n",
+      "Iteration   7 - Elapsed 0.24s - training error: 2.19261813\n",
+      "Iteration   8 - Elapsed 0.26s - training error: 2.19262004\n",
+      "Iteration   9 - Elapsed 0.29s - training error: 2.19261765\n",
+      "Iteration  10 - Elapsed 0.34s - training error: 2.19261789\n",
+      "Iteration  11 - Elapsed 0.38s - training error: 2.19261909\n",
+      "Iteration  12 - Elapsed 0.40s - training error: 2.19261885\n",
+      "Iteration  13 - Elapsed 0.43s - training error: 2.19261956\n",
+      "Iteration  14 - Elapsed 0.46s - training error: 2.19261932\n",
+      "Iteration  15 - Elapsed 0.49s - training error: 2.19261932\n",
+      "Iteration  16 - Elapsed 0.52s - training error: 2.19262099\n",
+      "Iteration  17 - Elapsed 0.54s - training error: 2.19262123\n",
+      "Iteration  18 - Elapsed 0.57s - training error: 2.19262147\n",
+      "Iteration  19 - Elapsed 0.60s - training error: 2.19262195\n",
+      "Iteration  20 - Elapsed 0.65s - training error: 2.19262338\n",
+      "Test RMSE: 2.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Learning on the california housing dataset\n",
+    "learn_with_kernel(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd29c886",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Adding KeOps Support\n",
+    "\n",
+    "We must inherit from `falkon.kernels.KeopsKernelMixin` and implement the method `keops_mmv_impl`.\n",
+    "\n",
+    "KeOps-enabled kernels will still use the implementation in the `compute` function for computing the kernel matrix itself, but will use KeOps to compute kernel-vector products (if the data dimension is small enough).\n",
+    "\n",
+    "This method is responsible for kernel-vector products, and it should contain:\n",
+    " 1. A formula definition (see https://www.kernel-operations.io/keops/api/math-operations.html for the appropriate syntax)\n",
+    " 2. A definition of all variables (again have a look at the KeOps documentation, or the implementation\n",
+    "     of other kernels within Falkon)\n",
+    " 3. A call to the `keops_mmv` method of the `KeopsKernelMixin` class, responsible for calling into\n",
+    "     the KeOps formula.\n",
+    "     \n",
+    "For our kernel we will use the `(X | Y)` syntax for the dot-product between samples, and then multiplication with the vector `v`. The aliases list maps the symbols used in the formula with the KeOps variable types.\n",
+    "\n",
+    "For more examples check the [KeOps documentatiaon](https://www.kernel-operations.io) or the implementation of existing kernels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8164d28e",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "class KeopsLinearKernel(DiffKernel, KeopsKernelMixin):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        super().__init__(\"my-keops-linear\", \n",
+    "                         options, \n",
+    "                         core_fn=None, \n",
+    "                         lengthscale=lengthscale)\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):\n",
+    "        scaled_X1 = X1 * self.lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            return torch.sum(scaled_X1 * X2, dim=-1)\n",
+    "        \n",
+    "        return torch.matmul(scaled_X1, X2.T)\n",
+    "\n",
+    "    def detach(self):\n",
+    "        return KeopsLinearKernel(\n",
+    "            lengthscale=self.lengthscale.detach(), \n",
+    "            options=self.params\n",
+    "        )\n",
+    "        \n",
+    "    def keops_mmv_impl(self, X1, X2, v, kernel, out, opt):\n",
+    "        # Keops formula for kernel-vector.\n",
+    "        formula = '(scale * (X | Y)) * v'\n",
+    "        aliases = [\n",
+    "            'X = Vi(%d)' % (X1.shape[1]),\n",
+    "            'Y = Vj(%d)' % (X2.shape[1]),\n",
+    "            'v = Vj(%d)' % (v.shape[1]),\n",
+    "            'scale = Pm(%d)' % (self.lengthscale.shape[0]),\n",
+    "        ]\n",
+    "        other_vars = [\n",
+    "            self.lengthscale.to(dtype=X1.dtype, device=X1.device),\n",
+    "        ]\n",
+    "        # Call to the executor of the formula.\n",
+    "        return self.keops_mmv(X1, X2, v, out, formula, aliases, other_vars, opt)\n",
+    "\n",
+    "    \n",
+    "    def compute_sparse(self, X1, X2, out: torch.Tensor, diag: bool, **kwargs) -> torch.Tensor:\n",
+    "        raise NotImplementedError(\"Sparse not implemented\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11528fb9",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### Test the KeOps kernel\n",
+    "\n",
+    "Note that KeOps will need to compile the kernels the first time they are run!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "12107005",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "lengthscale_init = torch.tensor([1.0]).requires_grad_()\n",
+    "k = KeopsLinearKernel(lengthscale_init, options=falkon.FalkonOptions(use_cpu=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c231eabc",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kernel-vector product\n",
+      "tensor([[-1.2121],\n",
+      "        [-0.1148],\n",
+      "        [ 2.2435],\n",
+      "        [ 0.9918]], grad_fn=<TilingGenredAutogradBackward>)\n",
+      "Gradients:\n",
+      "(tensor([1.9084]), tensor([[ 1.0124, -0.8363,  0.7706],\n",
+      "        [ 1.0124, -0.8363,  0.7706],\n",
+      "        [ 1.0124, -0.8363,  0.7706],\n",
+      "        [ 1.0124, -0.8363,  0.7706]], requires_grad=True))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# kernel-vector product + gradient\n",
+    "m1 = torch.randn(4, 3).requires_grad_()\n",
+    "m2 = torch.randn(2, 3)\n",
+    "v = torch.randn(2, 1)\n",
+    "k_mmv = k.mmv(m1, m2, v)\n",
+    "print(\"Kernel-vector product\")\n",
+    "print(k_mmv)\n",
+    "print(\"Gradients:\")\n",
+    "print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7d2b80e5",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.17s - training error: 2.27769995\n",
+      "Iteration   2 - Elapsed 0.34s - training error: 2.19313025\n",
+      "Iteration   3 - Elapsed 0.51s - training error: 2.19323778\n",
+      "Iteration   4 - Elapsed 0.66s - training error: 2.19308257\n",
+      "Iteration   5 - Elapsed 0.82s - training error: 2.19269753\n",
+      "Iteration   6 - Elapsed 0.98s - training error: 2.19266987\n",
+      "Iteration   7 - Elapsed 1.13s - training error: 2.19262886\n",
+      "Iteration   8 - Elapsed 1.29s - training error: 2.19262505\n",
+      "Iteration   9 - Elapsed 1.45s - training error: 2.19262052\n",
+      "Iteration  10 - Elapsed 1.76s - training error: 2.19260979\n",
+      "Iteration  11 - Elapsed 1.92s - training error: 2.19261813\n",
+      "Iteration  12 - Elapsed 2.08s - training error: 2.19261646\n",
+      "Iteration  13 - Elapsed 2.25s - training error: 2.19263911\n",
+      "Iteration  14 - Elapsed 2.42s - training error: 2.19263911\n",
+      "Iteration  15 - Elapsed 2.58s - training error: 2.19264960\n",
+      "Iteration  16 - Elapsed 2.74s - training error: 2.19265103\n",
+      "Iteration  17 - Elapsed 2.91s - training error: 2.19268680\n",
+      "Iteration  18 - Elapsed 3.07s - training error: 2.19269395\n",
+      "Iteration  19 - Elapsed 3.23s - training error: 2.19270301\n",
+      "Iteration  20 - Elapsed 3.55s - training error: 2.19275403\n",
+      "Test RMSE: 2.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Learning on the california housing dataset.\n",
+    "# Due to differences in floating point code, results may be slightly \n",
+    "# different from the other implementations.\n",
+    "learn_with_kernel(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fecea8f",
+   "metadata": {},
+   "source": [
+    "## Supporting Sparse Data\n",
+    "\n",
+    "Sparse support can be necessary for kernel learning in extremely high dimensions, when the inputs are sparse.\n",
+    "\n",
+    "Sparse support requires using special functions for common operations such as matrix multiplication. Falkon implements sparse tensors in a CSR format (PyTorch is slowly picking this format up, in place of COO), through the `falkon.sparse.SparseTensor` class.\n",
+    "\n",
+    "We will implement the `compute_sparse` method below, supporting both diagonal and full kernels.\n",
+    "However, only CPU support is added here (CUDA support is possible but requires a few more details), and differentiable sparse kernels are not supported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "98a92bf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from falkon.sparse import SparseTensor\n",
+    "from falkon.sparse import sparse_matmul, bdot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d9304478",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SparseLinearKernel(Kernel):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        # The base class takes as inputs a name for the kernel, and\n",
+    "        # an instance of `FalkonOptions`.\n",
+    "        super().__init__(\"sparse_linear\", options)\n",
+    "        \n",
+    "        self.lengthscale = lengthscale\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -> torch.Tensor:\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            # The dot-product row-by-row on `X1` and `X2` can be computed\n",
+    "            # on many rows at a time with matrix multiplication.\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_sparse(self, \n",
+    "                       X1: SparseTensor, \n",
+    "                       X2: SparseTensor, \n",
+    "                       out: torch.Tensor, \n",
+    "                       diag: bool,\n",
+    "                       **kwargs) -> torch.Tensor:\n",
+    "        # The inputs will be matrix X1(n*d) in CSR format, and X2(d*n) in CSC format.\n",
+    "        \n",
+    "        # To support different devices/data types, you must make sure\n",
+    "        # the lengthscale is compatible with the data.\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        \n",
+    "        if diag:\n",
+    "            # The diagonal is a dot-product between rows of X1 and X2.\n",
+    "            # The batched-dot is only implemented on CPU.\n",
+    "            out = bdot(X1, X2.transpose_csr(), out)\n",
+    "        else:\n",
+    "            # Otherwise we need to matrix-multiply. Note that X2 is already\n",
+    "            # transposed correctly.\n",
+    "            out = sparse_matmul(X1, X2, out)\n",
+    "\n",
+    "        out.mul_(lengthscale)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75a18424",
+   "metadata": {},
+   "source": [
+    "### Testing sparse support\n",
+    "\n",
+    "We generate two sparse matrices, and check that the sparse kernel is equivalent to the dense version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e6e4c0bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0., 5.],\n",
+       "        [1., 8.],\n",
+       "        [2., 0.]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indexptr = torch.tensor([0, 1, 3, 4], dtype=torch.long)\n",
+    "index = torch.tensor([1, 0, 1, 0], dtype=torch.long)\n",
+    "value = torch.tensor([5, 1, 8, 2], dtype=torch.float32)\n",
+    "sp1 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=\"csr\")\n",
+    "# Converted to dense:\n",
+    "dense1 = torch.from_numpy(sp1.to_scipy().todense())\n",
+    "dense1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7470a0b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0., 2.],\n",
+       "        [1., 0.],\n",
+       "        [3., 4.]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indexptr = torch.tensor([0, 1, 2, 4], dtype=torch.long)\n",
+    "index = torch.tensor([1, 0, 0, 1], dtype=torch.long)\n",
+    "value = torch.tensor([2, 1, 3, 4], dtype=torch.float32)\n",
+    "sp2 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=\"csr\")\n",
+    "dense2 = torch.from_numpy(sp2.to_scipy().todense())\n",
+    "dense2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f217cde0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the kernel\n",
+    "lengthscale_init = torch.tensor([1.0])\n",
+    "k = SparseLinearKernel(lengthscale_init, options=falkon.FalkonOptions())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "80f6a317",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[True, True, True],\n",
+       "        [True, True, True],\n",
+       "        [True, True, True]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k(sp1, sp2) == k(dense1, dense2)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/_sources/examples/examples.rst.txt b/_sources/examples/examples.rst.txt
index 56f7d26f..7c8e3611 100644
--- a/_sources/examples/examples.rst.txt
+++ b/_sources/examples/examples.rst.txt
@@ -3,32 +3,43 @@
 Examples
 ========
 
-Starting with simple kernel ridge regression, via classification, hyperparameter tuning, to large-scale GPU experiments,
-these notebooks cover all there is to know about Falkon.
-
- - `Kernel ridge regression`_ goes through the basic notions of the library with a simple example;
- - `Logistic Falkon tutorial`_ shows how to use the Logistic Falkon estimator, comparing the results with normal Falkon;
- - `Hyperparameter tuning`_ is a fully worked out example of parameter optimization with Falkon for a real-world multi-class problem;
- - `GPU training`_ details the options to maximize performance on multiple GPU systems.
-
 .. toctree::
     :maxdepth: 1
     :hidden:
 
-    ./simple_regression.ipynb
+    ./falkon_regression_tutorial.ipynb
     ./logistic_falkon.ipynb
-    ./hyperparameter_tuning.ipynb
-    ./performance_tuning.ipynb
-
+    ./falkon_cv.ipynb
+    ./custom_kernels.ipynb
+    ./hyperopt.ipynb
+    ./falkon_mnist.ipynb
 
 .. _Kernel ridge regression:
-    ./simple_regression.ipynb
+    ./falkon_regression_tutorial.ipynb
 
 .. _Logistic Falkon tutorial:
     ./logistic_falkon.ipynb
 
 .. _Hyperparameter tuning:
-    ./hyperparameter_tuning.ipynb
+    ./falkon_cv.ipynb
+
+.. _custom kernels:
+    ./custom_kernels.ipynb
+
+.. _Gradient hyperopt:
+    ./hyperopt.ipynb
+
+.. _MNIST example:
+    ./falkon_mnist.ipynb
+
+
+Starting with simple kernel ridge regression, via classification, hyperparameter tuning, to large-scale GPU experiments,
+these notebooks cover all there is to know about Falkon.
+
+ - `Kernel ridge regression`_ goes through the basic notions of the library with a simple example;
+ - `Logistic Falkon tutorial`_ shows how to use the Logistic Falkon estimator, comparing the results with normal Falkon;
+ - `Hyperparameter tuning`_ is a fully worked out example of optimizing hyperparameters with cross-validation for a real-world multi-class problem;
+ - `custom kernels`_ will walk you through the implementation of a custom kernel.
+ - `Gradient hyperopt`_: a tutorial on using the :mod:`~falkon.hopt` module for gradient-based hyperparameter optimization in Falkon.
+ - `MNIST example`_: A simple tutorial on using Falkon for MNIST digit classification.
 
-.. _GPU training:
-    ./performance_tuning.ipynb
diff --git a/_sources/examples/falkon_cv.ipynb.txt b/_sources/examples/falkon_cv.ipynb.txt
new file mode 100644
index 00000000..b07609b2
--- /dev/null
+++ b/_sources/examples/falkon_cv.ipynb.txt
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Hyperparameter Tuning with Falkon\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "We use Falkon for a multi-class problem (on the [digits](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits) dataset), showing how to integrate it into scikit-learn\n",
+    "for hyperparameter optimization.\n",
+    "\n",
+    "Since both `Falkon` and `LogisticFalkon` are estimators, and follow scikit-learn's API, integration is seamless"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection, metrics\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use the **digits** dataset, which is distributed alongside scikit-learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_digits(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "_ = ax.matshow(X[1].reshape((8, 8)), cmap='gray')\n",
+    "print(\"Label: %d\" % Y[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "As always with Falkon we must:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train)\n",
+    "Y_test = torch.from_numpy(Y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalizing the data is always a good idea. Here we use the global mean and standard deviation of the training set for z-score normalization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = X_train.mean()\n",
+    "train_std = X_train.std()\n",
+    "X_train -= train_mean\n",
+    "X_train /= train_std\n",
+    "X_test -= train_mean\n",
+    "X_test /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert labels to 1-hot\n",
+    "eye = torch.eye(10, dtype=torch.float32)\n",
+    "Y_train = eye[Y_train]\n",
+    "Y_test = eye[Y_test]\n",
+    "print(\"First label vector: \", Y_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search for the optimal parameters\n",
+    "\n",
+    "Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.\n",
+    "\n",
+    "Scikit-learn has great support for this, with the `GridSearchCV` class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.\n",
+    "\n",
+    "Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mclass_loss(true, pred):\n",
+    "    true = torch.argmax(true, dim=1)\n",
+    "    pred = torch.argmax(pred, dim=1)\n",
+    "    return torch.mean((true != pred).to(torch.float32))\n",
+    "mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter settings which will be explored by the grid-search are:\n",
+    " - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data)\n",
+    " - four different regularization values\n",
+    " - two different values for M: the number of inducing points. As we will see, a larger `M` is almost always better than a smaller one (but it leads to longer training times). \n",
+    "   Of course this is not the case if the dataset is easy to overfit, since reducing `M` may also provide additional regularization.\n",
+    "\n",
+    "When we create the estimator we pass it additional parameters via the `FalkonOptions` class.\n",
+    "In our case we want to ensure that the model runs on the CPU by setting `use_cpu=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_grid = {\n",
+    "    'kernel': [falkon.kernels.GaussianKernel(sigma=1), \n",
+    "               falkon.kernels.GaussianKernel(sigma=5), \n",
+    "               falkon.kernels.GaussianKernel(sigma=10),\n",
+    "               falkon.kernels.GaussianKernel(sigma=15),],\n",
+    "    'penalty': [1e-3, 1e-5, 1e-7, 1e-9],\n",
+    "    'M': [500, 1000],\n",
+    "}\n",
+    "estimator = falkon.Falkon(\n",
+    "    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000,  # Mandatory parameters, will be overridden\n",
+    "    maxiter=10, options=falkon.FalkonOptions(use_cpu=True))\n",
+    "\n",
+    "grid_search = model_selection.GridSearchCV(estimator, parameter_grid, scoring=mclass_scorer, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The best parameters are:  {'M': 500, 'kernel': GaussianKernel(sigma=Parameter containing:\n",
+      "tensor([10.], dtype=torch.float64)), 'penalty': 1e-07}\n",
+      "CPU times: user 52.3 s, sys: 1.78 s, total: 54.1 s\n",
+      "Wall time: 13.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "grid_search.fit(X_train, Y_train)\n",
+    "print(\"The best parameters are: \", grid_search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluating the model\n",
+    "We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 534 ms, sys: 23.8 ms, total: 558 ms\n",
+      "Wall time: 139 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "flk = grid_search.best_estimator_\n",
+    "flk.fit(X_train, Y_train)\n",
+    "test_pred = flk.predict(X_test)\n",
+    "train_pred = flk.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training error: 0.00%\n",
+      "Test error: 1.11%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training error: %.2f%%\" % (mclass_loss(Y_train, train_pred) * 100))\n",
+    "print(\"Test error: %.2f%%\" % (mclass_loss(Y_test, test_pred) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot grid-search results\n",
+    "\n",
+    "Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful.\n",
+    "If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.\n",
+    "\n",
+    "In the plot red indicates a high error, while darker blue indicates a low error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_df = pd.DataFrame.from_dict(grid_search.cv_results_)\n",
+    "res_df[\"M\"] = res_df.params.apply(lambda x: x.get(\"M\"))\n",
+    "res_df[\"penalty\"] = res_df.params.apply(lambda x: x.get(\"penalty\"))\n",
+    "res_df[\"sigma\"] = res_df.params.apply(lambda x: x.get(\"kernel\").sigma.item())\n",
+    "res_df = res_df[[\"mean_test_score\", \"M\", \"penalty\", \"sigma\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_heatmap(ax, df, xlabel, ylabel, value, scale):\n",
+    "    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)\n",
+    "    cmap = plt.cm.get_cmap('coolwarm_r', 20)\n",
+    "    ax.grid(False)\n",
+    "    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])\n",
+    "    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)\n",
+    "    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)\n",
+    "    ax.set_xticklabels(piv.columns, minor=False)\n",
+    "    ax.set_yticklabels(piv.index, minor=False)\n",
+    "    ax.set_xlabel(xlabel)\n",
+    "    ax.set_ylabel(ylabel)\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
+    "scale = (res_df[\"mean_test_score\"].min(), res_df[\"mean_test_score\"].max())\n",
+    "\n",
+    "c = plot_heatmap(axs[0], res_df[res_df.M == 500], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "axs[0].set_title(\"M = 500\")\n",
+    "c = plot_heatmap(axs[1], res_df[res_df.M == 1000], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "_ = axs[1].set_title(\"M = 1000\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/_sources/examples/falkon_mnist.ipynb.txt b/_sources/examples/falkon_mnist.ipynb.txt
new file mode 100644
index 00000000..8b57105f
--- /dev/null
+++ b/_sources/examples/falkon_mnist.ipynb.txt
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# MNIST Classification with Falkon"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "import falkon"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the MNIST dataset & load it in memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnist_train_dataset = torchvision.datasets.MNIST(\n",
+    "    root=\".\", train=True, download=True,\n",
+    "    transform=torchvision.transforms.ToTensor())\n",
+    "mnist_test_dataset = torchvision.datasets.MNIST(\n",
+    "    root=\".\", train=False, download=True,\n",
+    "    transform=torchvision.transforms.ToTensor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the whole dataset in memory\n",
+    "mnist_tr_img_list, mnist_tr_label_list = [], []\n",
+    "for i in range(len(mnist_train_dataset)):\n",
+    "    data_point = mnist_train_dataset[i]\n",
+    "    mnist_tr_img_list.append(data_point[0])\n",
+    "    mnist_tr_label_list.append(data_point[1])\n",
+    "mnist_ts_img_list, mnist_ts_label_list = [], []\n",
+    "for i in range(len(mnist_test_dataset)):\n",
+    "    data_point = mnist_test_dataset[i]\n",
+    "    mnist_ts_img_list.append(data_point[0])\n",
+    "    mnist_ts_label_list.append(data_point[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnist_tr_x = torch.vstack(mnist_tr_img_list)\n",
+    "mnist_tr_x = mnist_tr_x.reshape(mnist_tr_x.shape[0], -1)\n",
+    "mnist_ts_x = torch.vstack(mnist_ts_img_list)\n",
+    "mnist_ts_x = mnist_ts_x.reshape(mnist_ts_x.shape[0], -1)\n",
+    "mnist_tr_y = torch.tensor(mnist_tr_label_list)\n",
+    "mnist_ts_y = torch.tensor(mnist_ts_label_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4ee3780130>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(mnist_tr_x[0].reshape(28,28))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "We convert the labels to their one-hot representation. \n",
+    "This is the best way to run multi-class classification with Falkon which minimizes the squared error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one-hot labels\n",
+    "A = torch.eye(10, dtype=torch.float32)\n",
+    "mnist_tr_y = A[mnist_tr_y.to(torch.long), :]\n",
+    "mnist_ts_y = A[mnist_ts_y.to(torch.long), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classif_error(y_true, y_pred):\n",
+    "    y_true = torch.argmax(y_true, dim=1)\n",
+    "    y_pred = torch.argmax(y_pred, dim=1)\n",
+    "    err = y_true.flatten() != y_pred.flatten()\n",
+    "    return torch.mean(err.to(torch.float32))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run Falkon\n",
+    "\n",
+    "There are a few parameters which need to be provided to the algorithm\n",
+    "\n",
+    " - The `FalkonOptions` class is used to provide non-standard tuning knobs. It allows to, for example, tune the amount of GPU memory the algorithm can use, adjust the convergence tolerance, and decide whether certain parts of the algorithm are computed on CPU or GPU. \n",
+    "     \n",
+    "     It can be used with default parameters for most purposes!\n",
+    "     \n",
+    " - The **kernel** is the most important choice which depends on the data at hand. We use the `GaussianKernel` which is the most common option and initialize it with a length-scale of 15.\n",
+    " \n",
+    " - The **penalty** determines the amount of regularization. A higher value corresponds to more regularization.\n",
+    " \n",
+    " - The **number of centers** `M` strongly influences the time needed for fitting. By default the centers\n",
+    "     are chosen uniformly at random."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = falkon.FalkonOptions(use_cpu=True)\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=15)\n",
+    "flk = falkon.Falkon(kernel=kernel, \n",
+    "                    penalty=1e-8,\n",
+    "                    M=1000, \n",
+    "                    maxiter=10,\n",
+    "                    options=options,\n",
+    "                    error_every=1,\n",
+    "                    error_fn=classif_error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.55s - training error: 0.11998333\n",
+      "Iteration   2 - Elapsed 1.02s - training error: 0.07140000\n",
+      "Iteration   3 - Elapsed 1.50s - training error: 0.05766667\n",
+      "Iteration   4 - Elapsed 1.98s - training error: 0.05121667\n",
+      "Iteration   5 - Elapsed 2.46s - training error: 0.04776667\n",
+      "Iteration   6 - Elapsed 2.95s - training error: 0.04556667\n",
+      "Iteration   7 - Elapsed 3.45s - training error: 0.04376667\n",
+      "Iteration   8 - Elapsed 3.93s - training error: 0.04340000\n",
+      "Iteration   9 - Elapsed 4.42s - training error: 0.04286667\n",
+      "Iteration  10 - Elapsed 5.39s - training error: 0.04223333\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = flk.fit(mnist_tr_x, mnist_tr_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training error: 4.22%\n",
+      "Test error: 4.13%\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_pred = flk.predict(mnist_tr_x)\n",
+    "test_pred = flk.predict(mnist_ts_x)\n",
+    "\n",
+    "print(\"Training error: %.2f%%\" % (classif_error(train_pred, mnist_tr_y) * 100))\n",
+    "print(\"Test error: %.2f%%\" % (classif_error(test_pred, mnist_ts_y) * 100))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/_sources/examples/falkon_regression_tutorial.ipynb.txt b/_sources/examples/falkon_regression_tutorial.ipynb.txt
new file mode 100644
index 00000000..b0921424
--- /dev/null
+++ b/_sources/examples/falkon_regression_tutorial.ipynb.txt
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Falkon Regression Tutorial\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook introduces the main interface of the Falkon library, \n",
+    "using a toy regression problem.\n",
+    "\n",
+    "We will be using the Boston housing dataset which is included in `scikit-learn` to train a Falkon model.\n",
+    "Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions.\n",
+    "The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.\n",
+    "\n",
+    "After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test \n",
+    "set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the [model_selection module](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.fetch_california_housing(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "num_train = int(X.shape[0] * 0.8)\n",
+    "num_test = X.shape[0] - num_train\n",
+    "shuffle_idx = np.arange(X.shape[0])\n",
+    "np.random.shuffle(shuffle_idx)\n",
+    "train_idx = shuffle_idx[:num_train]\n",
+    "test_idx = shuffle_idx[num_train:]\n",
+    "\n",
+    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
+    "Xtest, Ytest = X[test_idx], Y[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Pre-process the data\n",
+    "\n",
+    "We must convert the numpy arrays to PyTorch tensors before using them in Falkon.\n",
+    "This is very easy and fast with the `torch.from_numpy` function.\n",
+    "\n",
+    "Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data:\n",
+    "convert it to have zero-mean and unit standard deviation.\n",
+    "We use the statistics of the training data to avoid leakage between the two sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# convert numpy -> pytorch\n",
+    "Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)\n",
+    "Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)\n",
+    "Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)\n",
+    "Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = Xtrain.mean(0, keepdim=True)\n",
+    "train_std = Xtrain.std(0, keepdim=True)\n",
+    "Xtrain -= train_mean\n",
+    "Xtrain /= train_std\n",
+    "Xtest -= train_mean\n",
+    "Xtest /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Create the Falkon model\n",
+    "\n",
+    "The Falkon object is the main API of this library. \n",
+    "It is similar in spirit to the fit-transform API of scikit-learn, while supporting some\n",
+    "additional features such as monitoring of validation error.\n",
+    "\n",
+    "While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much \n",
+    "larger datasets.\n",
+    "Here we only showcase some of the more basic options.\n",
+    "\n",
+    "Mandatory parameters are:\n",
+    " - the kernel function (here we use a linear kernel)\n",
+    " - the amount of regularization, which we set to some small positive value\n",
+    " - the number of inducing points M. We set M to 5000, which is a sizable portion of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
+     ]
+    }
+   ],
+   "source": [
+    "options = falkon.FalkonOptions(keops_active=\"no\")\n",
+    "\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=1, opt=options)\n",
+    "flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=5000, options=options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Training the model\n",
+    "\n",
+    "The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are\n",
+    "two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed.\n",
+    "To gain more insight in the various steps of the algorithm you can pass `debug=True` when creating the Falkon object. \n",
+    "\n",
+    "Model training will occur on the GPU, if it is available, and CUDA is properly installed, \n",
+    "or on the CPU as a fallback. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Falkon(M=5000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f65871c45e0>, kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64)), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='no', keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty=1e-05)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flk.fit(Xtrain, Ytrain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Evaluating model performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the `predict` method to obtain predictions on new data.\n",
+    "\n",
+    "Here we print the error on both train and test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training RMSE: 0.527\n",
+      "Test RMSE: 0.578\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_pred = flk.predict(Xtrain).reshape(-1, )\n",
+    "test_pred = flk.predict(Xtest).reshape(-1, )\n",
+    "\n",
+    "def rmse(true, pred):\n",
+    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
+    "\n",
+    "print(\"Training RMSE: %.3f\" % (rmse(train_pred, Ytrain)))\n",
+    "print(\"Test RMSE: %.3f\" % (rmse(test_pred, Ytest)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EoS8iYhBnugsQWapiA2fS0m7EsiBLbowuqaeRvoiIQRT6IiIGUeiLiBhEoS8iYhCFvoiIQRT6IiIGUeiLiBhEoS8iYhCFvoiIQZK+IjcajdLS0oLH46GlpYWLFy/S0dHB8PAwK1asYM+ePeTn5wNw7Ngx/H4/lmXR1NRETU1Nss2LiMgCJD3SP378OGVlZfHH3d3dVFdX09nZSXV1Nd3d3QCcPXuWQCBAe3s7TzzxBEeOHCEajSbbvIiILEBSoT86OsrJkyfZuHFjfFswGKShoQGAhoYGgsFgfHt9fT25ubkUFxdTUlJCf39/Ms2LiMgCJTW988ILL7Bjxw7Gx8fj28bGxnC73QC43W4uXLgAQCgUoqqqKv46j8dDKBSadb89PT309PQA0NraitfrTabMlHA6nRlRp8NK/s80DgAb9pMpsrE/mfCzZodM+d7YZSn0J+HQf/PNNyksLKSiooJ33nlnztfHYrF579vn8+Hz+eKPR0ZGEqoxlbxeb0bUGbNjysyy7NlPpsjC/mTCz5odMuV7Y5dM6k9paems2xMO/dOnT/PGG2/w1ltvEYlEGB8fp7Ozk8LCQsLhMG63m3A4TEFBAQBFRUWMjo7G3x8KhfB4PIk2LyIiCUj4mHf79u0cPnyYrq4udu/ezc0338zDDz9MbW0tvb29APT29rJmzRoAamtrCQQCTE1NMTQ0xODgIJWVlfb0QkRE5sX2m6g0NjbS0dGB3+/H6/XS3NwMQHl5OXV1dTQ3N2NZFrt27cLKonlWEZGlwBFbyGR7mpw7dy7dJcwpU+byDhz1J70PR5bNgWdjf/Z+f326y7BFpnxv7JJJ/bF9Tl9E0ufgax+lpd2968vT0q7YR/MrIiIGUeiLiBhEoS8iYhCFvoiIQRT6IiIGUeiLiBhEoS8iYhCFvoiIQXRxlsgSFBs4k56GdXHWkqeRvoiIQRT6IiIGUeiLiBhEoS8iYhCFvoiIQRT6IiIGUeiLiBhEoS8iYhCFvoiIQRT6IiIGUeiLiBhEoS8iYhCFvoiIQRT6IiIGUeiLiBhEoS8iYhCFvoiIQRT6IiIGUeiLiBhEoS8iYpCEb4w+MjJCV1cXn3zyCQ6HA5/Px+bNm7l48SIdHR0MDw+zYsUK9uzZQ35+PgDHjh3D7/djWRZNTU3U1NTY1Q8RkUUx84sD835t2OViJhKxpd2c//2pLfv5bwmHfk5ODvfddx8VFRWMj4/T0tLCLbfcwmuvvUZ1dTWNjY10d3fT3d3Njh07OHv2LIFAgPb2dsLhMAcOHOC5557DsnSwISKSKgmHvtvtxu12A7Bs2TLKysoIhUIEg0GefPJJABoaGnjyySfZsWMHwWCQ+vp6cnNzKS4upqSkhP7+flatWmVLRzLJQkYGtsu/PX1ti0jGSzj0/9PQ0BADAwNUVlYyNjYW/2Xgdru5cOECAKFQiKqqqvh7PB4PoVBo1v319PTQ09MDQGtrK16v144yF5XT6YzXGXa50laHw4YjJwdAFh2BqT/2sfu7+J/fm0y1kO+z5XDgsun7716k/5ekQ39iYoK2tjbuv/9+li9f/qWvi8Vi896nz+fD5/PFH4+MjCRVYyp4vd54nXbN6SUi5oomvxPLIha1YT+ZQv2xjd3fxf/83mSqhXyfXS4XEZu+/8n+v5SWls66PanhwvT0NG1tbdx5552sXbsWgMLCQsLhMADhcJiCggIAioqKGB0djb83FArh8XiSaV5ERBYo4dCPxWIcPnyYsrIytmzZEt9eW1tLb28vAL29vaxZsya+PRAIMDU1xdDQEIODg1RWViZZvoiILETC0zunT5/mxIkTrFy5kkcffRSAbdu20djYSEdHB36/H6/XS3NzMwDl5eXU1dXR3NyMZVns2rVLZ+6IiKRYwqF/00038bvf/W7W5/bt2zfr9q1bt7J169ZEmxQRkSRpqC0iYhBbTtmUL3pa58qLSIbSSF9ExCAa6YvIvB187SNb9+dynZ/Xee1715fb2q7JFPoiMm+xgTO27i8y3wvNFPq20fSOiIhBFPoiIgZR6IuIGEShLyJiEIW+iIhBFPoiIgZR6IuIGEShLyJiEIW+iIhBFPoiIgbRMgwikvHsXvNnIR5PW8uLQyN9ERGDKPRFRAyi6R0RyXh2r+5pMo30RUQMotAXETGIpndERK5iIfe8dlgWMdc8bgozDz+1ZS9X0khfRMQgCn0REYMo9EVEDKLQFxExiEJfRMQgCn0REYMo9EVEDKLQFxExSMovzjp16hRHjx4lGo2yceNGGhsbU12CiIixUhr60WiUI0eOsHfvXoqKinj88cepra3lhhtuWJT2UrkGt8t1nkgkkrL2REQSkdLpnf7+fkpKSrj++utxOp3U19cTDAZTWYKIiNFSOtIPhUIUFRXFHxcVFXHmzJVLpvb09NDT0wNAa2srpaWlCbV3aHti70ve2jS1KyJydSkd6cdisSu2ORyOK7b5fD5aW1tpbW1NRVm2aGlpSXcJtsmmvoD6k8myqS+wNPqT0tAvKipidHQ0/nh0dBS3253KEkREjJbS0L/xxhsZHBxkaGiI6elpAoEAtbW1qSxBRMRoKZ3Tz8nJYefOnTz11FNEo1HuuusuysvLU1nCovH5fOkuwTbZ1BdQfzJZNvUFlkZ/HLHZJtpFRCQr6YpcERGDKPRFRAyie+QmKZuWlTh06BAnT56ksLCQtra2dJeTtJGREbq6uvjkk09wOBz4fD42b96c7rISEolE2L9/P9PT08zMzLBu3TruvffedJeVtGg0SktLCx6PZ0mc7ng1P/rRj8jLy8OyLHJycjL2lHOFfhJSvazEYlu/fj133303XV1d6S7FFjk5Odx3331UVFQwPj5OS0sLt9xyy5L8fHJzc9m/fz95eXlMT0+zb98+ampqWLVqVbpLS8rx48cpKytjfHw83aXYYv/+/RQUFKS7jKvS9E4Ssm1ZidWrV5Ofn5/uMmzjdrupqKgAYNmyZZSVlREKhdJcVWIcDgd5eXkAzMzMMDMzM+uFjUvJ6OgoJ0+eZOPGjekuxSga6SdhvstKSPoNDQ0xMDBAZWVluktJWDQa5bHHHuPjjz/mW9/6FlVVVekuKSkvvPACO3bsyJpRPsBTTz0FwKZNmzL29E2FfhLmu6yEpNfExARtbW3cf//9LF++PN3lJMyyLH7+859z6dIlnnnmGT788ENWrlyZ7rIS8uabb1JYWEhFRQXvvPNOusuxxYEDB/B4PIyNjXHw4EFKS0tZvXp1usu6gkI/CVpWIvNNT0/T1tbGnXfeydq12bEQ3rXXXsvq1as5derUkg3906dP88Ybb/DWW28RiUQYHx+ns7OThx9+ON2lJczj8QBQWFjImjVr6O/vz8jQ15x+ErSsRGaLxWIcPnyYsrIytmzZku5yknLhwgUuXboEXD6Tp6+vj7KysjRXlbjt27dz+PBhurq62L17NzfffPOSDvyJiYn4NNXExAT//Oc/M/YXskb6Sci2ZSWeffZZ3n33XT799FMeeOAB7r33XjZs2JDushJ2+vRpTpw4wcqVK3n00UcB2LZtG1/72tfSXNnChcNhurq6iEajxGIx6urq+PrXv57usuT/jY2N8cwzzwCX/9B+xx13UFNTk96ivoSWYRARMYimd0REDKLQFxExiEJfRMQgCn0REYMo9EVEDKLQFxExiEJfRMQg/wcfh6NZb+HHugAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "hist_range = (min(Ytest.min(), test_pred.min()).item(), max(Ytest.max(), test_pred.max()).item())\n",
+    "ax.hist(Ytest.numpy(), bins=10, range=hist_range, alpha=0.7, label=\"True\")\n",
+    "ax.hist(test_pred.numpy(), bins=10, range=hist_range, alpha=0.7, label=\"Pred\")\n",
+    "ax.legend(loc=\"best\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/_sources/examples/hyperopt.ipynb.txt b/_sources/examples/hyperopt.ipynb.txt
new file mode 100644
index 00000000..f79b8a27
--- /dev/null
+++ b/_sources/examples/hyperopt.ipynb.txt
@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Automatic Hyperparameter Optimization"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection\n",
+    "import numpy as np\n",
+    "np.random.seed(30)\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee63dc4",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use the **digits** dataset, which is distributed alongside scikit-learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3da6ecb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_digits(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6b948acf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 648x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_digit(ax, x, y):\n",
+    "    ax.imshow(x.reshape((8, 8)), cmap='gray')\n",
+    "    ax.set_title(\"Label: %d\" % y)\n",
+    "    ax.set_axis_off()\n",
+    "\n",
+    "# Plot three sample images from the dataset\n",
+    "fig, ax = plt.subplots(ncols=3, figsize=(9, 6))\n",
+    "plot_digit(ax[0], X[3], Y[3])\n",
+    "plot_digit(ax[1], X[10], Y[10])\n",
+    "plot_digit(ax[2], X[42], Y[42])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aa131a8",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6b5535cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eef7ac94",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "As always with Falkon we must:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a3a1469f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train)\n",
+    "Y_test = torch.from_numpy(Y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12fcc9d3",
+   "metadata": {},
+   "source": [
+    "Normalizing the data is always a good idea, and it becomes even more important with automatic hyperparameter optimization. \n",
+    "\n",
+    "Here we use the global mean and standard deviation of the training set for **z-score normalization**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1450e0f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = X_train.mean()\n",
+    "train_std = X_train.std()\n",
+    "X_train -= train_mean\n",
+    "X_train /= train_std\n",
+    "X_test -= train_mean\n",
+    "X_test /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "938d4389",
+   "metadata": {},
+   "source": [
+    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification.\n",
+    "\n",
+    "We therefore convert the labels to a **1-hot representation**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87729460",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert labels to 1-hot\n",
+    "eye = torch.eye(10, dtype=torch.float32)\n",
+    "Y_train = eye[Y_train]\n",
+    "Y_test = eye[Y_test]\n",
+    "print(\"First label vector: \", Y_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66bf3881",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter Optimization\n",
+    "\n",
+    "The Falkon algorithm has three main kinds of hyper-parameters:\n",
+    " 1. The kernel parameters. Most commonly when using the Gaussian kernel these are the length-scales for each dimension in the data\n",
+    " 2. The amount of regularization $\\lambda$ (the penalty term, which helps prevent overfitting).\n",
+    " 3. The Nystrom centers. These are sometimes not considered hyper parameters, and they are chosen uniformly at random from the training set. However, it is possible to find better centers!\n",
+    " \n",
+    "In the [grid-search](falkon_cv.ipynb) notebook all three types of hyperparameters are considered, but only with a small number of options in each category.\n",
+    "\n",
+    "With automatic hyperparameter optimization, which is based on a gradient descent-type procedure, we can instead define a much larger search space for the hyperparameters.\n",
+    "\n",
+    "In particular, we will optimize the kernel length-scale (one for each dimension in the data), the regularization and the Nystrom centers. Optimizing the Nystrom centers is especially useful since it allows to reduce their number, thus speeding up the whole training and inference process!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1ad2b54d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mclass_loss(true, pred):\n",
+    "    true = torch.argmax(true, dim=1)\n",
+    "    pred = torch.argmax(pred, dim=1)\n",
+    "    return torch.mean((true != pred).to(torch.float32))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "475c9d14",
+   "metadata": {},
+   "source": [
+    "Several functions and classes used for hyperparameter optimization reside in the `falkon.hopt` module.\n",
+    "\n",
+    "Here we import the `NystromCompReg` class which defines the optimization objective."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "86095c91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import falkon.hopt\n",
+    "from falkon import FalkonOptions\n",
+    "from falkon.hopt.objectives import NystromCompReg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4e2132fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flk_opt = FalkonOptions(use_cpu=not torch.cuda.is_available())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b412208",
+   "metadata": {},
+   "source": [
+    "We have to initialize the hyperparameters to some default values. In particular we choose some random initial points from the dataset as the initial Nystrom centers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "414616aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma_init = torch.tensor([1.0] * X_train.shape[1], dtype=torch.float32).requires_grad_()\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=sigma_init, opt=flk_opt)\n",
+    "\n",
+    "penalty_init = torch.tensor(1e-5, dtype=torch.float32)\n",
+    "centers_init = X_train[np.random.choice(X_train.shape[0], size=(500, ), replace=False)].clone()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a311c944",
+   "metadata": {},
+   "source": [
+    "Now we initialize the loss function (`NystromCompReg`) and the optimization algorithm (Adam)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "38de0fde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = NystromCompReg(\n",
+    "    kernel=kernel, penalty_init=penalty_init, centers_init=centers_init,  # The initial hp values\n",
+    "    opt_penalty=True, opt_centers=True,  # Whether the various hps are to be optimized\n",
+    "    )\n",
+    "opt_hp = torch.optim.Adam(model.parameters(), lr=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e08fae3d",
+   "metadata": {},
+   "source": [
+    "And start training. Each iteration corresponds to a single gradient step over the whole dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "79a9552a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 Loss 40310.387 Error 1.67%\n",
+      "Epoch 1 Loss 35137.203 Error 1.67%\n",
+      "Epoch 2 Loss 24207.420 Error 1.74%\n",
+      "Epoch 3 Loss 17153.834 Error 2.02%\n",
+      "Epoch 4 Loss 12490.971 Error 1.95%\n",
+      "Epoch 5 Loss 9343.500 Error 1.67%\n",
+      "Epoch 6 Loss 7062.557 Error 1.32%\n",
+      "Epoch 7 Loss 5359.402 Error 1.18%\n",
+      "Epoch 8 Loss 4108.987 Error 1.04%\n",
+      "Epoch 9 Loss 3210.683 Error 0.90%\n",
+      "Epoch 10 Loss 2569.709 Error 0.63%\n",
+      "Epoch 11 Loss 2121.547 Error 0.49%\n",
+      "Epoch 12 Loss 1814.989 Error 0.49%\n",
+      "Epoch 13 Loss 1606.087 Error 0.42%\n",
+      "Epoch 14 Loss 1460.298 Error 0.42%\n",
+      "Epoch 15 Loss 1353.231 Error 0.21%\n",
+      "Epoch 16 Loss 1269.496 Error 0.21%\n",
+      "Epoch 17 Loss 1199.922 Error 0.14%\n",
+      "Epoch 18 Loss 1139.733 Error 0.14%\n",
+      "Epoch 19 Loss 1088.957 Error 0.07%\n",
+      "Epoch 20 Loss 1043.391 Error 0.07%\n",
+      "Epoch 21 Loss 1002.747 Error 0.07%\n",
+      "Epoch 22 Loss 966.971 Error 0.07%\n",
+      "Epoch 23 Loss 935.513 Error 0.07%\n",
+      "Epoch 24 Loss 907.745 Error 0.07%\n",
+      "Epoch 25 Loss 883.216 Error 0.07%\n",
+      "Epoch 26 Loss 861.607 Error 0.07%\n",
+      "Epoch 27 Loss 842.661 Error 0.07%\n",
+      "Epoch 28 Loss 826.113 Error 0.07%\n",
+      "Epoch 29 Loss 811.653 Error 0.07%\n",
+      "Epoch 30 Loss 798.939 Error 0.07%\n",
+      "Epoch 31 Loss 787.643 Error 0.07%\n",
+      "Epoch 32 Loss 777.481 Error 0.07%\n",
+      "Epoch 33 Loss 768.217 Error 0.07%\n",
+      "Epoch 34 Loss 759.674 Error 0.07%\n",
+      "Epoch 35 Loss 751.720 Error 0.07%\n",
+      "Epoch 36 Loss 744.265 Error 0.07%\n",
+      "Epoch 37 Loss 737.261 Error 0.07%\n",
+      "Epoch 38 Loss 730.671 Error 0.07%\n",
+      "Epoch 39 Loss 724.491 Error 0.07%\n",
+      "Epoch 40 Loss 718.708 Error 0.07%\n",
+      "Epoch 41 Loss 713.325 Error 0.07%\n",
+      "Epoch 42 Loss 708.335 Error 0.07%\n",
+      "Epoch 43 Loss 703.722 Error 0.07%\n",
+      "Epoch 44 Loss 699.472 Error 0.07%\n",
+      "Epoch 45 Loss 695.557 Error 0.07%\n",
+      "Epoch 46 Loss 691.935 Error 0.00%\n",
+      "Epoch 47 Loss 688.580 Error 0.00%\n",
+      "Epoch 48 Loss 685.445 Error 0.00%\n",
+      "Epoch 49 Loss 682.500 Error 0.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "tr_loss, tr_err = [], []\n",
+    "\n",
+    "for epoch in range(50):\n",
+    "    opt_hp.zero_grad()\n",
+    "    loss = model(X_train, Y_train)\n",
+    "    loss.backward()\n",
+    "    opt_hp.step()\n",
+    "\n",
+    "    tr_loss.append(loss.item())\n",
+    "    tr_err.append(mclass_loss(Y_train, model.predict(X_train)))\n",
+    "    print(f\"Epoch {epoch} Loss {tr_loss[-1]:.3f} Error {tr_err[-1] * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "86d2e2ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test error: 0.83%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the test error:\n",
+    "ts_preds = model.predict(X_test)\n",
+    "print(f\"Test error: {mclass_loss(Y_test, ts_preds) * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89df66d9",
+   "metadata": {},
+   "source": [
+    "The optimized parameters are available as attributes of the `model` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3fe66880",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final value of lambda: 4.428e-05\n",
+      "Final value of sigma: tensor([2.3884, 2.6252, 2.7859, 2.7036, 2.6799, 2.7698, 2.7344, 3.0071, 2.5923,\n",
+      "        2.7421, 2.7629, 2.7525, 2.6989, 2.7232, 2.8216, 2.8830, 2.6465, 2.7994,\n",
+      "        2.7406, 2.7082, 2.8053, 2.6552, 2.7757, 2.6979, 2.3884, 2.7573, 2.7242,\n",
+      "        2.6681, 2.7780, 2.7649, 2.7010, 2.7274, 2.3884, 2.6608, 2.7477, 2.7604,\n",
+      "        2.8015, 2.7581, 2.6359, 2.3884, 2.8348, 2.7656, 2.6553, 2.6672, 2.7252,\n",
+      "        2.7499, 2.6721, 2.6589, 2.9603, 2.7725, 2.7143, 2.7481, 2.7307, 2.7093,\n",
+      "        2.7251, 2.7213, 2.6326, 2.6092, 2.7600, 2.7744, 2.7885, 2.6976, 2.6838,\n",
+      "        2.7062])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Final value of lambda: %.3e\" % (model.penalty))\n",
+    "print(\"Final value of sigma: %s\" % (model.kernel.sigma.detach()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db747ea",
+   "metadata": {},
+   "source": [
+    "We can compare the obtained results with the grid-search notebook.\n",
+    "\n",
+    "A grid-search with 1000 centers and 32 grid-points resulted in choosing a model with sigma=5, and lambda=1e-7.\n",
+    "\n",
+    "The `NystromCompReg` objective with half the centers led to obtain a lower test error (0.83% vs. 1.11%) after 50 training epochs. However the obtained hyperparameters are quite different: lambda in particular is much higher at 1.1e-4.\n",
+    "\n",
+    "This objective in particular has quite a high bias and tends to choose simple models instead of more complex ones (remember that since lambda is a regularizer, it can be seen as one possible measure of model complexity). In practice this is often not a problem, as we observed in this case.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/_sources/examples/hyperparameter_tuning.ipynb.txt b/_sources/examples/hyperparameter_tuning.ipynb.txt
deleted file mode 100644
index aef681c6..00000000
--- a/_sources/examples/hyperparameter_tuning.ipynb.txt
+++ /dev/null
@@ -1,430 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Hyperparameter Tuning with Falkon\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "We use Falkon for a multi-class problem (on the [digits](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits) dataset), showing how to integrate it into scikit-learn\n",
-    "for hyperparameter optimization.\n",
-    "\n",
-    "Since both `Falkon` and `LogisticFalkon` are estimators, and follow scikit-learn's API, integration is seamless."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn import datasets, model_selection, metrics\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "import falkon"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the data\n",
-    "\n",
-    "We use the **digits** dataset, which is distributed alongside scikit-learn."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X, Y = datasets.load_digits(return_X_y=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Label: 1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "_ = ax.matshow(X[1].reshape((8, 8)), cmap='gray')\n",
-    "print(\"Label: %d\" % Y[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Split into training and test sets\n",
-    "\n",
-    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
-    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Preprocessing\n",
-    "\n",
-    "As always with Falkon we must:\n",
-    " 1. Convert from numpy arrays to torch tensors\n",
-    " 2. Convert data and labels to the same data-type (in this case float32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
-    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
-    "Y_train = torch.from_numpy(Y_train)\n",
-    "Y_test = torch.from_numpy(Y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Normalizing the data is always a good idea. Here we use the global mean and standard deviation of the training set for z-score normalization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# z-score normalization\n",
-    "train_mean = X_train.mean()\n",
-    "train_std = X_train.std()\n",
-    "X_train -= train_mean\n",
-    "X_train /= train_std\n",
-    "X_test -= train_mean\n",
-    "X_test /= train_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Convert labels to 1-hot\n",
-    "eye = torch.eye(10, dtype=torch.float32)\n",
-    "Y_train = eye[Y_train]\n",
-    "Y_test = eye[Y_test]\n",
-    "print(\"First label vector: \", Y_train[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Search for the optimal parameters\n",
-    "\n",
-    "Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.\n",
-    "\n",
-    "Scikit-learn has great support for this, with the `GridSearchCV` class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.\n",
-    "\n",
-    "Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mclass_loss(true, pred):\n",
-    "    true = torch.argmax(true, dim=1)\n",
-    "    pred = torch.argmax(pred, dim=1)\n",
-    "    return torch.mean((true != pred).to(torch.float32))\n",
-    "mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The parameter settings which will be explored by the grid-search are:\n",
-    " - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data)\n",
-    " - four different regularization values\n",
-    " - two different values for M: the number of inducing points. As we will see, a larger `M` is almost always better than a smaller one (but it leads to longer training times). \n",
-    "   Of course this is not the case if the dataset is easy to overfit, since reducing `M` may also provide additional regularization.\n",
-    "\n",
-    "When we create the estimator we pass it additional parameters via the `FalkonOptions` class.\n",
-    "In our case we want to ensure that the model runs on the CPU by setting `use_cpu=True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/helpers.py:31: UserWarning: Failed to initialize cuBLAS library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
-      "Error encountered was cublas library not found\n",
-      "  warnings.warn(get_error_str(\"cuBLAS\", e))\n"
-     ]
-    }
-   ],
-   "source": [
-    "parameter_grid = {\n",
-    "    'kernel': [falkon.kernels.GaussianKernel(sigma=1), \n",
-    "               falkon.kernels.GaussianKernel(sigma=5), \n",
-    "               falkon.kernels.GaussianKernel(sigma=10),\n",
-    "               falkon.kernels.GaussianKernel(sigma=15),],\n",
-    "    'penalty': [1e-3, 1e-5, 1e-7, 1e-9],\n",
-    "    'M': [500, 1000],\n",
-    "}\n",
-    "estimator = falkon.Falkon(\n",
-    "    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000, maxiter=10,\n",
-    "    options=falkon.FalkonOptions(use_cpu=True))\n",
-    "\n",
-    "grid_search = model_selection.GridSearchCV(estimator, parameter_grid, mclass_scorer, cv=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The best parameters are:  {'M': 1000, 'kernel': GaussianKernel(sigma=5.0), 'penalty': 1e-07}\n",
-      "CPU times: user 2min 5s, sys: 17 s, total: 2min 22s\n",
-      "Wall time: 35.6 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "grid_search.fit(X_train, Y_train)\n",
-    "print(\"The best parameters are: \", grid_search.best_params_)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Evaluating the model\n",
-    "We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.14 s, sys: 143 ms, total: 1.29 s\n",
-      "Wall time: 323 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "flk = grid_search.best_estimator_\n",
-    "flk.fit(X_train, Y_train)\n",
-    "test_pred = flk.predict(X_test)\n",
-    "train_pred = flk.predict(X_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training error: 0.00%\n",
-      "Test error: 1.11%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Training error: %.2f%%\" % (mclass_loss(Y_train, train_pred) * 100))\n",
-    "print(\"Test error: %.2f%%\" % (mclass_loss(Y_test, test_pred) * 100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot grid-search results\n",
-    "\n",
-    "Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful.\n",
-    "If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.\n",
-    "\n",
-    "In the plot red indicates a high error, while darker blue indicates a low error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res_df = pd.DataFrame.from_dict(grid_search.cv_results_)\n",
-    "res_df[\"M\"] = res_df.params.apply(lambda x: x.get(\"M\"))\n",
-    "res_df[\"penalty\"] = res_df.params.apply(lambda x: x.get(\"penalty\"))\n",
-    "res_df[\"sigma\"] = res_df.params.apply(lambda x: x.get(\"kernel\").sigma.item())\n",
-    "res_df = res_df[[\"mean_test_score\", \"M\", \"penalty\", \"sigma\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_heatmap(ax, df, xlabel, ylabel, value, scale):\n",
-    "    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)\n",
-    "    cmap = plt.cm.get_cmap('coolwarm_r', 20)\n",
-    "    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])\n",
-    "    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)\n",
-    "    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)\n",
-    "    ax.set_xticklabels(piv.columns, minor=False)\n",
-    "    ax.set_yticklabels(piv.index, minor=False)\n",
-    "    ax.set_xlabel(xlabel)\n",
-    "    ax.set_ylabel(ylabel)\n",
-    "    return c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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CPULIuVpu85swAQAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQV4X8N/97nd6//339eOPP/pzHgCAj5DbABCcIrxdeP/99+uzzz7T3/72N3Xs2FGpqanq1auXIiMj/TkfAKCWyG0ACE5eF/DevXurd+/eOnfunLZs2aKcnBxlZ2erV69eSk1NVZcuXfw5JwCghshtAAhOXhfwn0VHR2vAgAFq1KiRNmzYoM8//1z79u1TWFiYHnvsMXXt2tUfcwIAaoncBoDg4nUBr6ys1J49e5SXl6edO3eqQ4cOuu+++9xvZ27btk1vvPGGsrKy/DkvAMBL5DYABCevC3hGRoZiYmKUmpqqhx9+WHFxcR77e/furZycHJ8PCACoHXIbAIKT1wX8xRdfVLt27a65ZsaMGTc8EADAN8htAAhOXn8M4e9///tqt48fP95nwwAAfIfcBoDg5HUBv3z5cpVtLpdLlZWVPh0IAOAb5DYABKfrPoIyffp02Ww2VVRUVHmrsri4WB06dPDbcACAmiO3ASC4XbeADx48WJJ06NAhDRo0yL3dZrPJbrfzObIAEGTIbQAIbtct4AMHDpQktW/fXjfffLO/5wEA3CByGwCC2zUL+KZNmzxe79+/v9p1P99tAQAEFrkNAMHvmgX8008/9eqLEOQAEBzIbQAIftcs4Hw+LACEFnIbAIKf17+I579ZliXLstyvw8K8/jRDAEAAkNsAEDy8LuBOp1PLly/Xvn37dP78eY99a9as8flgAIAbQ24DQHDy+hbIsmXLFBERoenTp6tRo0aaO3euUlJS9Pjjj/tzPgBALZHbABCcvC7gBw4c0BNPPKGkpCTZbDYlJSXpiSee0HvvvefP+QAAtURuA0Bw8voRlLCwMIWHh0uSmjRpopKSEjVu3FhOp9NvwwGhxt68WaBHCCm5a7cEeoSQMnP0gBqtJ7eBazt78nSgRwgpBZ8EeoIQNLpztZu9LuC33Xabdu3apV69eqlbt25auHChIiMj1a5dO5/NCADwHXIbAIKT1wX86aefdv8L+jFjxmjDhg0qKyvT8OHD/TYcAKD2yG0ACE5eF/AmTZq4/x4ZGakRI0b4ZSAAgG+Q2wAQnLwu4C6XS5s3b9aRI0dUVlbmse+pp57y+WAAgBtDbgNAcPK6gC9evFhHjx5Vjx49ZLfb/TkTAMAHyG0ACE5eF/Ddu3dr8eLFHm9pAgCCF7kNAMHJ688Bb9asmSoqKvw5CwDAh8htAAhOXt8BT01N1WuvvaZf/epXatq0qce+Ll26+HwwAMCNIbcBIDh5XcA3btwoSfrrX//qsd1ms2nx4sW+nQoAcMPIbQAITl4X8CVLlvhzDgCAj5HbABCcvH4GXLrykVb79u3Tli1Xfn10WVlZlY+2AgAED3IbAIKP13fAv/32W82dO1cNGjRQcXGx+vbtq7179+qTTz7R5MmT/TkjAKAWyG0ACE5e3wHPysrSqFGjtGjRIkVEXOntnTp10tdff+234QAAtUduA0Bw8rqAHzt2THfeeafHtkaNGqm8vNznQwEAbhy5DQDByesCHh8fr8OHD3tsO3TokBITE30+FADgxpHbABCcvH4GfNSoUZozZ46GDh0ql8ul9evX64MPPlBmZqY/5wMA1BK5DQDByes74D169NDUqVNVUlKizp076/Tp03rhhRfUrVs3f84HAKglchsAgpPXd8BdLpe2b9+u3bt368yZM4qLi9NNN92kW265RZGRkf6cEQBQC+Q2AAQnrwt4VlaWioqKNG7cOMXHx+v06dNav369srOzNXHiRH/OCACoBXIbAIKT1wW8oKBAb7zxhpo0aSJJuuWWW3Tbbbfp6aef9ttwAIDaI7cBIDh5/Qx406ZNdenSJY9t5eXlio2N9flQAIAbR24DQHDy+g54amqqXnnlFaWnp8vhcKi4uFg5OTlKTU3Vl19+6V7XpUsXvwwKAKgZchsAgpPXBfzDDz+UJK1fv77K9p/32Ww2LV682IfjAQBqi9wGgODkdQFfsmSJP+cAAPgYuQ0AwcnrZ8ABAAAA3DgKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAzy+lNQbsTSpUu1c+dO2e12zZ8/v0bHHj58WEuWLFF5ebmSk5M1duxY2Ww2LVy4UEVFRZKkCxcuKCoqSq+99po/xgeAeofcBgD/MVLABw4cqPT09Fp9JFZWVpYyMjLUvn17vfrqqyosLFRycrImT57sXrNq1SpFRUX5cmQAqNfIbQDwHyMFvFOnTjp16pTHthMnTmj58uUqKSlRw4YNlZGRoZtvvtljzZkzZ3Tx4kV16NBB0pXf6lZQUKDk5GT3GsuytHXrVk2fPt3/JwIA9QS5DQD+Y6SAV2fZsmV6/PHH1aJFCx08eFDZ2dmaMWOGxxqn0ymHw+F+7XA45HQ6Pdbs27dPdrtdLVq0MDI3ANRX5DYA+EZACnhZWZn279+vBQsWuLe5XK4q6yzLuu7Xys/PV79+/a65Jjc3V7m5uZKkOXPm1HBaAIDJ3CazAdR1ASnglZWVatKkSZV/fFNZWakpU6ZIklJSUjRs2DAVFxe79xcXFysuLs79+vLly9q+fft1AzotLU1paWk+PAMAqF9M5jaZDaCuC0gBj4qKUkJCgrZu3ao+ffrIsiwdPXpUSUlJVcK9cePGOnDggNq3b6+8vDylp6e79/3nP/9Ry5YtPd7uBAD4HrkNAL5jpIAvWrRIe/fuVWlpqTIzMzVy5EhNmjRJWVlZWrdunVwul/r166ekpKQqx44fP15Lly5VeXm5unfv7vEPebx5/AQAUHPkNgD4j83y5oG9Omb1p/XulGvtj/PyAj1CSLE3bxboEULK2ZOnAz1CSPnsnwMCPUJAPDytKNAjhIwjew4GegTUYfx/XM29n9252u38JkwAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAGUcABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADIoI9ABAXXL25OlAjwAAAIIcd8ABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAGUcABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMCjCxDdZunSpdu7cKbvdrvnz59fo2MOHD2vJkiUqLy9XcnKyxo4dK5vNpiNHjigrK0tlZWWKj4/XpEmTFBUV5aczAID6hdwGAP8xcgd84MCBmjp1aq2OzcrKUkZGhl5//XWdOHFChYWFkqS33npLv/nNbzR//nz16tVLGzZs8OXIAFCvkdsA4D9GCninTp0UHR3tse3EiROaPXu2pkyZounTp+v777+vctyZM2d08eJFdejQQTabTampqSooKJAkFRUVqWPHjpKkrl276vPPP/f/iQBAPUFuA4D/BOwZ8GXLlmncuHGaO3euRo8erezs7CprnE6nHA6H+7XD4ZDT6ZQktWrVSjt27JAkbdu2TcXFxWYGB4B6itwGAN8w8gz4/yorK9P+/fu1YMEC9zaXy1VlnWVZV/0aTzzxhFasWKF33nlHKSkpioi4+qnk5uYqNzdXkjRnzpwbmBwA6ieTuU1mA6jrAlLAKysr1aRJE7322mtVtk+ZMkWSlJKSomHDhnncISkuLlZcXJwk6eabb9bLL78s6crbmjt37rzq90tLS1NaWpqvTwMA6g2TuU1mA6jrAlLAo6KilJCQoK1bt6pPnz6yLEtHjx5VUlJSlXBv3LixDhw4oPbt2ysvL0/p6emSpLNnz8put6uyslLr1q3T0KFDA3EqAFAvkNsA4DtGCviiRYu0d+9elZaWKjMzUyNHjtSkSZOUlZWldevWyeVyqV+/fkpKSqpy7Pjx47V06VKVl5ere/fuSk5OliTl5+crJydHktSrVy8NGjTIxKkAQL1AbgOA/9isaz2wV0et/rTenXKt/XFeXqBHAPCTz/45INAjBMTD04oCPULIOLLnYKBHQB1mb94s0COEnPezO1e7nd+ECQAAABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAGUcABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAAAIBBNsuyrEAPAQAAANQX3AEPEi+++GKgRwgpXK+a4XrVDNcL18PPSM1wvWqG61VzoXbNKOAAAACAQRRwAAAAwKDwmTNnzgz0ELiibdu2gR4hpHC9aobrVTNcL1wPPyM1w/WqGa5XzYXSNeMfYQIAAAAG8QgKAAAAYFBEoAeoT5YuXaqdO3fKbrdr/vz5VfZblqUVK1Zo165datiwoSZOnBhSb6f4w5NPPqlGjRopLCxM4eHhmjNnjsf++n7NqvuZOnfunBYuXKgffvhB8fHxmjx5sqKjo6scu3nzZq1bt06SdP/992vgwIEmRw+I6q7X2rVr9dFHHykmJkaS9OCDD+qOO+6ocmxhYaFWrFihyspKDRkyRPfdd5/R2WEemV1zZPa1kdk1V2dz24IxX331lfXNN99Yzz33XLX7v/jiC2v27NlWZWWltX//fuull14yPGHwmThxonX27Nmr7q/v16y6n6m3337bWr9+vWVZlrV+/Xrr7bffrnJcaWmp9eSTT1qlpaUef6/rqrtea9assf7xj39c87jLly9bTz31lHXixAmroqLCev75563vvvvO3+MiwMjsmiOzr43Mrrm6mts8gmJQp06dqv2v2p/t2LFDqampstls6tChg86fP68zZ84YnDD01PdrVt3PVEFBgQYMGCBJGjBggAoKCqocV1hYqK5duyo6OlrR0dHq2rWrCgsLjcwcSNf73+DVHDp0SImJiWrevLkiIiLUt2/faq8r6hYy2/fq+zUjs2uuruY2j6AEEafTqWbNmrlfOxwOOZ1OxcbGBnCqwJs9e7YkaejQoUpLS/PYxzWr6uzZs+7zj42NVUlJSZU1TqdTDofD/TouLk5Op9PYjMEmJydHeXl5atu2rR555JEqYf+/18vhcOjgwYOmx0SQIX+qR2bXDJldO6Ge2xTwIGJV84E0NpstAJMEj1mzZikuLk5nz57V73//e7Vs2VKdOnVy7+ea+U59vW7Dhg3TiBEjJElr1qzRqlWrNHHiRI81/JyhOvxcVEVmm1Ofr1tdyG0eQQkiDodDp0+fdr8uLi6u13cFpCv/lS9JdrtdPXv21KFDhzz2c82qstvt7rd0z5w54/5HKv8tLi5OxcXF7tf1+Q5U06ZNFRYWprCwMA0ZMkTffPNNlTUOh8PjevFzBon8qQ6ZXXNkds3VhdymgAeRlJQU5eXlybIsHThwQFFRUUH1w2JaWVmZLl686P77nj171Lp1a481XLOqUlJS9Mknn0iSPvnkE/Xs2bPKmu7du2v37t06d+6czp07p927d6t79+6mRw0K//386fbt29WqVasqa9q1a6fjx4/r1KlTcrlc2rJli1JSUkyOiSBE/ngis2uHzK65upDb/CIegxYtWqS9e/eqtLRUdrtdI0eOlMvlknTl7RTLsrR8+XLt3r1bkZGRmjhxotq1axfgqQPn5MmT+sMf/iBJunz5svTecH0AAARPSURBVPr376/7779fH3zwgSSumVT9z1TPnj21cOFCnT59Ws2aNdNzzz2n6OhoffPNN/rwww+VmZkpSdq0aZPWr18v6cpHWg0aNCiQp2JEddfrq6++0pEjR2Sz2RQfH68JEyYoNjZWTqdTb731ll566SVJ0s6dO7Vy5UpVVlZq0KBBuv/++wN8NvA3MrtmyOzrI7Nrrq7mNgUcAAAAMIhHUAAAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOXMe6dev05ptvBnoMAIAXyGyEAj6GEAAAADCIO+AAAACAQRGBHgAIJu+++67+/e9/6+LFi4qNjdX48eO1b98+nThxQpMmTZJ05VcFr1mzRmVlZbrrrrv08ccfKyMjQ127dtXatWt17NgxRUREaMeOHYqPj9dvf/tbff7553r//ffVoEEDZWZmqlu3bpKkjz/+WBs2bFBxcbFiYmJ07733aujQoYG8BAAQMshshCrugAM/KSoqUk5Ojl599VWtWrVK06ZNU3x8vMeaY8eOKTs7W5MmTdKyZct04cIFOZ1OjzVffPGFUlNTtWLFCt16662aPXu2LMvSm2++qQceeEDLli1zr7Xb7ZoyZYpWrlypiRMnauXKlTp8+LCR8wWAUEZmI5RRwIGfhIWFqaKiQseOHZPL5VJCQoISExM91mzbtk09evTQ7bffroiICI0aNarK17n99tvVvXt3hYeHq3fv3iopKdF9992niIgI9evXTz/88IPOnz8vSbrjjjuUmJgom82mTp06qWvXrvr666+NnC8AhDIyG6GMR1CAnyQmJmrMmDH6+9//rmPHjqlbt2565JFHPNY4nU41a9bM/bphw4a66aabPNbY7Xb33yMjIxUTE6OwsDD3a0kqKytTkyZNtGvXLr3zzjsqKiqSZVm6dOmSWrdu7a9TBIA6g8xGKKOAA/+lf//+6t+/vy5cuKBly5Zp9erVat68uXt/bGysioqK3K/Ly8tVWlpaq+9VUVGh+fPn66mnnlJKSooiIiI0b968Gz4HAKgvyGyEKh5BAX5SVFSkL7/8UhUVFYqMjFRkZKT7LsjPevfurS+++EL79++Xy+XS2rVra/39XC6XKioqFBMTo/DwcO3atUt79uy50dMAgHqBzEYo4w448JOKigqtXr1a33//vcLDw/WLX/xCEyZMUG5urntNq1atNG7cOC1atEiXLl3SXXfdpZiYGDVo0KDG369x48YaO3asFi5cqIqKCvXo0UMpKSm+PCUAqLPIbIQyfhEPcAPKyso0ZswYvf7660pISAj0OACAayCzESx4BAWooR07dujSpUsqKyvTqlWr1Lp16yoffQUACA5kNoIRj6AANbRjxw4tXrxYlmWpXbt2evbZZ2Wz2QI9FgCgGmQ2ghGPoAAAAAAG8QgKAAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACD/h9N3nvICoJJCAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
-    "scale = (res_df[\"mean_test_score\"].min(), res_df[\"mean_test_score\"].max())\n",
-    "\n",
-    "c = plot_heatmap(axs[0], res_df[res_df.M == 500], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
-    "axs[0].set_title(\"M = 500\")\n",
-    "c = plot_heatmap(axs[1], res_df[res_df.M == 1000], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
-    "_ = axs[1].set_title(\"M = 1000\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "source": [],
-    "metadata": {
-     "collapsed": false
-    }
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
diff --git a/_sources/examples/logistic_falkon.ipynb.txt b/_sources/examples/logistic_falkon.ipynb.txt
index c157bfb6..77b4104c 100644
--- a/_sources/examples/logistic_falkon.ipynb.txt
+++ b/_sources/examples/logistic_falkon.ipynb.txt
@@ -22,14 +22,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
      ]
     }
    ],
    "source": [
     "%matplotlib inline\n",
     "from sklearn import datasets, model_selection\n",
-    "import numpy as np\n",
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
     "plt.style.use('ggplot')\n",
@@ -45,7 +44,7 @@
     "\n",
     "We use a synthetic dataset in two dimensions, which consists of two concentric circles labeled with different classes.\n",
     "\n",
-    "We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 100000 samples to demonstrate the speed of the `Falkon` library."
+    "We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 10000 samples to demonstrate the speed of the `Falkon` library."
    ]
   },
   {
@@ -55,7 +54,7 @@
    "outputs": [],
    "source": [
     "X, Y = datasets.make_circles(\n",
-    "    n_samples=100_000, shuffle=False, noise=0.1, random_state=122)"
+    "    n_samples=10_000, shuffle=False, noise=0.1, random_state=122)"
    ]
   },
   {
@@ -65,7 +64,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -76,8 +75,8 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 7))\n",
-    "ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.1, marker='.')\n",
-    "_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.1, marker='.')"
+    "ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.3, marker='.')\n",
+    "_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.3, marker='.')"
    ]
   },
   {
@@ -161,20 +160,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
-      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "flk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "# We define some basic options to run on the CPU, and to disable keops\n",
+    "flk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=\"no\")\n",
     "\n",
-    "flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000)"
+    "flk_kernel = falkon.kernels.GaussianKernel(1, opt=flk_opt)\n",
+    "\n",
+    "flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000, options=flk_opt)"
    ]
   },
   {
@@ -202,15 +195,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logflk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "logflk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=\"no\")\n",
+    "\n",
+    "logflk_kernel = falkon.kernels.GaussianKernel(1, opt=logflk_opt)\n",
     "logloss = falkon.gsc_losses.LogisticLoss(logflk_kernel)\n",
     "\n",
-    "penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7, 1e-7]\n",
-    "iter_list = [3, 3, 3, 8, 8, 8]\n",
+    "penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7]\n",
+    "iter_list = [4, 4, 4, 8, 8]\n",
     "\n",
     "logflk = falkon.LogisticFalkon(\n",
     "    kernel=logflk_kernel, penalty_list=penalty_list, iter_list=iter_list, M=1000, loss=logloss,\n",
-    "    error_fn=binary_loss, error_every=1)"
+    "    error_fn=binary_loss, error_every=1, options=logflk_opt)"
    ]
   },
   {
@@ -219,10 +214,10 @@
    "source": [
     "## Train both models\n",
     "\n",
-    "Training Falkon for 20 iterations (default value) takes about 20s on a laptop.\n",
+    "Training Falkon for 20 iterations (default value) takes about half a secon on a laptop.\n",
     "\n",
-    "Clearly, since the logistic falkon runs about 33 iterations (the sum of values in `iter_list`), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns.\n",
-    "On the same laptop, the logistic falkon algorithm takes around 50s."
+    "Clearly, since the logistic falkon runs about 28 iterations (the sum of values in `iter_list`), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns.\n",
+    "On the same laptop, the logistic falkon algorithm takes around 1.5s."
    ]
   },
   {
@@ -234,14 +229,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 41s, sys: 66.6 ms, total: 1min 41s\n",
-      "Wall time: 25.4 s\n"
+      "CPU times: user 2.35 s, sys: 59.3 ms, total: 2.41 s\n",
+      "Wall time: 604 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Falkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f22222fc650>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-07, seed=None, weight_fn=None)"
+       "Falkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f9f7d142b20>, kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64)), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='no', keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=True, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty=1e-07)"
       ]
      },
      "execution_count": 10,
@@ -251,7 +247,7 @@
    ],
    "source": [
     "%%time\n",
-    "flk.fit(X_train, Y_train)"
+    "flk.fit(X_train, Y_train);"
    ]
   },
   {
@@ -263,27 +259,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 0 - penalty 1.000000e-03 - sub-iterations 3\n",
-      "Iteration   0 - Elapsed 4.02s - training loss 0.6910 - training error 0.2550 \n",
-      "Iteration 1 - penalty 1.000000e-05 - sub-iterations 3\n",
-      "Iteration   1 - Elapsed 9.15s - training loss 0.5639 - training error 0.1623 \n",
-      "Iteration 2 - penalty 1.000000e-07 - sub-iterations 3\n",
-      "Iteration   2 - Elapsed 14.68s - training loss 0.4032 - training error 0.1627 \n",
+      "Iteration 0 - penalty 1.000000e-03 - sub-iterations 4\n",
+      "Iteration   0 - Elapsed 0.27s - training loss 0.4697 - training error 0.1544 \n",
+      "Iteration 1 - penalty 1.000000e-05 - sub-iterations 4\n",
+      "Iteration   1 - Elapsed 0.51s - training loss 0.3774 - training error 0.1551 \n",
+      "Iteration 2 - penalty 1.000000e-07 - sub-iterations 4\n",
+      "Iteration   2 - Elapsed 0.74s - training loss 0.3575 - training error 0.1534 \n",
       "Iteration 3 - penalty 1.000000e-07 - sub-iterations 8\n",
-      "Iteration   3 - Elapsed 27.14s - training loss 0.3696 - training error 0.1607 \n",
+      "Iteration   3 - Elapsed 1.12s - training loss 0.3554 - training error 0.1530 \n",
       "Iteration 4 - penalty 1.000000e-07 - sub-iterations 8\n",
-      "Iteration   4 - Elapsed 39.60s - training loss 0.3655 - training error 0.1605 \n",
-      "Iteration 5 - penalty 1.000000e-07 - sub-iterations 8\n",
-      "Iteration   5 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
-      "Iteration   6 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
-      "CPU times: user 3min 46s, sys: 48 ms, total: 3min 46s\n",
-      "Wall time: 56.7 s\n"
+      "Iteration   4 - Elapsed 1.50s - training loss 0.3554 - training error 0.1530 \n",
+      "CPU times: user 6.39 s, sys: 30.9 ms, total: 6.42 s\n",
+      "Wall time: 1.6 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "LogisticFalkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f2222305590>, error_every=1, error_fn=<function binary_loss at 0x7f22a05ec950>, iter_list=[3, 3, 3, 8, 8, 8], kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07, 1e-07], seed=None)"
+       "LogisticFalkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f9f7d150250>, error_fn=<function binary_loss at 0x7f9f7d13eaf0>, iter_list=[4, 4, 4, 8, 8], kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='no', keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=True, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07])"
       ]
      },
      "execution_count": 11,
@@ -293,7 +288,7 @@
    ],
    "source": [
     "%%time\n",
-    "logflk.fit(X_train, Y_train)"
+    "logflk.fit(X_train, Y_train);"
    ]
   },
   {
@@ -317,8 +312,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Falkon model          -- Error: 15.81%\n",
-      "Logistic Falkon model -- Error: 15.63%\n"
+      "Falkon model          -- Error: 17.05%\n",
+      "Logistic Falkon model -- Error: 16.90%\n"
      ]
     }
    ],
@@ -342,7 +337,7 @@
     "In the plot we have the outer and inner circles which are correct predictions, and the circles in the middle which are the mispredicted points.\n",
     "Since we added lots of noise to the dataset, perfect predictions are not possible (there is no clear boundary between the two classes).\n",
     "\n",
-    "Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where a slightly lower classification error is desired."
+    "Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where the best possible classification error is desired."
    ]
   },
   {
@@ -354,17 +349,17 @@
     "def plot_predictions(preds, ax):\n",
     "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 0], \n",
     "               X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 1], \n",
-    "               alpha=0.1, marker='.', color='b')\n",
+    "               alpha=0.3, marker='.', color='b', label=\"correct cls 0\")\n",
     "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),0], \n",
     "               X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),1], \n",
-    "               alpha=0.1, marker='.', color='r')\n",
+    "               alpha=0.3, marker='.', color='r', label=\"correct cls 1\")\n",
     "\n",
     "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 0], \n",
     "               X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 1], \n",
-    "               alpha=0.2, marker='x', color='b')\n",
+    "               alpha=0.5, marker='x', color='b', label=\"wrong cls 0\")\n",
     "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),0], \n",
     "               X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),1], \n",
-    "               alpha=0.2, marker='x', color='r')"
+    "               alpha=0.5, marker='x', color='r', label=\"wrong cls 1\")"
    ]
   },
   {
@@ -374,7 +369,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x360 with 2 Axes>"
       ]
@@ -387,21 +382,17 @@
     "fig, ax = plt.subplots(ncols=2, figsize=(13, 5))\n",
     "plot_predictions(flk_pred, ax[0])\n",
     "ax[0].set_title(\"Falkon\")\n",
+    "ax[0].legend(loc='best')\n",
+    "\n",
     "plot_predictions(logflk_pred, ax[1])\n",
-    "_ = ax[1].set_title(\"Logistic Falkon\")"
+    "ax[1].set_title(\"Logistic Falkon\")\n",
+    "ax[1].legend(loc='best');\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -415,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.9"
   },
   "toc": {
    "base_numbering": 1,
@@ -433,4 +424,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/_sources/examples/performance_tuning.ipynb.txt b/_sources/examples/performance_tuning.ipynb.txt
deleted file mode 100644
index 0be2e993..00000000
--- a/_sources/examples/performance_tuning.ipynb.txt
+++ /dev/null
@@ -1,49 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Training on the GPU\n",
-    "\n",
-    "This notebook will be available soon"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/_sources/examples/simple_regression.ipynb.txt b/_sources/examples/simple_regression.ipynb.txt
deleted file mode 100644
index 68b25646..00000000
--- a/_sources/examples/simple_regression.ipynb.txt
+++ /dev/null
@@ -1,403 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Falkon Regression Tutorial\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "This notebook introduces the main interface of the Falkon library, \n",
-    "using a toy regression problem.\n",
-    "\n",
-    "We will be using the Boston housing dataset which is included in `scikit-learn` to train a Falkon model.\n",
-    "Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn import datasets\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "import falkon"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the data\n",
-    "\n",
-    "The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions.\n",
-    "The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.\n",
-    "\n",
-    "After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test \n",
-    "set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the [model_selection module](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "X, Y = datasets.load_boston(return_X_y=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "num_train = int(X.shape[0] * 0.8)\n",
-    "num_test = X.shape[0] - num_train\n",
-    "shuffle_idx = np.arange(X.shape[0])\n",
-    "np.random.shuffle(shuffle_idx)\n",
-    "train_idx = shuffle_idx[:num_train]\n",
-    "test_idx = shuffle_idx[num_train:]\n",
-    "\n",
-    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
-    "Xtest, Ytest = X[test_idx], Y[test_idx]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Pre-process the data\n",
-    "\n",
-    "We must convert the numpy arrays to PyTorch tensors before using them in Falkon.\n",
-    "This is very easy and fast with the `torch.from_numpy` function.\n",
-    "\n",
-    "Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data:\n",
-    "convert it to have zero-mean and unit standard deviation.\n",
-    "We use the statistics of the training data to avoid leakage between the two sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# convert numpy -> pytorch\n",
-    "Xtrain = torch.from_numpy(Xtrain)\n",
-    "Xtest = torch.from_numpy(Xtest)\n",
-    "Ytrain = torch.from_numpy(Ytrain)\n",
-    "Ytest = torch.from_numpy(Ytest)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# z-score normalization\n",
-    "train_mean = Xtrain.mean(0, keepdim=True)\n",
-    "train_std = Xtrain.std(0, keepdim=True)\n",
-    "Xtrain -= train_mean\n",
-    "Xtrain /= train_std\n",
-    "Xtest -= train_mean\n",
-    "Xtest /= train_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Create the Falkon model\n",
-    "\n",
-    "The Falkon object is the main API of this library. \n",
-    "It is similar in spirit to the fit-transform API of scikit-learn, while supporting some\n",
-    "additional features such as monitoring of validation error.\n",
-    "\n",
-    "While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much \n",
-    "larger datasets.\n",
-    "Here we only showcase some of the more basic options.\n",
-    "\n",
-    "Mandatory parameters are:\n",
-    " - the kernel function (here we use a linear kernel)\n",
-    " - the amount of regularization, which we set to some small positive value\n",
-    " - the number of inducing points M. \n",
-    "   Since the dataset is already very small, we use set `M=N` (every data-point is an inducing point). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
-      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
-     ]
-    }
-   ],
-   "source": [
-    "options = falkon.FalkonOptions()\n",
-    "kernel = falkon.kernels.GaussianKernel(sigma=5)\n",
-    "flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=Xtrain.shape[0], options=options)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Training the model\n",
-    "\n",
-    "The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are\n",
-    "two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed.\n",
-    "To gain more insight in the various steps of the algorithm you can pass `debug=True` when creating the Falkon object. \n",
-    "\n",
-    "Model training will occur on the GPU, if it is available, and CUDA is properly installed, \n",
-    "or on the CPU as a fallback. \n",
-    "\n",
-    "If KeOps has been installed alongside Falkon, the first run may take\n",
-    "a bit longer due to the compilation step of KeOps (to avoid using KeOps you can pass the argument `keops_active=False` to the `FalkonOptions` object)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stopping conjugate gradient descent at iteration 1. Solution has converged.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Falkon(M=404, center_selection=<falkon.center_selection.UniformSelector object at 0x7f31c8279190>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-05, seed=None, weight_fn=None)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flk.fit(Xtrain, Ytrain)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Evaluating model performance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the `predict` method to obtain predictions on new data.\n",
-    "\n",
-    "Here we print the error on both train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training RMSE: 1.794\n",
-      "Test RMSE: 3.534\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_pred = flk.predict(Xtrain).reshape(-1, )\n",
-    "test_pred = flk.predict(Xtest).reshape(-1, )\n",
-    "\n",
-    "def rmse(true, pred):\n",
-    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
-    "\n",
-    "print(\"Training RMSE: %.3f\" % (rmse(train_pred, Ytrain)))\n",
-    "print(\"Test RMSE: %.3f\" % (rmse(test_pred, Ytest)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f3148f13a50>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.hist(Ytest, alpha=0.7, label=\"True\")\n",
-    "ax.hist(test_pred, alpha=0.7, label=\"Pred\")\n",
-    "ax.legend(loc=\"best\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/_sources/get_started.rst.txt b/_sources/get_started.rst.txt
index db1b63e3..f1e96d01 100644
--- a/_sources/get_started.rst.txt
+++ b/_sources/get_started.rst.txt
@@ -29,15 +29,16 @@ few lines of code:
 Passing Options
 ~~~~~~~~~~~~~~~
 
-A number of different options exist for both the `Falkon` and `LogisticFalkon` estimators (see :ref:`falkon.Options <api_options>`).
-All options can be passed to the estimator through the `FalkonOptions` class, like so:
+A number of different options exist for both the :ref:`Falkon <falkon_model>` and :ref:`LogisticFalkon <log_falkon_model>`
+estimators (see :ref:`falkon.FalkonOptions <api_options>`).
+All options can be passed to the estimator through the :class:`~falkon.options.FalkonOptions` class, like so:
 
 .. code-block:: python
 
     from falkon import FalkonOptions, Falkon, kernels
 
     # Options to: increase the amount of output information; avoid using the KeOps library
-    options = FalkonOptions(debug=True, no_keops=True)
+    options = FalkonOptions(debug=True, keops_active="no")
     kernel = kernels.GaussianKernel(sigma=1.0)
 
     model = Falkon(kernel=kernel,
@@ -50,4 +51,4 @@ All options can be passed to the estimator through the `FalkonOptions` class, li
 More Examples
 ~~~~~~~~~~~~~
 
-For more detailed examples, have a look at the :ref:`example notebooks <examples>`.
\ No newline at end of file
+For more detailed examples, have a look at the :ref:`example notebooks <examples>`.
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
index a72bb123..851ad56f 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -9,8 +9,8 @@ Falkon
 A Python library for large-scale kernel methods, with optional (multi-)GPU acceleration.
 
 The library currently includes two solvers:
-one for approximate kernel ridge regression [2]_ which is extremely fast, and one for kernel logistic
-regression [3]_ which trades off lower speed for better accuracy on binary classification problems.
+one for approximate kernel ridge regression :ref:`[2] <flk_2>` which is extremely fast, and one for kernel logistic
+regression :ref:`[3] <log_flk>` which trades off lower speed for better accuracy on binary classification problems.
 
 The main features of Falkon are:
 
@@ -20,20 +20,20 @@ The main features of Falkon are:
  * *Sparse data support*
  * *Scikit-learn integration* - Our estimators follow the scikit-learn API
 
-For more details about the algorithms used, you can read our paper [1]_, or look at the source code
+For more details about the algorithms used, you can read :ref:`our paper <flk_1>`, or look at the source code
 `github.com/FalkonML/falkon <https://github.com/falkonml/falkon>`_ and at the :ref:`documentation <api_reference>`.
 Also, make sure to follow :ref:`the example notebooks <examples>` to find out about all of Falkon's features.
 
 Falkon is built on top of `PyTorch <https://pytorch.org/>`__ which is used to support both CPU and GPU tensor
 calculations, and `KeOps <https://www.kernel-operations.io/keops/index.html>`__ for fast kernel evaluations on the GPU.
 
-If you find this library useful for your research, please cite us [1]_!
+If you find this library useful for your research, please cite our paper :ref:`[1] <flk_1>`!
 
 Contents
 ========
 
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 2
 
    install
    get_started
@@ -44,14 +44,21 @@ Contents
 References
 ==========
 
-.. [1] Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
-       "Kernel methods through the roof: handling billions of points efficiently,"
-       arXiv:2006.10350, 2020.
-.. [2] Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, "FALKON: An optimal large
-       scale kernel method," Advances in Neural Information Processing Systems, 2017.
-.. [3] Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, "Globally Convergent
-      Newton Methods for Ill-conditioned Generalized Self-concordant Losses,"
-      Advances in Neural Information Processing Systems, 2019
+.. _flk_1:
+
+Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+"Kernel methods through the roof: handling billions of points efficiently,"
+Advancs in Neural Information Processing Systems, 2020.
+
+.. _flk_2:
+
+Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, "FALKON: An optimal large scale kernel method,"
+Advances in Neural Information Processing Systems, 2017.
+
+.. _log_flk:
+
+Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, "Globally Convergent Newton Methods for Ill-conditioned
+Generalized Self-concordant Losses," Advances in Neural Information Processing Systems, 2019.
 
 
 Indices and tables
diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt
index 34430468..8621b727 100644
--- a/_sources/install.rst.txt
+++ b/_sources/install.rst.txt
@@ -10,8 +10,8 @@ Falkon is only tested on Linux.
 
 GPU support is achieved via CUDA, so using Falkon from Windows will be tricky.
 
-Using the library from the CPU only is much more portable since there is no CUDA requirement. CPU-only Falkon
-is thus likely to work on Windows and Mac OS easily.
+Using the library from the CPU only is much more portable since there is no CUDA requirement, and it will likely work
+on Windows and Mac OS more easily.
 
 Prerequisites
 -------------
@@ -48,6 +48,14 @@ The patched version is available as a submodule of the Falkon repository. To ins
     $ cd falkon/
     $ pip install ./keops
 
+For use with **Google Colab** KeOps needs some additional packages. You can install them alongside KeOps by running
+
+.. code-block::
+
+    !pip install pykeops[colab] > install.log
+
+from within the Jupyter notebook (see the `KeOps docs <https://www.kernel-operations.io/keops/python/installation.html>`__
+for more information).
 
 Installing
 ----------
@@ -65,8 +73,14 @@ and PyTorch extensions which take some time to compile.
 Testing the installation
 ------------------------
 
-To check that everything works correctly you can follow the `Kernel ridge regression`_ notebook.
+To check that everything works correctly you can follow the `Kernel ridge regression <examples/falkon_regression_tutorial.ipynb>`_ notebook.
+
+
+
+Development
+-----------
 
+For development purposes the library should be installed in editable mode (i.e. `pip install -e .` from the
+falkon directory.
 
-.. _Kernel ridge regression:
-    examples/simple_regression.ipynb
+To build the documentation go into the `doc` directory and run `make html`.
diff --git a/_static/basic.css b/_static/basic.css
index be19270e..bf18350b 100644
--- a/_static/basic.css
+++ b/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -130,7 +130,7 @@ ul.search li a {
     font-weight: bold;
 }
 
-ul.search li div.context {
+ul.search li p.context {
     color: #888;
     margin: 2px 0 0 30px;
     text-align: left;
@@ -277,25 +277,25 @@ p.rubric {
     font-weight: bold;
 }
 
-img.align-left, .figure.align-left, object.align-left {
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
     clear: left;
     float: left;
     margin-right: 1em;
 }
 
-img.align-right, .figure.align-right, object.align-right {
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
     clear: right;
     float: right;
     margin-left: 1em;
 }
 
-img.align-center, .figure.align-center, object.align-center {
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
   display: block;
   margin-left: auto;
   margin-right: auto;
 }
 
-img.align-default, .figure.align-default {
+img.align-default, figure.align-default, .figure.align-default {
   display: block;
   margin-left: auto;
   margin-right: auto;
@@ -319,7 +319,8 @@ img.align-default, .figure.align-default {
 
 /* -- sidebars -------------------------------------------------------------- */
 
-div.sidebar {
+div.sidebar,
+aside.sidebar {
     margin: 0 0 0.5em 1em;
     border: 1px solid #ddb;
     padding: 7px;
@@ -377,12 +378,14 @@ div.body p.centered {
 /* -- content of sidebars/topics/admonitions -------------------------------- */
 
 div.sidebar > :last-child,
+aside.sidebar > :last-child,
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
 }
 
 div.sidebar::after,
+aside.sidebar::after,
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -455,20 +458,22 @@ td > :last-child {
 
 /* -- figures --------------------------------------------------------------- */
 
-div.figure {
+div.figure, figure {
     margin: 0.5em;
     padding: 0.5em;
 }
 
-div.figure p.caption {
+div.figure p.caption, figcaption {
     padding: 0.3em;
 }
 
-div.figure p.caption span.caption-number {
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
     font-style: italic;
 }
 
-div.figure p.caption span.caption-text {
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
 }
 
 /* -- field list styles ----------------------------------------------------- */
@@ -503,6 +508,63 @@ table.hlist td {
     vertical-align: top;
 }
 
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
 
 /* -- other body styles ----------------------------------------------------- */
 
@@ -629,14 +691,6 @@ dl.glossary dt {
     font-size: 1.1em;
 }
 
-.optional {
-    font-size: 1.3em;
-}
-
-.sig-paren {
-    font-size: larger;
-}
-
 .versionmodified {
     font-style: italic;
 }
@@ -677,8 +731,9 @@ dl.glossary dt {
 
 .classifier:before {
     font-style: normal;
-    margin: 0.5em;
+    margin: 0 0.5em;
     content: ":";
+    display: inline-block;
 }
 
 abbr, acronym {
@@ -702,6 +757,7 @@ span.pre {
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
+    white-space: nowrap;
 }
 
 div[class*="highlight-"] {
@@ -765,8 +821,12 @@ div.code-block-caption code {
 
 table.highlighttable td.linenos,
 span.linenos,
-div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
-    user-select: none;
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
 }
 
 div.code-block-caption span.caption-number {
@@ -781,16 +841,6 @@ div.literal-block-wrapper {
     margin: 1em 0;
 }
 
-code.descname {
-    background-color: transparent;
-    font-weight: bold;
-    font-size: 1.2em;
-}
-
-code.descclassname {
-    background-color: transparent;
-}
-
 code.xref, a code {
     background-color: transparent;
     font-weight: bold;
diff --git a/_static/css/theme.css b/_static/css/theme.css
index 8cd4f101..0d9ae7e1 100644
--- a/_static/css/theme.css
+++ b/_static/css/theme.css
@@ -1,4 +1,4 @@
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#7fbbe3;background:#e7f2fa;font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) format("woff2"),url(fonts/lato-normal-italic.woff?f28f2d6482446544ef1ea1ccc6dd5892) format("woff");font-weight:400;font-style:italic;font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:400;src:url(fonts/Roboto-Slab-Regular.woff2?7abf5b8d04d26a2cafea937019bca958) format("woff2"),url(fonts/Roboto-Slab-Regular.woff?c1be9284088d487c5e3ff0a10a92e58c) format("woff");font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:700;src:url(fonts/Roboto-Slab-Bold.woff2?9984f4a9bda09be08e83f2506954adbe) format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block}
\ No newline at end of file
diff --git a/_static/doctools.js b/_static/doctools.js
index 61ac9d26..e509e483 100644
--- a/_static/doctools.js
+++ b/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for all documentation.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -264,6 +264,9 @@ var Documentation = {
   hideSearchWords : function() {
     $('#searchbox .highlight-link').fadeOut(300);
     $('span.highlighted').removeClass('highlighted');
+    var url = new URL(window.location);
+    url.searchParams.delete('highlight');
+    window.history.replaceState({}, '', url);
   },
 
   /**
@@ -301,12 +304,14 @@ var Documentation = {
               window.location.href = prevHref;
               return false;
             }
+            break;
           case 39: // right
             var nextHref = $('link[rel="next"]').prop('href');
             if (nextHref) {
               window.location.href = nextHref;
               return false;
             }
+            break;
         }
       }
     });
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 567cdf71..ce4f220c 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.6.3',
+    VERSION: '0.7.2',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/_static/js/theme.js b/_static/js/theme.js
index 839d07e2..1fddb6ee 100644
--- a/_static/js/theme.js
+++ b/_static/js/theme.js
@@ -1 +1 @@
-!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return n[i].call(o.exports,o,o.exports,t),o.l=!0,o.exports}t.m=n,t.c=e,t.d=function(n,e,i){t.o(n,e)||Object.defineProperty(n,e,{enumerable:!0,get:i})},t.r=function(n){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(n,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(n,"__esModule",{value:!0})},t.t=function(n,e){if(1&e&&(n=t(n)),8&e)return n;if(4&e&&"object"==typeof n&&n&&n.__esModule)return n;var i=Object.create(null);if(t.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:n}),2&e&&"string"!=typeof n)for(var o in n)t.d(i,o,function(e){return n[e]}.bind(null,o));return i},t.n=function(n){var e=n&&n.__esModule?function(){return n.default}:function(){return n};return t.d(e,"a",e),e},t.o=function(n,e){return Object.prototype.hasOwnProperty.call(n,e)},t.p="",t(t.s=0)}([function(n,e,t){t(1),n.exports=t(3)},function(n,e,t){(function(){var e="undefined"!=typeof window?window.jQuery:t(2);n.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(n){var t=this;void 0===n&&(n=!0),t.isRunning||(t.isRunning=!0,e((function(e){t.init(e),t.reset(),t.win.on("hashchange",t.reset),n&&t.win.on("scroll",(function(){t.linkScroll||t.winScroll||(t.winScroll=!0,requestAnimationFrame((function(){t.onScroll()})))})),t.win.on("resize",(function(){t.winResize||(t.winResize=!0,requestAnimationFrame((function(){t.onResize()})))})),t.onResize()})))},enableSticky:function(){this.enable(!0)},init:function(n){n(document);var e=this;this.navBar=n("div.wy-side-scroll:first"),this.win=n(window),n(document).on("click","[data-toggle='wy-nav-top']",(function(){n("[data-toggle='wy-nav-shift']").toggleClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift")})).on("click",".wy-menu-vertical .current ul li a",(function(){var t=n(this);n("[data-toggle='wy-nav-shift']").removeClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift"),e.toggleCurrent(t),e.hashChange()})).on("click","[data-toggle='rst-current-version']",(function(){n("[data-toggle='rst-versions']").toggleClass("shift-up")})),n("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),n("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),n("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n('<span class="toctree-expand"></span>'),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}t.length>0&&($(".wy-menu-vertical .current").removeClass("current"),t.addClass("current"),t.closest("li.toctree-l1").addClass("current"),t.closest("li.toctree-l1").parent().addClass("current"),t.closest("li.toctree-l1").addClass("current"),t.closest("li.toctree-l2").addClass("current"),t.closest("li.toctree-l3").addClass("current"),t.closest("li.toctree-l4").addClass("current"),t.closest("li.toctree-l5").addClass("current"),t[0].scrollIntoView())}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current"),e.siblings().find("li.current").removeClass("current"),e.find("> ul li.current").removeClass("current"),e.toggleClass("current")}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return n=i+o,r}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()}).call(window)},function(n,e){n.exports=jQuery},function(n,e,t){}]);
\ No newline at end of file
+!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return n[i].call(o.exports,o,o.exports,t),o.l=!0,o.exports}t.m=n,t.c=e,t.d=function(n,e,i){t.o(n,e)||Object.defineProperty(n,e,{enumerable:!0,get:i})},t.r=function(n){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(n,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(n,"__esModule",{value:!0})},t.t=function(n,e){if(1&e&&(n=t(n)),8&e)return n;if(4&e&&"object"==typeof n&&n&&n.__esModule)return n;var i=Object.create(null);if(t.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:n}),2&e&&"string"!=typeof n)for(var o in n)t.d(i,o,function(e){return n[e]}.bind(null,o));return i},t.n=function(n){var e=n&&n.__esModule?function(){return n.default}:function(){return n};return t.d(e,"a",e),e},t.o=function(n,e){return Object.prototype.hasOwnProperty.call(n,e)},t.p="",t(t.s=0)}([function(n,e,t){t(1),n.exports=t(3)},function(n,e,t){(function(){var e="undefined"!=typeof window?window.jQuery:t(2);n.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(n){var t=this;void 0===n&&(n=!0),t.isRunning||(t.isRunning=!0,e((function(e){t.init(e),t.reset(),t.win.on("hashchange",t.reset),n&&t.win.on("scroll",(function(){t.linkScroll||t.winScroll||(t.winScroll=!0,requestAnimationFrame((function(){t.onScroll()})))})),t.win.on("resize",(function(){t.winResize||(t.winResize=!0,requestAnimationFrame((function(){t.onResize()})))})),t.onResize()})))},enableSticky:function(){this.enable(!0)},init:function(n){n(document);var e=this;this.navBar=n("div.wy-side-scroll:first"),this.win=n(window),n(document).on("click","[data-toggle='wy-nav-top']",(function(){n("[data-toggle='wy-nav-shift']").toggleClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift")})).on("click",".wy-menu-vertical .current ul li a",(function(){var t=n(this);n("[data-toggle='wy-nav-shift']").removeClass("shift"),n("[data-toggle='rst-versions']").toggleClass("shift"),e.toggleCurrent(t),e.hashChange()})).on("click","[data-toggle='rst-current-version']",(function(){n("[data-toggle='rst-versions']").toggleClass("shift-up")})),n("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),n("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),n("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),n(".wy-menu-vertical ul").not(".simple").siblings("a").each((function(){var t=n(this);expand=n('<button class="toctree-expand" title="Open/close menu"></button>'),expand.on("click",(function(n){return e.toggleCurrent(t),n.stopPropagation(),!1})),t.prepend(expand)}))},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),t=e.find('[href="'+n+'"]');if(0===t.length){var i=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}if(t.length>0){$(".wy-menu-vertical .current").removeClass("current").attr("aria-expanded","false"),t.addClass("current").attr("aria-expanded","true"),t.closest("li.toctree-l1").parent().addClass("current").attr("aria-expanded","true");for(let n=1;n<=10;n++)t.closest("li.toctree-l"+n).addClass("current").attr("aria-expanded","true");t[0].scrollIntoView()}}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return n=i+o,r}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()}).call(window)},function(n,e){n.exports=jQuery},function(n,e,t){}]);
\ No newline at end of file
diff --git a/_static/language_data.js b/_static/language_data.js
index 863704b3..ebe2f03b 100644
--- a/_static/language_data.js
+++ b/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/_static/pygments.css b/_static/pygments.css
index 20c4814d..691aeb82 100644
--- a/_static/pygments.css
+++ b/_static/pygments.css
@@ -1,5 +1,10 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #eeffcc; }
+.highlight { background: #eeffcc; }
 .highlight .c { color: #408090; font-style: italic } /* Comment */
 .highlight .err { border: 1px solid #FF0000 } /* Error */
 .highlight .k { color: #007020; font-weight: bold } /* Keyword */
diff --git a/_static/searchtools.js b/_static/searchtools.js
index 1a90152e..2d778593 100644
--- a/_static/searchtools.js
+++ b/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -282,7 +282,10 @@ var Search = {
                   complete: function(jqxhr, textstatus) {
                     var data = jqxhr.responseText;
                     if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
+                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
+                      if (summary) {
+                        listItem.append(summary);
+                      }
                     }
                     Search.output.append(listItem);
                     setTimeout(function() {
@@ -325,7 +328,9 @@ var Search = {
     var results = [];
 
     for (var prefix in objects) {
-      for (var name in objects[prefix]) {
+      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
+        var match = objects[prefix][iMatch];
+        var name = match[4];
         var fullname = (prefix ? prefix + '.' : '') + name;
         var fullnameLower = fullname.toLowerCase()
         if (fullnameLower.indexOf(object) > -1) {
@@ -339,7 +344,6 @@ var Search = {
           } else if (parts[parts.length - 1].indexOf(object) > -1) {
             score += Scorer.objPartialMatch;
           }
-          var match = objects[prefix][name];
           var objname = objnames[match[1]][2];
           var title = titles[match[0]];
           // If more than one term searched for, we require other words to be
@@ -498,6 +502,9 @@ var Search = {
    */
   makeSearchSummary : function(htmlText, keywords, hlwords) {
     var text = Search.htmlToText(htmlText);
+    if (text == "") {
+      return null;
+    }
     var textLower = text.toLowerCase();
     var start = 0;
     $.each(keywords, function() {
@@ -509,7 +516,7 @@ var Search = {
     var excerpt = ((start > 0) ? '...' : '') +
       $.trim(text.substr(start, 240)) +
       ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<div class="context"></div>').text(excerpt);
+    var rv = $('<p class="context"></p>').text(excerpt);
     $.each(hlwords, function() {
       rv = rv.highlightText(this, 'highlighted');
     });
diff --git a/_static/underscore-1.12.0.js b/_static/underscore-1.13.1.js
similarity index 94%
rename from _static/underscore-1.12.0.js
rename to _static/underscore-1.13.1.js
index 3af6352e..ffd77af9 100644
--- a/_static/underscore-1.12.0.js
+++ b/_static/underscore-1.13.1.js
@@ -1,19 +1,19 @@
 (function (global, factory) {
   typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
   typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = global || self, (function () {
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
     var current = global._;
     var exports = global._ = factory();
     exports.noConflict = function () { global._ = current; return exports; };
   }()));
 }(this, (function () {
-  //     Underscore.js 1.12.0
+  //     Underscore.js 1.13.1
   //     https://underscorejs.org
-  //     (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
   //     Underscore may be freely distributed under the MIT license.
 
   // Current version.
-  var VERSION = '1.12.0';
+  var VERSION = '1.13.1';
 
   // Establish the root object, `window` (`self`) in the browser, `global`
   // on the server, or `this` in some virtual machines. We use `self`
@@ -170,7 +170,7 @@
   var isArray = nativeIsArray || tagTester('Array');
 
   // Internal function to check whether `key` is an own property name of `obj`.
-  function has(obj, key) {
+  function has$1(obj, key) {
     return obj != null && hasOwnProperty.call(obj, key);
   }
 
@@ -181,7 +181,7 @@
   (function() {
     if (!isArguments(arguments)) {
       isArguments = function(obj) {
-        return has(obj, 'callee');
+        return has$1(obj, 'callee');
       };
     }
   }());
@@ -268,7 +268,7 @@
 
     // Constructor is a special case.
     var prop = 'constructor';
-    if (has(obj, prop) && !keys.contains(prop)) keys.push(prop);
+    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
 
     while (nonEnumIdx--) {
       prop = nonEnumerableProps[nonEnumIdx];
@@ -284,7 +284,7 @@
     if (!isObject(obj)) return [];
     if (nativeKeys) return nativeKeys(obj);
     var keys = [];
-    for (var key in obj) if (has(obj, key)) keys.push(key);
+    for (var key in obj) if (has$1(obj, key)) keys.push(key);
     // Ahem, IE < 9.
     if (hasEnumBug) collectNonEnumProps(obj, keys);
     return keys;
@@ -318,24 +318,24 @@
   // If Underscore is called as a function, it returns a wrapped object that can
   // be used OO-style. This wrapper holds altered versions of all functions added
   // through `_.mixin`. Wrapped objects may be chained.
-  function _(obj) {
-    if (obj instanceof _) return obj;
-    if (!(this instanceof _)) return new _(obj);
+  function _$1(obj) {
+    if (obj instanceof _$1) return obj;
+    if (!(this instanceof _$1)) return new _$1(obj);
     this._wrapped = obj;
   }
 
-  _.VERSION = VERSION;
+  _$1.VERSION = VERSION;
 
   // Extracts the result from a wrapped and chained object.
-  _.prototype.value = function() {
+  _$1.prototype.value = function() {
     return this._wrapped;
   };
 
   // Provide unwrapping proxies for some methods used in engine operations
   // such as arithmetic and JSON stringification.
-  _.prototype.valueOf = _.prototype.toJSON = _.prototype.value;
+  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
 
-  _.prototype.toString = function() {
+  _$1.prototype.toString = function() {
     return String(this._wrapped);
   };
 
@@ -370,8 +370,8 @@
   // Internal recursive comparison function for `_.isEqual`.
   function deepEq(a, b, aStack, bStack) {
     // Unwrap any wrapped objects.
-    if (a instanceof _) a = a._wrapped;
-    if (b instanceof _) b = b._wrapped;
+    if (a instanceof _$1) a = a._wrapped;
+    if (b instanceof _$1) b = b._wrapped;
     // Compare `[[Class]]` names.
     var className = toString.call(a);
     if (className !== toString.call(b)) return false;
@@ -463,7 +463,7 @@
       while (length--) {
         // Deep compare each member
         key = _keys[length];
-        if (!(has(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
       }
     }
     // Remove the first object from the stack of traversed objects.
@@ -642,15 +642,15 @@
 
   // Normalize a (deep) property `path` to array.
   // Like `_.iteratee`, this function can be customized.
-  function toPath(path) {
+  function toPath$1(path) {
     return isArray(path) ? path : [path];
   }
-  _.toPath = toPath;
+  _$1.toPath = toPath$1;
 
   // Internal wrapper for `_.toPath` to enable minification.
   // Similar to `cb` for `_.iteratee`.
-  function toPath$1(path) {
-    return _.toPath(path);
+  function toPath(path) {
+    return _$1.toPath(path);
   }
 
   // Internal function to obtain a nested property in `obj` along `path`.
@@ -668,19 +668,19 @@
   // `undefined`, return `defaultValue` instead.
   // The `path` is normalized through `_.toPath`.
   function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath$1(path));
+    var value = deepGet(object, toPath(path));
     return isUndefined(value) ? defaultValue : value;
   }
 
   // Shortcut function for checking if an object has a given property directly on
   // itself (in other words, not on a prototype). Unlike the internal `has`
   // function, this public version can also traverse nested properties.
-  function has$1(obj, path) {
-    path = toPath$1(path);
+  function has(obj, path) {
+    path = toPath(path);
     var length = path.length;
     for (var i = 0; i < length; i++) {
       var key = path[i];
-      if (!has(obj, key)) return false;
+      if (!has$1(obj, key)) return false;
       obj = obj[key];
     }
     return !!length;
@@ -703,7 +703,7 @@
   // Creates a function that, when passed an object, will traverse that object’s
   // properties down the given `path`, specified as an array of keys or indices.
   function property(path) {
-    path = toPath$1(path);
+    path = toPath(path);
     return function(obj) {
       return deepGet(obj, path);
     };
@@ -747,12 +747,12 @@
   function iteratee(value, context) {
     return baseIteratee(value, context, Infinity);
   }
-  _.iteratee = iteratee;
+  _$1.iteratee = iteratee;
 
   // The function we call internally to generate a callback. It invokes
   // `_.iteratee` if overridden, otherwise `baseIteratee`.
   function cb(value, context, argCount) {
-    if (_.iteratee !== iteratee) return _.iteratee(value, context);
+    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
     return baseIteratee(value, context, argCount);
   }
 
@@ -840,7 +840,7 @@
 
   // By default, Underscore uses ERB-style template delimiters. Change the
   // following template settings to use alternative delimiters.
-  var templateSettings = _.templateSettings = {
+  var templateSettings = _$1.templateSettings = {
     evaluate: /<%([\s\S]+?)%>/g,
     interpolate: /<%=([\s\S]+?)%>/g,
     escape: /<%-([\s\S]+?)%>/g
@@ -868,13 +868,20 @@
     return '\\' + escapes[match];
   }
 
+  // In order to prevent third-party code injection through
+  // `_.templateSettings.variable`, we test it against the following regular
+  // expression. It is intentionally a bit more liberal than just matching valid
+  // identifiers, but still prevents possible loopholes through defaults or
+  // destructuring assignment.
+  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
+
   // JavaScript micro-templating, similar to John Resig's implementation.
   // Underscore templating handles arbitrary delimiters, preserves whitespace,
   // and correctly escapes quotes within interpolated code.
   // NB: `oldSettings` only exists for backwards compatibility.
   function template(text, settings, oldSettings) {
     if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _.templateSettings);
+    settings = defaults({}, settings, _$1.templateSettings);
 
     // Combine delimiters into one regular expression via alternation.
     var matcher = RegExp([
@@ -903,8 +910,17 @@
     });
     source += "';\n";
 
-    // If a variable is not specified, place data values in local scope.
-    if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n';
+    var argument = settings.variable;
+    if (argument) {
+      // Insure against third-party code injection. (CVE-2021-23358)
+      if (!bareIdentifier.test(argument)) throw new Error(
+        'variable is not a bare identifier: ' + argument
+      );
+    } else {
+      // If a variable is not specified, place data values in local scope.
+      source = 'with(obj||{}){\n' + source + '}\n';
+      argument = 'obj';
+    }
 
     source = "var __t,__p='',__j=Array.prototype.join," +
       "print=function(){__p+=__j.call(arguments,'');};\n" +
@@ -912,18 +928,17 @@
 
     var render;
     try {
-      render = new Function(settings.variable || 'obj', '_', source);
+      render = new Function(argument, '_', source);
     } catch (e) {
       e.source = source;
       throw e;
     }
 
     var template = function(data) {
-      return render.call(this, data, _);
+      return render.call(this, data, _$1);
     };
 
     // Provide the compiled source as a convenience for precompilation.
-    var argument = settings.variable || 'obj';
     template.source = 'function(' + argument + '){\n' + source + '}';
 
     return template;
@@ -933,7 +948,7 @@
   // is invoked with its parent as context. Returns the value of the final
   // child, or `fallback` if any child is undefined.
   function result(obj, path, fallback) {
-    path = toPath$1(path);
+    path = toPath(path);
     var length = path.length;
     if (!length) {
       return isFunction$1(fallback) ? fallback.call(obj) : fallback;
@@ -959,7 +974,7 @@
 
   // Start chaining a wrapped Underscore object.
   function chain(obj) {
-    var instance = _(obj);
+    var instance = _$1(obj);
     instance._chain = true;
     return instance;
   }
@@ -993,7 +1008,7 @@
     return bound;
   });
 
-  partial.placeholder = _;
+  partial.placeholder = _$1;
 
   // Create a function bound to a given object (assigning `this`, and arguments,
   // optionally).
@@ -1012,7 +1027,7 @@
   var isArrayLike = createSizePropertyCheck(getLength);
 
   // Internal implementation of a recursive `flatten` function.
-  function flatten(input, depth, strict, output) {
+  function flatten$1(input, depth, strict, output) {
     output = output || [];
     if (!depth && depth !== 0) {
       depth = Infinity;
@@ -1025,7 +1040,7 @@
       if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
         // Flatten current level of array or arguments object.
         if (depth > 1) {
-          flatten(value, depth - 1, strict, output);
+          flatten$1(value, depth - 1, strict, output);
           idx = output.length;
         } else {
           var j = 0, len = value.length;
@@ -1042,7 +1057,7 @@
   // are the method names to be bound. Useful for ensuring that all callbacks
   // defined on an object belong to it.
   var bindAll = restArguments(function(obj, keys) {
-    keys = flatten(keys, false, false);
+    keys = flatten$1(keys, false, false);
     var index = keys.length;
     if (index < 1) throw new Error('bindAll must be passed function names');
     while (index--) {
@@ -1057,7 +1072,7 @@
     var memoize = function(key) {
       var cache = memoize.cache;
       var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has(cache, address)) cache[address] = func.apply(this, arguments);
+      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
       return cache[address];
     };
     memoize.cache = {};
@@ -1074,7 +1089,7 @@
 
   // Defers a function, scheduling it to run after the current call stack has
   // cleared.
-  var defer = partial(delay, _, 1);
+  var defer = partial(delay, _$1, 1);
 
   // Returns a function, that, when invoked, will only be triggered at most once
   // during a given window of time. Normally, the throttled function will run
@@ -1420,7 +1435,7 @@
     if (isFunction$1(path)) {
       func = path;
     } else {
-      path = toPath$1(path);
+      path = toPath(path);
       contextPath = path.slice(0, -1);
       path = path[path.length - 1];
     }
@@ -1562,7 +1577,7 @@
   // Groups the object's values by a criterion. Pass either a string attribute
   // to group by, or a function that returns the criterion.
   var groupBy = group(function(result, value, key) {
-    if (has(result, key)) result[key].push(value); else result[key] = [value];
+    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
   });
 
   // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
@@ -1575,7 +1590,7 @@
   // either a string attribute to count by, or a function that returns the
   // criterion.
   var countBy = group(function(result, value, key) {
-    if (has(result, key)) result[key]++; else result[key] = 1;
+    if (has$1(result, key)) result[key]++; else result[key] = 1;
   });
 
   // Split a collection into two arrays: one whose elements all pass the given
@@ -1618,7 +1633,7 @@
       keys = allKeys(obj);
     } else {
       iteratee = keyInObj;
-      keys = flatten(keys, false, false);
+      keys = flatten$1(keys, false, false);
       obj = Object(obj);
     }
     for (var i = 0, length = keys.length; i < length; i++) {
@@ -1636,7 +1651,7 @@
       iteratee = negate(iteratee);
       if (keys.length > 1) context = keys[1];
     } else {
-      keys = map(flatten(keys, false, false), String);
+      keys = map(flatten$1(keys, false, false), String);
       iteratee = function(value, key) {
         return !contains(keys, key);
       };
@@ -1681,14 +1696,14 @@
 
   // Flatten out an array, either recursively (by default), or up to `depth`.
   // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten$1(array, depth) {
-    return flatten(array, depth, false);
+  function flatten(array, depth) {
+    return flatten$1(array, depth, false);
   }
 
   // Take the difference between one array and a number of other arrays.
   // Only the elements present in just the first array will remain.
   var difference = restArguments(function(array, rest) {
-    rest = flatten(rest, true, true);
+    rest = flatten$1(rest, true, true);
     return filter(array, function(value){
       return !contains(rest, value);
     });
@@ -1734,7 +1749,7 @@
   // Produce an array that contains the union: each distinct element from all of
   // the passed-in arrays.
   var union = restArguments(function(arrays) {
-    return uniq(flatten(arrays, true, true));
+    return uniq(flatten$1(arrays, true, true));
   });
 
   // Produce an array that contains every item shared between all the
@@ -1821,26 +1836,26 @@
 
   // Helper function to continue chaining intermediate results.
   function chainResult(instance, obj) {
-    return instance._chain ? _(obj).chain() : obj;
+    return instance._chain ? _$1(obj).chain() : obj;
   }
 
   // Add your own custom functions to the Underscore object.
   function mixin(obj) {
     each(functions(obj), function(name) {
-      var func = _[name] = obj[name];
-      _.prototype[name] = function() {
+      var func = _$1[name] = obj[name];
+      _$1.prototype[name] = function() {
         var args = [this._wrapped];
         push.apply(args, arguments);
-        return chainResult(this, func.apply(_, args));
+        return chainResult(this, func.apply(_$1, args));
       };
     });
-    return _;
+    return _$1;
   }
 
   // Add all mutator `Array` functions to the wrapper.
   each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
     var method = ArrayProto[name];
-    _.prototype[name] = function() {
+    _$1.prototype[name] = function() {
       var obj = this._wrapped;
       if (obj != null) {
         method.apply(obj, arguments);
@@ -1855,7 +1870,7 @@
   // Add all accessor `Array` functions to the wrapper.
   each(['concat', 'join', 'slice'], function(name) {
     var method = ArrayProto[name];
-    _.prototype[name] = function() {
+    _$1.prototype[name] = function() {
       var obj = this._wrapped;
       if (obj != null) obj = method.apply(obj, arguments);
       return chainResult(this, obj);
@@ -1909,12 +1924,12 @@
     clone: clone,
     tap: tap,
     get: get,
-    has: has$1,
+    has: has,
     mapObject: mapObject,
     identity: identity,
     constant: constant,
     noop: noop,
-    toPath: toPath,
+    toPath: toPath$1,
     property: property,
     propertyOf: propertyOf,
     matcher: matcher,
@@ -1997,7 +2012,7 @@
     tail: rest,
     drop: rest,
     compact: compact,
-    flatten: flatten$1,
+    flatten: flatten,
     without: without,
     uniq: uniq,
     unique: uniq,
@@ -2011,17 +2026,17 @@
     range: range,
     chunk: chunk,
     mixin: mixin,
-    'default': _
+    'default': _$1
   };
 
   // Default Export
 
   // Add all of the Underscore functions to the wrapper object.
-  var _$1 = mixin(allExports);
+  var _ = mixin(allExports);
   // Legacy Node.js API.
-  _$1._ = _$1;
+  _._ = _;
 
-  return _$1;
+  return _;
 
 })));
-//# sourceMappingURL=underscore.js.map
+//# sourceMappingURL=underscore-umd.js.map
diff --git a/_static/underscore.js b/_static/underscore.js
index 166240ef..cf177d42 100644
--- a/_static/underscore.js
+++ b/_static/underscore.js
@@ -1,6 +1,6 @@
-!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n=n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
-//     Underscore.js 1.12.0
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
+//     Underscore.js 1.13.1
 //     https://underscorejs.org
-//     (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
 //     Underscore may be freely distributed under the MIT license.
-var n="1.12.0",r="object"==typeof self&&self.self===self&&self||"object"==typeof global&&global.global===global&&global||Function("return this")()||{},t=Array.prototype,e=Object.prototype,u="undefined"!=typeof Symbol?Symbol.prototype:null,o=t.push,i=t.slice,a=e.toString,f=e.hasOwnProperty,c="undefined"!=typeof ArrayBuffer,l="undefined"!=typeof DataView,s=Array.isArray,p=Object.keys,v=Object.create,h=c&&ArrayBuffer.isView,y=isNaN,g=isFinite,d=!{toString:null}.propertyIsEnumerable("toString"),b=["valueOf","isPrototypeOf","toString","propertyIsEnumerable","hasOwnProperty","toLocaleString"],m=Math.pow(2,53)-1;function j(n,r){return r=null==r?n.length-1:+r,function(){for(var t=Math.max(arguments.length-r,0),e=Array(t),u=0;u<t;u++)e[u]=arguments[u+r];switch(r){case 0:return n.call(this,e);case 1:return n.call(this,arguments[0],e);case 2:return n.call(this,arguments[0],arguments[1],e)}var o=Array(r+1);for(u=0;u<r;u++)o[u]=arguments[u];return o[r]=e,n.apply(this,o)}}function _(n){var r=typeof 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diff --git a/api_reference/center_selector.html b/api_reference/center_selector.html
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-  <meta name="generator" content="Docutils 0.17: http://docutils.sourceforge.net/" />
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-  <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-
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+  <title>falkon.center_selection &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-    <script type="text/javascript" src="../_static/js/theme.js"></script>
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+    <link rel="next" title="falkon.hopt" href="hopt.html" />
     <link rel="prev" title="falkon.sparse" href="sparse.html" /> 
 </head>
 
-<body class="wy-body-for-nav">
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+<body class="wy-body-for-nav"> 
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-          
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           </a>
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-            
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               <div class="version">
-                0.6.3
+                0.7.2
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@@ -77,18 +40,7 @@
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-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
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@@ -108,13 +61,13 @@
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-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
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@@ -129,19 +82,51 @@
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-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -228,77 +208,48 @@
 <li class="toctree-l3"><a class="reference internal" href="#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>
 
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 <style>
 /* CSS overrides for sphinx_rtd_theme */
@@ -324,12 +275,12 @@
 }
 </style>
 <section id="module-falkon.center_selection">
-<span id="falkon-center-selection"></span><h1>falkon.center_selection<a class="headerlink" href="#module-falkon.center_selection" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-center-selection"></span><h1>falkon.center_selection<a class="headerlink" href="#module-falkon.center_selection" title="Permalink to this headline"></a></h1>
 <section id="centerselector">
-<h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink to this headline">¶</a></h2>
+<h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.center_selection.CenterSelector">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></code><code class="sig-name descname"><span class="pre">CenterSelector</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.CenterSelector" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.CenterSelector">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">CenterSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.CenterSelector" title="Permalink to this definition"></a></dt>
 <dd><p>Create the center selector with a random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -337,8 +288,8 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.center_selection.CenterSelector.select">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">select</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.CenterSelector.select">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -358,8 +309,8 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.center_selection.CenterSelector.select_indices">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">select_indices</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select_indices" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.CenterSelector.select_indices">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -384,10 +335,10 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 
 </section>
 <section id="uniformselector">
-<h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalink to this headline">¶</a></h2>
+<h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.center_selection.UniformSelector">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></code><code class="sig-name descname"><span class="pre">UniformSelector</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.UniformSelector" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.UniformSelector">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">UniformSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.UniformSelector" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.center_selection.CenterSelector</span></code></a></p>
 <p>Center selector which chooses from the full dataset uniformly at random (without replacement)</p>
 <dl class="field-list simple">
@@ -399,8 +350,8 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.center_selection.UniformSelector.select">
-<code class="sig-name descname"><span class="pre">select</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.UniformSelector.select">
+<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
 <p>The selection strategy is uniformly at random. To control the randomness,
 pass an appropriate numpy random generator to this class’s constructor.</p>
@@ -430,8 +381,8 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.center_selection.UniformSelector.select_indices">
-<code class="sig-name descname"><span class="pre">select_indices</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select_indices" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.UniformSelector.select_indices">
+<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
 <p>The selection strategy is uniformly at random. To control the randomness,
 pass an appropriate numpy random generator to this class’s constructor.</p>
@@ -467,10 +418,10 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 
 </section>
 <section id="fixedselector">
-<h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to this headline">¶</a></h2>
+<h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.center_selection.FixedSelector">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></code><code class="sig-name descname"><span class="pre">FixedSelector</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_centers</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.FixedSelector" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.FixedSelector">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">FixedSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.FixedSelector" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.center_selection.CenterSelector</span></code></a></p>
 <p>Center selector which always picks the same centers.</p>
 <p>The fixed centers are specified at class initialization time.</p>
@@ -486,8 +437,8 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.center_selection.FixedSelector.select">
-<code class="sig-name descname"><span class="pre">select</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.FixedSelector.select">
+<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Returns the fixed centers with which this instance was created</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -515,8 +466,8 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.center_selection.FixedSelector.select_indices">
-<code class="sig-name descname"><span class="pre">select_indices</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select_indices" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.center_selection.FixedSelector.select_indices">
+<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Returns the fixed centers, and their indices with which this instance was created</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -553,49 +504,33 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 
 
            </div>
-           
           </div>
-          <footer>
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
         <a href="sparse.html" class="btn btn-neutral float-left" title="falkon.sparse" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="hopt.html" class="btn btn-neutral float-right" title="falkon.hopt" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-    <p>
-        &#169; Copyright 2020, Giacomo Meanti.
-
-    </p>
+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
   </div>
-    
-    
-    
-    Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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     <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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-    provided by <a href="https://readthedocs.org">Read the Docs</a>. 
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
 
 </footer>
         </div>
       </div>
-
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-
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-  
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+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
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diff --git a/api_reference/gsc_losses.html b/api_reference/gsc_losses.html
index 3dbe8114..a7d658e9 100644
--- a/api_reference/gsc_losses.html
+++ b/api_reference/gsc_losses.html
@@ -1,78 +1,40 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
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 <section id="module-falkon">
-<span id="falkon-gsc-losses"></span><h1>falkon.gsc_losses<a class="headerlink" href="#module-falkon" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-gsc-losses"></span><h1>falkon.gsc_losses<a class="headerlink" href="#module-falkon" title="Permalink to this headline"></a></h1>
 <section id="loss">
-<h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶</a></h2>
+<h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.gsc_losses.Loss">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></code><code class="sig-name descname"><span class="pre">Loss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.Loss" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.Loss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.Loss" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract generalized self-concordant loss function class.</p>
 <p>Such loss functions must be three times differentiable; but for the logistic Falkon algorithm
 only the first two derivatives are used.
@@ -363,8 +313,8 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶
 </dl>
 </div>
 <dl class="py method">
-<dt id="falkon.gsc_losses.Loss.__call__">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.__call__" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.Loss.__call__">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the loss for predicting <cite>y2</cite> with true labels <cite>y1</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -380,8 +330,8 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.Loss.ddf">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">ddf</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.ddf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.Loss.ddf">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.ddf" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the second derivative of the loss wrt <cite>y2</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -399,8 +349,8 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.Loss.df">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">df</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.df" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.Loss.df">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.df" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the derivative of the loss wrt <cite>y2</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -418,8 +368,8 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.Loss.knmp_grad">
-<code class="sig-name descname"><span class="pre">knmp_grad</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_grad" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.Loss.knmp_grad">
+<span class="sig-name descname"><span class="pre">knmp_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_grad" title="Permalink to this definition"></a></dt>
 <dd><p>Computes a kernel vector product where the vector is the first derivative of this loss</p>
 <p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
 number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
@@ -453,8 +403,8 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.Loss.knmp_hess">
-<code class="sig-name descname"><span class="pre">knmp_hess</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_hess" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.Loss.knmp_hess">
+<span class="sig-name descname"><span class="pre">knmp_hess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_hess" title="Permalink to this definition"></a></dt>
 <dd><p>Compute a kernel-vector product with a rescaling with the second derivative</p>
 <p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
 number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
@@ -487,10 +437,10 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">¶
 
 </section>
 <section id="logistic-loss">
-<h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to this headline">¶</a></h2>
+<h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.gsc_losses.LogisticLoss">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></code><code class="sig-name descname"><span class="pre">LogisticLoss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">LogisticLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper for the logistic loss, to be used in conjunction with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Usage of this loss assumes a binary classification problem with labels -1 and +1. For different
 choices of labels, see <a class="reference internal" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a>.</p>
@@ -510,8 +460,8 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 </pre></div>
 </div>
 <dl class="py method">
-<dt id="falkon.gsc_losses.LogisticLoss.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.__call__" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the logistic loss between two 1-dimensional tensors</p>
 <p>The formula used is <span class="math notranslate nohighlight">\(\log(1 + \exp(-y_1 * y_2))\)</span></p>
 <dl class="field-list simple">
@@ -528,8 +478,8 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.LogisticLoss.ddf">
-<code class="sig-name descname"><span class="pre">ddf</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.ddf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss.ddf">
+<span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.ddf" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the second derivative of the logistic loss with respect to <cite>y2</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -548,8 +498,8 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.LogisticLoss.df">
-<code class="sig-name descname"><span class="pre">df</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.df" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss.df">
+<span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.df" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the derivative of the logistic loss with respect to <cite>y2</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -571,10 +521,10 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 
 </section>
 <section id="weighted-binary-cross-entropy-loss">
-<h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-binary-cross-entropy-loss" title="Permalink to this headline">¶</a></h2>
+<h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-binary-cross-entropy-loss" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></code><code class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper for the weighted binary cross-entropy loss, to be used with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Using this loss assumes a binary classification problem with labels 0 and +1. Additionally,
 this loss allows to place a different weight to samples belonging to one of the two classes
@@ -599,8 +549,8 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 </pre></div>
 </div>
 <dl class="py method">
-<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.__call__" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the weighted BCE loss between two 1-dimensional tensors</p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -620,8 +570,8 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss.ddf">
-<code class="sig-name descname"><span class="pre">ddf</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.ddf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss.ddf">
+<span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.ddf" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the second derivative of the weighted BCE loss with respect to <cite>pred</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -640,8 +590,8 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.gsc_losses.WeightedCrossEntropyLoss.df">
-<code class="sig-name descname"><span class="pre">df</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.df" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss.df">
+<span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.df" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the derivative of the weighted BCE loss with respect to <cite>pred</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -666,50 +616,33 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 
 
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+<!DOCTYPE html>
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+<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#more-examples">More Examples</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+
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+.math {
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+</style>
+<section id="module-falkon.hopt">
+<span id="falkon-hopt"></span><h1>falkon.hopt<a class="headerlink" href="#module-falkon.hopt" title="Permalink to this headline"></a></h1>
+<section id="objectives">
+<h2>Objectives<a class="headerlink" href="#objectives" title="Permalink to this headline"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.objectives.HyperoptObjective">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.objectives.</span></span><span class="sig-name descname"><span class="pre">HyperoptObjective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.objectives.HyperoptObjective" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<section id="nystrom-complexity-regularization">
+<h3>Nystrom Complexity Regularization<a class="headerlink" href="#nystrom-complexity-regularization" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.NystromCompReg">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">NystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.NystromCompReg" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="stochastic-nystrom-computational-regularization">
+<h3>Stochastic Nystrom Computational Regularization<a class="headerlink" href="#stochastic-nystrom-computational-regularization" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.StochasticNystromCompReg">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">StochasticNystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_trace_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.StochasticNystromCompReg" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="complexity-regularization">
+<h3>Complexity Regularization<a class="headerlink" href="#complexity-regularization" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.CompReg">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">CompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.CompReg" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="generalized-cross-validation">
+<h3>Generalized Cross Validation<a class="headerlink" href="#generalized-cross-validation" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.GCV">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">GCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.GCV" title="Permalink to this definition"></a></dt>
+<dd><p>GCV objective is</p>
+<div class="math notranslate nohighlight">
+\[\dfrac{\dfrac{1}{n} \lVert (I - \widetilde{K}_\lambda \widetilde{K}) Y \rVert^2}
+      {\frac{1}{n} \mathrm{Tr}(I - \widetilde{K}_\lambda \widetilde{K}) }\]</div>
+<p>We must compute the two terms denoted as the numerator and the denominator.
+Using the usual names for matrix variable substitutions (taken from gpflow code), we have that
+the numerator can be computed as</p>
+<div class="math notranslate nohighlight">
+\[\dfrac{1}{n} \lVert (I - A^\top \mathrm{LB}^{-\top} \mathrm{LB}^{-1} A) Y \rVert^2\]</div>
+<p>We compute the terms inside the norm first, from right to left using matrix-vector multiplications
+and triangular solves. Finally we compute the norm.</p>
+<p>The denominator is far less efficient to compute, since it requires working with m*n matrices.
+It can be expressed in terms of the same matrices as above:</p>
+<div class="math notranslate nohighlight">
+\[\Big( \frac{1}{n} (\mathrm{Tr}(I) - \lVert \mathrm{LB}^{-1}A \rVert_F^2 ) \Big)^2\]</div>
+</dd></dl>
+
+</section>
+<section id="hold-out-cross-validation">
+<h3>Hold Out Cross Validation<a class="headerlink" href="#hold-out-cross-validation" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.HoldOut">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">HoldOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_pct</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_iter_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.HoldOut" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="leave-one-out-cross-validation">
+<h3>Leave One Out Cross Validation<a class="headerlink" href="#leave-one-out-cross-validation" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.LOOCV">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">LOOCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.LOOCV" title="Permalink to this definition"></a></dt>
+<dd><p>LOOCV objective is to minimize the PRESS error:</p>
+<div class="math notranslate nohighlight">
+\[\min \sum_{i=1}^n \big(e_{(i)}\big)_i^2\]</div>
+<p>whose components are calculated as</p>
+<div class="math notranslate nohighlight">
+\[\big(e_{(i)}\big)_i = (y_i - f(\alpha_{(i)})_i) = \dfrac{y_i - f(\alpha)_i}{1 - S_{ii}}\]</div>
+<p>where</p>
+<div class="math notranslate nohighlight">
+\[S = K_{nm} (\lambda K_{mm} + K_{nm}^\top K_{nm})^{-1} K_{nm}^\top\]</div>
+<p>So we first need to compute the N-KRR solution <span class="math notranslate nohighlight">\(\alpha\)</span> and then the diagonal of matrix
+<span class="math notranslate nohighlight">\(S\)</span>. For simplicity we use a direct solver the N-KRR problem, although Falkon could easily
+be substituted in.</p>
+</dd></dl>
+
+</section>
+<section id="sgpr">
+<h3>SGPR<a class="headerlink" href="#sgpr" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.hopt.objectives.SGPR">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">SGPR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.SGPR" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/api_reference/index.html b/api_reference/index.html
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@@ -1,76 +1,38 @@
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-
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-  <meta name="generator" content="Docutils 0.17: http://docutils.sourceforge.net/" />
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+  <title>API Reference &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+    <link rel="prev" title="MNIST Classification with Falkon" href="../examples/falkon_mnist.html" /> 
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@@ -101,6 +52,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
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@@ -109,13 +61,13 @@
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-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
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 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -130,19 +82,51 @@
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 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">API Reference</a><ul>
@@ -154,9 +138,12 @@
 </li>
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+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +189,12 @@
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
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@@ -229,75 +208,47 @@
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+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
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+<span id="id1"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline"></a></h1>
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 <ul>
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@@ -334,9 +285,12 @@
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 <li class="toctree-l1"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
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 <li class="toctree-l3"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -382,20 +336,12 @@
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-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -409,56 +355,52 @@
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -132,19 +84,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
@@ -156,9 +140,12 @@
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -204,20 +191,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -231,77 +210,48 @@
 <li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
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@@ -327,48 +277,41 @@
 }
 </style>
 <section id="module-falkon.kernels">
-<span id="falkon-kernels"></span><h1>falkon.kernels<a class="headerlink" href="#module-falkon.kernels" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-kernels"></span><h1>falkon.kernels<a class="headerlink" href="#module-falkon.kernels" title="Permalink to this headline"></a></h1>
 <section id="kernel">
-<h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline">¶</a></h2>
+<h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.kernels.kernel.Kernel">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.kernel.</span></code><code class="sig-name descname"><span class="pre">Kernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_type</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.kernel.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract kernel class. Kernels should inherit from this class, overriding appropriate methods.</p>
 <p>To extend Falkon with new kernels, you should read the documentation of this class
-carefully. In particular, you will <strong>need</strong> to implement <a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare" title="falkon.kernels.kernel.Kernel._prepare"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare()</span></code></a>, <a class="reference internal" href="#falkon.kernels.kernel.Kernel._apply" title="falkon.kernels.kernel.Kernel._apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_apply()</span></code></a> and
-<a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> methods.</p>
-<p>Other methods which should be optionally implemented are the sparse versions
-<a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare_sparse" title="falkon.kernels.kernel.Kernel._prepare_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare_sparse()</span></code></a> and <a class="reference internal" href="#falkon.kernels.kernel.Kernel._apply_sparse" title="falkon.kernels.kernel.Kernel._apply_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_apply_sparse()</span></code></a> (note that there is no <cite>_finalize_sparse</cite>,
-since the <cite>_finalize</cite> takes as input a partial kernel matrix, and even with sparse data,
-kernel matrices are assumed to be dense. Therefore, even for sparse data, the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a>
-method will be used.</p>
-<p>To provide a KeOps implementation, you will have to inherit also from the
-<a class="reference internal" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a> class, and implement its abstract methods. In case
-a KeOps implementation is provided, you should make sure to override the
-<a class="reference internal" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="falkon.kernels.kernel.Kernel._decide_mmv_impl"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_decide_mmv_impl()</span></code></a> and <a class="reference internal" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="falkon.kernels.kernel.Kernel._decide_dmmv_impl"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_decide_dmmv_impl()</span></code></a> so that the KeOps implementation is
-effectively used. Have a look at the <a class="reference internal" href="#falkon.kernels.PolynomialKernel" title="falkon.kernels.PolynomialKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.kernels.PolynomialKernel</span></code></a> class for
-an example of how to integrate KeOps in the kernel.</p>
+carefully, and take a look at the existing implementation of <a class="reference internal" href="#falkon.kernels.GaussianKernel" title="falkon.kernels.GaussianKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianKernel</span></code></a>
+or <a class="reference internal" href="#falkon.kernels.LinearKernel" title="falkon.kernels.LinearKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearKernel</span></code></a>. A walk-through for implementing a custom kernel
+is available as a <a class="reference internal" href="../examples/custom_kernels.html"><span class="doc">notebook</span></a>.</p>
+<p>There are several abstract methods which should be implemented, depending on the kind of operations
+which are supported by the implementing kernel.</p>
+<p>The <a class="reference internal" href="#falkon.kernels.kernel.Kernel.compute" title="falkon.kernels.kernel.Kernel.compute"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute()</span></code></a> method should compute the kernel matrix, without concerns for differentiability,
+while the <a class="reference internal" href="#falkon.kernels.kernel.Kernel.compute_sparse" title="falkon.kernels.kernel.Kernel.compute_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_sparse()</span></code></a> method is used to compute the kernel for sparse input matrices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>name</strong> – A short name for the kernel (e.g. “Gaussian”)</p></li>
-<li><p><strong>kernel_type</strong> – A short string describing the type of kernel. This may be used to create specialized
-functions in <a class="reference internal" href="mmv_ops.html#module-falkon.mmv_ops" title="falkon.mmv_ops"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.mmv_ops</span></code></a> which optimize for a specific kernel type.</p></li>
 <li><p><strong>opt</strong> – Base set of options to be used for operations involving this kernel.</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
-M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
 <li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
@@ -376,56 +319,14 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._apply">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_apply</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._apply" title="Permalink to this definition">¶</a></dt>
-<dd><p>Main kernel operation, usually matrix multiplication.</p>
-<p>This function will be called with two blocks of data which may be subsampled on the
-first and second dimension (i.e. X1 may be of size <cite>n x d</cite> where <cite>n &lt;&lt; N</cite> and <cite>d &lt;&lt; D</cite>).
-The output shall be stored in the <cite>out</cite> argument, and not be returned.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – (n x d) tensor. It is a block of the <cite>X1</cite> input matrix, possibly subsampled in the
-first dimension.</p></li>
-<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – (m x d) tensor. It is a block of the <cite>X2</cite> input matrix, possibly subsampled in the
-first dimension.</p></li>
-<li><p><strong>out</strong> (<em>torch.Tensor</em>) – (n x m) tensor. A tensor in which the output of the operation shall be accumulated.
-This tensor is initialized to 0 before calling <cite>_apply</cite>, but in case of subsampling
-of the data along the second dimension, multiple calls will be needed to compute a
-single (n x m) output block. In such case, the first call to this method will have
-a zeroed tensor, while subsequent calls will simply reuse the same object.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._apply_sparse">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_apply_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._apply_sparse" title="Permalink to this definition">¶</a></dt>
-<dd><p>Main kernel computation for sparse tensors.</p>
-<p>Unlike the :meth`_apply` method, the <cite>X1</cite> and <cite>X2</cite> tensors are only subsampled along
-the first dimension. Take note that the <cite>out</cite> tensor <strong>is not sparse</strong>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (n x D), with possibly n &lt;&lt; N.</p></li>
-<li><p><strong>X2</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (m x D), with possibly m &lt;&lt; M.</p></li>
-<li><p><strong>out</strong> (<em>torch.Tensor</em>) – Tensor of shape (n x m) which should hold a tile of the kernel. The output of this
-function (typically a matrix multiplication) should be placed in this tensor.</p></li>
-</ul>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._decide_dmmv_impl">
-<code class="sig-name descname"><span class="pre">_decide_dmmv_impl</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_dmmv_impl">
+<span class="sig-name descname"><span class="pre">_decide_dmmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>dmmv</cite> function to use for this data.</p>
 <p>Note that <cite>dmmv</cite> functions compute double kernel-vector products (see <a class="reference internal" href="#falkon.kernels.kernel.Kernel.dmmv" title="falkon.kernels.kernel.Kernel.dmmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dmmv()</span></code></a> for
 an explanation of what they are).</p>
@@ -450,8 +351,8 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._decide_mm_impl">
-<code class="sig-name descname"><span class="pre">_decide_mm_impl</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mm_impl" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_mm_impl">
+<span class="sig-name descname"><span class="pre">_decide_mm_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mm_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>mm</cite> function to use for this data.</p>
 <p>Note that <cite>mm</cite> functions compute the kernel itself so <strong>KeOps may not be used</strong>.</p>
 <dl class="field-list simple">
@@ -459,6 +360,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – First data matrix, of shape (N x D)</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – Second data matrix, of shape (M x D)</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
 <li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options. Options may be specified to force GPU or CPU usage.</p></li>
 </ul>
 </dd>
@@ -473,8 +375,8 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._decide_mmv_impl">
-<code class="sig-name descname"><span class="pre">_decide_mmv_impl</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_mmv_impl">
+<span class="sig-name descname"><span class="pre">_decide_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>mmv</cite> function to use for this data.</p>
 <p>Note that <cite>mmv</cite> functions compute the kernel-vector product</p>
 <dl class="field-list simple">
@@ -497,81 +399,59 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._finalize">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_finalize</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._finalize" title="Permalink to this definition">¶</a></dt>
-<dd><p>Final actions to be performed on a partial kernel matrix.</p>
-<p>All elementwise operations on the kernel matrix should be performed in this method.
-Operations should be performed inplace by modifying the matrix <cite>A</cite>, to improve memory
-efficiency. If operations are not in-place, out-of-memory errors are possible when
-using the GPU.</p>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.compute">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> - without regards for differentiability.</p>
+<p>The kernel matrix should be stored in <code class="docutils literal notranslate"><span class="pre">out</span></code> to ensure the correctness of allocatable
+memory computations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>A</strong> (<em>torch.Tensor</em>) – A (m x n) tile of the kernel matrix, as obtained by the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._apply" title="falkon.kernels.kernel.Kernel._apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_apply()</span></code></a> method.</p></li>
-<li><p><strong>d</strong> – Additional data, as computed by the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare" title="falkon.kernels.kernel.Kernel._prepare"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare()</span></code></a> method.</p></li>
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The left matrix for computing the kernel</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The right matrix for computing the kernel</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em>) – The output matrix into which implementing classes should store the kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – If true, <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> have the same shape, and only the diagonal of <code class="docutils literal notranslate"><span class="pre">k(X1,</span> <span class="pre">X2)</span></code>
+is to be computed and stored in <code class="docutils literal notranslate"><span class="pre">out</span></code>. Otherwise compute the full kernel matrix.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><em>A</em> – The same tensor as the input, if operations are performed in-place. Otherwise
-another tensor of the same shape.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._prepare">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_prepare</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">Any</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._prepare" title="Permalink to this definition">¶</a></dt>
-<dd><p>Pre-processing operations necessary to compute the kernel.</p>
-<p>This function will be called with two blocks of data which may be subsampled on the
-first dimension (i.e. X1 may be of size <cite>n x D</cite> where <cite>n &lt;&lt; N</cite>). The function should
-not modify <cite>X1</cite> and <cite>X2</cite>. If necessary, it may return some data which is then made available
-to the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> method.</p>
-<p>For example, in the Gaussian kernel, this method is used to compute the squared norms
-of the datasets.</p>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.compute_sparse">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute_sparse" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> which are two sparse matrices, storing the output
+in the dense matrix <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – (n x D) tensor. It is a block of the <cite>X1</cite> input matrix, possibly subsampled in the
-first dimension.</p></li>
-<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – (m x D) tensor. It is a block of the <cite>X2</cite> input matrix, possibly subsampled in the
-first dimension.</p></li>
+<li><p><strong>X1</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The left matrix for computing the kernel</p></li>
+<li><p><strong>X2</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The right matrix for computing the kernel</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em>) – The output matrix into which implementing classes should store the kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – If true, <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> have the same shape, and only the diagonal of <code class="docutils literal notranslate"><span class="pre">k(X1,</span> <span class="pre">X2)</span></code>
+is to be computed and stored in <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p></li>
+<li><p><strong>kwargs</strong> – <p>Additional keyword arguments which some sparse implementations might require. Currently
+the keyword arguments passed by the <a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.mmv_ops.fmmv.sparse_mmv_run_thread()</span></code></a>
+and <a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.mmv_ops.fmm.sparse_mm_run_thread()</span></code></a> functions are:</p>
+<ul>
+<li><p>X1_csr : the X1 matrix in CSR format</p></li>
+<li><p>X2_csr : the X2 matrix in CSR format</p></li>
 </ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p>Data which may be used for the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> method. If no such information is</p></li>
-<li><p><em>necessary, returns None.</em></p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel._prepare_sparse">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">_prepare_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._prepare_sparse" title="Permalink to this definition">¶</a></dt>
-<dd><p>Data preprocessing for sparse tensors.</p>
-<p>This is an equivalent to the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._prepare" title="falkon.kernels.kernel.Kernel._prepare"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_prepare()</span></code></a> method for sparse tensors.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (n x D), with possibly n &lt;&lt; N.</p></li>
-<li><p><strong>X2</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>falkon.sparse.sparse_tensor.SparseTensor</em></a>) – Sparse tensor of shape (m x D), with possibly m &lt;&lt; M.</p></li>
+</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p>Data derived from <cite>X1</cite> and <cite>X2</cite> which is needed by the <a class="reference internal" href="#falkon.kernels.kernel.Kernel._finalize" title="falkon.kernels.kernel.Kernel._finalize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">_finalize()</span></code></a> method when</p></li>
-<li><p><em>finishing to compute a kernel tile.</em></p></li>
-</ul>
-</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <cite>out</cite>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel.dmmv">
-<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.dmmv">
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -613,8 +493,8 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel.extra_mem">
-<code class="sig-name descname"><span class="pre">extra_mem</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; <span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.extra_mem" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.extra_mem">
+<span class="sig-name descname"><span class="pre">extra_mem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.extra_mem" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the amount of extra memory which will be needed when computing this kernel.</p>
 <p>Often kernel computation needs some extra memory allocations. To avoid using too large
 block-sizes which may lead to OOM errors, you should declare any such extra allocations
@@ -637,8 +517,8 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.kernel.Kernel.mmv">
-<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.mmv">
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -674,45 +554,167 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 </dd></dl>
 
+</section>
+<section id="diffkernel">
+<h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.kernels.keops_helpers.KeopsKernelMixin">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.keops_helpers.</span></code><code class="sig-name descname"><span class="pre">KeopsKernelMixin</span></code><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="py method">
-<dt id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_dmmv_helper">
-<code class="sig-name descname"><span class="pre">keops_dmmv_helper</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmv_fn</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_dmmv_helper" title="Permalink to this definition">¶</a></dt>
-<dd><p>performs fnc(X1*X2’, X1, X2)’ * ( fnc(X1*X2’, X1, X2) * v  +  w )</p>
+<dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.diff_kernel.</span></span><span class="sig-name descname"><span class="pre">DiffKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">core_fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kernel_params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel" title="Permalink to this definition"></a></dt>
+<dd><p>Abstract class for differentiable kernels.</p>
+<p>This class should be extended instead of <a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a> whenever designing
+a custom kernel to be used with automatic hyperparameter optimization (see the <a class="reference internal" href="hopt.html#module-falkon.hopt" title="falkon.hopt"><code class="xref py py-mod docutils literal notranslate"><span class="pre">hopt</span></code></a>
+module).</p>
+<p>Subclasses should implement the <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel.detach" title="falkon.kernels.diff_kernel.DiffKernel.detach"><code class="xref py py-meth docutils literal notranslate"><span class="pre">detach()</span></code></a> method to return a new instance of the kernel,
+with its parameters detached from the computation graph.</p>
+<p>The <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel.compute_diff" title="falkon.kernels.diff_kernel.DiffKernel.compute_diff"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_diff()</span></code></a> method should be overridden, unless the <code class="docutils literal notranslate"><span class="pre">core_fn</span></code> parameter is
+passed to the constructor.</p>
+<p>Hyperparameters to the concrete kernel instance (for example the length-scale of the Gaussian
+kernel) should be passed to the constructor of this class, in order to be registered
+as parameters of the computation graph. Even non-differentiable parameters should be provided
+as keywords (also non tensor arguments).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – N x D tensor</p></li>
-<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – M x D tensor</p></li>
-<li><p><strong>v</strong> (<em>torch.Tensor</em>) – M x T tensor. Often, T = 1 and this is a vector.</p></li>
-<li><p><strong>w</strong> (<em>torch.Tensor</em>) – N x T tensor. Often, T = 1 and this is a vector.</p></li>
-<li><p><strong>kernel</strong> (<a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – Kernel instance to calculate this kernel. This is only here to preserve API
-structure.</p></li>
-<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor in which to store the output (M x T)</p></li>
-<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Options to be passed downstream</p></li>
-<li><p><strong>mmv_fn</strong> (<em>Callable</em>) – The function which performs the mmv operation. Two mmv operations are (usually)
-needed for a dmmv operation.</p></li>
+<li><p><strong>name</strong> – A short name for the kernel (e.g. “Gaussian”)</p></li>
+<li><p><strong>options</strong> – Base set of options to be used for operations involving this kernel.</p></li>
+<li><p><strong>core_fn</strong> – Optional function which can be used to compute a kernel matrix.
+The signature of the function should be:
+<code class="docutils literal notranslate"><span class="pre">core_fn(X1,</span> <span class="pre">X2,</span> <span class="pre">out,</span> <span class="pre">diag,</span> <span class="pre">**kernel_parameters)`</span></code>
+where <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> are the input matrices, <code class="docutils literal notranslate"><span class="pre">out</span></code> is the output matrix (it will
+be <code class="docutils literal notranslate"><span class="pre">None</span></code> when called from <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel.compute_diff" title="falkon.kernels.diff_kernel.DiffKernel.compute_diff"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_diff()</span></code></a>), <code class="docutils literal notranslate"><span class="pre">diag</span></code> is a flag indicating
+that only the diagonal of the kernel matrix is to be computed, and <code class="docutils literal notranslate"><span class="pre">**kernel_parameters</span></code>
+includes all additional parameters belonging to the kernel (which are passed to the
+constructor of <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">DiffKernel</span></code></a>).</p></li>
+<li><p><strong>kernel_params</strong> – All parameters (differentiable and non-differentiable) to be used for this kernel.
+The values given are used to initialize the actual parameters - which will be copied in
+the constructor.</p></li>
 </ul>
 </dd>
 </dl>
-<p class="rubric">Notes</p>
-<p>The double MMV is implemented as two separate calls to the user-supplied
-<cite>mmv_fn</cite>. The first one calculates the inner part of the formula (NxT)
-while the second calculates the outer matrix-vector multiplication which</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel.compute_diff">
+<span class="sig-name descname"><span class="pre">compute_diff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.compute_diff" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>. The output should be differentiable with
+respect to <cite>X1</cite>, <cite>X2</cite>, and all kernel parameters returned by the <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel.diff_params" title="falkon.kernels.diff_kernel.DiffKernel.diff_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">diff_params()</span></code></a> method.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The left matrix for computing the kernel</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The right matrix for computing the kernel</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – If true, <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> have the same shape, and only the diagonal of <code class="docutils literal notranslate"><span class="pre">k(X1,</span> <span class="pre">X2)</span></code>
+is to be computed and stored in <code class="docutils literal notranslate"><span class="pre">out</span></code>. Otherwise compute the full kernel matrix.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The constructed kernel matrix.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel.detach">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">detach</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.detach" title="Permalink to this definition"></a></dt>
+<dd><p>Detaches all differentiable parameters of the kernel from the computation graph.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><em>k</em> – A new instance of the kernel sharing the same parameters, but detached from the
+computation graph.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel.diff_params">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diff_params</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.diff_params" title="Permalink to this definition"></a></dt>
+<dd><p>A dictionary mapping parameter names to their values for all <strong>differentiable</strong> parameters
+of the kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><em>params</em> – A dictionary mapping parameter names to their values</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="keopskernelmixin">
+<h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permalink to this headline"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.keops_helpers.</span></span><span class="sig-name descname"><span class="pre">KeopsKernelMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="Permalink to this definition"></a></dt>
+<dd><p>Abstract class for kernels which enables KeOps acceleration.</p>
+<p>This class should be extended when KeOps-accelerated kernel-vector products are required.
+Subclasses should implement the <a class="reference internal" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl"><code class="xref py py-meth docutils literal notranslate"><span class="pre">keops_mmv_impl()</span></code></a> method by defining an appropriate
+KeOps formula, and calling into KeOps for kernel vector product calculation (the helper method
+<a class="reference internal" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv" title="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">keops_mmv()</span></code></a> can be used to call into KeOps).</p>
+<p>For help in implementing a subclass, check the existing implementations
+(e.g. <a class="reference internal" href="#falkon.kernels.GaussianKernel" title="falkon.kernels.GaussianKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianKernel</span></code></a>), and the
+<a class="reference internal" href="../examples/custom_kernels.html"><span class="doc">Custom Kernels</span></a> notebook.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv">
+<span class="sig-name descname"><span class="pre">keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv" title="Permalink to this definition"></a></dt>
+<dd><p>Helper method to call into KeOps for kernel-vector products</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <code class="docutils literal notranslate"><span class="pre">T</span></code> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>formula</strong> (<em>str</em>) – The KeOps formula</p></li>
+<li><p><strong>aliases</strong> – Aliases referencing names in the formula with actual KeOps variables</p></li>
+<li><p><strong>other_vars</strong> – Kernel parameters to be used in the formula, other than <code class="docutils literal notranslate"><span class="pre">X1</span></code>, <code class="docutils literal notranslate"><span class="pre">X2</span></code> and <code class="docutils literal notranslate"><span class="pre">v</span></code>.</p></li>
+<li><p><strong>opt</strong> – Options to be used for computing the operation. Useful are the memory size options,
+CUDA options and KeOps options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>out</em> – The computed kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>, multiplied by vector <code class="docutils literal notranslate"><span class="pre">v</span></code>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">keops_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="Permalink to this definition"></a></dt>
+<dd><p>Implementation of the KeOps formula to compute a kernel-vector product.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <code class="docutils literal notranslate"><span class="pre">T</span></code> is very large the operations will be much slower.</p></li>
+<li><p><strong>kernel</strong> (<a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – Instance of this class. This is equal to <code class="docutils literal notranslate"><span class="pre">self</span></code> and can be ignored.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Options to be used for computing the operation. Useful are the memory size options,
+CUDA options and KeOps options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><em>out</em> – The computed kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>, multiplied by vector <code class="docutils literal notranslate"><span class="pre">v</span></code>.</p>
+</dd>
+</dl>
 </dd></dl>
 
 </dd></dl>
 
 </section>
 <section id="radial-kernels">
-<h2>Radial kernels<a class="headerlink" href="#radial-kernels" title="Permalink to this headline">¶</a></h2>
+<h2>Radial kernels<a class="headerlink" href="#radial-kernels" title="Permalink to this headline"></a></h2>
 <section id="gaussian-kernel">
-<h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalink to this headline">¶</a></h3>
+<h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.kernels.GaussianKernel">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">GaussianKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.GaussianKernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">GaussianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Gaussian kernel and related kernel-vector products</p>
 <p>The Gaussian kernel is one of the most common and effective kernel embeddings
 since it is infinite dimensional, and governed by a single parameter. The kernel length-scale
@@ -766,16 +768,17 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 <p>In both cases, the actual computation follows a different path, working on the expanded
 norm.</p>
 <dl class="py method">
-<dt id="falkon.kernels.GaussianKernel.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
-M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
 <li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
@@ -783,14 +786,14 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.GaussianKernel.dmmv">
-<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.dmmv">
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -832,8 +835,8 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.GaussianKernel.mmv">
-<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.mmv">
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -871,10 +874,10 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 
 </section>
 <section id="laplacian-kernel">
-<h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permalink to this headline">¶</a></h3>
+<h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.kernels.LaplacianKernel">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">LaplacianKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LaplacianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Laplacian kernel, and related kernel-vector products.</p>
 <p>The Laplacian kernel is similar to the Gaussian kernel, but less sensitive to changes
 in the parameter <cite>sigma</cite>.</p>
@@ -888,9 +891,76 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 <div class="math notranslate nohighlight">
 \[k(x, x') = \exp{-\frac{\lVert x - x' \rVert}{\sigma}}\]</div>
 <dl class="py method">
-<dt id="falkon.kernels.LaplacianKernel.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.dmmv">
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.dmmv" title="Permalink to this definition"></a></dt>
+<dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
+<p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
+where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
+we remove the additive factor.
+<strong>At least one of `w` and `v` must be provided</strong>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – A vector to compute matrix-vector products. This may also be a matrix of shape
+(N x T) but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (M x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([150, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.mmv">
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition"></a></dt>
+<dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -898,6 +968,72 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 N samples in D dimensions.</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
 M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+<li><p><strong>v</strong> (<em>torch.Tensor</em>) – A vector to compute the matrix-vector product. This may also be a matrix of shape
+(M x T), but if <cite>T</cite> is very large the operations will be much slower.</p></li>
+<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x T) to hold the output. If not provided it will
+be created.</p></li>
+<li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
+and CUDA options.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">out</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">torch.Size([100, 1])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="matern-kernel">
+<h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to this headline"></a></h3>
+<dl class="py class">
+<dt class="sig sig-object py" id="falkon.kernels.MaternKernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">MaternKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel" title="Permalink to this definition"></a></dt>
+<dd><p>Class for computing the Matern kernel, and related kernel-vector products.</p>
+<p>The Matern kernels define a generic class of kernel functions which includes the
+Laplacian and Gaussian kernels. The class is parametrized by ‘nu’. When <code class="docutils literal notranslate"><span class="pre">nu</span> <span class="pre">=</span> <span class="pre">0.5</span></code>
+this kernel is equivalent to the Laplacian kernel, when <code class="docutils literal notranslate"><span class="pre">nu</span> <span class="pre">=</span> <span class="pre">float('inf')</span></code>, the
+Matern kernel is equivalent to the Gaussian kernel.</p>
+<p>This class implements the Matern kernel only for the values of nu which have a closed
+form solution, which are 0.5, 1.5, 2.5, and infinity.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sigma</strong> – The length-scale of the Matern kernel. The length-scale can be either a scalar
+or a vector. Matrix-valued length-scales are not allowed for the Matern kernel.</p></li>
+<li><p><strong>nu</strong> – The parameter of the Matern kernel. It should be one of <cite>0.5</cite>, <cite>1.5</cite>, <cite>2.5</cite> or
+<cite>inf</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>While for <cite>nu = float(‘inf’)</cite> this kernel is equivalent to the <a class="reference internal" href="#falkon.kernels.GaussianKernel" title="falkon.kernels.GaussianKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianKernel</span></code></a>,
+this implementation is more general. Using the <a class="reference internal" href="#falkon.kernels.GaussianKernel" title="falkon.kernels.GaussianKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianKernel</span></code></a> directly
+may be computationally more efficient.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.kernels.MaternKernel.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.__call__" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
+N samples in D dimensions.</p></li>
+<li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
 <li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
@@ -905,14 +1041,14 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.LaplacianKernel.dmmv">
-<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.MaternKernel.dmmv">
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -954,8 +1090,8 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.LaplacianKernel.mmv">
-<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.MaternKernel.mmv">
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -994,38 +1130,39 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 </section>
 </section>
 <section id="dot-product-kernels">
-<h2>Dot-Product kernels<a class="headerlink" href="#dot-product-kernels" title="Permalink to this headline">¶</a></h2>
+<h2>Dot-Product kernels<a class="headerlink" href="#dot-product-kernels" title="Permalink to this headline"></a></h2>
 <section id="polynomial-kernel">
-<h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">¶</a></h3>
+<h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.kernels.PolynomialKernel">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">PolynomialKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">PolynomialKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Polynomial kernel with multiplicative and additive constants.</p>
 <p>Follows the formula</p>
 <div class="math notranslate nohighlight">
-\[(\alpha * X_1^\top X_2 + \beta)^{\mathrm{degree}}\]</div>
+\[(\gamma * X_1^\top X_2 + \beta)^{\mathrm{degree}}\]</div>
 <p>Where all operations apart from the matrix multiplication are taken element-wise.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>alpha</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
 <li><p><strong>beta</strong> (<em>float-like</em>) – Additive constant</p></li>
+<li><p><strong>gamma</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
 <li><p><strong>degree</strong> (<em>float-like</em>) – Power of the polynomial kernel</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options which will be used in downstream kernel operations.</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.kernels.PolynomialKernel.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
-M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
 <li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
@@ -1033,14 +1170,14 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.PolynomialKernel.dmmv">
-<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.dmmv">
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -1082,8 +1219,8 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.PolynomialKernel.mmv">
-<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.mmv">
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1121,10 +1258,10 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 
 </section>
 <section id="linear-kernel">
-<h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">¶</a></h3>
+<h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.kernels.LinearKernel">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">LinearKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.LinearKernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LinearKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Linear Kernel with optional scaling and translation parameters.</p>
 <p>The kernel implemented here is the covariance function in the original
 input space (i.e. <cite>X &#64; X.T</cite>) with optional parameters to translate
@@ -1133,7 +1270,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>beta</strong> (<em>float-like</em>) – Additive constant for the kernel, default: 0.0</p></li>
-<li><p><strong>sigma</strong> (<em>float-like</em>) – Multiplicative constant for the kernel. The kernel will
+<li><p><strong>gamma</strong> (<em>float-like</em>) – Multiplicative constant for the kernel. The kernel will
 be multiplied by the inverse of sigma squared. Default: 1.0</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options which will be used in downstream kernel operations.</p></li>
 </ul>
@@ -1147,16 +1284,17 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 </pre></div>
 </div>
 <dl class="py method">
-<dt id="falkon.kernels.LinearKernel.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dt class="sig sig-object py" id="falkon.kernels.LinearKernel.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
-M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
 <li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
@@ -1164,14 +1302,14 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.LinearKernel.dmmv">
-<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.LinearKernel.dmmv">
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -1213,8 +1351,8 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.LinearKernel.mmv">
-<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.LinearKernel.mmv">
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1252,10 +1390,10 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 
 </section>
 <section id="sigmoid-kernel">
-<h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink to this headline">¶</a></h3>
+<h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.kernels.SigmoidKernel">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></code><code class="sig-name descname"><span class="pre">SigmoidKernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">SigmoidKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Sigmoid (or hyperbolic tangent) kernel function, with additive and multiplicative constants.</p>
 <p>Follows the formula</p>
 <div class="math notranslate nohighlight">
@@ -1263,23 +1401,24 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>alpha</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
 <li><p><strong>beta</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
+<li><p><strong>gamma</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options which will be used in downstream kernel operations.</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.kernels.SigmoidKernel.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute the kernel matrix between <cite>X1</cite> and <cite>X2</cite></p>
+<dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition"></a></dt>
+<dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The second data-matrix for computing the kernel. Of shape (M x D):
-M samples in D dimensions. Set <cite>X2 == X1</cite> to compute a symmetric kernel.</p></li>
+M samples in D dimensions. Set <code class="docutils literal notranslate"><span class="pre">X2</span> <span class="pre">==</span> <span class="pre">X1</span></code> to compute a symmetric kernel.</p></li>
+<li><p><strong>diag</strong> (<em>bool</em>) – Whether to compute just the diagonal of the kernel matrix, or the whole matrix.</p></li>
 <li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Optional tensor of shape (N x M) to hold the output. If not provided it will
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
@@ -1287,14 +1426,14 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <cite>X1</cite> and <cite>X2</cite>.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.SigmoidKernel.dmmv">
-<code class="sig-name descname"><span class="pre">dmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.dmmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.dmmv">
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -1336,8 +1475,8 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.kernels.SigmoidKernel.mmv">
-<code class="sig-name descname"><span class="pre">mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.mmv">
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1379,50 +1518,33 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 
 
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@@ -1,78 +1,40 @@
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@@ -103,6 +54,7 @@
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
@@ -111,13 +63,13 @@
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -132,19 +84,51 @@
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 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
@@ -156,9 +140,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -204,20 +191,12 @@
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -231,77 +210,48 @@
 <li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>
 
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 /* CSS overrides for sphinx_rtd_theme */
@@ -327,7 +277,7 @@
 }
 </style>
 <section id="falkon-mmv-ops">
-<h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink to this headline">¶</a></h1>
+<h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink to this headline"></a></h1>
 <p>The algorithms to compute kernels and kernel-vector products blockwise on GPUs and CPU. The algorithms in this module
 are kernel agnostic. Refer to <a class="reference internal" href="kernels.html#module-falkon.kernels" title="falkon.kernels"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.kernels</span></code></a> for the actual kernel implementations.</p>
 <p>The KeOps wrapper only supports the <cite>mmv</cite> operation (kernel-vector products). The matrix-multiplication implementations
@@ -341,257 +291,168 @@ <h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink
 </ul>
 </div></blockquote>
 <span class="target" id="module-falkon.mmv_ops"></span><section id="run-keops-mmv">
-<h2>run_keops_mmv<a class="headerlink" href="#run-keops-mmv" title="Permalink to this headline">¶</a></h2>
+<h2>run_keops_mmv<a class="headerlink" href="#run-keops-mmv" title="Permalink to this headline"></a></h2>
 <p>A thin wrapper to KeOps is provided to allow for block-splitting and multiple GPU usage. This only supports
 kernel-vector products.</p>
 <dl class="py function">
-<dt id="falkon.mmv_ops.keops.run_keops_mmv">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.keops.</span></code><code class="sig-name descname"><span class="pre">run_keops_mmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'Sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.keops.run_keops_mmv" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.mmv_ops.keops.run_keops_mmv">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.keops.</span></span><span class="sig-name descname"><span class="pre">run_keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'Sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.keops.run_keops_mmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
-<section id="fmm-cpu">
-<h2>fmm_cpu<a class="headerlink" href="#fmm-cpu" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel calculation on the CPU.</p>
+<section id="fmm">
+<h2>fmm<a class="headerlink" href="#fmm" title="Permalink to this headline"></a></h2>
+<p>Block-wise kernel calculation. If the inputs require gradient, this function uses a differentiable implementation.</p>
 <dl class="py function">
-<dt id="falkon.mmv_ops.fmm_cpu.fmm_cpu">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cpu.</span></code><code class="sig-name descname"><span class="pre">fmm_cpu</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">falkon.options.BaseOptions</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cpu.fmm_cpu" title="Permalink to this definition">¶</a></dt>
-<dd><p>Compute kernel value on matrices X1 and X2: <code class="docutils literal notranslate"><span class="pre">out</span> <span class="pre">=</span> <span class="pre">kernel(X1,</span> <span class="pre">X2)</span></code></p>
+<dt class="sig sig-object py" id="falkon.mmv_ops.fmm.fmm">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">fmm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.fmm" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fmmv">
+<h2>fmmv<a class="headerlink" href="#fmmv" title="Permalink to this headline"></a></h2>
+<p>Block-wise kernel-vector products.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fmmv">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fmmv" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="fdmmv">
+<h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this headline"></a></h2>
+<p>Block-wise double kernel-vector products.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fdmmv">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fdmmv" title="Permalink to this definition"></a></dt>
+<dd><p>Double kernel-vector product</p>
+<p>Computes kernel <span class="math notranslate nohighlight">\(K = k(X_1, X_2)\)</span> and then the double kernel-vector
+product <span class="math notranslate nohighlight">\(K^{\top} (K v + w)\)</span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> – [N, D] array</p></li>
-<li><p><strong>X2</strong> – [M, D] array</p></li>
-<li><p><strong>kernel</strong> – Class representing the desired kernel function</p></li>
-<li><p><strong>out</strong> – Array for storing the kernel output. If None, will be allocated within the function.</p></li>
-<li><p><strong>opt</strong> – Basic options dictionary, used for determining available memory. Additionally, the
-<code class="xref py py-attr docutils literal notranslate"><span class="pre">no_single_kernel</span></code> option is used to determine the
-accumulator data type.</p></li>
+<li><p><strong>X1</strong> – <span class="math notranslate nohighlight">\(n \times d\)</span> input matrix.</p></li>
+<li><p><strong>X2</strong> – <span class="math notranslate nohighlight">\(m \times d\)</span> input matrix.</p></li>
+<li><p><strong>v</strong> – <span class="math notranslate nohighlight">\(m \times t\)</span> vector (to be multiplied by the kernel)</p></li>
+<li><p><strong>w</strong> – <span class="math notranslate nohighlight">\(n \times t\)</span> vector to be added to the first k-v product</p></li>
+<li><p><strong>kernel</strong> – Kernel object responsible for computing kernel blocks</p></li>
+<li><p><strong>out</strong> – Optional output matrix of size <span class="math notranslate nohighlight">\(m \times t\)</span>. If specified, the output will be
+stored in it, otherwise a new output matrix will be allocated</p></li>
+<li><p><strong>differentiable</strong> – Whether the inputs are intended to be differentiated with. Currently setting this
+to <code class="docutils literal notranslate"><span class="pre">True</span></code> results in a <code class="code docutils literal notranslate"><span class="pre">NotImplementedError</span></code>.</p></li>
+<li><p><strong>opt</strong> – Options to be used for this operation</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><em>out</em> – [N, M] array. The kernel between X1 and X2.</p>
+<dd class="field-even"><p><em>out</em> – Output of the double kernel-vector product. Will use the same storage as the <cite>out</cite>
+parameter if it was specified</p>
 </dd>
 </dl>
 </dd></dl>
 
 </section>
-<section id="fmm-cpu-sparse">
-<h2>fmm_cpu_sparse<a class="headerlink" href="#fmm-cpu-sparse" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel calculation on the CPU for sparse datasets.</p>
+<section id="incore-fmmv">
+<h2>incore_fmmv<a class="headerlink" href="#incore-fmmv" title="Permalink to this headline"></a></h2>
 <dl class="py function">
-<dt id="falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cpu.</span></code><code class="sig-name descname"><span class="pre">fmm_cpu_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">falkon.options.BaseOptions</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.mmv_ops.fmmv_incore.incore_fmmv">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
-<section id="fmm-cuda">
-<h2>fmm_cuda<a class="headerlink" href="#fmm-cuda" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel calculation on GPUs.</p>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmm_cuda.fmm_cuda">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cuda.</span></code><code class="sig-name descname"><span class="pre">fmm_cuda</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cuda.fmm_cuda" title="Permalink to this definition">¶</a></dt>
-<dd><p>performs fnc(X1*X2’, X1, X2) in blocks on multiple GPUs</p>
-</dd></dl>
-
-</section>
-<section id="fmm-cuda-sparse">
-<h2>fmm_cuda_sparse<a class="headerlink" href="#fmm-cuda-sparse" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel calculation on GPUs for sparse datasets.</p>
+<section id="incore-fdmmv">
+<h2>incore_fdmmv<a class="headerlink" href="#incore-fdmmv" title="Permalink to this headline"></a></h2>
 <dl class="py function">
-<dt id="falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm_cuda.</span></code><code class="sig-name descname"><span class="pre">fmm_cuda_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.mmv_ops.fmmv_incore.incore_fdmmv">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
-<section id="fmmv-cpu">
-<h2>fmmv_cpu<a class="headerlink" href="#fmmv-cpu" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel-vector products on CPU.</p>
+<section id="low-level-functions">
+<h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="Permalink to this headline"></a></h2>
+<p>The following are some of the low-level functions which help compute kernels and kernel-vector products block-wise.
+They are specialized for different input types.</p>
 <dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cpu.fmmv_cpu">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fmmv_cpu</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fmmv_cpu" title="Permalink to this definition">¶</a></dt>
-<dd><p>Blockwise kernel-vector product</p>
-<p>This function computes <code class="docutils literal notranslate"><span class="pre">kernel(X1,</span> <span class="pre">X2)</span> <span class="pre">&#64;</span> <span class="pre">v</span></code> in a blockwise fashion, to avoid having the
-whole N*M kernel matrix in memory at once.
-Note that while the principle is that of matrix-vector product, <cite>v</cite> can have more than
-one column.</p>
+<dt class="sig sig-object py" id="falkon.mmv_ops.fmm.sparse_mm_run_thread">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">sparse_mm_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comp_dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="Permalink to this definition"></a></dt>
+<dd><p>Inner loop to compute (part of) a kernel matrix for two sparse input tensors</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> – [N, D] array</p></li>
-<li><p><strong>X2</strong> – [M, D] array</p></li>
-<li><p><strong>v</strong> – [M, T] array</p></li>
-<li><p><strong>kernel</strong> – Class representing the desired kernel function</p></li>
-<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [N, T] array for storing the kernel-vector product output.
-If None, will be allocated within the function.</p></li>
-<li><p><strong>opt</strong> – Basic options dictionary, used for determining available memory.</p></li>
+<li><p><strong>m1</strong> – Left input tensor for computing the kernel</p></li>
+<li><p><strong>m2</strong> – Right input tensor for computing the kernel</p></li>
+<li><p><strong>out</strong> – Output dense matrix in which to store the result</p></li>
+<li><p><strong>kernel</strong> – Kernel object, used for computing the kernel. This must implement the
+<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel.compute_sparse" title="falkon.kernels.kernel.Kernel.compute_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">falkon.kernels.kernel.Kernel.compute_sparse()</span></code></a> method.</p></li>
+<li><p><strong>n</strong> – Block size for the first axis of <cite>m1</cite></p></li>
+<li><p><strong>m</strong> – Block size for the first ais of <cite>m2</cite></p></li>
+<li><p><strong>comp_dt</strong> – Data-type in which to run the actual calculations (may be different from the data-type
+of <cite>m1</cite> or <cite>m2</cite>).</p></li>
+<li><p><strong>dev</strong> – Device on which to run the calculations</p></li>
 </ul>
 </dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <cite>out</cite>.</p>
+</dd>
 </dl>
 </dd></dl>
 
-</section>
-<section id="fmmv-cpu-sparse">
-<h2>fmmv_cpu_sparse<a class="headerlink" href="#fmmv-cpu-sparse" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel-vector products on CPU for sparse datasets.</p>
 <dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fmmv_cpu_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">falkon.options.BaseOptions</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</section>
-<section id="fdmmv-cpu">
-<h2>fdmmv_cpu<a class="headerlink" href="#fdmmv-cpu" title="Permalink to this headline">¶</a></h2>
-<p>Blocked double kernel-vector products on CPU.</p>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cpu.fdmmv_cpu">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cpu</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu" title="Permalink to this definition">¶</a></dt>
-<dd><p>Calculate a double kernel-vector product.</p>
-<p>This function computes the following quantity: <code class="docutils literal notranslate"><span class="pre">kernel(X1,</span> <span class="pre">X2).T</span> <span class="pre">&#64;</span> <span class="pre">(kernel(X1,</span> <span class="pre">X2)</span> <span class="pre">&#64;</span> <span class="pre">v</span> <span class="pre">+</span> <span class="pre">w)</span></code>
-Where one of <cite>v</cite> or <cite>w</cite> can be empty.
-All arrays passed to this function must be 2-dimensional, although
-the second dimension can be unitary.</p>
-<p>The expression is not computed directly. We separate the computation
-into smaller blocks so as to reduce the total memory consumption (the
-large N*M kernel matrix is never wholly stored in RAM.)</p>
+<dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.sparse_mmv_run_thread">
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">sparse_mmv_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mem_needed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="Permalink to this definition"></a></dt>
+<dd><p>Inner loop to compute (part of) a kernel-vector product for sparse input matrices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X1</strong> – [N, D] array</p></li>
-<li><p><strong>X2</strong> – [M, D] array</p></li>
-<li><p><strong>v</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [M, T] array. But note that at least one of v or w must be specified.</p></li>
-<li><p><strong>w</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [N, T] array. But note that at least one of v or w must be specified.</p></li>
-<li><p><strong>kernel</strong> – Class representing the desired kernel function</p></li>
-<li><p><strong>out</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – [M, T] array for storing the kernel-vector product output.
-If None, will be allocated within the function.</p></li>
-<li><p><strong>opt</strong> – Basic options dictionary, used for determining available memory.</p></li>
+<li><p><strong>m1</strong> – Left input tensor for computing the kernel</p></li>
+<li><p><strong>m2</strong> – Right input tensor for computing the kernel</p></li>
+<li><p><strong>v</strong> – Dense vector to be multiplied by the kernel matrix</p></li>
+<li><p><strong>out</strong> – Dense output vector which should store the result of the kernel vector product on exit
+from this function.</p></li>
+<li><p><strong>kernel</strong> – Kernel object, used for computing the kernel. This must implement the
+<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel.compute_sparse" title="falkon.kernels.kernel.Kernel.compute_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">falkon.kernels.kernel.Kernel.compute_sparse()</span></code></a> method.</p></li>
+<li><p><strong>blk_n</strong> – Block size for the first axis of <cite>m1</cite></p></li>
+<li><p><strong>blk_m</strong> – Block size for the first ais of <cite>m2</cite></p></li>
+<li><p><strong>mem_needed</strong> – Memory needed for pre-allocations</p></li>
+<li><p><strong>dev</strong> – Device on which to run the calculations</p></li>
 </ul>
 </dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <cite>out</cite>.</p>
+</dd>
 </dl>
 </dd></dl>
 
-</section>
-<section id="fdmmv-cpu-sparse">
-<h2>fdmmv_cpu_sparse<a class="headerlink" href="#fdmmv-cpu-sparse" title="Permalink to this headline">¶</a></h2>
-<p>Blocked double kernel-vector products on CPU for sparse datasets.</p>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cpu.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cpu_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</section>
-<section id="fmmv-cuda">
-<h2>fmmv_cuda<a class="headerlink" href="#fmmv-cuda" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel-vector products on GPUs.</p>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cuda.fmmv_cuda">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fmmv_cuda</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fmmv_cuda" title="Permalink to this definition">¶</a></dt>
-<dd><p>X1 : N x D
-X2 : M x D
-v  : M x T</p>
-<p>performs  fnc(X1*X2’, X1, X2) * v   : N x T
-in blocks on multiple GPUs</p>
-</dd></dl>
-
-</section>
-<section id="fmmv-cuda-sparse">
-<h2>fmmv_cuda_sparse<a class="headerlink" href="#fmmv-cuda-sparse" title="Permalink to this headline">¶</a></h2>
-<p>Blocked kernel-vector products on GPUs for sparse datasets.</p>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fmmv_cuda_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</section>
-<section id="fdmmv-cuda">
-<h2>fdmmv_cuda<a class="headerlink" href="#fdmmv-cuda" title="Permalink to this headline">¶</a></h2>
-<p>Blocked double kernel-vector products on GPUs.</p>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cuda.fdmmv_cuda">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cuda</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda" title="Permalink to this definition">¶</a></dt>
-<dd><p>X1 : N x D
-X2 : M x D
-v  : M x T
-w  : N x T</p>
-<p>performs fnc(X1*X2’, X1, X2)’ * ( fnc(X1*X2’, X1, X2) * v  +  w )  : M x T
-in blocks on multiple GPUs</p>
-<p>Assume all inputs have the same data type</p>
-</dd></dl>
-
-</section>
-<section id="fdmmv-cuda-sparse">
-<h2>fdmmv_cuda_sparse<a class="headerlink" href="#fdmmv-cuda-sparse" title="Permalink to this headline">¶</a></h2>
-<p>Blocked double kernel-vector products on GPUs for sparse datasets.</p>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_cuda.</span></code><code class="sig-name descname"><span class="pre">fdmmv_cuda_sparse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</section>
-<section id="incore-fmmv">
-<h2>incore_fmmv<a class="headerlink" href="#incore-fmmv" title="Permalink to this headline">¶</a></h2>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_incore.incore_fmmv">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></code><code class="sig-name descname"><span class="pre">incore_fmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</section>
-<section id="incore-fdmmv">
-<h2>incore_fdmmv<a class="headerlink" href="#incore-fdmmv" title="Permalink to this headline">¶</a></h2>
-<dl class="py function">
-<dt id="falkon.mmv_ops.fmmv_incore.incore_fdmmv">
-<code class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></code><code class="sig-name descname"><span class="pre">incore_fdmmv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
 </section>
 </section>
 
 
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diff --git a/api_reference/models.html b/api_reference/models.html
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@@ -101,6 +52,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
 </ul>
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@@ -109,13 +61,13 @@
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 <li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -130,19 +82,51 @@
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 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
@@ -154,9 +138,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +189,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -229,77 +208,48 @@
 <li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>
 
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@@ -325,15 +275,15 @@
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 <section id="module-falkon.models">
-<span id="falkon-models"></span><h1>falkon.models<a class="headerlink" href="#module-falkon.models" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-models"></span><h1>falkon.models<a class="headerlink" href="#module-falkon.models" title="Permalink to this headline"></a></h1>
 <section id="falkon">
-<h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline">¶</a></h2>
+<span id="falkon-model"></span><h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.models.Falkon">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.models.</span></code><code class="sig-name descname"><span class="pre">Falkon</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.Falkon">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">Falkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition"></a></dt>
 <dd><p>Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
-projections and a fast optimization algorithm as described in <a class="reference internal" href="#flk-1" id="id1"><span>[flk_1]</span></a>, <a class="reference internal" href="#flk-2" id="id2"><span>[flk_2]</span></a>.</p>
+projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
 <p>Multiclass and multiple regression problems can all be tackled
 with this same object, for example by encoding multiple classes
 in a one-hot target matrix.</p>
@@ -347,8 +297,8 @@ <h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 and lower than the total number of training points. A larger
 <cite>M</cite> will typically lead to better accuracy but will use more
 computational resources. You can either specify the number of centers
-by setting this parameter, or by passing to this constructor a <cite>CenterSelctor</cite> class
-instance.</p></li>
+by setting this parameter, or by passing to this constructor a
+<a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.center_selection.CenterSelector</span></code></a> class instance.</p></li>
 <li><p><strong>center_selection</strong> (<em>str</em><em> or </em><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><em>falkon.center_selection.CenterSelector</em></a>) – The center selection algorithm. Implemented is only ‘uniform’
 selection which can choose each training sample with the same
 probability.</p></li>
@@ -388,21 +338,23 @@ <h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 <span class="gp">&gt;&gt;&gt; </span><span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 </pre></div>
 </div>
+<p>Warm restarts: run for 5 iterations, then use <cite>warm_start</cite> to run for 5 more iterations.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">maxiter</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">warm_start</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">beta_</span><span class="p">)</span>
+</pre></div>
+</div>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="flk-1"><span class="brackets"><a class="fn-backref" href="#id1">flk_1</a></span></dt>
-<dd><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
-scale kernel method,” Advances in Neural Information Processing Systems 29, 2017.</p>
-</dd>
-<dt class="label" id="flk-2"><span class="brackets"><a class="fn-backref" href="#id2">flk_2</a></span></dt>
-<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+<ul class="simple">
+<li><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
+scale kernel method,” Advances in Neural Information Processing Systems 29, 2017.</p></li>
+<li><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
 “Kernel methods through the roof: handling billions of points efficiently,”
-arXiv:2006.10350, 2020.</p>
-</dd>
-</dl>
+Advancs in Neural Information Processing Systems, 2020.</p></li>
+</ul>
 <dl class="py method">
-<dt id="falkon.models.Falkon.fit">
-<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.fit" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.Falkon.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon KRR model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -427,6 +379,11 @@ <h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 during the optimization iterations.
 If Yts is in Fortran order (i.e. column-contiguous) then we can avoid an
 extra copy of the data.</p></li>
+<li><p><strong>warm_start</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Specify a starting point for the conjugate gradient optimizer. If not specified, the
+initial point will be a tensor filled with zeros.
+Be aware that the starting point should not be in the parameter space, but in the
+preconditioner space (i.e. if initializing from a previous Falkon object, use the
+<cite>beta_</cite> field, not <cite>alpha_</cite>).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -436,8 +393,8 @@ <h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.Falkon.get_params">
-<code class="sig-name descname"><span class="pre">get_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.get_params" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.Falkon.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.get_params" title="Permalink to this definition"></a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -451,8 +408,8 @@ <h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.Falkon.predict">
-<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.models.Falkon.predict" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.Falkon.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.models.Falkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -466,8 +423,8 @@ <h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.Falkon.set_params">
-<code class="sig-name descname"><span class="pre">set_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.set_params" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.Falkon.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.set_params" title="Permalink to this definition"></a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
@@ -487,13 +444,13 @@ <h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 
 </section>
 <section id="logisticfalkon">
-<h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink to this headline">¶</a></h2>
+<span id="log-falkon-model"></span><h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.models.LogisticFalkon">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.models.</span></code><code class="sig-name descname"><span class="pre">LogisticFalkon</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_list</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_list</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><span class="pre">falkon.gsc_losses.Loss</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.LogisticFalkon">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">LogisticFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><span class="pre">falkon.gsc_losses.Loss</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon" title="Permalink to this definition"></a></dt>
 <dd><p>Falkon Logistic regression solver.</p>
 <p>This estimator object solves approximate logistic regression problems with Nystroem
-projections and a fast optimization algorithm as described in <a class="footnote-reference brackets" href="#id5" id="id3">1</a>, <a class="footnote-reference brackets" href="#id6" id="id4">2</a>.</p>
+projections and a fast optimization algorithm as described in  <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#log-flk"><span class="std std-ref">[3]</span></a>.</p>
 <p>This model can handle logistic regression, so it may be used in place of
 <a class="reference internal" href="#falkon.models.Falkon" title="falkon.models.Falkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.Falkon</span></code></a> (which uses the squared loss) when tackling binary
 classification problems.</p>
@@ -550,17 +507,13 @@ <h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink
 </pre></div>
 </div>
 <p class="rubric">References</p>
-<dl class="footnote brackets">
-<dt class="label" id="id5"><span class="brackets"><a class="fn-backref" href="#id3">1</a></span></dt>
-<dd><p>Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, “Globally Convergent Newton Methods
-for Ill-conditioned Generalized Self-concordant Losses,” NeurIPS 32, 2019.</p>
-</dd>
-<dt class="label" id="id6"><span class="brackets"><a class="fn-backref" href="#id4">2</a></span></dt>
-<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+<ul class="simple">
+<li><p>Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, “Globally Convergent Newton Methods
+for Ill-conditioned Generalized Self-concordant Losses,” NeurIPS 32, 2019.</p></li>
+<li><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
 “Kernel methods through the roof: handling billions of points efficiently,”
-arXiv:2006.10350, 2020.</p>
-</dd>
-</dl>
+Advancs in Neural Information Processing Systems, 2020.</p></li>
+</ul>
 <p class="rubric">Notes</p>
 <p>A rule of thumb for <strong>setting the `penalty_list`</strong> is to keep in mind the desired final
 regularization (1e-6 in the example above), and then create a short path of around three
@@ -571,8 +524,8 @@ <h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink
 iterations where the regularization is decreased, and then switch to a higher number of
 inner-steps (e.g. 8) for the remaining iterations.</p>
 <dl class="py method">
-<dt id="falkon.models.LogisticFalkon.fit">
-<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.fit" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.LogisticFalkon.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon Kernel Logistic Regression model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -606,8 +559,8 @@ <h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.LogisticFalkon.get_params">
-<code class="sig-name descname"><span class="pre">get_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.get_params" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.LogisticFalkon.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.get_params" title="Permalink to this definition"></a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -621,8 +574,8 @@ <h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.LogisticFalkon.predict">
-<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.models.LogisticFalkon.predict" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.LogisticFalkon.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.models.LogisticFalkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -636,8 +589,8 @@ <h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.LogisticFalkon.set_params">
-<code class="sig-name descname"><span class="pre">set_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.set_params" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.LogisticFalkon.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.set_params" title="Permalink to this definition"></a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
@@ -657,13 +610,13 @@ <h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink
 
 </section>
 <section id="incorefalkon">
-<h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to this headline">¶</a></h2>
+<span id="ic-falkon-model"></span><h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.models.InCoreFalkon">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.models.</span></code><code class="sig-name descname"><span class="pre">InCoreFalkon</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">InCoreFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition"></a></dt>
 <dd><p>In GPU core Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
-projections and a fast optimization algorithm as described in <a class="reference internal" href="#flkk-1" id="id7"><span>[flkk_1]</span></a>, <a class="reference internal" href="#flkk-2" id="id8"><span>[flkk_2]</span></a>.</p>
+projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
 <p>Multiclass and multiple regression problems can all be tackled
 with this same object, for example by encoding multiple classes
 in a one-hot target matrix.</p>
@@ -720,7 +673,7 @@ <h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to t
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<p>Running Falkon on a random dataset</p>
+<p>Running <a class="reference internal" href="#falkon.models.InCoreFalkon" title="falkon.models.InCoreFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a> on a randomly generated dataset</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">Y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span>
@@ -732,20 +685,16 @@ <h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to t
 </pre></div>
 </div>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="flkk-1"><span class="brackets"><a class="fn-backref" href="#id7">flkk_1</a></span></dt>
-<dd><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
-scale kernel method,” Advances in Neural Information Processing Systems 29, 2017.</p>
-</dd>
-<dt class="label" id="flkk-2"><span class="brackets"><a class="fn-backref" href="#id8">flkk_2</a></span></dt>
-<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+<ul class="simple">
+<li><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
+scale kernel method,” Advances in Neural Information Processing Systems 29, 2017.</p></li>
+<li><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
 “Kernel methods through the roof: handling billions of points efficiently,”
-arXiv:2006.10350, 2020.</p>
-</dd>
-</dl>
+Advancs in Neural Information Processing Systems, 2020.</p></li>
+</ul>
 <dl class="py method">
-<dt id="falkon.models.InCoreFalkon.fit">
-<code class="sig-name descname"><span class="pre">fit</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.fit" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon KRR model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -770,6 +719,11 @@ <h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to t
 during the optimization iterations.
 If Yts is in Fortran order (i.e. column-contiguous) then we can avoid an
 extra copy of the data. Must be a CUDA tensor.</p></li>
+<li><p><strong>warm_start</strong> (<em>torch.Tensor</em><em> or </em><em>None</em>) – Specify a starting point for the conjugate gradient optimizer. If not specified, the
+initial point will be a tensor filled with zeros.
+Be aware that the starting point should not be in the parameter space, but in the
+preconditioner space (i.e. if initializing from a previous Falkon object, use the
+<cite>beta_</cite> field, not <cite>alpha_</cite>).</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -779,8 +733,8 @@ <h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.InCoreFalkon.get_params">
-<code class="sig-name descname"><span class="pre">get_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.get_params" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.get_params" title="Permalink to this definition"></a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -794,8 +748,8 @@ <h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.InCoreFalkon.predict">
-<code class="sig-name descname"><span class="pre">predict</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.models.InCoreFalkon.predict" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.models.InCoreFalkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -809,8 +763,8 @@ <h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.models.InCoreFalkon.set_params">
-<code class="sig-name descname"><span class="pre">set_params</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.set_params" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.set_params" title="Permalink to this definition"></a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
@@ -833,50 +787,33 @@ <h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to t
 
 
            </div>
-           
           </div>
-          <footer>
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
-        <a href="kernels.html" class="btn btn-neutral float-right" title="falkon.kernels" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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     </div>
 
   <hr/>
 
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-    <p>
-        &#169; Copyright 2020, Giacomo Meanti.
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+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
   </div>
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-    
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
 
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diff --git a/api_reference/optimization.html b/api_reference/optimization.html
index d0b7968f..302d1adf 100644
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+++ b/api_reference/optimization.html
@@ -1,78 +1,40 @@
-
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 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
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+  <title>falkon.optim &mdash; falkon 0.7.2 documentation</title>
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-          
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+                0.7.2
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@@ -80,18 +42,7 @@
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-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
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 <li class="toctree-l1"><a class="reference internal" href="../install.html">Install</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#supported-platforms">Supported Platforms</a></li>
@@ -103,6 +54,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
@@ -111,13 +63,13 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -132,19 +84,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
@@ -156,9 +140,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -204,20 +191,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -231,77 +210,48 @@
 <li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
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 <section id="module-falkon.optim">
-<span id="falkon-optim"></span><h1>falkon.optim<a class="headerlink" href="#module-falkon.optim" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-optim"></span><h1>falkon.optim<a class="headerlink" href="#module-falkon.optim" title="Permalink to this headline"></a></h1>
 <section id="optimizer">
-<h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline">¶</a></h2>
+<h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.optim.Optimizer">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.optim.</span></code><code class="sig-name descname"><span class="pre">Optimizer</span></code><a class="headerlink" href="#falkon.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dt class="sig sig-object py" id="falkon.optim.Optimizer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">Optimizer</span></span><a class="headerlink" href="#falkon.optim.Optimizer" title="Permalink to this definition"></a></dt>
+<dd><p>Base class for optimizers. This is an empty shell at the moment.</p>
+</dd></dl>
 
 </section>
 <section id="conjugate-gradient-methods">
-<h2>Conjugate gradient methods<a class="headerlink" href="#conjugate-gradient-methods" title="Permalink to this headline">¶</a></h2>
+<h2>Conjugate gradient methods<a class="headerlink" href="#conjugate-gradient-methods" title="Permalink to this headline"></a></h2>
 <section id="conjugategradient">
-<h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Permalink to this headline">¶</a></h3>
+<h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.optim.ConjugateGradient">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.optim.</span></code><code class="sig-name descname"><span class="pre">ConjugateGradient</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.ConjugateGradientOptions</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.ConjugateGradient" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dt class="sig sig-object py" id="falkon.optim.ConjugateGradient">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">ConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.ConjugateGradientOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.ConjugateGradient" title="Permalink to this definition"></a></dt>
+<dd><dl class="py method">
+<dt class="sig sig-object py" id="falkon.optim.ConjugateGradient.solve">
+<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmv</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.optim.ConjugateGradient.solve" title="Permalink to this definition"></a></dt>
+<dd><p>Conjugate-gradient solver with optional support for preconditioning via generic MMV.</p>
+<p>This solver can be used for iterative solution of linear systems of the form $AX = B$ with
+respect to the <cite>X</cite> variable. Knowledge of <cite>A</cite> is only needed through matrix-vector
+multiplications with temporary solutions (must be provided through the <cite>mmv</cite> function).</p>
+<p>Preconditioning can be achieved by incorporating the preconditioner matrix in the <cite>mmv</cite>
+function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X0</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em>) – Initial solution for the solver. If not provided it will be a zero-tensor.</p></li>
+<li><p><strong>B</strong> (<em>torch.Tensor</em>) – Right-hand-side of the linear system to be solved.</p></li>
+<li><p><strong>mmv</strong> – User-provided function to perform matrix-vector multiplications with the design matrix
+<cite>A</cite>. The function must accept a single argument (the vector to be multiplied), and
+return the result of the matrix-vector multiplication.</p></li>
+<li><p><strong>max_iter</strong> (<em>int</em>) – Maximum number of iterations the solver will perform. Early stopping is implemented
+via the options passed in the constructor of this class (in particular look at
+<cite>cg_tolerance</cite> options)
+i + 1, X, e_train</p></li>
+<li><p><strong>callback</strong> – An optional, user-provided function which shall be called at the end of each iteration
+with the current solution. The arguments to the function are the iteration number,
+a tensor containing the current solution, and the total time elapsed from the beginning
+of training (note that this time explicitly excludes any time taken by the callback
+itself).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>The solution to the linear system <cite>X</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
 
 </section>
 <section id="falkonconjugategradient">
-<h3>FalkonConjugateGradient<a class="headerlink" href="#falkonconjugategradient" title="Permalink to this headline">¶</a></h3>
+<h3>FalkonConjugateGradient<a class="headerlink" href="#falkonconjugategradient" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.optim.FalkonConjugateGradient">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.optim.</span></code><code class="sig-name descname"><span class="pre">FalkonConjugateGradient</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner" title="falkon.preconditioner.preconditioner.Preconditioner"><span class="pre">falkon.preconditioner.preconditioner.Preconditioner</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.FalkonConjugateGradient" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.optim.FalkonConjugateGradient">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">FalkonConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner" title="falkon.preconditioner.preconditioner.Preconditioner"><span class="pre">falkon.preconditioner.preconditioner.Preconditioner</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.FalkonConjugateGradient" title="Permalink to this definition"></a></dt>
 <dd><p>Preconditioned conjugate gradient solver, optimized for the Falkon algorithm.</p>
 <p>The linear system solved is</p>
 <div class="math notranslate nohighlight">
@@ -390,50 +375,33 @@ <h3>FalkonConjugateGradient<a class="headerlink" href="#falkonconjugategradient"
 
 
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diff --git a/api_reference/options.html b/api_reference/options.html
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+++ b/api_reference/options.html
@@ -1,76 +1,38 @@
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-  <meta name="generator" content="Docutils 0.17: http://docutils.sourceforge.net/" />
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@@ -101,6 +52,7 @@
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@@ -109,13 +61,13 @@
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-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
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-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -130,19 +82,51 @@
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 </li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
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@@ -154,9 +138,12 @@
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+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +189,12 @@
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
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 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -229,77 +208,48 @@
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+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
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+</ul>
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@@ -325,12 +275,12 @@
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 </style>
 <section id="module-falkon.options">
-<span id="falkon-options"></span><span id="api-options"></span><h1>falkon.options<a class="headerlink" href="#module-falkon.options" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-options"></span><span id="api-options"></span><h1>falkon.options<a class="headerlink" href="#module-falkon.options" title="Permalink to this headline"></a></h1>
 <section id="falkonoptions">
-<h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to this headline">¶</a></h2>
+<h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.options.FalkonOptions">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.options.</span></code><code class="sig-name descname"><span class="pre">FalkonOptions</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keops_acc_dtype</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_sum_scheme</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_active</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_in_core</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_ooc</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lauum_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-13</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_preconditioner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_tolerance</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_full_gradient_every</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gpu_mem</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cpu_mem</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_arch_speed</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_single_kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">30000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_32</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">300000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_64</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">900000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">never_store_kernel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_fmm_streams</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.options.FalkonOptions" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.options.FalkonOptions">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.options.</span></span><span class="sig-name descname"><span class="pre">FalkonOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keops_acc_dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_sum_scheme</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_active</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_memory_slack</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_in_core</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_ooc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-13</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_tolerance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_full_gradient_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_differential_convergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_arch_speed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_single_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">30000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">300000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">900000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">never_store_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_kernel_d_threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_fmm_streams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.options.FalkonOptions" title="Permalink to this definition"></a></dt>
 <dd><p>Global options for Falkon.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -389,6 +339,11 @@ <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to
 True may (in case there would be enough RAM to store the kernel), increase the
 training time for Falkon since the K_NM matrix must be recomputed at every
 conjugate gradient iteration.</p></li>
+<li><p><strong>store_kernel_d_threshold</strong> (<em>int</em>) – <cite>default 1200</cite> - The minimum data-dimensionality (<cite>d</cite>) for which to consider whether to store
+the full Knm kernel matrix (between the data-points and the Nystrom centers). The final decision
+on whether the matrix is stored or not is based on the amount of memory available.
+Storing the Knm matrix may greatly reduce training and inference times, especially if <cite>d</cite> is
+large, or for kernels which are costly to compute.</p></li>
 <li><p><strong>num_fmm_streams</strong> (<em>int</em>) – <cite>default 2</cite> - The number of CUDA streams to use for evaluating kernels when CUDA is available.
 This number should be increased from its default value when the number of Nystroem centers is
 higher than around 5000.</p></li>
@@ -401,6 +356,9 @@ <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to
 ‘auto’ (the default setting) means that KeOps will be used if it is installed correctly,
 ‘no’ means keops will not be used, nor will it be imported, and ‘force’ means that if KeOps is
 not installed an error will be raised.</p></li>
+<li><p><strong>keops_memory_slack</strong> (<em>float</em>) – <cite>default 0.7</cite> - Controls the amount of slack used when calculating the matrix splits for KeOps.
+Since memory usage estimation for KeOps is hard, you may need to reduce this value if running
+out-of-GPU-memory when using KeOps. Typically this only occurs for large datasets.</p></li>
 <li><p><strong>cg_epsilon_32</strong> (<em>float</em>) – <cite>default 1e-7</cite> - Small added epsilon to prevent divide-by-zero errors in the conjugate
 gradient algorithm. Used for single precision data-types</p></li>
 <li><p><strong>cg_epsilon_64</strong> (<em>float</em>) – <cite>default 1e-15</cite> - Small added epsilon to prevent divide-by-zero errors in the conjugate
@@ -410,15 +368,18 @@ <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to
 <li><p><strong>cg_full_gradient_every</strong> (<em>int</em>) – <cite>default 10</cite> - How often to calculate the full gradient in the conjugate gradient algorithm.
 Full-gradient iterations take roughly twice the time as normal iterations, but they reset
 the error introduced by the other iterations.</p></li>
+<li><p><strong>cg_differential_convergence</strong> (<em>bool</em>) – <cite>default False</cite> - Differential convergence refers to a procedure relevant to the conjugate
+gradient optimizer, and only applies when multiple right-hand side vectors are used (e.g.
+in multi-class classification, or in hyperparameter optimization with the stochastic objective).
+If this flag is set, whenever the convergence criterion is met for single right-hand-sides,
+they are removed from the optimization procedure. If it is not set, all vectors must have
+converged for the optimization to stop. It is especially useful for hyperparameter optimization.</p></li>
 <li><p><strong>pc_epsilon_32</strong> (<em>float</em>) – <cite>default 1e-5</cite> - Epsilon used to increase the diagonal dominance of a matrix before its
 Cholesky decomposition (for single-precision data types).</p></li>
 <li><p><strong>pc_epsilon_64</strong> (<em>float</em>) – <cite>default 1e-13</cite> - Epsilon used to increase the diagonal dominance of a matrix before its
 Cholesky decomposition (for double-precision data types).</p></li>
 <li><p><strong>cpu_preconditioner</strong> (<em>bool</em>) – <cite>default False</cite> - Whether the preconditioner should be computed on the CPU. This setting
 overrides the <code class="xref py py-attr docutils literal notranslate"><span class="pre">FalkonOptions.use_cpu</span></code> option.</p></li>
-<li><p><strong>lauum_par_blk_multiplier</strong> (<em>int</em>) – <cite>default 8</cite> - Minimum number of tiles per-GPU for the LAUUM algorithm. This can be set
-quite high (e.g. 8) without too much performance degradation. Optimal settings will depend
-on the number of GPUs.</p></li>
 <li><p><strong>chol_force_in_core</strong> (<em>bool</em>) – <cite>default False</cite> - Whether to force in-core execution of the Cholesky decomposition. This will
 not work with matrices bigger than GPU memory.</p></li>
 <li><p><strong>chol_force_ooc</strong> (<em>bool</em>) – <cite>default False</cite> - Whether to force out-of-core (parallel) execution for the POTRF algorithm,
@@ -434,50 +395,33 @@ <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to
 
 
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@@ -229,77 +208,48 @@
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+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
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@@ -325,7 +275,7 @@
 }
 </style>
 <section id="falkon-ooc-ops">
-<h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink to this headline">¶</a></h1>
+<h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink to this headline"></a></h1>
 <p>The out-of-core algorithms for the Cholesky decomposition and the LAUUM operation are crucial for speeding up our
 library. To find out more about how they work, check the source code:</p>
 <blockquote>
@@ -336,10 +286,10 @@ <h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink
 </div></blockquote>
 <p>The following functions provide a higher-level interface to the two operations.</p>
 <span class="target" id="module-falkon.ooc_ops"></span><section id="gpu-cholesky">
-<h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to this headline">¶</a></h2>
+<h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to this headline"></a></h2>
 <dl class="py function">
-<dt id="falkon.ooc_ops.gpu_cholesky">
-<code class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></code><code class="sig-name descname"><span class="pre">gpu_cholesky</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.ooc_ops.gpu_cholesky" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.ooc_ops.gpu_cholesky">
+<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_cholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.ooc_ops.gpu_cholesky" title="Permalink to this definition"></a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -366,10 +316,10 @@ <h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to t
 
 </section>
 <section id="gpu-lauum">
-<h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this headline">¶</a></h2>
+<h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this headline"></a></h2>
 <dl class="py function">
-<dt id="falkon.ooc_ops.gpu_lauum">
-<code class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></code><code class="sig-name descname"><span class="pre">gpu_lauum</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_opposite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.ooc_ops.gpu_lauum" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.ooc_ops.gpu_lauum">
+<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_lauum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_opposite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.ooc_ops.gpu_lauum" title="Permalink to this definition"></a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -392,50 +342,33 @@ <h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this he
 
 
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diff --git a/api_reference/preconditioner.html b/api_reference/preconditioner.html
index 6ad810e3..8806b2a5 100644
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+++ b/api_reference/preconditioner.html
@@ -1,78 +1,40 @@
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@@ -80,18 +42,7 @@
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-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
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@@ -103,6 +54,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
@@ -111,13 +63,13 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -132,19 +84,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
@@ -156,9 +140,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -204,20 +191,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
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 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
@@ -231,77 +210,48 @@
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 </ul>
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+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
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 <style>
 /* CSS overrides for sphinx_rtd_theme */
@@ -327,12 +277,12 @@
 }
 </style>
 <section id="module-falkon.preconditioner">
-<span id="falkon-preconditioner"></span><h1>falkon.preconditioner<a class="headerlink" href="#module-falkon.preconditioner" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-preconditioner"></span><h1>falkon.preconditioner<a class="headerlink" href="#module-falkon.preconditioner" title="Permalink to this headline"></a></h1>
 <section id="preconditioner">
-<h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink to this headline">¶</a></h2>
+<h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.preconditioner.preconditioner.Preconditioner">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.preconditioner.preconditioner.</span></code><code class="sig-name descname"><span class="pre">Preconditioner</span></code><a class="headerlink" href="#falkon.preconditioner.preconditioner.Preconditioner" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.preconditioner.Preconditioner">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.preconditioner.</span></span><span class="sig-name descname"><span class="pre">Preconditioner</span></span><a class="headerlink" href="#falkon.preconditioner.preconditioner.Preconditioner" title="Permalink to this definition"></a></dt>
 <dd><p>Generic preconditioner class, used to accelerate solutions to linear systems.</p>
 <p>Given a system of equations <span class="math notranslate nohighlight">\(H\beta = Y\)</span>, where <span class="math notranslate nohighlight">\(H\)</span> typically contains in some
 form our data matrix <cite>X</cite> and <cite>Y</cite> contains the targets. We can use matrix <span class="math notranslate nohighlight">\(B\)</span> to
@@ -356,12 +306,12 @@ <h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink
 
 </section>
 <section id="cholesky-preconditioners">
-<h2>Cholesky preconditioners<a class="headerlink" href="#cholesky-preconditioners" title="Permalink to this headline">¶</a></h2>
+<h2>Cholesky preconditioners<a class="headerlink" href="#cholesky-preconditioners" title="Permalink to this headline"></a></h2>
 <section id="falkonpreconditioner">
-<h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title="Permalink to this headline">¶</a></h3>
+<h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.preconditioner.FalkonPreconditioner">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></code><code class="sig-name descname"><span class="pre">FalkonPreconditioner</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></span><span class="sig-name descname"><span class="pre">FalkonPreconditioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner" title="Permalink to this definition"></a></dt>
 <dd><p>Approximated Cholesky Preconditioner for FALKON.</p>
 <p>The preconditioner is based on the <span class="math notranslate nohighlight">\(K_{MM}\)</span> kernel between the
 inducing points. A two step approximation of the inverse matrix
@@ -405,8 +355,8 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.preconditioner.FalkonPreconditioner.apply">
-<code class="sig-name descname"><span class="pre">apply</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply" title="Permalink to this definition"></a></dt>
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(ATx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -427,8 +377,8 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.FalkonPreconditioner.apply_t">
-<code class="sig-name descname"><span class="pre">apply_t</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply_t" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.apply_t">
+<span class="sig-name descname"><span class="pre">apply_t</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply_t" title="Permalink to this definition"></a></dt>
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(A^\top T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -449,8 +399,8 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.FalkonPreconditioner.init">
-<code class="sig-name descname"><span class="pre">init</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_vec</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.init" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.init">
+<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.init" title="Permalink to this definition"></a></dt>
 <dd><p>Initialize the preconditioner matrix.</p>
 <p>This method must be called before the preconditioner can be used.</p>
 <dl class="field-list simple">
@@ -465,8 +415,8 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.FalkonPreconditioner.invA">
-<code class="sig-name descname"><span class="pre">invA</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invA" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invA">
+<span class="sig-name descname"><span class="pre">invA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invA" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Ax = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -487,8 +437,8 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.FalkonPreconditioner.invAt">
-<code class="sig-name descname"><span class="pre">invAt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invAt" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invAt">
+<span class="sig-name descname"><span class="pre">invAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invAt" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(A^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -509,8 +459,8 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.FalkonPreconditioner.invT">
-<code class="sig-name descname"><span class="pre">invT</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invT" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invT">
+<span class="sig-name descname"><span class="pre">invT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invT" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Tx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -531,8 +481,8 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.FalkonPreconditioner.invTt">
-<code class="sig-name descname"><span class="pre">invTt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invTt" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invTt">
+<span class="sig-name descname"><span class="pre">invTt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invTt" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -556,10 +506,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 </section>
 <section id="logisticpreconditioner">
-<h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" title="Permalink to this headline">¶</a></h3>
+<h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" title="Permalink to this headline"></a></h3>
 <dl class="py class">
-<dt id="falkon.preconditioner.LogisticPreconditioner">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></code><code class="sig-name descname"><span class="pre">LogisticPreconditioner</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></span><span class="sig-name descname"><span class="pre">LogisticPreconditioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner" title="Permalink to this definition"></a></dt>
 <dd><p>Approximate Cholesky Preconditioner for Logistic-FALKON.</p>
 <p>The preconditioner is based on the K_MM kernel between the
 inducing points. A two step approximation of the inverse matrix
@@ -617,8 +567,8 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 </dl>
 </div>
 <dl class="py method">
-<dt id="falkon.preconditioner.LogisticPreconditioner.apply">
-<code class="sig-name descname"><span class="pre">apply</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.apply" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.apply" title="Permalink to this definition"></a></dt>
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(ATx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -639,8 +589,8 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.LogisticPreconditioner.apply_t">
-<code class="sig-name descname"><span class="pre">apply_t</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.apply_t" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.apply_t">
+<span class="sig-name descname"><span class="pre">apply_t</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.apply_t" title="Permalink to this definition"></a></dt>
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(A^\top T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -661,8 +611,8 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.LogisticPreconditioner.init">
-<code class="sig-name descname"><span class="pre">init</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">None</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.init" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.init">
+<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.init" title="Permalink to this definition"></a></dt>
 <dd><p>Initialize the preconditioner matrix.</p>
 <p>This method must be called before the preconditioner becomes usable.</p>
 <dl class="field-list simple">
@@ -684,8 +634,8 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.LogisticPreconditioner.invA">
-<code class="sig-name descname"><span class="pre">invA</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invA" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.invA">
+<span class="sig-name descname"><span class="pre">invA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invA" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Ax = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -706,8 +656,8 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.LogisticPreconditioner.invAt">
-<code class="sig-name descname"><span class="pre">invAt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invAt" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.invAt">
+<span class="sig-name descname"><span class="pre">invAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invAt" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(A^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -728,8 +678,8 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.LogisticPreconditioner.invT">
-<code class="sig-name descname"><span class="pre">invT</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invT" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.invT">
+<span class="sig-name descname"><span class="pre">invT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invT" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Tx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -750,8 +700,8 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 </dd></dl>
 
 <dl class="py method">
-<dt id="falkon.preconditioner.LogisticPreconditioner.invTt">
-<code class="sig-name descname"><span class="pre">invTt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invTt" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.invTt">
+<span class="sig-name descname"><span class="pre">invTt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.invTt" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -779,50 +729,33 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 
 
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-          <footer>
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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diff --git a/api_reference/sparse.html b/api_reference/sparse.html
index 011cebfc..9c4384b7 100644
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+++ b/api_reference/sparse.html
@@ -1,76 +1,38 @@
-
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 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
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-  <meta charset="utf-8" />
-  <meta name="generator" content="Docutils 0.17: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-  <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+  <title>falkon.sparse &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-<body class="wy-body-for-nav">
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+<body class="wy-body-for-nav"> 
   <div class="wy-grid-for-nav">
-    
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-          
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-          
             <a href="../index.html" class="icon icon-home"> falkon
-          
-
-          
           </a>
-
-          
-            
-            
               <div class="version">
-                0.6.3
+                0.7.2
               </div>
-            
-          
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-          
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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@@ -78,18 +40,7 @@
     <input type="hidden" name="area" value="default" />
   </form>
 </div>
-
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-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../install.html">Install</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#supported-platforms">Supported Platforms</a></li>
@@ -101,6 +52,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
@@ -109,13 +61,13 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -130,19 +82,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="../examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
@@ -154,9 +138,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +189,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.sparse</a><ul>
@@ -229,77 +208,48 @@
 <li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
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@@ -325,12 +275,12 @@
 }
 </style>
 <section id="module-falkon.sparse">
-<span id="falkon-sparse"></span><h1>falkon.sparse<a class="headerlink" href="#module-falkon.sparse" title="Permalink to this headline">¶</a></h1>
+<span id="falkon-sparse"></span><h1>falkon.sparse<a class="headerlink" href="#module-falkon.sparse" title="Permalink to this headline"></a></h1>
 <section id="sparsetensor">
-<h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to this headline">¶</a></h2>
+<h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to this headline"></a></h2>
 <dl class="py class">
-<dt id="falkon.sparse.sparse_tensor.SparseTensor">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></code><code class="sig-name descname"><span class="pre">SparseTensor</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexptr</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_type</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><span class="pre">falkon.sparse.sparse_tensor.SparseType</span></a><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">csr</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.sparse.sparse_tensor.SparseTensor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></span><span class="sig-name descname"><span class="pre">SparseTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexptr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><span class="pre">falkon.sparse.sparse_tensor.SparseType</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">SparseType.CSR</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper class to represent sparse 2D matrices in CSR or CSC format.</p>
 <p>The wrapper holds three 1D torch tensors which give the sparse representation
 (an index pointer, an index and the non-zero values of the matrix).
@@ -352,8 +302,8 @@ <h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to t
 </dd>
 </dl>
 <dl class="py method">
-<dt id="falkon.sparse.sparse_tensor.SparseTensor.narrow_rows">
-<code class="sig-name descname"><span class="pre">narrow_rows</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.sparse.sparse_tensor.SparseTensor.narrow_rows">
+<span class="sig-name descname"><span class="pre">narrow_rows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows" title="Permalink to this definition"></a></dt>
 <dd><p>Select a subset of contiguous rows from the sparse matrix.
 If this is a CSC sparse matrix, instead of taking contiguous rows we take contiguous
 columns.</p>
@@ -375,17 +325,17 @@ <h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to t
 </dd></dl>
 
 <dl class="py class">
-<dt id="falkon.sparse.sparse_tensor.SparseType">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></code><code class="sig-name descname"><span class="pre">SparseType</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseType" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.sparse.sparse_tensor.SparseType">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></span><span class="sig-name descname"><span class="pre">SparseType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseType" title="Permalink to this definition"></a></dt>
 <dd><p>Whether a <cite>SparseTensor</cite> is in CSC or CSR format.</p>
 </dd></dl>
 
 </section>
 <section id="sparse-operations">
-<h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Permalink to this headline">¶</a></h2>
+<h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Permalink to this headline"></a></h2>
 <dl class="py function">
-<dt id="falkon.sparse.sparse_matmul">
-<code class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></code><code class="sig-name descname"><span class="pre">sparse_matmul</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.sparse.sparse_matmul" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.sparse.sparse_matmul">
+<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_matmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_matmul" title="Permalink to this definition"></a></dt>
 <dd><p>Sparse*Sparse matrix multiplication. Output will be copied into dense <cite>out</cite> matrix.</p>
 <p>This function can be applied to CPU or CUDA tensors (but all tensors must
 be  on the same device).</p>
@@ -404,8 +354,8 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 </dd></dl>
 
 <dl class="py function">
-<dt id="falkon.sparse.sparse_square_norm">
-<code class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></code><code class="sig-name descname"><span class="pre">sparse_square_norm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.sparse.sparse_square_norm" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.sparse.sparse_square_norm">
+<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_square_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_square_norm" title="Permalink to this definition"></a></dt>
 <dd><p>Row-wise squared l2 norm of a sparse 2D matrix.</p>
 <p>The operation is equivalent to squaring all elements of the matrix, and summing up the rows.</p>
 <dl class="field-list simple">
@@ -426,8 +376,8 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 </dd></dl>
 
 <dl class="py function">
-<dt id="falkon.sparse.sparse_norm">
-<code class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></code><code class="sig-name descname"><span class="pre">sparse_norm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> &#x2192; <span class="pre">torch.Tensor</span><a class="headerlink" href="#falkon.sparse.sparse_norm" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="falkon.sparse.sparse_norm">
+<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_norm" title="Permalink to this definition"></a></dt>
 <dd><p>Row-wise l2 norm of a sparse 2D matrix</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -451,50 +401,33 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 
 
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+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_mnist.html">MNIST Classification with Falkon</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
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+      <li>Implementing A Custom Kernel</li>
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+            <a href="../_sources/examples/custom_kernels.ipynb.txt" rel="nofollow"> View page source</a>
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+</style>
+<section id="Implementing-A-Custom-Kernel">
+<h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Custom-Kernel" title="Permalink to this headline"></a></h1>
+<p>In this notebook we will show how to implement a custom kernel in Falkon.</p>
+<p>There are several complementary parts to a kernel, which can be added to support different operations. We will go through them one-by-one in this notebook:</p>
+<ul class="simple">
+<li><p>Basic support: supports learning with Falkon!</p></li>
+<li><p>Autodiff support: supports automatic hyperparameter tuning (in the <code class="docutils literal notranslate"><span class="pre">hopt</span></code> module)</p></li>
+<li><p>KeOps support: faster kernel-vector products in low dimension</p></li>
+<li><p>Sparse support: support learning on sparse data.</p></li>
+</ul>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">import</span> <span class="nn">falkon</span>
+<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
+<span class="kn">from</span> <span class="nn">falkon.kernels</span> <span class="kn">import</span> <span class="n">Kernel</span><span class="p">,</span> <span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[pyKeOps]: Warning, no cuda detected. Switching to cpu only.
+</pre></div></div>
+</div>
+<section id="Setup-a-simple-problem-for-testing">
+<h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simple-problem-for-testing" title="Permalink to this headline"></a></h2>
+<p>Load and preprocess the <em>California housing</em> dataset. The <code class="docutils literal notranslate"><span class="pre">learn_with_kernel</span></code> function sets up Falkon for learning on the California housing datase with a given kernel.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">fetch_california_housing</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
+<span class="n">num_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">num_train</span>
+<span class="n">shuffle_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffle_idx</span><span class="p">)</span>
+<span class="n">train_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[:</span><span class="n">num_train</span><span class="p">]</span>
+<span class="n">test_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[</span><span class="n">num_train</span><span class="p">:]</span>
+
+<span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
+<span class="n">Xtest</span><span class="p">,</span> <span class="n">Ytest</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">test_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>
+<span class="c1"># convert numpy -&gt; pytorch</span>
+<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">Xtrain</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtrain</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">Xtest</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtest</span> <span class="o">/=</span> <span class="n">train_std</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="k">def</span> <span class="nf">learn_with_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">):</span>
+    <span class="n">flk_opt</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
+        <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">,</span>
+        <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">error_fn</span><span class="o">=</span><span class="n">rmse</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
+    <span class="n">ts_err</span> <span class="o">=</span> <span class="n">rmse</span><span class="p">(</span><span class="n">Ytest</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test RMSE: </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">ts_err</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Basic-Kernel-Implementation">
+<h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Implementation" title="Permalink to this headline"></a></h2>
+<p>We must inherit from the <code class="docutils literal notranslate"><span class="pre">falkon.kernels.Kernel</span></code> class, and implement: - <code class="docutils literal notranslate"><span class="pre">compute</span></code> method: the core of the kernel implementation. Given two input matrices (of size <span class="math notranslate nohighlight">\(n\times d\)</span> and <span class="math notranslate nohighlight">\(m\times d\)</span>), and an output matrix (of size <span class="math notranslate nohighlight">\(n\times m\)</span>), compute the kernel function between the two inputs and store it in the output.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>The additional `diag` parameter is a boolean flag. It indicates that a) $n$ is equal to $m$, b) only the diagonal of the kernel matrix should be computed.
+</pre></div>
+</div>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">compute_sparse</span></code> method: this should be used if you want your kernel to support sparse data. We will implement it in a later section.</p></li>
+</ul>
+<p>We will implement a <strong>linear</strong> kernel:</p>
+<div class="math notranslate nohighlight">
+\[k(x, x') = \sigma (x^\top x')\]</div>
+<p>the parameter <span class="math notranslate nohighlight">\(\sigma\)</span> is the <em>variance</em> of the kernel. It is the only hyperparameter.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">BasicLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
+        <span class="c1"># an instance of `FalkonOptions`.</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;basic_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
+
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="c1"># To support different devices/data types, you must make sure</span>
+        <span class="c1"># the lengthscale is compatible with the data.</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># The dot-product row-by-row on `X1` and `X2` can be computed</span>
+            <span class="c1"># on many rows at a time with matrix multiplication.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Test-the-basic-kernel">
+<h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" title="Permalink to this headline"></a></h3>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel</span>
+<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">BasicLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The kernel matrix</span>
+<span class="n">k</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+tensor([[-1.3538,  4.0383, -0.5058, -3.1306, -0.3159],
+        [-0.9498, -2.0581,  0.4684,  0.8994,  0.7577],
+        [ 0.3122, -0.1038, -0.5039,  2.5076, -0.4032],
+        [ 0.8383,  3.8545, -1.4094,  1.0497, -1.4979],
+        [ 0.8344, -4.5258,  2.9362, -7.7300,  2.0740]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Kernel-vector product</span>
+<span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">v</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+tensor([[6.1084],
+        [3.6743],
+        [1.2653],
+        [1.2448]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Double kernel-vector product</span>
+<span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">v</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">w</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+tensor([[ -3.6467],
+        [ -9.8628],
+        [  1.4857],
+        [-12.8557]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset</span>
+<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Iteration   1 - Elapsed 0.07s - training error: 2.36367178
+Iteration   2 - Elapsed 0.11s - training error: 2.19508219
+Iteration   3 - Elapsed 0.14s - training error: 2.19265079
+Iteration   4 - Elapsed 0.17s - training error: 2.19265032
+Iteration   5 - Elapsed 0.20s - training error: 2.19262338
+Iteration   6 - Elapsed 0.24s - training error: 2.19262123
+Iteration   7 - Elapsed 0.27s - training error: 2.19261861
+Iteration   8 - Elapsed 0.30s - training error: 2.19261885
+Iteration   9 - Elapsed 0.33s - training error: 2.19261789
+Iteration  10 - Elapsed 0.39s - training error: 2.19261765
+Iteration  11 - Elapsed 0.42s - training error: 2.19261956
+Iteration  12 - Elapsed 0.45s - training error: 2.19261932
+Iteration  13 - Elapsed 0.48s - training error: 2.19261909
+Iteration  14 - Elapsed 0.51s - training error: 2.19261813
+Iteration  15 - Elapsed 0.55s - training error: 2.19261885
+Iteration  16 - Elapsed 0.57s - training error: 2.19261742
+Iteration  17 - Elapsed 0.61s - training error: 2.19261813
+Iteration  18 - Elapsed 0.63s - training error: 2.19261980
+Iteration  19 - Elapsed 0.66s - training error: 2.19261956
+Iteration  20 - Elapsed 0.73s - training error: 2.19262052
+Test RMSE: 2.19
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Differentiable-Kernel">
+<h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" title="Permalink to this headline"></a></h2>
+<p>A differentiable kernel is needed for automatic hyperparameter optimization (see the <a class="reference internal" href="hyperopt.html"><span class="doc">notebook</span></a>).</p>
+<p>It requires inheriting from <code class="docutils literal notranslate"><span class="pre">falkon.kernels.DiffKernel</span></code>. In addition to the methods already discussed, we must implement: - <code class="docutils literal notranslate"><span class="pre">compute_diff</span></code>, which works similarly to the <code class="docutils literal notranslate"><span class="pre">compute</span></code> method but it does not have an <code class="docutils literal notranslate"><span class="pre">out</span></code> parameter. The implementation should be fully differentiable with respect to its inputs, and to the kernel hyperparameters. - <code class="docutils literal notranslate"><span class="pre">detach</span></code>, which essentially clones the kernel with the parameters <em>detached</em> from the computational graph.</p>
+<p>Another important difference from the basic kernel is the call to the <em>constructor</em>, which must include - All kernel hyperparameters as keyword arguments. These will be available as attributes on the class. Hyperparameters do not need to be tensors.</p>
+<p><strong>``core_fn`` parameter (optional)</strong></p>
+<p>The constructor can also <em>optionally</em> contain a <code class="docutils literal notranslate"><span class="pre">core_fn</span></code> parameter which can simplify implementation by uniting the <code class="docutils literal notranslate"><span class="pre">compute</span></code> and <code class="docutils literal notranslate"><span class="pre">compute_diff</span></code> implementations. Have a look at the implementation of kernels in <code class="docutils literal notranslate"><span class="pre">falkon.kernels.dot_prod_kernel.py</span></code> and <code class="docutils literal notranslate"><span class="pre">falkon.kernels.distance_kernel.py</span></code> for how to use the <code class="docutils literal notranslate"><span class="pre">core_fn</span></code> parameter.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DiffLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="c1"># Super-class constructor call. We do not specify core_fn</span>
+        <span class="c1"># but we must specify the hyperparameter of this kernel (lengthscale)</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;diff_linear&quot;</span><span class="p">,</span>
+                         <span class="n">options</span><span class="p">,</span>
+                         <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="c1"># The implementation here is similar to `compute` without in-place operations.</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Clones the class with detached hyperparameters</span>
+        <span class="k">return</span> <span class="n">DiffLinearKernel</span><span class="p">(</span>
+            <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
+            <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Test-the-differentiable-kernel">
+<h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differentiable-kernel" title="Permalink to this headline"></a></h3>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel, with a lengthscale which requires grad.</span>
+<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">DiffLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Kernel matrix. Notice how the outputs has a `grad_fn`</span>
+<span class="n">k_mat</span> <span class="o">=</span> <span class="n">k</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">k_mat</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+tensor([[ 2.7480,  1.6149, -1.2979, -2.3070, -1.1852],
+        [ 4.2437,  2.8397, -2.6248, -3.1610, -1.1940],
+        [ 2.6474,  0.9644, -0.4447, -1.1742, -1.0197],
+        [-3.4735,  0.4214, -1.9773,  0.3380,  2.2361],
+        [-1.8094, -0.2183, -0.5620,  1.8260,  1.8644]],
+       grad_fn=&lt;KernelMmFnFullBackward&gt;)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Gradient of the kernel with respect to the lengthscale.</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mat</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(tensor([-0.7049]),)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># kernel-vector product + gradient</span>
+<span class="n">m1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">m2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">k_mmv</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kernel-vector product&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">k_mmv</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Gradients:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mmv</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">,</span> <span class="n">m1</span><span class="p">]))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Kernel-vector product
+tensor([[ 0.0198],
+        [-1.6055],
+        [ 2.3654],
+        [-0.6039]], grad_fn=&lt;KernelMmvFnFullBackward&gt;)
+Gradients:
+(tensor([0.1758]), tensor([[ 0.6192,  1.2183, -0.2544],
+        [ 0.6192,  1.2183, -0.2544],
+        [ 0.6192,  1.2183, -0.2544],
+        [ 0.6192,  1.2183, -0.2544]]))
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset</span>
+<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Iteration   1 - Elapsed 0.06s - training error: 2.20815659
+Iteration   2 - Elapsed 0.10s - training error: 2.19324374
+Iteration   3 - Elapsed 0.12s - training error: 2.19264197
+Iteration   4 - Elapsed 0.15s - training error: 2.19263649
+Iteration   5 - Elapsed 0.18s - training error: 2.19262934
+Iteration   6 - Elapsed 0.21s - training error: 2.19261909
+Iteration   7 - Elapsed 0.24s - training error: 2.19261813
+Iteration   8 - Elapsed 0.26s - training error: 2.19262004
+Iteration   9 - Elapsed 0.29s - training error: 2.19261765
+Iteration  10 - Elapsed 0.34s - training error: 2.19261789
+Iteration  11 - Elapsed 0.38s - training error: 2.19261909
+Iteration  12 - Elapsed 0.40s - training error: 2.19261885
+Iteration  13 - Elapsed 0.43s - training error: 2.19261956
+Iteration  14 - Elapsed 0.46s - training error: 2.19261932
+Iteration  15 - Elapsed 0.49s - training error: 2.19261932
+Iteration  16 - Elapsed 0.52s - training error: 2.19262099
+Iteration  17 - Elapsed 0.54s - training error: 2.19262123
+Iteration  18 - Elapsed 0.57s - training error: 2.19262147
+Iteration  19 - Elapsed 0.60s - training error: 2.19262195
+Iteration  20 - Elapsed 0.65s - training error: 2.19262338
+Test RMSE: 2.19
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Adding-KeOps-Support">
+<h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title="Permalink to this headline"></a></h2>
+<p>We must inherit from <code class="docutils literal notranslate"><span class="pre">falkon.kernels.KeopsKernelMixin</span></code> and implement the method <code class="docutils literal notranslate"><span class="pre">keops_mmv_impl</span></code>.</p>
+<p>KeOps-enabled kernels will still use the implementation in the <code class="docutils literal notranslate"><span class="pre">compute</span></code> function for computing the kernel matrix itself, but will use KeOps to compute kernel-vector products (if the data dimension is small enough).</p>
+<p>This method is responsible for kernel-vector products, and it should contain: 1. A formula definition (see <a class="reference external" href="https://www.kernel-operations.io/keops/api/math-operations.html">https://www.kernel-operations.io/keops/api/math-operations.html</a> for the appropriate syntax) 2. A definition of all variables (again have a look at the KeOps documentation, or the implementation of other kernels within Falkon) 3. A call to the <code class="docutils literal notranslate"><span class="pre">keops_mmv</span></code> method of the <code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code> class, responsible for calling into the KeOps formula.</p>
+<p>For our kernel we will use the <code class="docutils literal notranslate"><span class="pre">(X</span> <span class="pre">|</span> <span class="pre">Y)</span></code> syntax for the dot-product between samples, and then multiplication with the vector <code class="docutils literal notranslate"><span class="pre">v</span></code>. The aliases list maps the symbols used in the formula with the KeOps variable types.</p>
+<p>For more examples check the <a class="reference external" href="https://www.kernel-operations.io">KeOps documentatiaon</a> or the implementation of existing kernels.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">KeopsLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;my-keops-linear&quot;</span><span class="p">,</span>
+                         <span class="n">options</span><span class="p">,</span>
+                         <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">KeopsLinearKernel</span><span class="p">(</span>
+            <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
+            <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">keops_mmv_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">opt</span><span class="p">):</span>
+        <span class="c1"># Keops formula for kernel-vector.</span>
+        <span class="n">formula</span> <span class="o">=</span> <span class="s1">&#39;(scale * (X | Y)) * v&#39;</span>
+        <span class="n">aliases</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="s1">&#39;X = Vi(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">X1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="s1">&#39;Y = Vj(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">X2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="s1">&#39;v = Vj(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="s1">&#39;scale = Pm(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
+        <span class="p">]</span>
+        <span class="n">other_vars</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+        <span class="p">]</span>
+        <span class="c1"># Call to the executor of the formula.</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">keops_mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">formula</span><span class="p">,</span> <span class="n">aliases</span><span class="p">,</span> <span class="n">other_vars</span><span class="p">,</span> <span class="n">opt</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Test-the-KeOps-kernel">
+<h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" title="Permalink to this headline"></a></h3>
+<p>Note that KeOps will need to compile the kernels the first time they are run!</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">KeopsLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># kernel-vector product + gradient</span>
+<span class="n">m1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">m2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">k_mmv</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kernel-vector product&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">k_mmv</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Gradients:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mmv</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">,</span> <span class="n">m1</span><span class="p">]))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Kernel-vector product
+tensor([[-1.2121],
+        [-0.1148],
+        [ 2.2435],
+        [ 0.9918]], grad_fn=&lt;TilingGenredAutogradBackward&gt;)
+Gradients:
+(tensor([1.9084]), tensor([[ 1.0124, -0.8363,  0.7706],
+        [ 1.0124, -0.8363,  0.7706],
+        [ 1.0124, -0.8363,  0.7706],
+        [ 1.0124, -0.8363,  0.7706]], requires_grad=True))
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset.</span>
+<span class="c1"># Due to differences in floating point code, results may be slightly</span>
+<span class="c1"># different from the other implementations.</span>
+<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Iteration   1 - Elapsed 0.17s - training error: 2.27769995
+Iteration   2 - Elapsed 0.34s - training error: 2.19313025
+Iteration   3 - Elapsed 0.51s - training error: 2.19323778
+Iteration   4 - Elapsed 0.66s - training error: 2.19308257
+Iteration   5 - Elapsed 0.82s - training error: 2.19269753
+Iteration   6 - Elapsed 0.98s - training error: 2.19266987
+Iteration   7 - Elapsed 1.13s - training error: 2.19262886
+Iteration   8 - Elapsed 1.29s - training error: 2.19262505
+Iteration   9 - Elapsed 1.45s - training error: 2.19262052
+Iteration  10 - Elapsed 1.76s - training error: 2.19260979
+Iteration  11 - Elapsed 1.92s - training error: 2.19261813
+Iteration  12 - Elapsed 2.08s - training error: 2.19261646
+Iteration  13 - Elapsed 2.25s - training error: 2.19263911
+Iteration  14 - Elapsed 2.42s - training error: 2.19263911
+Iteration  15 - Elapsed 2.58s - training error: 2.19264960
+Iteration  16 - Elapsed 2.74s - training error: 2.19265103
+Iteration  17 - Elapsed 2.91s - training error: 2.19268680
+Iteration  18 - Elapsed 3.07s - training error: 2.19269395
+Iteration  19 - Elapsed 3.23s - training error: 2.19270301
+Iteration  20 - Elapsed 3.55s - training error: 2.19275403
+Test RMSE: 2.19
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Supporting-Sparse-Data">
+<h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" title="Permalink to this headline"></a></h2>
+<p>Sparse support can be necessary for kernel learning in extremely high dimensions, when the inputs are sparse.</p>
+<p>Sparse support requires using special functions for common operations such as matrix multiplication. Falkon implements sparse tensors in a CSR format (PyTorch is slowly picking this format up, in place of COO), through the <code class="docutils literal notranslate"><span class="pre">falkon.sparse.SparseTensor</span></code> class.</p>
+<p>We will implement the <code class="docutils literal notranslate"><span class="pre">compute_sparse</span></code> method below, supporting both diagonal and full kernels. However, only CPU support is added here (CUDA support is possible but requires a few more details), and differentiable sparse kernels are not supported.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">SparseTensor</span>
+<span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">sparse_matmul</span><span class="p">,</span> <span class="n">bdot</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">SparseLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
+        <span class="c1"># an instance of `FalkonOptions`.</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;sparse_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
+
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># The dot-product row-by-row on `X1` and `X2` can be computed</span>
+            <span class="c1"># on many rows at a time with matrix multiplication.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                       <span class="n">X1</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
+                       <span class="n">X2</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
+                       <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+                       <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
+                       <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="c1"># The inputs will be matrix X1(n*d) in CSR format, and X2(d*n) in CSC format.</span>
+
+        <span class="c1"># To support different devices/data types, you must make sure</span>
+        <span class="c1"># the lengthscale is compatible with the data.</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="c1"># The diagonal is a dot-product between rows of X1 and X2.</span>
+            <span class="c1"># The batched-dot is only implemented on CPU.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">bdot</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">transpose_csr</span><span class="p">(),</span> <span class="n">out</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Otherwise we need to matrix-multiply. Note that X2 is already</span>
+            <span class="c1"># transposed correctly.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">sparse_matmul</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span>
+
+        <span class="n">out</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">lengthscale</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span>
+</pre></div>
+</div>
+</div>
+<section id="Testing-sparse-support">
+<h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" title="Permalink to this headline"></a></h3>
+<p>We generate two sparse matrices, and check that the sparse kernel is equivalent to the dense version.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">indexptr</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">sp1</span> <span class="o">=</span> <span class="n">SparseTensor</span><span class="p">(</span><span class="n">indexptr</span><span class="o">=</span><span class="n">indexptr</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">sparse_type</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
+<span class="c1"># Converted to dense:</span>
+<span class="n">dense1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">sp1</span><span class="o">.</span><span class="n">to_scipy</span><span class="p">()</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
+<span class="n">dense1</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+tensor([[0., 5.],
+        [1., 8.],
+        [2., 0.]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">indexptr</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">sp2</span> <span class="o">=</span> <span class="n">SparseTensor</span><span class="p">(</span><span class="n">indexptr</span><span class="o">=</span><span class="n">indexptr</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">sparse_type</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
+<span class="n">dense2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">sp2</span><span class="o">.</span><span class="n">to_scipy</span><span class="p">()</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
+<span class="n">dense2</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+tensor([[0., 2.],
+        [1., 0.],
+        [3., 4.]])
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel</span>
+<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">SparseLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">k</span><span class="p">(</span><span class="n">sp1</span><span class="p">,</span> <span class="n">sp2</span><span class="p">)</span> <span class="o">==</span> <span class="n">k</span><span class="p">(</span><span class="n">dense1</span><span class="p">,</span> <span class="n">dense2</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+tensor([[True, True, True],
+        [True, True, True],
+        [True, True, True]])
+</pre></div></div>
+</div>
+</section>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="falkon_cv.html" class="btn btn-neutral float-left" title="Hyperparameter Tuning with Falkon" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="hyperopt.html" class="btn btn-neutral float-right" title="Automatic Hyperparameter Optimization" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/examples/custom_kernels.ipynb b/examples/custom_kernels.ipynb
new file mode 100644
index 00000000..27f46126
--- /dev/null
+++ b/examples/custom_kernels.ipynb
@@ -0,0 +1,944 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "be9efd3e",
+   "metadata": {},
+   "source": [
+    "# Implementing A Custom Kernel\n",
+    "\n",
+    "In this notebook we will show how to implement a custom kernel in Falkon.\n",
+    "\n",
+    "There are several complementary parts to a kernel, which can be added to support different operations.\n",
+    "We will go through them one-by-one in this notebook:\n",
+    "\n",
+    " - Basic support: supports learning with Falkon!\n",
+    " - Autodiff support: supports automatic hyperparameter tuning (in the `hopt` module)\n",
+    " - KeOps support: faster kernel-vector products in low dimension\n",
+    " - Sparse support: support learning on sparse data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cc7a4428",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "\n",
+    "import falkon\n",
+    "from falkon import FalkonOptions\n",
+    "from falkon.kernels import Kernel, DiffKernel, KeopsKernelMixin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b37d1d0",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Setup a simple problem for testing\n",
+    "\n",
+    "Load and preprocess the *California housing* dataset. The `learn_with_kernel` function sets up Falkon for learning on the California housing datase with a given kernel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "be73c315",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.fetch_california_housing(return_X_y=True)\n",
+    "num_train = int(X.shape[0] * 0.8)\n",
+    "num_test = X.shape[0] - num_train\n",
+    "shuffle_idx = np.arange(X.shape[0])\n",
+    "np.random.shuffle(shuffle_idx)\n",
+    "train_idx = shuffle_idx[:num_train]\n",
+    "test_idx = shuffle_idx[num_train:]\n",
+    "\n",
+    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
+    "Xtest, Ytest = X[test_idx], Y[test_idx]\n",
+    "# convert numpy -> pytorch\n",
+    "Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)\n",
+    "Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)\n",
+    "Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)\n",
+    "Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)\n",
+    "# z-score normalization\n",
+    "train_mean = Xtrain.mean(0, keepdim=True)\n",
+    "train_std = Xtrain.std(0, keepdim=True)\n",
+    "Xtrain -= train_mean\n",
+    "Xtrain /= train_std\n",
+    "Xtest -= train_mean\n",
+    "Xtest /= train_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a58a7c95",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "def rmse(true, pred):\n",
+    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
+    "\n",
+    "def learn_with_kernel(kernel):\n",
+    "    flk_opt = FalkonOptions(use_cpu=True)\n",
+    "    model = falkon.Falkon(\n",
+    "        kernel=kernel, penalty=1e-5, M=1000, options=flk_opt,\n",
+    "        error_every=1, error_fn=rmse)\n",
+    "    model.fit(Xtrain, Ytrain)\n",
+    "    ts_err = rmse(Ytest, model.predict(Xtest))\n",
+    "    print(\"Test RMSE: %.2f\" % (ts_err))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d046959",
+   "metadata": {},
+   "source": [
+    "## Basic Kernel Implementation\n",
+    "\n",
+    "We must inherit from the `falkon.kernels.Kernel` class, and implement:\n",
+    " - `compute` method: the core of the kernel implementation. \n",
+    "     Given two input matrices (of size $n\\times d$ and $m\\times d$), and an output matrix (of size $n\\times m$), compute the kernel function between the two inputs and store it in the output.\n",
+    "     \n",
+    "     The additional `diag` parameter is a boolean flag. It indicates that a) $n$ is equal to $m$, b) only the diagonal of the kernel matrix should be computed.\n",
+    " - `compute_sparse` method: this should be used if you want your kernel to support sparse data. \n",
+    "     We will implement it in a later section.\n",
+    "     \n",
+    "We will implement a **linear** kernel:\n",
+    "$$k(x, x') = \\sigma (x^\\top x')$$\n",
+    "the parameter $\\sigma$ is the *variance* of the kernel. It is the only hyperparameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bfd90b30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BasicLinearKernel(Kernel):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        # The base class takes as inputs a name for the kernel, and\n",
+    "        # an instance of `FalkonOptions`.\n",
+    "        super().__init__(\"basic_linear\", options)\n",
+    "        \n",
+    "        self.lengthscale = lengthscale\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -> torch.Tensor:\n",
+    "        # To support different devices/data types, you must make sure\n",
+    "        # the lengthscale is compatible with the data.\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            # The dot-product row-by-row on `X1` and `X2` can be computed\n",
+    "            # on many rows at a time with matrix multiplication.\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_sparse(self, X1, X2, out, diag, **kwargs) -> torch.Tensor:\n",
+    "        raise NotImplementedError(\"Sparse not implemented\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f19f3e95",
+   "metadata": {},
+   "source": [
+    "### Test the basic kernel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7bfcd1be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the kernel\n",
+    "lengthscale_init = torch.tensor([1.0])\n",
+    "k = BasicLinearKernel(lengthscale_init, options=falkon.FalkonOptions())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "32b86480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.3538,  4.0383, -0.5058, -3.1306, -0.3159],\n",
+       "        [-0.9498, -2.0581,  0.4684,  0.8994,  0.7577],\n",
+       "        [ 0.3122, -0.1038, -0.5039,  2.5076, -0.4032],\n",
+       "        [ 0.8383,  3.8545, -1.4094,  1.0497, -1.4979],\n",
+       "        [ 0.8344, -4.5258,  2.9362, -7.7300,  2.0740]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The kernel matrix\n",
+    "k(torch.randn(5, 3), torch.randn(5, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a3134217",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[6.1084],\n",
+       "        [3.6743],\n",
+       "        [1.2653],\n",
+       "        [1.2448]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Kernel-vector product\n",
+    "k.mmv(torch.randn(4, 3), torch.randn(4, 3), v=torch.randn(4, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "53521733",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ -3.6467],\n",
+       "        [ -9.8628],\n",
+       "        [  1.4857],\n",
+       "        [-12.8557]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Double kernel-vector product\n",
+    "k.dmmv(torch.randn(3, 3), torch.randn(4, 3), v=torch.randn(4, 1), w=torch.randn(3, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "31837bb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.07s - training error: 2.36367178\n",
+      "Iteration   2 - Elapsed 0.11s - training error: 2.19508219\n",
+      "Iteration   3 - Elapsed 0.14s - training error: 2.19265079\n",
+      "Iteration   4 - Elapsed 0.17s - training error: 2.19265032\n",
+      "Iteration   5 - Elapsed 0.20s - training error: 2.19262338\n",
+      "Iteration   6 - Elapsed 0.24s - training error: 2.19262123\n",
+      "Iteration   7 - Elapsed 0.27s - training error: 2.19261861\n",
+      "Iteration   8 - Elapsed 0.30s - training error: 2.19261885\n",
+      "Iteration   9 - Elapsed 0.33s - training error: 2.19261789\n",
+      "Iteration  10 - Elapsed 0.39s - training error: 2.19261765\n",
+      "Iteration  11 - Elapsed 0.42s - training error: 2.19261956\n",
+      "Iteration  12 - Elapsed 0.45s - training error: 2.19261932\n",
+      "Iteration  13 - Elapsed 0.48s - training error: 2.19261909\n",
+      "Iteration  14 - Elapsed 0.51s - training error: 2.19261813\n",
+      "Iteration  15 - Elapsed 0.55s - training error: 2.19261885\n",
+      "Iteration  16 - Elapsed 0.57s - training error: 2.19261742\n",
+      "Iteration  17 - Elapsed 0.61s - training error: 2.19261813\n",
+      "Iteration  18 - Elapsed 0.63s - training error: 2.19261980\n",
+      "Iteration  19 - Elapsed 0.66s - training error: 2.19261956\n",
+      "Iteration  20 - Elapsed 0.73s - training error: 2.19262052\n",
+      "Test RMSE: 2.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Learning on the california housing dataset\n",
+    "learn_with_kernel(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1a4b946",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Differentiable Kernel\n",
+    "\n",
+    "A differentiable kernel is needed for automatic hyperparameter optimization (see the [notebook](hyperopt.ipynb)).\n",
+    "\n",
+    "It requires inheriting from `falkon.kernels.DiffKernel`. In addition to the methods already discussed, we must implement:\n",
+    " - `compute_diff`, which works similarly to the `compute` method but it does not have an `out` parameter. The implementation should be fully differentiable with respect to its inputs, and to the kernel hyperparameters.\n",
+    " - `detach`, which essentially clones the kernel with the parameters *detached* from the computational graph.\n",
+    " \n",
+    "Another important difference from the basic kernel is the call to the *constructor*, which must include\n",
+    " - All kernel hyperparameters as keyword arguments. These will be available as attributes on the class. Hyperparameters do not need to be tensors.\n",
+    "\n",
+    "**`core_fn` parameter (optional)**\n",
+    "\n",
+    "The constructor can also *optionally* contain a `core_fn` parameter which can simplify implementation by uniting the `compute` and `compute_diff` implementations. Have a look at the implementation of kernels in `falkon.kernels.dot_prod_kernel.py` and `falkon.kernels.distance_kernel.py` for how to use the `core_fn` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13c002e2",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "class DiffLinearKernel(DiffKernel):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        # Super-class constructor call. We do not specify core_fn\n",
+    "        # but we must specify the hyperparameter of this kernel (lengthscale)\n",
+    "        super().__init__(\"diff_linear\", \n",
+    "                         options, \n",
+    "                         core_fn=None, \n",
+    "                         lengthscale=lengthscale)\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):\n",
+    "        # The implementation here is similar to `compute` without in-place operations.\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            return torch.sum(scaled_X1 * X2, dim=-1)\n",
+    "        \n",
+    "        return torch.matmul(scaled_X1, X2.T)\n",
+    "\n",
+    "    def detach(self):\n",
+    "        # Clones the class with detached hyperparameters\n",
+    "        return DiffLinearKernel(\n",
+    "            lengthscale=self.lengthscale.detach(), \n",
+    "            options=self.params\n",
+    "        )\n",
+    "    \n",
+    "    def compute_sparse(self, X1, X2, out, diag, **kwargs) -> torch.Tensor:\n",
+    "        raise NotImplementedError(\"Sparse not implemented\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7dd73bf",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### Test the differentiable kernel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0f223c67",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize the kernel, with a lengthscale which requires grad.\n",
+    "lengthscale_init = torch.tensor([1.0]).requires_grad_()\n",
+    "k = DiffLinearKernel(lengthscale_init, options=falkon.FalkonOptions())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cc862c47",
+   "metadata": {
+    "hidden": true,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 2.7480,  1.6149, -1.2979, -2.3070, -1.1852],\n",
+       "        [ 4.2437,  2.8397, -2.6248, -3.1610, -1.1940],\n",
+       "        [ 2.6474,  0.9644, -0.4447, -1.1742, -1.0197],\n",
+       "        [-3.4735,  0.4214, -1.9773,  0.3380,  2.2361],\n",
+       "        [-1.8094, -0.2183, -0.5620,  1.8260,  1.8644]],\n",
+       "       grad_fn=<KernelMmFnFullBackward>)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Kernel matrix. Notice how the outputs has a `grad_fn`\n",
+    "k_mat = k(torch.randn(5, 3), torch.randn(5, 3))\n",
+    "k_mat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a22671f4",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([-0.7049]),)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Gradient of the kernel with respect to the lengthscale.\n",
+    "torch.autograd.grad(k_mat.sum(), k.lengthscale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d508d0d7",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kernel-vector product\n",
+      "tensor([[ 0.0198],\n",
+      "        [-1.6055],\n",
+      "        [ 2.3654],\n",
+      "        [-0.6039]], grad_fn=<KernelMmvFnFullBackward>)\n",
+      "Gradients:\n",
+      "(tensor([0.1758]), tensor([[ 0.6192,  1.2183, -0.2544],\n",
+      "        [ 0.6192,  1.2183, -0.2544],\n",
+      "        [ 0.6192,  1.2183, -0.2544],\n",
+      "        [ 0.6192,  1.2183, -0.2544]]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# kernel-vector product + gradient\n",
+    "m1 = torch.randn(4, 3).requires_grad_()\n",
+    "m2 = torch.randn(2, 3)\n",
+    "v = torch.randn(2, 1)\n",
+    "k_mmv = k.mmv(m1, m2, v)\n",
+    "print(\"Kernel-vector product\")\n",
+    "print(k_mmv)\n",
+    "print(\"Gradients:\")\n",
+    "print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c56f9a2a",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.06s - training error: 2.20815659\n",
+      "Iteration   2 - Elapsed 0.10s - training error: 2.19324374\n",
+      "Iteration   3 - Elapsed 0.12s - training error: 2.19264197\n",
+      "Iteration   4 - Elapsed 0.15s - training error: 2.19263649\n",
+      "Iteration   5 - Elapsed 0.18s - training error: 2.19262934\n",
+      "Iteration   6 - Elapsed 0.21s - training error: 2.19261909\n",
+      "Iteration   7 - Elapsed 0.24s - training error: 2.19261813\n",
+      "Iteration   8 - Elapsed 0.26s - training error: 2.19262004\n",
+      "Iteration   9 - Elapsed 0.29s - training error: 2.19261765\n",
+      "Iteration  10 - Elapsed 0.34s - training error: 2.19261789\n",
+      "Iteration  11 - Elapsed 0.38s - training error: 2.19261909\n",
+      "Iteration  12 - Elapsed 0.40s - training error: 2.19261885\n",
+      "Iteration  13 - Elapsed 0.43s - training error: 2.19261956\n",
+      "Iteration  14 - Elapsed 0.46s - training error: 2.19261932\n",
+      "Iteration  15 - Elapsed 0.49s - training error: 2.19261932\n",
+      "Iteration  16 - Elapsed 0.52s - training error: 2.19262099\n",
+      "Iteration  17 - Elapsed 0.54s - training error: 2.19262123\n",
+      "Iteration  18 - Elapsed 0.57s - training error: 2.19262147\n",
+      "Iteration  19 - Elapsed 0.60s - training error: 2.19262195\n",
+      "Iteration  20 - Elapsed 0.65s - training error: 2.19262338\n",
+      "Test RMSE: 2.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Learning on the california housing dataset\n",
+    "learn_with_kernel(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd29c886",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Adding KeOps Support\n",
+    "\n",
+    "We must inherit from `falkon.kernels.KeopsKernelMixin` and implement the method `keops_mmv_impl`.\n",
+    "\n",
+    "KeOps-enabled kernels will still use the implementation in the `compute` function for computing the kernel matrix itself, but will use KeOps to compute kernel-vector products (if the data dimension is small enough).\n",
+    "\n",
+    "This method is responsible for kernel-vector products, and it should contain:\n",
+    " 1. A formula definition (see https://www.kernel-operations.io/keops/api/math-operations.html for the appropriate syntax)\n",
+    " 2. A definition of all variables (again have a look at the KeOps documentation, or the implementation\n",
+    "     of other kernels within Falkon)\n",
+    " 3. A call to the `keops_mmv` method of the `KeopsKernelMixin` class, responsible for calling into\n",
+    "     the KeOps formula.\n",
+    "     \n",
+    "For our kernel we will use the `(X | Y)` syntax for the dot-product between samples, and then multiplication with the vector `v`. The aliases list maps the symbols used in the formula with the KeOps variable types.\n",
+    "\n",
+    "For more examples check the [KeOps documentatiaon](https://www.kernel-operations.io) or the implementation of existing kernels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8164d28e",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "class KeopsLinearKernel(DiffKernel, KeopsKernelMixin):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        super().__init__(\"my-keops-linear\", \n",
+    "                         options, \n",
+    "                         core_fn=None, \n",
+    "                         lengthscale=lengthscale)\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):\n",
+    "        scaled_X1 = X1 * self.lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            return torch.sum(scaled_X1 * X2, dim=-1)\n",
+    "        \n",
+    "        return torch.matmul(scaled_X1, X2.T)\n",
+    "\n",
+    "    def detach(self):\n",
+    "        return KeopsLinearKernel(\n",
+    "            lengthscale=self.lengthscale.detach(), \n",
+    "            options=self.params\n",
+    "        )\n",
+    "        \n",
+    "    def keops_mmv_impl(self, X1, X2, v, kernel, out, opt):\n",
+    "        # Keops formula for kernel-vector.\n",
+    "        formula = '(scale * (X | Y)) * v'\n",
+    "        aliases = [\n",
+    "            'X = Vi(%d)' % (X1.shape[1]),\n",
+    "            'Y = Vj(%d)' % (X2.shape[1]),\n",
+    "            'v = Vj(%d)' % (v.shape[1]),\n",
+    "            'scale = Pm(%d)' % (self.lengthscale.shape[0]),\n",
+    "        ]\n",
+    "        other_vars = [\n",
+    "            self.lengthscale.to(dtype=X1.dtype, device=X1.device),\n",
+    "        ]\n",
+    "        # Call to the executor of the formula.\n",
+    "        return self.keops_mmv(X1, X2, v, out, formula, aliases, other_vars, opt)\n",
+    "\n",
+    "    \n",
+    "    def compute_sparse(self, X1, X2, out: torch.Tensor, diag: bool, **kwargs) -> torch.Tensor:\n",
+    "        raise NotImplementedError(\"Sparse not implemented\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11528fb9",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "### Test the KeOps kernel\n",
+    "\n",
+    "Note that KeOps will need to compile the kernels the first time they are run!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "12107005",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "lengthscale_init = torch.tensor([1.0]).requires_grad_()\n",
+    "k = KeopsLinearKernel(lengthscale_init, options=falkon.FalkonOptions(use_cpu=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c231eabc",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kernel-vector product\n",
+      "tensor([[-1.2121],\n",
+      "        [-0.1148],\n",
+      "        [ 2.2435],\n",
+      "        [ 0.9918]], grad_fn=<TilingGenredAutogradBackward>)\n",
+      "Gradients:\n",
+      "(tensor([1.9084]), tensor([[ 1.0124, -0.8363,  0.7706],\n",
+      "        [ 1.0124, -0.8363,  0.7706],\n",
+      "        [ 1.0124, -0.8363,  0.7706],\n",
+      "        [ 1.0124, -0.8363,  0.7706]], requires_grad=True))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# kernel-vector product + gradient\n",
+    "m1 = torch.randn(4, 3).requires_grad_()\n",
+    "m2 = torch.randn(2, 3)\n",
+    "v = torch.randn(2, 1)\n",
+    "k_mmv = k.mmv(m1, m2, v)\n",
+    "print(\"Kernel-vector product\")\n",
+    "print(k_mmv)\n",
+    "print(\"Gradients:\")\n",
+    "print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7d2b80e5",
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.17s - training error: 2.27769995\n",
+      "Iteration   2 - Elapsed 0.34s - training error: 2.19313025\n",
+      "Iteration   3 - Elapsed 0.51s - training error: 2.19323778\n",
+      "Iteration   4 - Elapsed 0.66s - training error: 2.19308257\n",
+      "Iteration   5 - Elapsed 0.82s - training error: 2.19269753\n",
+      "Iteration   6 - Elapsed 0.98s - training error: 2.19266987\n",
+      "Iteration   7 - Elapsed 1.13s - training error: 2.19262886\n",
+      "Iteration   8 - Elapsed 1.29s - training error: 2.19262505\n",
+      "Iteration   9 - Elapsed 1.45s - training error: 2.19262052\n",
+      "Iteration  10 - Elapsed 1.76s - training error: 2.19260979\n",
+      "Iteration  11 - Elapsed 1.92s - training error: 2.19261813\n",
+      "Iteration  12 - Elapsed 2.08s - training error: 2.19261646\n",
+      "Iteration  13 - Elapsed 2.25s - training error: 2.19263911\n",
+      "Iteration  14 - Elapsed 2.42s - training error: 2.19263911\n",
+      "Iteration  15 - Elapsed 2.58s - training error: 2.19264960\n",
+      "Iteration  16 - Elapsed 2.74s - training error: 2.19265103\n",
+      "Iteration  17 - Elapsed 2.91s - training error: 2.19268680\n",
+      "Iteration  18 - Elapsed 3.07s - training error: 2.19269395\n",
+      "Iteration  19 - Elapsed 3.23s - training error: 2.19270301\n",
+      "Iteration  20 - Elapsed 3.55s - training error: 2.19275403\n",
+      "Test RMSE: 2.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Learning on the california housing dataset.\n",
+    "# Due to differences in floating point code, results may be slightly \n",
+    "# different from the other implementations.\n",
+    "learn_with_kernel(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fecea8f",
+   "metadata": {},
+   "source": [
+    "## Supporting Sparse Data\n",
+    "\n",
+    "Sparse support can be necessary for kernel learning in extremely high dimensions, when the inputs are sparse.\n",
+    "\n",
+    "Sparse support requires using special functions for common operations such as matrix multiplication. Falkon implements sparse tensors in a CSR format (PyTorch is slowly picking this format up, in place of COO), through the `falkon.sparse.SparseTensor` class.\n",
+    "\n",
+    "We will implement the `compute_sparse` method below, supporting both diagonal and full kernels.\n",
+    "However, only CPU support is added here (CUDA support is possible but requires a few more details), and differentiable sparse kernels are not supported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "98a92bf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from falkon.sparse import SparseTensor\n",
+    "from falkon.sparse import sparse_matmul, bdot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d9304478",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SparseLinearKernel(Kernel):\n",
+    "    def __init__(self, lengthscale, options):\n",
+    "        # The base class takes as inputs a name for the kernel, and\n",
+    "        # an instance of `FalkonOptions`.\n",
+    "        super().__init__(\"sparse_linear\", options)\n",
+    "        \n",
+    "        self.lengthscale = lengthscale\n",
+    "    \n",
+    "    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -> torch.Tensor:\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "\n",
+    "        scaled_X1 = X1 * lengthscale\n",
+    "        \n",
+    "        if diag:\n",
+    "            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))\n",
+    "        else:\n",
+    "            # The dot-product row-by-row on `X1` and `X2` can be computed\n",
+    "            # on many rows at a time with matrix multiplication.\n",
+    "            out = torch.matmul(scaled_X1, X2.T, out=out)\n",
+    "\n",
+    "        return out\n",
+    "    \n",
+    "    def compute_sparse(self, \n",
+    "                       X1: SparseTensor, \n",
+    "                       X2: SparseTensor, \n",
+    "                       out: torch.Tensor, \n",
+    "                       diag: bool,\n",
+    "                       **kwargs) -> torch.Tensor:\n",
+    "        # The inputs will be matrix X1(n*d) in CSR format, and X2(d*n) in CSC format.\n",
+    "        \n",
+    "        # To support different devices/data types, you must make sure\n",
+    "        # the lengthscale is compatible with the data.\n",
+    "        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)\n",
+    "        \n",
+    "        if diag:\n",
+    "            # The diagonal is a dot-product between rows of X1 and X2.\n",
+    "            # The batched-dot is only implemented on CPU.\n",
+    "            out = bdot(X1, X2.transpose_csr(), out)\n",
+    "        else:\n",
+    "            # Otherwise we need to matrix-multiply. Note that X2 is already\n",
+    "            # transposed correctly.\n",
+    "            out = sparse_matmul(X1, X2, out)\n",
+    "\n",
+    "        out.mul_(lengthscale)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75a18424",
+   "metadata": {},
+   "source": [
+    "### Testing sparse support\n",
+    "\n",
+    "We generate two sparse matrices, and check that the sparse kernel is equivalent to the dense version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e6e4c0bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0., 5.],\n",
+       "        [1., 8.],\n",
+       "        [2., 0.]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indexptr = torch.tensor([0, 1, 3, 4], dtype=torch.long)\n",
+    "index = torch.tensor([1, 0, 1, 0], dtype=torch.long)\n",
+    "value = torch.tensor([5, 1, 8, 2], dtype=torch.float32)\n",
+    "sp1 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=\"csr\")\n",
+    "# Converted to dense:\n",
+    "dense1 = torch.from_numpy(sp1.to_scipy().todense())\n",
+    "dense1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7470a0b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0., 2.],\n",
+       "        [1., 0.],\n",
+       "        [3., 4.]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indexptr = torch.tensor([0, 1, 2, 4], dtype=torch.long)\n",
+    "index = torch.tensor([1, 0, 0, 1], dtype=torch.long)\n",
+    "value = torch.tensor([2, 1, 3, 4], dtype=torch.float32)\n",
+    "sp2 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=\"csr\")\n",
+    "dense2 = torch.from_numpy(sp2.to_scipy().todense())\n",
+    "dense2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f217cde0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the kernel\n",
+    "lengthscale_init = torch.tensor([1.0])\n",
+    "k = SparseLinearKernel(lengthscale_init, options=falkon.FalkonOptions())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "80f6a317",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[True, True, True],\n",
+       "        [True, True, True],\n",
+       "        [True, True, True]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k(sp1, sp2) == k(dense1, dense2)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "toc": {
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+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/examples.html b/examples/examples.html
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+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
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+  <title>Examples &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+    <link rel="next" title="Falkon Regression Tutorial" href="falkon_regression_tutorial.html" />
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@@ -101,6 +52,7 @@
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
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 <li class="toctree-l1 current"><a class="current reference internal" href="#">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Load-the-data">Load the data</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
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-<li class="toctree-l2"><a class="reference internal" href="hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
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 </li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="performance_tuning.html">Training on the GPU</a></li>
+<li class="toctree-l2"><a class="reference internal" href="hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
@@ -154,9 +138,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +189,12 @@
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-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
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@@ -229,75 +208,47 @@
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 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
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 <section id="examples">
-<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1>
+<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline"></a></h1>
+<div class="toctree-wrapper compound">
+</div>
 <p>Starting with simple kernel ridge regression, via classification, hyperparameter tuning, to large-scale GPU experiments,
 these notebooks cover all there is to know about Falkon.</p>
 <blockquote>
 <div><ul class="simple">
-<li><p><a class="reference internal" href="simple_regression.html"><span class="doc">Kernel ridge regression</span></a> goes through the basic notions of the library with a simple example;</p></li>
+<li><p><a class="reference internal" href="falkon_regression_tutorial.html"><span class="doc">Kernel ridge regression</span></a> goes through the basic notions of the library with a simple example;</p></li>
 <li><p><a class="reference internal" href="logistic_falkon.html"><span class="doc">Logistic Falkon tutorial</span></a> shows how to use the Logistic Falkon estimator, comparing the results with normal Falkon;</p></li>
-<li><p><a class="reference internal" href="hyperparameter_tuning.html"><span class="doc">Hyperparameter tuning</span></a> is a fully worked out example of parameter optimization with Falkon for a real-world multi-class problem;</p></li>
-<li><p><a class="reference internal" href="performance_tuning.html"><span class="doc">GPU training</span></a> details the options to maximize performance on multiple GPU systems.</p></li>
+<li><p><a class="reference internal" href="falkon_cv.html"><span class="doc">Hyperparameter tuning</span></a> is a fully worked out example of optimizing hyperparameters with cross-validation for a real-world multi-class problem;</p></li>
+<li><p><a class="reference internal" href="custom_kernels.html"><span class="doc">custom kernels</span></a> will walk you through the implementation of a custom kernel.</p></li>
+<li><p><a class="reference internal" href="hyperopt.html"><span class="doc">Gradient hyperopt</span></a>: a tutorial on using the <a class="reference internal" href="../api_reference/hopt.html#module-falkon.hopt" title="falkon.hopt"><code class="xref py py-mod docutils literal notranslate"><span class="pre">hopt</span></code></a> module for gradient-based hyperparameter optimization in Falkon.</p></li>
+<li><p><a class="reference internal" href="falkon_mnist.html"><span class="doc">MNIST example</span></a>: A simple tutorial on using Falkon for MNIST digit classification.</p></li>
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+<li class="toctree-l2"><a class="reference internal" href="falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
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 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="performance_tuning.html">Training on the GPU</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
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+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
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+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_mnist.html">MNIST Classification with Falkon</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
+</li>
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+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
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 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
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 /* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
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+.prompt .copybtn {
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 </style>
 <section id="Hyperparameter-Tuning-with-Falkon">
-<h1>Hyperparameter Tuning with Falkon<a class="headerlink" href="#Hyperparameter-Tuning-with-Falkon" title="Permalink to this headline">¶</a></h1>
+<h1>Hyperparameter Tuning with Falkon<a class="headerlink" href="#Hyperparameter-Tuning-with-Falkon" title="Permalink to this headline"></a></h1>
 <section id="Introduction">
-<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline">¶</a></h2>
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline"></a></h2>
 <p>We use Falkon for a multi-class problem (on the <a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">digits</a> dataset), showing how to integrate it into scikit-learn for hyperparameter optimization.</p>
-<p>Since both <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> and <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> are estimators, and follow scikit-learn’s API, integration is seamless.</p>
-<div class="nbinput nblast docutils container">
+<p>Since both <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> and <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> are estimators, and follow scikit-learn’s API, integration is seamless</p>
+<div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
 <span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span><span class="p">,</span> <span class="n">metrics</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[pyKeOps]: Warning, no cuda detected. Switching to cpu only.
+</pre></div></div>
+</div>
 </section>
 <section id="Load-the-data">
-<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline">¶</a></h2>
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
 <p>We use the <strong>digits</strong> dataset, which is distributed alongside scikit-learn.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -610,8 +567,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
 <span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">Y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
 </pre></div>
@@ -629,33 +585,31 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/examples_hyperparameter_tuning_4_1.png" src="../_images/examples_hyperparameter_tuning_4_1.png" />
+<img alt="../_images/examples_falkon_cv_4_1.png" src="../_images/examples_falkon_cv_4_1.png" />
 </div>
 </div>
 </section>
 <section id="Split-into-training-and-test-sets">
-<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline">¶</a></h2>
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline"></a></h2>
 <p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
     <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </section>
 <section id="Data-Preprocessing">
-<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline">¶</a></h2>
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
 <p>As always with Falkon we must: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32)</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 <span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 <span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
 <span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
@@ -667,8 +621,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="c1"># z-score normalization</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
 <span class="n">train_mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
 <span class="n">train_std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
 <span class="n">X_train</span> <span class="o">-=</span> <span class="n">train_mean</span>
@@ -683,8 +636,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="c1"># Convert labels to 1-hot</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert labels to 1-hot</span>
 <span class="n">eye</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 <span class="n">Y_train</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_train</span><span class="p">]</span>
 <span class="n">Y_test</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_test</span><span class="p">]</span>
@@ -702,7 +654,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 </div>
 </section>
 <section id="Search-for-the-optimal-parameters">
-<h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the-optimal-parameters" title="Permalink to this headline">¶</a></h2>
+<h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the-optimal-parameters" title="Permalink to this headline"></a></h2>
 <p>Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.</p>
 <p>Scikit-learn has great support for this, with the <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.</p>
 <p>Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds.</p>
@@ -710,8 +662,7 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
     <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
@@ -722,12 +673,11 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <p>The parameter settings which will be explored by the grid-search are: - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data) - four different regularization values - two different values for M: the number of inducing points. As we will see, a larger <code class="docutils literal notranslate"><span class="pre">M</span></code> is almost always better than a smaller one (but it leads to longer training times). Of course this is not the case if the dataset is easy to overfit, since reducing <code class="docutils literal notranslate"><span class="pre">M</span></code> may
 also provide additional regularization.</p>
 <p>When we create the estimator we pass it additional parameters via the <code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code> class. In our case we want to ensure that the model runs on the CPU by setting <code class="docutils literal notranslate"><span class="pre">use_cpu=True</span></code>.</p>
-<div class="nbinput docutils container">
+<div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">parameter_grid</span> <span class="o">=</span> <span class="p">{</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parameter_grid</span> <span class="o">=</span> <span class="p">{</span>
     <span class="s1">&#39;kernel&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
                <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
                <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
@@ -736,29 +686,18 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
     <span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">],</span>
 <span class="p">}</span>
 <span class="n">estimator</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
-    <span class="n">kernel</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>  <span class="c1"># Mandatory parameters, will be overridden</span>
+    <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
 
-<span class="n">grid_search</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">parameter_grid</span><span class="p">,</span> <span class="n">mclass_scorer</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">grid_search</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">parameter_grid</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">mclass_scorer</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/helpers.py:31: UserWarning: Failed to initialize cuBLAS library; falling back to CPU. Set &#39;use_cpu&#39; to True to avoid this warning.
-Error encountered was cublas library not found
-  warnings.warn(get_error_str(&#34;cuBLAS&#34;, e))
-</pre></div></div>
-</div>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%%time</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
 <span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The best parameters are: &quot;</span><span class="p">,</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
 </pre></div>
@@ -769,20 +708,20 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-The best parameters are:  {&#39;M&#39;: 1000, &#39;kernel&#39;: GaussianKernel(sigma=5.0), &#39;penalty&#39;: 1e-07}
-CPU times: user 2min 5s, sys: 17 s, total: 2min 22s
-Wall time: 35.6 s
+The best parameters are:  {&#39;M&#39;: 500, &#39;kernel&#39;: GaussianKernel(sigma=Parameter containing:
+tensor([10.], dtype=torch.float64)), &#39;penalty&#39;: 1e-07}
+CPU times: user 52.3 s, sys: 1.78 s, total: 54.1 s
+Wall time: 13.6 s
 </pre></div></div>
 </div>
 <section id="Evaluating-the-model">
-<h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title="Permalink to this headline">¶</a></h3>
+<h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title="Permalink to this headline"></a></h3>
 <p>We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%%time</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
 <span class="n">flk</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator_</span>
 <span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
 <span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -795,16 +734,15 @@ <h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-CPU times: user 1.14 s, sys: 143 ms, total: 1.29 s
-Wall time: 323 ms
+CPU times: user 534 ms, sys: 23.8 ms, total: 558 ms
+Wall time: 139 ms
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">train_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">train_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">test_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
 </pre></div>
 </div>
@@ -821,15 +759,14 @@ <h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title
 </section>
 </section>
 <section id="Plot-grid-search-results">
-<h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-results" title="Permalink to this headline">¶</a></h2>
+<h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-results" title="Permalink to this headline"></a></h2>
 <p>Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful. If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.</p>
 <p>In the plot red indicates a high error, while darker blue indicates a low error.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">res_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">res_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
 <span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;M&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;M&quot;</span><span class="p">))</span>
 <span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;penalty&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;penalty&quot;</span><span class="p">))</span>
 <span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;sigma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;kernel&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
@@ -841,10 +778,10 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="k">def</span> <span class="nf">plot_heatmap</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_heatmap</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
     <span class="n">piv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">ylabel</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
     <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;coolwarm_r&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
     <span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">piv</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vmax</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
@@ -860,8 +797,7 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
 <span class="n">scale</span> <span class="o">=</span> <span class="p">(</span><span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
 
 <span class="n">c</span> <span class="o">=</span> <span class="n">plot_heatmap</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">res_df</span><span class="p">[</span><span class="n">res_df</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">500</span><span class="p">],</span> <span class="s2">&quot;sigma&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
@@ -875,7 +811,7 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/examples_hyperparameter_tuning_24_0.png" src="../_images/examples_hyperparameter_tuning_24_0.png" />
+<img alt="../_images/examples_falkon_cv_24_0.png" src="../_images/examples_falkon_cv_24_0.png" />
 </div>
 </div>
 </section>
@@ -883,50 +819,33 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 
 
            </div>
-           
           </div>
-          <footer>
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
-        <a href="performance_tuning.html" class="btn btn-neutral float-right" title="Training on the GPU" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
         <a href="logistic_falkon.html" class="btn btn-neutral float-left" title="Introducing Logistic Falkon" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="custom_kernels.html" class="btn btn-neutral float-right" title="Implementing A Custom Kernel" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-    <p>
-        &#169; Copyright 2020, Giacomo Meanti.
-
-    </p>
+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
   </div>
-    
-    
-    
-    Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
     <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    
-    provided by <a href="https://readthedocs.org">Read the Docs</a>. 
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
 
 </footer>
         </div>
       </div>
-
     </section>
-
   </div>
-  
-
-  <script type="text/javascript">
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
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-  </script>
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+  </script> 
 
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\ No newline at end of file
diff --git a/examples/falkon_cv.ipynb b/examples/falkon_cv.ipynb
new file mode 100644
index 00000000..b07609b2
--- /dev/null
+++ b/examples/falkon_cv.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Hyperparameter Tuning with Falkon\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "We use Falkon for a multi-class problem (on the [digits](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits) dataset), showing how to integrate it into scikit-learn\n",
+    "for hyperparameter optimization.\n",
+    "\n",
+    "Since both `Falkon` and `LogisticFalkon` are estimators, and follow scikit-learn's API, integration is seamless"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection, metrics\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use the **digits** dataset, which is distributed alongside scikit-learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_digits(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD8CAYAAACvvuKtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAALn0lEQVR4nO3d34tc9RnH8c+nmwR/xaxUK2LEVKgBEboJIVQCmh8qsUpy04sEFCot6UUrhhZEexP9B8ReFGGJGsEY0WhIkdYa0EWEVpvEtUY3Fg0Rk6hRzCZqoUF9ejEnZSsb98zs+Z6dPL5fMOTMzsx5nk3yme85Z86cryNCAPL43kw3AKBZhBpIhlADyRBqIBlCDSRDqIFk+iLUtlfbftv2O7bvLljnYdtHbe8rVWNCrctsv2h7zPabtu8sWOss26/afr2qdV+pWhNqDth+zfazLdQ6aPsN26O2dxeuNWh7u+391b/dNYXqLKx+n1O3E7Y3NrLyiJjRm6QBSe9KukLSHEmvS7qqUK1rJS2WtK+F3+sSSYur5bmS/lXw97Kk86rl2ZJekfSTwr/fbyU9LunZFv4uD0q6sHSdqtajkn5ZLc+RNNhCzQFJH0q6vIn19cNIvVTSOxFxICJOSnpC0toShSLiJUmfllj3JLU+iIi91fJnksYkXVqoVkTE59Xd2dWt2FlFtudLulnS5lI1ZoLt89V5439IkiLiZESMt1B6laR3I+K9JlbWD6G+VNL7E+4fUqH//DPF9gJJi9QZQUvVGLA9KumopF0RUayWpAck3SXp64I1JgpJz9veY3tDwTpXSPpY0iPVrsVm2+cWrHfKOknbmlpZP4Tak/wszbmrts+T9LSkjRFxolSdiPgqIoYkzZe01PbVJerYvkXS0YjYU2L9p7EsIhZLuknSr21fW6jOLHV2zx6MiEWSvpBU7BiPJNmeI2mNpKeaWmc/hPqQpMsm3J8v6cgM9dIo27PVCfTWiHimjZrV5uKIpNWFSiyTtMb2QXV2lVbafqxQLUlSRByp/jwqaYc6u2wlHJJ0aMJWznZ1Ql7STZL2RsRHTa2wH0L9D0k/sv3D6l1rnaQ/zXBP02bb6uybjUXE/YVrXWR7sFo+W9L1kvaXqBUR90TE/IhYoM6/1QsRcWuJWpJk+1zbc08tS7pRUpFPLyLiQ0nv215Y/WiVpLdK1JpgvRrc9JY6mxszKiK+tP0bSX9V5yjgwxHxZolatrdJWi7pQtuHJG2KiIdK1FJnRLtN0hvVvq4k/T4i/lyg1iWSHrU9oM4b9ZMRUfyjppZcLGlH5z1SsyQ9HhHPFax3h6St1QBzQNLtpQrZPkfSDZJ+1eh6q0PqAJLoh81vAA0i1EAyhBpIhlADyRBqIJm+CXXh0/9mrFbb9ahFrb4JtaQ2g9ZqqFuuR63veK1+CjWABhQ5+cQ2Z7Q04Morr+z6NcePH9e8efO6ft2sWd2fXHjs2DFdcMEFXb/u8OHDXb/m5MmTmjNnTtevO378eNevOVNExGRfhiLU/WxkZKS1WoODg63V2rRpU2u1du7c2Vqttp0u1Gx+A8kQaiAZQg0kQ6iBZAg1kAyhBpIh1EAyhBpIplao25oWB8D0TRnq6mJ2f1TnUqZXSVpv+6rSjQHoTZ2RurVpcQBMX51Q15oWx/YG27tLz0oI4NvV+WpOrWlxImJY0rDEFzqAmVRnpE47LQ6QUZ1Qp5wWB8hqys3vNqfFATB9tS53Uc3/VGIOKAAN44wyIBlCDSRDqIFkCDWQDKEGkiHUQDKEGkim+2kZ0Jrx8fHWal133XWt1VqxYkVrtTJfzP90GKmBZAg1kAyhBpIh1EAyhBpIhlADyRBqIBlCDSRDqIFkCDWQTJ0ZOh62fdT2vjYaAjA9dUbqLZJWF+4DQEOmDHVEvCTp0xZ6AdAA9qmBZBr76qXtDZI2NLU+AL1pLNTMpQX0Bza/gWTqfKS1TdLfJC20fcj2L8q3BaBXdebSWt9GIwCaweY3kAyhBpIh1EAyhBpIhlADyRBqIBlCDSTDtDtdGBoaarXe8uXLW63XltHR0ZluITVGaiAZQg0kQ6iBZAg1kAyhBpIh1EAyhBpIhlADyRBqIBlCDSRT5xpll9l+0faY7Tdt39lGYwB6U+fc7y8l/S4i9tqeK2mP7V0R8Vbh3gD0oM60Ox9ExN5q+TNJY5IuLd0YgN50tU9te4GkRZJeKdINgGmr/dVL2+dJelrSxog4McnjTLsD9IFaobY9W51Ab42IZyZ7DtPuAP2hztFvS3pI0lhE3F++JQDTUWefepmk2ySttD1a3X5auC8APaoz7c7LktxCLwAawBllQDKEGkiGUAPJEGogGUINJEOogWQINZAMoQaSOePn0tq4cWNrte69997WaknSvHnzWq3XlpGRkZluITVGaiAZQg0kQ6iBZAg1kAyhBpIh1EAyhBpIhlADyRBqIJk6Fx48y/artl+vpt25r43GAPSmzmmi/5G0MiI+ry4V/LLtv0TE3wv3BqAHdS48GJI+r+7Orm5c1xvoU7X2qW0P2B6VdFTSrohg2h2gT9UKdUR8FRFDkuZLWmr76m8+x/YG27tt7264RwBd6Orod0SMSxqRtHqSx4YjYklELGmmNQC9qHP0+yLbg9Xy2ZKul7S/cF8AelTn6Pclkh61PaDOm8CTEfFs2bYA9KrO0e9/qjMnNYAzAGeUAckQaiAZQg0kQ6iBZAg1kAyhBpIh1EAyhBpIxp1vVja8UjvlVzMHBwdbrXfs2LFW67Vl0aL2zmUaHR1trVbbIsKT/ZyRGkiGUAPJEGogGUINJEOogWQINZAMoQaSIdRAMoQaSIZQA8nUDnV1Qf/XbHPRQaCPdTNS3ylprFQjAJpRd9qd+ZJulrS5bDsApqvuSP2ApLskfX26JzDtDtAf6szQcYukoxGx59uex7Q7QH+oM1Ivk7TG9kFJT0haafuxol0B6NmUoY6IeyJifkQskLRO0gsRcWvxzgD0hM+pgWTqTJD3PxExos5UtgD6FCM1kAyhBpIh1EAyhBpIhlADyRBqIBlCDSTT1efUQBOGhoZaq5V52p3TYaQGkiHUQDKEGkiGUAPJEGogGUINJEOogWQINZAMoQaSIdRAMrVOE62uJPqZpK8kfcllgIH+1c253ysi4pNinQBoBJvfQDJ1Qx2Snre9x/aGyZ7AtDtAf6i7+b0sIo7Y/oGkXbb3R8RLE58QEcOShiXJdjTcJ4Caao3UEXGk+vOopB2SlpZsCkDv6kyQd67tuaeWJd0oaV/pxgD0ps7m98WSdtg+9fzHI+K5ol0B6NmUoY6IA5J+3EIvABrAR1pAMoQaSIZQA8kQaiAZQg0kQ6iBZAg1kAyhBpIh1EAyhBpIhlADyRBqIBlCDSRDqIFkCDWQDKEGkiHUQDKEGkimVqhtD9rebnu/7THb15RuDEBv6l73+w+SnouIn9meI+mcgj0BmIYpQ237fEnXSvq5JEXESUkny7YFoFd1Nr+vkPSxpEdsv2Z7c3X9bwB9qE6oZ0laLOnBiFgk6QtJd3/zScylBfSHOqE+JOlQRLxS3d+uTsj/T0QMR8QS5q4GZtaUoY6IDyW9b3th9aNVkt4q2hWAntU9+n2HpK3Vke8Dkm4v1xKA6agV6ogYlcRmNXAG4IwyIBlCDSRDqIFkCDWQDKEGkiHUQDKEGkiGUAPJ1D2jDJLGx8dbrbdz587Waq1du7a1WsuXL2+t1pYtW1qr1S8YqYFkCDWQDKEGkiHUQDKEGkiGUAPJEGogGUINJEOogWSmDLXthbZHJ9xO2N7YQm8AejDlaaIR8bakIUmyPSDpsKQdZdsC0KtuN79XSXo3It4r0QyA6es21OskbSvRCIBm1A51dc3vNZKeOs3jTLsD9IFuvnp5k6S9EfHRZA9GxLCkYUmyHQ30BqAH3Wx+rxeb3kDfqxVq2+dIukHSM2XbATBddafd+bek7xfuBUADOKMMSIZQA8kQaiAZQg0kQ6iBZAg1kAyhBpIh1EAyjmj+NG3bH0vq9uuZF0r6pPFmZr5W2/Wo9d2odXlEXDTZA0VC3QvbuyNiSbZabdejFrXY/AaSIdRAMv0U6uGktdquR63veK2+2acG0Ix+GqkBNIBQA8kQaiAZQg0kQ6iBZP4Lhj5lm5RUNJYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "_ = ax.matshow(X[1].reshape((8, 8)), cmap='gray')\n",
+    "print(\"Label: %d\" % Y[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "As always with Falkon we must:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train)\n",
+    "Y_test = torch.from_numpy(Y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalizing the data is always a good idea. Here we use the global mean and standard deviation of the training set for z-score normalization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = X_train.mean()\n",
+    "train_std = X_train.std()\n",
+    "X_train -= train_mean\n",
+    "X_train /= train_std\n",
+    "X_test -= train_mean\n",
+    "X_test /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert labels to 1-hot\n",
+    "eye = torch.eye(10, dtype=torch.float32)\n",
+    "Y_train = eye[Y_train]\n",
+    "Y_test = eye[Y_test]\n",
+    "print(\"First label vector: \", Y_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search for the optimal parameters\n",
+    "\n",
+    "Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.\n",
+    "\n",
+    "Scikit-learn has great support for this, with the `GridSearchCV` class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.\n",
+    "\n",
+    "Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mclass_loss(true, pred):\n",
+    "    true = torch.argmax(true, dim=1)\n",
+    "    pred = torch.argmax(pred, dim=1)\n",
+    "    return torch.mean((true != pred).to(torch.float32))\n",
+    "mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter settings which will be explored by the grid-search are:\n",
+    " - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data)\n",
+    " - four different regularization values\n",
+    " - two different values for M: the number of inducing points. As we will see, a larger `M` is almost always better than a smaller one (but it leads to longer training times). \n",
+    "   Of course this is not the case if the dataset is easy to overfit, since reducing `M` may also provide additional regularization.\n",
+    "\n",
+    "When we create the estimator we pass it additional parameters via the `FalkonOptions` class.\n",
+    "In our case we want to ensure that the model runs on the CPU by setting `use_cpu=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_grid = {\n",
+    "    'kernel': [falkon.kernels.GaussianKernel(sigma=1), \n",
+    "               falkon.kernels.GaussianKernel(sigma=5), \n",
+    "               falkon.kernels.GaussianKernel(sigma=10),\n",
+    "               falkon.kernels.GaussianKernel(sigma=15),],\n",
+    "    'penalty': [1e-3, 1e-5, 1e-7, 1e-9],\n",
+    "    'M': [500, 1000],\n",
+    "}\n",
+    "estimator = falkon.Falkon(\n",
+    "    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000,  # Mandatory parameters, will be overridden\n",
+    "    maxiter=10, options=falkon.FalkonOptions(use_cpu=True))\n",
+    "\n",
+    "grid_search = model_selection.GridSearchCV(estimator, parameter_grid, scoring=mclass_scorer, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The best parameters are:  {'M': 500, 'kernel': GaussianKernel(sigma=Parameter containing:\n",
+      "tensor([10.], dtype=torch.float64)), 'penalty': 1e-07}\n",
+      "CPU times: user 52.3 s, sys: 1.78 s, total: 54.1 s\n",
+      "Wall time: 13.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "grid_search.fit(X_train, Y_train)\n",
+    "print(\"The best parameters are: \", grid_search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluating the model\n",
+    "We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 534 ms, sys: 23.8 ms, total: 558 ms\n",
+      "Wall time: 139 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "flk = grid_search.best_estimator_\n",
+    "flk.fit(X_train, Y_train)\n",
+    "test_pred = flk.predict(X_test)\n",
+    "train_pred = flk.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training error: 0.00%\n",
+      "Test error: 1.11%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training error: %.2f%%\" % (mclass_loss(Y_train, train_pred) * 100))\n",
+    "print(\"Test error: %.2f%%\" % (mclass_loss(Y_test, test_pred) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot grid-search results\n",
+    "\n",
+    "Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful.\n",
+    "If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.\n",
+    "\n",
+    "In the plot red indicates a high error, while darker blue indicates a low error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_df = pd.DataFrame.from_dict(grid_search.cv_results_)\n",
+    "res_df[\"M\"] = res_df.params.apply(lambda x: x.get(\"M\"))\n",
+    "res_df[\"penalty\"] = res_df.params.apply(lambda x: x.get(\"penalty\"))\n",
+    "res_df[\"sigma\"] = res_df.params.apply(lambda x: x.get(\"kernel\").sigma.item())\n",
+    "res_df = res_df[[\"mean_test_score\", \"M\", \"penalty\", \"sigma\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_heatmap(ax, df, xlabel, ylabel, value, scale):\n",
+    "    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)\n",
+    "    cmap = plt.cm.get_cmap('coolwarm_r', 20)\n",
+    "    ax.grid(False)\n",
+    "    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])\n",
+    "    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)\n",
+    "    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)\n",
+    "    ax.set_xticklabels(piv.columns, minor=False)\n",
+    "    ax.set_yticklabels(piv.index, minor=False)\n",
+    "    ax.set_xlabel(xlabel)\n",
+    "    ax.set_ylabel(ylabel)\n",
+    "    return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
+    "scale = (res_df[\"mean_test_score\"].min(), res_df[\"mean_test_score\"].max())\n",
+    "\n",
+    "c = plot_heatmap(axs[0], res_df[res_df.M == 500], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "axs[0].set_title(\"M = 500\")\n",
+    "c = plot_heatmap(axs[1], res_df[res_df.M == 1000], \"sigma\", \"penalty\", \"mean_test_score\", scale)\n",
+    "_ = axs[1].set_title(\"M = 1000\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/falkon_mnist.html b/examples/falkon_mnist.html
new file mode 100644
index 00000000..32d949cf
--- /dev/null
+++ b/examples/falkon_mnist.html
@@ -0,0 +1,756 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>MNIST Classification with Falkon &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="API Reference" href="../api_reference/index.html" />
+    <link rel="prev" title="Automatic Hyperparameter Optimization" href="hyperopt.html" /> 
+</head>
+
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+          </a>
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+              </div>
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+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Testing">Testing</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Run-Falkon">Run Falkon</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
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+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
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+    margin: 0;
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+
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+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
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+  background: rgba(66, 165, 245, 0.2);
+}
+
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
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+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<section id="MNIST-Classification-with-Falkon">
+<h1>MNIST Classification with Falkon<a class="headerlink" href="#MNIST-Classification-with-Falkon" title="Permalink to this headline"></a></h1>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torchvision</span>
+
+<span class="kn">import</span> <span class="nn">falkon</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[pyKeOps]: Warning, no cuda detected. Switching to cpu only.
+</pre></div></div>
+</div>
+<section id="Download-the-MNIST-dataset-&amp;-load-it-in-memory">
+<h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href="#Download-the-MNIST-dataset-&-load-it-in-memory" title="Permalink to this headline"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mnist_train_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
+    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
+<span class="n">mnist_test_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
+    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load the whole dataset in memory</span>
+<span class="n">mnist_tr_img_list</span><span class="p">,</span> <span class="n">mnist_tr_label_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mnist_train_dataset</span><span class="p">)):</span>
+    <span class="n">data_point</span> <span class="o">=</span> <span class="n">mnist_train_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="n">mnist_tr_img_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">mnist_tr_label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">mnist_ts_img_list</span><span class="p">,</span> <span class="n">mnist_ts_label_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mnist_test_dataset</span><span class="p">)):</span>
+    <span class="n">data_point</span> <span class="o">=</span> <span class="n">mnist_test_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="n">mnist_ts_img_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">mnist_ts_label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mnist_tr_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">mnist_tr_img_list</span><span class="p">)</span>
+<span class="n">mnist_tr_x</span> <span class="o">=</span> <span class="n">mnist_tr_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">mnist_ts_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">mnist_ts_img_list</span><span class="p">)</span>
+<span class="n">mnist_ts_x</span> <span class="o">=</span> <span class="n">mnist_ts_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mnist_ts_x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">mnist_tr_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">mnist_tr_label_list</span><span class="p">)</span>
+<span class="n">mnist_ts_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">mnist_ts_label_list</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.image.AxesImage at 0x7f4ee3780130&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/examples_falkon_mnist_6_1.png" src="../_images/examples_falkon_mnist_6_1.png" />
+</div>
+</div>
+</section>
+<section id="Data-Preprocessing">
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
+<p>We convert the labels to their one-hot representation. This is the best way to run multi-class classification with Falkon which minimizes the squared error</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># one-hot labels</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">mnist_tr_y</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">mnist_tr_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">),</span> <span class="p">:]</span>
+<span class="n">mnist_ts_y</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">mnist_ts_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">),</span> <span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">classif_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
+    <span class="n">y_true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">err</span> <span class="o">=</span> <span class="n">y_true</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="o">!=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">err</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Run-Falkon">
+<h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this headline"></a></h2>
+<p>There are a few parameters which need to be provided to the algorithm</p>
+<ul>
+<li><p>The <code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code> class is used to provide non-standard tuning knobs. It allows to, for example, tune the amount of GPU memory the algorithm can use, adjust the convergence tolerance, and decide whether certain parts of the algorithm are computed on CPU or GPU.</p>
+<p>It can be used with default parameters for most purposes!</p>
+</li>
+<li><p>The <strong>kernel</strong> is the most important choice which depends on the data at hand. We use the <code class="docutils literal notranslate"><span class="pre">GaussianKernel</span></code> which is the most common option and initialize it with a length-scale of 15.</p></li>
+<li><p>The <strong>penalty</strong> determines the amount of regularization. A higher value corresponds to more regularization.</p></li>
+<li><p>The <strong>number of centers</strong> <code class="docutils literal notranslate"><span class="pre">M</span></code> strongly influences the time needed for fitting. By default the centers are chosen uniformly at random.</p></li>
+</ul>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
+                    <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span>
+                    <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+                    <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                    <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">,</span>
+                    <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">error_fn</span><span class="o">=</span><span class="n">classif_error</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">,</span> <span class="n">mnist_tr_y</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Iteration   1 - Elapsed 0.55s - training error: 0.11998333
+Iteration   2 - Elapsed 1.02s - training error: 0.07140000
+Iteration   3 - Elapsed 1.50s - training error: 0.05766667
+Iteration   4 - Elapsed 1.98s - training error: 0.05121667
+Iteration   5 - Elapsed 2.46s - training error: 0.04776667
+Iteration   6 - Elapsed 2.95s - training error: 0.04556667
+Iteration   7 - Elapsed 3.45s - training error: 0.04376667
+Iteration   8 - Elapsed 3.93s - training error: 0.04340000
+Iteration   9 - Elapsed 4.42s - training error: 0.04286667
+Iteration  10 - Elapsed 5.39s - training error: 0.04223333
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">)</span>
+<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">mnist_ts_x</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">classif_error</span><span class="p">(</span><span class="n">train_pred</span><span class="p">,</span> <span class="n">mnist_tr_y</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">classif_error</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">mnist_ts_y</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Training error: 4.22%
+Test error: 4.13%
+</pre></div></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/examples/falkon_mnist.ipynb b/examples/falkon_mnist.ipynb
new file mode 100644
index 00000000..b0483211
--- /dev/null
+++ b/examples/falkon_mnist.ipynb
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# MNIST Classification with Falkon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the MNIST dataset & load it in memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnist_train_dataset = torchvision.datasets.MNIST(\n",
+    "    root=\".\", train=True, download=True,\n",
+    "    transform=torchvision.transforms.ToTensor())\n",
+    "mnist_test_dataset = torchvision.datasets.MNIST(\n",
+    "    root=\".\", train=False, download=True,\n",
+    "    transform=torchvision.transforms.ToTensor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the whole dataset in memory\n",
+    "mnist_tr_img_list, mnist_tr_label_list = [], []\n",
+    "for i in range(len(mnist_train_dataset)):\n",
+    "    data_point = mnist_train_dataset[i]\n",
+    "    mnist_tr_img_list.append(data_point[0])\n",
+    "    mnist_tr_label_list.append(data_point[1])\n",
+    "mnist_ts_img_list, mnist_ts_label_list = [], []\n",
+    "for i in range(len(mnist_test_dataset)):\n",
+    "    data_point = mnist_test_dataset[i]\n",
+    "    mnist_ts_img_list.append(data_point[0])\n",
+    "    mnist_ts_label_list.append(data_point[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnist_tr_x = torch.vstack(mnist_tr_img_list)\n",
+    "mnist_tr_x = mnist_tr_x.reshape(mnist_tr_x.shape[0], -1)\n",
+    "mnist_ts_x = torch.vstack(mnist_ts_img_list)\n",
+    "mnist_ts_x = mnist_ts_x.reshape(mnist_ts_x.shape[0], -1)\n",
+    "mnist_tr_y = torch.tensor(mnist_tr_label_list)\n",
+    "mnist_ts_y = torch.tensor(mnist_ts_label_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4ee3780130>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(mnist_tr_x[0].reshape(28,28))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "We convert the labels to their one-hot representation. \n",
+    "This is the best way to run multi-class classification with Falkon which minimizes the squared error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one-hot labels\n",
+    "A = torch.eye(10, dtype=torch.float32)\n",
+    "mnist_tr_y = A[mnist_tr_y.to(torch.long), :]\n",
+    "mnist_ts_y = A[mnist_ts_y.to(torch.long), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def classif_error(y_true, y_pred):\n",
+    "    y_true = torch.argmax(y_true, dim=1)\n",
+    "    y_pred = torch.argmax(y_pred, dim=1)\n",
+    "    err = y_true.flatten() != y_pred.flatten()\n",
+    "    return torch.mean(err.to(torch.float32))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run Falkon\n",
+    "\n",
+    "There are a few parameters which need to be provided to the algorithm\n",
+    "\n",
+    " - The `FalkonOptions` class is used to provide non-standard tuning knobs. It allows to, for example, tune the amount of GPU memory the algorithm can use, adjust the convergence tolerance, and decide whether certain parts of the algorithm are computed on CPU or GPU. \n",
+    "     \n",
+    "     It can be used with default parameters for most purposes!\n",
+    "     \n",
+    " - The **kernel** is the most important choice which depends on the data at hand. We use the `GaussianKernel` which is the most common option and initialize it with a length-scale of 15.\n",
+    " \n",
+    " - The **penalty** determines the amount of regularization. A higher value corresponds to more regularization.\n",
+    " \n",
+    " - The **number of centers** `M` strongly influences the time needed for fitting. By default the centers\n",
+    "     are chosen uniformly at random."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = falkon.FalkonOptions(use_cpu=True)\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=15)\n",
+    "flk = falkon.Falkon(kernel=kernel, \n",
+    "                    penalty=1e-8,\n",
+    "                    M=1000, \n",
+    "                    maxiter=10,\n",
+    "                    options=options,\n",
+    "                    error_every=1,\n",
+    "                    error_fn=classif_error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration   1 - Elapsed 0.55s - training error: 0.11998333\n",
+      "Iteration   2 - Elapsed 1.02s - training error: 0.07140000\n",
+      "Iteration   3 - Elapsed 1.50s - training error: 0.05766667\n",
+      "Iteration   4 - Elapsed 1.98s - training error: 0.05121667\n",
+      "Iteration   5 - Elapsed 2.46s - training error: 0.04776667\n",
+      "Iteration   6 - Elapsed 2.95s - training error: 0.04556667\n",
+      "Iteration   7 - Elapsed 3.45s - training error: 0.04376667\n",
+      "Iteration   8 - Elapsed 3.93s - training error: 0.04340000\n",
+      "Iteration   9 - Elapsed 4.42s - training error: 0.04286667\n",
+      "Iteration  10 - Elapsed 5.39s - training error: 0.04223333\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = flk.fit(mnist_tr_x, mnist_tr_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training error: 4.22%\n",
+      "Test error: 4.13%\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_pred = flk.predict(mnist_tr_x)\n",
+    "test_pred = flk.predict(mnist_ts_x)\n",
+    "\n",
+    "print(\"Training error: %.2f%%\" % (classif_error(train_pred, mnist_tr_y) * 100))\n",
+    "print(\"Test error: %.2f%%\" % (classif_error(test_pred, mnist_ts_y) * 100))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/simple_regression.html b/examples/falkon_regression_tutorial.html
similarity index 71%
rename from examples/simple_regression.html
rename to examples/falkon_regression_tutorial.html
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--- a/examples/simple_regression.html
+++ b/examples/falkon_regression_tutorial.html
@@ -1,76 +1,40 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
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-  <meta charset="utf-8" />
-  <meta name="generator" content="Docutils 0.17: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  
-  <title>Falkon Regression Tutorial &mdash; falkon 0.6.3 documentation</title>
-  
-
-  
-  <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-
-  
-  
-
-  
-  
-
-  
-
-  
+  <title>Falkon Regression Tutorial &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-    
-      <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
         <script src="../_static/doctools.js"></script>
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-    
-    <script type="text/javascript" src="../_static/js/theme.js"></script>
-
-    
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Introducing Logistic Falkon" href="logistic_falkon.html" />
     <link rel="prev" title="Examples" href="examples.html" /> 
 </head>
 
-<body class="wy-body-for-nav">
-
-   
+<body class="wy-body-for-nav"> 
   <div class="wy-grid-for-nav">
-    
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-          
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-
-          
-            
-            
               <div class="version">
-                0.6.3
+                0.7.2
               </div>
-            
-          
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-          
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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@@ -78,18 +42,7 @@
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   </form>
 </div>
-
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-        </div>
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-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
-          
-            
-            
-              
-            
-            
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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 <li class="toctree-l1"><a class="reference internal" href="../install.html">Install</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#supported-platforms">Supported Platforms</a></li>
@@ -101,6 +54,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
@@ -130,19 +84,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
@@ -154,9 +140,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +191,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
@@ -229,77 +210,48 @@
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+          <div role="navigation" aria-label="Page navigation">
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-        
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-        
       <li>Falkon Regression Tutorial</li>
-    
-    
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-        
-          
-            <a href="../_sources/examples/simple_regression.ipynb.txt" rel="nofollow"> View page source</a>
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+            <a href="../_sources/examples/falkon_regression_tutorial.ipynb.txt" rel="nofollow"> View page source</a>
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 <style>
 /* CSS for nbsphinx extension */
@@ -510,7 +462,7 @@
 }
 
 /* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
-.prompt a.copybtn {
+.prompt .copybtn {
     display: none;
 }
 
@@ -571,17 +523,16 @@
 }
 </style>
 <section id="Falkon-Regression-Tutorial">
-<h1>Falkon Regression Tutorial<a class="headerlink" href="#Falkon-Regression-Tutorial" title="Permalink to this headline">¶</a></h1>
+<h1>Falkon Regression Tutorial<a class="headerlink" href="#Falkon-Regression-Tutorial" title="Permalink to this headline"></a></h1>
 <section id="Introduction">
-<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline">¶</a></h2>
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline"></a></h2>
 <p>This notebook introduces the main interface of the Falkon library, using a toy regression problem.</p>
 <p>We will be using the Boston housing dataset which is included in <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> to train a Falkon model. Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
 <span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">torch</span>
@@ -597,20 +548,19 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected
+[pyKeOps]: Warning, no cuda detected. Switching to cpu only.
 </pre></div></div>
 </div>
 </section>
 <section id="Load-the-data">
-<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline">¶</a></h2>
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
 <p>The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions. The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.</p>
 <p>After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the <a class="reference external" href="https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation">model_selection module</a>)</p>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_boston</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">fetch_california_housing</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -618,8 +568,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
 <span class="n">num_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">num_train</span>
 <span class="n">shuffle_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffle_idx</span><span class="p">)</span>
@@ -633,19 +582,18 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 </div>
 </section>
 <section id="Pre-process-the-data">
-<h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title="Permalink to this headline">¶</a></h2>
+<h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title="Permalink to this headline"></a></h2>
 <p>We must convert the numpy arrays to PyTorch tensors before using them in Falkon. This is very easy and fast with the <code class="docutils literal notranslate"><span class="pre">torch.from_numpy</span></code> function.</p>
 <p>Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data: convert it to have zero-mean and unit standard deviation. We use the statistics of the training data to avoid leakage between the two sets.</p>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="c1"># convert numpy -&gt; pytorch</span>
-<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span>
-<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span>
-<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span>
-<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># convert numpy -&gt; pytorch</span>
+<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -653,8 +601,7 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="c1"># z-score normalization</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
 <span class="n">train_mean</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">train_std</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">Xtrain</span> <span class="o">-=</span> <span class="n">train_mean</span>
@@ -666,18 +613,18 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
 </div>
 </section>
 <section id="Create-the-Falkon-model">
-<h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model" title="Permalink to this headline">¶</a></h2>
+<h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model" title="Permalink to this headline"></a></h2>
 <p>The Falkon object is the main API of this library. It is similar in spirit to the fit-transform API of scikit-learn, while supporting some additional features such as monitoring of validation error.</p>
 <p>While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much larger datasets. Here we only showcase some of the more basic options.</p>
-<p>Mandatory parameters are: - the kernel function (here we use a linear kernel) - the amount of regularization, which we set to some small positive value - the number of inducing points M. Since the dataset is already very small, we use set <code class="docutils literal notranslate"><span class="pre">M=N</span></code> (every data-point is an inducing point).</p>
+<p>Mandatory parameters are: - the kernel function (here we use a linear kernel) - the amount of regularization, which we set to some small positive value - the number of inducing points M. We set M to 5000, which is a sizable portion of the dataset.</p>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">()</span>
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="n">Xtrain</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
+
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -692,48 +639,38 @@ <h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model"
 </div>
 </section>
 <section id="Training-the-model">
-<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Permalink to this headline">¶</a></h2>
+<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Permalink to this headline"></a></h2>
 <p>The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed. To gain more insight in the various steps of the algorithm you can pass <code class="docutils literal notranslate"><span class="pre">debug=True</span></code> when creating the Falkon object.</p>
 <p>Model training will occur on the GPU, if it is available, and CUDA is properly installed, or on the CPU as a fallback.</p>
-<p>If KeOps has been installed alongside Falkon, the first run may take a bit longer due to the compilation step of KeOps (to avoid using KeOps you can pass the argument <code class="docutils literal notranslate"><span class="pre">keops_active=False</span></code> to the <code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code> object).</p>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
 </pre></div>
 </div>
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-<div class="nboutput docutils container">
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-</div>
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-Stopping conjugate gradient descent at iteration 1. Solution has converged.
-</pre></div></div>
-</div>
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-Falkon(M=404, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f31c8279190&gt;, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;auto&#39;, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-05, seed=None, weight_fn=None)
+Falkon(M=5000, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f65871c45e0&gt;, kernel=GaussianKernel(sigma=Parameter containing:
+tensor([1.], dtype=torch.float64)), options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;no&#39;, keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty=1e-05)
 </pre></div></div>
 </div>
 <p>Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations.</p>
 </section>
 <section id="Evaluating-model-performance">
-<h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-performance" title="Permalink to this headline">¶</a></h2>
+<h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-performance" title="Permalink to this headline"></a></h2>
 <p>Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method to obtain predictions on new data.</p>
 <p>Here we print the error on both train and test sets.</p>
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 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
 <span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
 
 <span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
@@ -749,8 +686,8 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Training RMSE: 1.794
-Test RMSE: 3.534
+Training RMSE: 0.527
+Test RMSE: 0.578
 </pre></div></div>
 </div>
 <p>Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels.</p>
@@ -758,37 +695,19 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">Ytest</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;True&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Pred&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;best&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">hist_range</span> <span class="o">=</span> <span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="nb">max</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">max</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">hist_range</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;True&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">test_pred</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">hist_range</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Pred&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;best&quot;</span><span class="p">);</span>
 </pre></div>
 </div>
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-<div class="nboutput docutils container">
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-</pre></div>
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-&lt;matplotlib.legend.Legend at 0x7f3148f13a50&gt;
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+<img alt="../_images/examples_falkon_regression_tutorial_17_0.png" src="../_images/examples_falkon_regression_tutorial_17_0.png" />
 </div>
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 </section>
@@ -796,50 +715,33 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 
 
            </div>
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-          <footer>
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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-        &#169; Copyright 2020, Giacomo Meanti.
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+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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diff --git a/examples/falkon_regression_tutorial.ipynb b/examples/falkon_regression_tutorial.ipynb
new file mode 100644
index 00000000..9a519e23
--- /dev/null
+++ b/examples/falkon_regression_tutorial.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Falkon Regression Tutorial\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook introduces the main interface of the Falkon library, \n",
+    "using a toy regression problem.\n",
+    "\n",
+    "We will be using the Boston housing dataset which is included in `scikit-learn` to train a Falkon model.\n",
+    "Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "import falkon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions.\n",
+    "The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.\n",
+    "\n",
+    "After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test \n",
+    "set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the [model_selection module](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.fetch_california_housing(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "num_train = int(X.shape[0] * 0.8)\n",
+    "num_test = X.shape[0] - num_train\n",
+    "shuffle_idx = np.arange(X.shape[0])\n",
+    "np.random.shuffle(shuffle_idx)\n",
+    "train_idx = shuffle_idx[:num_train]\n",
+    "test_idx = shuffle_idx[num_train:]\n",
+    "\n",
+    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
+    "Xtest, Ytest = X[test_idx], Y[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Pre-process the data\n",
+    "\n",
+    "We must convert the numpy arrays to PyTorch tensors before using them in Falkon.\n",
+    "This is very easy and fast with the `torch.from_numpy` function.\n",
+    "\n",
+    "Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data:\n",
+    "convert it to have zero-mean and unit standard deviation.\n",
+    "We use the statistics of the training data to avoid leakage between the two sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# convert numpy -> pytorch\n",
+    "Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)\n",
+    "Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)\n",
+    "Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)\n",
+    "Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = Xtrain.mean(0, keepdim=True)\n",
+    "train_std = Xtrain.std(0, keepdim=True)\n",
+    "Xtrain -= train_mean\n",
+    "Xtrain /= train_std\n",
+    "Xtest -= train_mean\n",
+    "Xtest /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Create the Falkon model\n",
+    "\n",
+    "The Falkon object is the main API of this library. \n",
+    "It is similar in spirit to the fit-transform API of scikit-learn, while supporting some\n",
+    "additional features such as monitoring of validation error.\n",
+    "\n",
+    "While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much \n",
+    "larger datasets.\n",
+    "Here we only showcase some of the more basic options.\n",
+    "\n",
+    "Mandatory parameters are:\n",
+    " - the kernel function (here we use a linear kernel)\n",
+    " - the amount of regularization, which we set to some small positive value\n",
+    " - the number of inducing points M. We set M to 5000, which is a sizable portion of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
+      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
+     ]
+    }
+   ],
+   "source": [
+    "options = falkon.FalkonOptions(keops_active=\"no\")\n",
+    "\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=1, opt=options)\n",
+    "flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=5000, options=options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Training the model\n",
+    "\n",
+    "The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are\n",
+    "two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed.\n",
+    "To gain more insight in the various steps of the algorithm you can pass `debug=True` when creating the Falkon object. \n",
+    "\n",
+    "Model training will occur on the GPU, if it is available, and CUDA is properly installed, \n",
+    "or on the CPU as a fallback. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Falkon(M=5000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f65871c45e0>, kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64)), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='no', keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty=1e-05)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flk.fit(Xtrain, Ytrain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Evaluating model performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the `predict` method to obtain predictions on new data.\n",
+    "\n",
+    "Here we print the error on both train and test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training RMSE: 0.527\n",
+      "Test RMSE: 0.578\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_pred = flk.predict(Xtrain).reshape(-1, )\n",
+    "test_pred = flk.predict(Xtest).reshape(-1, )\n",
+    "\n",
+    "def rmse(true, pred):\n",
+    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
+    "\n",
+    "print(\"Training RMSE: %.3f\" % (rmse(train_pred, Ytrain)))\n",
+    "print(\"Test RMSE: %.3f\" % (rmse(test_pred, Ytest)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "hist_range = (min(Ytest.min(), test_pred.min()).item(), max(Ytest.max(), test_pred.max()).item())\n",
+    "ax.hist(Ytest.numpy(), bins=10, range=hist_range, alpha=0.7, label=\"True\")\n",
+    "ax.hist(test_pred.numpy(), bins=10, range=hist_range, alpha=0.7, label=\"Pred\")\n",
+    "ax.legend(loc=\"best\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/hyperopt.html b/examples/hyperopt.html
new file mode 100644
index 00000000..ae29104c
--- /dev/null
+++ b/examples/hyperopt.html
@@ -0,0 +1,872 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+  <title>Automatic Hyperparameter Optimization &mdash; falkon 0.7.2 documentation</title>
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#development">Development</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../get_started.html">Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#passing-options">Passing Options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../get_started.html#more-examples">More Examples</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="examples.html">Examples</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html">Introducing Logistic Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-the-Falkon-model">Define the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Define-Logistic-Falkon-model">Define Logistic Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Train-both-models">Train both models</a></li>
+<li class="toctree-l3"><a class="reference internal" href="logistic_falkon.html#Testing">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#polynomial-kernel">Polynomial kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#linear-kernel">Linear kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#sigmoid-kernel">Sigmoid kernel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
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+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
+.prompt .copybtn {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
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+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
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+  border: none;
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+div.rendered_html th {
+  font-weight: bold;
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+div.rendered_html tbody tr:nth-child(odd) {
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+  background: rgba(66, 165, 245, 0.2);
+}
+
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
+.admonition > p:before {
+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<section id="Automatic-Hyperparameter-Optimization">
+<h1>Automatic Hyperparameter Optimization<a class="headerlink" href="#Automatic-Hyperparameter-Optimization" title="Permalink to this headline"></a></h1>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[pyKeOps]: Warning, no cuda detected. Switching to cpu only.
+</pre></div></div>
+</div>
+<section id="Load-the-data">
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
+<p>We use the <strong>digits</strong> dataset, which is distributed alongside scikit-learn.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">y</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
+
+<span class="c1"># Plot three sample images from the dataset</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">10</span><span class="p">])</span>
+<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">42</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">42</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/examples_hyperopt_4_0.png" src="../_images/examples_hyperopt_4_0.png" />
+</div>
+</div>
+</section>
+<section id="Split-into-training-and-test-sets">
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline"></a></h2>
+<p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Data-Preprocessing">
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
+<p>As always with Falkon we must: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Normalizing the data is always a good idea, and it becomes even more important with automatic hyperparameter optimization.</p>
+<p>Here we use the global mean and standard deviation of the training set for <strong>z-score normalization</strong>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+<span class="n">X_train</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_train</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">X_test</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_test</span> <span class="o">/=</span> <span class="n">train_std</span>
+</pre></div>
+</div>
+</div>
+<p>Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification.</p>
+<p>We therefore convert the labels to a <strong>1-hot representation</strong>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert labels to 1-hot</span>
+<span class="n">eye</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_train</span><span class="p">]</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_test</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label vector: &quot;</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])
+</pre></div></div>
+</div>
+</section>
+<section id="Hyperparameter-Optimization">
+<h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optimization" title="Permalink to this headline"></a></h2>
+<p>The Falkon algorithm has three main kinds of hyper-parameters: 1. The kernel parameters. Most commonly when using the Gaussian kernel these are the length-scales for each dimension in the data 2. The amount of regularization <span class="math notranslate nohighlight">\(\lambda\)</span> (the penalty term, which helps prevent overfitting). 3. The Nystrom centers. These are sometimes not considered hyper parameters, and they are chosen uniformly at random from the training set. However, it is possible to find better centers!</p>
+<p>In the <a class="reference internal" href="falkon_cv.html"><span class="doc">grid-search</span></a> notebook all three types of hyperparameters are considered, but only with a small number of options in each category.</p>
+<p>With automatic hyperparameter optimization, which is based on a gradient descent-type procedure, we can instead define a much larger search space for the hyperparameters.</p>
+<p>In particular, we will optimize the kernel length-scale (one for each dimension in the data), the regularization and the Nystrom centers. Optimizing the Nystrom centers is especially useful since it allows to reduce their number, thus speeding up the whole training and inference process!</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<p>Several functions and classes used for hyperparameter optimization reside in the <code class="docutils literal notranslate"><span class="pre">falkon.hopt</span></code> module.</p>
+<p>Here we import the <code class="docutils literal notranslate"><span class="pre">NystromCompReg</span></code> class which defines the optimization objective.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">falkon.hopt</span>
+<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
+<span class="kn">from</span> <span class="nn">falkon.hopt.objectives</span> <span class="kn">import</span> <span class="n">NystromCompReg</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk_opt</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<p>We have to initialize the hyperparameters to some default values. In particular we choose some random initial points from the dataset as the initial Nystrom centers.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sigma_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="n">sigma_init</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
+
+<span class="n">penalty_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">centers_init</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="p">),</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Now we initialize the loss function (<code class="docutils literal notranslate"><span class="pre">NystromCompReg</span></code>) and the optimization algorithm (Adam).</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">NystromCompReg</span><span class="p">(</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty_init</span><span class="o">=</span><span class="n">penalty_init</span><span class="p">,</span> <span class="n">centers_init</span><span class="o">=</span><span class="n">centers_init</span><span class="p">,</span>  <span class="c1"># The initial hp values</span>
+    <span class="n">opt_penalty</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">opt_centers</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>  <span class="c1"># Whether the various hps are to be optimized</span>
+    <span class="p">)</span>
+<span class="n">opt_hp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>And start training. Each iteration corresponds to a single gradient step over the whole dataset.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tr_loss</span><span class="p">,</span> <span class="n">tr_err</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
+    <span class="n">opt_hp</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+    <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+    <span class="n">opt_hp</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+
+    <span class="n">tr_loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+    <span class="n">tr_err</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2"> Loss </span><span class="si">{</span><span class="n">tr_loss</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Error </span><span class="si">{</span><span class="n">tr_err</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Epoch 0 Loss 40310.387 Error 1.67%
+Epoch 1 Loss 35137.203 Error 1.67%
+Epoch 2 Loss 24207.420 Error 1.74%
+Epoch 3 Loss 17153.834 Error 2.02%
+Epoch 4 Loss 12490.971 Error 1.95%
+Epoch 5 Loss 9343.500 Error 1.67%
+Epoch 6 Loss 7062.557 Error 1.32%
+Epoch 7 Loss 5359.402 Error 1.18%
+Epoch 8 Loss 4108.987 Error 1.04%
+Epoch 9 Loss 3210.683 Error 0.90%
+Epoch 10 Loss 2569.709 Error 0.63%
+Epoch 11 Loss 2121.547 Error 0.49%
+Epoch 12 Loss 1814.989 Error 0.49%
+Epoch 13 Loss 1606.087 Error 0.42%
+Epoch 14 Loss 1460.298 Error 0.42%
+Epoch 15 Loss 1353.231 Error 0.21%
+Epoch 16 Loss 1269.496 Error 0.21%
+Epoch 17 Loss 1199.922 Error 0.14%
+Epoch 18 Loss 1139.733 Error 0.14%
+Epoch 19 Loss 1088.957 Error 0.07%
+Epoch 20 Loss 1043.391 Error 0.07%
+Epoch 21 Loss 1002.747 Error 0.07%
+Epoch 22 Loss 966.971 Error 0.07%
+Epoch 23 Loss 935.513 Error 0.07%
+Epoch 24 Loss 907.745 Error 0.07%
+Epoch 25 Loss 883.216 Error 0.07%
+Epoch 26 Loss 861.607 Error 0.07%
+Epoch 27 Loss 842.661 Error 0.07%
+Epoch 28 Loss 826.113 Error 0.07%
+Epoch 29 Loss 811.653 Error 0.07%
+Epoch 30 Loss 798.939 Error 0.07%
+Epoch 31 Loss 787.643 Error 0.07%
+Epoch 32 Loss 777.481 Error 0.07%
+Epoch 33 Loss 768.217 Error 0.07%
+Epoch 34 Loss 759.674 Error 0.07%
+Epoch 35 Loss 751.720 Error 0.07%
+Epoch 36 Loss 744.265 Error 0.07%
+Epoch 37 Loss 737.261 Error 0.07%
+Epoch 38 Loss 730.671 Error 0.07%
+Epoch 39 Loss 724.491 Error 0.07%
+Epoch 40 Loss 718.708 Error 0.07%
+Epoch 41 Loss 713.325 Error 0.07%
+Epoch 42 Loss 708.335 Error 0.07%
+Epoch 43 Loss 703.722 Error 0.07%
+Epoch 44 Loss 699.472 Error 0.07%
+Epoch 45 Loss 695.557 Error 0.07%
+Epoch 46 Loss 691.935 Error 0.00%
+Epoch 47 Loss 688.580 Error 0.00%
+Epoch 48 Loss 685.445 Error 0.00%
+Epoch 49 Loss 682.500 Error 0.00%
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Evaluate the test error:</span>
+<span class="n">ts_preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test error: </span><span class="si">{</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">ts_preds</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Test error: 0.83%
+</pre></div></div>
+</div>
+<p>The optimized parameters are available as attributes of the <code class="docutils literal notranslate"><span class="pre">model</span></code> object:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final value of lambda: </span><span class="si">%.3e</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">penalty</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final value of sigma: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">kernel</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">detach</span><span class="p">()))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Final value of lambda: 4.428e-05
+Final value of sigma: tensor([2.3884, 2.6252, 2.7859, 2.7036, 2.6799, 2.7698, 2.7344, 3.0071, 2.5923,
+        2.7421, 2.7629, 2.7525, 2.6989, 2.7232, 2.8216, 2.8830, 2.6465, 2.7994,
+        2.7406, 2.7082, 2.8053, 2.6552, 2.7757, 2.6979, 2.3884, 2.7573, 2.7242,
+        2.6681, 2.7780, 2.7649, 2.7010, 2.7274, 2.3884, 2.6608, 2.7477, 2.7604,
+        2.8015, 2.7581, 2.6359, 2.3884, 2.8348, 2.7656, 2.6553, 2.6672, 2.7252,
+        2.7499, 2.6721, 2.6589, 2.9603, 2.7725, 2.7143, 2.7481, 2.7307, 2.7093,
+        2.7251, 2.7213, 2.6326, 2.6092, 2.7600, 2.7744, 2.7885, 2.6976, 2.6838,
+        2.7062])
+</pre></div></div>
+</div>
+<p>We can compare the obtained results with the grid-search notebook.</p>
+<p>A grid-search with 1000 centers and 32 grid-points resulted in choosing a model with sigma=5, and lambda=1e-7.</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">NystromCompReg</span></code> objective with half the centers led to obtain a lower test error (0.83% vs. 1.11%) after 50 training epochs. However the obtained hyperparameters are quite different: lambda in particular is much higher at 1.1e-4.</p>
+<p>This objective in particular has quite a high bias and tends to choose simple models instead of more complex ones (remember that since lambda is a regularizer, it can be seen as one possible measure of model complexity). In practice this is often not a problem, as we observed in this case.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
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+        <a href="custom_kernels.html" class="btn btn-neutral float-left" title="Implementing A Custom Kernel" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
+  </div>
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+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/examples/hyperopt.ipynb b/examples/hyperopt.ipynb
new file mode 100644
index 00000000..32bddaff
--- /dev/null
+++ b/examples/hyperopt.ipynb
@@ -0,0 +1,498 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b57c79fd",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Automatic Hyperparameter Optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b44861fd",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from sklearn import datasets, model_selection\n",
+    "import numpy as np\n",
+    "np.random.seed(30)\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('ggplot')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee63dc4",
+   "metadata": {},
+   "source": [
+    "## Load the data\n",
+    "\n",
+    "We use the **digits** dataset, which is distributed alongside scikit-learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3da6ecb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = datasets.load_digits(return_X_y=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6b948acf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 648x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_digit(ax, x, y):\n",
+    "    ax.imshow(x.reshape((8, 8)), cmap='gray')\n",
+    "    ax.set_title(\"Label: %d\" % y)\n",
+    "    ax.set_axis_off()\n",
+    "\n",
+    "# Plot three sample images from the dataset\n",
+    "fig, ax = plt.subplots(ncols=3, figsize=(9, 6))\n",
+    "plot_digit(ax[0], X[3], Y[3])\n",
+    "plot_digit(ax[1], X[10], Y[10])\n",
+    "plot_digit(ax[2], X[42], Y[42])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aa131a8",
+   "metadata": {},
+   "source": [
+    "## Split into training and test sets\n",
+    "\n",
+    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6b5535cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eef7ac94",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "As always with Falkon we must:\n",
+    " 1. Convert from numpy arrays to torch tensors\n",
+    " 2. Convert data and labels to the same data-type (in this case float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a3a1469f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
+    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
+    "Y_train = torch.from_numpy(Y_train)\n",
+    "Y_test = torch.from_numpy(Y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12fcc9d3",
+   "metadata": {},
+   "source": [
+    "Normalizing the data is always a good idea, and it becomes even more important with automatic hyperparameter optimization. \n",
+    "\n",
+    "Here we use the global mean and standard deviation of the training set for **z-score normalization**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1450e0f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# z-score normalization\n",
+    "train_mean = X_train.mean()\n",
+    "train_std = X_train.std()\n",
+    "X_train -= train_mean\n",
+    "X_train /= train_std\n",
+    "X_test -= train_mean\n",
+    "X_test /= train_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "938d4389",
+   "metadata": {},
+   "source": [
+    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification.\n",
+    "\n",
+    "We therefore convert the labels to a **1-hot representation**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87729460",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert labels to 1-hot\n",
+    "eye = torch.eye(10, dtype=torch.float32)\n",
+    "Y_train = eye[Y_train]\n",
+    "Y_test = eye[Y_test]\n",
+    "print(\"First label vector: \", Y_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66bf3881",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter Optimization\n",
+    "\n",
+    "The Falkon algorithm has three main kinds of hyper-parameters:\n",
+    " 1. The kernel parameters. Most commonly when using the Gaussian kernel these are the length-scales for each dimension in the data\n",
+    " 2. The amount of regularization $\\lambda$ (the penalty term, which helps prevent overfitting).\n",
+    " 3. The Nystrom centers. These are sometimes not considered hyper parameters, and they are chosen uniformly at random from the training set. However, it is possible to find better centers!\n",
+    " \n",
+    "In the [grid-search](falkon_cv.ipynb) notebook all three types of hyperparameters are considered, but only with a small number of options in each category.\n",
+    "\n",
+    "With automatic hyperparameter optimization, which is based on a gradient descent-type procedure, we can instead define a much larger search space for the hyperparameters.\n",
+    "\n",
+    "In particular, we will optimize the kernel length-scale (one for each dimension in the data), the regularization and the Nystrom centers. Optimizing the Nystrom centers is especially useful since it allows to reduce their number, thus speeding up the whole training and inference process!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1ad2b54d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mclass_loss(true, pred):\n",
+    "    true = torch.argmax(true, dim=1)\n",
+    "    pred = torch.argmax(pred, dim=1)\n",
+    "    return torch.mean((true != pred).to(torch.float32))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "475c9d14",
+   "metadata": {},
+   "source": [
+    "Several functions and classes used for hyperparameter optimization reside in the `falkon.hopt` module.\n",
+    "\n",
+    "Here we import the `NystromCompReg` class which defines the optimization objective."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "86095c91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import falkon.hopt\n",
+    "from falkon import FalkonOptions\n",
+    "from falkon.hopt.objectives import NystromCompReg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4e2132fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flk_opt = FalkonOptions(use_cpu=not torch.cuda.is_available())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b412208",
+   "metadata": {},
+   "source": [
+    "We have to initialize the hyperparameters to some default values. In particular we choose some random initial points from the dataset as the initial Nystrom centers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "414616aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma_init = torch.tensor([1.0] * X_train.shape[1], dtype=torch.float32).requires_grad_()\n",
+    "kernel = falkon.kernels.GaussianKernel(sigma=sigma_init, opt=flk_opt)\n",
+    "\n",
+    "penalty_init = torch.tensor(1e-5, dtype=torch.float32)\n",
+    "centers_init = X_train[np.random.choice(X_train.shape[0], size=(500, ), replace=False)].clone()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a311c944",
+   "metadata": {},
+   "source": [
+    "Now we initialize the loss function (`NystromCompReg`) and the optimization algorithm (Adam)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "38de0fde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = NystromCompReg(\n",
+    "    kernel=kernel, penalty_init=penalty_init, centers_init=centers_init,  # The initial hp values\n",
+    "    opt_penalty=True, opt_centers=True,  # Whether the various hps are to be optimized\n",
+    "    )\n",
+    "opt_hp = torch.optim.Adam(model.parameters(), lr=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e08fae3d",
+   "metadata": {},
+   "source": [
+    "And start training. Each iteration corresponds to a single gradient step over the whole dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "79a9552a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 Loss 40310.387 Error 1.67%\n",
+      "Epoch 1 Loss 35137.203 Error 1.67%\n",
+      "Epoch 2 Loss 24207.420 Error 1.74%\n",
+      "Epoch 3 Loss 17153.834 Error 2.02%\n",
+      "Epoch 4 Loss 12490.971 Error 1.95%\n",
+      "Epoch 5 Loss 9343.500 Error 1.67%\n",
+      "Epoch 6 Loss 7062.557 Error 1.32%\n",
+      "Epoch 7 Loss 5359.402 Error 1.18%\n",
+      "Epoch 8 Loss 4108.987 Error 1.04%\n",
+      "Epoch 9 Loss 3210.683 Error 0.90%\n",
+      "Epoch 10 Loss 2569.709 Error 0.63%\n",
+      "Epoch 11 Loss 2121.547 Error 0.49%\n",
+      "Epoch 12 Loss 1814.989 Error 0.49%\n",
+      "Epoch 13 Loss 1606.087 Error 0.42%\n",
+      "Epoch 14 Loss 1460.298 Error 0.42%\n",
+      "Epoch 15 Loss 1353.231 Error 0.21%\n",
+      "Epoch 16 Loss 1269.496 Error 0.21%\n",
+      "Epoch 17 Loss 1199.922 Error 0.14%\n",
+      "Epoch 18 Loss 1139.733 Error 0.14%\n",
+      "Epoch 19 Loss 1088.957 Error 0.07%\n",
+      "Epoch 20 Loss 1043.391 Error 0.07%\n",
+      "Epoch 21 Loss 1002.747 Error 0.07%\n",
+      "Epoch 22 Loss 966.971 Error 0.07%\n",
+      "Epoch 23 Loss 935.513 Error 0.07%\n",
+      "Epoch 24 Loss 907.745 Error 0.07%\n",
+      "Epoch 25 Loss 883.216 Error 0.07%\n",
+      "Epoch 26 Loss 861.607 Error 0.07%\n",
+      "Epoch 27 Loss 842.661 Error 0.07%\n",
+      "Epoch 28 Loss 826.113 Error 0.07%\n",
+      "Epoch 29 Loss 811.653 Error 0.07%\n",
+      "Epoch 30 Loss 798.939 Error 0.07%\n",
+      "Epoch 31 Loss 787.643 Error 0.07%\n",
+      "Epoch 32 Loss 777.481 Error 0.07%\n",
+      "Epoch 33 Loss 768.217 Error 0.07%\n",
+      "Epoch 34 Loss 759.674 Error 0.07%\n",
+      "Epoch 35 Loss 751.720 Error 0.07%\n",
+      "Epoch 36 Loss 744.265 Error 0.07%\n",
+      "Epoch 37 Loss 737.261 Error 0.07%\n",
+      "Epoch 38 Loss 730.671 Error 0.07%\n",
+      "Epoch 39 Loss 724.491 Error 0.07%\n",
+      "Epoch 40 Loss 718.708 Error 0.07%\n",
+      "Epoch 41 Loss 713.325 Error 0.07%\n",
+      "Epoch 42 Loss 708.335 Error 0.07%\n",
+      "Epoch 43 Loss 703.722 Error 0.07%\n",
+      "Epoch 44 Loss 699.472 Error 0.07%\n",
+      "Epoch 45 Loss 695.557 Error 0.07%\n",
+      "Epoch 46 Loss 691.935 Error 0.00%\n",
+      "Epoch 47 Loss 688.580 Error 0.00%\n",
+      "Epoch 48 Loss 685.445 Error 0.00%\n",
+      "Epoch 49 Loss 682.500 Error 0.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "tr_loss, tr_err = [], []\n",
+    "\n",
+    "for epoch in range(50):\n",
+    "    opt_hp.zero_grad()\n",
+    "    loss = model(X_train, Y_train)\n",
+    "    loss.backward()\n",
+    "    opt_hp.step()\n",
+    "\n",
+    "    tr_loss.append(loss.item())\n",
+    "    tr_err.append(mclass_loss(Y_train, model.predict(X_train)))\n",
+    "    print(f\"Epoch {epoch} Loss {tr_loss[-1]:.3f} Error {tr_err[-1] * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "86d2e2ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test error: 0.83%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the test error:\n",
+    "ts_preds = model.predict(X_test)\n",
+    "print(f\"Test error: {mclass_loss(Y_test, ts_preds) * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89df66d9",
+   "metadata": {},
+   "source": [
+    "The optimized parameters are available as attributes of the `model` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3fe66880",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final value of lambda: 4.428e-05\n",
+      "Final value of sigma: tensor([2.3884, 2.6252, 2.7859, 2.7036, 2.6799, 2.7698, 2.7344, 3.0071, 2.5923,\n",
+      "        2.7421, 2.7629, 2.7525, 2.6989, 2.7232, 2.8216, 2.8830, 2.6465, 2.7994,\n",
+      "        2.7406, 2.7082, 2.8053, 2.6552, 2.7757, 2.6979, 2.3884, 2.7573, 2.7242,\n",
+      "        2.6681, 2.7780, 2.7649, 2.7010, 2.7274, 2.3884, 2.6608, 2.7477, 2.7604,\n",
+      "        2.8015, 2.7581, 2.6359, 2.3884, 2.8348, 2.7656, 2.6553, 2.6672, 2.7252,\n",
+      "        2.7499, 2.6721, 2.6589, 2.9603, 2.7725, 2.7143, 2.7481, 2.7307, 2.7093,\n",
+      "        2.7251, 2.7213, 2.6326, 2.6092, 2.7600, 2.7744, 2.7885, 2.6976, 2.6838,\n",
+      "        2.7062])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Final value of lambda: %.3e\" % (model.penalty))\n",
+    "print(\"Final value of sigma: %s\" % (model.kernel.sigma.detach()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db747ea",
+   "metadata": {},
+   "source": [
+    "We can compare the obtained results with the grid-search notebook.\n",
+    "\n",
+    "A grid-search with 1000 centers and 32 grid-points resulted in choosing a model with sigma=5, and lambda=1e-7.\n",
+    "\n",
+    "The `NystromCompReg` objective with half the centers led to obtain a lower test error (0.83% vs. 1.11%) after 50 training epochs. However the obtained hyperparameters are quite different: lambda in particular is much higher at 1.1e-4.\n",
+    "\n",
+    "This objective in particular has quite a high bias and tends to choose simple models instead of more complex ones (remember that since lambda is a regularizer, it can be seen as one possible measure of model complexity). In practice this is often not a problem, as we observed in this case.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/hyperparameter_tuning.ipynb b/examples/hyperparameter_tuning.ipynb
deleted file mode 100644
index 53bb0f90..00000000
--- a/examples/hyperparameter_tuning.ipynb
+++ /dev/null
@@ -1,430 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Hyperparameter Tuning with Falkon\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "We use Falkon for a multi-class problem (on the [digits](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits) dataset), showing how to integrate it into scikit-learn\n",
-    "for hyperparameter optimization.\n",
-    "\n",
-    "Since both `Falkon` and `LogisticFalkon` are estimators, and follow scikit-learn's API, integration is seamless."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn import datasets, model_selection, metrics\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "import falkon"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the data\n",
-    "\n",
-    "We use the **digits** dataset, which is distributed alongside scikit-learn."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X, Y = datasets.load_digits(return_X_y=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Label: 1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "_ = ax.matshow(X[1].reshape((8, 8)), cmap='gray')\n",
-    "print(\"Label: %d\" % Y[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Split into training and test sets\n",
-    "\n",
-    "We split the data into a training set with 80% of the samples and a test set with the remaining 20%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(\n",
-    "    X, Y, test_size=0.2, random_state=10, shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data Preprocessing\n",
-    "\n",
-    "As always with Falkon we must:\n",
-    " 1. Convert from numpy arrays to torch tensors\n",
-    " 2. Convert data and labels to the same data-type (in this case float32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = torch.from_numpy(X_train).to(dtype=torch.float32)\n",
-    "X_test = torch.from_numpy(X_test).to(dtype=torch.float32)\n",
-    "Y_train = torch.from_numpy(Y_train)\n",
-    "Y_test = torch.from_numpy(Y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Normalizing the data is always a good idea. Here we use the global mean and standard deviation of the training set for z-score normalization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# z-score normalization\n",
-    "train_mean = X_train.mean()\n",
-    "train_std = X_train.std()\n",
-    "X_train -= train_mean\n",
-    "X_train /= train_std\n",
-    "X_test -= train_mean\n",
-    "X_test /= train_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First label vector:  tensor([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Convert labels to 1-hot\n",
-    "eye = torch.eye(10, dtype=torch.float32)\n",
-    "Y_train = eye[Y_train]\n",
-    "Y_test = eye[Y_test]\n",
-    "print(\"First label vector: \", Y_train[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Search for the optimal parameters\n",
-    "\n",
-    "Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.\n",
-    "\n",
-    "Scikit-learn has great support for this, with the `GridSearchCV` class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.\n",
-    "\n",
-    "Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mclass_loss(true, pred):\n",
-    "    true = torch.argmax(true, dim=1)\n",
-    "    pred = torch.argmax(pred, dim=1)\n",
-    "    return torch.mean((true != pred).to(torch.float32))\n",
-    "mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The parameter settings which will be explored by the grid-search are:\n",
-    " - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data)\n",
-    " - four different regularization values\n",
-    " - two different values for M: the number of inducing points. As we will see, a larger `M` is almost always better than a smaller one (but it leads to longer training times). \n",
-    "   Of course this is not the case if the dataset is easy to overfit, since reducing `M` may also provide additional regularization.\n",
-    "\n",
-    "When we create the estimator we pass it additional parameters via the `FalkonOptions` class.\n",
-    "In our case we want to ensure that the model runs on the CPU by setting `use_cpu=True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/helpers.py:31: UserWarning: Failed to initialize cuBLAS library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
-      "Error encountered was cublas library not found\n",
-      "  warnings.warn(get_error_str(\"cuBLAS\", e))\n"
-     ]
-    }
-   ],
-   "source": [
-    "parameter_grid = {\n",
-    "    'kernel': [falkon.kernels.GaussianKernel(sigma=1), \n",
-    "               falkon.kernels.GaussianKernel(sigma=5), \n",
-    "               falkon.kernels.GaussianKernel(sigma=10),\n",
-    "               falkon.kernels.GaussianKernel(sigma=15),],\n",
-    "    'penalty': [1e-3, 1e-5, 1e-7, 1e-9],\n",
-    "    'M': [500, 1000],\n",
-    "}\n",
-    "estimator = falkon.Falkon(\n",
-    "    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000, maxiter=10,\n",
-    "    options=falkon.FalkonOptions(use_cpu=True))\n",
-    "\n",
-    "grid_search = model_selection.GridSearchCV(estimator, parameter_grid, mclass_scorer, cv=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The best parameters are:  {'M': 1000, 'kernel': GaussianKernel(sigma=5.0), 'penalty': 1e-07}\n",
-      "CPU times: user 2min 5s, sys: 17 s, total: 2min 22s\n",
-      "Wall time: 35.6 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "grid_search.fit(X_train, Y_train)\n",
-    "print(\"The best parameters are: \", grid_search.best_params_)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Evaluating the model\n",
-    "We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.14 s, sys: 143 ms, total: 1.29 s\n",
-      "Wall time: 323 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "flk = grid_search.best_estimator_\n",
-    "flk.fit(X_train, Y_train)\n",
-    "test_pred = flk.predict(X_test)\n",
-    "train_pred = flk.predict(X_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training error: 0.00%\n",
-      "Test error: 1.11%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Training error: %.2f%%\" % (mclass_loss(Y_train, train_pred) * 100))\n",
-    "print(\"Test error: %.2f%%\" % (mclass_loss(Y_test, test_pred) * 100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot grid-search results\n",
-    "\n",
-    "Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful.\n",
-    "If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.\n",
-    "\n",
-    "In the plot red indicates a high error, while darker blue indicates a low error."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res_df = pd.DataFrame.from_dict(grid_search.cv_results_)\n",
-    "res_df[\"M\"] = res_df.params.apply(lambda x: x.get(\"M\"))\n",
-    "res_df[\"penalty\"] = res_df.params.apply(lambda x: x.get(\"penalty\"))\n",
-    "res_df[\"sigma\"] = res_df.params.apply(lambda x: x.get(\"kernel\").sigma.item())\n",
-    "res_df = res_df[[\"mean_test_score\", \"M\", \"penalty\", \"sigma\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_heatmap(ax, df, xlabel, ylabel, value, scale):\n",
-    "    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)\n",
-    "    cmap = plt.cm.get_cmap('coolwarm_r', 20)\n",
-    "    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])\n",
-    "    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)\n",
-    "    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)\n",
-    "    ax.set_xticklabels(piv.columns, minor=False)\n",
-    "    ax.set_yticklabels(piv.index, minor=False)\n",
-    "    ax.set_xlabel(xlabel)\n",
-    "    ax.set_ylabel(ylabel)\n",
-    "    return c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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diff --git a/examples/logistic_falkon.html b/examples/logistic_falkon.html
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-<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Introducing Logistic Falkon</a><ul>
@@ -132,19 +84,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="performance_tuning.html">Training on the GPU</a></li>
+<li class="toctree-l2"><a class="reference internal" href="hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
@@ -156,9 +140,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -204,20 +191,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
@@ -231,77 +210,48 @@
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>
 
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 <style>
 /* CSS for nbsphinx extension */
@@ -512,7 +462,7 @@
 }
 
 /* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
-.prompt a.copybtn {
+.prompt .copybtn {
     display: none;
 }
 
@@ -573,19 +523,17 @@
 }
 </style>
 <section id="Introducing-Logistic-Falkon">
-<h1>Introducing Logistic Falkon<a class="headerlink" href="#Introducing-Logistic-Falkon" title="Permalink to this headline">¶</a></h1>
+<h1>Introducing Logistic Falkon<a class="headerlink" href="#Introducing-Logistic-Falkon" title="Permalink to this headline"></a></h1>
 <section id="Introduction">
-<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline">¶</a></h2>
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline"></a></h2>
 <p>In this notebook we use a synthetic dataset to show how the <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> estimator works.</p>
 <p>We compare Falkon – which is trained on the squared loss – to its logistic loss version, and verify that on a binary classification problem logistic falkon achieves better accuracy.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
 <span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
@@ -599,21 +547,20 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected
+[pyKeOps]: Warning, no cuda detected. Switching to cpu only.
 </pre></div></div>
 </div>
 </section>
 <section id="Load-the-data">
-<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline">¶</a></h2>
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
 <p>We use a synthetic dataset in two dimensions, which consists of two concentric circles labeled with different classes.</p>
-<p>We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 100000 samples to demonstrate the speed of the <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> library.</p>
+<p>We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 10000 samples to demonstrate the speed of the <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> library.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_circles</span><span class="p">(</span>
-    <span class="n">n_samples</span><span class="o">=</span><span class="mi">100_000</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">122</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_circles</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">10_000</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">122</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -621,10 +568,9 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
-<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -637,21 +583,20 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 </div>
 </section>
 <section id="Split-into-training-and-test-sets">
-<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline">¶</a></h2>
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline"></a></h2>
 <p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
     <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </section>
 <section id="Data-Preprocessing">
-<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline">¶</a></h2>
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
 <p>The minimal preprocessing steps are: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32) 3. Reshape the labels so that they have two dimensions. This is not strictly necessary, but Falkon internally works with 2D tensors, so the output of the <code class="docutils literal notranslate"><span class="pre">Falkon.predict</span></code> method will always be 2D. 4. Change the labels from 0, 1 to -1, 1. Note that Logistic Falkon uses the following formula for the logistic loss:</p>
 <div class="math notranslate nohighlight">
 \[\log(1 + e^{-y_1 y_2})\]</div>
@@ -662,8 +607,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 <span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 <span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 <span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -674,49 +618,40 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">Y_train</span><span class="p">[</span><span class="n">Y_train</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Y_train</span><span class="p">[</span><span class="n">Y_train</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
 <span class="n">Y_test</span><span class="p">[</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
 </pre></div>
 </div>
 </div>
 </section>
 <section id="Define-the-Falkon-model">
-<h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model" title="Permalink to this headline">¶</a></h2>
+<h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model" title="Permalink to this headline"></a></h2>
 <p>We use the same base parameters for both models: a small amount of regularization (<span class="math notranslate nohighlight">\(10^{-7}\)</span>) and a Gaussian kernel with <span class="math notranslate nohighlight">\(\sigma = 5\)</span>. The number of inducing points is set to 1000 which is adequate for the problem which is very easy.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="k">def</span> <span class="nf">binary_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">binary_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
-<div class="nbinput docutils container">
+<div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">flk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We define some basic options to run on the CPU, and to disable keops</span>
+<span class="n">flk_opt</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
+
+<span class="n">flk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
 
-<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">flk_kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">flk_kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set &#39;use_cpu&#39; to True to avoid this warning.
-  warnings.warn(get_error_str(&#34;CUDA&#34;, None))
-</pre></div></div>
-</div>
 </section>
 <section id="Define-Logistic-Falkon-model">
-<h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Falkon-model" title="Permalink to this headline">¶</a></h2>
+<h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Falkon-model" title="Permalink to this headline"></a></h2>
 <p>The logistic Falkon estimator uses the same base parameters as Falkon.</p>
 <p>However, instead of specifying a single value for regularization, we need to specify a <em>regularization path</em>: a series of decreasing amounts of regularization. For each regularization value we also need to specify the <em>number of iterations</em> of conjugate gradient descent, which will be performed for that specific regularization value.</p>
 <p>We validated empirically on a wide number of binary classification problems that a good scheme to set the regularization path is to use three short (i.e. 3 iterations) runs with increasing regularization, and then a few longer (here we used 8 iterations) runs with the final regularization value (here <code class="docutils literal notranslate"><span class="pre">1e-7</span></code>, the same as for Falkon).</p>
@@ -726,31 +661,31 @@ <h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Fal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">logflk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">logflk_opt</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
+
+<span class="n">logflk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">logflk_opt</span><span class="p">)</span>
 <span class="n">logloss</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">gsc_losses</span><span class="o">.</span><span class="n">LogisticLoss</span><span class="p">(</span><span class="n">logflk_kernel</span><span class="p">)</span>
 
-<span class="n">penalty_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">]</span>
-<span class="n">iter_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
+<span class="n">penalty_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">]</span>
+<span class="n">iter_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
 
 <span class="n">logflk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">LogisticFalkon</span><span class="p">(</span>
     <span class="n">kernel</span><span class="o">=</span><span class="n">logflk_kernel</span><span class="p">,</span> <span class="n">penalty_list</span><span class="o">=</span><span class="n">penalty_list</span><span class="p">,</span> <span class="n">iter_list</span><span class="o">=</span><span class="n">iter_list</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">logloss</span><span class="p">,</span>
-    <span class="n">error_fn</span><span class="o">=</span><span class="n">binary_loss</span><span class="p">,</span> <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">error_fn</span><span class="o">=</span><span class="n">binary_loss</span><span class="p">,</span> <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">logflk_opt</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </section>
 <section id="Train-both-models">
-<h2>Train both models<a class="headerlink" href="#Train-both-models" title="Permalink to this headline">¶</a></h2>
-<p>Training Falkon for 20 iterations (default value) takes about 20s on a laptop.</p>
-<p>Clearly, since the logistic falkon runs about 33 iterations (the sum of values in <code class="docutils literal notranslate"><span class="pre">iter_list</span></code>), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns. On the same laptop, the logistic falkon algorithm takes around 50s.</p>
+<h2>Train both models<a class="headerlink" href="#Train-both-models" title="Permalink to this headline"></a></h2>
+<p>Training Falkon for 20 iterations (default value) takes about half a secon on a laptop.</p>
+<p>Clearly, since the logistic falkon runs about 28 iterations (the sum of values in <code class="docutils literal notranslate"><span class="pre">iter_list</span></code>), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns. On the same laptop, the logistic falkon algorithm takes around 1.5s.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%%time</span>
-<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">);</span>
 </pre></div>
 </div>
 </div>
@@ -759,8 +694,8 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-CPU times: user 1min 41s, sys: 66.6 ms, total: 1min 41s
-Wall time: 25.4 s
+CPU times: user 2.35 s, sys: 59.3 ms, total: 2.41 s
+Wall time: 604 ms
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -769,16 +704,16 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Falkon(M=1000, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f22222fc650&gt;, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;auto&#39;, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-07, seed=None, weight_fn=None)
+Falkon(M=1000, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f9f7d142b20&gt;, kernel=GaussianKernel(sigma=Parameter containing:
+tensor([1.], dtype=torch.float64)), options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;no&#39;, keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=True, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty=1e-07)
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="o">%%time</span>
-<span class="n">logflk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">logflk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">);</span>
 </pre></div>
 </div>
 </div>
@@ -787,21 +722,18 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Iteration 0 - penalty 1.000000e-03 - sub-iterations 3
-Iteration   0 - Elapsed 4.02s - training loss 0.6910 - training error 0.2550
-Iteration 1 - penalty 1.000000e-05 - sub-iterations 3
-Iteration   1 - Elapsed 9.15s - training loss 0.5639 - training error 0.1623
-Iteration 2 - penalty 1.000000e-07 - sub-iterations 3
-Iteration   2 - Elapsed 14.68s - training loss 0.4032 - training error 0.1627
+Iteration 0 - penalty 1.000000e-03 - sub-iterations 4
+Iteration   0 - Elapsed 0.27s - training loss 0.4697 - training error 0.1544
+Iteration 1 - penalty 1.000000e-05 - sub-iterations 4
+Iteration   1 - Elapsed 0.51s - training loss 0.3774 - training error 0.1551
+Iteration 2 - penalty 1.000000e-07 - sub-iterations 4
+Iteration   2 - Elapsed 0.74s - training loss 0.3575 - training error 0.1534
 Iteration 3 - penalty 1.000000e-07 - sub-iterations 8
-Iteration   3 - Elapsed 27.14s - training loss 0.3696 - training error 0.1607
+Iteration   3 - Elapsed 1.12s - training loss 0.3554 - training error 0.1530
 Iteration 4 - penalty 1.000000e-07 - sub-iterations 8
-Iteration   4 - Elapsed 39.60s - training loss 0.3655 - training error 0.1605
-Iteration 5 - penalty 1.000000e-07 - sub-iterations 8
-Iteration   5 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605
-Iteration   6 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605
-CPU times: user 3min 46s, sys: 48 ms, total: 3min 46s
-Wall time: 56.7 s
+Iteration   4 - Elapsed 1.50s - training loss 0.3554 - training error 0.1530
+CPU times: user 6.39 s, sys: 30.9 ms, total: 6.42 s
+Wall time: 1.6 s
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -810,20 +742,21 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-LogisticFalkon(M=1000, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f2222305590&gt;, error_every=1, error_fn=&lt;function binary_loss at 0x7f22a05ec950&gt;, iter_list=[3, 3, 3, 8, 8, 8], kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;auto&#39;, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07, 1e-07], seed=None)
+LogisticFalkon(M=1000, center_selection=&lt;falkon.center_selection.UniformSelector object at 0x7f9f7d150250&gt;, error_fn=&lt;function binary_loss at 0x7f9f7d13eaf0&gt;, iter_list=[4, 4, 4, 8, 8], kernel=GaussianKernel(sigma=Parameter containing:
+tensor([1.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=Parameter containing:
+tensor([1.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype=&#39;auto&#39;, keops_sum_scheme=&#39;auto&#39;, keops_active=&#39;no&#39;, keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=True, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07])
 </pre></div></div>
 </div>
 </section>
 <section id="Testing">
-<h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headline">¶</a></h2>
+<h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headline"></a></h2>
 <p>However, the price paid for with a higher training time, leads to lower training error.</p>
 <p>We found that, on a variety of binary classification datasets, logistic falkon obtains a slightly lower error than the vanilla Falkon algorithm.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">flk_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
 <span class="n">flk_err</span> <span class="o">=</span> <span class="n">binary_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">flk_pred</span><span class="p">)</span>
 
 <span class="n">logflk_pred</span> <span class="o">=</span> <span class="n">logflk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -839,35 +772,34 @@ <h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headli
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Falkon model          -- Error: 15.81%
-Logistic Falkon model -- Error: 15.63%
+Falkon model          -- Error: 17.05%
+Logistic Falkon model -- Error: 16.90%
 </pre></div></div>
 </div>
 </section>
 </section>
 <section id="Plot-predictions">
-<h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permalink to this headline">¶</a></h1>
+<h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permalink to this headline"></a></h1>
 <p>In the plot we have the outer and inner circles which are correct predictions, and the circles in the middle which are the mispredicted points. Since we added lots of noise to the dataset, perfect predictions are not possible (there is no clear boundary between the two classes).</p>
-<p>Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where a slightly lower classification error is desired.</p>
+<p>Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where the best possible classification error is desired.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="k">def</span> <span class="nf">plot_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
                <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;correct cls 0&quot;</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
                <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;correct cls 1&quot;</span><span class="p">)</span>
 
     <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
                <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;wrong cls 0&quot;</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
                <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;wrong cls 1&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -875,13 +807,15 @@ <h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
 <span class="n">plot_predictions</span><span class="p">(</span><span class="n">flk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Falkon&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">)</span>
+
 <span class="n">plot_predictions</span><span class="p">(</span><span class="n">logflk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon&quot;</span><span class="p">)</span>
-</pre></div>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">);</span>
+<br/></pre></div>
 </div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -891,63 +825,37 @@ <h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permal
 <img alt="../_images/examples_logistic_falkon_22_0.png" src="../_images/examples_logistic_falkon_22_0.png" />
 </div>
 </div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
-<span></span>
-</pre></div>
-</div>
-</div>
 </section>
 
 
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-           
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-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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   <hr/>
 
   <div role="contentinfo">
-    <p>
-        &#169; Copyright 2020, Giacomo Meanti.
-
-    </p>
+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
   </div>
-    
-    
-    
-    Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
     <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    
-    provided by <a href="https://readthedocs.org">Read the Docs</a>. 
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
 
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+  <script>
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diff --git a/examples/logistic_falkon.ipynb b/examples/logistic_falkon.ipynb
index c157bfb6..6dc97caf 100644
--- a/examples/logistic_falkon.ipynb
+++ b/examples/logistic_falkon.ipynb
@@ -22,14 +22,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
+      "[pyKeOps]: Warning, no cuda detected. Switching to cpu only.\n"
      ]
     }
    ],
    "source": [
     "%matplotlib inline\n",
     "from sklearn import datasets, model_selection\n",
-    "import numpy as np\n",
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
     "plt.style.use('ggplot')\n",
@@ -45,7 +44,7 @@
     "\n",
     "We use a synthetic dataset in two dimensions, which consists of two concentric circles labeled with different classes.\n",
     "\n",
-    "We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 100000 samples to demonstrate the speed of the `Falkon` library."
+    "We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 10000 samples to demonstrate the speed of the `Falkon` library."
    ]
   },
   {
@@ -55,7 +54,7 @@
    "outputs": [],
    "source": [
     "X, Y = datasets.make_circles(\n",
-    "    n_samples=100_000, shuffle=False, noise=0.1, random_state=122)"
+    "    n_samples=10_000, shuffle=False, noise=0.1, random_state=122)"
    ]
   },
   {
@@ -65,7 +64,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x504 with 1 Axes>"
       ]
@@ -76,8 +75,8 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 7))\n",
-    "ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.1, marker='.')\n",
-    "_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.1, marker='.')"
+    "ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.3, marker='.')\n",
+    "_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.3, marker='.')"
    ]
   },
   {
@@ -161,20 +160,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
-      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "flk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "# We define some basic options to run on the CPU, and to disable keops\n",
+    "flk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=\"no\")\n",
     "\n",
-    "flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000)"
+    "flk_kernel = falkon.kernels.GaussianKernel(1, opt=flk_opt)\n",
+    "\n",
+    "flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000, options=flk_opt)"
    ]
   },
   {
@@ -202,15 +195,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "logflk_kernel = falkon.kernels.GaussianKernel(5)\n",
+    "logflk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=\"no\")\n",
+    "\n",
+    "logflk_kernel = falkon.kernels.GaussianKernel(1, opt=logflk_opt)\n",
     "logloss = falkon.gsc_losses.LogisticLoss(logflk_kernel)\n",
     "\n",
-    "penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7, 1e-7]\n",
-    "iter_list = [3, 3, 3, 8, 8, 8]\n",
+    "penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7]\n",
+    "iter_list = [4, 4, 4, 8, 8]\n",
     "\n",
     "logflk = falkon.LogisticFalkon(\n",
     "    kernel=logflk_kernel, penalty_list=penalty_list, iter_list=iter_list, M=1000, loss=logloss,\n",
-    "    error_fn=binary_loss, error_every=1)"
+    "    error_fn=binary_loss, error_every=1, options=logflk_opt)"
    ]
   },
   {
@@ -219,10 +214,10 @@
    "source": [
     "## Train both models\n",
     "\n",
-    "Training Falkon for 20 iterations (default value) takes about 20s on a laptop.\n",
+    "Training Falkon for 20 iterations (default value) takes about half a secon on a laptop.\n",
     "\n",
-    "Clearly, since the logistic falkon runs about 33 iterations (the sum of values in `iter_list`), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns.\n",
-    "On the same laptop, the logistic falkon algorithm takes around 50s."
+    "Clearly, since the logistic falkon runs about 28 iterations (the sum of values in `iter_list`), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns.\n",
+    "On the same laptop, the logistic falkon algorithm takes around 1.5s."
    ]
   },
   {
@@ -234,14 +229,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 41s, sys: 66.6 ms, total: 1min 41s\n",
-      "Wall time: 25.4 s\n"
+      "CPU times: user 2.35 s, sys: 59.3 ms, total: 2.41 s\n",
+      "Wall time: 604 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Falkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f22222fc650>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-07, seed=None, weight_fn=None)"
+       "Falkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f9f7d142b20>, kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64)), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='no', keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=True, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty=1e-07)"
       ]
      },
      "execution_count": 10,
@@ -251,7 +247,7 @@
    ],
    "source": [
     "%%time\n",
-    "flk.fit(X_train, Y_train)"
+    "flk.fit(X_train, Y_train);"
    ]
   },
   {
@@ -263,27 +259,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 0 - penalty 1.000000e-03 - sub-iterations 3\n",
-      "Iteration   0 - Elapsed 4.02s - training loss 0.6910 - training error 0.2550 \n",
-      "Iteration 1 - penalty 1.000000e-05 - sub-iterations 3\n",
-      "Iteration   1 - Elapsed 9.15s - training loss 0.5639 - training error 0.1623 \n",
-      "Iteration 2 - penalty 1.000000e-07 - sub-iterations 3\n",
-      "Iteration   2 - Elapsed 14.68s - training loss 0.4032 - training error 0.1627 \n",
+      "Iteration 0 - penalty 1.000000e-03 - sub-iterations 4\n",
+      "Iteration   0 - Elapsed 0.27s - training loss 0.4697 - training error 0.1544 \n",
+      "Iteration 1 - penalty 1.000000e-05 - sub-iterations 4\n",
+      "Iteration   1 - Elapsed 0.51s - training loss 0.3774 - training error 0.1551 \n",
+      "Iteration 2 - penalty 1.000000e-07 - sub-iterations 4\n",
+      "Iteration   2 - Elapsed 0.74s - training loss 0.3575 - training error 0.1534 \n",
       "Iteration 3 - penalty 1.000000e-07 - sub-iterations 8\n",
-      "Iteration   3 - Elapsed 27.14s - training loss 0.3696 - training error 0.1607 \n",
+      "Iteration   3 - Elapsed 1.12s - training loss 0.3554 - training error 0.1530 \n",
       "Iteration 4 - penalty 1.000000e-07 - sub-iterations 8\n",
-      "Iteration   4 - Elapsed 39.60s - training loss 0.3655 - training error 0.1605 \n",
-      "Iteration 5 - penalty 1.000000e-07 - sub-iterations 8\n",
-      "Iteration   5 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
-      "Iteration   6 - Elapsed 52.04s - training loss 0.3637 - training error 0.1605 \n",
-      "CPU times: user 3min 46s, sys: 48 ms, total: 3min 46s\n",
-      "Wall time: 56.7 s\n"
+      "Iteration   4 - Elapsed 1.50s - training loss 0.3554 - training error 0.1530 \n",
+      "CPU times: user 6.39 s, sys: 30.9 ms, total: 6.42 s\n",
+      "Wall time: 1.6 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "LogisticFalkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f2222305590>, error_every=1, error_fn=<function binary_loss at 0x7f22a05ec950>, iter_list=[3, 3, 3, 8, 8, 8], kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07, 1e-07], seed=None)"
+       "LogisticFalkon(M=1000, center_selection=<falkon.center_selection.UniformSelector object at 0x7f9f7d150250>, error_fn=<function binary_loss at 0x7f9f7d13eaf0>, iter_list=[4, 4, 4, 8, 8], kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64)), loss=LogisticLoss(kernel=GaussianKernel(sigma=Parameter containing:\n",
+       "tensor([1.], dtype=torch.float64))), options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='no', keops_memory_slack=0.7, chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, cg_differential_convergence=False, debug=False, use_cpu=True, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, store_kernel_d_threshold=1200, num_fmm_streams=2), penalty_list=[0.001, 1e-05, 1e-07, 1e-07, 1e-07])"
       ]
      },
      "execution_count": 11,
@@ -293,7 +288,7 @@
    ],
    "source": [
     "%%time\n",
-    "logflk.fit(X_train, Y_train)"
+    "logflk.fit(X_train, Y_train);"
    ]
   },
   {
@@ -317,8 +312,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Falkon model          -- Error: 15.81%\n",
-      "Logistic Falkon model -- Error: 15.63%\n"
+      "Falkon model          -- Error: 17.05%\n",
+      "Logistic Falkon model -- Error: 16.90%\n"
      ]
     }
    ],
@@ -342,7 +337,7 @@
     "In the plot we have the outer and inner circles which are correct predictions, and the circles in the middle which are the mispredicted points.\n",
     "Since we added lots of noise to the dataset, perfect predictions are not possible (there is no clear boundary between the two classes).\n",
     "\n",
-    "Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where a slightly lower classification error is desired."
+    "Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where the best possible classification error is desired."
    ]
   },
   {
@@ -354,17 +349,17 @@
     "def plot_predictions(preds, ax):\n",
     "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 0], \n",
     "               X_test[((Y_test == -1) & (preds.sign() == Y_test)).reshape(-1), 1], \n",
-    "               alpha=0.1, marker='.', color='b')\n",
+    "               alpha=0.3, marker='.', color='b', label=\"correct cls 0\")\n",
     "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),0], \n",
     "               X_test[((Y_test == 1) & (preds.sign() == Y_test)).reshape(-1),1], \n",
-    "               alpha=0.1, marker='.', color='r')\n",
+    "               alpha=0.3, marker='.', color='r', label=\"correct cls 1\")\n",
     "\n",
     "    ax.scatter(X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 0], \n",
     "               X_test[((Y_test == -1) & (preds.sign() != Y_test)).reshape(-1), 1], \n",
-    "               alpha=0.2, marker='x', color='b')\n",
+    "               alpha=0.5, marker='x', color='b', label=\"wrong cls 0\")\n",
     "    ax.scatter(X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),0], \n",
     "               X_test[((Y_test == 1) & (preds.sign() != Y_test)).reshape(-1),1], \n",
-    "               alpha=0.2, marker='x', color='r')"
+    "               alpha=0.5, marker='x', color='r', label=\"wrong cls 1\")"
    ]
   },
   {
@@ -374,7 +369,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x360 with 2 Axes>"
       ]
@@ -387,21 +382,17 @@
     "fig, ax = plt.subplots(ncols=2, figsize=(13, 5))\n",
     "plot_predictions(flk_pred, ax[0])\n",
     "ax[0].set_title(\"Falkon\")\n",
+    "ax[0].legend(loc='best')\n",
+    "\n",
     "plot_predictions(logflk_pred, ax[1])\n",
-    "_ = ax[1].set_title(\"Logistic Falkon\")"
+    "ax[1].set_title(\"Logistic Falkon\")\n",
+    "ax[1].legend(loc='best');\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -415,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.9"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/performance_tuning.html b/examples/performance_tuning.html
deleted file mode 100644
index 5dc11457..00000000
--- a/examples/performance_tuning.html
+++ /dev/null
@@ -1,380 +0,0 @@
-
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-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
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-  
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-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
-</ul>
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-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
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-<section id="Training-on-the-GPU">
-<h1>Training on the GPU<a class="headerlink" href="#Training-on-the-GPU" title="Permalink to this headline">¶</a></h1>
-<p>This notebook will be available soon</p>
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diff --git a/examples/performance_tuning.ipynb b/examples/performance_tuning.ipynb
deleted file mode 100644
index 0be2e993..00000000
--- a/examples/performance_tuning.ipynb
+++ /dev/null
@@ -1,49 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Training on the GPU\n",
-    "\n",
-    "This notebook will be available soon"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/simple_regression.ipynb b/examples/simple_regression.ipynb
deleted file mode 100644
index 68b25646..00000000
--- a/examples/simple_regression.ipynb
+++ /dev/null
@@ -1,403 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Falkon Regression Tutorial\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "This notebook introduces the main interface of the Falkon library, \n",
-    "using a toy regression problem.\n",
-    "\n",
-    "We will be using the Boston housing dataset which is included in `scikit-learn` to train a Falkon model.\n",
-    "Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyKeOps]: cuInit failed with error code 100: no CUDA-capable device is detected\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn import datasets\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "import falkon"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the data\n",
-    "\n",
-    "The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions.\n",
-    "The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.\n",
-    "\n",
-    "After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test \n",
-    "set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the [model_selection module](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "X, Y = datasets.load_boston(return_X_y=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "num_train = int(X.shape[0] * 0.8)\n",
-    "num_test = X.shape[0] - num_train\n",
-    "shuffle_idx = np.arange(X.shape[0])\n",
-    "np.random.shuffle(shuffle_idx)\n",
-    "train_idx = shuffle_idx[:num_train]\n",
-    "test_idx = shuffle_idx[num_train:]\n",
-    "\n",
-    "Xtrain, Ytrain = X[train_idx], Y[train_idx]\n",
-    "Xtest, Ytest = X[test_idx], Y[test_idx]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Pre-process the data\n",
-    "\n",
-    "We must convert the numpy arrays to PyTorch tensors before using them in Falkon.\n",
-    "This is very easy and fast with the `torch.from_numpy` function.\n",
-    "\n",
-    "Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data:\n",
-    "convert it to have zero-mean and unit standard deviation.\n",
-    "We use the statistics of the training data to avoid leakage between the two sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# convert numpy -> pytorch\n",
-    "Xtrain = torch.from_numpy(Xtrain)\n",
-    "Xtest = torch.from_numpy(Xtest)\n",
-    "Ytrain = torch.from_numpy(Ytrain)\n",
-    "Ytest = torch.from_numpy(Ytest)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# z-score normalization\n",
-    "train_mean = Xtrain.mean(0, keepdim=True)\n",
-    "train_std = Xtrain.std(0, keepdim=True)\n",
-    "Xtrain -= train_mean\n",
-    "Xtrain /= train_std\n",
-    "Xtest -= train_mean\n",
-    "Xtest /= train_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Create the Falkon model\n",
-    "\n",
-    "The Falkon object is the main API of this library. \n",
-    "It is similar in spirit to the fit-transform API of scikit-learn, while supporting some\n",
-    "additional features such as monitoring of validation error.\n",
-    "\n",
-    "While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much \n",
-    "larger datasets.\n",
-    "Here we only showcase some of the more basic options.\n",
-    "\n",
-    "Mandatory parameters are:\n",
-    " - the kernel function (here we use a linear kernel)\n",
-    " - the amount of regularization, which we set to some small positive value\n",
-    " - the number of inducing points M. \n",
-    "   Since the dataset is already very small, we use set `M=N` (every data-point is an inducing point). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/giacomo/Dropbox/unige/falkon/falkon/falkon/utils/switches.py:25: UserWarning: Failed to initialize CUDA library; falling back to CPU. Set 'use_cpu' to True to avoid this warning.\n",
-      "  warnings.warn(get_error_str(\"CUDA\", None))\n"
-     ]
-    }
-   ],
-   "source": [
-    "options = falkon.FalkonOptions()\n",
-    "kernel = falkon.kernels.GaussianKernel(sigma=5)\n",
-    "flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=Xtrain.shape[0], options=options)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Training the model\n",
-    "\n",
-    "The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are\n",
-    "two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed.\n",
-    "To gain more insight in the various steps of the algorithm you can pass `debug=True` when creating the Falkon object. \n",
-    "\n",
-    "Model training will occur on the GPU, if it is available, and CUDA is properly installed, \n",
-    "or on the CPU as a fallback. \n",
-    "\n",
-    "If KeOps has been installed alongside Falkon, the first run may take\n",
-    "a bit longer due to the compilation step of KeOps (to avoid using KeOps you can pass the argument `keops_active=False` to the `FalkonOptions` object)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stopping conjugate gradient descent at iteration 1. Solution has converged.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Falkon(M=404, center_selection=<falkon.center_selection.UniformSelector object at 0x7f31c8279190>, error_every=1, error_fn=None, kernel=GaussianKernel(sigma=tensor([5.], dtype=torch.float64)), maxiter=20, options=FalkonOptions(keops_acc_dtype='auto', keops_sum_scheme='auto', keops_active='auto', chol_force_in_core=False, chol_force_ooc=False, chol_par_blk_multiplier=2, lauum_par_blk_multiplier=8, pc_epsilon_32=1e-05, pc_epsilon_64=1e-13, cpu_preconditioner=False, cg_epsilon_32=1e-07, cg_epsilon_64=1e-15, cg_tolerance=1e-07, cg_full_gradient_every=10, debug=False, use_cpu=False, max_gpu_mem=inf, max_cpu_mem=inf, compute_arch_speed=False, no_single_kernel=True, min_cuda_pc_size_32=10000, min_cuda_pc_size_64=30000, min_cuda_iter_size_32=300000000, min_cuda_iter_size_64=900000000, never_store_kernel=False, num_fmm_streams=2), penalty=1e-05, seed=None, weight_fn=None)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flk.fit(Xtrain, Ytrain)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Evaluating model performance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the `predict` method to obtain predictions on new data.\n",
-    "\n",
-    "Here we print the error on both train and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training RMSE: 1.794\n",
-      "Test RMSE: 3.534\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_pred = flk.predict(Xtrain).reshape(-1, )\n",
-    "test_pred = flk.predict(Xtest).reshape(-1, )\n",
-    "\n",
-    "def rmse(true, pred):\n",
-    "    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))\n",
-    "\n",
-    "print(\"Training RMSE: %.3f\" % (rmse(train_pred, Ytrain)))\n",
-    "print(\"Test RMSE: %.3f\" % (rmse(test_pred, Ytest)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally we plot the model predictions to check that the distribution of our predictions is close to that of the labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f3148f13a50>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+  <title>Index &mdash; falkon 0.7.2 documentation</title>
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-          
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-
-          
-            
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               <div class="version">
-                0.6.3
+                0.7.2
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-          
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@@ -75,18 +37,7 @@
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 <li class="toctree-l1"><a class="reference internal" href="install.html">Install</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="install.html#supported-platforms">Supported Platforms</a></li>
@@ -98,6 +49,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
@@ -106,13 +58,13 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -127,19 +79,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
@@ -151,9 +135,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -199,20 +186,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
@@ -226,73 +205,46 @@
 <li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>
 
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+             
 
 <h1 id="index">Index</h1>
 
@@ -304,6 +256,7 @@ <h1 id="index">Index</h1>
  | <a href="#E"><strong>E</strong></a>
  | <a href="#F"><strong>F</strong></a>
  | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
  | <a href="#I"><strong>I</strong></a>
  | <a href="#K"><strong>K</strong></a>
  | <a href="#L"><strong>L</strong></a>
@@ -334,6 +287,8 @@ <h2 id="_">_</h2>
         <li><a href="api_reference/kernels.html#falkon.kernels.LaplacianKernel.__call__">(falkon.kernels.LaplacianKernel method)</a>
 </li>
         <li><a href="api_reference/kernels.html#falkon.kernels.LinearKernel.__call__">(falkon.kernels.LinearKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.MaternKernel.__call__">(falkon.kernels.MaternKernel method)</a>
 </li>
         <li><a href="api_reference/kernels.html#falkon.kernels.PolynomialKernel.__call__">(falkon.kernels.PolynomialKernel method)</a>
 </li>
@@ -342,21 +297,11 @@ <h2 id="_">_</h2>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._apply">_apply() (falkon.kernels.kernel.Kernel method)</a>
-</li>
-      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._apply_sparse">_apply_sparse() (falkon.kernels.kernel.Kernel method)</a>
-</li>
       <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._decide_dmmv_impl">_decide_dmmv_impl() (falkon.kernels.kernel.Kernel method)</a>
 </li>
       <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._decide_mm_impl">_decide_mm_impl() (falkon.kernels.kernel.Kernel method)</a>
 </li>
       <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._decide_mmv_impl">_decide_mmv_impl() (falkon.kernels.kernel.Kernel method)</a>
-</li>
-      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._finalize">_finalize() (falkon.kernels.kernel.Kernel method)</a>
-</li>
-      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._prepare">_prepare() (falkon.kernels.kernel.Kernel method)</a>
-</li>
-      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel._prepare_sparse">_prepare_sparse() (falkon.kernels.kernel.Kernel method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -385,9 +330,17 @@ <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_reference/center_selector.html#falkon.center_selection.CenterSelector">CenterSelector (class in falkon.center_selection)</a>
+</li>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.CompReg">CompReg (class in falkon.hopt.objectives)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel.compute">compute() (falkon.kernels.kernel.Kernel method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel.compute_diff">compute_diff() (falkon.kernels.diff_kernel.DiffKernel method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.kernel.Kernel.compute_sparse">compute_sparse() (falkon.kernels.kernel.Kernel method)</a>
+</li>
       <li><a href="api_reference/optimization.html#falkon.optim.ConjugateGradient">ConjugateGradient (class in falkon.optim)</a>
 </li>
   </ul></td>
@@ -404,6 +357,8 @@ <h2 id="D">D</h2>
         <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss.ddf">(falkon.gsc_losses.WeightedCrossEntropyLoss method)</a>
 </li>
       </ul></li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel.detach">detach() (falkon.kernels.diff_kernel.DiffKernel method)</a>
+</li>
       <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss.df">df() (falkon.gsc_losses.LogisticLoss method)</a>
 
       <ul>
@@ -412,8 +367,12 @@ <h2 id="D">D</h2>
         <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss.df">(falkon.gsc_losses.WeightedCrossEntropyLoss method)</a>
 </li>
       </ul></li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel.diff_params">diff_params (falkon.kernels.diff_kernel.DiffKernel property)</a>
+</li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel">DiffKernel (class in falkon.kernels.diff_kernel)</a>
+</li>
       <li><a href="api_reference/kernels.html#falkon.kernels.GaussianKernel.dmmv">dmmv() (falkon.kernels.GaussianKernel method)</a>
 
       <ul>
@@ -422,6 +381,8 @@ <h2 id="D">D</h2>
         <li><a href="api_reference/kernels.html#falkon.kernels.LaplacianKernel.dmmv">(falkon.kernels.LaplacianKernel method)</a>
 </li>
         <li><a href="api_reference/kernels.html#falkon.kernels.LinearKernel.dmmv">(falkon.kernels.LinearKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.MaternKernel.dmmv">(falkon.kernels.MaternKernel method)</a>
 </li>
         <li><a href="api_reference/kernels.html#falkon.kernels.PolynomialKernel.dmmv">(falkon.kernels.PolynomialKernel method)</a>
 </li>
@@ -456,6 +417,13 @@ <h2 id="F">F</h2>
 
       <ul>
         <li><a href="api_reference/center_selector.html#module-falkon.center_selection">module</a>
+</li>
+      </ul></li>
+      <li>
+    falkon.hopt
+
+      <ul>
+        <li><a href="api_reference/hopt.html#module-falkon.hopt">module</a>
 </li>
       </ul></li>
       <li>
@@ -493,6 +461,8 @@ <h2 id="F">F</h2>
         <li><a href="api_reference/optimization.html#module-falkon.optim">module</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li>
     falkon.options
 
@@ -514,21 +484,13 @@ <h2 id="F">F</h2>
         <li><a href="api_reference/sparse.html#module-falkon.sparse">module</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_reference/optimization.html#falkon.optim.FalkonConjugateGradient">FalkonConjugateGradient (class in falkon.optim)</a>
 </li>
       <li><a href="api_reference/options.html#falkon.options.FalkonOptions">FalkonOptions (class in falkon.options)</a>
 </li>
       <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner">FalkonPreconditioner (class in falkon.preconditioner)</a>
 </li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu">fdmmv_cpu() (in module falkon.mmv_ops.fmmv_cpu)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fdmmv_cpu_sparse">fdmmv_cpu_sparse() (in module falkon.mmv_ops.fmmv_cpu)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda">fdmmv_cuda() (in module falkon.mmv_ops.fmmv_cuda)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fdmmv_cuda_sparse">fdmmv_cuda_sparse() (in module falkon.mmv_ops.fmmv_cuda)</a>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv">fdmmv() (in module falkon.mmv_ops.fmmv)</a>
 </li>
       <li><a href="api_reference/models.html#falkon.models.Falkon.fit">fit() (falkon.models.Falkon method)</a>
 
@@ -540,21 +502,9 @@ <h2 id="F">F</h2>
       </ul></li>
       <li><a href="api_reference/center_selector.html#falkon.center_selection.FixedSelector">FixedSelector (class in falkon.center_selection)</a>
 </li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cpu.fmm_cpu">fmm_cpu() (in module falkon.mmv_ops.fmm_cpu)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cpu.fmm_cpu_sparse">fmm_cpu_sparse() (in module falkon.mmv_ops.fmm_cpu)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cuda.fmm_cuda">fmm_cuda() (in module falkon.mmv_ops.fmm_cuda)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm_cuda.fmm_cuda_sparse">fmm_cuda_sparse() (in module falkon.mmv_ops.fmm_cuda)</a>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm">fmm() (in module falkon.mmv_ops.fmm)</a>
 </li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fmmv_cpu">fmmv_cpu() (in module falkon.mmv_ops.fmmv_cpu)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cpu.fmmv_cpu_sparse">fmmv_cpu_sparse() (in module falkon.mmv_ops.fmmv_cpu)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fmmv_cuda">fmmv_cuda() (in module falkon.mmv_ops.fmmv_cuda)</a>
-</li>
-      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_cuda.fmmv_cuda_sparse">fmmv_cuda_sparse() (in module falkon.mmv_ops.fmmv_cuda)</a>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv">fmmv() (in module falkon.mmv_ops.fmmv)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -563,6 +513,8 @@ <h2 id="G">G</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_reference/kernels.html#falkon.kernels.GaussianKernel">GaussianKernel (class in falkon.kernels)</a>
+</li>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.GCV">GCV (class in falkon.hopt.objectives)</a>
 </li>
       <li><a href="api_reference/models.html#falkon.models.Falkon.get_params">get_params() (falkon.models.Falkon method)</a>
 
@@ -581,6 +533,18 @@ <h2 id="G">G</h2>
   </ul></td>
 </tr></table>
 
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.HoldOut">HoldOut (class in falkon.hopt.objectives)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective">HyperoptObjective (class in falkon.hopt.objectives.objectives)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 <h2 id="I">I</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -628,7 +592,9 @@ <h2 id="I">I</h2>
 <h2 id="K">K</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_dmmv_helper">keops_dmmv_helper() (falkon.kernels.keops_helpers.KeopsKernelMixin method)</a>
+      <li><a href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv">keops_mmv() (falkon.kernels.keops_helpers.KeopsKernelMixin method)</a>
+</li>
+      <li><a href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl">keops_mmv_impl() (falkon.kernels.keops_helpers.KeopsKernelMixin method)</a>
 </li>
       <li><a href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin">KeopsKernelMixin (class in falkon.kernels.keops_helpers)</a>
 </li>
@@ -657,6 +623,8 @@ <h2 id="L">L</h2>
       <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss">LogisticLoss (class in falkon.gsc_losses)</a>
 </li>
       <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner">LogisticPreconditioner (class in falkon.preconditioner)</a>
+</li>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.LOOCV">LOOCV (class in falkon.hopt.objectives)</a>
 </li>
       <li><a href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss">Loss (class in falkon.gsc_losses)</a>
 </li>
@@ -666,6 +634,8 @@ <h2 id="L">L</h2>
 <h2 id="M">M</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/kernels.html#falkon.kernels.MaternKernel">MaternKernel (class in falkon.kernels)</a>
+</li>
       <li><a href="api_reference/kernels.html#falkon.kernels.GaussianKernel.mmv">mmv() (falkon.kernels.GaussianKernel method)</a>
 
       <ul>
@@ -674,6 +644,8 @@ <h2 id="M">M</h2>
         <li><a href="api_reference/kernels.html#falkon.kernels.LaplacianKernel.mmv">(falkon.kernels.LaplacianKernel method)</a>
 </li>
         <li><a href="api_reference/kernels.html#falkon.kernels.LinearKernel.mmv">(falkon.kernels.LinearKernel method)</a>
+</li>
+        <li><a href="api_reference/kernels.html#falkon.kernels.MaternKernel.mmv">(falkon.kernels.MaternKernel method)</a>
 </li>
         <li><a href="api_reference/kernels.html#falkon.kernels.PolynomialKernel.mmv">(falkon.kernels.PolynomialKernel method)</a>
 </li>
@@ -687,6 +659,8 @@ <h2 id="M">M</h2>
         <li><a href="api_reference/gsc_losses.html#module-falkon">falkon</a>
 </li>
         <li><a href="api_reference/center_selector.html#module-falkon.center_selection">falkon.center_selection</a>
+</li>
+        <li><a href="api_reference/hopt.html#module-falkon.hopt">falkon.hopt</a>
 </li>
         <li><a href="api_reference/kernels.html#module-falkon.kernels">falkon.kernels</a>
 </li>
@@ -712,6 +686,10 @@ <h2 id="N">N</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows">narrow_rows() (falkon.sparse.sparse_tensor.SparseTensor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.NystromCompReg">NystromCompReg (class in falkon.hopt.objectives)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -779,11 +757,19 @@ <h2 id="S">S</h2>
         <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.set_params">(falkon.models.LogisticFalkon method)</a>
 </li>
       </ul></li>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.SGPR">SGPR (class in falkon.hopt.objectives)</a>
+</li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_reference/kernels.html#falkon.kernels.SigmoidKernel">SigmoidKernel (class in falkon.kernels)</a>
+</li>
+      <li><a href="api_reference/optimization.html#falkon.optim.ConjugateGradient.solve">solve() (falkon.optim.ConjugateGradient method)</a>
 </li>
       <li><a href="api_reference/sparse.html#falkon.sparse.sparse_matmul">sparse_matmul() (in module falkon.sparse)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread">sparse_mm_run_thread() (in module falkon.mmv_ops.fmm)</a>
+</li>
+      <li><a href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread">sparse_mmv_run_thread() (in module falkon.mmv_ops.fmmv)</a>
 </li>
       <li><a href="api_reference/sparse.html#falkon.sparse.sparse_norm">sparse_norm() (in module falkon.sparse)</a>
 </li>
@@ -792,6 +778,8 @@ <h2 id="S">S</h2>
       <li><a href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor">SparseTensor (class in falkon.sparse.sparse_tensor)</a>
 </li>
       <li><a href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType">SparseType (class in falkon.sparse.sparse_tensor)</a>
+</li>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.StochasticNystromCompReg">StochasticNystromCompReg (class in falkon.hopt.objectives)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -815,46 +803,30 @@ <h2 id="W">W</h2>
 
 
            </div>
-           
           </div>
           <footer>
 
   <hr/>
 
   <div role="contentinfo">
-    <p>
-        &#169; Copyright 2020, Giacomo Meanti.
-
-    </p>
+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
   </div>
-    
-    
-    
-    Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
     <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    
-    provided by <a href="https://readthedocs.org">Read the Docs</a>. 
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
 
 </footer>
         </div>
       </div>
-
     </section>
-
   </div>
-  
-
-  <script type="text/javascript">
+  <script>
       jQuery(function () {
           SphinxRtdTheme.Navigation.enable(true);
       });
-  </script>
-
-  
-  
-    
-   
+  </script> 
 
 </body>
 </html>
\ No newline at end of file
diff --git a/get_started.html b/get_started.html
index 9e40a84a..d6aff22e 100644
--- a/get_started.html
+++ b/get_started.html
@@ -1,76 +1,38 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" />
-  <meta name="generator" content="Docutils 0.17: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  
-  <title>Getting Started &mdash; falkon 0.6.3 documentation</title>
-  
-
-  
-  <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-
-  
-  
-
-  
-  
-
-  
-
-  
+  <title>Getting Started &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-    
-      <script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
         <script src="_static/underscore.js"></script>
         <script src="_static/doctools.js"></script>
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-    
-    <script type="text/javascript" src="_static/js/theme.js"></script>
-
-    
+    <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="Examples" href="examples/examples.html" />
     <link rel="prev" title="Install" href="install.html" /> 
 </head>
 
-<body class="wy-body-for-nav">
-
-   
+<body class="wy-body-for-nav"> 
   <div class="wy-grid-for-nav">
-    
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-          
-
-          
             <a href="index.html" class="icon icon-home"> falkon
-          
-
-          
           </a>
-
-          
-            
-            
               <div class="version">
-                0.6.3
+                0.7.2
               </div>
-            
-          
-
-          
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
@@ -78,18 +40,7 @@
     <input type="hidden" name="area" value="default" />
   </form>
 </div>
-
-          
-        </div>
-
-        
-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
-          
-            
-            
-              
-            
-            
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="install.html">Install</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="install.html#supported-platforms">Supported Platforms</a></li>
@@ -101,6 +52,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Getting Started</a><ul>
@@ -109,13 +61,13 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -130,19 +82,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
@@ -154,9 +138,12 @@
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 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
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 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +189,12 @@
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-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
@@ -229,75 +208,47 @@
 <li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
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+<li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
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@@ -323,7 +274,7 @@
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 <section id="getting-started">
-<span id="get-started"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h1>
+<span id="get-started"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline"></a></h1>
 <p>Once Falkon is installed, getting started is easy. The basic setup to use the <cite>Falkon</cite> estimator only requires
 few lines of code:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
@@ -345,13 +296,14 @@
 </pre></div>
 </div>
 <section id="passing-options">
-<h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalink to this headline">¶</a></h2>
-<p>A number of different options exist for both the <cite>Falkon</cite> and <cite>LogisticFalkon</cite> estimators (see <a class="reference internal" href="api_reference/options.html#api-options"><span class="std std-ref">falkon.Options</span></a>).
-All options can be passed to the estimator through the <cite>FalkonOptions</cite> class, like so:</p>
+<h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalink to this headline"></a></h2>
+<p>A number of different options exist for both the <a class="reference internal" href="api_reference/models.html#falkon-model"><span class="std std-ref">Falkon</span></a> and <a class="reference internal" href="api_reference/models.html#log-falkon-model"><span class="std std-ref">LogisticFalkon</span></a>
+estimators (see <a class="reference internal" href="api_reference/options.html#api-options"><span class="std std-ref">falkon.FalkonOptions</span></a>).
+All options can be passed to the estimator through the <a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a> class, like so:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span><span class="p">,</span> <span class="n">Falkon</span><span class="p">,</span> <span class="n">kernels</span>
 
 <span class="c1"># Options to: increase the amount of output information; avoid using the KeOps library</span>
-<span class="n">options</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">no_keops</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">options</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
 <span class="n">kernel</span> <span class="o">=</span> <span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
@@ -363,57 +315,40 @@ <h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalin
 </div>
 </section>
 <section id="more-examples">
-<h2>More Examples<a class="headerlink" href="#more-examples" title="Permalink to this headline">¶</a></h2>
+<h2>More Examples<a class="headerlink" href="#more-examples" title="Permalink to this headline"></a></h2>
 <p>For more detailed examples, have a look at the <a class="reference internal" href="examples/examples.html#examples"><span class="std std-ref">example notebooks</span></a>.</p>
 </section>
 </section>
 
 
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diff --git a/index.html b/index.html
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@@ -100,6 +51,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
@@ -108,13 +60,13 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -129,19 +81,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
@@ -153,9 +137,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -201,20 +188,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
@@ -228,75 +207,47 @@
 <li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>
 
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 <style>
 /* CSS overrides for sphinx_rtd_theme */
@@ -322,11 +273,11 @@
 }
 </style>
 <section id="falkon">
-<h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline">¶</a></h1>
+<h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline"></a></h1>
 <p>A Python library for large-scale kernel methods, with optional (multi-)GPU acceleration.</p>
 <p>The library currently includes two solvers:
-one for approximate kernel ridge regression <a class="footnote-reference brackets" href="#id6" id="id1">2</a> which is extremely fast, and one for kernel logistic
-regression <a class="footnote-reference brackets" href="#id7" id="id2">3</a> which trades off lower speed for better accuracy on binary classification problems.</p>
+one for approximate kernel ridge regression <a class="reference internal" href="#flk-2"><span class="std std-ref">[2]</span></a> which is extremely fast, and one for kernel logistic
+regression <a class="reference internal" href="#log-flk"><span class="std std-ref">[3]</span></a> which trades off lower speed for better accuracy on binary classification problems.</p>
 <p>The main features of Falkon are:</p>
 <blockquote>
 <div><ul class="simple">
@@ -337,26 +288,22 @@ <h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 <li><p><em>Scikit-learn integration</em> - Our estimators follow the scikit-learn API</p></li>
 </ul>
 </div></blockquote>
-<p>For more details about the algorithms used, you can read our paper <a class="footnote-reference brackets" href="#id5" id="id3">1</a>, or look at the source code
+<p>For more details about the algorithms used, you can read <a class="reference internal" href="#flk-1"><span class="std std-ref">our paper</span></a>, or look at the source code
 <a class="reference external" href="https://github.com/falkonml/falkon">github.com/FalkonML/falkon</a> and at the <a class="reference internal" href="api_reference/index.html#api-reference"><span class="std std-ref">documentation</span></a>.
 Also, make sure to follow <a class="reference internal" href="examples/examples.html#examples"><span class="std std-ref">the example notebooks</span></a> to find out about all of Falkon’s features.</p>
 <p>Falkon is built on top of <a class="reference external" href="https://pytorch.org/">PyTorch</a> which is used to support both CPU and GPU tensor
 calculations, and <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">KeOps</a> for fast kernel evaluations on the GPU.</p>
-<p>If you find this library useful for your research, please cite us <a class="footnote-reference brackets" href="#id5" id="id4">1</a>!</p>
+<p>If you find this library useful for your research, please cite our paper <a class="reference internal" href="#flk-1"><span class="std std-ref">[1]</span></a>!</p>
 <section id="contents">
-<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">¶</a></h2>
+<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this headline"></a></h2>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Install</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="install.html#supported-platforms">Supported Platforms</a></li>
-<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install.html#keops">KeOps</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a></li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
@@ -366,98 +313,34 @@ <h2>Contents<a class="headerlink" href="#contents" title="Permalink to this head
 </li>
 <li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a></li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a></li>
 </ul>
 </li>
 </ul>
 </div>
 </section>
 <section id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
-<dl class="footnote brackets">
-<dt class="label" id="id5"><span class="brackets">1</span><span class="fn-backref">(<a href="#id3">1</a>,<a href="#id4">2</a>)</span></dt>
-<dd><p>Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
+<h2>References<a class="headerlink" href="#references" title="Permalink to this headline"></a></h2>
+<p id="flk-1">Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
 “Kernel methods through the roof: handling billions of points efficiently,”
-arXiv:2006.10350, 2020.</p>
-</dd>
-<dt class="label" id="id6"><span class="brackets"><a class="fn-backref" href="#id1">2</a></span></dt>
-<dd><p>Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large
-scale kernel method,” Advances in Neural Information Processing Systems, 2017.</p>
-</dd>
-<dt class="label" id="id7"><span class="brackets"><a class="fn-backref" href="#id2">3</a></span></dt>
-<dd><p>Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, “Globally Convergent
-Newton Methods for Ill-conditioned Generalized Self-concordant Losses,”
-Advances in Neural Information Processing Systems, 2019</p>
-</dd>
-</dl>
+Advancs in Neural Information Processing Systems, 2020.</p>
+<p id="flk-2">Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco, “FALKON: An optimal large scale kernel method,”
+Advances in Neural Information Processing Systems, 2017.</p>
+<p id="log-flk">Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi, “Globally Convergent Newton Methods for Ill-conditioned
+Generalized Self-concordant Losses,” Advances in Neural Information Processing Systems, 2019.</p>
 </section>
 <section id="indices-and-tables">
-<h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h2>
+<h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h2>
 <ul class="simple">
 <li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
 <li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
@@ -468,49 +351,32 @@ <h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Pe
 
 
            </div>
-           
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-          <footer>
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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   <hr/>
 
   <div role="contentinfo">
-    <p>
-        &#169; Copyright 2020, Giacomo Meanti.
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+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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-  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+  <title>Install &mdash; falkon 0.7.2 documentation</title>
+      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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-            
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-                0.6.3
+                0.7.2
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 <li class="toctree-l1 current"><a class="current reference internal" href="#">Install</a><ul>
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@@ -101,6 +52,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#installing">Installing</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#testing-the-installation">Testing the installation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#development">Development</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="get_started.html">Getting Started</a><ul>
@@ -109,13 +61,13 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="examples/examples.html">Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="examples/simple_regression.html">Falkon Regression Tutorial</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Pre-process-the-data">Pre-process the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Create-the-Falkon-model">Create the Falkon model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Training-the-model">Training the model</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/simple_regression.html#Evaluating-model-performance">Evaluating model performance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_regression_tutorial.html">Falkon Regression Tutorial</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Pre-process-the-data">Pre-process the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Create-the-Falkon-model">Create the Falkon model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Training-the-model">Training the model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_regression_tutorial.html#Evaluating-model-performance">Evaluating model performance</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html">Introducing Logistic Falkon</a><ul>
@@ -130,19 +82,51 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="examples/logistic_falkon.html#Plot-predictions">Plot predictions</a></li>
-<li class="toctree-l2"><a class="reference internal" href="examples/hyperparameter_tuning.html">Hyperparameter Tuning with Falkon</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Introduction">Introduction</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Load-the-data">Load the data</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Data-Preprocessing">Data Preprocessing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="examples/hyperparameter_tuning.html#Evaluating-the-model">Evaluating the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_cv.html">Hyperparameter Tuning with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Introduction">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/falkon_cv.html#Evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Plot-grid-search-results">Plot grid-search results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Differentiable-Kernel">Differentiable Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-differentiable-kernel">Test the differentiable kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-KeOps-kernel">Test the KeOps kernel</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Supporting-Sparse-Data">Supporting Sparse Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Testing-sparse-support">Testing sparse support</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="examples/hyperparameter_tuning.html#Plot-grid-search-results">Plot grid-search results</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="examples/performance_tuning.html">Training on the GPU</a></li>
+<li class="toctree-l2"><a class="reference internal" href="examples/hyperopt.html">Automatic Hyperparameter Optimization</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Load-the-data">Load the data</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Split-into-training-and-test-sets">Split into training and test sets</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/hyperopt.html#Hyperparameter-Optimization">Hyperparameter Optimization</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/falkon_mnist.html">MNIST Classification with Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Run-Falkon">Run Falkon</a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
@@ -154,9 +138,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#dot-product-kernels">Dot-Product kernels</a><ul>
@@ -202,20 +189,12 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu">fmm_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cpu-sparse">fmm_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda">fmm_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm-cuda-sparse">fmm_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu">fmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cpu-sparse">fmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu">fdmmv_cpu</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cpu-sparse">fdmmv_cpu_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda">fmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv-cuda-sparse">fmmv_cuda_sparse</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda">fdmmv_cuda</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
@@ -229,75 +208,47 @@
 <li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
+</li>
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@@ -323,18 +274,18 @@
 }
 </style>
 <section id="install">
-<span id="id1"></span><h1>Install<a class="headerlink" href="#install" title="Permalink to this headline">¶</a></h1>
+<span id="id1"></span><h1>Install<a class="headerlink" href="#install" title="Permalink to this headline"></a></h1>
 <section id="supported-platforms">
-<h2>Supported Platforms<a class="headerlink" href="#supported-platforms" title="Permalink to this headline">¶</a></h2>
+<h2>Supported Platforms<a class="headerlink" href="#supported-platforms" title="Permalink to this headline"></a></h2>
 <p>Falkon is only tested on Linux.</p>
 <p>GPU support is achieved via CUDA, so using Falkon from Windows will be tricky.</p>
-<p>Using the library from the CPU only is much more portable since there is no CUDA requirement. CPU-only Falkon
-is thus likely to work on Windows and Mac OS easily.</p>
+<p>Using the library from the CPU only is much more portable since there is no CUDA requirement, and it will likely work
+on Windows and Mac OS more easily.</p>
 </section>
 <section id="prerequisites">
-<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h2>
 <section id="pytorch-and-cuda">
-<h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permalink to this headline">¶</a></h3>
+<h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permalink to this headline"></a></h3>
 <p>Falkon depends on PyTorch, and on the NVIDIA Toolkit (for NVIDIA GPU support) which is usually installed
 alongside PyTorch.
 PyTorch can be installed in several ways by following the instructions at <a class="reference external" href="https://pytorch.org/get-started/locally/">Install PyTorch</a>.
@@ -345,13 +296,13 @@ <h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permal
 </div>
 </section>
 <section id="intel-mkl">
-<h3>Intel MKL<a class="headerlink" href="#intel-mkl" title="Permalink to this headline">¶</a></h3>
+<h3>Intel MKL<a class="headerlink" href="#intel-mkl" title="Permalink to this headline"></a></h3>
 <p>If Falkon is not installed with GPU support, it will try to link to <a class="reference external" href="https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html">Intel MKL</a>
 to speed-up certain sparse operations. MKL shared libraries are usually distributed with numpy, so this should not be a problem.
 In case sparse matrix support is not needed, the MKL library will not be loaded.</p>
 </section>
 <section id="keops">
-<h3>KeOps<a class="headerlink" href="#keops" title="Permalink to this headline">¶</a></h3>
+<h3>KeOps<a class="headerlink" href="#keops" title="Permalink to this headline"></a></h3>
 <p>Falkon uses a patched version of <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">KeOps</a>, therefore until the
 patches are merged in the KeOps library, users will need to uninstall existing KeOps installations (if present), and
 install our patched version.</p>
@@ -361,10 +312,16 @@ <h3>KeOps<a class="headerlink" href="#keops" title="Permalink to this headline">
 $ pip install ./keops
 </pre></div>
 </div>
+<p>For use with <strong>Google Colab</strong> KeOps needs some additional packages. You can install them alongside KeOps by running</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!pip install pykeops[colab] &gt; install.log
+</pre></div>
+</div>
+<p>from within the Jupyter notebook (see the <a class="reference external" href="https://www.kernel-operations.io/keops/python/installation.html">KeOps docs</a>
+for more information).</p>
 </section>
 </section>
 <section id="installing">
-<h2>Installing<a class="headerlink" href="#installing" title="Permalink to this headline">¶</a></h2>
+<h2>Installing<a class="headerlink" href="#installing" title="Permalink to this headline"></a></h2>
 <p>After having installed KeOps, you are ready to install Falkon with <cite>pip</cite> (from the root of the git tree):</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install .
 </pre></div>
@@ -373,57 +330,46 @@ <h2>Installing<a class="headerlink" href="#installing" title="Permalink to this
 and PyTorch extensions which take some time to compile.</p>
 </section>
 <section id="testing-the-installation">
-<h2>Testing the installation<a class="headerlink" href="#testing-the-installation" title="Permalink to this headline">¶</a></h2>
-<p>To check that everything works correctly you can follow the <a class="reference internal" href="examples/simple_regression.html"><span class="doc">Kernel ridge regression</span></a> notebook.</p>
+<h2>Testing the installation<a class="headerlink" href="#testing-the-installation" title="Permalink to this headline"></a></h2>
+<p>To check that everything works correctly you can follow the <a class="reference internal" href="examples/falkon_regression_tutorial.html"><span class="doc">Kernel ridge regression</span></a> notebook.</p>
+</section>
+<section id="development">
+<h2>Development<a class="headerlink" href="#development" title="Permalink to this headline"></a></h2>
+<p>For development purposes the library should be installed in editable mode (i.e. <cite>pip install -e .</cite> from the
+falkon directory.</p>
+<p>To build the documentation go into the <cite>doc</cite> directory and run <cite>make html</cite>.</p>
 </section>
 </section>
 
 
            </div>
-           
           </div>
-          <footer>
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
-        <a href="get_started.html" class="btn btn-neutral float-right" title="Getting Started" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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-    <p>
-        &#169; Copyright 2020, Giacomo Meanti.
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+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
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-    
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-    provided by <a href="https://readthedocs.org">Read the Docs</a>. 
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
 
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+  <script>
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\ No newline at end of file
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+  <title>Python Module Index &mdash; falkon 0.7.2 documentation</title>
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@@ -109,13 +61,13 @@
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Setup-a-simple-problem-for-testing">Setup a simple problem for testing</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="examples/custom_kernels.html#Test-the-basic-kernel">Test the basic kernel</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Adding-KeOps-Support">Adding KeOps Support</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_cv.html#Search-for-the-optimal-parameters">Search for the optimal parameters</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="examples/custom_kernels.html">Implementing A Custom Kernel</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="examples/custom_kernels.html#Basic-Kernel-Implementation">Basic Kernel Implementation</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Download-the-MNIST-dataset-&amp;-load-it-in-memory">Download the MNIST dataset &amp; load it in memory</a></li>
+<li class="toctree-l3"><a class="reference internal" href="examples/falkon_mnist.html#Data-Preprocessing">Data Preprocessing</a></li>
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 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#matern-kernel">Matern kernel</a></li>
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@@ -202,20 +189,12 @@
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-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv-cuda-sparse">fdmmv_cuda_sparse</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
@@ -229,71 +208,46 @@
 <li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#generalized-cross-validation">Generalized Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#hold-out-cross-validation">Hold Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#leave-one-out-cross-validation">Leave One Out Cross Validation</a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#sgpr">SGPR</a></li>
+</ul>
+</li>
+</ul>
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 </li>
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+    <p>&#169; Copyright 2020, Giacomo Meanti, Alessandro Rudi.</p>
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Reference","falkon.kernels","falkon.mmv_ops","falkon.models","falkon.optim","falkon.options","falkon.ooc_ops","falkon.preconditioner","falkon.sparse","Implementing A Custom Kernel","Examples","Hyperparameter Tuning with Falkon","MNIST Classification with Falkon","Falkon Regression Tutorial","Automatic Hyperparameter Optimization","Introducing Logistic Falkon","Getting Started","Falkon","Install"],titleterms:{"function":5,A:12,One:2,ad:12,api:3,automat:17,basic:12,binari:1,both:18,center_select:0,centerselector:0,choleski:10,classif:15,complex:2,comput:2,conjug:7,conjugategradi:7,content:20,creat:16,cross:[1,2],cuda:21,custom:12,data:[12,14,15,16,17,18],dataset:15,defin:18,develop:21,differenti:12,diffkernel:4,dot:4,download:15,entropi:1,evalu:[14,16],exampl:[13,19],falkon:[0,1,2,4,5,6,7,8,9,10,11,14,15,16,18,20],falkonconjugategradi:7,falkonopt:8,falkonprecondition:10,fdmmv:5,fixedselector:0,fmm:5,fmmv:5,gaussian:4,gener:2,get:19,gpu_choleski:9,gpu_lauum:9,gradient:7,grid:14,gsc_loss:1,hold:2,hopt:2,hyperparamet:[14,17],implement:12,incore_fdmmv:5,incore_fmmv:5,incorefalkon:6,indic:20,instal:21,intel:21,introduc:18,introduct:[14,16,18],keop:[12,21],keopskernelmixin:4,kernel:[4,12],laplacian:4,leav:2,level:5,linear:4,load:[14,15,16,17,18],logist:[1,18],logisticfalkon:6,logisticprecondition:10,loss:1,low:5,matern:4,memori:15,method:7,mkl:21,mmv_op:5,mnist:15,model:[6,14,16,18],more:19,nystrom:2,object:2,ooc_op:9,oper:11,optim:[7,14,17],option:[8,19],out:2,paramet:14,pass:19,perform:16,platform:21,plot:[14,18],polynomi:4,pre:16,precondition:10,predict:18,preprocess:[14,15,17,18],prerequisit:21,problem:12,process:16,product:4,pytorch:21,radial:4,refer:[3,20],regress:16,regular:2,result:14,run:15,run_keops_mmv:5,search:14,set:[14,17,18],setup:12,sgpr:2,sigmoid:4,simpl:12,spars:[11,12],sparsetensor:11,split:[14,17,18],start:19,stochast:2,support:[12,21],tabl:20,test:[12,14,17,18,21],train:[14,16,17,18],tune:14,tutori:16,uniformselector:0,valid:2,weight:1}})
\ No newline at end of file

From 43dab6dc75de1ba0d168869e22a7589c2ddc8c85 Mon Sep 17 00:00:00 2001
From: Giodiro <Giodiro@users.noreply.github.com>
Date: Thu, 16 Jun 2022 22:07:32 +0000
Subject: [PATCH 3/7] deploy: 0802115fe53ceb3ecd123247ade2dd81ffd23531

---
 .buildinfo                                    |   2 +-
 _sources/install.rst.txt                      |  26 +-
 .../_sphinx_javascript_frameworks_compat.js   | 134 +++
 _static/basic.css                             |  38 +-
 _static/doctools.js                           | 450 +++++-----
 _static/documentation_options.js              |   8 +-
 _static/{jquery-3.5.1.js => jquery-3.6.0.js}  | 227 ++---
 _static/jquery.js                             |   4 +-
 _static/language_data.js                      | 100 +--
 _static/searchtools.js                        | 788 +++++++++---------
 api_reference/center_selector.html            |  32 +-
 api_reference/gsc_losses.html                 |  42 +-
 api_reference/hopt.html                       |  40 +-
 api_reference/index.html                      |   8 +-
 api_reference/kernels.html                    | 114 +--
 api_reference/mmv_ops.html                    |  40 +-
 api_reference/models.html                     |  46 +-
 api_reference/optimization.html               |  22 +-
 api_reference/options.html                    |  10 +-
 api_reference/outofcore.html                  |  38 +-
 api_reference/preconditioner.html             |  36 +-
 api_reference/sparse.html                     |  22 +-
 examples/custom_kernels.html                  |  34 +-
 examples/examples.html                        |   8 +-
 examples/falkon_cv.html                       |  30 +-
 examples/falkon_mnist.html                    |  22 +-
 examples/falkon_regression_tutorial.html      |  28 +-
 examples/hyperopt.html                        |  24 +-
 examples/hyperopt.ipynb                       |   4 +-
 examples/logistic_falkon.html                 |  34 +-
 genindex.html                                 |   6 +-
 get_started.html                              |  12 +-
 index.html                                    |  14 +-
 install.html                                  |  44 +-
 objects.inv                                   | Bin 5566 -> 5555 bytes
 py-modindex.html                              |   6 +-
 search.html                                   |   6 +-
 searchindex.js                                |   2 +-
 38 files changed, 1264 insertions(+), 1237 deletions(-)
 create mode 100644 _static/_sphinx_javascript_frameworks_compat.js
 rename _static/{jquery-3.5.1.js => jquery-3.6.0.js} (98%)

diff --git a/.buildinfo b/.buildinfo
index 4d019a0b..01a24165 100644
--- a/.buildinfo
+++ b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 36fa1b01701632ed1db9d55e2124877f
+config: 01cb727235985ea8210a828cb4b7007b
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt
index 8621b727..3673ae56 100644
--- a/_sources/install.rst.txt
+++ b/_sources/install.rst.txt
@@ -34,37 +34,15 @@ If Falkon is not installed with GPU support, it will try to link to `Intel MKL <
 to speed-up certain sparse operations. MKL shared libraries are usually distributed with numpy, so this should not be a problem.
 In case sparse matrix support is not needed, the MKL library will not be loaded.
 
-KeOps
-~~~~~
-Falkon uses a patched version of `KeOps <https://www.kernel-operations.io/keops/index.html>`__, therefore until the
-patches are merged in the KeOps library, users will need to uninstall existing KeOps installations (if present), and
-install our patched version.
-
-The patched version is available as a submodule of the Falkon repository. To install it, you can do the following:
-
-.. code-block:: bash
-
-    $ git clone --recurse-submodules https://github.com/FalkonML/falkon.git
-    $ cd falkon/
-    $ pip install ./keops
-
-For use with **Google Colab** KeOps needs some additional packages. You can install them alongside KeOps by running
-
-.. code-block::
-
-    !pip install pykeops[colab] > install.log
-
-from within the Jupyter notebook (see the `KeOps docs <https://www.kernel-operations.io/keops/python/installation.html>`__
-for more information).
 
 Installing
 ----------
 
-After having installed KeOps, you are ready to install Falkon with `pip` (from the root of the git tree):
+You can install Falkon
 
 .. code-block:: bash
 
-   $ pip install .
+   $ pip install git+https://github.com/falkonml/falkon.git
 
 This will take a while since there are both Cython extensions which need to be compiled (if Cython is installed),
 and PyTorch extensions which take some time to compile.
diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 00000000..8549469d
--- /dev/null
+++ b/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,134 @@
+/*
+ * _sphinx_javascript_frameworks_compat.js
+ * ~~~~~~~~~~
+ *
+ * Compatability shim for jQuery and underscores.js.
+ *
+ * WILL BE REMOVED IN Sphinx 6.0
+ * xref RemovedInSphinx60Warning
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/_static/basic.css b/_static/basic.css
index bf18350b..7d5974c3 100644
--- a/_static/basic.css
+++ b/_static/basic.css
@@ -222,7 +222,7 @@ table.modindextable td {
 /* -- general body styles --------------------------------------------------- */
 
 div.body {
-    min-width: 450px;
+    min-width: 360px;
     max-width: 800px;
 }
 
@@ -236,7 +236,6 @@ div.body p, div.body dd, div.body li, div.body blockquote {
 a.headerlink {
     visibility: hidden;
 }
-
 a.brackets:before,
 span.brackets > a:before{
     content: "[";
@@ -247,6 +246,7 @@ span.brackets > a:after {
     content: "]";
 }
 
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -334,13 +334,11 @@ aside.sidebar {
 p.sidebar-title {
     font-weight: bold;
 }
-
 div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
-
 div.topic {
     border: 1px solid #ccc;
     padding: 7px;
@@ -428,10 +426,6 @@ table.docutils td, table.docutils th {
     border-bottom: 1px solid #aaa;
 }
 
-table.footnote td, table.footnote th {
-    border: 0 !important;
-}
-
 th {
     text-align: left;
     padding-right: 5px;
@@ -615,6 +609,7 @@ ul.simple p {
     margin-bottom: 0;
 }
 
+/* Docutils 0.17 and older (footnotes & citations) */
 dl.footnote > dt,
 dl.citation > dt {
     float: left;
@@ -632,6 +627,33 @@ dl.citation > dd:after {
     clear: both;
 }
 
+/* Docutils 0.18+ (footnotes & citations) */
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+/* Footnotes & citations ends */
+
 dl.field-list {
     display: grid;
     grid-template-columns: fit-content(30%) auto;
diff --git a/_static/doctools.js b/_static/doctools.js
index e509e483..c3db08d1 100644
--- a/_static/doctools.js
+++ b/_static/doctools.js
@@ -2,325 +2,263 @@
  * doctools.js
  * ~~~~~~~~~~~
  *
- * Sphinx JavaScript utilities for all documentation.
+ * Base JavaScript utilities for all Sphinx HTML documentation.
  *
  * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
+"use strict";
 
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
-  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
-    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
-    "profile", "profileEnd"];
-  window.console = {};
-  for (var i = 0; i < names.length; ++i)
-    window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
-  if (!x) {
-    return x
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
   }
-  return decodeURIComponent(x.replace(/\+/g, ' '));
 };
 
 /**
- * small helper function to urlencode strings
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
  */
-jQuery.urlencode = encodeURIComponent;
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
 
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-  if (typeof s === 'undefined')
-    s = document.location.search;
-  var parts = s.substr(s.indexOf('?') + 1).split('&');
-  var result = {};
-  for (var i = 0; i < parts.length; i++) {
-    var tmp = parts[i].split('=', 2);
-    var key = jQuery.urldecode(tmp[0]);
-    var value = jQuery.urldecode(tmp[1]);
-    if (key in result)
-      result[key].push(value);
-    else
-      result[key] = [value];
-  }
-  return result;
-};
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
 
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-  function highlight(node, addItems) {
-    if (node.nodeType === 3) {
-      var val = node.nodeValue;
-      var pos = val.toLowerCase().indexOf(text);
-      if (pos >= 0 &&
-          !jQuery(node.parentNode).hasClass(className) &&
-          !jQuery(node.parentNode).hasClass("nohighlight")) {
-        var span;
-        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-        if (isInSVG) {
-          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-        } else {
-          span = document.createElement("span");
-          span.className = className;
-        }
-        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
           document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling));
-        node.nodeValue = val.substr(0, pos);
-        if (isInSVG) {
-          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-          var bbox = node.parentElement.getBBox();
-          rect.x.baseVal.value = bbox.x;
-          rect.y.baseVal.value = bbox.y;
-          rect.width.baseVal.value = bbox.width;
-          rect.height.baseVal.value = bbox.height;
-          rect.setAttribute('class', className);
-          addItems.push({
-              "parent": node.parentNode,
-              "target": rect});
-        }
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
       }
     }
-    else if (!jQuery(node).is("button, select, textarea")) {
-      jQuery.each(node.childNodes, function() {
-        highlight(this, addItems);
-      });
-    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
   }
-  var addItems = [];
-  var result = this.each(function() {
-    highlight(this, addItems);
-  });
-  for (var i = 0; i < addItems.length; ++i) {
-    jQuery(addItems[i].parent).before(addItems[i].target);
-  }
-  return result;
 };
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-  jQuery.uaMatch = function(ua) {
-    ua = ua.toLowerCase();
-
-    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-      /(msie) ([\w.]+)/.exec(ua) ||
-      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-      [];
-
-    return {
-      browser: match[ 1 ] || "",
-      version: match[ 2 ] || "0"
-    };
-  };
-  jQuery.browser = {};
-  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
 
 /**
  * Small JavaScript module for the documentation.
  */
-var Documentation = {
-
-  init : function() {
-    this.fixFirefoxAnchorBug();
-    this.highlightSearchWords();
-    this.initIndexTable();
-    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
-      this.initOnKeyListeners();
-    }
+const Documentation = {
+  init: () => {
+    Documentation.highlightSearchWords();
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
   },
 
   /**
    * i18n support
    */
-  TRANSLATIONS : {},
-  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
-  LOCALE : 'unknown',
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
 
   // gettext and ngettext don't access this so that the functions
   // can safely bound to a different name (_ = Documentation.gettext)
-  gettext : function(string) {
-    var translated = Documentation.TRANSLATIONS[string];
-    if (typeof translated === 'undefined')
-      return string;
-    return (typeof translated === 'string') ? translated : translated[0];
-  },
-
-  ngettext : function(singular, plural, n) {
-    var translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated === 'undefined')
-      return (n == 1) ? singular : plural;
-    return translated[Documentation.PLURALEXPR(n)];
-  },
-
-  addTranslations : function(catalog) {
-    for (var key in catalog.messages)
-      this.TRANSLATIONS[key] = catalog.messages[key];
-    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
-    this.LOCALE = catalog.locale;
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
   },
 
-  /**
-   * add context elements like header anchor links
-   */
-  addContextElements : function() {
-    $('div[id] > :header:first').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this headline')).
-      appendTo(this);
-    });
-    $('dt[id]').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this definition')).
-      appendTo(this);
-    });
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
   },
 
-  /**
-   * workaround a firefox stupidity
-   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
-   */
-  fixFirefoxAnchorBug : function() {
-    if (document.location.hash && $.browser.mozilla)
-      window.setTimeout(function() {
-        document.location.href += '';
-      }, 10);
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
   },
 
   /**
    * highlight the search words provided in the url in the text
    */
-  highlightSearchWords : function() {
-    var params = $.getQueryParameters();
-    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
-    if (terms.length) {
-      var body = $('div.body');
-      if (!body.length) {
-        body = $('body');
-      }
-      window.setTimeout(function() {
-        $.each(terms, function() {
-          body.highlightText(this.toLowerCase(), 'highlighted');
-        });
-      }, 10);
-      $('<p class="highlight-link"><a href="javascript:Documentation.' +
-        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
-          .appendTo($('#searchbox'));
-    }
-  },
+  highlightSearchWords: () => {
+    const highlight =
+      new URLSearchParams(window.location.search).get("highlight") || "";
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
 
-  /**
-   * init the domain index toggle buttons
-   */
-  initIndexTable : function() {
-    var togglers = $('img.toggler').click(function() {
-      var src = $(this).attr('src');
-      var idnum = $(this).attr('id').substr(7);
-      $('tr.cg-' + idnum).toggle();
-      if (src.substr(-9) === 'minus.png')
-        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
-      else
-        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
-    }).css('display', '');
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
-        togglers.click();
-    }
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:Documentation.hideSearchWords()">' +
+            Documentation.gettext("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
   },
 
   /**
    * helper function to hide the search marks again
    */
-  hideSearchWords : function() {
-    $('#searchbox .highlight-link').fadeOut(300);
-    $('span.highlighted').removeClass('highlighted');
-    var url = new URL(window.location);
-    url.searchParams.delete('highlight');
-    window.history.replaceState({}, '', url);
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    const url = new URL(window.location);
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
   },
 
   /**
-   * make the url absolute
+   * helper function to focus on search bar
    */
-  makeURL : function(relativeURL) {
-    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
   },
 
   /**
-   * get the current relative url
+   * Initialise the domain index toggle buttons
    */
-  getCurrentURL : function() {
-    var path = document.location.pathname;
-    var parts = path.split(/\//);
-    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
-      if (this === '..')
-        parts.pop();
-    });
-    var url = parts.join('/');
-    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
   },
 
-  initOnKeyListeners: function() {
-    $(document).keydown(function(event) {
-      var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box, textarea, dropdown or button
-      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
-          && !event.shiftKey) {
-        switch (event.keyCode) {
-          case 37: // left
-            var prevHref = $('link[rel="prev"]').prop('href');
-            if (prevHref) {
-              window.location.href = prevHref;
-              return false;
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    const blacklistedElements = new Set([
+      "TEXTAREA",
+      "INPUT",
+      "SELECT",
+      "BUTTON",
+    ]);
+    document.addEventListener("keydown", (event) => {
+      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
+      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
             }
             break;
-          case 39: // right
-            var nextHref = $('link[rel="next"]').prop('href');
-            if (nextHref) {
-              window.location.href = nextHref;
-              return false;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
             }
             break;
+          case "Escape":
+            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+            Documentation.hideSearchWords();
+            event.preventDefault();
         }
       }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
     });
-  }
+  },
 };
 
 // quick alias for translations
-_ = Documentation.gettext;
+const _ = Documentation.gettext;
 
-$(document).ready(function() {
-  Documentation.init();
-});
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index ce4f220c..0e817375 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,12 +1,14 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.7.2',
-    LANGUAGE: 'None',
+    VERSION: '0.7.5',
+    LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
     FILE_SUFFIX: '.html',
     LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
     SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: false,
 };
\ No newline at end of file
diff --git a/_static/jquery-3.5.1.js b/_static/jquery-3.6.0.js
similarity index 98%
rename from _static/jquery-3.5.1.js
rename to _static/jquery-3.6.0.js
index 50937333..fc6c299b 100644
--- a/_static/jquery-3.5.1.js
+++ b/_static/jquery-3.6.0.js
@@ -1,15 +1,15 @@
 /*!
- * jQuery JavaScript Library v3.5.1
+ * jQuery JavaScript Library v3.6.0
  * https://jquery.com/
  *
  * Includes Sizzle.js
  * https://sizzlejs.com/
  *
- * Copyright JS Foundation and other contributors
+ * Copyright OpenJS Foundation and other contributors
  * Released under the MIT license
  * https://jquery.org/license
  *
- * Date: 2020-05-04T22:49Z
+ * Date: 2021-03-02T17:08Z
  */
 ( function( global, factory ) {
 
@@ -76,12 +76,16 @@ var support = {};
 
 var isFunction = function isFunction( obj ) {
 
-      // Support: Chrome <=57, Firefox <=52
-      // In some browsers, typeof returns "function" for HTML <object> elements
-      // (i.e., `typeof document.createElement( "object" ) === "function"`).
-      // We don't want to classify *any* DOM node as a function.
-      return typeof obj === "function" && typeof obj.nodeType !== "number";
-  };
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
 
 
 var isWindow = function isWindow( obj ) {
@@ -147,7 +151,7 @@ function toType( obj ) {
 
 
 var
-	version = "3.5.1",
+	version = "3.6.0",
 
 	// Define a local copy of jQuery
 	jQuery = function( selector, context ) {
@@ -401,7 +405,7 @@ jQuery.extend( {
 			if ( isArrayLike( Object( arr ) ) ) {
 				jQuery.merge( ret,
 					typeof arr === "string" ?
-					[ arr ] : arr
+						[ arr ] : arr
 				);
 			} else {
 				push.call( ret, arr );
@@ -496,9 +500,9 @@ if ( typeof Symbol === "function" ) {
 
 // Populate the class2type map
 jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( _i, name ) {
-	class2type[ "[object " + name + "]" ] = name.toLowerCase();
-} );
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
 
 function isArrayLike( obj ) {
 
@@ -518,14 +522,14 @@ function isArrayLike( obj ) {
 }
 var Sizzle =
 /*!
- * Sizzle CSS Selector Engine v2.3.5
+ * Sizzle CSS Selector Engine v2.3.6
  * https://sizzlejs.com/
  *
  * Copyright JS Foundation and other contributors
  * Released under the MIT license
  * https://js.foundation/
  *
- * Date: 2020-03-14
+ * Date: 2021-02-16
  */
 ( function( window ) {
 var i,
@@ -1108,8 +1112,8 @@ support = Sizzle.support = {};
  * @returns {Boolean} True iff elem is a non-HTML XML node
  */
 isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem.namespaceURI,
-		docElem = ( elem.ownerDocument || elem ).documentElement;
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
 
 	// Support: IE <=8
 	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
@@ -3024,9 +3028,9 @@ var rneedsContext = jQuery.expr.match.needsContext;
 
 function nodeName( elem, name ) {
 
-  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
 
-};
+}
 var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
 
 
@@ -3997,8 +4001,8 @@ jQuery.extend( {
 			resolveContexts = Array( i ),
 			resolveValues = slice.call( arguments ),
 
-			// the master Deferred
-			master = jQuery.Deferred(),
+			// the primary Deferred
+			primary = jQuery.Deferred(),
 
 			// subordinate callback factory
 			updateFunc = function( i ) {
@@ -4006,30 +4010,30 @@ jQuery.extend( {
 					resolveContexts[ i ] = this;
 					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
 					if ( !( --remaining ) ) {
-						master.resolveWith( resolveContexts, resolveValues );
+						primary.resolveWith( resolveContexts, resolveValues );
 					}
 				};
 			};
 
 		// Single- and empty arguments are adopted like Promise.resolve
 		if ( remaining <= 1 ) {
-			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
 				!remaining );
 
 			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( master.state() === "pending" ||
+			if ( primary.state() === "pending" ||
 				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
 
-				return master.then();
+				return primary.then();
 			}
 		}
 
 		// Multiple arguments are aggregated like Promise.all array elements
 		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
 		}
 
-		return master.promise();
+		return primary.promise();
 	}
 } );
 
@@ -4180,8 +4184,8 @@ var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
 			for ( ; i < len; i++ ) {
 				fn(
 					elems[ i ], key, raw ?
-					value :
-					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
 				);
 			}
 		}
@@ -5089,10 +5093,7 @@ function buildFragment( elems, context, scripts, selection, ignored ) {
 }
 
 
-var
-	rkeyEvent = /^key/,
-	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
-	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
 
 function returnTrue() {
 	return true;
@@ -5387,8 +5388,8 @@ jQuery.event = {
 			event = jQuery.event.fix( nativeEvent ),
 
 			handlers = (
-					dataPriv.get( this, "events" ) || Object.create( null )
-				)[ event.type ] || [],
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
 			special = jQuery.event.special[ event.type ] || {};
 
 		// Use the fix-ed jQuery.Event rather than the (read-only) native event
@@ -5512,12 +5513,12 @@ jQuery.event = {
 			get: isFunction( hook ) ?
 				function() {
 					if ( this.originalEvent ) {
-							return hook( this.originalEvent );
+						return hook( this.originalEvent );
 					}
 				} :
 				function() {
 					if ( this.originalEvent ) {
-							return this.originalEvent[ name ];
+						return this.originalEvent[ name ];
 					}
 				},
 
@@ -5656,7 +5657,13 @@ function leverageNative( el, type, expectSync ) {
 						// Cancel the outer synthetic event
 						event.stopImmediatePropagation();
 						event.preventDefault();
-						return result.value;
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
 					}
 
 				// If this is an inner synthetic event for an event with a bubbling surrogate
@@ -5821,34 +5828,7 @@ jQuery.each( {
 	targetTouches: true,
 	toElement: true,
 	touches: true,
-
-	which: function( event ) {
-		var button = event.button;
-
-		// Add which for key events
-		if ( event.which == null && rkeyEvent.test( event.type ) ) {
-			return event.charCode != null ? event.charCode : event.keyCode;
-		}
-
-		// Add which for click: 1 === left; 2 === middle; 3 === right
-		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
-			if ( button & 1 ) {
-				return 1;
-			}
-
-			if ( button & 2 ) {
-				return 3;
-			}
-
-			if ( button & 4 ) {
-				return 2;
-			}
-
-			return 0;
-		}
-
-		return event.which;
-	}
+	which: true
 }, jQuery.event.addProp );
 
 jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
@@ -5874,6 +5854,12 @@ jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateTyp
 			return true;
 		},
 
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
 		delegateType: delegateType
 	};
 } );
@@ -6541,6 +6527,10 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
 		// set in CSS while `offset*` properties report correct values.
 		// Behavior in IE 9 is more subtle than in newer versions & it passes
 		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
 		reliableTrDimensions: function() {
 			var table, tr, trChild, trStyle;
 			if ( reliableTrDimensionsVal == null ) {
@@ -6548,17 +6538,32 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
 				tr = document.createElement( "tr" );
 				trChild = document.createElement( "div" );
 
-				table.style.cssText = "position:absolute;left:-11111px";
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
 				tr.style.height = "1px";
 				trChild.style.height = "9px";
 
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
 				documentElement
 					.appendChild( table )
 					.appendChild( tr )
 					.appendChild( trChild );
 
 				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
 
 				documentElement.removeChild( table );
 			}
@@ -7022,10 +7027,10 @@ jQuery.each( [ "height", "width" ], function( _i, dimension ) {
 					// Running getBoundingClientRect on a disconnected node
 					// in IE throws an error.
 					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-						swap( elem, cssShow, function() {
-							return getWidthOrHeight( elem, dimension, extra );
-						} ) :
-						getWidthOrHeight( elem, dimension, extra );
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
 			}
 		},
 
@@ -7084,7 +7089,7 @@ jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
 					swap( elem, { marginLeft: 0 }, function() {
 						return elem.getBoundingClientRect().left;
 					} )
-				) + "px";
+			) + "px";
 		}
 	}
 );
@@ -7223,7 +7228,7 @@ Tween.propHooks = {
 			if ( jQuery.fx.step[ tween.prop ] ) {
 				jQuery.fx.step[ tween.prop ]( tween );
 			} else if ( tween.elem.nodeType === 1 && (
-					jQuery.cssHooks[ tween.prop ] ||
+				jQuery.cssHooks[ tween.prop ] ||
 					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
 				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
 			} else {
@@ -7468,7 +7473,7 @@ function defaultPrefilter( elem, props, opts ) {
 
 			anim.done( function() {
 
-			/* eslint-enable no-loop-func */
+				/* eslint-enable no-loop-func */
 
 				// The final step of a "hide" animation is actually hiding the element
 				if ( !hidden ) {
@@ -7588,7 +7593,7 @@ function Animation( elem, properties, options ) {
 			tweens: [],
 			createTween: function( prop, end ) {
 				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
 				animation.tweens.push( tween );
 				return tween;
 			},
@@ -7761,7 +7766,8 @@ jQuery.fn.extend( {
 					anim.stop( true );
 				}
 			};
-			doAnimation.finish = doAnimation;
+
+		doAnimation.finish = doAnimation;
 
 		return empty || optall.queue === false ?
 			this.each( doAnimation ) :
@@ -8401,8 +8407,8 @@ jQuery.fn.extend( {
 				if ( this.setAttribute ) {
 					this.setAttribute( "class",
 						className || value === false ?
-						"" :
-						dataPriv.get( this, "__className__" ) || ""
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
 					);
 				}
 			}
@@ -8417,7 +8423,7 @@ jQuery.fn.extend( {
 		while ( ( elem = this[ i++ ] ) ) {
 			if ( elem.nodeType === 1 &&
 				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-					return true;
+				return true;
 			}
 		}
 
@@ -8707,9 +8713,7 @@ jQuery.extend( jQuery.event, {
 				special.bindType || type;
 
 			// jQuery handler
-			handle = (
-					dataPriv.get( cur, "events" ) || Object.create( null )
-				)[ event.type ] &&
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
 				dataPriv.get( cur, "handle" );
 			if ( handle ) {
 				handle.apply( cur, data );
@@ -8856,7 +8860,7 @@ var rquery = ( /\?/ );
 
 // Cross-browser xml parsing
 jQuery.parseXML = function( data ) {
-	var xml;
+	var xml, parserErrorElem;
 	if ( !data || typeof data !== "string" ) {
 		return null;
 	}
@@ -8865,12 +8869,17 @@ jQuery.parseXML = function( data ) {
 	// IE throws on parseFromString with invalid input.
 	try {
 		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {
-		xml = undefined;
-	}
+	} catch ( e ) {}
 
-	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
-		jQuery.error( "Invalid XML: " + data );
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
 	}
 	return xml;
 };
@@ -8971,16 +8980,14 @@ jQuery.fn.extend( {
 			// Can add propHook for "elements" to filter or add form elements
 			var elements = jQuery.prop( this, "elements" );
 			return elements ? jQuery.makeArray( elements ) : this;
-		} )
-		.filter( function() {
+		} ).filter( function() {
 			var type = this.type;
 
 			// Use .is( ":disabled" ) so that fieldset[disabled] works
 			return this.name && !jQuery( this ).is( ":disabled" ) &&
 				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
 				( this.checked || !rcheckableType.test( type ) );
-		} )
-		.map( function( _i, elem ) {
+		} ).map( function( _i, elem ) {
 			var val = jQuery( this ).val();
 
 			if ( val == null ) {
@@ -9033,7 +9040,8 @@ var
 
 	// Anchor tag for parsing the document origin
 	originAnchor = document.createElement( "a" );
-	originAnchor.href = location.href;
+
+originAnchor.href = location.href;
 
 // Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
 function addToPrefiltersOrTransports( structure ) {
@@ -9414,8 +9422,8 @@ jQuery.extend( {
 			// Context for global events is callbackContext if it is a DOM node or jQuery collection
 			globalEventContext = s.context &&
 				( callbackContext.nodeType || callbackContext.jquery ) ?
-					jQuery( callbackContext ) :
-					jQuery.event,
+				jQuery( callbackContext ) :
+				jQuery.event,
 
 			// Deferreds
 			deferred = jQuery.Deferred(),
@@ -9727,8 +9735,10 @@ jQuery.extend( {
 				response = ajaxHandleResponses( s, jqXHR, responses );
 			}
 
-			// Use a noop converter for missing script
-			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
 				s.converters[ "text script" ] = function() {};
 			}
 
@@ -10466,12 +10476,6 @@ jQuery.offset = {
 			options.using.call( elem, props );
 
 		} else {
-			if ( typeof props.top === "number" ) {
-				props.top += "px";
-			}
-			if ( typeof props.left === "number" ) {
-				props.left += "px";
-			}
 			curElem.css( props );
 		}
 	}
@@ -10640,8 +10644,11 @@ jQuery.each( [ "top", "left" ], function( _i, prop ) {
 
 // Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
 jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
-		function( defaultExtra, funcName ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
 
 		// Margin is only for outerHeight, outerWidth
 		jQuery.fn[ funcName ] = function( margin, value ) {
@@ -10726,7 +10733,8 @@ jQuery.fn.extend( {
 	}
 } );
 
-jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
 	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
 	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
 	function( _i, name ) {
@@ -10737,7 +10745,8 @@ jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
 				this.on( name, null, data, fn ) :
 				this.trigger( name );
 		};
-	} );
+	}
+);
 
 
 
diff --git a/_static/jquery.js b/_static/jquery.js
index b0614034..c4c6022f 100644
--- a/_static/jquery.js
+++ b/_static/jquery.js
@@ -1,2 +1,2 @@
-/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.5.1",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new 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diff --git a/_static/language_data.js b/_static/language_data.js
index ebe2f03b..2e22b06a 100644
--- a/_static/language_data.js
+++ b/_static/language_data.js
@@ -10,7 +10,7 @@
  *
  */
 
-var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
 /* Non-minified version is copied as a separate JS file, is available */
@@ -197,101 +197,3 @@ var Stemmer = function() {
   }
 }
 
-
-
-
-var splitChars = (function() {
-    var result = {};
-    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
-         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
-         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
-         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
-         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
-         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
-         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
-         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
-         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
-         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
-    var i, j, start, end;
-    for (i = 0; i < singles.length; i++) {
-        result[singles[i]] = true;
-    }
-    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
-         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
-         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
-         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
-         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
-         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
-         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
-         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
-         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
-         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
-         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
-         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
-         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
-         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
-         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
-         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
-         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
-         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
-         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
-         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
-         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
-         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
-         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
-         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
-         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
-         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
-         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
-         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
-         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
-         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
-         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
-         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
-         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
-         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
-         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
-         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
-         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
-         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
-         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
-         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
-         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
-         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
-         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
-         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
-         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
-         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
-         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
-         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
-         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
-    for (i = 0; i < ranges.length; i++) {
-        start = ranges[i][0];
-        end = ranges[i][1];
-        for (j = start; j <= end; j++) {
-            result[j] = true;
-        }
-    }
-    return result;
-})();
-
-function splitQuery(query) {
-    var result = [];
-    var start = -1;
-    for (var i = 0; i < query.length; i++) {
-        if (splitChars[query.charCodeAt(i)]) {
-            if (start !== -1) {
-                result.push(query.slice(start, i));
-                start = -1;
-            }
-        } else if (start === -1) {
-            start = i;
-        }
-    }
-    if (start !== -1) {
-        result.push(query.slice(start));
-    }
-    return result;
-}
-
-
diff --git a/_static/searchtools.js b/_static/searchtools.js
index 2d778593..ac4d5861 100644
--- a/_static/searchtools.js
+++ b/_static/searchtools.js
@@ -8,18 +8,20 @@
  * :license: BSD, see LICENSE for details.
  *
  */
+"use strict";
 
-if (!Scorer) {
-  /**
-   * Simple result scoring code.
-   */
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
   var Scorer = {
     // Implement the following function to further tweak the score for each result
-    // The function takes a result array [filename, title, anchor, descr, score]
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
     // and returns the new score.
     /*
-    score: function(result) {
-      return result[4];
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
     },
     */
 
@@ -28,9 +30,11 @@ if (!Scorer) {
     // or matches in the last dotted part of the object name
     objPartialMatch: 6,
     // Additive scores depending on the priority of the object
-    objPrio: {0:  15,   // used to be importantResults
-              1:  5,   // used to be objectResults
-              2: -5},  // used to be unimportantResults
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
     //  Used when the priority is not in the mapping.
     objPrioDefault: 0,
 
@@ -39,456 +43,455 @@ if (!Scorer) {
     partialTitle: 7,
     // query found in terms
     term: 5,
-    partialTerm: 2
+    partialTerm: 2,
   };
 }
 
-if (!splitQuery) {
-  function splitQuery(query) {
-    return query.split(/\s+/);
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, highlightTerms, searchTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+
+  const [docName, title, anchor, descr] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = docUrlRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = docUrlRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  const params = new URLSearchParams();
+  params.set("highlight", [...highlightTerms].join(" "));
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + "?" + params.toString() + anchor;
+  linkEl.innerHTML = title;
+  if (descr)
+    listItem.appendChild(document.createElement("span")).innerText =
+      " (" + descr + ")";
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, highlightTerms)
+          );
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  highlightTerms,
+  searchTerms
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), highlightTerms, searchTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
+      5
+    );
   }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
 }
 
 /**
  * Search Module
  */
-var Search = {
-
-  _index : null,
-  _queued_query : null,
-  _pulse_status : -1,
-
-  htmlToText : function(htmlString) {
-      var virtualDocument = document.implementation.createHTMLDocument('virtual');
-      var htmlElement = $(htmlString, virtualDocument);
-      htmlElement.find('.headerlink').remove();
-      docContent = htmlElement.find('[role=main]')[0];
-      if(docContent === undefined) {
-          console.warn("Content block not found. Sphinx search tries to obtain it " +
-                       "via '[role=main]'. Could you check your theme or template.");
-          return "";
-      }
-      return docContent.textContent || docContent.innerText;
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = document
+      .createRange()
+      .createContextualFragment(htmlString);
+    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
   },
 
-  init : function() {
-      var params = $.getQueryParameters();
-      if (params.q) {
-          var query = params.q[0];
-          $('input[name="q"]')[0].value = query;
-          this.performSearch(query);
-      }
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
   },
 
-  loadIndex : function(url) {
-    $.ajax({type: "GET", url: url, data: null,
-            dataType: "script", cache: true,
-            complete: function(jqxhr, textstatus) {
-              if (textstatus != "success") {
-                document.getElementById("searchindexloader").src = url;
-              }
-            }});
-  },
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
 
-  setIndex : function(index) {
-    var q;
-    this._index = index;
-    if ((q = this._queued_query) !== null) {
-      this._queued_query = null;
-      Search.query(q);
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
     }
   },
 
-  hasIndex : function() {
-      return this._index !== null;
-  },
+  hasIndex: () => Search._index !== null,
 
-  deferQuery : function(query) {
-      this._queued_query = query;
-  },
+  deferQuery: (query) => (Search._queued_query = query),
 
-  stopPulse : function() {
-      this._pulse_status = 0;
-  },
+  stopPulse: () => (Search._pulse_status = -1),
 
-  startPulse : function() {
-    if (this._pulse_status >= 0)
-        return;
-    function pulse() {
-      var i;
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
       Search._pulse_status = (Search._pulse_status + 1) % 4;
-      var dotString = '';
-      for (i = 0; i < Search._pulse_status; i++)
-        dotString += '.';
-      Search.dots.text(dotString);
-      if (Search._pulse_status > -1)
-        window.setTimeout(pulse, 500);
-    }
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
     pulse();
   },
 
   /**
    * perform a search for something (or wait until index is loaded)
    */
-  performSearch : function(query) {
+  performSearch: (query) => {
     // create the required interface elements
-    this.out = $('#search-results');
-    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
-    this.dots = $('<span></span>').appendTo(this.title);
-    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
-    this.output = $('<ul class="search"/>').appendTo(this.out);
-
-    $('#search-progress').text(_('Preparing search...'));
-    this.startPulse();
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
 
     // index already loaded, the browser was quick!
-    if (this.hasIndex())
-      this.query(query);
-    else
-      this.deferQuery(query);
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
   },
 
   /**
    * execute search (requires search index to be loaded)
    */
-  query : function(query) {
-    var i;
-
-    // stem the searchterms and add them to the correct list
-    var stemmer = new Stemmer();
-    var searchterms = [];
-    var excluded = [];
-    var hlterms = [];
-    var tmp = splitQuery(query);
-    var objectterms = [];
-    for (i = 0; i < tmp.length; i++) {
-      if (tmp[i] !== "") {
-          objectterms.push(tmp[i].toLowerCase());
-      }
+  query: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
 
-      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
-        // skip this "word"
-        continue;
-      }
       // stem the word
-      var word = stemmer.stemWord(tmp[i].toLowerCase());
-      // prevent stemmer from cutting word smaller than two chars
-      if(word.length < 3 && tmp[i].length >= 3) {
-        word = tmp[i];
-      }
-      var toAppend;
+      let word = stemmer.stemWord(queryTermLower);
       // select the correct list
-      if (word[0] == '-') {
-        toAppend = excluded;
-        word = word.substr(1);
-      }
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
       else {
-        toAppend = searchterms;
-        hlterms.push(tmp[i].toLowerCase());
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
       }
-      // only add if not already in the list
-      if (!$u.contains(toAppend, word))
-        toAppend.push(word);
-    }
-    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
-
-    // console.debug('SEARCH: searching for:');
-    // console.info('required: ', searchterms);
-    // console.info('excluded: ', excluded);
+    });
 
-    // prepare search
-    var terms = this._index.terms;
-    var titleterms = this._index.titleterms;
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
 
-    // array of [filename, title, anchor, descr, score]
-    var results = [];
-    $('#search-progress').empty();
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
 
     // lookup as object
-    for (i = 0; i < objectterms.length; i++) {
-      var others = [].concat(objectterms.slice(0, i),
-                             objectterms.slice(i+1, objectterms.length));
-      results = results.concat(this.performObjectSearch(objectterms[i], others));
-    }
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
 
     // lookup as search terms in fulltext
-    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      for (i = 0; i < results.length; i++)
-        results[i][4] = Scorer.score(results[i]);
-    }
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
 
     // now sort the results by score (in opposite order of appearance, since the
     // display function below uses pop() to retrieve items) and then
     // alphabetically
-    results.sort(function(a, b) {
-      var left = a[4];
-      var right = b[4];
-      if (left > right) {
-        return 1;
-      } else if (left < right) {
-        return -1;
-      } else {
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
         // same score: sort alphabetically
-        left = a[1].toLowerCase();
-        right = b[1].toLowerCase();
-        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
       }
+      return leftScore > rightScore ? 1 : -1;
     });
 
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
     // for debugging
     //Search.lastresults = results.slice();  // a copy
-    //console.info('search results:', Search.lastresults);
+    // console.info("search results:", Search.lastresults);
 
     // print the results
-    var resultCount = results.length;
-    function displayNextItem() {
-      // results left, load the summary and display it
-      if (results.length) {
-        var item = results.pop();
-        var listItem = $('<li></li>');
-        var requestUrl = "";
-        var linkUrl = "";
-        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
-          // dirhtml builder
-          var dirname = item[0] + '/';
-          if (dirname.match(/\/index\/$/)) {
-            dirname = dirname.substring(0, dirname.length-6);
-          } else if (dirname == 'index/') {
-            dirname = '';
-          }
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
-          linkUrl = requestUrl;
-
-        } else {
-          // normal html builders
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-        }
-        listItem.append($('<a/>').attr('href',
-            linkUrl +
-            highlightstring + item[2]).html(item[1]));
-        if (item[3]) {
-          listItem.append($('<span> (' + item[3] + ')</span>'));
-          Search.output.append(listItem);
-          setTimeout(function() {
-            displayNextItem();
-          }, 5);
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
-          $.ajax({url: requestUrl,
-                  dataType: "text",
-                  complete: function(jqxhr, textstatus) {
-                    var data = jqxhr.responseText;
-                    if (data !== '' && data !== undefined) {
-                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
-                      if (summary) {
-                        listItem.append(summary);
-                      }
-                    }
-                    Search.output.append(listItem);
-                    setTimeout(function() {
-                      displayNextItem();
-                    }, 5);
-                  }});
-        } else {
-          // no source available, just display title
-          Search.output.append(listItem);
-          setTimeout(function() {
-            displayNextItem();
-          }, 5);
-        }
-      }
-      // search finished, update title and status message
-      else {
-        Search.stopPulse();
-        Search.title.text(_('Search Results'));
-        if (!resultCount)
-          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
-        else
-            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
-        Search.status.fadeIn(500);
-      }
-    }
-    displayNextItem();
+    _displayNextItem(results, results.length, highlightTerms, searchTerms);
   },
 
   /**
    * search for object names
    */
-  performObjectSearch : function(object, otherterms) {
-    var filenames = this._index.filenames;
-    var docnames = this._index.docnames;
-    var objects = this._index.objects;
-    var objnames = this._index.objnames;
-    var titles = this._index.titles;
-
-    var i;
-    var results = [];
-
-    for (var prefix in objects) {
-      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
-        var match = objects[prefix][iMatch];
-        var name = match[4];
-        var fullname = (prefix ? prefix + '.' : '') + name;
-        var fullnameLower = fullname.toLowerCase()
-        if (fullnameLower.indexOf(object) > -1) {
-          var score = 0;
-          var parts = fullnameLower.split('.');
-          // check for different match types: exact matches of full name or
-          // "last name" (i.e. last dotted part)
-          if (fullnameLower == object || parts[parts.length - 1] == object) {
-            score += Scorer.objNameMatch;
-          // matches in last name
-          } else if (parts[parts.length - 1].indexOf(object) > -1) {
-            score += Scorer.objPartialMatch;
-          }
-          var objname = objnames[match[1]][2];
-          var title = titles[match[0]];
-          // If more than one term searched for, we require other words to be
-          // found in the name/title/description
-          if (otherterms.length > 0) {
-            var haystack = (prefix + ' ' + name + ' ' +
-                            objname + ' ' + title).toLowerCase();
-            var allfound = true;
-            for (i = 0; i < otherterms.length; i++) {
-              if (haystack.indexOf(otherterms[i]) == -1) {
-                allfound = false;
-                break;
-              }
-            }
-            if (!allfound) {
-              continue;
-            }
-          }
-          var descr = objname + _(', in ') + title;
-
-          var anchor = match[3];
-          if (anchor === '')
-            anchor = fullname;
-          else if (anchor == '-')
-            anchor = objnames[match[1]][1] + '-' + fullname;
-          // add custom score for some objects according to scorer
-          if (Scorer.objPrio.hasOwnProperty(match[2])) {
-            score += Scorer.objPrio[match[2]];
-          } else {
-            score += Scorer.objPrioDefault;
-          }
-          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
-        }
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
       }
-    }
 
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
     return results;
   },
 
-  /**
-   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
-   */
-  escapeRegExp : function(string) {
-    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-  },
-
   /**
    * search for full-text terms in the index
    */
-  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
-    var docnames = this._index.docnames;
-    var filenames = this._index.filenames;
-    var titles = this._index.titles;
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const docNames = Search._index.docnames;
+    const filenames = Search._index.filenames;
+    const titles = Search._index.titles;
 
-    var i, j, file;
-    var fileMap = {};
-    var scoreMap = {};
-    var results = [];
+    const scoreMap = new Map();
+    const fileMap = new Map();
 
     // perform the search on the required terms
-    for (i = 0; i < searchterms.length; i++) {
-      var word = searchterms[i];
-      var files = [];
-      var _o = [
-        {files: terms[word], score: Scorer.term},
-        {files: titleterms[word], score: Scorer.title}
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
       ];
       // add support for partial matches
       if (word.length > 2) {
-        var word_regex = this.escapeRegExp(word);
-        for (var w in terms) {
-          if (w.match(word_regex) && !terms[word]) {
-            _o.push({files: terms[w], score: Scorer.partialTerm})
-          }
-        }
-        for (var w in titleterms) {
-          if (w.match(word_regex) && !titleterms[word]) {
-              _o.push({files: titleterms[w], score: Scorer.partialTitle})
-          }
-        }
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
       }
 
       // no match but word was a required one
-      if ($u.every(_o, function(o){return o.files === undefined;})) {
-        break;
-      }
+      if (arr.every((record) => record.files === undefined)) return;
+
       // found search word in contents
-      $u.each(_o, function(o) {
-        var _files = o.files;
-        if (_files === undefined)
-          return
-
-        if (_files.length === undefined)
-          _files = [_files];
-        files = files.concat(_files);
-
-        // set score for the word in each file to Scorer.term
-        for (j = 0; j < _files.length; j++) {
-          file = _files[j];
-          if (!(file in scoreMap))
-            scoreMap[file] = {};
-          scoreMap[file][word] = o.score;
-        }
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
       });
 
       // create the mapping
-      for (j = 0; j < files.length; j++) {
-        file = files[j];
-        if (file in fileMap && fileMap[file].indexOf(word) === -1)
-          fileMap[file].push(word);
-        else
-          fileMap[file] = [word];
-      }
-    }
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
 
     // now check if the files don't contain excluded terms
-    for (file in fileMap) {
-      var valid = true;
-
+    const results = [];
+    for (const [file, wordList] of fileMap) {
       // check if all requirements are matched
-      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
-        searchterms.filter(function(term){return term.length > 2}).length
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
       if (
-        fileMap[file].length != searchterms.length &&
-        fileMap[file].length != filteredTermCount
-      ) continue;
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
 
       // ensure that none of the excluded terms is in the search result
-      for (i = 0; i < excluded.length; i++) {
-        if (terms[excluded[i]] == file ||
-            titleterms[excluded[i]] == file ||
-            $u.contains(terms[excluded[i]] || [], file) ||
-            $u.contains(titleterms[excluded[i]] || [], file)) {
-          valid = false;
-          break;
-        }
-      }
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
 
-      // if we have still a valid result we can add it to the result list
-      if (valid) {
-        // select one (max) score for the file.
-        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
-        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
-        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
-      }
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
     }
     return results;
   },
@@ -496,34 +499,33 @@ var Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, hlwords is the list of normal, unstemmed
+   * of stemmed words, highlightWords is the list of normal, unstemmed
    * words. the first one is used to find the occurrence, the
    * latter for highlighting it.
    */
-  makeSearchSummary : function(htmlText, keywords, hlwords) {
-    var text = Search.htmlToText(htmlText);
-    if (text == "") {
-      return null;
-    }
-    var textLower = text.toLowerCase();
-    var start = 0;
-    $.each(keywords, function() {
-      var i = textLower.indexOf(this.toLowerCase());
-      if (i > -1)
-        start = i;
-    });
-    start = Math.max(start - 120, 0);
-    var excerpt = ((start > 0) ? '...' : '') +
-      $.trim(text.substr(start, 240)) +
-      ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<p class="context"></p>').text(excerpt);
-    $.each(hlwords, function() {
-      rv = rv.highlightText(this, 'highlighted');
-    });
-    return rv;
-  }
+  makeSearchSummary: (htmlText, keywords, highlightWords) => {
+    const text = Search.htmlToText(htmlText).toLowerCase();
+    if (text === "") return null;
+
+    const actualStartPosition = [...keywords]
+      .map((k) => text.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("div");
+    summary.classList.add("context");
+    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
+
+    highlightWords.forEach((highlightWord) =>
+      _highlightText(summary, highlightWord, "highlighted")
+    );
+
+    return summary;
+  },
 };
 
-$(document).ready(function() {
-  Search.init();
-});
+_ready(Search.init);
diff --git a/api_reference/center_selector.html b/api_reference/center_selector.html
index 43f68060..9dcb5769 100644
--- a/api_reference/center_selector.html
+++ b/api_reference/center_selector.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.center_selection &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.center_selection &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -275,9 +275,9 @@
 }
 </style>
 <section id="module-falkon.center_selection">
-<span id="falkon-center-selection"></span><h1>falkon.center_selection<a class="headerlink" href="#module-falkon.center_selection" title="Permalink to this headline"></a></h1>
+<span id="falkon-center-selection"></span><h1>falkon.center_selection<a class="headerlink" href="#module-falkon.center_selection" title="Permalink to this heading"></a></h1>
 <section id="centerselector">
-<h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink to this headline"></a></h2>
+<h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.center_selection.CenterSelector">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">CenterSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.CenterSelector" title="Permalink to this definition"></a></dt>
@@ -289,7 +289,7 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.CenterSelector.select">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -310,7 +310,7 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.CenterSelector.select_indices">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select_indices" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -335,11 +335,11 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 
 </section>
 <section id="uniformselector">
-<h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalink to this headline"></a></h2>
+<h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.center_selection.UniformSelector">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">UniformSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.UniformSelector" title="Permalink to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.center_selection.CenterSelector</span></code></a></p>
+<dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></p>
 <p>Center selector which chooses from the full dataset uniformly at random (without replacement)</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -351,7 +351,7 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.UniformSelector.select">
-<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
 <p>The selection strategy is uniformly at random. To control the randomness,
 pass an appropriate numpy random generator to this class’s constructor.</p>
@@ -382,7 +382,7 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.UniformSelector.select_indices">
-<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select_indices" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
 <p>The selection strategy is uniformly at random. To control the randomness,
 pass an appropriate numpy random generator to this class’s constructor.</p>
@@ -418,11 +418,11 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 
 </section>
 <section id="fixedselector">
-<h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to this headline"></a></h2>
+<h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.center_selection.FixedSelector">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">FixedSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.FixedSelector" title="Permalink to this definition"></a></dt>
-<dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.center_selection.CenterSelector</span></code></a></p>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">FixedSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.FixedSelector" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></p>
 <p>Center selector which always picks the same centers.</p>
 <p>The fixed centers are specified at class initialization time.</p>
 <dl class="field-list simple">
@@ -438,7 +438,7 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.FixedSelector.select">
-<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Returns the fixed centers with which this instance was created</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -467,7 +467,7 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.FixedSelector.select_indices">
-<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select_indices" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Returns the fixed centers, and their indices with which this instance was created</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/api_reference/gsc_losses.html b/api_reference/gsc_losses.html
index a7d658e9..bcc08b83 100644
--- a/api_reference/gsc_losses.html
+++ b/api_reference/gsc_losses.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.gsc_losses &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.gsc_losses &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -277,12 +277,12 @@
 }
 </style>
 <section id="module-falkon">
-<span id="falkon-gsc-losses"></span><h1>falkon.gsc_losses<a class="headerlink" href="#module-falkon" title="Permalink to this headline"></a></h1>
+<span id="falkon-gsc-losses"></span><h1>falkon.gsc_losses<a class="headerlink" href="#module-falkon" title="Permalink to this heading"></a></h1>
 <section id="loss">
-<h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline"></a></h2>
+<h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.Loss" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.Loss" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract generalized self-concordant loss function class.</p>
 <p>Such loss functions must be three times differentiable; but for the logistic Falkon algorithm
 only the first two derivatives are used.
@@ -314,7 +314,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss.__call__">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.__call__" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the loss for predicting <cite>y2</cite> with true labels <cite>y1</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -331,7 +331,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss.ddf">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.ddf" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.ddf" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the second derivative of the loss wrt <cite>y2</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -350,7 +350,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss.df">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.df" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.df" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the derivative of the loss wrt <cite>y2</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -369,7 +369,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss.knmp_grad">
-<span class="sig-name descname"><span class="pre">knmp_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_grad" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">knmp_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_grad" title="Permalink to this definition"></a></dt>
 <dd><p>Computes a kernel vector product where the vector is the first derivative of this loss</p>
 <p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
 number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
@@ -404,7 +404,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss.knmp_hess">
-<span class="sig-name descname"><span class="pre">knmp_hess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_hess" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">knmp_hess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_hess" title="Permalink to this definition"></a></dt>
 <dd><p>Compute a kernel-vector product with a rescaling with the second derivative</p>
 <p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
 number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
@@ -437,10 +437,10 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this headline">
 
 </section>
 <section id="logistic-loss">
-<h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to this headline"></a></h2>
+<h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">LogisticLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">LogisticLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper for the logistic loss, to be used in conjunction with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Usage of this loss assumes a binary classification problem with labels -1 and +1. For different
 choices of labels, see <a class="reference internal" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a>.</p>
@@ -461,7 +461,7 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the logistic loss between two 1-dimensional tensors</p>
 <p>The formula used is <span class="math notranslate nohighlight">\(\log(1 + \exp(-y_1 * y_2))\)</span></p>
 <dl class="field-list simple">
@@ -479,7 +479,7 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss.ddf">
-<span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.ddf" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.ddf" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the second derivative of the logistic loss with respect to <cite>y2</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -499,7 +499,7 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss.df">
-<span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.df" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss.df" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the derivative of the logistic loss with respect to <cite>y2</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -521,10 +521,10 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 
 </section>
 <section id="weighted-binary-cross-entropy-loss">
-<h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-binary-cross-entropy-loss" title="Permalink to this headline"></a></h2>
+<h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-binary-cross-entropy-loss" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper for the weighted binary cross-entropy loss, to be used with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Using this loss assumes a binary classification problem with labels 0 and +1. Additionally,
 this loss allows to place a different weight to samples belonging to one of the two classes
@@ -550,7 +550,7 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the weighted BCE loss between two 1-dimensional tensors</p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -571,7 +571,7 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss.ddf">
-<span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.ddf" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.ddf" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the second derivative of the weighted BCE loss with respect to <cite>pred</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
@@ -591,7 +591,7 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss.df">
-<span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.df" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss.df" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the derivative of the weighted BCE loss with respect to <cite>pred</cite></p>
 <p>The formula used is</p>
 <div class="math notranslate nohighlight">
diff --git a/api_reference/hopt.html b/api_reference/hopt.html
index e659f62e..b28d84b0 100644
--- a/api_reference/hopt.html
+++ b/api_reference/hopt.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.hopt &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.hopt &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
@@ -32,7 +33,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -48,7 +49,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -276,43 +276,43 @@
 }
 </style>
 <section id="module-falkon.hopt">
-<span id="falkon-hopt"></span><h1>falkon.hopt<a class="headerlink" href="#module-falkon.hopt" title="Permalink to this headline"></a></h1>
+<span id="falkon-hopt"></span><h1>falkon.hopt<a class="headerlink" href="#module-falkon.hopt" title="Permalink to this heading"></a></h1>
 <section id="objectives">
-<h2>Objectives<a class="headerlink" href="#objectives" title="Permalink to this headline"></a></h2>
+<h2>Objectives<a class="headerlink" href="#objectives" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.objectives.HyperoptObjective">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.objectives.</span></span><span class="sig-name descname"><span class="pre">HyperoptObjective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.objectives.HyperoptObjective" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.objectives.</span></span><span class="sig-name descname"><span class="pre">HyperoptObjective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.objectives.HyperoptObjective" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <section id="nystrom-complexity-regularization">
-<h3>Nystrom Complexity Regularization<a class="headerlink" href="#nystrom-complexity-regularization" title="Permalink to this headline"></a></h3>
+<h3>Nystrom Complexity Regularization<a class="headerlink" href="#nystrom-complexity-regularization" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.NystromCompReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">NystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.NystromCompReg" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">NystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.NystromCompReg" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="stochastic-nystrom-computational-regularization">
-<h3>Stochastic Nystrom Computational Regularization<a class="headerlink" href="#stochastic-nystrom-computational-regularization" title="Permalink to this headline"></a></h3>
+<h3>Stochastic Nystrom Computational Regularization<a class="headerlink" href="#stochastic-nystrom-computational-regularization" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.StochasticNystromCompReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">StochasticNystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_trace_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.StochasticNystromCompReg" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">StochasticNystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_trace_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.StochasticNystromCompReg" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="complexity-regularization">
-<h3>Complexity Regularization<a class="headerlink" href="#complexity-regularization" title="Permalink to this headline"></a></h3>
+<h3>Complexity Regularization<a class="headerlink" href="#complexity-regularization" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.CompReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">CompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.CompReg" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">CompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.CompReg" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="generalized-cross-validation">
-<h3>Generalized Cross Validation<a class="headerlink" href="#generalized-cross-validation" title="Permalink to this headline"></a></h3>
+<h3>Generalized Cross Validation<a class="headerlink" href="#generalized-cross-validation" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.GCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">GCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.GCV" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">GCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.GCV" title="Permalink to this definition"></a></dt>
 <dd><p>GCV objective is</p>
 <div class="math notranslate nohighlight">
 \[\dfrac{\dfrac{1}{n} \lVert (I - \widetilde{K}_\lambda \widetilde{K}) Y \rVert^2}
@@ -332,18 +332,18 @@ <h3>Generalized Cross Validation<a class="headerlink" href="#generalized-cross-v
 
 </section>
 <section id="hold-out-cross-validation">
-<h3>Hold Out Cross Validation<a class="headerlink" href="#hold-out-cross-validation" title="Permalink to this headline"></a></h3>
+<h3>Hold Out Cross Validation<a class="headerlink" href="#hold-out-cross-validation" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.HoldOut">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">HoldOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_pct</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_iter_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.HoldOut" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">HoldOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_pct</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_iter_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.HoldOut" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="leave-one-out-cross-validation">
-<h3>Leave One Out Cross Validation<a class="headerlink" href="#leave-one-out-cross-validation" title="Permalink to this headline"></a></h3>
+<h3>Leave One Out Cross Validation<a class="headerlink" href="#leave-one-out-cross-validation" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.LOOCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">LOOCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.LOOCV" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">LOOCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.LOOCV" title="Permalink to this definition"></a></dt>
 <dd><p>LOOCV objective is to minimize the PRESS error:</p>
 <div class="math notranslate nohighlight">
 \[\min \sum_{i=1}^n \big(e_{(i)}\big)_i^2\]</div>
@@ -360,10 +360,10 @@ <h3>Leave One Out Cross Validation<a class="headerlink" href="#leave-one-out-cro
 
 </section>
 <section id="sgpr">
-<h3>SGPR<a class="headerlink" href="#sgpr" title="Permalink to this headline"></a></h3>
+<h3>SGPR<a class="headerlink" href="#sgpr" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.SGPR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">SGPR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">falkon.kernels.diff_kernel.DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.distributions.transforms.Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.SGPR" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">SGPR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.SGPR" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
diff --git a/api_reference/index.html b/api_reference/index.html
index 32f33704..293e352d 100644
--- a/api_reference/index.html
+++ b/api_reference/index.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>API Reference &mdash; falkon 0.7.2 documentation</title>
+  <title>API Reference &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -274,7 +274,7 @@
 }
 </style>
 <section id="api-reference">
-<span id="id1"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this headline"></a></h1>
+<span id="id1"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="models.html">falkon.models</a><ul>
diff --git a/api_reference/kernels.html b/api_reference/kernels.html
index 65eaffe2..4f80ea01 100644
--- a/api_reference/kernels.html
+++ b/api_reference/kernels.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.kernels &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.kernels &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -277,12 +277,12 @@
 }
 </style>
 <section id="module-falkon.kernels">
-<span id="falkon-kernels"></span><h1>falkon.kernels<a class="headerlink" href="#module-falkon.kernels" title="Permalink to this headline"></a></h1>
+<span id="falkon-kernels"></span><h1>falkon.kernels<a class="headerlink" href="#module-falkon.kernels" title="Permalink to this heading"></a></h1>
 <section id="kernel">
-<h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline"></a></h2>
+<h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.kernel.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.kernel.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract kernel class. Kernels should inherit from this class, overriding appropriate methods.</p>
 <p>To extend Falkon with new kernels, you should read the documentation of this class
 carefully, and take a look at the existing implementation of <a class="reference internal" href="#falkon.kernels.GaussianKernel" title="falkon.kernels.GaussianKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianKernel</span></code></a>
@@ -302,7 +302,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -326,7 +326,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_dmmv_impl">
-<span class="sig-name descname"><span class="pre">_decide_dmmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">_decide_dmmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>dmmv</cite> function to use for this data.</p>
 <p>Note that <cite>dmmv</cite> functions compute double kernel-vector products (see <a class="reference internal" href="#falkon.kernels.kernel.Kernel.dmmv" title="falkon.kernels.kernel.Kernel.dmmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dmmv()</span></code></a> for
 an explanation of what they are).</p>
@@ -352,7 +352,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_mm_impl">
-<span class="sig-name descname"><span class="pre">_decide_mm_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mm_impl" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">_decide_mm_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mm_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>mm</cite> function to use for this data.</p>
 <p>Note that <cite>mm</cite> functions compute the kernel itself so <strong>KeOps may not be used</strong>.</p>
 <dl class="field-list simple">
@@ -376,7 +376,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_mmv_impl">
-<span class="sig-name descname"><span class="pre">_decide_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">_decide_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>mmv</cite> function to use for this data.</p>
 <p>Note that <cite>mmv</cite> functions compute the kernel-vector product</p>
 <dl class="field-list simple">
@@ -400,7 +400,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.compute">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> - without regards for differentiability.</p>
 <p>The kernel matrix should be stored in <code class="docutils literal notranslate"><span class="pre">out</span></code> to ensure the correctness of allocatable
 memory computations.</p>
@@ -422,7 +422,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.compute_sparse">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute_sparse" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute_sparse" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> which are two sparse matrices, storing the output
 in the dense matrix <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p>
 <dl class="field-list simple">
@@ -451,7 +451,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -518,7 +518,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -556,7 +556,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this headline
 
 </section>
 <section id="diffkernel">
-<h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this headline"></a></h2>
+<h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.diff_kernel.</span></span><span class="sig-name descname"><span class="pre">DiffKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">core_fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kernel_params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel" title="Permalink to this definition"></a></dt>
@@ -593,7 +593,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel.compute_diff">
-<span class="sig-name descname"><span class="pre">compute_diff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.compute_diff" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">compute_diff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.compute_diff" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>. The output should be differentiable with
 respect to <cite>X1</cite>, <cite>X2</cite>, and all kernel parameters returned by the <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel.diff_params" title="falkon.kernels.diff_kernel.DiffKernel.diff_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">diff_params()</span></code></a> method.</p>
 <dl class="field-list simple">
@@ -613,7 +613,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel.detach">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">detach</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.detach" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">detach</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.detach" title="Permalink to this definition"></a></dt>
 <dd><p>Detaches all differentiable parameters of the kernel from the computation graph.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -625,7 +625,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 
 <dl class="py property">
 <dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel.diff_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diff_params</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.diff_params" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diff_params</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.diff_params" title="Permalink to this definition"></a></dt>
 <dd><p>A dictionary mapping parameter names to their values for all <strong>differentiable</strong> parameters
 of the kernel.</p>
 <dl class="field-list simple">
@@ -639,10 +639,10 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 
 </section>
 <section id="keopskernelmixin">
-<h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permalink to this headline"></a></h2>
+<h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.keops_helpers.</span></span><span class="sig-name descname"><span class="pre">KeopsKernelMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.keops_helpers.</span></span><span class="sig-name descname"><span class="pre">KeopsKernelMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract class for kernels which enables KeOps acceleration.</p>
 <p>This class should be extended when KeOps-accelerated kernel-vector products are required.
 Subclasses should implement the <a class="reference internal" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl"><code class="xref py py-meth docutils literal notranslate"><span class="pre">keops_mmv_impl()</span></code></a> method by defining an appropriate
@@ -653,7 +653,7 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 <a class="reference internal" href="../examples/custom_kernels.html"><span class="doc">Custom Kernels</span></a> notebook.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv">
-<span class="sig-name descname"><span class="pre">keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Helper method to call into KeOps for kernel-vector products</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -681,7 +681,7 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">keops_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">keops_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Implementation of the KeOps formula to compute a kernel-vector product.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -709,12 +709,12 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 
 </section>
 <section id="radial-kernels">
-<h2>Radial kernels<a class="headerlink" href="#radial-kernels" title="Permalink to this headline"></a></h2>
+<h2>Radial kernels<a class="headerlink" href="#radial-kernels" title="Permalink to this heading"></a></h2>
 <section id="gaussian-kernel">
-<h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalink to this headline"></a></h3>
+<h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">GaussianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">GaussianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Gaussian kernel and related kernel-vector products</p>
 <p>The Gaussian kernel is one of the most common and effective kernel embeddings
 since it is infinite dimensional, and governed by a single parameter. The kernel length-scale
@@ -769,7 +769,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 norm.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -793,7 +793,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -836,7 +836,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -874,10 +874,10 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 
 </section>
 <section id="laplacian-kernel">
-<h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permalink to this headline"></a></h3>
+<h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LaplacianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LaplacianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Laplacian kernel, and related kernel-vector products.</p>
 <p>The Laplacian kernel is similar to the Gaussian kernel, but less sensitive to changes
 in the parameter <cite>sigma</cite>.</p>
@@ -892,7 +892,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 \[k(x, x') = \exp{-\frac{\lVert x - x' \rVert}{\sigma}}\]</div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -916,7 +916,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -959,7 +959,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -997,10 +997,10 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 
 </section>
 <section id="matern-kernel">
-<h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to this headline"></a></h3>
+<h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">MaternKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">MaternKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Matern kernel, and related kernel-vector products.</p>
 <p>The Matern kernels define a generic class of kernel functions which includes the
 Laplacian and Gaussian kernels. The class is parametrized by ‘nu’. When <code class="docutils literal notranslate"><span class="pre">nu</span> <span class="pre">=</span> <span class="pre">0.5</span></code>
@@ -1024,7 +1024,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 may be computationally more efficient.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1048,7 +1048,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -1091,7 +1091,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1130,12 +1130,12 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 </section>
 </section>
 <section id="dot-product-kernels">
-<h2>Dot-Product kernels<a class="headerlink" href="#dot-product-kernels" title="Permalink to this headline"></a></h2>
+<h2>Dot-Product kernels<a class="headerlink" href="#dot-product-kernels" title="Permalink to this heading"></a></h2>
 <section id="polynomial-kernel">
-<h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline"></a></h3>
+<h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">PolynomialKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">PolynomialKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Polynomial kernel with multiplicative and additive constants.</p>
 <p>Follows the formula</p>
 <div class="math notranslate nohighlight">
@@ -1153,7 +1153,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1177,7 +1177,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -1220,7 +1220,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1258,14 +1258,14 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 
 </section>
 <section id="linear-kernel">
-<h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline"></a></h3>
+<h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LinearKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LinearKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Linear Kernel with optional scaling and translation parameters.</p>
 <p>The kernel implemented here is the covariance function in the original
 input space (i.e. <cite>X &#64; X.T</cite>) with optional parameters to translate
-and scale the kernel: <cite>beta + 1/(sigma**2) * X &#64; X.T</cite></p>
+and scale the kernel: <cite>beta + gamma * X &#64; X.T</cite></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -1277,15 +1277,15 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">LinearKernel</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">LinearKernel</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># 100 samples in 3 dimensions</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">kernel_matrix</span> <span class="o">=</span> <span class="n">k</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">kernel_matrix</span><span class="p">,</span> <span class="n">X</span> <span class="o">@</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">kernel_matrix</span><span class="p">,</span> <span class="n">X</span> <span class="o">@</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1309,7 +1309,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -1352,7 +1352,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1390,10 +1390,10 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 
 </section>
 <section id="sigmoid-kernel">
-<h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink to this headline"></a></h3>
+<h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">SigmoidKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">SigmoidKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Sigmoid (or hyperbolic tangent) kernel function, with additive and multiplicative constants.</p>
 <p>Follows the formula</p>
 <div class="math notranslate nohighlight">
@@ -1409,7 +1409,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1433,7 +1433,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -1476,7 +1476,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/api_reference/mmv_ops.html b/api_reference/mmv_ops.html
index 504a7649..da027132 100644
--- a/api_reference/mmv_ops.html
+++ b/api_reference/mmv_ops.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.mmv_ops &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.mmv_ops &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -277,7 +277,7 @@
 }
 </style>
 <section id="falkon-mmv-ops">
-<h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink to this headline"></a></h1>
+<h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink to this heading"></a></h1>
 <p>The algorithms to compute kernels and kernel-vector products blockwise on GPUs and CPU. The algorithms in this module
 are kernel agnostic. Refer to <a class="reference internal" href="kernels.html#module-falkon.kernels" title="falkon.kernels"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.kernels</span></code></a> for the actual kernel implementations.</p>
 <p>The KeOps wrapper only supports the <cite>mmv</cite> operation (kernel-vector products). The matrix-multiplication implementations
@@ -291,39 +291,39 @@ <h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink
 </ul>
 </div></blockquote>
 <span class="target" id="module-falkon.mmv_ops"></span><section id="run-keops-mmv">
-<h2>run_keops_mmv<a class="headerlink" href="#run-keops-mmv" title="Permalink to this headline"></a></h2>
+<h2>run_keops_mmv<a class="headerlink" href="#run-keops-mmv" title="Permalink to this heading"></a></h2>
 <p>A thin wrapper to KeOps is provided to allow for block-splitting and multiple GPU usage. This only supports
 kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.keops.run_keops_mmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.keops.</span></span><span class="sig-name descname"><span class="pre">run_keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'Sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.keops.run_keops_mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.keops.</span></span><span class="sig-name descname"><span class="pre">run_keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'Sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.keops.run_keops_mmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="fmm">
-<h2>fmm<a class="headerlink" href="#fmm" title="Permalink to this headline"></a></h2>
+<h2>fmm<a class="headerlink" href="#fmm" title="Permalink to this heading"></a></h2>
 <p>Block-wise kernel calculation. If the inputs require gradient, this function uses a differentiable implementation.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmm.fmm">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">fmm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.fmm" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">fmm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseOptions</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.fmm" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="fmmv">
-<h2>fmmv<a class="headerlink" href="#fmmv" title="Permalink to this headline"></a></h2>
+<h2>fmmv<a class="headerlink" href="#fmmv" title="Permalink to this heading"></a></h2>
 <p>Block-wise kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="fdmmv">
-<h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this headline"></a></h2>
+<h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this heading"></a></h2>
 <p>Block-wise double kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fdmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fdmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fdmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Double kernel-vector product</p>
 <p>Computes kernel <span class="math notranslate nohighlight">\(K = k(X_1, X_2)\)</span> and then the double kernel-vector
 product <span class="math notranslate nohighlight">\(K^{\top} (K v + w)\)</span>.</p>
@@ -351,28 +351,28 @@ <h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this headline">
 
 </section>
 <section id="incore-fmmv">
-<h2>incore_fmmv<a class="headerlink" href="#incore-fmmv" title="Permalink to this headline"></a></h2>
+<h2>incore_fmmv<a class="headerlink" href="#incore-fmmv" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv_incore.incore_fmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="incore-fdmmv">
-<h2>incore_fdmmv<a class="headerlink" href="#incore-fdmmv" title="Permalink to this headline"></a></h2>
+<h2>incore_fdmmv<a class="headerlink" href="#incore-fdmmv" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv_incore.incore_fdmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
 <section id="low-level-functions">
-<h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="Permalink to this headline"></a></h2>
+<h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="Permalink to this heading"></a></h2>
 <p>The following are some of the low-level functions which help compute kernels and kernel-vector products block-wise.
 They are specialized for different input types.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmm.sparse_mm_run_thread">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">sparse_mm_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comp_dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">sparse_mm_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comp_dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="Permalink to this definition"></a></dt>
 <dd><p>Inner loop to compute (part of) a kernel matrix for two sparse input tensors</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -387,6 +387,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 <li><p><strong>comp_dt</strong> – Data-type in which to run the actual calculations (may be different from the data-type
 of <cite>m1</cite> or <cite>m2</cite>).</p></li>
 <li><p><strong>dev</strong> – Device on which to run the calculations</p></li>
+<li><p><strong>tid</strong> – Thread ID. If on the main thread this will be -1</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -397,7 +398,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.sparse_mmv_run_thread">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">sparse_mmv_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mem_needed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">sparse_mmv_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mem_needed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="Permalink to this definition"></a></dt>
 <dd><p>Inner loop to compute (part of) a kernel-vector product for sparse input matrices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -413,6 +414,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 <li><p><strong>blk_m</strong> – Block size for the first ais of <cite>m2</cite></p></li>
 <li><p><strong>mem_needed</strong> – Memory needed for pre-allocations</p></li>
 <li><p><strong>dev</strong> – Device on which to run the calculations</p></li>
+<li><p><strong>tid</strong> – Thread ID or -1 if on main thread</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
diff --git a/api_reference/models.html b/api_reference/models.html
index 42d82d92..924626c3 100644
--- a/api_reference/models.html
+++ b/api_reference/models.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.models &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.models &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -275,12 +275,12 @@
 }
 </style>
 <section id="module-falkon.models">
-<span id="falkon-models"></span><h1>falkon.models<a class="headerlink" href="#module-falkon.models" title="Permalink to this headline"></a></h1>
+<span id="falkon-models"></span><h1>falkon.models<a class="headerlink" href="#module-falkon.models" title="Permalink to this heading"></a></h1>
 <section id="falkon">
-<span id="falkon-model"></span><h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline"></a></h2>
+<span id="falkon-model"></span><h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.Falkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">Falkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">Falkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition"></a></dt>
 <dd><p>Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
 projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
@@ -315,9 +315,10 @@
 <cite>error_every</cite> iterations. If set to 1 then the error will be
 calculated at each iteration. If set to None, it will never be
 calculated.</p></li>
-<li><p><strong>weight_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – <p>A function which appropriately weighs the target tensor (i.e. Y). This function is used
-for weighted least-squares, it should accept a single argument which represents a subset
-of the targets, and return a vector of weights corresponding to the input targets.</p>
+<li><p><strong>weight_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – <p>A function for giving different weights to different samples. This is used
+for weighted least-squares, it should accept three arguments: <cite>Y</cite>, <cite>X</cite>, <cite>indices</cite> which
+represent the samples for which weights need to be computed, and return a vector of
+weights corresponding to the input targets.</p>
 <p>As an example, in the setting of binary classification Y can be -1 or +1. To give more
 importance to errors on the negative class, pass a <cite>weight_fn</cite> which returns 2 whenever
 the target is -1.</p>
@@ -354,7 +355,7 @@
 </ul>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.Falkon.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.fit" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon KRR model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -409,7 +410,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.Falkon.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.models.Falkon.predict" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.Falkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -444,10 +445,10 @@
 
 </section>
 <section id="logisticfalkon">
-<span id="log-falkon-model"></span><h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink to this headline"></a></h2>
+<span id="log-falkon-model"></span><h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.LogisticFalkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">LogisticFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><span class="pre">falkon.gsc_losses.Loss</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">LogisticFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><span class="pre">Loss</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon" title="Permalink to this definition"></a></dt>
 <dd><p>Falkon Logistic regression solver.</p>
 <p>This estimator object solves approximate logistic regression problems with Nystroem
 projections and a fast optimization algorithm as described in  <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#log-flk"><span class="std std-ref">[3]</span></a>.</p>
@@ -525,7 +526,7 @@
 inner-steps (e.g. 8) for the remaining iterations.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.LogisticFalkon.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.fit" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon Kernel Logistic Regression model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -575,7 +576,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.LogisticFalkon.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.models.LogisticFalkon.predict" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.LogisticFalkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -610,10 +611,10 @@
 
 </section>
 <section id="incorefalkon">
-<span id="ic-falkon-model"></span><h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to this headline"></a></h2>
+<span id="ic-falkon-model"></span><h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">InCoreFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">falkon.center_selection.CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">InCoreFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition"></a></dt>
 <dd><p>In GPU core Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
 projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
@@ -660,9 +661,10 @@
 <cite>error_every</cite> iterations. If set to 1 then the error will be
 calculated at each iteration. If set to None, it will never be
 calculated.</p></li>
-<li><p><strong>weight_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – <p>A function which appropriately weighs the target tensor (i.e. Y). This function is used
-for weighted least-squares, it should accept a single argument which represents a subset
-of the targets, and return a vector of weights corresponding to the input targets.</p>
+<li><p><strong>weight_fn</strong> (<em>Callable</em><em> or </em><em>None</em>) – <p>A function for giving different weights to different samples. This is used
+for weighted least-squares, it should accept three arguments: <cite>Y</cite>, <cite>X</cite>, <cite>indices</cite> which
+represent the samples for which weights need to be computed, and return a vector of
+weights corresponding to the input targets.</p>
 <p>As an example, in the setting of binary classification Y can be -1 or +1. To give more
 importance to errors on the negative class, pass a <cite>weight_fn</cite> which returns 2 whenever
 the target is -1.</p>
@@ -694,7 +696,7 @@
 </ul>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.fit" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon KRR model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -749,7 +751,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.models.InCoreFalkon.predict" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.InCoreFalkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/api_reference/optimization.html b/api_reference/optimization.html
index 302d1adf..5deed2f7 100644
--- a/api_reference/optimization.html
+++ b/api_reference/optimization.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.optim &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.optim &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -277,9 +277,9 @@
 }
 </style>
 <section id="module-falkon.optim">
-<span id="falkon-optim"></span><h1>falkon.optim<a class="headerlink" href="#module-falkon.optim" title="Permalink to this headline"></a></h1>
+<span id="falkon-optim"></span><h1>falkon.optim<a class="headerlink" href="#module-falkon.optim" title="Permalink to this heading"></a></h1>
 <section id="optimizer">
-<h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline"></a></h2>
+<h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.optim.Optimizer">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">Optimizer</span></span><a class="headerlink" href="#falkon.optim.Optimizer" title="Permalink to this definition"></a></dt>
@@ -288,15 +288,15 @@ <h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this he
 
 </section>
 <section id="conjugate-gradient-methods">
-<h2>Conjugate gradient methods<a class="headerlink" href="#conjugate-gradient-methods" title="Permalink to this headline"></a></h2>
+<h2>Conjugate gradient methods<a class="headerlink" href="#conjugate-gradient-methods" title="Permalink to this heading"></a></h2>
 <section id="conjugategradient">
-<h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Permalink to this headline"></a></h3>
+<h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.optim.ConjugateGradient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">ConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">falkon.options.ConjugateGradientOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.ConjugateGradient" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">ConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">ConjugateGradientOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.ConjugateGradient" title="Permalink to this definition"></a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="falkon.optim.ConjugateGradient.solve">
-<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmv</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.optim.ConjugateGradient.solve" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmv</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.optim.ConjugateGradient.solve" title="Permalink to this definition"></a></dt>
 <dd><p>Conjugate-gradient solver with optional support for preconditioning via generic MMV.</p>
 <p>This solver can be used for iterative solution of linear systems of the form $AX = B$ with
 respect to the <cite>X</cite> variable. Knowledge of <cite>A</cite> is only needed through matrix-vector
@@ -332,10 +332,10 @@ <h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Perm
 
 </section>
 <section id="falkonconjugategradient">
-<h3>FalkonConjugateGradient<a class="headerlink" href="#falkonconjugategradient" title="Permalink to this headline"></a></h3>
+<h3>FalkonConjugateGradient<a class="headerlink" href="#falkonconjugategradient" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.optim.FalkonConjugateGradient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">FalkonConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">falkon.kernels.kernel.Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner" title="falkon.preconditioner.preconditioner.Preconditioner"><span class="pre">falkon.preconditioner.preconditioner.Preconditioner</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.FalkonConjugateGradient" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">FalkonConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner" title="falkon.preconditioner.preconditioner.Preconditioner"><span class="pre">Preconditioner</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.FalkonConjugateGradient" title="Permalink to this definition"></a></dt>
 <dd><p>Preconditioned conjugate gradient solver, optimized for the Falkon algorithm.</p>
 <p>The linear system solved is</p>
 <div class="math notranslate nohighlight">
diff --git a/api_reference/options.html b/api_reference/options.html
index d87b7599..2ba10e74 100644
--- a/api_reference/options.html
+++ b/api_reference/options.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.options &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.options &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -275,9 +275,9 @@
 }
 </style>
 <section id="module-falkon.options">
-<span id="falkon-options"></span><span id="api-options"></span><h1>falkon.options<a class="headerlink" href="#module-falkon.options" title="Permalink to this headline"></a></h1>
+<span id="falkon-options"></span><span id="api-options"></span><h1>falkon.options<a class="headerlink" href="#module-falkon.options" title="Permalink to this heading"></a></h1>
 <section id="falkonoptions">
-<h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to this headline"></a></h2>
+<h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.options.FalkonOptions">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.options.</span></span><span class="sig-name descname"><span class="pre">FalkonOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keops_acc_dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_sum_scheme</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_active</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_memory_slack</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_in_core</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_ooc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-13</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_tolerance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_full_gradient_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_differential_convergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_arch_speed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_single_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">30000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">300000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">900000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">never_store_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_kernel_d_threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_fmm_streams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.options.FalkonOptions" title="Permalink to this definition"></a></dt>
diff --git a/api_reference/outofcore.html b/api_reference/outofcore.html
index b21ab9bf..13fe2d0f 100644
--- a/api_reference/outofcore.html
+++ b/api_reference/outofcore.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.ooc_ops &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.ooc_ops &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -275,7 +275,7 @@
 }
 </style>
 <section id="falkon-ooc-ops">
-<h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink to this headline"></a></h1>
+<h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink to this heading"></a></h1>
 <p>The out-of-core algorithms for the Cholesky decomposition and the LAUUM operation are crucial for speeding up our
 library. To find out more about how they work, check the source code:</p>
 <blockquote>
@@ -286,15 +286,15 @@ <h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink
 </div></blockquote>
 <p>The following functions provide a higher-level interface to the two operations.</p>
 <span class="target" id="module-falkon.ooc_ops"></span><section id="gpu-cholesky">
-<h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to this headline"></a></h2>
+<h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.ooc_ops.gpu_cholesky">
-<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_cholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.ooc_ops.gpu_cholesky" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_cholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.ooc_ops.gpu_cholesky" title="Permalink to this definition"></a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>A</strong> (<em>torch.Tensor</em>) – 2D positive-definite matrix of size (n x n) that will be factorized as
-A = U.T &#64; U (if <cite>upper</cite> is True) or A = L &#64; L.T if <cite>upper</cite>
+<code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">=</span> <span class="pre">U.T</span> <span class="pre">&#64;</span> <span class="pre">U</span></code> (if <cite>upper</cite> is True) or <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">=</span> <span class="pre">L</span> <span class="pre">&#64;</span> <span class="pre">L.T</span></code> if <cite>upper</cite>
 is False.</p></li>
 <li><p><strong>upper</strong> (<em>bool</em>) – Whether the triangle which should be factorized is the upper or lower of <cite>A</cite>.</p></li>
 <li><p><strong>clean</strong> (<em>bool</em>) – Whether the “other” triangle of the output matrix (the one that
@@ -316,23 +316,29 @@ <h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to t
 
 </section>
 <section id="gpu-lauum">
-<h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this headline"></a></h2>
+<h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.ooc_ops.gpu_lauum">
-<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_lauum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_opposite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.ooc_ops.gpu_lauum" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_lauum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_opposite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.ooc_ops.gpu_lauum" title="Permalink to this definition"></a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>A</strong> (<em>torch.Tensor</em>) – (N x N) positive-definite matrix that will be factorized as
-A = U.T &#64; U (if <cite>upper</cite> is True) or A = L &#64; L.T if <cite>upper</cite>
-is False.</p></li>
-<li><p><strong>overwrite</strong> (<em>bool</em>) – Whether to overwrite matrix A or to output the result in a new
-buffer.</p></li>
+<li><p><strong>A</strong> (<em>torch.Tensor</em>) – N-by-N triangular matrix.</p></li>
+<li><p><strong>upper</strong> (<em>bool</em>) – Whether the input matrix is upper or lower triangular.</p></li>
+<li><p><strong>overwrite</strong> (<em>bool</em>) – Whether to overwrite matrix A or to output the result in a new buffer.</p></li>
+<li><p><strong>write_opposite</strong> (<em>bool</em>) – Independently of the <code class="docutils literal notranslate"><span class="pre">overwrite</span></code> parameter, whether to write the result of the
+triangular multiplication on the ‘opposite’ side of <code class="docutils literal notranslate"><span class="pre">A</span></code>. For example, if <code class="docutils literal notranslate"><span class="pre">upper</span> <span class="pre">==</span> <span class="pre">True</span></code>
+and <code class="docutils literal notranslate"><span class="pre">overwrite</span> <span class="pre">==</span> <span class="pre">False</span></code>, then the result will be written on the lower triangular part
+of the input matrix <code class="docutils literal notranslate"><span class="pre">A</span></code>.
+While independent, this is mostly useful when <code class="docutils literal notranslate"><span class="pre">overwrite</span> <span class="pre">==</span> <span class="pre">False</span></code>, since it can
+effectively avoid allocating a new tensor, and at the same time preserve the original data.</p></li>
+<li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em> or </em><em>None</em>) – Options for the LAUUM operation. The only relevant options are the one connected to
+GPU memory usage.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – A (N x N) tensor. This will share the same memory as the input tensor <cite>A</cite> if <cite>overwrite</cite>
-is set to True, otherwise it will be a newly allocated tensor.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – A (N x N) tensor. This will share the same memory as the input tensor <code class="docutils literal notranslate"><span class="pre">A</span></code> if <code class="docutils literal notranslate"><span class="pre">overwrite</span></code>
+is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, otherwise it will be a newly allocated tensor.</p>
 </dd>
 </dl>
 </dd></dl>
diff --git a/api_reference/preconditioner.html b/api_reference/preconditioner.html
index 8806b2a5..64cf0081 100644
--- a/api_reference/preconditioner.html
+++ b/api_reference/preconditioner.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.preconditioner &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.preconditioner &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -277,9 +277,9 @@
 }
 </style>
 <section id="module-falkon.preconditioner">
-<span id="falkon-preconditioner"></span><h1>falkon.preconditioner<a class="headerlink" href="#module-falkon.preconditioner" title="Permalink to this headline"></a></h1>
+<span id="falkon-preconditioner"></span><h1>falkon.preconditioner<a class="headerlink" href="#module-falkon.preconditioner" title="Permalink to this heading"></a></h1>
 <section id="preconditioner">
-<h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink to this headline"></a></h2>
+<h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.preconditioner.preconditioner.Preconditioner">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.preconditioner.</span></span><span class="sig-name descname"><span class="pre">Preconditioner</span></span><a class="headerlink" href="#falkon.preconditioner.preconditioner.Preconditioner" title="Permalink to this definition"></a></dt>
@@ -306,12 +306,12 @@ <h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink
 
 </section>
 <section id="cholesky-preconditioners">
-<h2>Cholesky preconditioners<a class="headerlink" href="#cholesky-preconditioners" title="Permalink to this headline"></a></h2>
+<h2>Cholesky preconditioners<a class="headerlink" href="#cholesky-preconditioners" title="Permalink to this heading"></a></h2>
 <section id="falkonpreconditioner">
-<h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title="Permalink to this headline"></a></h3>
+<h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></span><span class="sig-name descname"><span class="pre">FalkonPreconditioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></span><span class="sig-name descname"><span class="pre">FalkonPreconditioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner" title="Permalink to this definition"></a></dt>
 <dd><p>Approximated Cholesky Preconditioner for FALKON.</p>
 <p>The preconditioner is based on the <span class="math notranslate nohighlight">\(K_{MM}\)</span> kernel between the
 inducing points. A two step approximation of the inverse matrix
@@ -356,7 +356,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply" title="Permalink to this definition"></a></dt>
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(ATx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -378,7 +378,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.apply_t">
-<span class="sig-name descname"><span class="pre">apply_t</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply_t" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">apply_t</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.apply_t" title="Permalink to this definition"></a></dt>
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(A^\top T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -400,7 +400,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.init">
-<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.init" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.init" title="Permalink to this definition"></a></dt>
 <dd><p>Initialize the preconditioner matrix.</p>
 <p>This method must be called before the preconditioner can be used.</p>
 <dl class="field-list simple">
@@ -416,7 +416,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invA">
-<span class="sig-name descname"><span class="pre">invA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invA" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">invA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invA" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Ax = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -438,7 +438,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invAt">
-<span class="sig-name descname"><span class="pre">invAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invAt" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">invAt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invAt" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(A^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -460,7 +460,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invT">
-<span class="sig-name descname"><span class="pre">invT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invT" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">invT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invT" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Tx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -482,7 +482,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.invTt">
-<span class="sig-name descname"><span class="pre">invTt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invTt" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">invTt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.invTt" title="Permalink to this definition"></a></dt>
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
@@ -506,10 +506,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 </section>
 <section id="logisticpreconditioner">
-<h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" title="Permalink to this headline"></a></h3>
+<h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></span><span class="sig-name descname"><span class="pre">LogisticPreconditioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">falkon.options.FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.preconditioner.</span></span><span class="sig-name descname"><span class="pre">LogisticPreconditioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner" title="Permalink to this definition"></a></dt>
 <dd><p>Approximate Cholesky Preconditioner for Logistic-FALKON.</p>
 <p>The preconditioner is based on the K_MM kernel between the
 inducing points. A two step approximation of the inverse matrix
@@ -612,7 +612,7 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.init">
-<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.init" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.init" title="Permalink to this definition"></a></dt>
 <dd><p>Initialize the preconditioner matrix.</p>
 <p>This method must be called before the preconditioner becomes usable.</p>
 <dl class="field-list simple">
diff --git a/api_reference/sparse.html b/api_reference/sparse.html
index 9c4384b7..5a0bcf3e 100644
--- a/api_reference/sparse.html
+++ b/api_reference/sparse.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.sparse &mdash; falkon 0.7.2 documentation</title>
+  <title>falkon.sparse &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -275,12 +275,12 @@
 }
 </style>
 <section id="module-falkon.sparse">
-<span id="falkon-sparse"></span><h1>falkon.sparse<a class="headerlink" href="#module-falkon.sparse" title="Permalink to this headline"></a></h1>
+<span id="falkon-sparse"></span><h1>falkon.sparse<a class="headerlink" href="#module-falkon.sparse" title="Permalink to this heading"></a></h1>
 <section id="sparsetensor">
-<h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to this headline"></a></h2>
+<h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_tensor.SparseTensor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></span><span class="sig-name descname"><span class="pre">SparseTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexptr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><span class="pre">falkon.sparse.sparse_tensor.SparseType</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">SparseType.CSR</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></span><span class="sig-name descname"><span class="pre">SparseTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexptr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><span class="pre">SparseType</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">SparseType.CSR</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper class to represent sparse 2D matrices in CSR or CSC format.</p>
 <p>The wrapper holds three 1D torch tensors which give the sparse representation
 (an index pointer, an index and the non-zero values of the matrix).
@@ -303,7 +303,7 @@ <h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_tensor.SparseTensor.narrow_rows">
-<span class="sig-name descname"><span class="pre">narrow_rows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">narrow_rows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows" title="Permalink to this definition"></a></dt>
 <dd><p>Select a subset of contiguous rows from the sparse matrix.
 If this is a CSC sparse matrix, instead of taking contiguous rows we take contiguous
 columns.</p>
@@ -332,10 +332,10 @@ <h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to t
 
 </section>
 <section id="sparse-operations">
-<h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Permalink to this headline"></a></h2>
+<h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_matmul">
-<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_matmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_matmul" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_matmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_matmul" title="Permalink to this definition"></a></dt>
 <dd><p>Sparse*Sparse matrix multiplication. Output will be copied into dense <cite>out</cite> matrix.</p>
 <p>This function can be applied to CPU or CUDA tensors (but all tensors must
 be  on the same device).</p>
@@ -355,7 +355,7 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_square_norm">
-<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_square_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_square_norm" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_square_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_square_norm" title="Permalink to this definition"></a></dt>
 <dd><p>Row-wise squared l2 norm of a sparse 2D matrix.</p>
 <p>The operation is equivalent to squaring all elements of the matrix, and summing up the rows.</p>
 <dl class="field-list simple">
@@ -377,7 +377,7 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_norm">
-<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">falkon.sparse.sparse_tensor.SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_norm" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_norm" title="Permalink to this definition"></a></dt>
 <dd><p>Row-wise l2 norm of a sparse 2D matrix</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/examples/custom_kernels.html b/examples/custom_kernels.html
index 8a9ebbfb..b47b9533 100644
--- a/examples/custom_kernels.html
+++ b/examples/custom_kernels.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Implementing A Custom Kernel &mdash; falkon 0.7.2 documentation</title>
+  <title>Implementing A Custom Kernel &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -467,6 +467,7 @@
 }
 
 /* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
 div.rendered_html table {
   border: none;
   border-collapse: collapse;
@@ -475,10 +476,14 @@
   font-size: 12px;
   table-layout: fixed;
 }
+.jp-RenderedHTMLCommon thead,
 div.rendered_html thead {
   border-bottom: 1px solid black;
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+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
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 div.rendered_html tr,
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@@ -490,12 +495,15 @@
   max-width: none;
   border: none;
 }
+.jp-RenderedHTMLCommon th,
 div.rendered_html th {
   font-weight: bold;
 }
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
 div.rendered_html tbody tr:nth-child(odd) {
   background: #f5f5f5;
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+.jp-RenderedHTMLCommon tbody tr:hover,
 div.rendered_html tbody tr:hover {
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@@ -523,7 +531,7 @@
 }
 </style>
 <section id="Implementing-A-Custom-Kernel">
-<h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Custom-Kernel" title="Permalink to this headline"></a></h1>
+<h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Custom-Kernel" title="Permalink to this heading"></a></h1>
 <p>In this notebook we will show how to implement a custom kernel in Falkon.</p>
 <p>There are several complementary parts to a kernel, which can be added to support different operations. We will go through them one-by-one in this notebook:</p>
 <ul class="simple">
@@ -558,7 +566,7 @@ <h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Cust
 </pre></div></div>
 </div>
 <section id="Setup-a-simple-problem-for-testing">
-<h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simple-problem-for-testing" title="Permalink to this headline"></a></h2>
+<h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simple-problem-for-testing" title="Permalink to this heading"></a></h2>
 <p>Load and preprocess the <em>California housing</em> dataset. The <code class="docutils literal notranslate"><span class="pre">learn_with_kernel</span></code> function sets up Falkon for learning on the California housing datase with a given kernel.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -609,7 +617,7 @@ <h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simpl
 </div>
 </section>
 <section id="Basic-Kernel-Implementation">
-<h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Implementation" title="Permalink to this headline"></a></h2>
+<h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Implementation" title="Permalink to this heading"></a></h2>
 <p>We must inherit from the <code class="docutils literal notranslate"><span class="pre">falkon.kernels.Kernel</span></code> class, and implement: - <code class="docutils literal notranslate"><span class="pre">compute</span></code> method: the core of the kernel implementation. Given two input matrices (of size <span class="math notranslate nohighlight">\(n\times d\)</span> and <span class="math notranslate nohighlight">\(m\times d\)</span>), and an output matrix (of size <span class="math notranslate nohighlight">\(n\times m\)</span>), compute the kernel function between the two inputs and store it in the output.</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>The additional `diag` parameter is a boolean flag. It indicates that a) $n$ is equal to $m$, b) only the diagonal of the kernel matrix should be computed.
 </pre></div>
@@ -655,7 +663,7 @@ <h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Impleme
 </div>
 </div>
 <section id="Test-the-basic-kernel">
-<h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" title="Permalink to this headline"></a></h3>
+<h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" title="Permalink to this heading"></a></h3>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
@@ -770,7 +778,7 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 </section>
 </section>
 <section id="Differentiable-Kernel">
-<h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" title="Permalink to this headline"></a></h2>
+<h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" title="Permalink to this heading"></a></h2>
 <p>A differentiable kernel is needed for automatic hyperparameter optimization (see the <a class="reference internal" href="hyperopt.html"><span class="doc">notebook</span></a>).</p>
 <p>It requires inheriting from <code class="docutils literal notranslate"><span class="pre">falkon.kernels.DiffKernel</span></code>. In addition to the methods already discussed, we must implement: - <code class="docutils literal notranslate"><span class="pre">compute_diff</span></code>, which works similarly to the <code class="docutils literal notranslate"><span class="pre">compute</span></code> method but it does not have an <code class="docutils literal notranslate"><span class="pre">out</span></code> parameter. The implementation should be fully differentiable with respect to its inputs, and to the kernel hyperparameters. - <code class="docutils literal notranslate"><span class="pre">detach</span></code>, which essentially clones the kernel with the parameters <em>detached</em> from the computational graph.</p>
 <p>Another important difference from the basic kernel is the call to the <em>constructor</em>, which must include - All kernel hyperparameters as keyword arguments. These will be available as attributes on the class. Hyperparameters do not need to be tensors.</p>
@@ -822,7 +830,7 @@ <h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" tit
 </div>
 </div>
 <section id="Test-the-differentiable-kernel">
-<h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differentiable-kernel" title="Permalink to this headline"></a></h3>
+<h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differentiable-kernel" title="Permalink to this heading"></a></h3>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
@@ -948,7 +956,7 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 </section>
 </section>
 <section id="Adding-KeOps-Support">
-<h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title="Permalink to this headline"></a></h2>
+<h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title="Permalink to this heading"></a></h2>
 <p>We must inherit from <code class="docutils literal notranslate"><span class="pre">falkon.kernels.KeopsKernelMixin</span></code> and implement the method <code class="docutils literal notranslate"><span class="pre">keops_mmv_impl</span></code>.</p>
 <p>KeOps-enabled kernels will still use the implementation in the <code class="docutils literal notranslate"><span class="pre">compute</span></code> function for computing the kernel matrix itself, but will use KeOps to compute kernel-vector products (if the data dimension is small enough).</p>
 <p>This method is responsible for kernel-vector products, and it should contain: 1. A formula definition (see <a class="reference external" href="https://www.kernel-operations.io/keops/api/math-operations.html">https://www.kernel-operations.io/keops/api/math-operations.html</a> for the appropriate syntax) 2. A definition of all variables (again have a look at the KeOps documentation, or the implementation of other kernels within Falkon) 3. A call to the <code class="docutils literal notranslate"><span class="pre">keops_mmv</span></code> method of the <code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code> class, responsible for calling into the KeOps formula.</p>
@@ -1012,7 +1020,7 @@ <h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title
 </div>
 </div>
 <section id="Test-the-KeOps-kernel">
-<h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" title="Permalink to this headline"></a></h3>
+<h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" title="Permalink to this heading"></a></h3>
 <p>Note that KeOps will need to compile the kernels the first time they are run!</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
@@ -1098,7 +1106,7 @@ <h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" tit
 </section>
 </section>
 <section id="Supporting-Sparse-Data">
-<h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" title="Permalink to this headline"></a></h2>
+<h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" title="Permalink to this heading"></a></h2>
 <p>Sparse support can be necessary for kernel learning in extremely high dimensions, when the inputs are sparse.</p>
 <p>Sparse support requires using special functions for common operations such as matrix multiplication. Falkon implements sparse tensors in a CSR format (PyTorch is slowly picking this format up, in place of COO), through the <code class="docutils literal notranslate"><span class="pre">falkon.sparse.SparseTensor</span></code> class.</p>
 <p>We will implement the <code class="docutils literal notranslate"><span class="pre">compute_sparse</span></code> method below, supporting both diagonal and full kernels. However, only CPU support is added here (CUDA support is possible but requires a few more details), and differentiable sparse kernels are not supported.</p>
@@ -1164,7 +1172,7 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 </div>
 </div>
 <section id="Testing-sparse-support">
-<h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" title="Permalink to this headline"></a></h3>
+<h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" title="Permalink to this heading"></a></h3>
 <p>We generate two sparse matrices, and check that the sparse kernel is equivalent to the dense version.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
diff --git a/examples/examples.html b/examples/examples.html
index 51059a8f..2033892a 100644
--- a/examples/examples.html
+++ b/examples/examples.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Examples &mdash; falkon 0.7.2 documentation</title>
+  <title>Examples &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -274,7 +274,7 @@
 }
 </style>
 <section id="examples">
-<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline"></a></h1>
+<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this heading"></a></h1>
 <div class="toctree-wrapper compound">
 </div>
 <p>Starting with simple kernel ridge regression, via classification, hyperparameter tuning, to large-scale GPU experiments,
diff --git a/examples/falkon_cv.html b/examples/falkon_cv.html
index fcbc429f..a5c2a7f9 100644
--- a/examples/falkon_cv.html
+++ b/examples/falkon_cv.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Hyperparameter Tuning with Falkon &mdash; falkon 0.7.2 documentation</title>
+  <title>Hyperparameter Tuning with Falkon &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -467,6 +467,7 @@
 }
 
 /* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
 div.rendered_html table {
   border: none;
   border-collapse: collapse;
@@ -475,10 +476,14 @@
   font-size: 12px;
   table-layout: fixed;
 }
+.jp-RenderedHTMLCommon thead,
 div.rendered_html thead {
   border-bottom: 1px solid black;
   vertical-align: bottom;
 }
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
 div.rendered_html tr,
 div.rendered_html th,
 div.rendered_html td {
@@ -490,12 +495,15 @@
   max-width: none;
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 }
+.jp-RenderedHTMLCommon th,
 div.rendered_html th {
   font-weight: bold;
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+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
 div.rendered_html tbody tr:nth-child(odd) {
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@@ -523,9 +531,9 @@
 }
 </style>
 <section id="Hyperparameter-Tuning-with-Falkon">
-<h1>Hyperparameter Tuning with Falkon<a class="headerlink" href="#Hyperparameter-Tuning-with-Falkon" title="Permalink to this headline"></a></h1>
+<h1>Hyperparameter Tuning with Falkon<a class="headerlink" href="#Hyperparameter-Tuning-with-Falkon" title="Permalink to this heading"></a></h1>
 <section id="Introduction">
-<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline"></a></h2>
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this heading"></a></h2>
 <p>We use Falkon for a multi-class problem (on the <a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">digits</a> dataset), showing how to integrate it into scikit-learn for hyperparameter optimization.</p>
 <p>Since both <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> and <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> are estimators, and follow scikit-learn’s API, integration is seamless</p>
 <div class="nbinput docutils container">
@@ -553,7 +561,7 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </div>
 </section>
 <section id="Load-the-data">
-<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this heading"></a></h2>
 <p>We use the <strong>digits</strong> dataset, which is distributed alongside scikit-learn.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -590,7 +598,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 </div>
 </section>
 <section id="Split-into-training-and-test-sets">
-<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline"></a></h2>
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this heading"></a></h2>
 <p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -603,7 +611,7 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 </div>
 </section>
 <section id="Data-Preprocessing">
-<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this heading"></a></h2>
 <p>As always with Falkon we must: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32)</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -654,7 +662,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 </div>
 </section>
 <section id="Search-for-the-optimal-parameters">
-<h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the-optimal-parameters" title="Permalink to this headline"></a></h2>
+<h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the-optimal-parameters" title="Permalink to this heading"></a></h2>
 <p>Since Falkon (with the Gaussian kernel) has only 3 important hyperparameters, it is entirely feasible to run a grid search over them to find the best parameter settings.</p>
 <p>Scikit-learn has great support for this, with the <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> class. For each paramater setting it will run 5-fold cross-validation on the training set, to determine which has the best results.</p>
 <p>Given the dataset is quite small, and Falkon is fast, we can run 160 model evaluations in around 40 seconds.</p>
@@ -715,7 +723,7 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 </pre></div></div>
 </div>
 <section id="Evaluating-the-model">
-<h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title="Permalink to this headline"></a></h3>
+<h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title="Permalink to this heading"></a></h3>
 <p>We evaluate the model on the held-out set and see that we obtain a respectable 1% error on 10 classes.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
@@ -759,7 +767,7 @@ <h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title
 </section>
 </section>
 <section id="Plot-grid-search-results">
-<h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-results" title="Permalink to this headline"></a></h2>
+<h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-results" title="Permalink to this heading"></a></h2>
 <p>Plotting results from a grid-search is always useful, since it shows the range of parameters which were successful. If the initial grid was too coarse, one could then run a second grid search to obtain even better accuracy.</p>
 <p>In the plot red indicates a high error, while darker blue indicates a low error.</p>
 <div class="nbinput nblast docutils container">
diff --git a/examples/falkon_mnist.html b/examples/falkon_mnist.html
index 32d949cf..d3138a07 100644
--- a/examples/falkon_mnist.html
+++ b/examples/falkon_mnist.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>MNIST Classification with Falkon &mdash; falkon 0.7.2 documentation</title>
+  <title>MNIST Classification with Falkon &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -467,6 +467,7 @@
 }
 
 /* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
 div.rendered_html table {
   border: none;
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   table-layout: fixed;
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+.jp-RenderedHTMLCommon thead,
 div.rendered_html thead {
   border-bottom: 1px solid black;
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+.jp-RenderedHTMLCommon tr,
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+.jp-RenderedHTMLCommon th,
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   font-weight: bold;
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+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
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+.jp-RenderedHTMLCommon tbody tr:hover,
 div.rendered_html tbody tr:hover {
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@@ -523,7 +531,7 @@
 }
 </style>
 <section id="MNIST-Classification-with-Falkon">
-<h1>MNIST Classification with Falkon<a class="headerlink" href="#MNIST-Classification-with-Falkon" title="Permalink to this headline"></a></h1>
+<h1>MNIST Classification with Falkon<a class="headerlink" href="#MNIST-Classification-with-Falkon" title="Permalink to this heading"></a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -548,7 +556,7 @@ <h1>MNIST Classification with Falkon<a class="headerlink" href="#MNIST-Classific
 </pre></div></div>
 </div>
 <section id="Download-the-MNIST-dataset-&amp;-load-it-in-memory">
-<h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href="#Download-the-MNIST-dataset-&-load-it-in-memory" title="Permalink to this headline"></a></h2>
+<h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href="#Download-the-MNIST-dataset-&-load-it-in-memory" title="Permalink to this heading"></a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -620,7 +628,7 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 </div>
 </section>
 <section id="Data-Preprocessing">
-<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this heading"></a></h2>
 <p>We convert the labels to their one-hot representation. This is the best way to run multi-class classification with Falkon which minimizes the squared error</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
@@ -647,7 +655,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 </div>
 </section>
 <section id="Run-Falkon">
-<h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this headline"></a></h2>
+<h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this heading"></a></h2>
 <p>There are a few parameters which need to be provided to the algorithm</p>
 <ul>
 <li><p>The <code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code> class is used to provide non-standard tuning knobs. It allows to, for example, tune the amount of GPU memory the algorithm can use, adjust the convergence tolerance, and decide whether certain parts of the algorithm are computed on CPU or GPU.</p>
diff --git a/examples/falkon_regression_tutorial.html b/examples/falkon_regression_tutorial.html
index 86b9b960..b650b385 100644
--- a/examples/falkon_regression_tutorial.html
+++ b/examples/falkon_regression_tutorial.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Falkon Regression Tutorial &mdash; falkon 0.7.2 documentation</title>
+  <title>Falkon Regression Tutorial &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -467,6 +467,7 @@
 }
 
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+.jp-RenderedHTMLCommon table,
 div.rendered_html table {
   border: none;
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   font-size: 12px;
   table-layout: fixed;
 }
+.jp-RenderedHTMLCommon thead,
 div.rendered_html thead {
   border-bottom: 1px solid black;
   vertical-align: bottom;
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+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
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 div.rendered_html tr,
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@@ -523,9 +531,9 @@
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 </style>
 <section id="Falkon-Regression-Tutorial">
-<h1>Falkon Regression Tutorial<a class="headerlink" href="#Falkon-Regression-Tutorial" title="Permalink to this headline"></a></h1>
+<h1>Falkon Regression Tutorial<a class="headerlink" href="#Falkon-Regression-Tutorial" title="Permalink to this heading"></a></h1>
 <section id="Introduction">
-<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline"></a></h2>
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this heading"></a></h2>
 <p>This notebook introduces the main interface of the Falkon library, using a toy regression problem.</p>
 <p>We will be using the Boston housing dataset which is included in <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> to train a Falkon model. Since the dataset is very small, it is not necessary to use the Nystroem approximation here. It is however useful to demonstrate the simple API offered by Falkon.</p>
 <div class="nbinput docutils container">
@@ -553,7 +561,7 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </div>
 </section>
 <section id="Load-the-data">
-<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this heading"></a></h2>
 <p>The Boston housing dataset poses a regression problem with 506 data points in 13 dimensions. The goal is to predict house prices given some attributes including criminality rates, air pollution, property value, etc.</p>
 <p>After loading the data, we split it into two parts: a training set (containing 80% of the points) and a test set with the remaining 20%. Data splitting could alternatively be done using some scikit-learn utilities (found in the <a class="reference external" href="https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation">model_selection module</a>)</p>
 <div class="nbinput nblast docutils container">
@@ -582,7 +590,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 </div>
 </section>
 <section id="Pre-process-the-data">
-<h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title="Permalink to this headline"></a></h2>
+<h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title="Permalink to this heading"></a></h2>
 <p>We must convert the numpy arrays to PyTorch tensors before using them in Falkon. This is very easy and fast with the <code class="docutils literal notranslate"><span class="pre">torch.from_numpy</span></code> function.</p>
 <p>Another preprocessing step which is often necessary with kernel methods is to normalize the z-score of the data: convert it to have zero-mean and unit standard deviation. We use the statistics of the training data to avoid leakage between the two sets.</p>
 <div class="nbinput nblast docutils container">
@@ -613,7 +621,7 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
 </div>
 </section>
 <section id="Create-the-Falkon-model">
-<h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model" title="Permalink to this headline"></a></h2>
+<h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model" title="Permalink to this heading"></a></h2>
 <p>The Falkon object is the main API of this library. It is similar in spirit to the fit-transform API of scikit-learn, while supporting some additional features such as monitoring of validation error.</p>
 <p>While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much larger datasets. Here we only showcase some of the more basic options.</p>
 <p>Mandatory parameters are: - the kernel function (here we use a linear kernel) - the amount of regularization, which we set to some small positive value - the number of inducing points M. We set M to 5000, which is a sizable portion of the dataset.</p>
@@ -639,7 +647,7 @@ <h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model"
 </div>
 </section>
 <section id="Training-the-model">
-<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Permalink to this headline"></a></h2>
+<h2>Training the model<a class="headerlink" href="#Training-the-model" title="Permalink to this heading"></a></h2>
 <p>The Falkon model is trained using the preconditioned conjugate gradient algorithm (TODO: Add a reference). Thus there are two steps to the algorithm: first the preconditioner is computed, and then the conjugate gradient iterations are performed. To gain more insight in the various steps of the algorithm you can pass <code class="docutils literal notranslate"><span class="pre">debug=True</span></code> when creating the Falkon object.</p>
 <p>Model training will occur on the GPU, if it is available, and CUDA is properly installed, or on the CPU as a fallback.</p>
 <div class="nbinput docutils container">
@@ -663,7 +671,7 @@ <h2>Training the model<a class="headerlink" href="#Training-the-model" title="Pe
 <p>Optimization converges very quickly to a minimum, where convergence is detected by checking the change model parameters between iterations.</p>
 </section>
 <section id="Evaluating-model-performance">
-<h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-performance" title="Permalink to this headline"></a></h2>
+<h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-performance" title="Permalink to this heading"></a></h2>
 <p>Since the problem is regression a natural error metric is the RMSE. Given a fitted model, we can run the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method to obtain predictions on new data.</p>
 <p>Here we print the error on both train and test sets.</p>
 <div class="nbinput docutils container">
diff --git a/examples/hyperopt.html b/examples/hyperopt.html
index ae29104c..b95ee0f0 100644
--- a/examples/hyperopt.html
+++ b/examples/hyperopt.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Automatic Hyperparameter Optimization &mdash; falkon 0.7.2 documentation</title>
+  <title>Automatic Hyperparameter Optimization &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -467,6 +467,7 @@
 }
 
 /* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
 div.rendered_html table {
   border: none;
   border-collapse: collapse;
@@ -475,10 +476,14 @@
   font-size: 12px;
   table-layout: fixed;
 }
+.jp-RenderedHTMLCommon thead,
 div.rendered_html thead {
   border-bottom: 1px solid black;
   vertical-align: bottom;
 }
+.jp-RenderedHTMLCommon tr,
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 div.rendered_html tr,
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+.jp-RenderedHTMLCommon th,
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 div.rendered_html tbody tr:nth-child(odd) {
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 }
+.jp-RenderedHTMLCommon tbody tr:hover,
 div.rendered_html tbody tr:hover {
   background: rgba(66, 165, 245, 0.2);
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@@ -523,7 +531,7 @@
 }
 </style>
 <section id="Automatic-Hyperparameter-Optimization">
-<h1>Automatic Hyperparameter Optimization<a class="headerlink" href="#Automatic-Hyperparameter-Optimization" title="Permalink to this headline"></a></h1>
+<h1>Automatic Hyperparameter Optimization<a class="headerlink" href="#Automatic-Hyperparameter-Optimization" title="Permalink to this heading"></a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -547,7 +555,7 @@ <h1>Automatic Hyperparameter Optimization<a class="headerlink" href="#Automatic-
 </pre></div></div>
 </div>
 <section id="Load-the-data">
-<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this heading"></a></h2>
 <p>We use the <strong>digits</strong> dataset, which is distributed alongside scikit-learn.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -583,7 +591,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 </div>
 </section>
 <section id="Split-into-training-and-test-sets">
-<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline"></a></h2>
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this heading"></a></h2>
 <p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -596,7 +604,7 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 </div>
 </section>
 <section id="Data-Preprocessing">
-<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this heading"></a></h2>
 <p>As always with Falkon we must: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32)</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -649,7 +657,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 </div>
 </section>
 <section id="Hyperparameter-Optimization">
-<h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optimization" title="Permalink to this headline"></a></h2>
+<h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optimization" title="Permalink to this heading"></a></h2>
 <p>The Falkon algorithm has three main kinds of hyper-parameters: 1. The kernel parameters. Most commonly when using the Gaussian kernel these are the length-scales for each dimension in the data 2. The amount of regularization <span class="math notranslate nohighlight">\(\lambda\)</span> (the penalty term, which helps prevent overfitting). 3. The Nystrom centers. These are sometimes not considered hyper parameters, and they are chosen uniformly at random from the training set. However, it is possible to find better centers!</p>
 <p>In the <a class="reference internal" href="falkon_cv.html"><span class="doc">grid-search</span></a> notebook all three types of hyperparameters are considered, but only with a small number of options in each category.</p>
 <p>With automatic hyperparameter optimization, which is based on a gradient descent-type procedure, we can instead define a much larger search space for the hyperparameters.</p>
diff --git a/examples/hyperopt.ipynb b/examples/hyperopt.ipynb
index 32bddaff..2e5be028 100644
--- a/examples/hyperopt.ipynb
+++ b/examples/hyperopt.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b57c79fd",
+   "id": "07982d3f",
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b44861fd",
+   "id": "6dca605d",
    "metadata": {
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     "pycharm": {
diff --git a/examples/logistic_falkon.html b/examples/logistic_falkon.html
index 65549a84..606b0957 100644
--- a/examples/logistic_falkon.html
+++ b/examples/logistic_falkon.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Introducing Logistic Falkon &mdash; falkon 0.7.2 documentation</title>
+  <title>Introducing Logistic Falkon &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
         <script src="../_static/underscore.js"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -33,7 +34,7 @@
             <a href="../index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +50,6 @@
 <li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../install.html#installing">Installing</a></li>
@@ -467,6 +467,7 @@
 }
 
 /* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
 div.rendered_html table {
   border: none;
   border-collapse: collapse;
@@ -475,10 +476,14 @@
   font-size: 12px;
   table-layout: fixed;
 }
+.jp-RenderedHTMLCommon thead,
 div.rendered_html thead {
   border-bottom: 1px solid black;
   vertical-align: bottom;
 }
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
 div.rendered_html tr,
 div.rendered_html th,
 div.rendered_html td {
@@ -490,12 +495,15 @@
   max-width: none;
   border: none;
 }
+.jp-RenderedHTMLCommon th,
 div.rendered_html th {
   font-weight: bold;
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+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
 div.rendered_html tbody tr:nth-child(odd) {
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+.jp-RenderedHTMLCommon tbody tr:hover,
 div.rendered_html tbody tr:hover {
   background: rgba(66, 165, 245, 0.2);
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@@ -523,9 +531,9 @@
 }
 </style>
 <section id="Introducing-Logistic-Falkon">
-<h1>Introducing Logistic Falkon<a class="headerlink" href="#Introducing-Logistic-Falkon" title="Permalink to this headline"></a></h1>
+<h1>Introducing Logistic Falkon<a class="headerlink" href="#Introducing-Logistic-Falkon" title="Permalink to this heading"></a></h1>
 <section id="Introduction">
-<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this headline"></a></h2>
+<h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this heading"></a></h2>
 <p>In this notebook we use a synthetic dataset to show how the <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> estimator works.</p>
 <p>We compare Falkon – which is trained on the squared loss – to its logistic loss version, and verify that on a binary classification problem logistic falkon achieves better accuracy.</p>
 <div class="nbinput docutils container">
@@ -552,7 +560,7 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </div>
 </section>
 <section id="Load-the-data">
-<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this headline"></a></h2>
+<h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to this heading"></a></h2>
 <p>We use a synthetic dataset in two dimensions, which consists of two concentric circles labeled with different classes.</p>
 <p>We introduce quite a bit of noise so that the estimators will not trivially achieve zero error, and we draw 10000 samples to demonstrate the speed of the <code class="docutils literal notranslate"><span class="pre">Falkon</span></code> library.</p>
 <div class="nbinput nblast docutils container">
@@ -583,7 +591,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 </div>
 </section>
 <section id="Split-into-training-and-test-sets">
-<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this headline"></a></h2>
+<h2>Split into training and test sets<a class="headerlink" href="#Split-into-training-and-test-sets" title="Permalink to this heading"></a></h2>
 <p>We split the data into a training set with 80% of the samples and a test set with the remaining 20%.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -596,7 +604,7 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 </div>
 </section>
 <section id="Data-Preprocessing">
-<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this headline"></a></h2>
+<h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this heading"></a></h2>
 <p>The minimal preprocessing steps are: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32) 3. Reshape the labels so that they have two dimensions. This is not strictly necessary, but Falkon internally works with 2D tensors, so the output of the <code class="docutils literal notranslate"><span class="pre">Falkon.predict</span></code> method will always be 2D. 4. Change the labels from 0, 1 to -1, 1. Note that Logistic Falkon uses the following formula for the logistic loss:</p>
 <div class="math notranslate nohighlight">
 \[\log(1 + e^{-y_1 y_2})\]</div>
@@ -625,7 +633,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 </div>
 </section>
 <section id="Define-the-Falkon-model">
-<h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model" title="Permalink to this headline"></a></h2>
+<h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model" title="Permalink to this heading"></a></h2>
 <p>We use the same base parameters for both models: a small amount of regularization (<span class="math notranslate nohighlight">\(10^{-7}\)</span>) and a Gaussian kernel with <span class="math notranslate nohighlight">\(\sigma = 5\)</span>. The number of inducing points is set to 1000 which is adequate for the problem which is very easy.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -651,7 +659,7 @@ <h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model"
 </div>
 </section>
 <section id="Define-Logistic-Falkon-model">
-<h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Falkon-model" title="Permalink to this headline"></a></h2>
+<h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Falkon-model" title="Permalink to this heading"></a></h2>
 <p>The logistic Falkon estimator uses the same base parameters as Falkon.</p>
 <p>However, instead of specifying a single value for regularization, we need to specify a <em>regularization path</em>: a series of decreasing amounts of regularization. For each regularization value we also need to specify the <em>number of iterations</em> of conjugate gradient descent, which will be performed for that specific regularization value.</p>
 <p>We validated empirically on a wide number of binary classification problems that a good scheme to set the regularization path is to use three short (i.e. 3 iterations) runs with increasing regularization, and then a few longer (here we used 8 iterations) runs with the final regularization value (here <code class="docutils literal notranslate"><span class="pre">1e-7</span></code>, the same as for Falkon).</p>
@@ -677,7 +685,7 @@ <h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Fal
 </div>
 </section>
 <section id="Train-both-models">
-<h2>Train both models<a class="headerlink" href="#Train-both-models" title="Permalink to this headline"></a></h2>
+<h2>Train both models<a class="headerlink" href="#Train-both-models" title="Permalink to this heading"></a></h2>
 <p>Training Falkon for 20 iterations (default value) takes about half a secon on a laptop.</p>
 <p>Clearly, since the logistic falkon runs about 28 iterations (the sum of values in <code class="docutils literal notranslate"><span class="pre">iter_list</span></code>), it is necessarily going to be slower. Further, logistic falkon needs to recompute part of the preconditioner at every step of the Newton method leading to further slowdowns. On the same laptop, the logistic falkon algorithm takes around 1.5s.</p>
 <div class="nbinput docutils container">
@@ -749,7 +757,7 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 </div>
 </section>
 <section id="Testing">
-<h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headline"></a></h2>
+<h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this heading"></a></h2>
 <p>However, the price paid for with a higher training time, leads to lower training error.</p>
 <p>We found that, on a variety of binary classification datasets, logistic falkon obtains a slightly lower error than the vanilla Falkon algorithm.</p>
 <div class="nbinput docutils container">
@@ -779,7 +787,7 @@ <h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headli
 </section>
 </section>
 <section id="Plot-predictions">
-<h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permalink to this headline"></a></h1>
+<h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permalink to this heading"></a></h1>
 <p>In the plot we have the outer and inner circles which are correct predictions, and the circles in the middle which are the mispredicted points. Since we added lots of noise to the dataset, perfect predictions are not possible (there is no clear boundary between the two classes).</p>
 <p>Here the error difference between Falkon and Logistic Falkon is very hard to distinguish by eye. However, there may be other applications where the best possible classification error is desired.</p>
 <div class="nbinput nblast docutils container">
diff --git a/genindex.html b/genindex.html
index 9d965654..6d968226 100644
--- a/genindex.html
+++ b/genindex.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Index &mdash; falkon 0.7.2 documentation</title>
+  <title>Index &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -13,6 +13,7 @@
         <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
         <script src="_static/underscore.js"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
@@ -28,7 +29,7 @@
             <a href="index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -44,7 +45,6 @@
 <li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
diff --git a/get_started.html b/get_started.html
index d6aff22e..87212055 100644
--- a/get_started.html
+++ b/get_started.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Getting Started &mdash; falkon 0.7.2 documentation</title>
+  <title>Getting Started &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
         <script src="_static/underscore.js"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
@@ -274,7 +274,7 @@
 }
 </style>
 <section id="getting-started">
-<span id="get-started"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline"></a></h1>
+<span id="get-started"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading"></a></h1>
 <p>Once Falkon is installed, getting started is easy. The basic setup to use the <cite>Falkon</cite> estimator only requires
 few lines of code:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
@@ -296,7 +296,7 @@
 </pre></div>
 </div>
 <section id="passing-options">
-<h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalink to this headline"></a></h2>
+<h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalink to this heading"></a></h2>
 <p>A number of different options exist for both the <a class="reference internal" href="api_reference/models.html#falkon-model"><span class="std std-ref">Falkon</span></a> and <a class="reference internal" href="api_reference/models.html#log-falkon-model"><span class="std std-ref">LogisticFalkon</span></a>
 estimators (see <a class="reference internal" href="api_reference/options.html#api-options"><span class="std std-ref">falkon.FalkonOptions</span></a>).
 All options can be passed to the estimator through the <a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a> class, like so:</p>
@@ -315,7 +315,7 @@ <h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalin
 </div>
 </section>
 <section id="more-examples">
-<h2>More Examples<a class="headerlink" href="#more-examples" title="Permalink to this headline"></a></h2>
+<h2>More Examples<a class="headerlink" href="#more-examples" title="Permalink to this heading"></a></h2>
 <p>For more detailed examples, have a look at the <a class="reference internal" href="examples/examples.html#examples"><span class="std std-ref">example notebooks</span></a>.</p>
 </section>
 </section>
diff --git a/index.html b/index.html
index 689856c1..662ec8e4 100644
--- a/index.html
+++ b/index.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Falkon &mdash; falkon 0.7.2 documentation</title>
+  <title>Falkon &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
         <script src="_static/underscore.js"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
@@ -30,7 +31,7 @@
             <a href="#" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -46,7 +47,6 @@
 <li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="install.html#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="install.html#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="install.html#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="install.html#installing">Installing</a></li>
@@ -273,7 +273,7 @@
 }
 </style>
 <section id="falkon">
-<h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline"></a></h1>
+<h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this heading"></a></h1>
 <p>A Python library for large-scale kernel methods, with optional (multi-)GPU acceleration.</p>
 <p>The library currently includes two solvers:
 one for approximate kernel ridge regression <a class="reference internal" href="#flk-2"><span class="std std-ref">[2]</span></a> which is extremely fast, and one for kernel logistic
@@ -295,7 +295,7 @@ <h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this headline
 calculations, and <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">KeOps</a> for fast kernel evaluations on the GPU.</p>
 <p>If you find this library useful for your research, please cite our paper <a class="reference internal" href="#flk-1"><span class="std std-ref">[1]</span></a>!</p>
 <section id="contents">
-<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this headline"></a></h2>
+<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this heading"></a></h2>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Install</a><ul>
@@ -330,7 +330,7 @@ <h2>Contents<a class="headerlink" href="#contents" title="Permalink to this head
 </div>
 </section>
 <section id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this headline"></a></h2>
+<h2>References<a class="headerlink" href="#references" title="Permalink to this heading"></a></h2>
 <p id="flk-1">Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi,
 “Kernel methods through the roof: handling billions of points efficiently,”
 Advancs in Neural Information Processing Systems, 2020.</p>
@@ -340,7 +340,7 @@ <h2>References<a class="headerlink" href="#references" title="Permalink to this
 Generalized Self-concordant Losses,” Advances in Neural Information Processing Systems, 2019.</p>
 </section>
 <section id="indices-and-tables">
-<h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h2>
+<h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading"></a></h2>
 <ul class="simple">
 <li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
 <li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
diff --git a/install.html b/install.html
index 4c9a8efd..7d9ff387 100644
--- a/install.html
+++ b/install.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Install &mdash; falkon 0.7.2 documentation</title>
+  <title>Install &mdash; falkon 0.7.5 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -14,6 +14,7 @@
         <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
         <script src="_static/underscore.js"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
@@ -31,7 +32,7 @@
             <a href="index.html" class="icon icon-home"> falkon
           </a>
               <div class="version">
-                0.7.2
+                0.7.5
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -47,7 +48,6 @@
 <li class="toctree-l2"><a class="reference internal" href="#prerequisites">Prerequisites</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#pytorch-and-cuda">PyTorch and CUDA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#intel-mkl">Intel MKL</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#keops">KeOps</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#installing">Installing</a></li>
@@ -274,18 +274,18 @@
 }
 </style>
 <section id="install">
-<span id="id1"></span><h1>Install<a class="headerlink" href="#install" title="Permalink to this headline"></a></h1>
+<span id="id1"></span><h1>Install<a class="headerlink" href="#install" title="Permalink to this heading"></a></h1>
 <section id="supported-platforms">
-<h2>Supported Platforms<a class="headerlink" href="#supported-platforms" title="Permalink to this headline"></a></h2>
+<h2>Supported Platforms<a class="headerlink" href="#supported-platforms" title="Permalink to this heading"></a></h2>
 <p>Falkon is only tested on Linux.</p>
 <p>GPU support is achieved via CUDA, so using Falkon from Windows will be tricky.</p>
 <p>Using the library from the CPU only is much more portable since there is no CUDA requirement, and it will likely work
 on Windows and Mac OS more easily.</p>
 </section>
 <section id="prerequisites">
-<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h2>
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this heading"></a></h2>
 <section id="pytorch-and-cuda">
-<h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permalink to this headline"></a></h3>
+<h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permalink to this heading"></a></h3>
 <p>Falkon depends on PyTorch, and on the NVIDIA Toolkit (for NVIDIA GPU support) which is usually installed
 alongside PyTorch.
 PyTorch can be installed in several ways by following the instructions at <a class="reference external" href="https://pytorch.org/get-started/locally/">Install PyTorch</a>.
@@ -296,45 +296,27 @@ <h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permal
 </div>
 </section>
 <section id="intel-mkl">
-<h3>Intel MKL<a class="headerlink" href="#intel-mkl" title="Permalink to this headline"></a></h3>
+<h3>Intel MKL<a class="headerlink" href="#intel-mkl" title="Permalink to this heading"></a></h3>
 <p>If Falkon is not installed with GPU support, it will try to link to <a class="reference external" href="https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html">Intel MKL</a>
 to speed-up certain sparse operations. MKL shared libraries are usually distributed with numpy, so this should not be a problem.
 In case sparse matrix support is not needed, the MKL library will not be loaded.</p>
 </section>
-<section id="keops">
-<h3>KeOps<a class="headerlink" href="#keops" title="Permalink to this headline"></a></h3>
-<p>Falkon uses a patched version of <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">KeOps</a>, therefore until the
-patches are merged in the KeOps library, users will need to uninstall existing KeOps installations (if present), and
-install our patched version.</p>
-<p>The patched version is available as a submodule of the Falkon repository. To install it, you can do the following:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ git clone --recurse-submodules https://github.com/FalkonML/falkon.git
-$ <span class="nb">cd</span> falkon/
-$ pip install ./keops
-</pre></div>
-</div>
-<p>For use with <strong>Google Colab</strong> KeOps needs some additional packages. You can install them alongside KeOps by running</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!pip install pykeops[colab] &gt; install.log
-</pre></div>
-</div>
-<p>from within the Jupyter notebook (see the <a class="reference external" href="https://www.kernel-operations.io/keops/python/installation.html">KeOps docs</a>
-for more information).</p>
-</section>
 </section>
 <section id="installing">
-<h2>Installing<a class="headerlink" href="#installing" title="Permalink to this headline"></a></h2>
-<p>After having installed KeOps, you are ready to install Falkon with <cite>pip</cite> (from the root of the git tree):</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install .
+<h2>Installing<a class="headerlink" href="#installing" title="Permalink to this heading"></a></h2>
+<p>You can install Falkon</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install git+https://github.com/falkonml/falkon.git
 </pre></div>
 </div>
 <p>This will take a while since there are both Cython extensions which need to be compiled (if Cython is installed),
 and PyTorch extensions which take some time to compile.</p>
 </section>
 <section id="testing-the-installation">
-<h2>Testing the installation<a class="headerlink" href="#testing-the-installation" title="Permalink to this headline"></a></h2>
+<h2>Testing the installation<a class="headerlink" href="#testing-the-installation" title="Permalink to this heading"></a></h2>
 <p>To check that everything works correctly you can follow the <a class="reference internal" href="examples/falkon_regression_tutorial.html"><span class="doc">Kernel ridge regression</span></a> notebook.</p>
 </section>
 <section id="development">
-<h2>Development<a class="headerlink" href="#development" title="Permalink to this headline"></a></h2>
+<h2>Development<a class="headerlink" href="#development" title="Permalink to this heading"></a></h2>
 <p>For development purposes the library should be installed in editable mode (i.e. <cite>pip install -e .</cite> from the
 falkon directory.</p>
 <p>To build the documentation go into the <cite>doc</cite> directory and run <cite>make html</cite>.</p>
diff --git a/objects.inv b/objects.inv
index 8fc6f6b9a2f62720644e4d179fae39e94b9ebfb5..a60df1700c75483b51d06599ba141a36b8c4604c 100644
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\ No newline at end of file

From 1731aee53b34c05a93ad56b50f048b4f395b30b3 Mon Sep 17 00:00:00 2001
From: Giodiro <Giodiro@users.noreply.github.com>
Date: Wed, 28 Dec 2022 19:09:32 +0000
Subject: [PATCH 4/7] deploy: e0f35851b00e181d6f2a5c52fc1aa160b1e7b0d6

---
 _static/basic.css                        |  31 +-
 _static/css/badge_only.css               |   2 +-
 _static/css/theme.css                    |   4 +-
 _static/doctools.js                      | 130 +----
 _static/documentation_options.js         |   2 +-
 _static/searchtools.js                   |  91 +++-
 _static/sphinx_highlight.js              | 144 +++++
 api_reference/center_selector.html       | 142 ++++-
 api_reference/gsc_losses.html            | 142 ++++-
 api_reference/hopt.html                  | 142 ++++-
 api_reference/index.html                 | 275 ++++++++--
 api_reference/kernels.html               | 142 ++++-
 api_reference/mmv_ops.html               | 142 ++++-
 api_reference/models.html                | 142 ++++-
 api_reference/optimization.html          | 142 ++++-
 api_reference/options.html               | 147 ++++-
 api_reference/outofcore.html             | 142 ++++-
 api_reference/preconditioner.html        | 142 ++++-
 api_reference/sparse.html                | 142 ++++-
 examples/custom_kernels.html             | 661 +++++++++++++----------
 examples/examples.html                   | 140 ++++-
 examples/falkon_cv.html                  | 305 +++++++----
 examples/falkon_mnist.html               | 253 ++++++---
 examples/falkon_regression_tutorial.html | 231 +++++---
 examples/hyperopt.html                   | 277 ++++++----
 examples/hyperopt.ipynb                  |   4 +-
 examples/logistic_falkon.html            | 273 ++++++----
 genindex.html                            | 140 ++++-
 get_started.html                         | 140 ++++-
 index.html                               | 140 ++++-
 install.html                             | 140 ++++-
 py-modindex.html                         | 140 ++++-
 search.html                              | 140 ++++-
 searchindex.js                           |   2 +-
 34 files changed, 3733 insertions(+), 1499 deletions(-)
 create mode 100644 _static/sphinx_highlight.js

diff --git a/_static/basic.css b/_static/basic.css
index 7d5974c3..eeb0519a 100644
--- a/_static/basic.css
+++ b/_static/basic.css
@@ -608,8 +608,6 @@ ol.simple p,
 ul.simple p {
     margin-bottom: 0;
 }
-
-/* Docutils 0.17 and older (footnotes & citations) */
 dl.footnote > dt,
 dl.citation > dt {
     float: left;
@@ -627,33 +625,6 @@ dl.citation > dd:after {
     clear: both;
 }
 
-/* Docutils 0.18+ (footnotes & citations) */
-aside.footnote > span,
-div.citation > span {
-    float: left;
-}
-aside.footnote > span:last-of-type,
-div.citation > span:last-of-type {
-  padding-right: 0.5em;
-}
-aside.footnote > p {
-  margin-left: 2em;
-}
-div.citation > p {
-  margin-left: 4em;
-}
-aside.footnote > p:last-of-type,
-div.citation > p:last-of-type {
-    margin-bottom: 0em;
-}
-aside.footnote > p:last-of-type:after,
-div.citation > p:last-of-type:after {
-    content: "";
-    clear: both;
-}
-
-/* Footnotes & citations ends */
-
 dl.field-list {
     display: grid;
     grid-template-columns: fit-content(30%) auto;
@@ -665,11 +636,11 @@ dl.field-list > dt {
     padding-left: 0.5em;
     padding-right: 5px;
 }
-
 dl.field-list > dt:after {
     content: ":";
 }
 
+
 dl.field-list > dd {
     padding-left: 0.5em;
     margin-top: 0em;
diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css
index e380325b..c718cee4 100644
--- a/_static/css/badge_only.css
+++ b/_static/css/badge_only.css
@@ -1 +1 @@
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diff --git a/_static/css/theme.css b/_static/css/theme.css
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format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block}
\ No newline at end of file
diff --git a/_static/doctools.js b/_static/doctools.js
index c3db08d1..527b876c 100644
--- a/_static/doctools.js
+++ b/_static/doctools.js
@@ -10,6 +10,13 @@
  */
 "use strict";
 
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
 const _ready = (callback) => {
   if (document.readyState !== "loading") {
     callback();
@@ -18,73 +25,11 @@ const _ready = (callback) => {
   }
 };
 
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
 /**
  * Small JavaScript module for the documentation.
  */
 const Documentation = {
   init: () => {
-    Documentation.highlightSearchWords();
     Documentation.initDomainIndexTable();
     Documentation.initOnKeyListeners();
   },
@@ -126,51 +71,6 @@ const Documentation = {
     Documentation.LOCALE = catalog.locale;
   },
 
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:Documentation.hideSearchWords()">' +
-            Documentation.gettext("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-  },
-
   /**
    * helper function to focus on search bar
    */
@@ -210,15 +110,11 @@ const Documentation = {
     )
       return;
 
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
     document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
 
       if (!event.shiftKey) {
         switch (event.key) {
@@ -240,10 +136,6 @@ const Documentation = {
               event.preventDefault();
             }
             break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
         }
       }
 
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 0e817375..5f348b0a 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -10,5 +10,5 @@ var DOCUMENTATION_OPTIONS = {
     SOURCELINK_SUFFIX: '.txt',
     NAVIGATION_WITH_KEYS: false,
     SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
+    ENABLE_SEARCH_SHORTCUTS: true,
 };
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
index ac4d5861..e89e34d4 100644
--- a/_static/searchtools.js
+++ b/_static/searchtools.js
@@ -57,14 +57,14 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, highlightTerms, searchTerms) => {
+const _displayItem = (item, searchTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
   const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
 
-  const [docName, title, anchor, descr] = item;
+  const [docName, title, anchor, descr, score, _filename] = item;
 
   let listItem = document.createElement("li");
   let requestUrl;
@@ -82,13 +82,12 @@ const _displayItem = (item, highlightTerms, searchTerms) => {
     requestUrl = docUrlRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
   let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
   linkEl.innerHTML = title;
   if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
+    listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
   else if (showSearchSummary)
     fetch(requestUrl)
@@ -96,7 +95,7 @@ const _displayItem = (item, highlightTerms, searchTerms) => {
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
+            Search.makeSearchSummary(data, searchTerms)
           );
       });
   Search.output.appendChild(listItem);
@@ -116,15 +115,14 @@ const _finishSearch = (resultCount) => {
 const _displayNextItem = (
   results,
   resultCount,
-  highlightTerms,
   searchTerms
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
+    _displayItem(results.pop(), searchTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms),
       5
     );
   }
@@ -155,10 +153,8 @@ const Search = {
   _pulse_status: -1,
 
   htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
     const docContent = htmlElement.querySelector('[role="main"]');
     if (docContent !== undefined) return docContent.textContent;
     console.warn(
@@ -239,6 +235,12 @@ const Search = {
    * execute search (requires search index to be loaded)
    */
   query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -266,6 +268,10 @@ const Search = {
       }
     });
 
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
     // console.debug("SEARCH: searching for:");
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
@@ -274,6 +280,40 @@ const Search = {
     let results = [];
     _removeChildren(document.getElementById("search-progress"));
 
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
     // lookup as object
     objectTerms.forEach((term) =>
       results.push(...Search.performObjectSearch(term, objectTerms))
@@ -320,7 +360,7 @@ const Search = {
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
+    _displayNextItem(results, results.length, searchTerms);
   },
 
   /**
@@ -401,8 +441,8 @@ const Search = {
     // prepare search
     const terms = Search._index.terms;
     const titleTerms = Search._index.titleterms;
-    const docNames = Search._index.docnames;
     const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
     const titles = Search._index.titles;
 
     const scoreMap = new Map();
@@ -499,16 +539,15 @@ const Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
+   * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
     if (text === "") return null;
 
+    const textLower = text.toLowerCase();
     const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
+      .map((k) => textLower.indexOf(k.toLowerCase()))
       .filter((i) => i > -1)
       .slice(-1)[0];
     const startWithContext = Math.max(actualStartPosition - 120, 0);
@@ -516,13 +555,9 @@ const Search = {
     const top = startWithContext === 0 ? "" : "...";
     const tail = startWithContext + 240 < text.length ? "..." : "";
 
-    let summary = document.createElement("div");
+    let summary = document.createElement("p");
     summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
 
     return summary;
   },
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 00000000..aae669d7
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,144 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(SphinxHighlight.highlightSearchWords);
+_ready(SphinxHighlight.initEscapeListener);
diff --git a/api_reference/center_selector.html b/api_reference/center_selector.html
index 9dcb5769..7d9c1a58 100644
--- a/api_reference/center_selector.html
+++ b/api_reference/center_selector.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,9 +322,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.center_selection</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.center_selection</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/center_selector.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/api_reference/gsc_losses.html b/api_reference/gsc_losses.html
index bcc08b83..9229e3f5 100644
--- a/api_reference/gsc_losses.html
+++ b/api_reference/gsc_losses.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
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+        <script src="../_static/sphinx_highlight.js"></script>
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@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
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   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.gsc_losses</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.gsc_losses</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/gsc_losses.rst.txt" rel="nofollow"> View page source</a>
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diff --git a/api_reference/hopt.html b/api_reference/hopt.html
index b28d84b0..b1b04e4e 100644
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@@ -132,15 +133,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -156,17 +175,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -175,7 +209,10 @@
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+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -184,33 +221,80 @@
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#complexity-regularization">Complexity Regularization</a></li>
@@ -239,9 +323,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
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-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.hopt</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.hopt</li>
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diff --git a/api_reference/index.html b/api_reference/index.html
index 293e352d..fb6772db 100644
--- a/api_reference/index.html
+++ b/api_reference/index.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+        <script src="../_static/sphinx_highlight.js"></script>
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     <script src="../_static/js/theme.js"></script>
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@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
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 </li>
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
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-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
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 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
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 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,8 +322,8 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>API Reference</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">API Reference</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/index.rst.txt" rel="nofollow"> View page source</a>
       </li>
@@ -278,15 +362,33 @@
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l2"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l2"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l2"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l2"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -302,17 +404,32 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l2"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -321,7 +438,10 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l2"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -330,33 +450,80 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l2"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l2"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l2"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l2"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l2"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l2"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
diff --git a/api_reference/kernels.html b/api_reference/kernels.html
index 4f80ea01..50c535ac 100644
--- a/api_reference/kernels.html
+++ b/api_reference/kernels.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
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+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.kernels</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.kernels</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/kernels.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/api_reference/mmv_ops.html b/api_reference/mmv_ops.html
index da027132..ee6099be 100644
--- a/api_reference/mmv_ops.html
+++ b/api_reference/mmv_ops.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
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+        <script src="../_static/sphinx_highlight.js"></script>
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@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
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 </li>
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-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.mmv_ops</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.mmv_ops</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/mmv_ops.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/api_reference/models.html b/api_reference/models.html
index 924626c3..869e473a 100644
--- a/api_reference/models.html
+++ b/api_reference/models.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
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 </li>
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
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-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
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 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,9 +322,9 @@
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           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.models</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.models</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/models.rst.txt" rel="nofollow"> View page source</a>
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diff --git a/api_reference/optimization.html b/api_reference/optimization.html
index 5deed2f7..bb0888b5 100644
--- a/api_reference/optimization.html
+++ b/api_reference/optimization.html
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         <script src="../_static/underscore.js"></script>
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+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -133,15 +134,33 @@
 </li>
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 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
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@@ -157,17 +176,32 @@
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
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-<li class="toctree-l3"><a class="reference internal" href="#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
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           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.optim</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.optim</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/optimization.rst.txt" rel="nofollow"> View page source</a>
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diff --git a/api_reference/options.html b/api_reference/options.html
index 2ba10e74..3cfd719e 100644
--- a/api_reference/options.html
+++ b/api_reference/options.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <link rel="index" title="Index" href="../genindex.html" />
@@ -131,15 +132,33 @@
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,9 +322,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.options</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.options</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/options.rst.txt" rel="nofollow"> View page source</a>
       </li>
@@ -280,7 +364,7 @@
 <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.options.FalkonOptions">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.options.</span></span><span class="sig-name descname"><span class="pre">FalkonOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keops_acc_dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_sum_scheme</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_active</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_memory_slack</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_in_core</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_ooc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-13</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_tolerance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_full_gradient_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_differential_convergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_arch_speed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_single_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">30000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">300000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">900000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">never_store_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_kernel_d_threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_fmm_streams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.options.FalkonOptions" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.options.</span></span><span class="sig-name descname"><span class="pre">FalkonOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keops_acc_dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_sum_scheme</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_active</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_memory_slack</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_in_core</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_ooc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-13</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_tolerance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_full_gradient_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_differential_convergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_arch_speed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_single_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">30000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">300000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">900000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">never_store_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_kernel_d_threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_fmm_streams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_slack</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.9</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.options.FalkonOptions" title="Permalink to this definition"></a></dt>
 <dd><p>Global options for Falkon.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -347,6 +431,9 @@ <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to
 <li><p><strong>num_fmm_streams</strong> (<em>int</em>) – <cite>default 2</cite> - The number of CUDA streams to use for evaluating kernels when CUDA is available.
 This number should be increased from its default value when the number of Nystroem centers is
 higher than around 5000.</p></li>
+<li><p><strong>memory_slack</strong> (<em>float</em>) – <cite>default 0.9</cite> - Controls the amount of slack in GPU memory when calculating the size of matrix
+splits for kernel-vector multiplications. This can be reduced if out-of-memory errors occur
+on the GPU.</p></li>
 <li><p><strong>keops_acc_dtype</strong> (<em>str</em>) – <cite>default “auto”</cite> - A string describing the accumulator data-type for KeOps.
 For more information refer to the
 <a class="reference external" href="https://www.kernel-operations.io/keops/python/api/pytorch/Genred_torch.html?highlight=genred#pykeops.torch.Genred">KeOps documentation</a></p></li>
diff --git a/api_reference/outofcore.html b/api_reference/outofcore.html
index 13fe2d0f..a297d1d1 100644
--- a/api_reference/outofcore.html
+++ b/api_reference/outofcore.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,9 +322,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.ooc_ops</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.ooc_ops</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/outofcore.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/api_reference/preconditioner.html b/api_reference/preconditioner.html
index 64cf0081..594c2c64 100644
--- a/api_reference/preconditioner.html
+++ b/api_reference/preconditioner.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
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+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
 </li>
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.preconditioner</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.preconditioner</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/preconditioner.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/api_reference/sparse.html b/api_reference/sparse.html
index 5a0bcf3e..ad93cc8f 100644
--- a/api_reference/sparse.html
+++ b/api_reference/sparse.html
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+        <script src="../_static/sphinx_highlight.js"></script>
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@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">API Reference</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
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 </li>
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-<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
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-<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
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+</li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
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+</ul>
+</li>
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-<li class="toctree-l3"><a class="reference internal" href="#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
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 </ul>
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 <li class="toctree-l2"><a class="reference internal" href="center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,9 +322,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="index.html">API Reference</a> &raquo;</li>
-      <li>falkon.sparse</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
+      <li class="breadcrumb-item active">falkon.sparse</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/sparse.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/examples/custom_kernels.html b/examples/custom_kernels.html
index b47b9533..37cd9c42 100644
--- a/examples/custom_kernels.html
+++ b/examples/custom_kernels.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="examples.html">Examples</a> &raquo;</li>
-      <li>Implementing A Custom Kernel</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
+      <li class="breadcrumb-item active">Implementing A Custom Kernel</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/custom_kernels.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
@@ -362,10 +446,11 @@
     }
 }
 
-/* disable scrollbars on prompts */
+/* disable scrollbars and line breaks on prompts */
 div.nbinput.container div.prompt pre,
 div.nboutput.container div.prompt pre {
     overflow: hidden;
+    white-space: pre;
 }
 
 /* input/output area */
@@ -544,16 +629,16 @@ <h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Cust
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
+plt.style.use(&#39;ggplot&#39;)
 
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+from sklearn import datasets
+import torch
+import numpy as np
 
-<span class="kn">import</span> <span class="nn">falkon</span>
-<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
-<span class="kn">from</span> <span class="nn">falkon.kernels</span> <span class="kn">import</span> <span class="n">Kernel</span><span class="p">,</span> <span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span>
+import falkon
+from falkon import FalkonOptions
+from falkon.kernels import Kernel, DiffKernel, KeopsKernelMixin
 </pre></div>
 </div>
 </div>
@@ -572,28 +657,28 @@ <h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simpl
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">fetch_california_housing</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
-<span class="n">num_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">num_train</span>
-<span class="n">shuffle_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffle_idx</span><span class="p">)</span>
-<span class="n">train_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[:</span><span class="n">num_train</span><span class="p">]</span>
-<span class="n">test_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[</span><span class="n">num_train</span><span class="p">:]</span>
-
-<span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
-<span class="n">Xtest</span><span class="p">,</span> <span class="n">Ytest</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">test_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>
-<span class="c1"># convert numpy -&gt; pytorch</span>
-<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="c1"># z-score normalization</span>
-<span class="n">train_mean</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">train_std</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">Xtrain</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">Xtrain</span> <span class="o">/=</span> <span class="n">train_std</span>
-<span class="n">Xtest</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">Xtest</span> <span class="o">/=</span> <span class="n">train_std</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.fetch_california_housing(return_X_y=True)
+num_train = int(X.shape[0] * 0.8)
+num_test = X.shape[0] - num_train
+shuffle_idx = np.arange(X.shape[0])
+np.random.shuffle(shuffle_idx)
+train_idx = shuffle_idx[:num_train]
+test_idx = shuffle_idx[num_train:]
+
+Xtrain, Ytrain = X[train_idx], Y[train_idx]
+Xtest, Ytest = X[test_idx], Y[test_idx]
+# convert numpy -&gt; pytorch
+Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)
+Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)
+Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)
+Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)
+# z-score normalization
+train_mean = Xtrain.mean(0, keepdim=True)
+train_std = Xtrain.std(0, keepdim=True)
+Xtrain -= train_mean
+Xtrain /= train_std
+Xtest -= train_mean
+Xtest /= train_std
 </pre></div>
 </div>
 </div>
@@ -601,17 +686,17 @@ <h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simpl
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-
-<span class="k">def</span> <span class="nf">learn_with_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">):</span>
-    <span class="n">flk_opt</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
-        <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">,</span>
-        <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">error_fn</span><span class="o">=</span><span class="n">rmse</span><span class="p">)</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
-    <span class="n">ts_err</span> <span class="o">=</span> <span class="n">rmse</span><span class="p">(</span><span class="n">Ytest</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test RMSE: </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">ts_err</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def rmse(true, pred):
+    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))
+
+def learn_with_kernel(kernel):
+    flk_opt = FalkonOptions(use_cpu=True)
+    model = falkon.Falkon(
+        kernel=kernel, penalty=1e-5, M=1000, options=flk_opt,
+        error_every=1, error_fn=rmse)
+    model.fit(Xtrain, Ytrain)
+    ts_err = rmse(Ytest, model.predict(Xtest))
+    print(&quot;Test RMSE: %.2f&quot; % (ts_err))
 </pre></div>
 </div>
 </div>
@@ -633,32 +718,32 @@ <h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Impleme
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">BasicLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
-        <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
-        <span class="c1"># an instance of `FalkonOptions`.</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;basic_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class BasicLinearKernel(Kernel):
+    def __init__(self, lengthscale, options):
+        # The base class takes as inputs a name for the kernel, and
+        # an instance of `FalkonOptions`.
+        super().__init__(&quot;basic_linear&quot;, options)
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
+        self.lengthscale = lengthscale
 
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="c1"># To support different devices/data types, you must make sure</span>
-        <span class="c1"># the lengthscale is compatible with the data.</span>
-        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -&gt; torch.Tensor:
+        # To support different devices/data types, you must make sure
+        # the lengthscale is compatible with the data.
+        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
 
-        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+        scaled_X1 = X1 * lengthscale
 
-        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
-            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># The dot-product row-by-row on `X1` and `X2` can be computed</span>
-            <span class="c1"># on many rows at a time with matrix multiplication.</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+        if diag:
+            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
+        else:
+            # The dot-product row-by-row on `X1` and `X2` can be computed
+            # on many rows at a time with matrix multiplication.
+            out = torch.matmul(scaled_X1, X2.T, out=out)
 
-        <span class="k">return</span> <span class="n">out</span>
+        return out
 
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
+    def compute_sparse(self, X1, X2, out, diag, **kwargs) -&gt; torch.Tensor:
+        raise NotImplementedError(&quot;Sparse not implemented&quot;)
 </pre></div>
 </div>
 </div>
@@ -668,9 +753,9 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel</span>
-<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span>
-<span class="n">k</span> <span class="o">=</span> <span class="n">BasicLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Initialize the kernel
+lengthscale_init = torch.tensor([1.0])
+k = BasicLinearKernel(lengthscale_init, options=falkon.FalkonOptions())
 </pre></div>
 </div>
 </div>
@@ -678,8 +763,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The kernel matrix</span>
-<span class="n">k</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># The kernel matrix
+k(torch.randn(5, 3), torch.randn(5, 3))
 </pre></div>
 </div>
 </div>
@@ -700,8 +785,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Kernel-vector product</span>
-<span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">v</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Kernel-vector product
+k.mmv(torch.randn(4, 3), torch.randn(4, 3), v=torch.randn(4, 1))
 </pre></div>
 </div>
 </div>
@@ -721,8 +806,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Double kernel-vector product</span>
-<span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">v</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">w</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Double kernel-vector product
+k.dmmv(torch.randn(3, 3), torch.randn(4, 3), v=torch.randn(4, 1), w=torch.randn(3, 1))
 </pre></div>
 </div>
 </div>
@@ -742,8 +827,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset</span>
-<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Learning on the california housing dataset
+learn_with_kernel(k)
 </pre></div>
 </div>
 </div>
@@ -788,44 +873,44 @@ <h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DiffLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
-        <span class="c1"># Super-class constructor call. We do not specify core_fn</span>
-        <span class="c1"># but we must specify the hyperparameter of this kernel (lengthscale)</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;diff_linear&quot;</span><span class="p">,</span>
-                         <span class="n">options</span><span class="p">,</span>
-                         <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                         <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
-        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
-        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
-            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">out</span>
-
-    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
-        <span class="c1"># The implementation here is similar to `compute` without in-place operations.</span>
-        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
-
-        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="c1"># Clones the class with detached hyperparameters</span>
-        <span class="k">return</span> <span class="n">DiffLinearKernel</span><span class="p">(</span>
-            <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
-            <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class DiffLinearKernel(DiffKernel):
+    def __init__(self, lengthscale, options):
+        # Super-class constructor call. We do not specify core_fn
+        # but we must specify the hyperparameter of this kernel (lengthscale)
+        super().__init__(&quot;diff_linear&quot;,
+                         options,
+                         core_fn=None,
+                         lengthscale=lengthscale)
+
+    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):
+        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
+        scaled_X1 = X1 * lengthscale
+        if diag:
+            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
+        else:
+            out = torch.matmul(scaled_X1, X2.T, out=out)
+
+        return out
+
+    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):
+        # The implementation here is similar to `compute` without in-place operations.
+        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
+        scaled_X1 = X1 * lengthscale
+
+        if diag:
+            return torch.sum(scaled_X1 * X2, dim=-1)
+
+        return torch.matmul(scaled_X1, X2.T)
+
+    def detach(self):
+        # Clones the class with detached hyperparameters
+        return DiffLinearKernel(
+            lengthscale=self.lengthscale.detach(),
+            options=self.params
+        )
+
+    def compute_sparse(self, X1, X2, out, diag, **kwargs) -&gt; torch.Tensor:
+        raise NotImplementedError(&quot;Sparse not implemented&quot;)
 </pre></div>
 </div>
 </div>
@@ -835,9 +920,9 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel, with a lengthscale which requires grad.</span>
-<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
-<span class="n">k</span> <span class="o">=</span> <span class="n">DiffLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Initialize the kernel, with a lengthscale which requires grad.
+lengthscale_init = torch.tensor([1.0]).requires_grad_()
+k = DiffLinearKernel(lengthscale_init, options=falkon.FalkonOptions())
 </pre></div>
 </div>
 </div>
@@ -845,9 +930,9 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Kernel matrix. Notice how the outputs has a `grad_fn`</span>
-<span class="n">k_mat</span> <span class="o">=</span> <span class="n">k</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="n">k_mat</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Kernel matrix. Notice how the outputs has a `grad_fn`
+k_mat = k(torch.randn(5, 3), torch.randn(5, 3))
+k_mat
 </pre></div>
 </div>
 </div>
@@ -869,8 +954,8 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Gradient of the kernel with respect to the lengthscale.</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mat</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Gradient of the kernel with respect to the lengthscale.
+torch.autograd.grad(k_mat.sum(), k.lengthscale)
 </pre></div>
 </div>
 </div>
@@ -887,15 +972,15 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># kernel-vector product + gradient</span>
-<span class="n">m1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
-<span class="n">m2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">k_mmv</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kernel-vector product&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">k_mmv</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Gradients:&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mmv</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">,</span> <span class="n">m1</span><span class="p">]))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># kernel-vector product + gradient
+m1 = torch.randn(4, 3).requires_grad_()
+m2 = torch.randn(2, 3)
+v = torch.randn(2, 1)
+k_mmv = k.mmv(m1, m2, v)
+print(&quot;Kernel-vector product&quot;)
+print(k_mmv)
+print(&quot;Gradients:&quot;)
+print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))
 </pre></div>
 </div>
 </div>
@@ -920,8 +1005,8 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset</span>
-<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Learning on the california housing dataset
+learn_with_kernel(k)
 </pre></div>
 </div>
 </div>
@@ -966,56 +1051,56 @@ <h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">KeopsLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;my-keops-linear&quot;</span><span class="p">,</span>
-                         <span class="n">options</span><span class="p">,</span>
-                         <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                         <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
-        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
-
-        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
-            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">out</span>
-
-    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
-        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span>
-
-        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">KeopsLinearKernel</span><span class="p">(</span>
-            <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
-            <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">keops_mmv_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">opt</span><span class="p">):</span>
-        <span class="c1"># Keops formula for kernel-vector.</span>
-        <span class="n">formula</span> <span class="o">=</span> <span class="s1">&#39;(scale * (X | Y)) * v&#39;</span>
-        <span class="n">aliases</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="s1">&#39;X = Vi(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">X1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="s1">&#39;Y = Vj(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">X2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="s1">&#39;v = Vj(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <span class="s1">&#39;scale = Pm(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-        <span class="p">]</span>
-        <span class="n">other_vars</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
-        <span class="p">]</span>
-        <span class="c1"># Call to the executor of the formula.</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">keops_mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">formula</span><span class="p">,</span> <span class="n">aliases</span><span class="p">,</span> <span class="n">other_vars</span><span class="p">,</span> <span class="n">opt</span><span class="p">)</span>
-
-
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class KeopsLinearKernel(DiffKernel, KeopsKernelMixin):
+    def __init__(self, lengthscale, options):
+        super().__init__(&quot;my-keops-linear&quot;,
+                         options,
+                         core_fn=None,
+                         lengthscale=lengthscale)
+
+    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):
+        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
+        scaled_X1 = X1 * lengthscale
+
+        if diag:
+            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
+        else:
+            out = torch.matmul(scaled_X1, X2.T, out=out)
+
+        return out
+
+    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):
+        scaled_X1 = X1 * self.lengthscale
+
+        if diag:
+            return torch.sum(scaled_X1 * X2, dim=-1)
+
+        return torch.matmul(scaled_X1, X2.T)
+
+    def detach(self):
+        return KeopsLinearKernel(
+            lengthscale=self.lengthscale.detach(),
+            options=self.params
+        )
+
+    def keops_mmv_impl(self, X1, X2, v, kernel, out, opt):
+        # Keops formula for kernel-vector.
+        formula = &#39;(scale * (X | Y)) * v&#39;
+        aliases = [
+            &#39;X = Vi(%d)&#39; % (X1.shape[1]),
+            &#39;Y = Vj(%d)&#39; % (X2.shape[1]),
+            &#39;v = Vj(%d)&#39; % (v.shape[1]),
+            &#39;scale = Pm(%d)&#39; % (self.lengthscale.shape[0]),
+        ]
+        other_vars = [
+            self.lengthscale.to(dtype=X1.dtype, device=X1.device),
+        ]
+        # Call to the executor of the formula.
+        return self.keops_mmv(X1, X2, v, out, formula, aliases, other_vars, opt)
+
+
+    def compute_sparse(self, X1, X2, out: torch.Tensor, diag: bool, **kwargs) -&gt; torch.Tensor:
+        raise NotImplementedError(&quot;Sparse not implemented&quot;)
 </pre></div>
 </div>
 </div>
@@ -1026,8 +1111,8 @@ <h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
-<span class="n">k</span> <span class="o">=</span> <span class="n">KeopsLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>lengthscale_init = torch.tensor([1.0]).requires_grad_()
+k = KeopsLinearKernel(lengthscale_init, options=falkon.FalkonOptions(use_cpu=True))
 </pre></div>
 </div>
 </div>
@@ -1035,15 +1120,15 @@ <h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># kernel-vector product + gradient</span>
-<span class="n">m1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
-<span class="n">m2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">k_mmv</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kernel-vector product&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">k_mmv</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Gradients:&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mmv</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">,</span> <span class="n">m1</span><span class="p">]))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># kernel-vector product + gradient
+m1 = torch.randn(4, 3).requires_grad_()
+m2 = torch.randn(2, 3)
+v = torch.randn(2, 1)
+k_mmv = k.mmv(m1, m2, v)
+print(&quot;Kernel-vector product&quot;)
+print(k_mmv)
+print(&quot;Gradients:&quot;)
+print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))
 </pre></div>
 </div>
 </div>
@@ -1068,10 +1153,10 @@ <h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset.</span>
-<span class="c1"># Due to differences in floating point code, results may be slightly</span>
-<span class="c1"># different from the other implementations.</span>
-<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Learning on the california housing dataset.
+# Due to differences in floating point code, results may be slightly
+# different from the other implementations.
+learn_with_kernel(k)
 </pre></div>
 </div>
 </div>
@@ -1114,8 +1199,8 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">SparseTensor</span>
-<span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">sparse_matmul</span><span class="p">,</span> <span class="n">bdot</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from falkon.sparse import SparseTensor
+from falkon.sparse import sparse_matmul, bdot
 </pre></div>
 </div>
 </div>
@@ -1123,51 +1208,51 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">SparseLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
-        <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
-        <span class="c1"># an instance of `FalkonOptions`.</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;sparse_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
-
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
-
-        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
-            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># The dot-product row-by-row on `X1` and `X2` can be computed</span>
-            <span class="c1"># on many rows at a time with matrix multiplication.</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">out</span>
-
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                       <span class="n">X1</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
-                       <span class="n">X2</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
-                       <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
-                       <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-                       <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="c1"># The inputs will be matrix X1(n*d) in CSR format, and X2(d*n) in CSC format.</span>
-
-        <span class="c1"># To support different devices/data types, you must make sure</span>
-        <span class="c1"># the lengthscale is compatible with the data.</span>
-        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
-            <span class="c1"># The diagonal is a dot-product between rows of X1 and X2.</span>
-            <span class="c1"># The batched-dot is only implemented on CPU.</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">bdot</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">transpose_csr</span><span class="p">(),</span> <span class="n">out</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Otherwise we need to matrix-multiply. Note that X2 is already</span>
-            <span class="c1"># transposed correctly.</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="n">sparse_matmul</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span>
-
-        <span class="n">out</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">lengthscale</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">out</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class SparseLinearKernel(Kernel):
+    def __init__(self, lengthscale, options):
+        # The base class takes as inputs a name for the kernel, and
+        # an instance of `FalkonOptions`.
+        super().__init__(&quot;sparse_linear&quot;, options)
+
+        self.lengthscale = lengthscale
+
+    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -&gt; torch.Tensor:
+        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
+
+        scaled_X1 = X1 * lengthscale
+
+        if diag:
+            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
+        else:
+            # The dot-product row-by-row on `X1` and `X2` can be computed
+            # on many rows at a time with matrix multiplication.
+            out = torch.matmul(scaled_X1, X2.T, out=out)
+
+        return out
+
+    def compute_sparse(self,
+                       X1: SparseTensor,
+                       X2: SparseTensor,
+                       out: torch.Tensor,
+                       diag: bool,
+                       **kwargs) -&gt; torch.Tensor:
+        # The inputs will be matrix X1(n*d) in CSR format, and X2(d*n) in CSC format.
+
+        # To support different devices/data types, you must make sure
+        # the lengthscale is compatible with the data.
+        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
+
+        if diag:
+            # The diagonal is a dot-product between rows of X1 and X2.
+            # The batched-dot is only implemented on CPU.
+            out = bdot(X1, X2.transpose_csr(), out)
+        else:
+            # Otherwise we need to matrix-multiply. Note that X2 is already
+            # transposed correctly.
+            out = sparse_matmul(X1, X2, out)
+
+        out.mul_(lengthscale)
+        return out
 </pre></div>
 </div>
 </div>
@@ -1178,13 +1263,13 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">indexptr</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
-<span class="n">index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
-<span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">sp1</span> <span class="o">=</span> <span class="n">SparseTensor</span><span class="p">(</span><span class="n">indexptr</span><span class="o">=</span><span class="n">indexptr</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">sparse_type</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
-<span class="c1"># Converted to dense:</span>
-<span class="n">dense1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">sp1</span><span class="o">.</span><span class="n">to_scipy</span><span class="p">()</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
-<span class="n">dense1</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>indexptr = torch.tensor([0, 1, 3, 4], dtype=torch.long)
+index = torch.tensor([1, 0, 1, 0], dtype=torch.long)
+value = torch.tensor([5, 1, 8, 2], dtype=torch.float32)
+sp1 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=&quot;csr&quot;)
+# Converted to dense:
+dense1 = torch.from_numpy(sp1.to_scipy().todense())
+dense1
 </pre></div>
 </div>
 </div>
@@ -1203,12 +1288,12 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">indexptr</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
-<span class="n">index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
-<span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">sp2</span> <span class="o">=</span> <span class="n">SparseTensor</span><span class="p">(</span><span class="n">indexptr</span><span class="o">=</span><span class="n">indexptr</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">sparse_type</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
-<span class="n">dense2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">sp2</span><span class="o">.</span><span class="n">to_scipy</span><span class="p">()</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
-<span class="n">dense2</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>indexptr = torch.tensor([0, 1, 2, 4], dtype=torch.long)
+index = torch.tensor([1, 0, 0, 1], dtype=torch.long)
+value = torch.tensor([2, 1, 3, 4], dtype=torch.float32)
+sp2 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=&quot;csr&quot;)
+dense2 = torch.from_numpy(sp2.to_scipy().todense())
+dense2
 </pre></div>
 </div>
 </div>
@@ -1227,9 +1312,9 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel</span>
-<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span>
-<span class="n">k</span> <span class="o">=</span> <span class="n">SparseLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Initialize the kernel
+lengthscale_init = torch.tensor([1.0])
+k = SparseLinearKernel(lengthscale_init, options=falkon.FalkonOptions())
 </pre></div>
 </div>
 </div>
@@ -1237,7 +1322,7 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">k</span><span class="p">(</span><span class="n">sp1</span><span class="p">,</span> <span class="n">sp2</span><span class="p">)</span> <span class="o">==</span> <span class="n">k</span><span class="p">(</span><span class="n">dense1</span><span class="p">,</span> <span class="n">dense2</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>k(sp1, sp2) == k(dense1, dense2)
 <br/></pre></div>
 </div>
 </div>
diff --git a/examples/examples.html b/examples/examples.html
index 2033892a..0d13e45b 100644
--- a/examples/examples.html
+++ b/examples/examples.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,8 +322,8 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>Examples</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">Examples</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/examples.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/examples/falkon_cv.html b/examples/falkon_cv.html
index a5c2a7f9..93c8f712 100644
--- a/examples/falkon_cv.html
+++ b/examples/falkon_cv.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="examples.html">Examples</a> &raquo;</li>
-      <li>Hyperparameter Tuning with Falkon</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
+      <li class="breadcrumb-item active">Hyperparameter Tuning with Falkon</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/falkon_cv.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
@@ -362,10 +446,11 @@
     }
 }
 
-/* disable scrollbars on prompts */
+/* disable scrollbars and line breaks on prompts */
 div.nbinput.container div.prompt pre,
 div.nboutput.container div.prompt pre {
     overflow: hidden;
+    white-space: pre;
 }
 
 /* input/output area */
@@ -540,14 +625,14 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span><span class="p">,</span> <span class="n">metrics</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+from sklearn import datasets, model_selection, metrics
+import numpy as np
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+import falkon
 </pre></div>
 </div>
 </div>
@@ -567,7 +652,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.load_digits(return_X_y=True)
 </pre></div>
 </div>
 </div>
@@ -575,9 +660,9 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">Y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
+_ = ax.matshow(X[1].reshape((8, 8)), cmap=&#39;gray&#39;)
+print(&quot;Label: %d&quot; % Y[1])
 </pre></div>
 </div>
 </div>
@@ -604,8 +689,8 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
+    X, Y, test_size=0.2, random_state=10, shuffle=True)
 </pre></div>
 </div>
 </div>
@@ -617,10 +702,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
-<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train = torch.from_numpy(X_train).to(dtype=torch.float32)
+X_test = torch.from_numpy(X_test).to(dtype=torch.float32)
+Y_train = torch.from_numpy(Y_train)
+Y_test = torch.from_numpy(Y_test)
 </pre></div>
 </div>
 </div>
@@ -629,13 +714,13 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
-<span class="n">train_mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="n">train_std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
-<span class="n">X_train</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">X_train</span> <span class="o">/=</span> <span class="n">train_std</span>
-<span class="n">X_test</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">X_test</span> <span class="o">/=</span> <span class="n">train_std</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># z-score normalization
+train_mean = X_train.mean()
+train_std = X_train.std()
+X_train -= train_mean
+X_train /= train_std
+X_test -= train_mean
+X_test /= train_std
 </pre></div>
 </div>
 </div>
@@ -644,11 +729,11 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert labels to 1-hot</span>
-<span class="n">eye</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Y_train</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_train</span><span class="p">]</span>
-<span class="n">Y_test</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_test</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label vector: &quot;</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Convert labels to 1-hot
+eye = torch.eye(10, dtype=torch.float32)
+Y_train = eye[Y_train]
+Y_test = eye[Y_test]
+print(&quot;First label vector: &quot;, Y_train[0])
 </pre></div>
 </div>
 </div>
@@ -670,11 +755,11 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
-    <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
-<span class="n">mclass_scorer</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">mclass_loss</span><span class="p">,</span> <span class="n">greater_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def mclass_loss(true, pred):
+    true = torch.argmax(true, dim=1)
+    pred = torch.argmax(pred, dim=1)
+    return torch.mean((true != pred).to(torch.float32))
+mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)
 </pre></div>
 </div>
 </div>
@@ -685,19 +770,19 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parameter_grid</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s1">&#39;kernel&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
-               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
-               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">15</span><span class="p">),],</span>
-    <span class="s1">&#39;penalty&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-9</span><span class="p">],</span>
-    <span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">],</span>
-<span class="p">}</span>
-<span class="n">estimator</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
-    <span class="n">kernel</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>  <span class="c1"># Mandatory parameters, will be overridden</span>
-    <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
-
-<span class="n">grid_search</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">parameter_grid</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">mclass_scorer</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>parameter_grid = {
+    &#39;kernel&#39;: [falkon.kernels.GaussianKernel(sigma=1),
+               falkon.kernels.GaussianKernel(sigma=5),
+               falkon.kernels.GaussianKernel(sigma=10),
+               falkon.kernels.GaussianKernel(sigma=15),],
+    &#39;penalty&#39;: [1e-3, 1e-5, 1e-7, 1e-9],
+    &#39;M&#39;: [500, 1000],
+}
+estimator = falkon.Falkon(
+    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000,  # Mandatory parameters, will be overridden
+    maxiter=10, options=falkon.FalkonOptions(use_cpu=True))
+
+grid_search = model_selection.GridSearchCV(estimator, parameter_grid, scoring=mclass_scorer, cv=5)
 </pre></div>
 </div>
 </div>
@@ -705,9 +790,9 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
-<span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The best parameters are: &quot;</span><span class="p">,</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
+grid_search.fit(X_train, Y_train)
+print(&quot;The best parameters are: &quot;, grid_search.best_params_)
 </pre></div>
 </div>
 </div>
@@ -729,11 +814,11 @@ <h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
-<span class="n">flk</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator_</span>
-<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
-<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
+flk = grid_search.best_estimator_
+flk.fit(X_train, Y_train)
+test_pred = flk.predict(X_test)
+train_pred = flk.predict(X_train)
 </pre></div>
 </div>
 </div>
@@ -750,8 +835,8 @@ <h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">train_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">test_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;Training error: %.2f%%&quot; % (mclass_loss(Y_train, train_pred) * 100))
+print(&quot;Test error: %.2f%%&quot; % (mclass_loss(Y_test, test_pred) * 100))
 </pre></div>
 </div>
 </div>
@@ -774,11 +859,11 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">res_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
-<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;M&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;M&quot;</span><span class="p">))</span>
-<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;penalty&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;penalty&quot;</span><span class="p">))</span>
-<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;sigma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;kernel&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-<span class="n">res_df</span> <span class="o">=</span> <span class="n">res_df</span><span class="p">[[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="s2">&quot;M&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;sigma&quot;</span><span class="p">]]</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>res_df = pd.DataFrame.from_dict(grid_search.cv_results_)
+res_df[&quot;M&quot;] = res_df.params.apply(lambda x: x.get(&quot;M&quot;))
+res_df[&quot;penalty&quot;] = res_df.params.apply(lambda x: x.get(&quot;penalty&quot;))
+res_df[&quot;sigma&quot;] = res_df.params.apply(lambda x: x.get(&quot;kernel&quot;).sigma.item())
+res_df = res_df[[&quot;mean_test_score&quot;, &quot;M&quot;, &quot;penalty&quot;, &quot;sigma&quot;]]
 </pre></div>
 </div>
 </div>
@@ -786,18 +871,18 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_heatmap</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
-    <span class="n">piv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">ylabel</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
-    <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;coolwarm_r&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">piv</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vmax</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">c</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def plot_heatmap(ax, df, xlabel, ylabel, value, scale):
+    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)
+    cmap = plt.cm.get_cmap(&#39;coolwarm_r&#39;, 20)
+    ax.grid(False)
+    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])
+    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)
+    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)
+    ax.set_xticklabels(piv.columns, minor=False)
+    ax.set_yticklabels(piv.index, minor=False)
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    return c
 </pre></div>
 </div>
 </div>
@@ -805,13 +890,13 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">scale</span> <span class="o">=</span> <span class="p">(</span><span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, axs = plt.subplots(ncols=2, figsize=(12, 5))
+scale = (res_df[&quot;mean_test_score&quot;].min(), res_df[&quot;mean_test_score&quot;].max())
 
-<span class="n">c</span> <span class="o">=</span> <span class="n">plot_heatmap</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">res_df</span><span class="p">[</span><span class="n">res_df</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">500</span><span class="p">],</span> <span class="s2">&quot;sigma&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
-<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;M = 500&quot;</span><span class="p">)</span>
-<span class="n">c</span> <span class="o">=</span> <span class="n">plot_heatmap</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">res_df</span><span class="p">[</span><span class="n">res_df</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">1000</span><span class="p">],</span> <span class="s2">&quot;sigma&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
-<span class="n">_</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;M = 1000&quot;</span><span class="p">)</span>
+c = plot_heatmap(axs[0], res_df[res_df.M == 500], &quot;sigma&quot;, &quot;penalty&quot;, &quot;mean_test_score&quot;, scale)
+axs[0].set_title(&quot;M = 500&quot;)
+c = plot_heatmap(axs[1], res_df[res_df.M == 1000], &quot;sigma&quot;, &quot;penalty&quot;, &quot;mean_test_score&quot;, scale)
+_ = axs[1].set_title(&quot;M = 1000&quot;)
 </pre></div>
 </div>
 </div>
diff --git a/examples/falkon_mnist.html b/examples/falkon_mnist.html
index d3138a07..08af8861 100644
--- a/examples/falkon_mnist.html
+++ b/examples/falkon_mnist.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="examples.html">Examples</a> &raquo;</li>
-      <li>MNIST Classification with Falkon</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
+      <li class="breadcrumb-item active">MNIST Classification with Falkon</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/falkon_mnist.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
@@ -362,10 +446,11 @@
     }
 }
 
-/* disable scrollbars on prompts */
+/* disable scrollbars and line breaks on prompts */
 div.nbinput.container div.prompt pre,
 div.nboutput.container div.prompt pre {
     overflow: hidden;
+    white-space: pre;
 }
 
 /* input/output area */
@@ -536,14 +621,14 @@ <h1>MNIST Classification with Falkon<a class="headerlink" href="#MNIST-Classific
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+import matplotlib.pyplot as plt
+plt.style.use(&#39;ggplot&#39;)
 
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torchvision</span>
+import torch
+import torchvision
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+import falkon
 </pre></div>
 </div>
 </div>
@@ -561,12 +646,12 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mnist_train_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
-    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
-<span class="n">mnist_test_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
-    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>mnist_train_dataset = torchvision.datasets.MNIST(
+    root=&quot;.&quot;, train=True, download=True,
+    transform=torchvision.transforms.ToTensor())
+mnist_test_dataset = torchvision.datasets.MNIST(
+    root=&quot;.&quot;, train=False, download=True,
+    transform=torchvision.transforms.ToTensor())
 </pre></div>
 </div>
 </div>
@@ -574,17 +659,17 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load the whole dataset in memory</span>
-<span class="n">mnist_tr_img_list</span><span class="p">,</span> <span class="n">mnist_tr_label_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mnist_train_dataset</span><span class="p">)):</span>
-    <span class="n">data_point</span> <span class="o">=</span> <span class="n">mnist_train_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-    <span class="n">mnist_tr_img_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">mnist_tr_label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">mnist_ts_img_list</span><span class="p">,</span> <span class="n">mnist_ts_label_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mnist_test_dataset</span><span class="p">)):</span>
-    <span class="n">data_point</span> <span class="o">=</span> <span class="n">mnist_test_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-    <span class="n">mnist_ts_img_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">mnist_ts_label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Load the whole dataset in memory
+mnist_tr_img_list, mnist_tr_label_list = [], []
+for i in range(len(mnist_train_dataset)):
+    data_point = mnist_train_dataset[i]
+    mnist_tr_img_list.append(data_point[0])
+    mnist_tr_label_list.append(data_point[1])
+mnist_ts_img_list, mnist_ts_label_list = [], []
+for i in range(len(mnist_test_dataset)):
+    data_point = mnist_test_dataset[i]
+    mnist_ts_img_list.append(data_point[0])
+    mnist_ts_label_list.append(data_point[1])
 </pre></div>
 </div>
 </div>
@@ -592,12 +677,12 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mnist_tr_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">mnist_tr_img_list</span><span class="p">)</span>
-<span class="n">mnist_tr_x</span> <span class="o">=</span> <span class="n">mnist_tr_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">mnist_ts_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">mnist_ts_img_list</span><span class="p">)</span>
-<span class="n">mnist_ts_x</span> <span class="o">=</span> <span class="n">mnist_ts_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mnist_ts_x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">mnist_tr_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">mnist_tr_label_list</span><span class="p">)</span>
-<span class="n">mnist_ts_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">mnist_ts_label_list</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>mnist_tr_x = torch.vstack(mnist_tr_img_list)
+mnist_tr_x = mnist_tr_x.reshape(mnist_tr_x.shape[0], -1)
+mnist_ts_x = torch.vstack(mnist_ts_img_list)
+mnist_ts_x = mnist_ts_x.reshape(mnist_ts_x.shape[0], -1)
+mnist_tr_y = torch.tensor(mnist_tr_label_list)
+mnist_ts_y = torch.tensor(mnist_ts_label_list)
 </pre></div>
 </div>
 </div>
@@ -605,8 +690,8 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
+ax.imshow(mnist_tr_x[0].reshape(28,28))
 </pre></div>
 </div>
 </div>
@@ -634,10 +719,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># one-hot labels</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">mnist_tr_y</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">mnist_tr_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">),</span> <span class="p">:]</span>
-<span class="n">mnist_ts_y</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">mnist_ts_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">),</span> <span class="p">:]</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># one-hot labels
+A = torch.eye(10, dtype=torch.float32)
+mnist_tr_y = A[mnist_tr_y.to(torch.long), :]
+mnist_ts_y = A[mnist_ts_y.to(torch.long), :]
 </pre></div>
 </div>
 </div>
@@ -645,11 +730,11 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">classif_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
-    <span class="n">y_true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">err</span> <span class="o">=</span> <span class="n">y_true</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="o">!=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">err</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def classif_error(y_true, y_pred):
+    y_true = torch.argmax(y_true, dim=1)
+    y_pred = torch.argmax(y_pred, dim=1)
+    err = y_true.flatten() != y_pred.flatten()
+    return torch.mean(err.to(torch.float32))
 </pre></div>
 </div>
 </div>
@@ -669,15 +754,15 @@ <h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
-                    <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span>
-                    <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                    <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-                    <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">,</span>
-                    <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                    <span class="n">error_fn</span><span class="o">=</span><span class="n">classif_error</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>options = falkon.FalkonOptions(use_cpu=True)
+kernel = falkon.kernels.GaussianKernel(sigma=15)
+flk = falkon.Falkon(kernel=kernel,
+                    penalty=1e-8,
+                    M=1000,
+                    maxiter=10,
+                    options=options,
+                    error_every=1,
+                    error_fn=classif_error)
 </pre></div>
 </div>
 </div>
@@ -685,7 +770,7 @@ <h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">,</span> <span class="n">mnist_tr_y</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>_ = flk.fit(mnist_tr_x, mnist_tr_y)
 </pre></div>
 </div>
 </div>
@@ -710,11 +795,11 @@ <h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">)</span>
-<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">mnist_ts_x</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>train_pred = flk.predict(mnist_tr_x)
+test_pred = flk.predict(mnist_ts_x)
 
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">classif_error</span><span class="p">(</span><span class="n">train_pred</span><span class="p">,</span> <span class="n">mnist_tr_y</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">classif_error</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">mnist_ts_y</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+print(&quot;Training error: %.2f%%&quot; % (classif_error(train_pred, mnist_tr_y) * 100))
+print(&quot;Test error: %.2f%%&quot; % (classif_error(test_pred, mnist_ts_y) * 100))
 <br/></pre></div>
 </div>
 </div>
diff --git a/examples/falkon_regression_tutorial.html b/examples/falkon_regression_tutorial.html
index b650b385..290b6c9d 100644
--- a/examples/falkon_regression_tutorial.html
+++ b/examples/falkon_regression_tutorial.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="examples.html">Examples</a> &raquo;</li>
-      <li>Falkon Regression Tutorial</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
+      <li class="breadcrumb-item active">Falkon Regression Tutorial</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/falkon_regression_tutorial.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
@@ -362,10 +446,11 @@
     }
 }
 
-/* disable scrollbars on prompts */
+/* disable scrollbars and line breaks on prompts */
 div.nbinput.container div.prompt pre,
 div.nboutput.container div.prompt pre {
     overflow: hidden;
+    white-space: pre;
 }
 
 /* input/output area */
@@ -540,14 +625,14 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+from sklearn import datasets
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+plt.style.use(&#39;ggplot&#39;)
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+import falkon
 </pre></div>
 </div>
 </div>
@@ -568,7 +653,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">fetch_california_housing</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.fetch_california_housing(return_X_y=True)
 </pre></div>
 </div>
 </div>
@@ -576,15 +661,15 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
-<span class="n">num_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">num_train</span>
-<span class="n">shuffle_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffle_idx</span><span class="p">)</span>
-<span class="n">train_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[:</span><span class="n">num_train</span><span class="p">]</span>
-<span class="n">test_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[</span><span class="n">num_train</span><span class="p">:]</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>num_train = int(X.shape[0] * 0.8)
+num_test = X.shape[0] - num_train
+shuffle_idx = np.arange(X.shape[0])
+np.random.shuffle(shuffle_idx)
+train_idx = shuffle_idx[:num_train]
+test_idx = shuffle_idx[num_train:]
 
-<span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
-<span class="n">Xtest</span><span class="p">,</span> <span class="n">Ytest</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">test_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>
+Xtrain, Ytrain = X[train_idx], Y[train_idx]
+Xtest, Ytest = X[test_idx], Y[test_idx]
 </pre></div>
 </div>
 </div>
@@ -597,11 +682,11 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># convert numpy -&gt; pytorch</span>
-<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># convert numpy -&gt; pytorch
+Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)
+Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)
+Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)
+Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)
 </pre></div>
 </div>
 </div>
@@ -609,13 +694,13 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
-<span class="n">train_mean</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">train_std</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">Xtrain</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">Xtrain</span> <span class="o">/=</span> <span class="n">train_std</span>
-<span class="n">Xtest</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">Xtest</span> <span class="o">/=</span> <span class="n">train_std</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># z-score normalization
+train_mean = Xtrain.mean(0, keepdim=True)
+train_std = Xtrain.std(0, keepdim=True)
+Xtrain -= train_mean
+Xtrain /= train_std
+Xtest -= train_mean
+Xtest /= train_std
 </pre></div>
 </div>
 </div>
@@ -629,10 +714,10 @@ <h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model"
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>options = falkon.FalkonOptions(keops_active=&quot;no&quot;)
 
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
-<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+kernel = falkon.kernels.GaussianKernel(sigma=1, opt=options)
+flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=5000, options=options)
 </pre></div>
 </div>
 </div>
@@ -654,7 +739,7 @@ <h2>Training the model<a class="headerlink" href="#Training-the-model" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>flk.fit(Xtrain, Ytrain)
 </pre></div>
 </div>
 </div>
@@ -678,14 +763,14 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
-<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>train_pred = flk.predict(Xtrain).reshape(-1, )
+test_pred = flk.predict(Xtest).reshape(-1, )
 
-<span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+def rmse(true, pred):
+    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))
 
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training RMSE: </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">train_pred</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test RMSE: </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">Ytest</span><span class="p">)))</span>
+print(&quot;Training RMSE: %.3f&quot; % (rmse(train_pred, Ytrain)))
+print(&quot;Test RMSE: %.3f&quot; % (rmse(test_pred, Ytest)))
 </pre></div>
 </div>
 </div>
@@ -703,11 +788,11 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">hist_range</span> <span class="o">=</span> <span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="nb">max</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">max</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">hist_range</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;True&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">test_pred</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">hist_range</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Pred&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;best&quot;</span><span class="p">);</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
+hist_range = (min(Ytest.min(), test_pred.min()).item(), max(Ytest.max(), test_pred.max()).item())
+ax.hist(Ytest.numpy(), bins=10, range=hist_range, alpha=0.7, label=&quot;True&quot;)
+ax.hist(test_pred.numpy(), bins=10, range=hist_range, alpha=0.7, label=&quot;Pred&quot;)
+ax.legend(loc=&quot;best&quot;);
 </pre></div>
 </div>
 </div>
diff --git a/examples/hyperopt.html b/examples/hyperopt.html
index b95ee0f0..54e13fb8 100644
--- a/examples/hyperopt.html
+++ b/examples/hyperopt.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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         <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="examples.html">Examples</a> &raquo;</li>
-      <li>Automatic Hyperparameter Optimization</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
+      <li class="breadcrumb-item active">Automatic Hyperparameter Optimization</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/hyperopt.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
@@ -362,10 +446,11 @@
     }
 }
 
-/* disable scrollbars on prompts */
+/* disable scrollbars and line breaks on prompts */
 div.nbinput.container div.prompt pre,
 div.nboutput.container div.prompt pre {
     overflow: hidden;
+    white-space: pre;
 }
 
 /* input/output area */
@@ -536,13 +621,13 @@ <h1>Automatic Hyperparameter Optimization<a class="headerlink" href="#Automatic-
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+from sklearn import datasets, model_selection
+import numpy as np
+np.random.seed(30)
+import torch
+import matplotlib.pyplot as plt
+plt.style.use(&#39;ggplot&#39;)
 </pre></div>
 </div>
 </div>
@@ -561,7 +646,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.load_digits(return_X_y=True)
 </pre></div>
 </div>
 </div>
@@ -569,16 +654,16 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">y</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def plot_digit(ax, x, y):
+    ax.imshow(x.reshape((8, 8)), cmap=&#39;gray&#39;)
+    ax.set_title(&quot;Label: %d&quot; % y)
+    ax.set_axis_off()
 
-<span class="c1"># Plot three sample images from the dataset</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
-<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
-<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">10</span><span class="p">])</span>
-<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">42</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">42</span><span class="p">])</span>
+# Plot three sample images from the dataset
+fig, ax = plt.subplots(ncols=3, figsize=(9, 6))
+plot_digit(ax[0], X[3], Y[3])
+plot_digit(ax[1], X[10], Y[10])
+plot_digit(ax[2], X[42], Y[42])
 </pre></div>
 </div>
 </div>
@@ -597,8 +682,8 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
+    X, Y, test_size=0.2, random_state=10, shuffle=True)
 </pre></div>
 </div>
 </div>
@@ -610,10 +695,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
-<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train = torch.from_numpy(X_train).to(dtype=torch.float32)
+X_test = torch.from_numpy(X_test).to(dtype=torch.float32)
+Y_train = torch.from_numpy(Y_train)
+Y_test = torch.from_numpy(Y_test)
 </pre></div>
 </div>
 </div>
@@ -623,13 +708,13 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
-<span class="n">train_mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="n">train_std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
-<span class="n">X_train</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">X_train</span> <span class="o">/=</span> <span class="n">train_std</span>
-<span class="n">X_test</span> <span class="o">-=</span> <span class="n">train_mean</span>
-<span class="n">X_test</span> <span class="o">/=</span> <span class="n">train_std</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># z-score normalization
+train_mean = X_train.mean()
+train_std = X_train.std()
+X_train -= train_mean
+X_train /= train_std
+X_test -= train_mean
+X_test /= train_std
 </pre></div>
 </div>
 </div>
@@ -639,11 +724,11 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert labels to 1-hot</span>
-<span class="n">eye</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Y_train</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_train</span><span class="p">]</span>
-<span class="n">Y_test</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_test</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label vector: &quot;</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Convert labels to 1-hot
+eye = torch.eye(10, dtype=torch.float32)
+Y_train = eye[Y_train]
+Y_test = eye[Y_test]
+print(&quot;First label vector: &quot;, Y_train[0])
 </pre></div>
 </div>
 </div>
@@ -666,10 +751,10 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
-    <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def mclass_loss(true, pred):
+    true = torch.argmax(true, dim=1)
+    pred = torch.argmax(pred, dim=1)
+    return torch.mean((true != pred).to(torch.float32))
 </pre></div>
 </div>
 </div>
@@ -679,9 +764,9 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">falkon.hopt</span>
-<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
-<span class="kn">from</span> <span class="nn">falkon.hopt.objectives</span> <span class="kn">import</span> <span class="n">NystromCompReg</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import falkon.hopt
+from falkon import FalkonOptions
+from falkon.hopt.objectives import NystromCompReg
 </pre></div>
 </div>
 </div>
@@ -689,7 +774,7 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk_opt</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>flk_opt = FalkonOptions(use_cpu=not torch.cuda.is_available())
 </pre></div>
 </div>
 </div>
@@ -698,11 +783,11 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sigma_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
-<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="n">sigma_init</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>sigma_init = torch.tensor([1.0] * X_train.shape[1], dtype=torch.float32).requires_grad_()
+kernel = falkon.kernels.GaussianKernel(sigma=sigma_init, opt=flk_opt)
 
-<span class="n">penalty_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">centers_init</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="p">),</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+penalty_init = torch.tensor(1e-5, dtype=torch.float32)
+centers_init = X_train[np.random.choice(X_train.shape[0], size=(500, ), replace=False)].clone()
 </pre></div>
 </div>
 </div>
@@ -711,11 +796,11 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">NystromCompReg</span><span class="p">(</span>
-    <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty_init</span><span class="o">=</span><span class="n">penalty_init</span><span class="p">,</span> <span class="n">centers_init</span><span class="o">=</span><span class="n">centers_init</span><span class="p">,</span>  <span class="c1"># The initial hp values</span>
-    <span class="n">opt_penalty</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">opt_centers</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>  <span class="c1"># Whether the various hps are to be optimized</span>
-    <span class="p">)</span>
-<span class="n">opt_hp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>model = NystromCompReg(
+    kernel=kernel, penalty_init=penalty_init, centers_init=centers_init,  # The initial hp values
+    opt_penalty=True, opt_centers=True,  # Whether the various hps are to be optimized
+    )
+opt_hp = torch.optim.Adam(model.parameters(), lr=0.1)
 </pre></div>
 </div>
 </div>
@@ -724,17 +809,17 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tr_loss</span><span class="p">,</span> <span class="n">tr_err</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>tr_loss, tr_err = [], []
 
-<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
-    <span class="n">opt_hp</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
-    <span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
-    <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-    <span class="n">opt_hp</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+for epoch in range(50):
+    opt_hp.zero_grad()
+    loss = model(X_train, Y_train)
+    loss.backward()
+    opt_hp.step()
 
-    <span class="n">tr_loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-    <span class="n">tr_err</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2"> Loss </span><span class="si">{</span><span class="n">tr_loss</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Error </span><span class="si">{</span><span class="n">tr_err</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+    tr_loss.append(loss.item())
+    tr_err.append(mclass_loss(Y_train, model.predict(X_train)))
+    print(f&quot;Epoch {epoch} Loss {tr_loss[-1]:.3f} Error {tr_err[-1] * 100:.2f}%&quot;)
 </pre></div>
 </div>
 </div>
@@ -799,9 +884,9 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Evaluate the test error:</span>
-<span class="n">ts_preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test error: </span><span class="si">{</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">ts_preds</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Evaluate the test error:
+ts_preds = model.predict(X_test)
+print(f&quot;Test error: {mclass_loss(Y_test, ts_preds) * 100:.2f}%&quot;)
 </pre></div>
 </div>
 </div>
@@ -818,8 +903,8 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final value of lambda: </span><span class="si">%.3e</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">penalty</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final value of sigma: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">kernel</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">detach</span><span class="p">()))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;Final value of lambda: %.3e&quot; % (model.penalty))
+print(&quot;Final value of sigma: %s&quot; % (model.kernel.sigma.detach()))
 </pre></div>
 </div>
 </div>
diff --git a/examples/hyperopt.ipynb b/examples/hyperopt.ipynb
index 2e5be028..4d7de093 100644
--- a/examples/hyperopt.ipynb
+++ b/examples/hyperopt.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "07982d3f",
+   "id": "16505af2",
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6dca605d",
+   "id": "ab47d6b1",
    "metadata": {
     "collapsed": false,
     "pycharm": {
diff --git a/examples/logistic_falkon.html b/examples/logistic_falkon.html
index 606b0957..34fcb83a 100644
--- a/examples/logistic_falkon.html
+++ b/examples/logistic_falkon.html
@@ -16,6 +16,7 @@
         <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
@@ -133,15 +134,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -157,17 +176,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -176,7 +210,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -185,33 +222,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -240,9 +324,9 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-          <li><a href="examples.html">Examples</a> &raquo;</li>
-      <li>Introducing Logistic Falkon</li>
+      <li><a href="../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
+      <li class="breadcrumb-item active">Introducing Logistic Falkon</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/logistic_falkon.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
@@ -362,10 +446,11 @@
     }
 }
 
-/* disable scrollbars on prompts */
+/* disable scrollbars and line breaks on prompts */
 div.nbinput.container div.prompt pre,
 div.nboutput.container div.prompt pre {
     overflow: hidden;
+    white-space: pre;
 }
 
 /* input/output area */
@@ -540,13 +625,13 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+from sklearn import datasets, model_selection
+import torch
+import matplotlib.pyplot as plt
+plt.style.use(&#39;ggplot&#39;)
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+import falkon
 </pre></div>
 </div>
 </div>
@@ -567,8 +652,8 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_circles</span><span class="p">(</span>
-    <span class="n">n_samples</span><span class="o">=</span><span class="mi">10_000</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">122</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.make_circles(
+    n_samples=10_000, shuffle=False, noise=0.1, random_state=122)
 </pre></div>
 </div>
 </div>
@@ -576,9 +661,9 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
-<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots(figsize=(7, 7))
+ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.3, marker=&#39;.&#39;)
+_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.3, marker=&#39;.&#39;)
 </pre></div>
 </div>
 </div>
@@ -597,8 +682,8 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
+    X, Y, test_size=0.2, random_state=10, shuffle=True)
 </pre></div>
 </div>
 </div>
@@ -615,10 +700,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train = torch.from_numpy(X_train).to(dtype=torch.float32)
+X_test = torch.from_numpy(X_test).to(dtype=torch.float32)
+Y_train = torch.from_numpy(Y_train).to(dtype=torch.float32).reshape(-1, 1)
+Y_test = torch.from_numpy(Y_test).to(dtype=torch.float32).reshape(-1, 1)
 </pre></div>
 </div>
 </div>
@@ -626,8 +711,8 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Y_train</span><span class="p">[</span><span class="n">Y_train</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-<span class="n">Y_test</span><span class="p">[</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>Y_train[Y_train == 0] = -1
+Y_test[Y_test == 0] = -1
 </pre></div>
 </div>
 </div>
@@ -639,8 +724,8 @@ <h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model"
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">binary_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def binary_loss(true, pred):
+    return torch.mean((true != torch.sign(pred)).to(torch.float32))
 </pre></div>
 </div>
 </div>
@@ -648,12 +733,12 @@ <h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model"
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We define some basic options to run on the CPU, and to disable keops</span>
-<span class="n">flk_opt</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># We define some basic options to run on the CPU, and to disable keops
+flk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=&quot;no&quot;)
 
-<span class="n">flk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
+flk_kernel = falkon.kernels.GaussianKernel(1, opt=flk_opt)
 
-<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">flk_kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
+flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000, options=flk_opt)
 </pre></div>
 </div>
 </div>
@@ -669,17 +754,17 @@ <h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Fal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">logflk_opt</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>logflk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=&quot;no&quot;)
 
-<span class="n">logflk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">logflk_opt</span><span class="p">)</span>
-<span class="n">logloss</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">gsc_losses</span><span class="o">.</span><span class="n">LogisticLoss</span><span class="p">(</span><span class="n">logflk_kernel</span><span class="p">)</span>
+logflk_kernel = falkon.kernels.GaussianKernel(1, opt=logflk_opt)
+logloss = falkon.gsc_losses.LogisticLoss(logflk_kernel)
 
-<span class="n">penalty_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">]</span>
-<span class="n">iter_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
+penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7]
+iter_list = [4, 4, 4, 8, 8]
 
-<span class="n">logflk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">LogisticFalkon</span><span class="p">(</span>
-    <span class="n">kernel</span><span class="o">=</span><span class="n">logflk_kernel</span><span class="p">,</span> <span class="n">penalty_list</span><span class="o">=</span><span class="n">penalty_list</span><span class="p">,</span> <span class="n">iter_list</span><span class="o">=</span><span class="n">iter_list</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">logloss</span><span class="p">,</span>
-    <span class="n">error_fn</span><span class="o">=</span><span class="n">binary_loss</span><span class="p">,</span> <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">logflk_opt</span><span class="p">)</span>
+logflk = falkon.LogisticFalkon(
+    kernel=logflk_kernel, penalty_list=penalty_list, iter_list=iter_list, M=1000, loss=logloss,
+    error_fn=binary_loss, error_every=1, options=logflk_opt)
 </pre></div>
 </div>
 </div>
@@ -692,8 +777,8 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
-<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">);</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
+flk.fit(X_train, Y_train);
 </pre></div>
 </div>
 </div>
@@ -720,8 +805,8 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
-<span class="n">logflk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">);</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
+logflk.fit(X_train, Y_train);
 </pre></div>
 </div>
 </div>
@@ -764,14 +849,14 @@ <h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headin
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">flk_err</span> <span class="o">=</span> <span class="n">binary_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">flk_pred</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>flk_pred = flk.predict(X_test)
+flk_err = binary_loss(Y_test, flk_pred)
 
-<span class="n">logflk_pred</span> <span class="o">=</span> <span class="n">logflk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">logflk_err</span> <span class="o">=</span> <span class="n">binary_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">logflk_pred</span><span class="p">)</span>
+logflk_pred = logflk.predict(X_test)
+logflk_err = binary_loss(Y_test, logflk_pred)
 
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Falkon model          -- Error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">flk_err</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon model -- Error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">logflk_err</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+print(&quot;Falkon model          -- Error: %.2f%%&quot; % (flk_err * 100))
+print(&quot;Logistic Falkon model -- Error: %.2f%%&quot; % (logflk_err * 100))
 </pre></div>
 </div>
 </div>
@@ -794,20 +879,20 @@ <h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
-               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;correct cls 0&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
-               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;correct cls 1&quot;</span><span class="p">)</span>
-
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
-               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;wrong cls 0&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
-               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
-               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;wrong cls 1&quot;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def plot_predictions(preds, ax):
+    ax.scatter(X_test[((Y_test == -1) &amp; (preds.sign() == Y_test)).reshape(-1), 0],
+               X_test[((Y_test == -1) &amp; (preds.sign() == Y_test)).reshape(-1), 1],
+               alpha=0.3, marker=&#39;.&#39;, color=&#39;b&#39;, label=&quot;correct cls 0&quot;)
+    ax.scatter(X_test[((Y_test == 1) &amp; (preds.sign() == Y_test)).reshape(-1),0],
+               X_test[((Y_test == 1) &amp; (preds.sign() == Y_test)).reshape(-1),1],
+               alpha=0.3, marker=&#39;.&#39;, color=&#39;r&#39;, label=&quot;correct cls 1&quot;)
+
+    ax.scatter(X_test[((Y_test == -1) &amp; (preds.sign() != Y_test)).reshape(-1), 0],
+               X_test[((Y_test == -1) &amp; (preds.sign() != Y_test)).reshape(-1), 1],
+               alpha=0.5, marker=&#39;x&#39;, color=&#39;b&#39;, label=&quot;wrong cls 0&quot;)
+    ax.scatter(X_test[((Y_test == 1) &amp; (preds.sign() != Y_test)).reshape(-1),0],
+               X_test[((Y_test == 1) &amp; (preds.sign() != Y_test)).reshape(-1),1],
+               alpha=0.5, marker=&#39;x&#39;, color=&#39;r&#39;, label=&quot;wrong cls 1&quot;)
 </pre></div>
 </div>
 </div>
@@ -815,14 +900,14 @@ <h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">plot_predictions</span><span class="p">(</span><span class="n">flk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Falkon&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots(ncols=2, figsize=(13, 5))
+plot_predictions(flk_pred, ax[0])
+ax[0].set_title(&quot;Falkon&quot;)
+ax[0].legend(loc=&#39;best&#39;)
 
-<span class="n">plot_predictions</span><span class="p">(</span><span class="n">logflk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">);</span>
+plot_predictions(logflk_pred, ax[1])
+ax[1].set_title(&quot;Logistic Falkon&quot;)
+ax[1].legend(loc=&#39;best&#39;);
 <br/></pre></div>
 </div>
 </div>
diff --git a/genindex.html b/genindex.html
index 6d968226..18ba01d1 100644
--- a/genindex.html
+++ b/genindex.html
@@ -15,6 +15,7 @@
         <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
+        <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="#" />
@@ -128,15 +129,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -152,17 +171,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -171,7 +205,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -180,33 +217,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -235,8 +319,8 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>Index</li>
+      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">Index</li>
       <li class="wy-breadcrumbs-aside">
       </li>
   </ul>
diff --git a/get_started.html b/get_started.html
index 87212055..d28bcd25 100644
--- a/get_started.html
+++ b/get_started.html
@@ -16,6 +16,7 @@
         <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
+        <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
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 <li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,8 +322,8 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>Getting Started</li>
+      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">Getting Started</li>
       <li class="wy-breadcrumbs-aside">
             <a href="_sources/get_started.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/index.html b/index.html
index 662ec8e4..bb00ae30 100644
--- a/index.html
+++ b/index.html
@@ -16,6 +16,7 @@
         <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
+        <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -130,15 +131,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -154,17 +173,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -173,7 +207,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -182,33 +219,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -237,8 +321,8 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="#" class="icon icon-home"></a> &raquo;</li>
-      <li>Falkon</li>
+      <li><a href="#" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">Falkon</li>
       <li class="wy-breadcrumbs-aside">
             <a href="_sources/index.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/install.html b/install.html
index 7d9ff387..54951a3b 100644
--- a/install.html
+++ b/install.html
@@ -16,6 +16,7 @@
         <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
+        <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,8 +322,8 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>Install</li>
+      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">Install</li>
       <li class="wy-breadcrumbs-aside">
             <a href="_sources/install.rst.txt" rel="nofollow"> View page source</a>
       </li>
diff --git a/py-modindex.html b/py-modindex.html
index 5442a837..f8507841 100644
--- a/py-modindex.html
+++ b/py-modindex.html
@@ -15,6 +15,7 @@
         <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
+        <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,8 +322,8 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>Python Module Index</li>
+      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">Python Module Index</li>
       <li class="wy-breadcrumbs-aside">
       </li>
   </ul>
diff --git a/search.html b/search.html
index 593f0335..13e12b57 100644
--- a/search.html
+++ b/search.html
@@ -16,6 +16,7 @@
         <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
         <script src="_static/doctools.js"></script>
+        <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <script src="_static/searchtools.js"></script>
@@ -131,15 +132,33 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="api_reference/index.html">API Reference</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/models.html">falkon.models</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#falkon">Falkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.Falkon"><code class="docutils literal notranslate"><span class="pre">Falkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#logisticfalkon">LogisticFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.LogisticFalkon"><code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/models.html#incorefalkon">InCoreFalkon</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/models.html#falkon.models.InCoreFalkon"><code class="docutils literal notranslate"><span class="pre">InCoreFalkon</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/kernels.html">falkon.kernels</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#kernel">Kernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.kernel.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#diffkernel">DiffKernel</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.diff_kernel.DiffKernel"><code class="docutils literal notranslate"><span class="pre">DiffKernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#keopskernelmixin">KeopsKernelMixin</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#falkon.kernels.keops_helpers.KeopsKernelMixin"><code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/kernels.html#radial-kernels">Radial kernels</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#gaussian-kernel">Gaussian kernel</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/kernels.html#laplacian-kernel">Laplacian kernel</a></li>
@@ -155,17 +174,32 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/options.html">falkon.options</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/options.html#falkonoptions">FalkonOptions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions"><code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/gsc_losses.html">falkon.gsc_losses</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#loss">Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.Loss"><code class="docutils literal notranslate"><span class="pre">Loss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#logistic-loss">Logistic loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.LogisticLoss"><code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/gsc_losses.html#weighted-binary-cross-entropy-loss">Weighted binary cross entropy loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/gsc_losses.html#falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/preconditioner.html">falkon.preconditioner</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#preconditioner">Preconditioner</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkon.preconditioner.preconditioner.Preconditioner"><code class="docutils literal notranslate"><span class="pre">Preconditioner</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/preconditioner.html#cholesky-preconditioners">Cholesky preconditioners</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#falkonpreconditioner">FalkonPreconditioner</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/preconditioner.html#logisticpreconditioner">LogisticPreconditioner</a></li>
@@ -174,7 +208,10 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/optimization.html">falkon.optim</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#optimizer">Optimizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkon.optim.Optimizer"><code class="docutils literal notranslate"><span class="pre">Optimizer</span></code></a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/optimization.html#conjugate-gradient-methods">Conjugate gradient methods</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#conjugategradient">ConjugateGradient</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/optimization.html#falkonconjugategradient">FalkonConjugateGradient</a></li>
@@ -183,33 +220,80 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/outofcore.html">falkon.ooc_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-cholesky">gpu_cholesky</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky"><code class="docutils literal notranslate"><span class="pre">gpu_cholesky()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/outofcore.html#gpu-lauum">gpu_lauum</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum"><code class="docutils literal notranslate"><span class="pre">gpu_lauum()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/mmv_ops.html">falkon.mmv_ops</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#run-keops-mmv">run_keops_mmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.keops.run_keops_mmv"><code class="docutils literal notranslate"><span class="pre">run_keops_mmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmm">fmm</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.fmm"><code class="docutils literal notranslate"><span class="pre">fmm()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fmmv">fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fmmv"><code class="docutils literal notranslate"><span class="pre">fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#fdmmv">fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.fdmmv"><code class="docutils literal notranslate"><span class="pre">fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fmmv">incore_fmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fmmv"><code class="docutils literal notranslate"><span class="pre">incore_fmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#incore-fdmmv">incore_fdmmv</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv_incore.incore_fdmmv"><code class="docutils literal notranslate"><span class="pre">incore_fdmmv()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/mmv_ops.html#low-level-functions">Low-level functions</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mm_run_thread()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="docutils literal notranslate"><span class="pre">sparse_mmv_run_thread()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/sparse.html">falkon.sparse</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparsetensor">SparseTensor</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseTensor"><code class="docutils literal notranslate"><span class="pre">SparseTensor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_tensor.SparseType"><code class="docutils literal notranslate"><span class="pre">SparseType</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/sparse.html#sparse-operations">Sparse operations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_matmul"><code class="docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_square_norm"><code class="docutils literal notranslate"><span class="pre">sparse_square_norm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/sparse.html#falkon.sparse.sparse_norm"><code class="docutils literal notranslate"><span class="pre">sparse_norm()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/center_selector.html">falkon.center_selection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a></li>
-<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a></li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#centerselector">CenterSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.CenterSelector"><code class="docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#uniformselector">UniformSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.UniformSelector"><code class="docutils literal notranslate"><span class="pre">UniformSelector</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="api_reference/center_selector.html#fixedselector">FixedSelector</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/center_selector.html#falkon.center_selection.FixedSelector"><code class="docutils literal notranslate"><span class="pre">FixedSelector</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="api_reference/hopt.html">falkon.hopt</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="api_reference/hopt.html#objectives">Objectives</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#falkon.hopt.objectives.objectives.HyperoptObjective"><code class="docutils literal notranslate"><span class="pre">HyperoptObjective</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#nystrom-complexity-regularization">Nystrom Complexity Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#stochastic-nystrom-computational-regularization">Stochastic Nystrom Computational Regularization</a></li>
 <li class="toctree-l4"><a class="reference internal" href="api_reference/hopt.html#complexity-regularization">Complexity Regularization</a></li>
@@ -238,8 +322,8 @@
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   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>Search</li>
+      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li class="breadcrumb-item active">Search</li>
       <li class="wy-breadcrumbs-aside">
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diff --git a/searchindex.js b/searchindex.js
index 28009f48..e59fd891 100644
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"Optimizer": [[7, "optimizer"]], "Conjugate gradient methods": [[7, "conjugate-gradient-methods"]], "ConjugateGradient": [[7, "conjugategradient"]], "FalkonConjugateGradient": [[7, "falkonconjugategradient"]], "falkon.options": [[8, "module-falkon.options"]], "FalkonOptions": [[8, "falkonoptions"]], "falkon.ooc_ops": [[9, "falkon-ooc-ops"]], "gpu_cholesky": [[9, "gpu-cholesky"]], "gpu_lauum": [[9, "gpu-lauum"]], "falkon.preconditioner": [[10, "module-falkon.preconditioner"]], "Preconditioner": [[10, "preconditioner"]], "Cholesky preconditioners": [[10, "cholesky-preconditioners"]], "FalkonPreconditioner": [[10, "falkonpreconditioner"]], "LogisticPreconditioner": [[10, "logisticpreconditioner"]], "falkon.sparse": [[11, "module-falkon.sparse"]], "SparseTensor": [[11, "sparsetensor"]], "Sparse operations": [[11, "sparse-operations"]], "Implementing A Custom Kernel": [[12, "Implementing-A-Custom-Kernel"]], "Setup a simple problem for testing": [[12, "Setup-a-simple-problem-for-testing"]], "Basic Kernel Implementation": [[12, "Basic-Kernel-Implementation"]], "Test the basic kernel": [[12, "Test-the-basic-kernel"]], "Differentiable Kernel": [[12, "Differentiable-Kernel"]], "Test the differentiable kernel": [[12, "Test-the-differentiable-kernel"]], "Adding KeOps Support": [[12, "Adding-KeOps-Support"]], "Test the KeOps kernel": [[12, "Test-the-KeOps-kernel"]], "Supporting Sparse Data": [[12, "Supporting-Sparse-Data"]], "Testing sparse support": [[12, "Testing-sparse-support"]], "Examples": [[13, "examples"]], "Hyperparameter Tuning with Falkon": [[14, "Hyperparameter-Tuning-with-Falkon"]], "Introduction": [[14, "Introduction"], [16, "Introduction"], [18, "Introduction"]], "Load the data": [[14, "Load-the-data"], [16, "Load-the-data"], [17, "Load-the-data"], [18, "Load-the-data"]], "Split into training and test sets": [[14, "Split-into-training-and-test-sets"], [17, "Split-into-training-and-test-sets"], [18, "Split-into-training-and-test-sets"]], "Data Preprocessing": [[14, "Data-Preprocessing"], [15, "Data-Preprocessing"], [17, "Data-Preprocessing"], [18, "Data-Preprocessing"]], "Search for the optimal parameters": [[14, "Search-for-the-optimal-parameters"]], "Evaluating the model": [[14, "Evaluating-the-model"]], "Plot grid-search results": [[14, "Plot-grid-search-results"]], "MNIST Classification with Falkon": [[15, "MNIST-Classification-with-Falkon"]], "Download the MNIST dataset & load it in memory": [[15, "Download-the-MNIST-dataset-&-load-it-in-memory"]], "Run Falkon": [[15, "Run-Falkon"]], "Falkon Regression Tutorial": [[16, "Falkon-Regression-Tutorial"]], "Pre-process the data": [[16, "Pre-process-the-data"]], "Create the Falkon model": [[16, "Create-the-Falkon-model"]], "Training the model": [[16, "Training-the-model"]], "Evaluating model performance": [[16, "Evaluating-model-performance"]], "Automatic Hyperparameter Optimization": [[17, "Automatic-Hyperparameter-Optimization"]], "Hyperparameter Optimization": [[17, "Hyperparameter-Optimization"]], "Introducing Logistic Falkon": [[18, "Introducing-Logistic-Falkon"]], "Define the Falkon model": [[18, "Define-the-Falkon-model"]], "Define Logistic Falkon model": [[18, "Define-Logistic-Falkon-model"]], "Train both models": [[18, "Train-both-models"]], "Testing": [[18, "Testing"]], "Plot predictions": [[18, "Plot-predictions"]], "Getting Started": [[19, "getting-started"]], "Passing Options": [[19, "passing-options"]], "More Examples": [[19, "more-examples"]], "Contents": [[20, "contents"]], "References": [[20, "references"]], "Indices and tables": [[20, "indices-and-tables"]], "Install": [[21, "install"]], "Supported Platforms": [[21, "supported-platforms"]], "Prerequisites": [[21, "prerequisites"]], "PyTorch and CUDA": [[21, "pytorch-and-cuda"]], "Intel MKL": [[21, "intel-mkl"]], "Installing": [[21, "installing"]], "Testing the installation": [[21, "testing-the-installation"]], "Development": [[21, "development"]]}, "indexentries": {"centerselector 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"falkon.preconditioner.LogisticPreconditioner.init"]], "inva() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invA"]], "inva() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invA"]], "invat() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invAt"]], "invat() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invAt"]], "invt() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invT"]], "invt() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invT"]], "invtt() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invTt"]], "invtt() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invTt"]], "sparsetensor (class in falkon.sparse.sparse_tensor)": [[11, "falkon.sparse.sparse_tensor.SparseTensor"]], "sparsetype (class in falkon.sparse.sparse_tensor)": [[11, "falkon.sparse.sparse_tensor.SparseType"]], "falkon.sparse": [[11, "module-falkon.sparse"]], "narrow_rows() (falkon.sparse.sparse_tensor.sparsetensor method)": [[11, "falkon.sparse.sparse_tensor.SparseTensor.narrow_rows"]], "sparse_matmul() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_matmul"]], "sparse_norm() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_norm"]], "sparse_square_norm() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_square_norm"]]}})
\ No newline at end of file

From 86bf1728349b64d8aa2c90fff1a65f4cf921c9be Mon Sep 17 00:00:00 2001
From: Giodiro <Giodiro@users.noreply.github.com>
Date: Tue, 20 Jun 2023 00:39:58 +0000
Subject: [PATCH 5/7] deploy: c26396b2041fa9148aa61e2134e46323eb70cbff

---
 .buildinfo                                    |     2 +-
 _sources/install.rst.txt                      |    44 +-
 .../_sphinx_javascript_frameworks_compat.js   |    17 +-
 _static/basic.css                             |    52 +-
 _static/css/theme.css                         |     2 +-
 _static/doctools.js                           |     2 +-
 _static/documentation_options.js              |     2 +-
 _static/jquery-3.6.0.js                       | 10881 ----------------
 _static/language_data.js                      |     2 +-
 _static/nbsphinx-broken-thumbnail.svg         |     9 +
 _static/nbsphinx-code-cells.css               |   259 +
 _static/nbsphinx-gallery.css                  |    31 +
 _static/nbsphinx-no-thumbnail.svg             |     9 +
 _static/searchtools.js                        |     2 +-
 _static/underscore-1.13.1.js                  |  2042 ---
 _static/underscore.js                         |     6 -
 api_reference/center_selector.html            |    93 +-
 api_reference/gsc_losses.html                 |   105 +-
 api_reference/hopt.html                       |    61 +-
 api_reference/index.html                      |    45 +-
 api_reference/kernels.html                    |   269 +-
 api_reference/mmv_ops.html                    |    69 +-
 api_reference/models.html                     |   111 +-
 api_reference/optimization.html               |    55 +-
 api_reference/options.html                    |    47 +-
 api_reference/outofcore.html                  |    53 +-
 api_reference/preconditioner.html             |   105 +-
 api_reference/sparse.html                     |    69 +-
 examples/custom_kernels.html                  |   817 +-
 examples/examples.html                        |    45 +-
 examples/falkon_cv.html                       |   463 +-
 examples/falkon_mnist.html                    |   409 +-
 examples/falkon_regression_tutorial.html      |   387 +-
 examples/hyperopt.html                        |   437 +-
 examples/hyperopt.ipynb                       |     4 +-
 examples/logistic_falkon.html                 |   431 +-
 genindex.html                                 |    17 +-
 get_started.html                              |    45 +-
 index.html                                    |    45 +-
 install.html                                  |   116 +-
 objects.inv                                   |   Bin 5555 -> 5555 bytes
 py-modindex.html                              |    17 +-
 search.html                                   |    17 +-
 searchindex.js                                |     2 +-
 44 files changed, 1599 insertions(+), 16097 deletions(-)
 delete mode 100644 _static/jquery-3.6.0.js
 create mode 100644 _static/nbsphinx-broken-thumbnail.svg
 create mode 100644 _static/nbsphinx-code-cells.css
 create mode 100644 _static/nbsphinx-gallery.css
 create mode 100644 _static/nbsphinx-no-thumbnail.svg
 delete mode 100644 _static/underscore-1.13.1.js
 delete mode 100644 _static/underscore.js

diff --git a/.buildinfo b/.buildinfo
index 01a24165..682a0128 100644
--- a/.buildinfo
+++ b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 01cb727235985ea8210a828cb4b7007b
+config: f53041f638e13eeb8dbee54aa3fe71e7
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt
index 3673ae56..31a4631a 100644
--- a/_sources/install.rst.txt
+++ b/_sources/install.rst.txt
@@ -35,17 +35,51 @@ to speed-up certain sparse operations. MKL shared libraries are usually distribu
 In case sparse matrix support is not needed, the MKL library will not be loaded.
 
 
+
 Installing
 ----------
 
-You can install Falkon
+There are three ways of installing Falkon:
+
+1. **From source** by running
+
+    .. code-block:: bash
+
+      $ pip install git+https://github.com/falkonml/falkon.git
+
+2. **From pypi with JIT compilation** (the C++ extension will be compiled when the library is first used):
+
+    .. code-block:: bash
+
+      $ pip install falkon
+
+3. **From pre-built wheels** which are available for the following versions of PyTorch and CUDA:
+
+     ============== ========= ========= ========= ========= =========
+      Linux          `cu113`   `cu115`   `cu116`   `cu117`   `cu118`
+     ============== ========= ========= ========= ========= =========
+      torch 1.11.0   ✅         ✅
+      torch 1.13.0                       ✅         ✅
+      torch 2.0.0                                  ✅         ✅
+     ============== ========= ========= ========= ========= =========
+
+    As an example, if you **already have installed** PyTorch 1.13 and CUDA 11.7 on your system, you should run
+
+    .. code-block:: bash
+
+      $ pip install falkon -f https://falkon.dibris.unige.it/torch-1.13.0_cu117.html
+
+    Similarly for CPU packages
+
+    .. code-block:: bash
 
-.. code-block:: bash
+      $ pip install falkon -f https://falkon.dibris.unige.it/torch-2.0.0_cpu.html
 
-   $ pip install git+https://github.com/falkonml/falkon.git
+    please check `here <https://falkon.dibris.unige.it/index.html>`__ for a list of supported wheels.
 
-This will take a while since there are both Cython extensions which need to be compiled (if Cython is installed),
-and PyTorch extensions which take some time to compile.
+For options 1 and 2, you will need the CUDA toolkit to be setup properly on your system in order to compile the sources.
+Compilation may take a few minutes. To speed it up you can try to install ``ninja`` (``pip install ninja``) which
+parallelizes the build process.
 
 
 Testing the installation
diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js
index 8549469d..81415803 100644
--- a/_static/_sphinx_javascript_frameworks_compat.js
+++ b/_static/_sphinx_javascript_frameworks_compat.js
@@ -1,20 +1,9 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
- * Compatability shim for jQuery and underscores.js.
- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
+/* Compatability shim for jQuery and underscores.js.
  *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
  */
 
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-
 /**
  * small helper function to urldecode strings
  *
diff --git a/_static/basic.css b/_static/basic.css
index eeb0519a..7577acb1 100644
--- a/_static/basic.css
+++ b/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -236,16 +236,6 @@ div.body p, div.body dd, div.body li, div.body blockquote {
 a.headerlink {
     visibility: hidden;
 }
-a.brackets:before,
-span.brackets > a:before{
-    content: "[";
-}
-
-a.brackets:after,
-span.brackets > a:after {
-    content: "]";
-}
-
 
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
@@ -334,11 +324,17 @@ aside.sidebar {
 p.sidebar-title {
     font-weight: bold;
 }
+
+nav.contents,
+aside.topic,
 div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
 div.topic {
     border: 1px solid #ccc;
     padding: 7px;
@@ -377,6 +373,8 @@ div.body p.centered {
 
 div.sidebar > :last-child,
 aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
@@ -384,6 +382,8 @@ div.admonition > :last-child {
 
 div.sidebar::after,
 aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -608,19 +608,27 @@ ol.simple p,
 ul.simple p {
     margin-bottom: 0;
 }
-dl.footnote > dt,
-dl.citation > dt {
+
+aside.footnote > span,
+div.citation > span {
     float: left;
-    margin-right: 0.5em;
 }
-
-dl.footnote > dd,
-dl.citation > dd {
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
     margin-bottom: 0em;
 }
-
-dl.footnote > dd:after,
-dl.citation > dd:after {
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
     content: "";
     clear: both;
 }
@@ -636,10 +644,6 @@ dl.field-list > dt {
     padding-left: 0.5em;
     padding-right: 5px;
 }
-dl.field-list > dt:after {
-    content: ":";
-}
-
 
 dl.field-list > dd {
     padding-left: 0.5em;
diff --git a/_static/css/theme.css b/_static/css/theme.css
index 09a1af86..19a446a0 100644
--- a/_static/css/theme.css
+++ b/_static/css/theme.css
@@ -1,4 +1,4 @@
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.viewcode-back{display:block;float:right}.rst-content p.rubric{margin-bottom:12px;font-weight:700}.rst-content code.download,.rst-content tt.download{background:inherit;padding:inherit;font-weight:400;font-family:inherit;font-size:inherit;color:inherit;border:inherit;white-space:inherit}.rst-content code.download span:first-child,.rst-content tt.download span:first-child{-webkit-font-smoothing:subpixel-antialiased}.rst-content code.download span:first-child:before,.rst-content tt.download span:first-child:before{margin-right:4px}.rst-content .guilabel,.rst-content .menuselection{font-size:80%;font-weight:700;border-radius:4px;padding:2.4px 6px;margin:auto 2px}.rst-content .guilabel,.rst-content .menuselection{border:1px solid #7fbbe3;background:#e7f2fa}.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>.kbd,.rst-content :not(dl.option-list)>:not(dt):not(kbd):not(.kbd)>kbd{color:inherit;font-size:80%;background-color:#fff;border:1px solid #a6a6a6;border-radius:4px;box-shadow:0 2px grey;padding:2.4px 6px;margin:auto 0}.rst-content .versionmodified{font-style:italic}@media screen and (max-width:480px){.rst-content .sidebar{width:100%}}span[id*=MathJax-Span]{color:#404040}.math{text-align:center}@font-face{font-family:Lato;src:url(fonts/lato-normal.woff2?bd03a2cc277bbbc338d464e679fe9942) format("woff2"),url(fonts/lato-normal.woff?27bd77b9162d388cb8d4c4217c7c5e2a) format("woff");font-weight:400;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold.woff2?cccb897485813c7c256901dbca54ecf2) format("woff2"),url(fonts/lato-bold.woff?d878b6c29b10beca227e9eef4246111b) format("woff");font-weight:700;font-style:normal;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-bold-italic.woff2?0b6bb6725576b072c5d0b02ecdd1900d) format("woff2"),url(fonts/lato-bold-italic.woff?9c7e4e9eb485b4a121c760e61bc3707c) format("woff");font-weight:700;font-style:italic;font-display:block}@font-face{font-family:Lato;src:url(fonts/lato-normal-italic.woff2?4eb103b4d12be57cb1d040ed5e162e9d) format("woff2"),url(fonts/lato-normal-italic.woff?f28f2d6482446544ef1ea1ccc6dd5892) format("woff");font-weight:400;font-style:italic;font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:400;src:url(fonts/Roboto-Slab-Regular.woff2?7abf5b8d04d26a2cafea937019bca958) format("woff2"),url(fonts/Roboto-Slab-Regular.woff?c1be9284088d487c5e3ff0a10a92e58c) format("woff");font-display:block}@font-face{font-family:Roboto Slab;font-style:normal;font-weight:700;src:url(fonts/Roboto-Slab-Bold.woff2?9984f4a9bda09be08e83f2506954adbe) format("woff2"),url(fonts/Roboto-Slab-Bold.woff?bed5564a116b05148e3b3bea6fb1162a) format("woff");font-display:block}
\ No newline at end of file
diff --git a/_static/doctools.js b/_static/doctools.js
index 527b876c..d06a71d7 100644
--- a/_static/doctools.js
+++ b/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 5f348b0a..45b5ce16 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.7.5',
+    VERSION: '0.8.2',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/_static/jquery-3.6.0.js b/_static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299b..00000000
--- a/_static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML <object> elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.6.0",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-						[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the primary Deferred
-			primary = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return primary.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
-		}
-
-		return primary.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-	which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-			) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-				/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-
-		doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
-
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( {
-		padding: "inner" + name,
-		content: type,
-		"": "outer" + name
-	}, function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each(
-	( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	}
-);
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
-	define( "jquery", [], function() {
-		return jQuery;
-	} );
-}
-
-
-
-
-var
-
-	// Map over jQuery in case of overwrite
-	_jQuery = window.jQuery,
-
-	// Map over the $ in case of overwrite
-	_$ = window.$;
-
-jQuery.noConflict = function( deep ) {
-	if ( window.$ === jQuery ) {
-		window.$ = _$;
-	}
-
-	if ( deep && window.jQuery === jQuery ) {
-		window.jQuery = _jQuery;
-	}
-
-	return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
-	window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/_static/language_data.js b/_static/language_data.js
index 2e22b06a..250f5665 100644
--- a/_static/language_data.js
+++ b/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/_static/nbsphinx-broken-thumbnail.svg b/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 00000000..4919ca88
--- /dev/null
+++ b/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
+  <path d="m 80.856887,38.34474 h 2.903414 l 7.066397,13.332221 V 38.34474 h 2.092166 V 54.281494 H 90.01545 L 82.949053,40.949273 v 13.332221 h -2.092166 z m -13.812565,0 h 10.076555 v 1.814633 h -7.920343 v 4.718048 h 7.589439 v 1.814634 h -7.589439 v 5.774805 h 8.11248 v 1.814634 H 67.044322 Z m -14.335606,0 h 2.156212 v 6.735493 L 62.01672,38.34474 h 2.775323 l -7.909669,7.429324 8.475407,8.50743 h -2.839368 l -7.653485,-7.674833 v 7.674833 h -2.156212 z m -10.738365,1.462381 q -2.348349,0 -3.73601,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010158 1.376987,4.760746 1.387661,1.750587 3.73601,1.750587 2.34835,0 3.714663,-1.750587 1.376986,-1.750588 1.376986,-4.760746 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714663,-1.750588 z m 0,-1.750588 q 3.351736,0 5.358507,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358507,2.241606 -3.362409,0 -5.379855,-2.241606 -2.006772,-2.241607 -2.006772,-6.020315 0,-3.778708 2.006772,-6.020314 2.017446,-2.252281 5.379855,-2.252281 z m -14.100771,8.75294 q 0.693831,0.234835 1.344964,1.003385 0.661808,0.768551 1.323615,2.113515 l 2.188235,4.355121 H 30.410068 L 28.371273,50.193231 Q 27.581374,48.592083 26.834172,48.069042 26.097644,47.546 24.816726,47.546 h -2.34835 v 6.735494 H 20.312164 V 38.34474 h 4.867489 q 2.732625,0 4.077588,1.142152 1.344964,1.142151 1.344964,3.447804 0,1.505078 -0.704505,2.49779 -0.69383,0.992711 -2.02812,1.376987 z m -5.401204,-6.692797 v 5.657388 h 2.711277 q 1.55845,0 2.348349,-0.715179 0.800574,-0.725854 0.800574,-2.124189 0,-1.398336 -0.800574,-2.102841 -0.789899,-0.715179 -2.348349,-0.715179 z M 7.4709616,46.670707 v 5.838851 H 10.92944 q 1.739914,0 2.57251,-0.71518 0.843271,-0.725853 0.843271,-2.209583 0,-1.494404 -0.843271,-2.198909 -0.832596,-0.715179 -2.57251,-0.715179 z m 0,-6.554031 v 4.803443 h 3.1916204 q 1.579799,0 2.34835,-0.587088 0.779225,-0.597761 0.779225,-1.814634 0,-1.206197 -0.779225,-1.803959 -0.768551,-0.597762 -2.34835,-0.597762 z M 5.3147497,38.34474 h 5.5079473 q 2.465767,0 3.800057,1.024734 1.334289,1.024734 1.334289,2.914088 0,1.462382 -0.683156,2.327001 -0.683156,0.86462 -2.006772,1.078106 1.590474,0.341579 2.465768,1.430359 0.885968,1.078106 0.885968,2.700602 0,2.134863 -1.451707,3.298364 -1.451707,1.1635 -4.130961,1.1635 H 5.3147497 Z M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 h -5.657391 z" />
+</svg>
diff --git a/_static/nbsphinx-code-cells.css b/_static/nbsphinx-code-cells.css
new file mode 100644
index 00000000..a3fb27c3
--- /dev/null
+++ b/_static/nbsphinx-code-cells.css
@@ -0,0 +1,259 @@
+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
diff --git a/_static/nbsphinx-gallery.css b/_static/nbsphinx-gallery.css
new file mode 100644
index 00000000..365c27a9
--- /dev/null
+++ b/_static/nbsphinx-gallery.css
@@ -0,0 +1,31 @@
+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
+.nbsphinx-gallery img {
+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
diff --git a/_static/nbsphinx-no-thumbnail.svg b/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 00000000..9dca7588
--- /dev/null
+++ b/_static/nbsphinx-no-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: #ccc; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: #999; }
+    }
+  </style>
+  <path d="M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 H 10.915948 Z M 65.497127,39.809967 q -2.34835,0 -3.736011,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010157 1.376987,4.760745 1.387661,1.750588 3.736011,1.750588 2.348349,0 3.714662,-1.750588 1.376986,-1.750588 1.376986,-4.760745 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714662,-1.750588 z m 0,-1.750588 q 3.351735,0 5.358506,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358506,2.241606 -3.36241,0 -5.379856,-2.241606 -2.006771,-2.241607 -2.006771,-6.020315 0,-3.778708 2.006771,-6.020314 2.017446,-2.252281 5.379856,-2.252281 z m -22.821688,0.288206 h 2.903414 L 52.64525,51.679807 V 38.347585 h 2.092166 V 54.28434 H 51.834001 L 44.767605,40.952119 V 54.28434 h -2.092166 z" />
+</svg>
diff --git a/_static/searchtools.js b/_static/searchtools.js
index e89e34d4..97d56a74 100644
--- a/_static/searchtools.js
+++ b/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/_static/underscore-1.13.1.js b/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af9..00000000
--- a/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-  typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
-    var current = global._;
-    var exports = global._ = factory();
-    exports.noConflict = function () { global._ = current; return exports; };
-  }()));
-}(this, (function () {
-  //     Underscore.js 1.13.1
-  //     https://underscorejs.org
-  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-  //     Underscore may be freely distributed under the MIT license.
-
-  // Current version.
-  var VERSION = '1.13.1';
-
-  // Establish the root object, `window` (`self`) in the browser, `global`
-  // on the server, or `this` in some virtual machines. We use `self`
-  // instead of `window` for `WebWorker` support.
-  var root = typeof self == 'object' && self.self === self && self ||
-            typeof global == 'object' && global.global === global && global ||
-            Function('return this')() ||
-            {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
-  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var push = ArrayProto.push,
-      slice = ArrayProto.slice,
-      toString = ObjProto.toString,
-      hasOwnProperty = ObjProto.hasOwnProperty;
-
-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has$1(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has$1(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has$1(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _$1(obj) {
-    if (obj instanceof _$1) return obj;
-    if (!(this instanceof _$1)) return new _$1(obj);
-    this._wrapped = obj;
-  }
-
-  _$1.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _$1.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
-  _$1.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _$1) a = a._wrapped;
-    if (b instanceof _$1) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath$1(path) {
-    return isArray(path) ? path : [path];
-  }
-  _$1.toPath = toPath$1;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath(path) {
-    return _$1.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has(obj, path) {
-    path = toPath(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has$1(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _$1.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _$1.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // In order to prevent third-party code injection through
-  // `_.templateSettings.variable`, we test it against the following regular
-  // expression. It is intentionally a bit more liberal than just matching valid
-  // identifiers, but still prevents possible loopholes through defaults or
-  // destructuring assignment.
-  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _$1.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    var argument = settings.variable;
-    if (argument) {
-      // Insure against third-party code injection. (CVE-2021-23358)
-      if (!bareIdentifier.test(argument)) throw new Error(
-        'variable is not a bare identifier: ' + argument
-      );
-    } else {
-      // If a variable is not specified, place data values in local scope.
-      source = 'with(obj||{}){\n' + source + '}\n';
-      argument = 'obj';
-    }
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(argument, '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _$1);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _$1(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _$1;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten$1(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten$1(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten$1(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _$1, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria;
-      var b = right.criteria;
-      if (a !== b) {
-        if (a > b || a === void 0) return 1;
-        if (a < b || b === void 0) return -1;
-      }
-      return left.index - right.index;
-    }), 'value');
-  }
-
-  // An internal function used for aggregate "group by" operations.
-  function group(behavior, partition) {
-    return function(obj, iteratee, context) {
-      var result = partition ? [[], []] : {};
-      iteratee = cb(iteratee, context);
-      each(obj, function(value, index) {
-        var key = iteratee(value, index, obj);
-        behavior(result, value, key);
-      });
-      return result;
-    };
-  }
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
-
-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
-    if (isString(obj)) {
-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
-
-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
-    if (isFunction$1(iteratee)) {
-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten$1(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten$1(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N.
-  function initial(array, n, guard) {
-    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
-  }
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  function first(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[0];
-    return initial(array, array.length - n);
-  }
-
-  // Returns everything but the first entry of the `array`. Especially useful on
-  // the `arguments` object. Passing an **n** will return the rest N values in the
-  // `array`.
-  function rest(array, n, guard) {
-    return slice.call(array, n == null || guard ? 1 : n);
-  }
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array.
-  function last(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[array.length - 1];
-    return rest(array, Math.max(0, array.length - n));
-  }
-
-  // Trim out all falsy values from an array.
-  function compact(array) {
-    return filter(array, Boolean);
-  }
-
-  // Flatten out an array, either recursively (by default), or up to `depth`.
-  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten(array, depth) {
-    return flatten$1(array, depth, false);
-  }
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  var difference = restArguments(function(array, rest) {
-    rest = flatten$1(rest, true, true);
-    return filter(array, function(value){
-      return !contains(rest, value);
-    });
-  });
-
-  // Return a version of the array that does not contain the specified value(s).
-  var without = restArguments(function(array, otherArrays) {
-    return difference(array, otherArrays);
-  });
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // The faster algorithm will not work with an iteratee if the iteratee
-  // is not a one-to-one function, so providing an iteratee will disable
-  // the faster algorithm.
-  function uniq(array, isSorted, iteratee, context) {
-    if (!isBoolean(isSorted)) {
-      context = iteratee;
-      iteratee = isSorted;
-      isSorted = false;
-    }
-    if (iteratee != null) iteratee = cb(iteratee, context);
-    var result = [];
-    var seen = [];
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var value = array[i],
-          computed = iteratee ? iteratee(value, i, array) : value;
-      if (isSorted && !iteratee) {
-        if (!i || seen !== computed) result.push(value);
-        seen = computed;
-      } else if (iteratee) {
-        if (!contains(seen, computed)) {
-          seen.push(computed);
-          result.push(value);
-        }
-      } else if (!contains(result, value)) {
-        result.push(value);
-      }
-    }
-    return result;
-  }
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  var union = restArguments(function(arrays) {
-    return uniq(flatten$1(arrays, true, true));
-  });
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays.
-  function intersection(array) {
-    var result = [];
-    var argsLength = arguments.length;
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var item = array[i];
-      if (contains(result, item)) continue;
-      var j;
-      for (j = 1; j < argsLength; j++) {
-        if (!contains(arguments[j], item)) break;
-      }
-      if (j === argsLength) result.push(item);
-    }
-    return result;
-  }
-
-  // Complement of zip. Unzip accepts an array of arrays and groups
-  // each array's elements on shared indices.
-  function unzip(array) {
-    var length = array && max(array, getLength).length || 0;
-    var result = Array(length);
-
-    for (var index = 0; index < length; index++) {
-      result[index] = pluck(array, index);
-    }
-    return result;
-  }
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  var zip = restArguments(unzip);
-
-  // Converts lists into objects. Pass either a single array of `[key, value]`
-  // pairs, or two parallel arrays of the same length -- one of keys, and one of
-  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
-  function object(list, values) {
-    var result = {};
-    for (var i = 0, length = getLength(list); i < length; i++) {
-      if (values) {
-        result[list[i]] = values[i];
-      } else {
-        result[list[i][0]] = list[i][1];
-      }
-    }
-    return result;
-  }
-
-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
-  // [the Python documentation](https://docs.python.org/library/functions.html#range).
-  function range(start, stop, step) {
-    if (stop == null) {
-      stop = start || 0;
-      start = 0;
-    }
-    if (!step) {
-      step = stop < start ? -1 : 1;
-    }
-
-    var length = Math.max(Math.ceil((stop - start) / step), 0);
-    var range = Array(length);
-
-    for (var idx = 0; idx < length; idx++, start += step) {
-      range[idx] = start;
-    }
-
-    return range;
-  }
-
-  // Chunk a single array into multiple arrays, each containing `count` or fewer
-  // items.
-  function chunk(array, count) {
-    if (count == null || count < 1) return [];
-    var result = [];
-    var i = 0, length = array.length;
-    while (i < length) {
-      result.push(slice.call(array, i, i += count));
-    }
-    return result;
-  }
-
-  // Helper function to continue chaining intermediate results.
-  function chainResult(instance, obj) {
-    return instance._chain ? _$1(obj).chain() : obj;
-  }
-
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diff --git a/_static/underscore.js b/_static/underscore.js
deleted file mode 100644
index cf177d42..00000000
--- a/_static/underscore.js
+++ /dev/null
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diff --git a/api_reference/center_selector.html b/api_reference/center_selector.html
index 7d9c1a58..c6d89f4e 100644
--- a/api_reference/center_selector.html
+++ b/api_reference/center_selector.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.center_selection &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.center_selection &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
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         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
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@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.center_selection</li>
       <li class="wy-breadcrumbs-aside">
@@ -334,31 +337,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
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-.nboutput.nblast.container {
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-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon.center_selection">
+  <section id="module-falkon.center_selection">
 <span id="falkon-center-selection"></span><h1>falkon.center_selection<a class="headerlink" href="#module-falkon.center_selection" title="Permalink to this heading"></a></h1>
 <section id="centerselector">
 <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink to this heading"></a></h2>
@@ -367,22 +346,22 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">CenterSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_gen</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.CenterSelector" title="Permalink to this definition"></a></dt>
 <dd><p>Create the center selector with a random number generator</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>random_gen</strong> – A numpy random number generator object or a random seed.</p>
 </dd>
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.CenterSelector.select">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> – The full input dataset (or a representation of it)</p></li>
 <li><p><strong>Y</strong> – The full input labels (this may be None)</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>X_centers</em> – If Y is None this is the only output: M centers selected from the data.</p></li>
 <li><p><em>Y_centers</em> – If Y is not empty, a set of label centers shall be returned as well.</p></li>
@@ -394,16 +373,16 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.CenterSelector.select_indices">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select_indices" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.CenterSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method for selecting <cite>M</cite> centers from the data.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> – The full input dataset (or a representation of it)</p></li>
 <li><p><strong>Y</strong> – The full input labels (this may be None)</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>X_centers</em> – If Y is None this is the only output: M centers selected from the data.</p></li>
 <li><p><em>Y_centers</em> – If Y is not empty, a set of label centers shall be returned as well.</p></li>
@@ -426,7 +405,7 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 <dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></p>
 <p>Center selector which chooses from the full dataset uniformly at random (without replacement)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>random_gen</strong> – A numpy random number generator object or a random seed.</p></li>
 <li><p><strong>num_centers</strong> – The number of centers which should be selected by this class.</p></li>
@@ -435,12 +414,12 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.UniformSelector.select">
-<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
 <p>The selection strategy is uniformly at random. To control the randomness,
 pass an appropriate numpy random generator to this class’s constructor.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> – N x D tensor containing the whole input dataset. If N is lower than the number of
 centers this class is programmed to pick, a warning will be raised and only N centers
@@ -452,7 +431,7 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 <a class="reference internal" href="models.html#falkon.models.Falkon" title="falkon.models.Falkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.Falkon</span></code></a>) only require the centers from the predictors.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>X_M</em> – The randomly selected centers. They will be in a new, memory-contiguous tensor.
 All characteristics of the input tensor will be preserved.</p></li>
@@ -466,14 +445,14 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.UniformSelector.select_indices">
-<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select_indices" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.UniformSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Select M observations from 2D tensor <cite>X</cite>, preserving device and memory order.</p>
 <p>The selection strategy is uniformly at random. To control the randomness,
 pass an appropriate numpy random generator to this class’s constructor.</p>
 <p>This method behaves the same as <a class="reference internal" href="#falkon.center_selection.UniformSelector.select" title="falkon.center_selection.UniformSelector.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select()</span></code></a> but additionally returns a <cite>LongTensor</cite>
 containing the indices of the chosen points.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> – N x D tensor containing the whole input dataset. If N is lower than the number of
 centers this class is programmed to pick, a warning will be raised and only N centers
@@ -485,7 +464,7 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 <a class="reference internal" href="models.html#falkon.models.Falkon" title="falkon.models.Falkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.models.Falkon</span></code></a>) only require the centers from the predictors.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>(X_M, indices)</em> – The randomly selected centers and the corresponding indices.
 The centers will be stored in a new, memory-contiguous tensor and all
@@ -505,12 +484,12 @@ <h2>UniformSelector<a class="headerlink" href="#uniformselector" title="Permalin
 <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.center_selection.FixedSelector">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">FixedSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.FixedSelector" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.center_selection.</span></span><span class="sig-name descname"><span class="pre">FixedSelector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.center_selection.FixedSelector" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <a class="reference internal" href="#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">CenterSelector</span></code></a></p>
 <p>Center selector which always picks the same centers.</p>
 <p>The fixed centers are specified at class initialization time.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>centers</strong> – Tensor of data-centers to be used.</p></li>
 <li><p><strong>y_centers</strong> – Optional tensor of label-centers to be used. If this is <cite>None</cite>, calling <a class="reference internal" href="#falkon.center_selection.FixedSelector.select" title="falkon.center_selection.FixedSelector.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select()</span></code></a> with
@@ -522,10 +501,10 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.FixedSelector.select">
-<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select" title="Permalink to this definition"></a></dt>
 <dd><p>Returns the fixed centers with which this instance was created</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> – This parameter is ignored. The centers returned are the ones passed in the class’s
 constructor.</p></li>
@@ -534,7 +513,7 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 and Y-centers.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>X_M</em> – The fixed centers as given in the class constructor</p></li>
 <li><p><em>(X_M, Y_M)</em> – The X-centers and Y-centers as given in the class constructor. This tuple is only
@@ -542,7 +521,7 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Raises</dt>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>RuntimeError</strong> – If parameter <cite>Y</cite> is not None but the <cite>y_centers</cite> tensor passed to the class constructor
     is <cite>None</cite>.</p>
 </dd>
@@ -551,10 +530,10 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.center_selection.FixedSelector.select_indices">
-<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select_indices" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">select_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.center_selection.FixedSelector.select_indices" title="Permalink to this definition"></a></dt>
 <dd><p>Returns the fixed centers, and their indices with which this instance was created</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> – This parameter is ignored. The centers returned are the ones passed in the class’s
 constructor.</p></li>
@@ -563,7 +542,7 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 Y-centers, and indices.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>(X_M, indices)</em> – The fixed centers and the indices as given in the class constructor</p></li>
 <li><p><em>(X_M, Y_M, indices)</em> – The X-centers, Y-centers and indices as given in the class constructor.
@@ -571,7 +550,7 @@ <h2>FixedSelector<a class="headerlink" href="#fixedselector" title="Permalink to
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Raises</dt>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>RuntimeError</strong> – If the indices passed to the class constructor are <cite>None</cite>.</p></li>
 <li><p><strong>RuntimeError</strong> – If parameter <cite>Y</cite> is not None but the <cite>y_centers</cite> tensor passed to the class constructor
diff --git a/api_reference/gsc_losses.html b/api_reference/gsc_losses.html
index 9229e3f5..ffccf0f2 100644
--- a/api_reference/gsc_losses.html
+++ b/api_reference/gsc_losses.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.gsc_losses &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.gsc_losses &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +31,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +327,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.gsc_losses</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,37 +339,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon">
+  <section id="module-falkon">
 <span id="falkon-gsc-losses"></span><h1>falkon.gsc_losses<a class="headerlink" href="#module-falkon" title="Permalink to this heading"></a></h1>
 <section id="loss">
 <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.Loss" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.Loss" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract generalized self-concordant loss function class.</p>
 <p>Such loss functions must be three times differentiable; but for the logistic Falkon algorithm
 only the first two derivatives are used.
@@ -378,7 +357,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 calculate kernel-vector products using the loss derivatives for vectors. These functions are
 specific to the logistic Falkon algorithm.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>name</strong> – A descriptive name for the loss function (e.g. “logistic”, “softmax”)</p></li>
 <li><p><strong>kernel</strong> – The kernel function used for training a LogFalkon model</p></li>
@@ -401,13 +380,13 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 <em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the loss for predicting <cite>y2</cite> with true labels <cite>y1</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>y1</strong> (<em>torch.Tensor</em>) – One of the two inputs to the loss. This should be interpreted as the <cite>true</cite> labels.</p></li>
 <li><p><strong>y2</strong> (<em>torch.Tensor</em>) – The other loss input. Should be interpreted as the predicted labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>torch.Tensor</em> – The loss calculated for the two inputs.</p>
 </dd>
 </dl>
@@ -418,14 +397,14 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 <em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ddf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.ddf" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the second derivative of the loss wrt <cite>y2</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>y1</strong> (<em>torch.Tensor</em>) – One of the two inputs to the loss. This should be interpreted as the <cite>true</cite> labels.</p></li>
 <li><p><strong>y2</strong> (<em>torch.Tensor</em>) – The other loss input. Should be interpreted as the predicted labels. The derivative
 should be computed with respect to this tensor.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>torch.Tensor</em> – The second derivative of the loss with respect to <cite>y2</cite>. It will be a tensor of the
 same shape as the two inputs.</p>
 </dd>
@@ -437,14 +416,14 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 <em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">df</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.df" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract method. Should return the derivative of the loss wrt <cite>y2</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>y1</strong> (<em>torch.Tensor</em>) – One of the two inputs to the loss. This should be interpreted as the <cite>true</cite> labels.</p></li>
 <li><p><strong>y2</strong> (<em>torch.Tensor</em>) – The other loss input. Should be interpreted as the predicted labels. The derivative
 should be computed with respect to this tensor.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>torch.Tensor</em> – The derivative of the loss with respect to <cite>y2</cite>. It will be a tensor of the same shape
 as the two inputs.</p>
 </dd>
@@ -453,14 +432,14 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss.knmp_grad">
-<span class="sig-name descname"><span class="pre">knmp_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_grad" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">knmp_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_grad" title="Permalink to this definition"></a></dt>
 <dd><p>Computes a kernel vector product where the vector is the first derivative of this loss</p>
 <p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
 number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
 <div class="math notranslate nohighlight">
 \[\dfrac{1}{n} K(X_c, X) &#64; (\mathcal{l}'(Y, K(X, X_c) &#64; u))\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>torch.Tensor</em>) – Data matrix of shape (n x d) with <cite>n</cite> samples in <cite>d</cite> dimensions.</p></li>
 <li><p><strong>Xc</strong> (<em>torch.Tensor</em>) – Center matrix of shape (m x d) with <cite>m</cite> centers in <cite>d</cite> dimensions.</p></li>
@@ -474,7 +453,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 class instance.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><strong>grad_mul</strong> (<em>torch.Tensor</em>) – A tensor of shape (m x 1) coming from the multiplication of the kernel matrix
 <cite>K(Xc, X)</cite> and the loss calculated on predictions with weights <cite>u</cite>.
@@ -488,14 +467,14 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.gsc_losses.Loss.knmp_hess">
-<span class="sig-name descname"><span class="pre">knmp_hess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_hess" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">knmp_hess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">u</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.gsc_losses.Loss.knmp_hess" title="Permalink to this definition"></a></dt>
 <dd><p>Compute a kernel-vector product with a rescaling with the second derivative</p>
 <p>Given kernel function <span class="math notranslate nohighlight">\(K\)</span>, the loss represented by this class <span class="math notranslate nohighlight">\(\mathcal{l}\)</span>,
 number of samples <span class="math notranslate nohighlight">\(n\)</span>, this function follows equation</p>
 <div class="math notranslate nohighlight">
 \[\dfrac{1}{n} K(X_c, X) &#64; (\mathcal{l}''(Y, f) * K(X, X_c) &#64; u)\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>torch.Tensor</em>) – Data matrix of shape (n x d) with <cite>n</cite> samples in <cite>d</cite> dimensions.</p></li>
 <li><p><strong>Xc</strong> (<em>torch.Tensor</em>) – Center matrix of shape (m x d) with <cite>m</cite> centers in <cite>d</cite> dimensions.</p></li>
@@ -511,7 +490,7 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 class instance.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>A tensor of shape (m x t), the output of the computation.</em></p>
 </dd>
 </dl>
@@ -524,12 +503,12 @@ <h2>Loss<a class="headerlink" href="#loss" title="Permalink to this heading">
 <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">LogisticLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">LogisticLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper for the logistic loss, to be used in conjunction with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Usage of this loss assumes a binary classification problem with labels -1 and +1. For different
 choices of labels, see <a class="reference internal" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel function used for training a <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> model</p></li>
 <li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options container. Will be passed to the kernel when computing kernel-vector
@@ -549,13 +528,13 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 <dd><p>Compute the logistic loss between two 1-dimensional tensors</p>
 <p>The formula used is <span class="math notranslate nohighlight">\(\log(1 + \exp(-y_1 * y_2))\)</span></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>y1</strong> – The first input tensor. Must be 1D</p></li>
 <li><p><strong>y2</strong> – The second input tensor. Must be 1D</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>loss</em> – The logistic loss between the two input vectors.</p>
 </dd>
 </dl>
@@ -569,13 +548,13 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 <div class="math notranslate nohighlight">
 \[y_1^2 \dfrac{1}{1 + \exp(-y_1 * y_2)} \dfrac{1}{1 + \exp(y_1 * y_2)}\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>y1</strong> – The first input tensor. Must be 1D</p></li>
 <li><p><strong>y2</strong> – The second input tensor. Must be 1D</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>dd_loss</em> – The second derivative of the logistic loss, calculated between the two input vectors.</p>
 </dd>
 </dl>
@@ -589,13 +568,13 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 <div class="math notranslate nohighlight">
 \[\dfrac{-y_1}{1 + \exp(y_1 * y_2)}\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>y1</strong> – The first input tensor. Must be 1D</p></li>
 <li><p><strong>y2</strong> – The second input tensor. Must be 1D</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>d_loss</em> – The derivative of the logistic loss, calculated between the two input vectors.</p>
 </dd>
 </dl>
@@ -608,13 +587,13 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-binary-cross-entropy-loss" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper for the weighted binary cross-entropy loss, to be used with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Using this loss assumes a binary classification problem with labels 0 and +1. Additionally,
 this loss allows to place a different weight to samples belonging to one of the two classes
 (see the <cite>neg_weight</cite> parameter).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel function used for training a <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> model</p></li>
 <li><p><strong>neg_weight</strong> (<em>float</em>) – The weight to be assigned to samples belonging to the negative (0-labeled) class.
@@ -640,14 +619,14 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 <div class="math notranslate nohighlight">
 \[\mathrm{true} * \log(1 + e^{-\mathrm{pred}}) + w * (1 - \mathrm{true}) * \log(1 + e^{\mathrm{pred}})\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>true</strong> – The label tensor. Must be 1D, with values 0 or 1.</p></li>
 <li><p><strong>pred</strong> – The prediction tensor. Must be 1D. These are “logits” so need not be scaled before
 hand.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>loss</em> – The weighted BCE loss between the two input vectors.</p>
 </dd>
 </dl>
@@ -661,13 +640,13 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 <div class="math notranslate nohighlight">
 \[\dfrac{-(\mathrm{true} * (w - 1) - w) * e^{\mathrm{pred}}}{(e^{\mathrm{pred}} + 1)^2}\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>true</strong> – The label tensor. Must be 1D</p></li>
 <li><p><strong>pred</strong> – The prediction tensor. Must be 1D</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>dd_loss</em> – The second derivative of the weighted BCE loss between the two input vectors.</p>
 </dd>
 </dl>
@@ -681,13 +660,13 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 <div class="math notranslate nohighlight">
 \[\dfrac{-(w * \mathrm{true} - w) * e^{\mathrm{pred}} - \mathrm{true}}{e^{\mathrm{pred}} + 1}\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>true</strong> – The label tensor. Must be 1D</p></li>
 <li><p><strong>pred</strong> – The prediction tensor. Must be 1D</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>d_loss</em> – The derivative of the weighted BCE loss between the two input vectors.</p>
 </dd>
 </dl>
diff --git a/api_reference/hopt.html b/api_reference/hopt.html
index b1b04e4e..ff33ec0c 100644
--- a/api_reference/hopt.html
+++ b/api_reference/hopt.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.hopt &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.hopt &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -31,14 +30,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -323,7 +326,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.hopt</li>
       <li class="wy-breadcrumbs-aside">
@@ -335,44 +338,20 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon.hopt">
+  <section id="module-falkon.hopt">
 <span id="falkon-hopt"></span><h1>falkon.hopt<a class="headerlink" href="#module-falkon.hopt" title="Permalink to this heading"></a></h1>
 <section id="objectives">
 <h2>Objectives<a class="headerlink" href="#objectives" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.objectives.HyperoptObjective">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.objectives.</span></span><span class="sig-name descname"><span class="pre">HyperoptObjective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.objectives.HyperoptObjective" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.objectives.</span></span><span class="sig-name descname"><span class="pre">HyperoptObjective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.objectives.HyperoptObjective" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <section id="nystrom-complexity-regularization">
 <h3>Nystrom Complexity Regularization<a class="headerlink" href="#nystrom-complexity-regularization" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.NystromCompReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">NystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.NystromCompReg" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">NystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.NystromCompReg" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -380,7 +359,7 @@ <h3>Nystrom Complexity Regularization<a class="headerlink" href="#nystrom-comple
 <h3>Stochastic Nystrom Computational Regularization<a class="headerlink" href="#stochastic-nystrom-computational-regularization" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.StochasticNystromCompReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">StochasticNystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_trace_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.StochasticNystromCompReg" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">StochasticNystromCompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">flk_maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_trace_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.StochasticNystromCompReg" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -388,7 +367,7 @@ <h3>Stochastic Nystrom Computational Regularization<a class="headerlink" href="#
 <h3>Complexity Regularization<a class="headerlink" href="#complexity-regularization" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.CompReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">CompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.CompReg" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">CompReg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.CompReg" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -396,7 +375,7 @@ <h3>Complexity Regularization<a class="headerlink" href="#complexity-regularizat
 <h3>Generalized Cross Validation<a class="headerlink" href="#generalized-cross-validation" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.GCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">GCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.GCV" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">GCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.GCV" title="Permalink to this definition"></a></dt>
 <dd><p>GCV objective is</p>
 <div class="math notranslate nohighlight">
 \[\dfrac{\dfrac{1}{n} \lVert (I - \widetilde{K}_\lambda \widetilde{K}) Y \rVert^2}
@@ -419,7 +398,7 @@ <h3>Generalized Cross Validation<a class="headerlink" href="#generalized-cross-v
 <h3>Hold Out Cross Validation<a class="headerlink" href="#hold-out-cross-validation" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.HoldOut">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">HoldOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_pct</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_iter_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.HoldOut" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">HoldOut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_pct</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_iter_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.HoldOut" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -427,7 +406,7 @@ <h3>Hold Out Cross Validation<a class="headerlink" href="#hold-out-cross-validat
 <h3>Leave One Out Cross Validation<a class="headerlink" href="#leave-one-out-cross-validation" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.LOOCV">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">LOOCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.LOOCV" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">LOOCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.LOOCV" title="Permalink to this definition"></a></dt>
 <dd><p>LOOCV objective is to minimize the PRESS error:</p>
 <div class="math notranslate nohighlight">
 \[\min \sum_{i=1}^n \big(e_{(i)}\big)_i^2\]</div>
@@ -447,7 +426,7 @@ <h3>Leave One Out Cross Validation<a class="headerlink" href="#leave-one-out-cro
 <h3>SGPR<a class="headerlink" href="#sgpr" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.hopt.objectives.SGPR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">SGPR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Transform</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.SGPR" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.hopt.objectives.</span></span><span class="sig-name descname"><span class="pre">SGPR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.diff_kernel.DiffKernel" title="falkon.kernels.diff_kernel.DiffKernel"><span class="pre">DiffKernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_centers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pen_transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Transform</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.hopt.objectives.SGPR" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
diff --git a/api_reference/index.html b/api_reference/index.html
index fb6772db..a91939f4 100644
--- a/api_reference/index.html
+++ b/api_reference/index.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>API Reference &mdash; falkon 0.7.5 documentation</title>
+  <title>API Reference &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">API Reference</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/api_reference/index.rst.txt" rel="nofollow"> View page source</a>
@@ -333,31 +336,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="api-reference">
+  <section id="api-reference">
 <span id="id1"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
diff --git a/api_reference/kernels.html b/api_reference/kernels.html
index 50c535ac..372ef60f 100644
--- a/api_reference/kernels.html
+++ b/api_reference/kernels.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.kernels &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.kernels &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +31,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +327,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.kernels</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,37 +339,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
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-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
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-.nblast.container + .nbinput.container {
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-.math {
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-}
-</style>
-<section id="module-falkon.kernels">
+  <section id="module-falkon.kernels">
 <span id="falkon-kernels"></span><h1>falkon.kernels<a class="headerlink" href="#module-falkon.kernels" title="Permalink to this heading"></a></h1>
 <section id="kernel">
 <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.kernel.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.kernel.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract kernel class. Kernels should inherit from this class, overriding appropriate methods.</p>
 <p>To extend Falkon with new kernels, you should read the documentation of this class
 carefully, and take a look at the existing implementation of <a class="reference internal" href="#falkon.kernels.GaussianKernel" title="falkon.kernels.GaussianKernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianKernel</span></code></a>
@@ -377,7 +356,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <p>The <a class="reference internal" href="#falkon.kernels.kernel.Kernel.compute" title="falkon.kernels.kernel.Kernel.compute"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute()</span></code></a> method should compute the kernel matrix, without concerns for differentiability,
 while the <a class="reference internal" href="#falkon.kernels.kernel.Kernel.compute_sparse" title="falkon.kernels.kernel.Kernel.compute_sparse"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_sparse()</span></code></a> method is used to compute the kernel for sparse input matrices.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>name</strong> – A short name for the kernel (e.g. “Gaussian”)</p></li>
 <li><p><strong>opt</strong> – Base set of options to be used for operations involving this kernel.</p></li>
@@ -386,10 +365,10 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -402,7 +381,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
@@ -410,12 +389,12 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_dmmv_impl">
-<span class="sig-name descname"><span class="pre">_decide_dmmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">_decide_dmmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_dmmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>dmmv</cite> function to use for this data.</p>
 <p>Note that <cite>dmmv</cite> functions compute double kernel-vector products (see <a class="reference internal" href="#falkon.kernels.kernel.Kernel.dmmv" title="falkon.kernels.kernel.Kernel.dmmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dmmv()</span></code></a> for
 an explanation of what they are).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – First data matrix, of shape (N x D)</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – Second data matrix, of shape (M x D)</p></li>
@@ -424,7 +403,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options. Options may be specified to force GPU or CPU usage.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>dmmv_fn</em> – A function which allows to perform the <cite>mmv</cite> operation.</p>
 </dd>
 </dl>
@@ -440,7 +419,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <dd><p>Choose which <cite>mm</cite> function to use for this data.</p>
 <p>Note that <cite>mm</cite> functions compute the kernel itself so <strong>KeOps may not be used</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – First data matrix, of shape (N x D)</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – Second data matrix, of shape (M x D)</p></li>
@@ -448,7 +427,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options. Options may be specified to force GPU or CPU usage.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>mm_fn</em> – A function which allows to perform the <cite>mm</cite> operation.</p>
 </dd>
 </dl>
@@ -460,11 +439,11 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel._decide_mmv_impl">
-<span class="sig-name descname"><span class="pre">_decide_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">_decide_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel._decide_mmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Choose which <cite>mmv</cite> function to use for this data.</p>
 <p>Note that <cite>mmv</cite> functions compute the kernel-vector product</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – First data matrix, of shape (N x D)</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – Second data matrix, of shape (M x D)</p></li>
@@ -472,7 +451,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Falkon options. Options may be specified to force GPU or CPU usage.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>mmv_fn</em> – A function which allows to perform the <cite>mmv</cite> operation.</p>
 </dd>
 </dl>
@@ -489,7 +468,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <p>The kernel matrix should be stored in <code class="docutils literal notranslate"><span class="pre">out</span></code> to ensure the correctness of allocatable
 memory computations.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The left matrix for computing the kernel</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The right matrix for computing the kernel</p></li>
@@ -498,7 +477,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 is to be computed and stored in <code class="docutils literal notranslate"><span class="pre">out</span></code>. Otherwise compute the full kernel matrix.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p>
 </dd>
 </dl>
@@ -510,7 +489,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> which are two sparse matrices, storing the output
 in the dense matrix <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The left matrix for computing the kernel</p></li>
 <li><p><strong>X2</strong> (<a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The right matrix for computing the kernel</p></li>
@@ -527,7 +506,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 </p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <cite>out</cite>.</p>
 </dd>
 </dl>
@@ -535,14 +514,14 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
 we remove the additive factor.
 <strong>At least one of `w` and `v` must be provided</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -558,7 +537,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
 </dd>
 </dl>
@@ -578,7 +557,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.extra_mem">
-<span class="sig-name descname"><span class="pre">extra_mem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.extra_mem" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">extra_mem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_differentiable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_sparse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">density2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.extra_mem" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the amount of extra memory which will be needed when computing this kernel.</p>
 <p>Often kernel computation needs some extra memory allocations. To avoid using too large
 block-sizes which may lead to OOM errors, you should declare any such extra allocations
@@ -588,24 +567,34 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 the return dictionary will be: <cite>{‘nd’: 1}</cite>. Other possible coefficients are on <cite>d</cite>, <cite>n</cite>, <cite>m</cite>
 which are respectively the data-dimension, the number of data-points in the first data
 matrix and the number of data-points in the second matrix. Pairwise combinations of the
-three dimensions are possible (i.e. <cite>nd</cite>, <cite>nm</cite>, <cite>md</cite>).
+three dimensions are possible (i.e. <cite>nd</cite>, <cite>nm</cite>, <cite>md</cite>), and a special key ‘0’ can be
+used to specify a base memory needed independently of data dimensions.
 Make sure to specify the dictionary keys as is written here since they will not be
 recognized otherwise.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p><strong>extra_allocs</strong> (<em>dictionary</em>) – A dictionary from strings indicating on which dimensions the extra-allocation is
-needed (allowed strings: <cite>‘n’, ‘m’, ‘d’, ‘nm’, ‘nd’, ‘md’</cite>) to floating-point numbers
-indicating how many extra-allocations are needed.</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>is_differentiable</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>is_sparse</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>dtype</strong> (<em>torch.dtype</em><em> or </em><em>np.dtype</em>) – </p></li>
+<li><p><strong>density1</strong> (<em>float</em><em> or </em><em>None</em>) – </p></li>
+<li><p><strong>density2</strong> (<em>float</em><em> or </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>extra_allocs</strong> (<em>dictionary</em>) – A dictionary from strings indicating on which dimensions the extra-allocation is
+needed (allowed strings: <cite>‘n’, ‘m’, ‘d’, ‘nm’, ‘nd’, ‘md’, ‘0’</cite>) to floating-point
+numbers indicating how many extra-allocations are needed.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -619,7 +608,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
 </dd>
 </dl>
@@ -657,7 +646,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 as parameters of the computation graph. Even non-differentiable parameters should be provided
 as keywords (also non tensor arguments).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>name</strong> – A short name for the kernel (e.g. “Gaussian”)</p></li>
 <li><p><strong>options</strong> – Base set of options to be used for operations involving this kernel.</p></li>
@@ -681,7 +670,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 <dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>. The output should be differentiable with
 respect to <cite>X1</cite>, <cite>X2</cite>, and all kernel parameters returned by the <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel.diff_params" title="falkon.kernels.diff_kernel.DiffKernel.diff_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">diff_params()</span></code></a> method.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The left matrix for computing the kernel</p></li>
 <li><p><strong>X2</strong> (<em>torch.Tensor</em>) – The right matrix for computing the kernel</p></li>
@@ -689,7 +678,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 is to be computed and stored in <code class="docutils literal notranslate"><span class="pre">out</span></code>. Otherwise compute the full kernel matrix.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The constructed kernel matrix.</p>
 </dd>
 </dl>
@@ -700,7 +689,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 <em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">detach</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.detach" title="Permalink to this definition"></a></dt>
 <dd><p>Detaches all differentiable parameters of the kernel from the computation graph.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p><em>k</em> – A new instance of the kernel sharing the same parameters, but detached from the
 computation graph.</p>
 </dd>
@@ -713,7 +702,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 <dd><p>A dictionary mapping parameter names to their values for all <strong>differentiable</strong> parameters
 of the kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p><em>params</em> – A dictionary mapping parameter names to their values</p>
 </dd>
 </dl>
@@ -726,7 +715,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.keops_helpers.</span></span><span class="sig-name descname"><span class="pre">KeopsKernelMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.keops_helpers.</span></span><span class="sig-name descname"><span class="pre">KeopsKernelMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin" title="Permalink to this definition"></a></dt>
 <dd><p>Abstract class for kernels which enables KeOps acceleration.</p>
 <p>This class should be extended when KeOps-accelerated kernel-vector products are required.
 Subclasses should implement the <a class="reference internal" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl"><code class="xref py py-meth docutils literal notranslate"><span class="pre">keops_mmv_impl()</span></code></a> method by defining an appropriate
@@ -737,10 +726,10 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 <a class="reference internal" href="../examples/custom_kernels.html"><span class="doc">Custom Kernels</span></a> notebook.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv">
-<span class="sig-name descname"><span class="pre">keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Helper method to call into KeOps for kernel-vector products</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -757,7 +746,7 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 CUDA options and KeOps options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>out</em> – The computed kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>, multiplied by vector <code class="docutils literal notranslate"><span class="pre">v</span></code>.</p>
 </dd>
 </dl>
@@ -768,7 +757,7 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 <em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">keops_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Implementation of the KeOps formula to compute a kernel-vector product.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -783,7 +772,7 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 CUDA options and KeOps options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>out</em> – The computed kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>, multiplied by vector <code class="docutils literal notranslate"><span class="pre">v</span></code>.</p>
 </dd>
 </dl>
@@ -798,7 +787,7 @@ <h2>Radial kernels<a class="headerlink" href="#radial-kernels" title="Permalink
 <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">GaussianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">GaussianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Gaussian kernel and related kernel-vector products</p>
 <p>The Gaussian kernel is one of the most common and effective kernel embeddings
 since it is infinite dimensional, and governed by a single parameter. The kernel length-scale
@@ -808,7 +797,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 On the opposite side of the spectrum, a small sigma means that only nearby points will
 influence the kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>sigma</strong> – The length-scale of the kernel.
 This can be a scalar, and then it corresponds to the standard deviation
@@ -853,10 +842,10 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 norm.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -869,7 +858,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
@@ -877,14 +866,14 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
 we remove the additive factor.
 <strong>At least one of `w` and `v` must be provided</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -900,7 +889,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
 </dd>
 </dl>
@@ -920,10 +909,10 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -937,7 +926,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
 </dd>
 </dl>
@@ -961,12 +950,12 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LaplacianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LaplacianKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Laplacian kernel, and related kernel-vector products.</p>
 <p>The Laplacian kernel is similar to the Gaussian kernel, but less sensitive to changes
 in the parameter <cite>sigma</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>sigma</strong> – The length-scale of the Laplacian kernel</p>
 </dd>
 </dl>
@@ -976,10 +965,10 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 \[k(x, x') = \exp{-\frac{\lVert x - x' \rVert}{\sigma}}\]</div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -992,7 +981,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
@@ -1000,14 +989,14 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
 we remove the additive factor.
 <strong>At least one of `w` and `v` must be provided</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1023,7 +1012,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
 </dd>
 </dl>
@@ -1043,10 +1032,10 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1060,7 +1049,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
 </dd>
 </dl>
@@ -1084,7 +1073,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">MaternKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">MaternKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Class for computing the Matern kernel, and related kernel-vector products.</p>
 <p>The Matern kernels define a generic class of kernel functions which includes the
 Laplacian and Gaussian kernels. The class is parametrized by ‘nu’. When <code class="docutils literal notranslate"><span class="pre">nu</span> <span class="pre">=</span> <span class="pre">0.5</span></code>
@@ -1093,7 +1082,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 <p>This class implements the Matern kernel only for the values of nu which have a closed
 form solution, which are 0.5, 1.5, 2.5, and infinity.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>sigma</strong> – The length-scale of the Matern kernel. The length-scale can be either a scalar
 or a vector. Matrix-valued length-scales are not allowed for the Matern kernel.</p></li>
@@ -1108,10 +1097,10 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 may be computationally more efficient.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1124,7 +1113,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
@@ -1132,14 +1121,14 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
 we remove the additive factor.
 <strong>At least one of `w` and `v` must be provided</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1155,7 +1144,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
 </dd>
 </dl>
@@ -1175,10 +1164,10 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1192,7 +1181,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
 </dd>
 </dl>
@@ -1219,14 +1208,14 @@ <h2>Dot-Product kernels<a class="headerlink" href="#dot-product-kernels" title="
 <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">PolynomialKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">PolynomialKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Polynomial kernel with multiplicative and additive constants.</p>
 <p>Follows the formula</p>
 <div class="math notranslate nohighlight">
 \[(\gamma * X_1^\top X_2 + \beta)^{\mathrm{degree}}\]</div>
 <p>Where all operations apart from the matrix multiplication are taken element-wise.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>beta</strong> (<em>float-like</em>) – Additive constant</p></li>
 <li><p><strong>gamma</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
@@ -1237,10 +1226,10 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1253,7 +1242,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
@@ -1261,14 +1250,14 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
 we remove the additive factor.
 <strong>At least one of `w` and `v` must be provided</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1284,7 +1273,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
 </dd>
 </dl>
@@ -1304,10 +1293,10 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1321,7 +1310,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
 </dd>
 </dl>
@@ -1345,13 +1334,13 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LinearKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">LinearKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Linear Kernel with optional scaling and translation parameters.</p>
 <p>The kernel implemented here is the covariance function in the original
 input space (i.e. <cite>X &#64; X.T</cite>) with optional parameters to translate
 and scale the kernel: <cite>beta + gamma * X &#64; X.T</cite></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>beta</strong> (<em>float-like</em>) – Additive constant for the kernel, default: 0.0</p></li>
 <li><p><strong>gamma</strong> (<em>float-like</em>) – Multiplicative constant for the kernel. The kernel will
@@ -1364,15 +1353,15 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">LinearKernel</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># 100 samples in 3 dimensions</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">kernel_matrix</span> <span class="o">=</span> <span class="n">k</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">kernel_matrix</span><span class="p">,</span> <span class="n">X</span> <span class="o">@</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">kernel_matrix</span><span class="p">,</span> <span class="n">X</span> <span class="o">@</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
 </pre></div>
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1385,7 +1374,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
@@ -1393,14 +1382,14 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
 we remove the additive factor.
 <strong>At least one of `w` and `v` must be provided</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1416,7 +1405,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
 </dd>
 </dl>
@@ -1436,10 +1425,10 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1453,7 +1442,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
 </dd>
 </dl>
@@ -1477,13 +1466,13 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">SigmoidKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.kernels.</span></span><span class="sig-name descname"><span class="pre">SigmoidKernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel" title="Permalink to this definition"></a></dt>
 <dd><p>Sigmoid (or hyperbolic tangent) kernel function, with additive and multiplicative constants.</p>
 <p>Follows the formula</p>
 <div class="math notranslate nohighlight">
 \[k(x, y) = \tanh(\alpha x^\top y + \beta)\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>beta</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
 <li><p><strong>gamma</strong> (<em>float-like</em>) – Multiplicative constant</p></li>
@@ -1493,10 +1482,10 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1509,7 +1498,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>.</p>
 </dd>
 </dl>
@@ -1517,14 +1506,14 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
 we remove the additive factor.
 <strong>At least one of `w` and `v` must be provided</strong>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1540,7 +1529,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (M x T) output.</p>
 </dd>
 </dl>
@@ -1560,10 +1549,10 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> (<em>torch.Tensor</em>) – The first data-matrix for computing the kernel. Of shape (N x D):
 N samples in D dimensions.</p></li>
@@ -1577,7 +1566,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 and CUDA options.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The (N x T) output.</p>
 </dd>
 </dl>
diff --git a/api_reference/mmv_ops.html b/api_reference/mmv_ops.html
index ee6099be..b0da287c 100644
--- a/api_reference/mmv_ops.html
+++ b/api_reference/mmv_ops.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.mmv_ops &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.mmv_ops &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
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@@ -32,14 +31,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +327,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.mmv_ops</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,31 +339,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="falkon-mmv-ops">
+  <section id="falkon-mmv-ops">
 <h1>falkon.mmv_ops<a class="headerlink" href="#falkon-mmv-ops" title="Permalink to this heading"></a></h1>
 <p>The algorithms to compute kernels and kernel-vector products blockwise on GPUs and CPU. The algorithms in this module
 are kernel agnostic. Refer to <a class="reference internal" href="kernels.html#module-falkon.kernels" title="falkon.kernels"><code class="xref py py-mod docutils literal notranslate"><span class="pre">falkon.kernels</span></code></a> for the actual kernel implementations.</p>
@@ -380,7 +359,7 @@ <h2>run_keops_mmv<a class="headerlink" href="#run-keops-mmv" title="Permalink to
 kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.keops.run_keops_mmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.keops.</span></span><span class="sig-name descname"><span class="pre">run_keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'Sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.keops.run_keops_mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.keops.</span></span><span class="sig-name descname"><span class="pre">run_keops_mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">formula</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aliases</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'Sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.keops.run_keops_mmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -389,7 +368,7 @@ <h2>fmm<a class="headerlink" href="#fmm" title="Permalink to this heading"></
 <p>Block-wise kernel calculation. If the inputs require gradient, this function uses a differentiable implementation.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmm.fmm">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">fmm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseOptions</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.fmm" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">fmm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.fmm" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -398,7 +377,7 @@ <h2>fmmv<a class="headerlink" href="#fmmv" title="Permalink to this heading">
 <p>Block-wise kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -407,12 +386,12 @@ <h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this heading">
 <p>Block-wise double kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fdmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fdmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fdmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Double kernel-vector product</p>
 <p>Computes kernel <span class="math notranslate nohighlight">\(K = k(X_1, X_2)\)</span> and then the double kernel-vector
 product <span class="math notranslate nohighlight">\(K^{\top} (K v + w)\)</span>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X1</strong> – <span class="math notranslate nohighlight">\(n \times d\)</span> input matrix.</p></li>
 <li><p><strong>X2</strong> – <span class="math notranslate nohighlight">\(m \times d\)</span> input matrix.</p></li>
@@ -426,7 +405,7 @@ <h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this heading">
 <li><p><strong>opt</strong> – Options to be used for this operation</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>out</em> – Output of the double kernel-vector product. Will use the same storage as the <cite>out</cite>
 parameter if it was specified</p>
 </dd>
@@ -438,7 +417,7 @@ <h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this heading">
 <h2>incore_fmmv<a class="headerlink" href="#incore-fmmv" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv_incore.incore_fmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -446,7 +425,7 @@ <h2>incore_fmmv<a class="headerlink" href="#incore-fmmv" title="Permalink to thi
 <h2>incore_fdmmv<a class="headerlink" href="#incore-fdmmv" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv_incore.incore_fdmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv_incore.</span></span><span class="sig-name descname"><span class="pre">incore_fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv_incore.incore_fdmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -459,7 +438,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 <span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">sparse_mm_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comp_dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="Permalink to this definition"></a></dt>
 <dd><p>Inner loop to compute (part of) a kernel matrix for two sparse input tensors</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>m1</strong> – Left input tensor for computing the kernel</p></li>
 <li><p><strong>m2</strong> – Right input tensor for computing the kernel</p></li>
@@ -474,7 +453,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 <li><p><strong>tid</strong> – Thread ID. If on the main thread this will be -1</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <cite>out</cite>.</p>
 </dd>
 </dl>
@@ -485,7 +464,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 <span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">sparse_mmv_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mem_needed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="Permalink to this definition"></a></dt>
 <dd><p>Inner loop to compute (part of) a kernel-vector product for sparse input matrices.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>m1</strong> – Left input tensor for computing the kernel</p></li>
 <li><p><strong>m2</strong> – Right input tensor for computing the kernel</p></li>
@@ -501,7 +480,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 <li><p><strong>tid</strong> – Thread ID or -1 if on main thread</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <cite>out</cite>.</p>
 </dd>
 </dl>
diff --git a/api_reference/models.html b/api_reference/models.html
index 869e473a..a723c07c 100644
--- a/api_reference/models.html
+++ b/api_reference/models.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.models &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.models &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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         <script src="../_static/jquery.js"></script>
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         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
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@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.models</li>
       <li class="wy-breadcrumbs-aside">
@@ -334,37 +337,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon.models">
+  <section id="module-falkon.models">
 <span id="falkon-models"></span><h1>falkon.models<a class="headerlink" href="#module-falkon.models" title="Permalink to this heading"></a></h1>
 <section id="falkon">
 <span id="falkon-model"></span><h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.Falkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">Falkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">Falkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition"></a></dt>
 <dd><p>Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
 projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
@@ -372,7 +351,7 @@
 with this same object, for example by encoding multiple classes
 in a one-hot target matrix.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>kernel</strong> – Object representing the kernel function used for KRR.</p></li>
 <li><p><strong>penalty</strong> (<em>float</em>) – Amount of regularization to apply to the problem.
@@ -439,10 +418,10 @@
 </ul>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.Falkon.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.fit" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon KRR model.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>torch.Tensor</em>) – The tensor of training data, of shape [num_samples, num_dimensions].
 If X is in Fortran order (i.e. column-contiguous) then we can avoid
@@ -471,7 +450,7 @@
 <cite>beta_</cite> field, not <cite>alpha_</cite>).</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>model</strong> (<em>Falkon</em>) – The fitted model</p>
 </dd>
 </dl>
@@ -482,11 +461,11 @@
 <span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.get_params" title="Permalink to this definition"></a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
 contained subobjects that are estimators.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>params</strong> (<em>dict</em>) – Parameter names mapped to their values.</p>
 </dd>
 </dl>
@@ -497,10 +476,10 @@
 <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.Falkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>X</strong> (<em>torch.Tensor</em>) – Tensor of test data points, of shape [num_samples, num_dimensions].</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>predictions</strong> (<em>torch.Tensor</em>) – Prediction tensor of shape [num_samples, num_outputs] for all
 data points.</p>
 </dd>
@@ -516,10 +495,10 @@
 parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s
 possible to update each component of a nested object.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>self</strong> (<em>estimator instance</em>) – Estimator instance.</p>
 </dd>
 </dl>
@@ -532,7 +511,7 @@
 <span id="log-falkon-model"></span><h2>LogisticFalkon<a class="headerlink" href="#logisticfalkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.LogisticFalkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">LogisticFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><span class="pre">Loss</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">LogisticFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="gsc_losses.html#falkon.gsc_losses.Loss" title="falkon.gsc_losses.Loss"><span class="pre">Loss</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon" title="Permalink to this definition"></a></dt>
 <dd><p>Falkon Logistic regression solver.</p>
 <p>This estimator object solves approximate logistic regression problems with Nystroem
 projections and a fast optimization algorithm as described in  <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#log-flk"><span class="std std-ref">[3]</span></a>.</p>
@@ -546,7 +525,7 @@
 of the base algorithm, how many CG iterations to use. For guidance on how to set these
 parameters, see below.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>kernel</strong> – Object representing the kernel function used for KRR.</p></li>
 <li><p><strong>penalty_list</strong> (<em>List</em><em>[</em><em>float</em><em>]</em>) – Amount of regularization to use for each iteration of the base algorithm. The length
@@ -610,10 +589,10 @@
 inner-steps (e.g. 8) for the remaining iterations.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.LogisticFalkon.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.fit" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon Kernel Logistic Regression model.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>torch.Tensor</em>) – The tensor of training data, of shape [num_samples, num_dimensions].
 If X is in Fortran order (i.e. column-contiguous) then we can avoid
@@ -637,7 +616,7 @@
 extra copy of the data.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>model</strong> (<em>LogisticFalkon</em>) – The fitted model</p>
 </dd>
 </dl>
@@ -648,11 +627,11 @@
 <span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.get_params" title="Permalink to this definition"></a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
 contained subobjects that are estimators.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>params</strong> (<em>dict</em>) – Parameter names mapped to their values.</p>
 </dd>
 </dl>
@@ -663,10 +642,10 @@
 <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.LogisticFalkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>X</strong> (<em>torch.Tensor</em>) – Tensor of test data points, of shape [num_samples, num_dimensions].</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>predictions</strong> (<em>torch.Tensor</em>) – Prediction tensor of shape [num_samples, num_outputs] for all
 data points.</p>
 </dd>
@@ -682,10 +661,10 @@
 parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s
 possible to update each component of a nested object.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>self</strong> (<em>estimator instance</em>) – Estimator instance.</p>
 </dd>
 </dl>
@@ -698,7 +677,7 @@
 <span id="ic-falkon-model"></span><h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">InCoreFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">InCoreFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition"></a></dt>
 <dd><p>In GPU core Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
 projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
@@ -718,7 +697,7 @@
 </div></blockquote>
 <p>Using multiple GPUs is not possible with this model.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>kernel</strong> – Object representing the kernel function used for KRR.</p></li>
 <li><p><strong>penalty</strong> (<em>float</em>) – Amount of regularization to apply to the problem.
@@ -780,10 +759,10 @@
 </ul>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.fit" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.fit" title="Permalink to this definition"></a></dt>
 <dd><p>Fits the Falkon KRR model.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>torch.Tensor</em>) – The tensor of training data, of shape [num_samples, num_dimensions].
 If X is in Fortran order (i.e. column-contiguous) then we can avoid
@@ -812,7 +791,7 @@
 <cite>beta_</cite> field, not <cite>alpha_</cite>).</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>model</strong> (<em>InCoreFalkon</em>) – The fitted model</p>
 </dd>
 </dl>
@@ -823,11 +802,11 @@
 <span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.get_params" title="Permalink to this definition"></a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
 contained subobjects that are estimators.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>params</strong> (<em>dict</em>) – Parameter names mapped to their values.</p>
 </dd>
 </dl>
@@ -838,10 +817,10 @@
 <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.InCoreFalkon.predict" title="Permalink to this definition"></a></dt>
 <dd><p>Makes predictions on data <cite>X</cite> using the learned model.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>X</strong> (<em>torch.Tensor</em>) – Tensor of test data points, of shape [num_samples, num_dimensions].</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>predictions</strong> (<em>torch.Tensor</em>) – Prediction tensor of shape [num_samples, num_outputs] for all
 data points.</p>
 </dd>
@@ -857,10 +836,10 @@
 parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s
 possible to update each component of a nested object.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>self</strong> (<em>estimator instance</em>) – Estimator instance.</p>
 </dd>
 </dl>
diff --git a/api_reference/optimization.html b/api_reference/optimization.html
index bb0888b5..b20f24fb 100644
--- a/api_reference/optimization.html
+++ b/api_reference/optimization.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.optim &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.optim &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +31,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +327,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.optim</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,31 +339,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon.optim">
+  <section id="module-falkon.optim">
 <span id="falkon-optim"></span><h1>falkon.optim<a class="headerlink" href="#module-falkon.optim" title="Permalink to this heading"></a></h1>
 <section id="optimizer">
 <h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this heading"></a></h2>
@@ -377,10 +356,10 @@ <h2>Conjugate gradient methods<a class="headerlink" href="#conjugate-gradient-me
 <h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Permalink to this heading"></a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.optim.ConjugateGradient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">ConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">ConjugateGradientOptions</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.ConjugateGradient" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.optim.</span></span><span class="sig-name descname"><span class="pre">ConjugateGradient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ConjugateGradientOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.optim.ConjugateGradient" title="Permalink to this definition"></a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="falkon.optim.ConjugateGradient.solve">
-<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmv</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.optim.ConjugateGradient.solve" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmv</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.optim.ConjugateGradient.solve" title="Permalink to this definition"></a></dt>
 <dd><p>Conjugate-gradient solver with optional support for preconditioning via generic MMV.</p>
 <p>This solver can be used for iterative solution of linear systems of the form $AX = B$ with
 respect to the <cite>X</cite> variable. Knowledge of <cite>A</cite> is only needed through matrix-vector
@@ -388,7 +367,7 @@ <h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Perm
 <p>Preconditioning can be achieved by incorporating the preconditioner matrix in the <cite>mmv</cite>
 function.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X0</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em>) – Initial solution for the solver. If not provided it will be a zero-tensor.</p></li>
 <li><p><strong>B</strong> (<em>torch.Tensor</em>) – Right-hand-side of the linear system to be solved.</p></li>
@@ -406,7 +385,7 @@ <h3>ConjugateGradient<a class="headerlink" href="#conjugategradient" title="Perm
 itself).</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>The solution to the linear system <cite>X</cite>.</p>
 </dd>
 </dl>
@@ -432,7 +411,7 @@ <h3>FalkonConjugateGradient<a class="headerlink" href="#falkonconjugategradient"
 <div class="math notranslate nohighlight">
 \[H = K_{nm}^\top K_{nm} + \lambda n K_{mm}\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>kernel</strong> – The kernel class used for the CG algorithm</p></li>
 <li><p><strong>preconditioner</strong> – The approximate Falkon preconditioner. The class should allow triangular solves with
diff --git a/api_reference/options.html b/api_reference/options.html
index 3cfd719e..0d088130 100644
--- a/api_reference/options.html
+++ b/api_reference/options.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.options &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.options &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.options</li>
       <li class="wy-breadcrumbs-aside">
@@ -334,31 +337,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon.options">
+  <section id="module-falkon.options">
 <span id="falkon-options"></span><span id="api-options"></span><h1>falkon.options<a class="headerlink" href="#module-falkon.options" title="Permalink to this heading"></a></h1>
 <section id="falkonoptions">
 <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to this heading"></a></h2>
@@ -367,7 +346,7 @@ <h2>FalkonOptions<a class="headerlink" href="#falkonoptions" title="Permalink to
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.options.</span></span><span class="sig-name descname"><span class="pre">FalkonOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keops_acc_dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_sum_scheme</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_active</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keops_memory_slack</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_in_core</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_force_ooc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chol_par_blk_multiplier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pc_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-13</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_preconditioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_epsilon_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_tolerance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_full_gradient_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cg_differential_convergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cpu_mem</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_arch_speed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_single_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_pc_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">30000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">300000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_cuda_iter_size_64</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">900000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">never_store_kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_kernel_d_threshold</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_fmm_streams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_slack</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.9</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.options.FalkonOptions" title="Permalink to this definition"></a></dt>
 <dd><p>Global options for Falkon.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>debug</strong> (<em>bool</em>) – <cite>default False</cite> - When set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, the estimators will print extensive debugging information.
 Set it if you want to dig deeper.</p></li>
diff --git a/api_reference/outofcore.html b/api_reference/outofcore.html
index a297d1d1..1fa0089b 100644
--- a/api_reference/outofcore.html
+++ b/api_reference/outofcore.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.ooc_ops &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.ooc_ops &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
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         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.ooc_ops</li>
       <li class="wy-breadcrumbs-aside">
@@ -334,31 +337,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="falkon-ooc-ops">
+  <section id="falkon-ooc-ops">
 <h1>falkon.ooc_ops<a class="headerlink" href="#falkon-ooc-ops" title="Permalink to this heading"></a></h1>
 <p>The out-of-core algorithms for the Cholesky decomposition and the LAUUM operation are crucial for speeding up our
 library. To find out more about how they work, check the source code:</p>
@@ -375,7 +354,7 @@ <h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to t
 <dt class="sig sig-object py" id="falkon.ooc_ops.gpu_cholesky">
 <span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_cholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.ooc_ops.gpu_cholesky" title="Permalink to this definition"></a></dt>
 <dd><dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>A</strong> (<em>torch.Tensor</em>) – 2D positive-definite matrix of size (n x n) that will be factorized as
 <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">=</span> <span class="pre">U.T</span> <span class="pre">&#64;</span> <span class="pre">U</span></code> (if <cite>upper</cite> is True) or <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">=</span> <span class="pre">L</span> <span class="pre">&#64;</span> <span class="pre">L.T</span></code> if <cite>upper</cite>
@@ -403,9 +382,9 @@ <h2>gpu_cholesky<a class="headerlink" href="#gpu-cholesky" title="Permalink to t
 <h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this heading"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.ooc_ops.gpu_lauum">
-<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_lauum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_opposite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.ooc_ops.gpu_lauum" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.ooc_ops.</span></span><span class="sig-name descname"><span class="pre">gpu_lauum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_opposite</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.ooc_ops.gpu_lauum" title="Permalink to this definition"></a></dt>
 <dd><dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>A</strong> (<em>torch.Tensor</em>) – N-by-N triangular matrix.</p></li>
 <li><p><strong>upper</strong> (<em>bool</em>) – Whether the input matrix is upper or lower triangular.</p></li>
@@ -420,7 +399,7 @@ <h2>gpu_lauum<a class="headerlink" href="#gpu-lauum" title="Permalink to this he
 GPU memory usage.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – A (N x N) tensor. This will share the same memory as the input tensor <code class="docutils literal notranslate"><span class="pre">A</span></code> if <code class="docutils literal notranslate"><span class="pre">overwrite</span></code>
 is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, otherwise it will be a newly allocated tensor.</p>
 </dd>
diff --git a/api_reference/preconditioner.html b/api_reference/preconditioner.html
index 594c2c64..d0482d2b 100644
--- a/api_reference/preconditioner.html
+++ b/api_reference/preconditioner.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.preconditioner &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.preconditioner &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
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         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +31,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +327,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.preconditioner</li>
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@@ -336,31 +339,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon.preconditioner">
+  <section id="module-falkon.preconditioner">
 <span id="falkon-preconditioner"></span><h1>falkon.preconditioner<a class="headerlink" href="#module-falkon.preconditioner" title="Permalink to this heading"></a></h1>
 <section id="preconditioner">
 <h2>Preconditioner<a class="headerlink" href="#preconditioner" title="Permalink to this heading"></a></h2>
@@ -408,7 +387,7 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 <p>Whenever we want to use one of the two triangles we must reset the matrix diagonal, since
 it is shared between the two matrices.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>penalty</strong> (<em>float</em>) – The regularization parameter for KRR. Must be greater than 0.</p></li>
 <li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel object. This is used to compute the M*M kernel
@@ -444,10 +423,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(ATx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -466,10 +445,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(A^\top T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -484,11 +463,11 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.FalkonPreconditioner.init">
-<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.init" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_vec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.preconditioner.FalkonPreconditioner.init" title="Permalink to this definition"></a></dt>
 <dd><p>Initialize the preconditioner matrix.</p>
 <p>This method must be called before the preconditioner can be used.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>torch.Tensor</em>) – The (M x D) matrix of Nystroem centers</p></li>
 <li><p><strong>weight_vec</strong> – An optional vector of size (M x 1) which is used for reweighted least-squares.
@@ -504,10 +483,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Ax = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -526,10 +505,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(A^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -548,10 +527,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Tx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -570,10 +549,10 @@ <h3>FalkonPreconditioner<a class="headerlink" href="#falkonpreconditioner" title
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -611,7 +590,7 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 <cite>W</cite> is of size <cite>M</cite> and is the only difference with respect to the normal FALKON preconditioner
 (<a class="reference internal" href="#falkon.preconditioner.FalkonPreconditioner" title="falkon.preconditioner.FalkonPreconditioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">falkon.preconditioner.FalkonPreconditioner</span></code></a>).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>kernel</strong> (<a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><em>falkon.kernels.kernel.Kernel</em></a>) – The kernel object. This is used to compute the M*M kernel
 between inducing points. This kernel is then overwritten by
@@ -656,10 +635,10 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(ATx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -678,10 +657,10 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 <dd><p>Solve two systems of equations <span class="math notranslate nohighlight">\(A^\top T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -696,11 +675,11 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.preconditioner.LogisticPreconditioner.init">
-<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.init" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#falkon.preconditioner.LogisticPreconditioner.init" title="Permalink to this definition"></a></dt>
 <dd><p>Initialize the preconditioner matrix.</p>
 <p>This method must be called before the preconditioner becomes usable.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>torch.Tensor</em>) – (M x D) matrix of Nystroem centers</p></li>
 <li><p><strong>Y</strong> (<em>torch.Tensor</em>) – (M x 1) vector of targets corresponding to the Nystroem centers <cite>X</cite></p></li>
@@ -723,10 +702,10 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Ax = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -745,10 +724,10 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(A^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -767,10 +746,10 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(Tx = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
@@ -789,10 +768,10 @@ <h3>LogisticPreconditioner<a class="headerlink" href="#logisticpreconditioner" t
 <dd><p>Solve the system of equations <span class="math notranslate nohighlight">\(T^\top x = v\)</span> for unknown vector <span class="math notranslate nohighlight">\(x\)</span>.</p>
 <p>Multiple right-hand sides are supported (by simply passing a 2D tensor for <cite>v</cite>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>v</strong> – The right-hand side of the triangular system of equations</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>x</em> – The solution, computed with the <cite>trsm</cite> function.</p>
 </dd>
 </dl>
diff --git a/api_reference/sparse.html b/api_reference/sparse.html
index ad93cc8f..ac43f214 100644
--- a/api_reference/sparse.html
+++ b/api_reference/sparse.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.sparse &mdash; falkon 0.7.5 documentation</title>
+  <title>falkon.sparse &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
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@@ -322,7 +325,7 @@
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           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="index.html">API Reference</a></li>
       <li class="breadcrumb-item active">falkon.sparse</li>
       <li class="wy-breadcrumbs-aside">
@@ -334,37 +337,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="module-falkon.sparse">
+  <section id="module-falkon.sparse">
 <span id="falkon-sparse"></span><h1>falkon.sparse<a class="headerlink" href="#module-falkon.sparse" title="Permalink to this heading"></a></h1>
 <section id="sparsetensor">
 <h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_tensor.SparseTensor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></span><span class="sig-name descname"><span class="pre">SparseTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexptr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><span class="pre">SparseType</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">SparseType.CSR</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.sparse.sparse_tensor.</span></span><span class="sig-name descname"><span class="pre">SparseTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexptr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseType" title="falkon.sparse.sparse_tensor.SparseType"><span class="pre">SparseType</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">SparseType.CSR</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor" title="Permalink to this definition"></a></dt>
 <dd><p>Wrapper class to represent sparse 2D matrices in CSR or CSC format.</p>
 <p>The wrapper holds three 1D torch tensors which give the sparse representation
 (an index pointer, an index and the non-zero values of the matrix).
@@ -373,7 +352,7 @@ <h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to t
 It does <strong>not</strong> define any mathematical function on sparse matrices, which are
 instead defined separately (see <a class="reference internal" href="#falkon.sparse.sparse_matmul" title="falkon.sparse.sparse_matmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">sparse_matmul()</span></code></a> for example).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>indexptr</strong> (<em>torch.Tensor</em>) – Array of row (or column for CSC data) pointers into the
 <cite>index</cite> and <cite>data</cite> arrays. Should be either of type long or int.</p></li>
@@ -387,18 +366,18 @@ <h2>SparseTensor<a class="headerlink" href="#sparsetensor" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_tensor.SparseTensor.narrow_rows">
-<span class="sig-name descname"><span class="pre">narrow_rows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">narrow_rows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></span><a class="headerlink" href="#falkon.sparse.sparse_tensor.SparseTensor.narrow_rows" title="Permalink to this definition"></a></dt>
 <dd><p>Select a subset of contiguous rows from the sparse matrix.
 If this is a CSC sparse matrix, instead of taking contiguous rows we take contiguous
 columns.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>start</strong> (<em>int</em><em> or </em><em>None</em>) – The index of the first row to select. If None will be assumed to be 0.</p></li>
 <li><p><strong>length</strong> (<em>int</em><em> or </em><em>None</em>) – The number of rows to select. If None will be assumed to be all rows after <cite>start</cite>.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><em>SparseTensor</em> – A new <a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparseTensor</span></code></a> object with <cite>length</cite> rows.</p>
 </dd>
 </dl>
@@ -424,14 +403,14 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 <p>This function can be applied to CPU or CUDA tensors (but all tensors must
 be  on the same device).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>A</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – N x D, sparse matrix.</p></li>
 <li><p><strong>B</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – D x M, sparse matrix</p></li>
 <li><p><strong>out</strong> (<em>torch.Tensor</em>) – Dense N x M tensor, it will hold the output of the multiplication.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The same tensor as the input <cite>out</cite> parameter.</p>
 </dd>
 </dl>
@@ -443,7 +422,7 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 <dd><p>Row-wise squared l2 norm of a sparse 2D matrix.</p>
 <p>The operation is equivalent to squaring all elements of the matrix, and summing up the rows.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>A</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The 2D matrix. Since we compute row-wise norms, the matrix must be in CSR format (for
 efficiency).</p></li>
@@ -451,7 +430,7 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 of the squared-norm operation.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The same tensor as the input <cite>out</cite> parameter.</p>
 </dd>
 </dl>
@@ -461,10 +440,10 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.sparse.sparse_norm">
-<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_norm" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.sparse.</span></span><span class="sig-name descname"><span class="pre">sparse_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.sparse.sparse_norm" title="Permalink to this definition"></a></dt>
 <dd><p>Row-wise l2 norm of a sparse 2D matrix</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>A</strong> (<a class="reference internal" href="#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><em>SparseTensor</em></a>) – The 2D matrix. Since we compute row-wise norms, the matrix must be in CSR format (for
 efficiency).</p></li>
@@ -472,7 +451,7 @@ <h2>Sparse operations<a class="headerlink" href="#sparse-operations" title="Perm
 of the norm operation.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The same tensor as the input <cite>out</cite> parameter.</p>
 </dd>
 </dl>
diff --git a/examples/custom_kernels.html b/examples/custom_kernels.html
index 37cd9c42..ed7df3d2 100644
--- a/examples/custom_kernels.html
+++ b/examples/custom_kernels.html
@@ -1,20 +1,20 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Implementing A Custom Kernel &mdash; falkon 0.7.5 documentation</title>
+  <title>Implementing A Custom Kernel &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
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@@ -32,14 +32,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +328,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
       <li class="breadcrumb-item active">Implementing A Custom Kernel</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,286 +340,7 @@
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-</style>
-<section id="Implementing-A-Custom-Kernel">
+  <section id="Implementing-A-Custom-Kernel">
 <h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Custom-Kernel" title="Permalink to this heading"></a></h1>
 <p>In this notebook we will show how to implement a custom kernel in Falkon.</p>
 <p>There are several complementary parts to a kernel, which can be added to support different operations. We will go through them one-by-one in this notebook:</p>
@@ -629,16 +354,16 @@ <h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Cust
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
-plt.style.use(&#39;ggplot&#39;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-from sklearn import datasets
-import torch
-import numpy as np
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
-import falkon
-from falkon import FalkonOptions
-from falkon.kernels import Kernel, DiffKernel, KeopsKernelMixin
+<span class="kn">import</span> <span class="nn">falkon</span>
+<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
+<span class="kn">from</span> <span class="nn">falkon.kernels</span> <span class="kn">import</span> <span class="n">Kernel</span><span class="p">,</span> <span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span>
 </pre></div>
 </div>
 </div>
@@ -657,28 +382,28 @@ <h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simpl
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.fetch_california_housing(return_X_y=True)
-num_train = int(X.shape[0] * 0.8)
-num_test = X.shape[0] - num_train
-shuffle_idx = np.arange(X.shape[0])
-np.random.shuffle(shuffle_idx)
-train_idx = shuffle_idx[:num_train]
-test_idx = shuffle_idx[num_train:]
-
-Xtrain, Ytrain = X[train_idx], Y[train_idx]
-Xtest, Ytest = X[test_idx], Y[test_idx]
-# convert numpy -&gt; pytorch
-Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)
-Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)
-Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)
-Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)
-# z-score normalization
-train_mean = Xtrain.mean(0, keepdim=True)
-train_std = Xtrain.std(0, keepdim=True)
-Xtrain -= train_mean
-Xtrain /= train_std
-Xtest -= train_mean
-Xtest /= train_std
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">fetch_california_housing</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
+<span class="n">num_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">num_train</span>
+<span class="n">shuffle_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffle_idx</span><span class="p">)</span>
+<span class="n">train_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[:</span><span class="n">num_train</span><span class="p">]</span>
+<span class="n">test_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[</span><span class="n">num_train</span><span class="p">:]</span>
+
+<span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
+<span class="n">Xtest</span><span class="p">,</span> <span class="n">Ytest</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">test_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>
+<span class="c1"># convert numpy -&gt; pytorch</span>
+<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">Xtrain</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtrain</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">Xtest</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtest</span> <span class="o">/=</span> <span class="n">train_std</span>
 </pre></div>
 </div>
 </div>
@@ -686,17 +411,17 @@ <h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simpl
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def rmse(true, pred):
-    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))
-
-def learn_with_kernel(kernel):
-    flk_opt = FalkonOptions(use_cpu=True)
-    model = falkon.Falkon(
-        kernel=kernel, penalty=1e-5, M=1000, options=flk_opt,
-        error_every=1, error_fn=rmse)
-    model.fit(Xtrain, Ytrain)
-    ts_err = rmse(Ytest, model.predict(Xtest))
-    print(&quot;Test RMSE: %.2f&quot; % (ts_err))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="k">def</span> <span class="nf">learn_with_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">):</span>
+    <span class="n">flk_opt</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
+        <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">,</span>
+        <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">error_fn</span><span class="o">=</span><span class="n">rmse</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
+    <span class="n">ts_err</span> <span class="o">=</span> <span class="n">rmse</span><span class="p">(</span><span class="n">Ytest</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test RMSE: </span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">ts_err</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -718,32 +443,32 @@ <h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Impleme
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class BasicLinearKernel(Kernel):
-    def __init__(self, lengthscale, options):
-        # The base class takes as inputs a name for the kernel, and
-        # an instance of `FalkonOptions`.
-        super().__init__(&quot;basic_linear&quot;, options)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">BasicLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
+        <span class="c1"># an instance of `FalkonOptions`.</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;basic_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
 
-        self.lengthscale = lengthscale
+        <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
 
-    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -&gt; torch.Tensor:
-        # To support different devices/data types, you must make sure
-        # the lengthscale is compatible with the data.
-        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="c1"># To support different devices/data types, you must make sure</span>
+        <span class="c1"># the lengthscale is compatible with the data.</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
 
-        scaled_X1 = X1 * lengthscale
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
 
-        if diag:
-            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
-        else:
-            # The dot-product row-by-row on `X1` and `X2` can be computed
-            # on many rows at a time with matrix multiplication.
-            out = torch.matmul(scaled_X1, X2.T, out=out)
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># The dot-product row-by-row on `X1` and `X2` can be computed</span>
+            <span class="c1"># on many rows at a time with matrix multiplication.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
 
-        return out
+        <span class="k">return</span> <span class="n">out</span>
 
-    def compute_sparse(self, X1, X2, out, diag, **kwargs) -&gt; torch.Tensor:
-        raise NotImplementedError(&quot;Sparse not implemented&quot;)
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -753,9 +478,9 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Initialize the kernel
-lengthscale_init = torch.tensor([1.0])
-k = BasicLinearKernel(lengthscale_init, options=falkon.FalkonOptions())
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel</span>
+<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">BasicLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
@@ -763,8 +488,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># The kernel matrix
-k(torch.randn(5, 3), torch.randn(5, 3))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The kernel matrix</span>
+<span class="n">k</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -785,8 +510,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Kernel-vector product
-k.mmv(torch.randn(4, 3), torch.randn(4, 3), v=torch.randn(4, 1))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Kernel-vector product</span>
+<span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">v</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -806,8 +531,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Double kernel-vector product
-k.dmmv(torch.randn(3, 3), torch.randn(4, 3), v=torch.randn(4, 1), w=torch.randn(3, 1))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Double kernel-vector product</span>
+<span class="n">k</span><span class="o">.</span><span class="n">dmmv</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">v</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">w</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -827,8 +552,8 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Learning on the california housing dataset
-learn_with_kernel(k)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset</span>
+<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -873,44 +598,44 @@ <h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class DiffLinearKernel(DiffKernel):
-    def __init__(self, lengthscale, options):
-        # Super-class constructor call. We do not specify core_fn
-        # but we must specify the hyperparameter of this kernel (lengthscale)
-        super().__init__(&quot;diff_linear&quot;,
-                         options,
-                         core_fn=None,
-                         lengthscale=lengthscale)
-
-    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):
-        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
-        scaled_X1 = X1 * lengthscale
-        if diag:
-            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
-        else:
-            out = torch.matmul(scaled_X1, X2.T, out=out)
-
-        return out
-
-    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):
-        # The implementation here is similar to `compute` without in-place operations.
-        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
-        scaled_X1 = X1 * lengthscale
-
-        if diag:
-            return torch.sum(scaled_X1 * X2, dim=-1)
-
-        return torch.matmul(scaled_X1, X2.T)
-
-    def detach(self):
-        # Clones the class with detached hyperparameters
-        return DiffLinearKernel(
-            lengthscale=self.lengthscale.detach(),
-            options=self.params
-        )
-
-    def compute_sparse(self, X1, X2, out, diag, **kwargs) -&gt; torch.Tensor:
-        raise NotImplementedError(&quot;Sparse not implemented&quot;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DiffLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="c1"># Super-class constructor call. We do not specify core_fn</span>
+        <span class="c1"># but we must specify the hyperparameter of this kernel (lengthscale)</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;diff_linear&quot;</span><span class="p">,</span>
+                         <span class="n">options</span><span class="p">,</span>
+                         <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="c1"># The implementation here is similar to `compute` without in-place operations.</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Clones the class with detached hyperparameters</span>
+        <span class="k">return</span> <span class="n">DiffLinearKernel</span><span class="p">(</span>
+            <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
+            <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -920,9 +645,9 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Initialize the kernel, with a lengthscale which requires grad.
-lengthscale_init = torch.tensor([1.0]).requires_grad_()
-k = DiffLinearKernel(lengthscale_init, options=falkon.FalkonOptions())
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel, with a lengthscale which requires grad.</span>
+<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">DiffLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
@@ -930,9 +655,9 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Kernel matrix. Notice how the outputs has a `grad_fn`
-k_mat = k(torch.randn(5, 3), torch.randn(5, 3))
-k_mat
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Kernel matrix. Notice how the outputs has a `grad_fn`</span>
+<span class="n">k_mat</span> <span class="o">=</span> <span class="n">k</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">k_mat</span>
 </pre></div>
 </div>
 </div>
@@ -954,8 +679,8 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Gradient of the kernel with respect to the lengthscale.
-torch.autograd.grad(k_mat.sum(), k.lengthscale)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Gradient of the kernel with respect to the lengthscale.</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mat</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -972,15 +697,15 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># kernel-vector product + gradient
-m1 = torch.randn(4, 3).requires_grad_()
-m2 = torch.randn(2, 3)
-v = torch.randn(2, 1)
-k_mmv = k.mmv(m1, m2, v)
-print(&quot;Kernel-vector product&quot;)
-print(k_mmv)
-print(&quot;Gradients:&quot;)
-print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># kernel-vector product + gradient</span>
+<span class="n">m1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">m2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">k_mmv</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kernel-vector product&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">k_mmv</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Gradients:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mmv</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">,</span> <span class="n">m1</span><span class="p">]))</span>
 </pre></div>
 </div>
 </div>
@@ -1005,8 +730,8 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Learning on the california housing dataset
-learn_with_kernel(k)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset</span>
+<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1051,56 +776,56 @@ <h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class KeopsLinearKernel(DiffKernel, KeopsKernelMixin):
-    def __init__(self, lengthscale, options):
-        super().__init__(&quot;my-keops-linear&quot;,
-                         options,
-                         core_fn=None,
-                         lengthscale=lengthscale)
-
-    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool):
-        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
-        scaled_X1 = X1 * lengthscale
-
-        if diag:
-            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
-        else:
-            out = torch.matmul(scaled_X1, X2.T, out=out)
-
-        return out
-
-    def compute_diff(self, X1: torch.Tensor, X2: torch.Tensor, diag: bool):
-        scaled_X1 = X1 * self.lengthscale
-
-        if diag:
-            return torch.sum(scaled_X1 * X2, dim=-1)
-
-        return torch.matmul(scaled_X1, X2.T)
-
-    def detach(self):
-        return KeopsLinearKernel(
-            lengthscale=self.lengthscale.detach(),
-            options=self.params
-        )
-
-    def keops_mmv_impl(self, X1, X2, v, kernel, out, opt):
-        # Keops formula for kernel-vector.
-        formula = &#39;(scale * (X | Y)) * v&#39;
-        aliases = [
-            &#39;X = Vi(%d)&#39; % (X1.shape[1]),
-            &#39;Y = Vj(%d)&#39; % (X2.shape[1]),
-            &#39;v = Vj(%d)&#39; % (v.shape[1]),
-            &#39;scale = Pm(%d)&#39; % (self.lengthscale.shape[0]),
-        ]
-        other_vars = [
-            self.lengthscale.to(dtype=X1.dtype, device=X1.device),
-        ]
-        # Call to the executor of the formula.
-        return self.keops_mmv(X1, X2, v, out, formula, aliases, other_vars, opt)
-
-
-    def compute_sparse(self, X1, X2, out: torch.Tensor, diag: bool, **kwargs) -&gt; torch.Tensor:
-        raise NotImplementedError(&quot;Sparse not implemented&quot;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">KeopsLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;my-keops-linear&quot;</span><span class="p">,</span>
+                         <span class="n">options</span><span class="p">,</span>
+                         <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">KeopsLinearKernel</span><span class="p">(</span>
+            <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
+            <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">keops_mmv_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">opt</span><span class="p">):</span>
+        <span class="c1"># Keops formula for kernel-vector.</span>
+        <span class="n">formula</span> <span class="o">=</span> <span class="s1">&#39;(scale * (X | Y)) * v&#39;</span>
+        <span class="n">aliases</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="s1">&#39;X = Vi(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">X1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="s1">&#39;Y = Vj(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">X2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="s1">&#39;v = Vj(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="s1">&#39;scale = Pm(</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
+        <span class="p">]</span>
+        <span class="n">other_vars</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+        <span class="p">]</span>
+        <span class="c1"># Call to the executor of the formula.</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">keops_mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">formula</span><span class="p">,</span> <span class="n">aliases</span><span class="p">,</span> <span class="n">other_vars</span><span class="p">,</span> <span class="n">opt</span><span class="p">)</span>
+
+
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1111,8 +836,8 @@ <h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>lengthscale_init = torch.tensor([1.0]).requires_grad_()
-k = KeopsLinearKernel(lengthscale_init, options=falkon.FalkonOptions(use_cpu=True))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">KeopsLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -1120,15 +845,15 @@ <h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># kernel-vector product + gradient
-m1 = torch.randn(4, 3).requires_grad_()
-m2 = torch.randn(2, 3)
-v = torch.randn(2, 1)
-k_mmv = k.mmv(m1, m2, v)
-print(&quot;Kernel-vector product&quot;)
-print(k_mmv)
-print(&quot;Gradients:&quot;)
-print(torch.autograd.grad(k_mmv.sum(), [k.lengthscale, m1]))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># kernel-vector product + gradient</span>
+<span class="n">m1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">m2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">k_mmv</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">mmv</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kernel-vector product&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">k_mmv</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Gradients:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">k_mmv</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="p">[</span><span class="n">k</span><span class="o">.</span><span class="n">lengthscale</span><span class="p">,</span> <span class="n">m1</span><span class="p">]))</span>
 </pre></div>
 </div>
 </div>
@@ -1153,10 +878,10 @@ <h3>Test the KeOps kernel<a class="headerlink" href="#Test-the-KeOps-kernel" tit
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Learning on the california housing dataset.
-# Due to differences in floating point code, results may be slightly
-# different from the other implementations.
-learn_with_kernel(k)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning on the california housing dataset.</span>
+<span class="c1"># Due to differences in floating point code, results may be slightly</span>
+<span class="c1"># different from the other implementations.</span>
+<span class="n">learn_with_kernel</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1199,8 +924,8 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from falkon.sparse import SparseTensor
-from falkon.sparse import sparse_matmul, bdot
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">SparseTensor</span>
+<span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">sparse_matmul</span><span class="p">,</span> <span class="n">bdot</span>
 </pre></div>
 </div>
 </div>
@@ -1208,51 +933,51 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>class SparseLinearKernel(Kernel):
-    def __init__(self, lengthscale, options):
-        # The base class takes as inputs a name for the kernel, and
-        # an instance of `FalkonOptions`.
-        super().__init__(&quot;sparse_linear&quot;, options)
-
-        self.lengthscale = lengthscale
-
-    def compute(self, X1: torch.Tensor, X2: torch.Tensor, out: torch.Tensor, diag: bool) -&gt; torch.Tensor:
-        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
-
-        scaled_X1 = X1 * lengthscale
-
-        if diag:
-            out.copy_(torch.sum(scaled_X1 * X2, dim=-1))
-        else:
-            # The dot-product row-by-row on `X1` and `X2` can be computed
-            # on many rows at a time with matrix multiplication.
-            out = torch.matmul(scaled_X1, X2.T, out=out)
-
-        return out
-
-    def compute_sparse(self,
-                       X1: SparseTensor,
-                       X2: SparseTensor,
-                       out: torch.Tensor,
-                       diag: bool,
-                       **kwargs) -&gt; torch.Tensor:
-        # The inputs will be matrix X1(n*d) in CSR format, and X2(d*n) in CSC format.
-
-        # To support different devices/data types, you must make sure
-        # the lengthscale is compatible with the data.
-        lengthscale = self.lengthscale.to(device=X1.device, dtype=X1.dtype)
-
-        if diag:
-            # The diagonal is a dot-product between rows of X1 and X2.
-            # The batched-dot is only implemented on CPU.
-            out = bdot(X1, X2.transpose_csr(), out)
-        else:
-            # Otherwise we need to matrix-multiply. Note that X2 is already
-            # transposed correctly.
-            out = sparse_matmul(X1, X2, out)
-
-        out.mul_(lengthscale)
-        return out
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">SparseLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+        <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
+        <span class="c1"># an instance of `FalkonOptions`.</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;sparse_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
+
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">scaled_X1</span> <span class="o">*</span> <span class="n">X2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># The dot-product row-by-row on `X1` and `X2` can be computed</span>
+            <span class="c1"># on many rows at a time with matrix multiplication.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">out</span>
+
+    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                       <span class="n">X1</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
+                       <span class="n">X2</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
+                       <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+                       <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
+                       <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="c1"># The inputs will be matrix X1(n*d) in CSR format, and X2(d*n) in CSC format.</span>
+
+        <span class="c1"># To support different devices/data types, you must make sure</span>
+        <span class="c1"># the lengthscale is compatible with the data.</span>
+        <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
+            <span class="c1"># The diagonal is a dot-product between rows of X1 and X2.</span>
+            <span class="c1"># The batched-dot is only implemented on CPU.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">bdot</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">transpose_csr</span><span class="p">(),</span> <span class="n">out</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Otherwise we need to matrix-multiply. Note that X2 is already</span>
+            <span class="c1"># transposed correctly.</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="n">sparse_matmul</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span>
+
+        <span class="n">out</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">lengthscale</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span>
 </pre></div>
 </div>
 </div>
@@ -1263,13 +988,13 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>indexptr = torch.tensor([0, 1, 3, 4], dtype=torch.long)
-index = torch.tensor([1, 0, 1, 0], dtype=torch.long)
-value = torch.tensor([5, 1, 8, 2], dtype=torch.float32)
-sp1 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=&quot;csr&quot;)
-# Converted to dense:
-dense1 = torch.from_numpy(sp1.to_scipy().todense())
-dense1
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">indexptr</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">sp1</span> <span class="o">=</span> <span class="n">SparseTensor</span><span class="p">(</span><span class="n">indexptr</span><span class="o">=</span><span class="n">indexptr</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">sparse_type</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
+<span class="c1"># Converted to dense:</span>
+<span class="n">dense1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">sp1</span><span class="o">.</span><span class="n">to_scipy</span><span class="p">()</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
+<span class="n">dense1</span>
 </pre></div>
 </div>
 </div>
@@ -1288,12 +1013,12 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>indexptr = torch.tensor([0, 1, 2, 4], dtype=torch.long)
-index = torch.tensor([1, 0, 0, 1], dtype=torch.long)
-value = torch.tensor([2, 1, 3, 4], dtype=torch.float32)
-sp2 = SparseTensor(indexptr=indexptr, index=index, data=value, size=(3, 2), sparse_type=&quot;csr&quot;)
-dense2 = torch.from_numpy(sp2.to_scipy().todense())
-dense2
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">indexptr</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+<span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">sp2</span> <span class="o">=</span> <span class="n">SparseTensor</span><span class="p">(</span><span class="n">indexptr</span><span class="o">=</span><span class="n">indexptr</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">value</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">sparse_type</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
+<span class="n">dense2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">sp2</span><span class="o">.</span><span class="n">to_scipy</span><span class="p">()</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
+<span class="n">dense2</span>
 </pre></div>
 </div>
 </div>
@@ -1312,9 +1037,9 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Initialize the kernel
-lengthscale_init = torch.tensor([1.0])
-k = SparseLinearKernel(lengthscale_init, options=falkon.FalkonOptions())
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize the kernel</span>
+<span class="n">lengthscale_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">])</span>
+<span class="n">k</span> <span class="o">=</span> <span class="n">SparseLinearKernel</span><span class="p">(</span><span class="n">lengthscale_init</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
@@ -1322,7 +1047,7 @@ <h3>Testing sparse support<a class="headerlink" href="#Testing-sparse-support" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>k(sp1, sp2) == k(dense1, dense2)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">k</span><span class="p">(</span><span class="n">sp1</span><span class="p">,</span> <span class="n">sp2</span><span class="p">)</span> <span class="o">==</span> <span class="n">k</span><span class="p">(</span><span class="n">dense1</span><span class="p">,</span> <span class="n">dense2</span><span class="p">)</span>
 <br/></pre></div>
 </div>
 </div>
diff --git a/examples/examples.html b/examples/examples.html
index 0d13e45b..17b0c279 100644
--- a/examples/examples.html
+++ b/examples/examples.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Examples &mdash; falkon 0.7.5 documentation</title>
+  <title>Examples &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">Examples</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/examples/examples.rst.txt" rel="nofollow"> View page source</a>
@@ -333,31 +336,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="examples">
+  <section id="examples">
 <span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this heading"></a></h1>
 <div class="toctree-wrapper compound">
 </div>
diff --git a/examples/falkon_cv.html b/examples/falkon_cv.html
index 93c8f712..ddbd542c 100644
--- a/examples/falkon_cv.html
+++ b/examples/falkon_cv.html
@@ -1,20 +1,20 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Hyperparameter Tuning with Falkon &mdash; falkon 0.7.5 documentation</title>
+  <title>Hyperparameter Tuning with Falkon &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +32,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +328,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
       <li class="breadcrumb-item active">Hyperparameter Tuning with Falkon</li>
       <li class="wy-breadcrumbs-aside">
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-<section id="Hyperparameter-Tuning-with-Falkon">
+  <section id="Hyperparameter-Tuning-with-Falkon">
 <h1>Hyperparameter Tuning with Falkon<a class="headerlink" href="#Hyperparameter-Tuning-with-Falkon" title="Permalink to this heading"></a></h1>
 <section id="Introduction">
 <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this heading"></a></h2>
@@ -625,14 +350,14 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
-from sklearn import datasets, model_selection, metrics
-import numpy as np
-import pandas as pd
-import torch
-import matplotlib.pyplot as plt
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span><span class="p">,</span> <span class="n">metrics</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 
-import falkon
+<span class="kn">import</span> <span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -652,7 +377,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.load_digits(return_X_y=True)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -660,9 +385,9 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
-_ = ax.matshow(X[1].reshape((8, 8)), cmap=&#39;gray&#39;)
-print(&quot;Label: %d&quot; % Y[1])
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">Y</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -689,8 +414,8 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
-    X, Y, test_size=0.2, random_state=10, shuffle=True)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -702,10 +427,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train = torch.from_numpy(X_train).to(dtype=torch.float32)
-X_test = torch.from_numpy(X_test).to(dtype=torch.float32)
-Y_train = torch.from_numpy(Y_train)
-Y_test = torch.from_numpy(Y_test)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -714,26 +439,26 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># z-score normalization
-train_mean = X_train.mean()
-train_std = X_train.std()
-X_train -= train_mean
-X_train /= train_std
-X_test -= train_mean
-X_test /= train_std
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+<span class="n">X_train</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_train</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">X_test</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_test</span> <span class="o">/=</span> <span class="n">train_std</span>
 </pre></div>
 </div>
 </div>
-<p>Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation.</p>
+<p>Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification. We therefore convert the labels to a 1-hot representation.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Convert labels to 1-hot
-eye = torch.eye(10, dtype=torch.float32)
-Y_train = eye[Y_train]
-Y_test = eye[Y_test]
-print(&quot;First label vector: &quot;, Y_train[0])
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert labels to 1-hot</span>
+<span class="n">eye</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_train</span><span class="p">]</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_test</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label vector: &quot;</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -755,11 +480,11 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def mclass_loss(true, pred):
-    true = torch.argmax(true, dim=1)
-    pred = torch.argmax(pred, dim=1)
-    return torch.mean((true != pred).to(torch.float32))
-mclass_scorer = metrics.make_scorer(mclass_loss, greater_is_better=False)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
+<span class="n">mclass_scorer</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">mclass_loss</span><span class="p">,</span> <span class="n">greater_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -770,19 +495,19 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>parameter_grid = {
-    &#39;kernel&#39;: [falkon.kernels.GaussianKernel(sigma=1),
-               falkon.kernels.GaussianKernel(sigma=5),
-               falkon.kernels.GaussianKernel(sigma=10),
-               falkon.kernels.GaussianKernel(sigma=15),],
-    &#39;penalty&#39;: [1e-3, 1e-5, 1e-7, 1e-9],
-    &#39;M&#39;: [500, 1000],
-}
-estimator = falkon.Falkon(
-    kernel=falkon.kernels.GaussianKernel(1), penalty=1e-3, M=1000,  # Mandatory parameters, will be overridden
-    maxiter=10, options=falkon.FalkonOptions(use_cpu=True))
-
-grid_search = model_selection.GridSearchCV(estimator, parameter_grid, scoring=mclass_scorer, cv=5)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parameter_grid</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;kernel&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
+               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
+               <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">15</span><span class="p">),],</span>
+    <span class="s1">&#39;penalty&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-9</span><span class="p">],</span>
+    <span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">],</span>
+<span class="p">}</span>
+<span class="n">estimator</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>  <span class="c1"># Mandatory parameters, will be overridden</span>
+    <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+<span class="n">grid_search</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">parameter_grid</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">mclass_scorer</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -790,9 +515,9 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
-grid_search.fit(X_train, Y_train)
-print(&quot;The best parameters are: &quot;, grid_search.best_params_)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The best parameters are: &quot;</span><span class="p">,</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -814,11 +539,11 @@ <h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
-flk = grid_search.best_estimator_
-flk.fit(X_train, Y_train)
-test_pred = flk.predict(X_test)
-train_pred = flk.predict(X_train)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator_</span>
+<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -835,8 +560,8 @@ <h3>Evaluating the model<a class="headerlink" href="#Evaluating-the-model" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;Training error: %.2f%%&quot; % (mclass_loss(Y_train, train_pred) * 100))
-print(&quot;Test error: %.2f%%&quot; % (mclass_loss(Y_test, test_pred) * 100))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">train_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">test_pred</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -859,11 +584,11 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>res_df = pd.DataFrame.from_dict(grid_search.cv_results_)
-res_df[&quot;M&quot;] = res_df.params.apply(lambda x: x.get(&quot;M&quot;))
-res_df[&quot;penalty&quot;] = res_df.params.apply(lambda x: x.get(&quot;penalty&quot;))
-res_df[&quot;sigma&quot;] = res_df.params.apply(lambda x: x.get(&quot;kernel&quot;).sigma.item())
-res_df = res_df[[&quot;mean_test_score&quot;, &quot;M&quot;, &quot;penalty&quot;, &quot;sigma&quot;]]
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">res_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
+<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;M&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;M&quot;</span><span class="p">))</span>
+<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;penalty&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;penalty&quot;</span><span class="p">))</span>
+<span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;sigma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res_df</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;kernel&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+<span class="n">res_df</span> <span class="o">=</span> <span class="n">res_df</span><span class="p">[[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="s2">&quot;M&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;sigma&quot;</span><span class="p">]]</span>
 </pre></div>
 </div>
 </div>
@@ -871,18 +596,18 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def plot_heatmap(ax, df, xlabel, ylabel, value, scale):
-    piv = pd.pivot_table(df, index=ylabel, columns=xlabel, values=value)
-    cmap = plt.cm.get_cmap(&#39;coolwarm_r&#39;, 20)
-    ax.grid(False)
-    c = ax.pcolormesh(piv, cmap=cmap, vmin=scale[0], vmax=scale[1])
-    ax.set_yticks(np.arange(piv.shape[0]) + 0.5, minor=False)
-    ax.set_xticks(np.arange(piv.shape[1]) + 0.5, minor=False)
-    ax.set_xticklabels(piv.columns, minor=False)
-    ax.set_yticklabels(piv.index, minor=False)
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel(ylabel)
-    return c
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_heatmap</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
+    <span class="n">piv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">ylabel</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
+    <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;coolwarm_r&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">piv</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vmax</span><span class="o">=</span><span class="n">scale</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">piv</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">c</span>
 </pre></div>
 </div>
 </div>
@@ -890,13 +615,13 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, axs = plt.subplots(ncols=2, figsize=(12, 5))
-scale = (res_df[&quot;mean_test_score&quot;].min(), res_df[&quot;mean_test_score&quot;].max())
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">scale</span> <span class="o">=</span> <span class="p">(</span><span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">res_df</span><span class="p">[</span><span class="s2">&quot;mean_test_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
 
-c = plot_heatmap(axs[0], res_df[res_df.M == 500], &quot;sigma&quot;, &quot;penalty&quot;, &quot;mean_test_score&quot;, scale)
-axs[0].set_title(&quot;M = 500&quot;)
-c = plot_heatmap(axs[1], res_df[res_df.M == 1000], &quot;sigma&quot;, &quot;penalty&quot;, &quot;mean_test_score&quot;, scale)
-_ = axs[1].set_title(&quot;M = 1000&quot;)
+<span class="n">c</span> <span class="o">=</span> <span class="n">plot_heatmap</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">res_df</span><span class="p">[</span><span class="n">res_df</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">500</span><span class="p">],</span> <span class="s2">&quot;sigma&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;M = 500&quot;</span><span class="p">)</span>
+<span class="n">c</span> <span class="o">=</span> <span class="n">plot_heatmap</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">res_df</span><span class="p">[</span><span class="n">res_df</span><span class="o">.</span><span class="n">M</span> <span class="o">==</span> <span class="mi">1000</span><span class="p">],</span> <span class="s2">&quot;sigma&quot;</span><span class="p">,</span> <span class="s2">&quot;penalty&quot;</span><span class="p">,</span> <span class="s2">&quot;mean_test_score&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;M = 1000&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
diff --git a/examples/falkon_mnist.html b/examples/falkon_mnist.html
index 08af8861..19119540 100644
--- a/examples/falkon_mnist.html
+++ b/examples/falkon_mnist.html
@@ -1,20 +1,20 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>MNIST Classification with Falkon &mdash; falkon 0.7.5 documentation</title>
+  <title>MNIST Classification with Falkon &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
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@@ -32,14 +32,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +328,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
       <li class="breadcrumb-item active">MNIST Classification with Falkon</li>
       <li class="wy-breadcrumbs-aside">
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-<section id="MNIST-Classification-with-Falkon">
+  <section id="MNIST-Classification-with-Falkon">
 <h1>MNIST Classification with Falkon<a class="headerlink" href="#MNIST-Classification-with-Falkon" title="Permalink to this heading"></a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
-import matplotlib.pyplot as plt
-plt.style.use(&#39;ggplot&#39;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-import torch
-import torchvision
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torchvision</span>
 
-import falkon
+<span class="kn">import</span> <span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -646,12 +371,12 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>mnist_train_dataset = torchvision.datasets.MNIST(
-    root=&quot;.&quot;, train=True, download=True,
-    transform=torchvision.transforms.ToTensor())
-mnist_test_dataset = torchvision.datasets.MNIST(
-    root=&quot;.&quot;, train=False, download=True,
-    transform=torchvision.transforms.ToTensor())
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mnist_train_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
+    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
+<span class="n">mnist_test_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
+    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
@@ -659,17 +384,17 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Load the whole dataset in memory
-mnist_tr_img_list, mnist_tr_label_list = [], []
-for i in range(len(mnist_train_dataset)):
-    data_point = mnist_train_dataset[i]
-    mnist_tr_img_list.append(data_point[0])
-    mnist_tr_label_list.append(data_point[1])
-mnist_ts_img_list, mnist_ts_label_list = [], []
-for i in range(len(mnist_test_dataset)):
-    data_point = mnist_test_dataset[i]
-    mnist_ts_img_list.append(data_point[0])
-    mnist_ts_label_list.append(data_point[1])
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load the whole dataset in memory</span>
+<span class="n">mnist_tr_img_list</span><span class="p">,</span> <span class="n">mnist_tr_label_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mnist_train_dataset</span><span class="p">)):</span>
+    <span class="n">data_point</span> <span class="o">=</span> <span class="n">mnist_train_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="n">mnist_tr_img_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">mnist_tr_label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">mnist_ts_img_list</span><span class="p">,</span> <span class="n">mnist_ts_label_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mnist_test_dataset</span><span class="p">)):</span>
+    <span class="n">data_point</span> <span class="o">=</span> <span class="n">mnist_test_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="n">mnist_ts_img_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">mnist_ts_label_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -677,12 +402,12 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>mnist_tr_x = torch.vstack(mnist_tr_img_list)
-mnist_tr_x = mnist_tr_x.reshape(mnist_tr_x.shape[0], -1)
-mnist_ts_x = torch.vstack(mnist_ts_img_list)
-mnist_ts_x = mnist_ts_x.reshape(mnist_ts_x.shape[0], -1)
-mnist_tr_y = torch.tensor(mnist_tr_label_list)
-mnist_ts_y = torch.tensor(mnist_ts_label_list)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mnist_tr_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">mnist_tr_img_list</span><span class="p">)</span>
+<span class="n">mnist_tr_x</span> <span class="o">=</span> <span class="n">mnist_tr_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">mnist_ts_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">mnist_ts_img_list</span><span class="p">)</span>
+<span class="n">mnist_ts_x</span> <span class="o">=</span> <span class="n">mnist_ts_x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mnist_ts_x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">mnist_tr_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">mnist_tr_label_list</span><span class="p">)</span>
+<span class="n">mnist_ts_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">mnist_ts_label_list</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -690,8 +415,8 @@ <h2>Download the MNIST dataset &amp; load it in memory<a class="headerlink" href
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
-ax.imshow(mnist_tr_x[0].reshape(28,28))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -719,10 +444,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># one-hot labels
-A = torch.eye(10, dtype=torch.float32)
-mnist_tr_y = A[mnist_tr_y.to(torch.long), :]
-mnist_ts_y = A[mnist_ts_y.to(torch.long), :]
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># one-hot labels</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">mnist_tr_y</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">mnist_tr_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">),</span> <span class="p">:]</span>
+<span class="n">mnist_ts_y</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">mnist_ts_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">),</span> <span class="p">:]</span>
 </pre></div>
 </div>
 </div>
@@ -730,11 +455,11 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def classif_error(y_true, y_pred):
-    y_true = torch.argmax(y_true, dim=1)
-    y_pred = torch.argmax(y_pred, dim=1)
-    err = y_true.flatten() != y_pred.flatten()
-    return torch.mean(err.to(torch.float32))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">classif_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
+    <span class="n">y_true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">err</span> <span class="o">=</span> <span class="n">y_true</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="o">!=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">err</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -754,15 +479,15 @@ <h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>options = falkon.FalkonOptions(use_cpu=True)
-kernel = falkon.kernels.GaussianKernel(sigma=15)
-flk = falkon.Falkon(kernel=kernel,
-                    penalty=1e-8,
-                    M=1000,
-                    maxiter=10,
-                    options=options,
-                    error_every=1,
-                    error_fn=classif_error)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
+                    <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span>
+                    <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+                    <span class="n">maxiter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                    <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">,</span>
+                    <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">error_fn</span><span class="o">=</span><span class="n">classif_error</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -770,7 +495,7 @@ <h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>_ = flk.fit(mnist_tr_x, mnist_tr_y)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">,</span> <span class="n">mnist_tr_y</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -795,11 +520,11 @@ <h2>Run Falkon<a class="headerlink" href="#Run-Falkon" title="Permalink to this
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>train_pred = flk.predict(mnist_tr_x)
-test_pred = flk.predict(mnist_ts_x)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">mnist_tr_x</span><span class="p">)</span>
+<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">mnist_ts_x</span><span class="p">)</span>
 
-print(&quot;Training error: %.2f%%&quot; % (classif_error(train_pred, mnist_tr_y) * 100))
-print(&quot;Test error: %.2f%%&quot; % (classif_error(test_pred, mnist_ts_y) * 100))
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">classif_error</span><span class="p">(</span><span class="n">train_pred</span><span class="p">,</span> <span class="n">mnist_tr_y</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">classif_error</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">mnist_ts_y</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
 <br/></pre></div>
 </div>
 </div>
diff --git a/examples/falkon_regression_tutorial.html b/examples/falkon_regression_tutorial.html
index 290b6c9d..8d77dbc4 100644
--- a/examples/falkon_regression_tutorial.html
+++ b/examples/falkon_regression_tutorial.html
@@ -1,20 +1,20 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Falkon Regression Tutorial &mdash; falkon 0.7.5 documentation</title>
+  <title>Falkon Regression Tutorial &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +32,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +328,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
       <li class="breadcrumb-item active">Falkon Regression Tutorial</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,286 +340,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
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-div.nboutput.container div.output_area .highlight {
-    border: none;
-    padding: 0;
-    margin: 0;
-    box-shadow: none;
-}
-
-div.nbinput.container > div[class*=highlight],
-div.nboutput.container > div[class*=highlight] {
-    margin: 0;
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-
-div.nbinput.container div.prompt *,
-div.nboutput.container div.prompt * {
-    background: none;
-}
-
-div.nboutput.container div.output_area .highlight,
-div.nboutput.container div.output_area pre {
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-div.nboutput.container div.output_area div.highlight {
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-.math {
-    text-align: unset;
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-</style>
-<section id="Falkon-Regression-Tutorial">
+  <section id="Falkon-Regression-Tutorial">
 <h1>Falkon Regression Tutorial<a class="headerlink" href="#Falkon-Regression-Tutorial" title="Permalink to this heading"></a></h1>
 <section id="Introduction">
 <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this heading"></a></h2>
@@ -625,14 +350,14 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
-from sklearn import datasets
-import numpy as np
-import torch
-import matplotlib.pyplot as plt
-plt.style.use(&#39;ggplot&#39;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-import falkon
+<span class="kn">import</span> <span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -653,7 +378,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.fetch_california_housing(return_X_y=True)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">fetch_california_housing</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -661,15 +386,15 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>num_train = int(X.shape[0] * 0.8)
-num_test = X.shape[0] - num_train
-shuffle_idx = np.arange(X.shape[0])
-np.random.shuffle(shuffle_idx)
-train_idx = shuffle_idx[:num_train]
-test_idx = shuffle_idx[num_train:]
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
+<span class="n">num_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">num_train</span>
+<span class="n">shuffle_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffle_idx</span><span class="p">)</span>
+<span class="n">train_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[:</span><span class="n">num_train</span><span class="p">]</span>
+<span class="n">test_idx</span> <span class="o">=</span> <span class="n">shuffle_idx</span><span class="p">[</span><span class="n">num_train</span><span class="p">:]</span>
 
-Xtrain, Ytrain = X[train_idx], Y[train_idx]
-Xtest, Ytest = X[test_idx], Y[test_idx]
+<span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
+<span class="n">Xtest</span><span class="p">,</span> <span class="n">Ytest</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">test_idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>
 </pre></div>
 </div>
 </div>
@@ -682,11 +407,11 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># convert numpy -&gt; pytorch
-Xtrain = torch.from_numpy(Xtrain).to(dtype=torch.float32)
-Xtest = torch.from_numpy(Xtest).to(dtype=torch.float32)
-Ytrain = torch.from_numpy(Ytrain).to(dtype=torch.float32)
-Ytest = torch.from_numpy(Ytest).to(dtype=torch.float32)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># convert numpy -&gt; pytorch</span>
+<span class="n">Xtrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Xtest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytrain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytrain</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Ytest</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Ytest</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -694,13 +419,13 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># z-score normalization
-train_mean = Xtrain.mean(0, keepdim=True)
-train_std = Xtrain.std(0, keepdim=True)
-Xtrain -= train_mean
-Xtrain /= train_std
-Xtest -= train_mean
-Xtest /= train_std
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">Xtrain</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">Xtrain</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtrain</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">Xtest</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">Xtest</span> <span class="o">/=</span> <span class="n">train_std</span>
 </pre></div>
 </div>
 </div>
@@ -714,10 +439,10 @@ <h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model"
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>options = falkon.FalkonOptions(keops_active=&quot;no&quot;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">options</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
 
-kernel = falkon.kernels.GaussianKernel(sigma=1, opt=options)
-flk = falkon.Falkon(kernel=kernel, penalty=1e-5, M=5000, options=options)
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -739,7 +464,7 @@ <h2>Training the model<a class="headerlink" href="#Training-the-model" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>flk.fit(Xtrain, Ytrain)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -763,14 +488,14 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>train_pred = flk.predict(Xtrain).reshape(-1, )
-test_pred = flk.predict(Xtest).reshape(-1, )
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
 
-def rmse(true, pred):
-    return torch.sqrt(torch.mean((true.reshape(-1, 1) - pred.reshape(-1, 1))**2))
+<span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
 
-print(&quot;Training RMSE: %.3f&quot; % (rmse(train_pred, Ytrain)))
-print(&quot;Test RMSE: %.3f&quot; % (rmse(test_pred, Ytest)))
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training RMSE: </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">train_pred</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test RMSE: </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">test_pred</span><span class="p">,</span> <span class="n">Ytest</span><span class="p">)))</span>
 </pre></div>
 </div>
 </div>
@@ -788,11 +513,11 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
-hist_range = (min(Ytest.min(), test_pred.min()).item(), max(Ytest.max(), test_pred.max()).item())
-ax.hist(Ytest.numpy(), bins=10, range=hist_range, alpha=0.7, label=&quot;True&quot;)
-ax.hist(test_pred.numpy(), bins=10, range=hist_range, alpha=0.7, label=&quot;Pred&quot;)
-ax.legend(loc=&quot;best&quot;);
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">hist_range</span> <span class="o">=</span> <span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="nb">max</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">max</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">Ytest</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">hist_range</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;True&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">test_pred</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">hist_range</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Pred&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;best&quot;</span><span class="p">);</span>
 </pre></div>
 </div>
 </div>
diff --git a/examples/hyperopt.html b/examples/hyperopt.html
index 54e13fb8..1754577e 100644
--- a/examples/hyperopt.html
+++ b/examples/hyperopt.html
@@ -1,20 +1,20 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Automatic Hyperparameter Optimization &mdash; falkon 0.7.5 documentation</title>
+  <title>Automatic Hyperparameter Optimization &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +32,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +328,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
       <li class="breadcrumb-item active">Automatic Hyperparameter Optimization</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,298 +340,19 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS for nbsphinx extension */
-
-/* remove conflicting styling from Sphinx themes */
-div.nbinput.container div.prompt *,
-div.nboutput.container div.prompt *,
-div.nbinput.container div.input_area pre,
-div.nboutput.container div.output_area pre,
-div.nbinput.container div.input_area .highlight,
-div.nboutput.container div.output_area .highlight {
-    border: none;
-    padding: 0;
-    margin: 0;
-    box-shadow: none;
-}
-
-div.nbinput.container > div[class*=highlight],
-div.nboutput.container > div[class*=highlight] {
-    margin: 0;
-}
-
-div.nbinput.container div.prompt *,
-div.nboutput.container div.prompt * {
-    background: none;
-}
-
-div.nboutput.container div.output_area .highlight,
-div.nboutput.container div.output_area pre {
-    background: unset;
-}
-
-div.nboutput.container div.output_area div.highlight {
-    color: unset;  /* override Pygments text color */
-}
-
-/* avoid gaps between output lines */
-div.nboutput.container div[class*=highlight] pre {
-    line-height: normal;
-}
-
-/* input/output containers */
-div.nbinput.container,
-div.nboutput.container {
-    display: -webkit-flex;
-    display: flex;
-    align-items: flex-start;
-    margin: 0;
-    width: 100%;
-}
-@media (max-width: 540px) {
-    div.nbinput.container,
-    div.nboutput.container {
-        flex-direction: column;
-    }
-}
-
-/* input container */
-div.nbinput.container {
-    padding-top: 5px;
-}
-
-/* last container */
-div.nblast.container {
-    padding-bottom: 5px;
-}
-
-/* input prompt */
-div.nbinput.container div.prompt pre {
-    color: #307FC1;
-}
-
-/* output prompt */
-div.nboutput.container div.prompt pre {
-    color: #BF5B3D;
-}
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-<section id="Automatic-Hyperparameter-Optimization">
+  <section id="Automatic-Hyperparameter-Optimization">
 <h1>Automatic Hyperparameter Optimization<a class="headerlink" href="#Automatic-Hyperparameter-Optimization" title="Permalink to this heading"></a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
-from sklearn import datasets, model_selection
-import numpy as np
-np.random.seed(30)
-import torch
-import matplotlib.pyplot as plt
-plt.style.use(&#39;ggplot&#39;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -646,7 +371,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.load_digits(return_X_y=True)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -654,16 +379,16 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def plot_digit(ax, x, y):
-    ax.imshow(x.reshape((8, 8)), cmap=&#39;gray&#39;)
-    ax.set_title(&quot;Label: %d&quot; % y)
-    ax.set_axis_off()
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">y</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
 
-# Plot three sample images from the dataset
-fig, ax = plt.subplots(ncols=3, figsize=(9, 6))
-plot_digit(ax[0], X[3], Y[3])
-plot_digit(ax[1], X[10], Y[10])
-plot_digit(ax[2], X[42], Y[42])
+<span class="c1"># Plot three sample images from the dataset</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">10</span><span class="p">])</span>
+<span class="n">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="mi">42</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="mi">42</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -682,8 +407,8 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
-    X, Y, test_size=0.2, random_state=10, shuffle=True)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -695,10 +420,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train = torch.from_numpy(X_train).to(dtype=torch.float32)
-X_test = torch.from_numpy(X_test).to(dtype=torch.float32)
-Y_train = torch.from_numpy(Y_train)
-Y_test = torch.from_numpy(Y_test)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -708,27 +433,27 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># z-score normalization
-train_mean = X_train.mean()
-train_std = X_train.std()
-X_train -= train_mean
-X_train /= train_std
-X_test -= train_mean
-X_test /= train_std
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># z-score normalization</span>
+<span class="n">train_mean</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">train_std</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
+<span class="n">X_train</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_train</span> <span class="o">/=</span> <span class="n">train_std</span>
+<span class="n">X_test</span> <span class="o">-=</span> <span class="n">train_mean</span>
+<span class="n">X_test</span> <span class="o">/=</span> <span class="n">train_std</span>
 </pre></div>
 </div>
 </div>
-<p>Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification.</p>
+<p>Since Falkon optimizes with respect to the square loss, using ordinal labels (e.g. 1, 4, 5) is not ideal since closeness in the natural numbers is meaningless for classification.</p>
 <p>We therefore convert the labels to a <strong>1-hot representation</strong>.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Convert labels to 1-hot
-eye = torch.eye(10, dtype=torch.float32)
-Y_train = eye[Y_train]
-Y_test = eye[Y_test]
-print(&quot;First label vector: &quot;, Y_train[0])
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert labels to 1-hot</span>
+<span class="n">eye</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_train</span><span class="p">]</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">eye</span><span class="p">[</span><span class="n">Y_test</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label vector: &quot;</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -751,10 +476,10 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def mclass_loss(true, pred):
-    true = torch.argmax(true, dim=1)
-    pred = torch.argmax(pred, dim=1)
-    return torch.mean((true != pred).to(torch.float32))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -764,9 +489,9 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import falkon.hopt
-from falkon import FalkonOptions
-from falkon.hopt.objectives import NystromCompReg
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">falkon.hopt</span>
+<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
+<span class="kn">from</span> <span class="nn">falkon.hopt.objectives</span> <span class="kn">import</span> <span class="n">NystromCompReg</span>
 </pre></div>
 </div>
 </div>
@@ -774,7 +499,7 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>flk_opt = FalkonOptions(use_cpu=not torch.cuda.is_available())
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk_opt</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
@@ -783,11 +508,11 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>sigma_init = torch.tensor([1.0] * X_train.shape[1], dtype=torch.float32).requires_grad_()
-kernel = falkon.kernels.GaussianKernel(sigma=sigma_init, opt=flk_opt)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sigma_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span>
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="n">sigma_init</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
 
-penalty_init = torch.tensor(1e-5, dtype=torch.float32)
-centers_init = X_train[np.random.choice(X_train.shape[0], size=(500, ), replace=False)].clone()
+<span class="n">penalty_init</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">centers_init</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="p">),</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)]</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
@@ -796,11 +521,11 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>model = NystromCompReg(
-    kernel=kernel, penalty_init=penalty_init, centers_init=centers_init,  # The initial hp values
-    opt_penalty=True, opt_centers=True,  # Whether the various hps are to be optimized
-    )
-opt_hp = torch.optim.Adam(model.parameters(), lr=0.1)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">NystromCompReg</span><span class="p">(</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty_init</span><span class="o">=</span><span class="n">penalty_init</span><span class="p">,</span> <span class="n">centers_init</span><span class="o">=</span><span class="n">centers_init</span><span class="p">,</span>  <span class="c1"># The initial hp values</span>
+    <span class="n">opt_penalty</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">opt_centers</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>  <span class="c1"># Whether the various hps are to be optimized</span>
+    <span class="p">)</span>
+<span class="n">opt_hp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -809,17 +534,17 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>tr_loss, tr_err = [], []
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tr_loss</span><span class="p">,</span> <span class="n">tr_err</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
 
-for epoch in range(50):
-    opt_hp.zero_grad()
-    loss = model(X_train, Y_train)
-    loss.backward()
-    opt_hp.step()
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
+    <span class="n">opt_hp</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
+    <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+    <span class="n">opt_hp</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
 
-    tr_loss.append(loss.item())
-    tr_err.append(mclass_loss(Y_train, model.predict(X_train)))
-    print(f&quot;Epoch {epoch} Loss {tr_loss[-1]:.3f} Error {tr_err[-1] * 100:.2f}%&quot;)
+    <span class="n">tr_loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+    <span class="n">tr_err</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2"> Loss </span><span class="si">{</span><span class="n">tr_loss</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Error </span><span class="si">{</span><span class="n">tr_err</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -884,9 +609,9 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Evaluate the test error:
-ts_preds = model.predict(X_test)
-print(f&quot;Test error: {mclass_loss(Y_test, ts_preds) * 100:.2f}%&quot;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Evaluate the test error:</span>
+<span class="n">ts_preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test error: </span><span class="si">{</span><span class="n">mclass_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span><span class="w"> </span><span class="n">ts_preds</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -903,8 +628,8 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>print(&quot;Final value of lambda: %.3e&quot; % (model.penalty))
-print(&quot;Final value of sigma: %s&quot; % (model.kernel.sigma.detach()))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final value of lambda: </span><span class="si">%.3e</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">penalty</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final value of sigma: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">kernel</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">detach</span><span class="p">()))</span>
 </pre></div>
 </div>
 </div>
@@ -926,7 +651,7 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 </div>
 <p>We can compare the obtained results with the grid-search notebook.</p>
 <p>A grid-search with 1000 centers and 32 grid-points resulted in choosing a model with sigma=5, and lambda=1e-7.</p>
-<p>The <code class="docutils literal notranslate"><span class="pre">NystromCompReg</span></code> objective with half the centers led to obtain a lower test error (0.83% vs. 1.11%) after 50 training epochs. However the obtained hyperparameters are quite different: lambda in particular is much higher at 1.1e-4.</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">NystromCompReg</span></code> objective with half the centers led to obtain a lower test error (0.83% vs. 1.11%) after 50 training epochs. However the obtained hyperparameters are quite different: lambda in particular is much higher at 1.1e-4.</p>
 <p>This objective in particular has quite a high bias and tends to choose simple models instead of more complex ones (remember that since lambda is a regularizer, it can be seen as one possible measure of model complexity). In practice this is often not a problem, as we observed in this case.</p>
 </section>
 </section>
diff --git a/examples/hyperopt.ipynb b/examples/hyperopt.ipynb
index 4d7de093..26837d5a 100644
--- a/examples/hyperopt.ipynb
+++ b/examples/hyperopt.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "16505af2",
+   "id": "702cd386",
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "ab47d6b1",
+   "id": "7364c275",
    "metadata": {
     "collapsed": false,
     "pycharm": {
diff --git a/examples/logistic_falkon.html b/examples/logistic_falkon.html
index 34fcb83a..57daf7af 100644
--- a/examples/logistic_falkon.html
+++ b/examples/logistic_falkon.html
@@ -1,20 +1,20 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Introducing Logistic Falkon &mdash; falkon 0.7.5 documentation</title>
+  <title>Introducing Logistic Falkon &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
         <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
         <script src="../_static/doctools.js"></script>
         <script src="../_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -32,14 +32,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -324,7 +328,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a></li>
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
           <li class="breadcrumb-item"><a href="examples.html">Examples</a></li>
       <li class="breadcrumb-item active">Introducing Logistic Falkon</li>
       <li class="wy-breadcrumbs-aside">
@@ -336,286 +340,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS for nbsphinx extension */
-
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-div.nboutput.container div.output_area pre,
-div.nbinput.container div.input_area .highlight,
-div.nboutput.container div.output_area .highlight {
-    border: none;
-    padding: 0;
-    margin: 0;
-    box-shadow: none;
-}
-
-div.nbinput.container > div[class*=highlight],
-div.nboutput.container > div[class*=highlight] {
-    margin: 0;
-}
-
-div.nbinput.container div.prompt *,
-div.nboutput.container div.prompt * {
-    background: none;
-}
-
-div.nboutput.container div.output_area .highlight,
-div.nboutput.container div.output_area pre {
-    background: unset;
-}
-
-div.nboutput.container div.output_area div.highlight {
-    color: unset;  /* override Pygments text color */
-}
-
-/* avoid gaps between output lines */
-div.nboutput.container div[class*=highlight] pre {
-    line-height: normal;
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-div.nbinput.container,
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-    display: -webkit-flex;
-    display: flex;
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-    margin: 0;
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-@media (max-width: 540px) {
-    div.nbinput.container,
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-        flex-direction: column;
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-    text-align: left;
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-<section id="Introducing-Logistic-Falkon">
+  <section id="Introducing-Logistic-Falkon">
 <h1>Introducing Logistic Falkon<a class="headerlink" href="#Introducing-Logistic-Falkon" title="Permalink to this heading"></a></h1>
 <section id="Introduction">
 <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to this heading"></a></h2>
@@ -625,13 +350,13 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
-from sklearn import datasets, model_selection
-import torch
-import matplotlib.pyplot as plt
-plt.style.use(&#39;ggplot&#39;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-import falkon
+<span class="kn">import</span> <span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -652,8 +377,8 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X, Y = datasets.make_circles(
-    n_samples=10_000, shuffle=False, noise=0.1, random_state=122)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">make_circles</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">10_000</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">122</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -661,9 +386,9 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots(figsize=(7, 7))
-ax.scatter(X[Y == 0,0], X[Y == 0,1], alpha=0.3, marker=&#39;.&#39;)
-_ = ax.scatter(X[Y == 1,0], X[Y == 1,1], alpha=0.3, marker=&#39;.&#39;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">Y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -682,8 +407,8 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
-    X, Y, test_size=0.2, random_state=10, shuffle=True)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">Y_test</span> <span class="o">=</span> <span class="n">model_selection</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -700,10 +425,10 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>X_train = torch.from_numpy(X_train).to(dtype=torch.float32)
-X_test = torch.from_numpy(X_test).to(dtype=torch.float32)
-Y_train = torch.from_numpy(Y_train).to(dtype=torch.float32).reshape(-1, 1)
-Y_test = torch.from_numpy(Y_test).to(dtype=torch.float32).reshape(-1, 1)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">Y_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">Y_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -711,8 +436,8 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>Y_train[Y_train == 0] = -1
-Y_test[Y_test == 0] = -1
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Y_train</span><span class="p">[</span><span class="n">Y_train</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+<span class="n">Y_test</span><span class="p">[</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
 </pre></div>
 </div>
 </div>
@@ -724,8 +449,8 @@ <h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model"
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def binary_loss(true, pred):
-    return torch.mean((true != torch.sign(pred)).to(torch.float32))
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">binary_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -733,12 +458,12 @@ <h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model"
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># We define some basic options to run on the CPU, and to disable keops
-flk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=&quot;no&quot;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We define some basic options to run on the CPU, and to disable keops</span>
+<span class="n">flk_opt</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
 
-flk_kernel = falkon.kernels.GaussianKernel(1, opt=flk_opt)
+<span class="n">flk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
 
-flk = falkon.Falkon(kernel=flk_kernel, penalty=1e-7, M=1000, options=flk_opt)
+<span class="n">flk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">flk_kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -747,24 +472,24 @@ <h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model"
 <h2>Define Logistic Falkon model<a class="headerlink" href="#Define-Logistic-Falkon-model" title="Permalink to this heading"></a></h2>
 <p>The logistic Falkon estimator uses the same base parameters as Falkon.</p>
 <p>However, instead of specifying a single value for regularization, we need to specify a <em>regularization path</em>: a series of decreasing amounts of regularization. For each regularization value we also need to specify the <em>number of iterations</em> of conjugate gradient descent, which will be performed for that specific regularization value.</p>
-<p>We validated empirically on a wide number of binary classification problems that a good scheme to set the regularization path is to use three short (i.e. 3 iterations) runs with increasing regularization, and then a few longer (here we used 8 iterations) runs with the final regularization value (here <code class="docutils literal notranslate"><span class="pre">1e-7</span></code>, the same as for Falkon).</p>
+<p>We validated empirically on a wide number of binary classification problems that a good scheme to set the regularization path is to use three short (i.e. 3 iterations) runs with increasing regularization, and then a few longer (here we used 8 iterations) runs with the final regularization value (here <code class="docutils literal notranslate"><span class="pre">1e-7</span></code>, the same as for Falkon).</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">LogisticFalkon</span></code> estimator also accepts a mandatory <code class="docutils literal notranslate"><span class="pre">loss</span></code> parameter, which should be set to an instance of the <code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code> class. While the <code class="docutils literal notranslate"><span class="pre">LogisticLoss</span></code> is the only implemented loss at the moment, the learning algorithm is defined for any <em>generalized self-concordant</em> loss, and we plan to extend the library to support more functions.</p>
 <p>An additional feature we show here is <strong>error monitoring</strong>: By passing an error function to the estimator (see parameter <code class="docutils literal notranslate"><span class="pre">error_fn</span></code>), the estimator will print the training error at every iteration (how often such prints occur is governed by the <code class="docutils literal notranslate"><span class="pre">error_every</span></code> parameter). This can be very useful to determine if it is possible to stop training early, and in general to monitor the training process.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>logflk_opt = falkon.FalkonOptions(use_cpu=True, keops_active=&quot;no&quot;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">logflk_opt</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
 
-logflk_kernel = falkon.kernels.GaussianKernel(1, opt=logflk_opt)
-logloss = falkon.gsc_losses.LogisticLoss(logflk_kernel)
+<span class="n">logflk_kernel</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">opt</span><span class="o">=</span><span class="n">logflk_opt</span><span class="p">)</span>
+<span class="n">logloss</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">gsc_losses</span><span class="o">.</span><span class="n">LogisticLoss</span><span class="p">(</span><span class="n">logflk_kernel</span><span class="p">)</span>
 
-penalty_list = [1e-3, 1e-5, 1e-7, 1e-7, 1e-7]
-iter_list = [4, 4, 4, 8, 8]
+<span class="n">penalty_list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e-3</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">]</span>
+<span class="n">iter_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
 
-logflk = falkon.LogisticFalkon(
-    kernel=logflk_kernel, penalty_list=penalty_list, iter_list=iter_list, M=1000, loss=logloss,
-    error_fn=binary_loss, error_every=1, options=logflk_opt)
+<span class="n">logflk</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">LogisticFalkon</span><span class="p">(</span>
+    <span class="n">kernel</span><span class="o">=</span><span class="n">logflk_kernel</span><span class="p">,</span> <span class="n">penalty_list</span><span class="o">=</span><span class="n">penalty_list</span><span class="p">,</span> <span class="n">iter_list</span><span class="o">=</span><span class="n">iter_list</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">logloss</span><span class="p">,</span>
+    <span class="n">error_fn</span><span class="o">=</span><span class="n">binary_loss</span><span class="p">,</span> <span class="n">error_every</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">logflk_opt</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -777,8 +502,8 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
-flk.fit(X_train, Y_train);
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">flk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">);</span>
 </pre></div>
 </div>
 </div>
@@ -805,8 +530,8 @@ <h2>Train both models<a class="headerlink" href="#Train-both-models" title="Perm
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%%time
-logflk.fit(X_train, Y_train);
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">logflk</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">);</span>
 </pre></div>
 </div>
 </div>
@@ -849,14 +574,14 @@ <h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headin
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>flk_pred = flk.predict(X_test)
-flk_err = binary_loss(Y_test, flk_pred)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flk_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">flk_err</span> <span class="o">=</span> <span class="n">binary_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">flk_pred</span><span class="p">)</span>
 
-logflk_pred = logflk.predict(X_test)
-logflk_err = binary_loss(Y_test, logflk_pred)
+<span class="n">logflk_pred</span> <span class="o">=</span> <span class="n">logflk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">logflk_err</span> <span class="o">=</span> <span class="n">binary_loss</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">logflk_pred</span><span class="p">)</span>
 
-print(&quot;Falkon model          -- Error: %.2f%%&quot; % (flk_err * 100))
-print(&quot;Logistic Falkon model -- Error: %.2f%%&quot; % (logflk_err * 100))
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Falkon model          -- Error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">flk_err</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon model -- Error: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">logflk_err</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -879,20 +604,20 @@ <h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def plot_predictions(preds, ax):
-    ax.scatter(X_test[((Y_test == -1) &amp; (preds.sign() == Y_test)).reshape(-1), 0],
-               X_test[((Y_test == -1) &amp; (preds.sign() == Y_test)).reshape(-1), 1],
-               alpha=0.3, marker=&#39;.&#39;, color=&#39;b&#39;, label=&quot;correct cls 0&quot;)
-    ax.scatter(X_test[((Y_test == 1) &amp; (preds.sign() == Y_test)).reshape(-1),0],
-               X_test[((Y_test == 1) &amp; (preds.sign() == Y_test)).reshape(-1),1],
-               alpha=0.3, marker=&#39;.&#39;, color=&#39;r&#39;, label=&quot;correct cls 1&quot;)
-
-    ax.scatter(X_test[((Y_test == -1) &amp; (preds.sign() != Y_test)).reshape(-1), 0],
-               X_test[((Y_test == -1) &amp; (preds.sign() != Y_test)).reshape(-1), 1],
-               alpha=0.5, marker=&#39;x&#39;, color=&#39;b&#39;, label=&quot;wrong cls 0&quot;)
-    ax.scatter(X_test[((Y_test == 1) &amp; (preds.sign() != Y_test)).reshape(-1),0],
-               X_test[((Y_test == 1) &amp; (preds.sign() != Y_test)).reshape(-1),1],
-               alpha=0.5, marker=&#39;x&#39;, color=&#39;r&#39;, label=&quot;wrong cls 1&quot;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;correct cls 0&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;correct cls 1&quot;</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;wrong cls 0&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">0</span><span class="p">],</span>
+               <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">!=</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">],</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;wrong cls 1&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -900,14 +625,14 @@ <h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots(ncols=2, figsize=(13, 5))
-plot_predictions(flk_pred, ax[0])
-ax[0].set_title(&quot;Falkon&quot;)
-ax[0].legend(loc=&#39;best&#39;)
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">13</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plot_predictions</span><span class="p">(</span><span class="n">flk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Falkon&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">)</span>
 
-plot_predictions(logflk_pred, ax[1])
-ax[1].set_title(&quot;Logistic Falkon&quot;)
-ax[1].legend(loc=&#39;best&#39;);
+<span class="n">plot_predictions</span><span class="p">(</span><span class="n">logflk_pred</span><span class="p">,</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Logistic Falkon&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">);</span>
 <br/></pre></div>
 </div>
 </div>
diff --git a/genindex.html b/genindex.html
index 18ba01d1..f2f7f833 100644
--- a/genindex.html
+++ b/genindex.html
@@ -3,17 +3,16 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Index &mdash; falkon 0.7.5 documentation</title>
+  <title>Index &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/doctools.js"></script>
         <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -27,14 +26,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -319,7 +322,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">Index</li>
       <li class="wy-breadcrumbs-aside">
       </li>
diff --git a/get_started.html b/get_started.html
index d28bcd25..ae769a25 100644
--- a/get_started.html
+++ b/get_started.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Getting Started &mdash; falkon 0.7.5 documentation</title>
+  <title>Getting Started &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/doctools.js"></script>
         <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">Getting Started</li>
       <li class="wy-breadcrumbs-aside">
             <a href="_sources/get_started.rst.txt" rel="nofollow"> View page source</a>
@@ -333,31 +336,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="getting-started">
+  <section id="getting-started">
 <span id="get-started"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading"></a></h1>
 <p>Once Falkon is installed, getting started is easy. The basic setup to use the <cite>Falkon</cite> estimator only requires
 few lines of code:</p>
diff --git a/index.html b/index.html
index bb00ae30..6d3fa228 100644
--- a/index.html
+++ b/index.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Falkon &mdash; falkon 0.7.5 documentation</title>
+  <title>Falkon &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/doctools.js"></script>
         <script src="_static/sphinx_highlight.js"></script>
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@@ -29,14 +28,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="#" class="icon icon-home"> falkon
+
+          
+          
+          <a href="#" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -321,7 +324,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="#" class="icon icon-home"></a></li>
+      <li><a href="#" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">Falkon</li>
       <li class="wy-breadcrumbs-aside">
             <a href="_sources/index.rst.txt" rel="nofollow"> View page source</a>
@@ -332,31 +335,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
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-
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-.nblast.container + .nbinput.container {
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-}
-
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-.math {
-    text-align: unset;
-}
-</style>
-<section id="falkon">
+  <section id="falkon">
 <h1>Falkon<a class="headerlink" href="#falkon" title="Permalink to this heading"></a></h1>
 <p>A Python library for large-scale kernel methods, with optional (multi-)GPU acceleration.</p>
 <p>The library currently includes two solvers:
diff --git a/install.html b/install.html
index 54951a3b..ca957a74 100644
--- a/install.html
+++ b/install.html
@@ -1,20 +1,19 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" >
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Install &mdash; falkon 0.7.5 documentation</title>
+  <title>Install &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/doctools.js"></script>
         <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">Install</li>
       <li class="wy-breadcrumbs-aside">
             <a href="_sources/install.rst.txt" rel="nofollow"> View page source</a>
@@ -333,31 +336,7 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  
-<style>
-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
-
-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
-
-.admonition > p:before {
-    margin-right: 4px;  /* make room for the exclamation icon */
-}
-
-/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
-.math {
-    text-align: unset;
-}
-</style>
-<section id="install">
+  <section id="install">
 <span id="id1"></span><h1>Install<a class="headerlink" href="#install" title="Permalink to this heading"></a></h1>
 <section id="supported-platforms">
 <h2>Supported Platforms<a class="headerlink" href="#supported-platforms" title="Permalink to this heading"></a></h2>
@@ -388,12 +367,75 @@ <h3>Intel MKL<a class="headerlink" href="#intel-mkl" title="Permalink to this he
 </section>
 <section id="installing">
 <h2>Installing<a class="headerlink" href="#installing" title="Permalink to this heading"></a></h2>
-<p>You can install Falkon</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install git+https://github.com/falkonml/falkon.git
+<p>There are three ways of installing Falkon:</p>
+<ol class="arabic">
+<li><p><strong>From source</strong> by running</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/falkonml/falkon.git
 </pre></div>
 </div>
-<p>This will take a while since there are both Cython extensions which need to be compiled (if Cython is installed),
-and PyTorch extensions which take some time to compile.</p>
+</div></blockquote>
+</li>
+<li><p><strong>From pypi with JIT compilation</strong> (the C++ extension will be compiled when the library is first used):</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon
+</pre></div>
+</div>
+</div></blockquote>
+</li>
+<li><p><strong>From pre-built wheels</strong> which are available for the following versions of PyTorch and CUDA:</p>
+<blockquote>
+<div><blockquote>
+<div><table class="docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Linux</p></th>
+<th class="head"><p><cite>cu113</cite></p></th>
+<th class="head"><p><cite>cu115</cite></p></th>
+<th class="head"><p><cite>cu116</cite></p></th>
+<th class="head"><p><cite>cu117</cite></p></th>
+<th class="head"><p><cite>cu118</cite></p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>torch 1.11.0</p></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p>torch 1.13.0</p></td>
+<td></td>
+<td></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>torch 2.0.0</p></td>
+<td></td>
+<td></td>
+<td></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+</tr>
+</tbody>
+</table>
+</div></blockquote>
+<p>As an example, if you <strong>already have installed</strong> PyTorch 1.13 and CUDA 11.7 on your system, you should run</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon<span class="w"> </span>-f<span class="w"> </span>https://falkon.dibris.unige.it/torch-1.13.0_cu117.html
+</pre></div>
+</div>
+<p>Similarly for CPU packages</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon<span class="w"> </span>-f<span class="w"> </span>https://falkon.dibris.unige.it/torch-2.0.0_cpu.html
+</pre></div>
+</div>
+<p>please check <a class="reference external" href="https://falkon.dibris.unige.it/index.html">here</a> for a list of supported wheels.</p>
+</div></blockquote>
+</li>
+</ol>
+<p>For options 1 and 2, you will need the CUDA toolkit to be setup properly on your system in order to compile the sources.
+Compilation may take a few minutes. To speed it up you can try to install <code class="docutils literal notranslate"><span class="pre">ninja</span></code> (<code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">ninja</span></code>) which
+parallelizes the build process.</p>
 </section>
 <section id="testing-the-installation">
 <h2>Testing the installation<a class="headerlink" href="#testing-the-installation" title="Permalink to this heading"></a></h2>
diff --git a/objects.inv b/objects.inv
index a60df1700c75483b51d06599ba141a36b8c4604c..6614a67ff99df1e7607be53765b1dfd07f35fac4 100644
GIT binary patch
delta 14
Vcmdn2y;*yL4YP%w(MJ0cQ2-_^1f2i?

delta 14
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index f8507841..b0ff257c 100644
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@@ -3,17 +3,16 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Python Module Index &mdash; falkon 0.7.5 documentation</title>
+  <title>Python Module Index &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/doctools.js"></script>
         <script src="_static/sphinx_highlight.js"></script>
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       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">Python Module Index</li>
       <li class="wy-breadcrumbs-aside">
       </li>
diff --git a/search.html b/search.html
index 13e12b57..a57aa87a 100644
--- a/search.html
+++ b/search.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Search &mdash; falkon 0.7.5 documentation</title>
+  <title>Search &mdash; falkon 0.8.2 documentation</title>
       <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
     
@@ -11,10 +11,9 @@
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
         <script src="_static/doctools.js"></script>
         <script src="_static/sphinx_highlight.js"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
@@ -30,14 +29,18 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="index.html" class="icon icon-home"> falkon
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            falkon
           </a>
               <div class="version">
-                0.7.5
+                0.8.2
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="#" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
   </form>
@@ -322,7 +325,7 @@
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home"></a></li>
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
       <li class="breadcrumb-item active">Search</li>
       <li class="wy-breadcrumbs-aside">
       </li>
diff --git a/searchindex.js b/searchindex.js
index e59fd891..72aa7ada 100644
--- a/searchindex.js
+++ b/searchindex.js
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"Basic Kernel Implementation": [[12, "Basic-Kernel-Implementation"]], "Test the basic kernel": [[12, "Test-the-basic-kernel"]], "Differentiable Kernel": [[12, "Differentiable-Kernel"]], "Test the differentiable kernel": [[12, "Test-the-differentiable-kernel"]], "Adding KeOps Support": [[12, "Adding-KeOps-Support"]], "Test the KeOps kernel": [[12, "Test-the-KeOps-kernel"]], "Supporting Sparse Data": [[12, "Supporting-Sparse-Data"]], "Testing sparse support": [[12, "Testing-sparse-support"]], "Examples": [[13, "examples"]], "Hyperparameter Tuning with Falkon": [[14, "Hyperparameter-Tuning-with-Falkon"]], "Introduction": [[14, "Introduction"], [16, "Introduction"], [18, "Introduction"]], "Load the data": [[14, "Load-the-data"], [16, "Load-the-data"], [17, "Load-the-data"], [18, "Load-the-data"]], "Split into training and test sets": [[14, "Split-into-training-and-test-sets"], [17, "Split-into-training-and-test-sets"], [18, "Split-into-training-and-test-sets"]], "Data Preprocessing": [[14, "Data-Preprocessing"], [15, "Data-Preprocessing"], [17, "Data-Preprocessing"], [18, "Data-Preprocessing"]], "Search for the optimal parameters": [[14, "Search-for-the-optimal-parameters"]], "Evaluating the model": [[14, "Evaluating-the-model"]], "Plot grid-search results": [[14, "Plot-grid-search-results"]], "MNIST Classification with Falkon": [[15, "MNIST-Classification-with-Falkon"]], "Download the MNIST dataset & load it in memory": [[15, "Download-the-MNIST-dataset-&-load-it-in-memory"]], "Run Falkon": [[15, "Run-Falkon"]], "Falkon Regression Tutorial": [[16, "Falkon-Regression-Tutorial"]], "Pre-process the data": [[16, "Pre-process-the-data"]], "Create the Falkon model": [[16, "Create-the-Falkon-model"]], "Training the model": [[16, "Training-the-model"]], "Evaluating model performance": [[16, "Evaluating-model-performance"]], "Automatic Hyperparameter Optimization": [[17, "Automatic-Hyperparameter-Optimization"]], "Hyperparameter Optimization": [[17, "Hyperparameter-Optimization"]], "Introducing Logistic Falkon": [[18, "Introducing-Logistic-Falkon"]], "Define the Falkon model": [[18, "Define-the-Falkon-model"]], "Define Logistic Falkon model": [[18, "Define-Logistic-Falkon-model"]], "Train both models": [[18, "Train-both-models"]], "Testing": [[18, "Testing"]], "Plot predictions": [[18, "Plot-predictions"]], "Getting Started": [[19, "getting-started"]], "Passing Options": [[19, "passing-options"]], "More Examples": [[19, "more-examples"]], "Contents": [[20, "contents"]], "References": [[20, "references"]], "Indices and tables": [[20, "indices-and-tables"]], "Install": [[21, "install"]], "Supported Platforms": [[21, "supported-platforms"]], "Prerequisites": [[21, "prerequisites"]], "PyTorch and CUDA": [[21, "pytorch-and-cuda"]], "Intel MKL": [[21, "intel-mkl"]], "Installing": [[21, "installing"]], "Testing the installation": [[21, "testing-the-installation"]], "Development": [[21, "development"]]}, "indexentries": {"centerselector 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[[4, "falkon.kernels.LinearKernel.__call__"]], "__call__() (falkon.kernels.maternkernel method)": [[4, "falkon.kernels.MaternKernel.__call__"]], "__call__() (falkon.kernels.polynomialkernel method)": [[4, "falkon.kernels.PolynomialKernel.__call__"]], "__call__() (falkon.kernels.sigmoidkernel method)": [[4, "falkon.kernels.SigmoidKernel.__call__"]], "__call__() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.__call__"]], "_decide_dmmv_impl() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel._decide_dmmv_impl"]], "_decide_mm_impl() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel._decide_mm_impl"]], "_decide_mmv_impl() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel._decide_mmv_impl"]], "compute() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.compute"]], "compute_diff() (falkon.kernels.diff_kernel.diffkernel method)": [[4, "falkon.kernels.diff_kernel.DiffKernel.compute_diff"]], "compute_sparse() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.compute_sparse"]], "detach() (falkon.kernels.diff_kernel.diffkernel method)": [[4, "falkon.kernels.diff_kernel.DiffKernel.detach"]], "diff_params (falkon.kernels.diff_kernel.diffkernel property)": [[4, "falkon.kernels.diff_kernel.DiffKernel.diff_params"]], "dmmv() (falkon.kernels.gaussiankernel method)": [[4, "falkon.kernels.GaussianKernel.dmmv"]], "dmmv() (falkon.kernels.laplaciankernel method)": [[4, "falkon.kernels.LaplacianKernel.dmmv"]], "dmmv() (falkon.kernels.linearkernel method)": [[4, "falkon.kernels.LinearKernel.dmmv"]], "dmmv() (falkon.kernels.maternkernel method)": [[4, "falkon.kernels.MaternKernel.dmmv"]], "dmmv() (falkon.kernels.polynomialkernel method)": [[4, "falkon.kernels.PolynomialKernel.dmmv"]], "dmmv() (falkon.kernels.sigmoidkernel method)": [[4, "falkon.kernels.SigmoidKernel.dmmv"]], "dmmv() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.dmmv"]], "extra_mem() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.extra_mem"]], "falkon.kernels": [[4, "module-falkon.kernels"]], "keops_mmv() (falkon.kernels.keops_helpers.keopskernelmixin method)": [[4, "falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv"]], "keops_mmv_impl() (falkon.kernels.keops_helpers.keopskernelmixin method)": [[4, "falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl"]], "mmv() (falkon.kernels.gaussiankernel method)": [[4, "falkon.kernels.GaussianKernel.mmv"]], "mmv() (falkon.kernels.laplaciankernel method)": [[4, "falkon.kernels.LaplacianKernel.mmv"]], "mmv() (falkon.kernels.linearkernel method)": [[4, "falkon.kernels.LinearKernel.mmv"]], "mmv() (falkon.kernels.maternkernel method)": [[4, "falkon.kernels.MaternKernel.mmv"]], "mmv() (falkon.kernels.polynomialkernel method)": [[4, "falkon.kernels.PolynomialKernel.mmv"]], "mmv() (falkon.kernels.sigmoidkernel method)": [[4, "falkon.kernels.SigmoidKernel.mmv"]], "mmv() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.mmv"]], "falkon.mmv_ops": [[5, "module-falkon.mmv_ops"]], "fdmmv() (in module falkon.mmv_ops.fmmv)": [[5, "falkon.mmv_ops.fmmv.fdmmv"]], "fmm() (in module falkon.mmv_ops.fmm)": [[5, "falkon.mmv_ops.fmm.fmm"]], "fmmv() (in module falkon.mmv_ops.fmmv)": [[5, "falkon.mmv_ops.fmmv.fmmv"]], "incore_fdmmv() (in module falkon.mmv_ops.fmmv_incore)": [[5, "falkon.mmv_ops.fmmv_incore.incore_fdmmv"]], "incore_fmmv() (in module falkon.mmv_ops.fmmv_incore)": [[5, "falkon.mmv_ops.fmmv_incore.incore_fmmv"]], "run_keops_mmv() (in module falkon.mmv_ops.keops)": [[5, "falkon.mmv_ops.keops.run_keops_mmv"]], "sparse_mm_run_thread() (in module falkon.mmv_ops.fmm)": [[5, "falkon.mmv_ops.fmm.sparse_mm_run_thread"]], "sparse_mmv_run_thread() (in module falkon.mmv_ops.fmmv)": [[5, "falkon.mmv_ops.fmmv.sparse_mmv_run_thread"]], "falkon (class in falkon.models)": [[6, "falkon.models.Falkon"]], "incorefalkon (class in falkon.models)": [[6, "falkon.models.InCoreFalkon"]], "logisticfalkon (class in falkon.models)": [[6, "falkon.models.LogisticFalkon"]], "falkon.models": [[6, "module-falkon.models"]], "fit() (falkon.models.falkon method)": [[6, "falkon.models.Falkon.fit"]], "fit() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.fit"]], "fit() (falkon.models.logisticfalkon method)": [[6, "falkon.models.LogisticFalkon.fit"]], "get_params() (falkon.models.falkon method)": [[6, "falkon.models.Falkon.get_params"]], "get_params() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.get_params"]], "get_params() (falkon.models.logisticfalkon method)": [[6, "falkon.models.LogisticFalkon.get_params"]], "predict() (falkon.models.falkon method)": [[6, "falkon.models.Falkon.predict"]], "predict() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.predict"]], "predict() (falkon.models.logisticfalkon method)": [[6, "falkon.models.LogisticFalkon.predict"]], "set_params() (falkon.models.falkon method)": [[6, "falkon.models.Falkon.set_params"]], "set_params() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.set_params"]], "set_params() (falkon.models.logisticfalkon method)": [[6, "falkon.models.LogisticFalkon.set_params"]], "conjugategradient (class in falkon.optim)": [[7, "falkon.optim.ConjugateGradient"]], "falkonconjugategradient (class in falkon.optim)": [[7, "falkon.optim.FalkonConjugateGradient"]], "optimizer (class in falkon.optim)": [[7, "falkon.optim.Optimizer"]], "falkon.optim": [[7, "module-falkon.optim"]], "solve() (falkon.optim.conjugategradient method)": [[7, "falkon.optim.ConjugateGradient.solve"]], "falkonoptions (class in falkon.options)": [[8, "falkon.options.FalkonOptions"]], "falkon.options": [[8, "module-falkon.options"]], "falkon.ooc_ops": [[9, "module-falkon.ooc_ops"]], "gpu_cholesky() (in module falkon.ooc_ops)": [[9, "falkon.ooc_ops.gpu_cholesky"]], "gpu_lauum() (in module falkon.ooc_ops)": [[9, "falkon.ooc_ops.gpu_lauum"]], "falkonpreconditioner (class in falkon.preconditioner)": [[10, "falkon.preconditioner.FalkonPreconditioner"]], "logisticpreconditioner (class in falkon.preconditioner)": [[10, "falkon.preconditioner.LogisticPreconditioner"]], "preconditioner (class in falkon.preconditioner.preconditioner)": [[10, "falkon.preconditioner.preconditioner.Preconditioner"]], "apply() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.apply"]], "apply() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.apply"]], "apply_t() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.apply_t"]], "apply_t() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.apply_t"]], "falkon.preconditioner": [[10, "module-falkon.preconditioner"]], "init() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.init"]], "init() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.init"]], "inva() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invA"]], "inva() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invA"]], "invat() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invAt"]], "invat() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invAt"]], "invt() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invT"]], "invt() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invT"]], "invtt() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invTt"]], "invtt() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invTt"]], "sparsetensor (class in falkon.sparse.sparse_tensor)": [[11, "falkon.sparse.sparse_tensor.SparseTensor"]], "sparsetype (class in falkon.sparse.sparse_tensor)": [[11, "falkon.sparse.sparse_tensor.SparseType"]], "falkon.sparse": [[11, "module-falkon.sparse"]], "narrow_rows() (falkon.sparse.sparse_tensor.sparsetensor method)": [[11, "falkon.sparse.sparse_tensor.SparseTensor.narrow_rows"]], "sparse_matmul() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_matmul"]], "sparse_norm() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_norm"]], "sparse_square_norm() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_square_norm"]]}})
\ No newline at end of file

From 7e252f13863469a72793da05e3a4e168a0e72422 Mon Sep 17 00:00:00 2001
From: Giodiro <Giodiro@users.noreply.github.com>
Date: Wed, 16 Jul 2025 14:13:49 +0000
Subject: [PATCH 6/7] deploy: 44c3fff34e2f4ed9611b2b9d5b46291a0cb9b1a2

---
 .buildinfo                                    |   2 +-
 _sources/install.rst.txt                      |  26 +-
 _static/basic.css                             |  18 ++
 _static/css/badge_only.css                    |   2 +-
 _static/css/theme.css                         |   2 +-
 _static/documentation_options.js              |   2 +-
 _static/fonts/Lato/lato-bold.eot              | Bin 0 -> 256056 bytes
 _static/fonts/Lato/lato-bold.ttf              | Bin 0 -> 600856 bytes
 _static/fonts/Lato/lato-bold.woff             | Bin 0 -> 309728 bytes
 _static/fonts/Lato/lato-bold.woff2            | Bin 0 -> 184912 bytes
 _static/fonts/Lato/lato-bolditalic.eot        | Bin 0 -> 266158 bytes
 _static/fonts/Lato/lato-bolditalic.ttf        | Bin 0 -> 622572 bytes
 _static/fonts/Lato/lato-bolditalic.woff       | Bin 0 -> 323344 bytes
 _static/fonts/Lato/lato-bolditalic.woff2      | Bin 0 -> 193308 bytes
 _static/fonts/Lato/lato-italic.eot            | Bin 0 -> 268604 bytes
 _static/fonts/Lato/lato-italic.ttf            | Bin 0 -> 639388 bytes
 _static/fonts/Lato/lato-italic.woff           | Bin 0 -> 328412 bytes
 _static/fonts/Lato/lato-italic.woff2          | Bin 0 -> 195704 bytes
 _static/fonts/Lato/lato-regular.eot           | Bin 0 -> 253461 bytes
 _static/fonts/Lato/lato-regular.ttf           | Bin 0 -> 607720 bytes
 _static/fonts/Lato/lato-regular.woff          | Bin 0 -> 309192 bytes
 _static/fonts/Lato/lato-regular.woff2         | Bin 0 -> 182708 bytes
 .../fonts/RobotoSlab/roboto-slab-v7-bold.eot  | Bin 0 -> 79520 bytes
 .../fonts/RobotoSlab/roboto-slab-v7-bold.ttf  | Bin 0 -> 170616 bytes
 .../fonts/RobotoSlab/roboto-slab-v7-bold.woff | Bin 0 -> 87624 bytes
 .../RobotoSlab/roboto-slab-v7-bold.woff2      | Bin 0 -> 67312 bytes
 .../RobotoSlab/roboto-slab-v7-regular.eot     | Bin 0 -> 78331 bytes
 .../RobotoSlab/roboto-slab-v7-regular.ttf     | Bin 0 -> 169064 bytes
 .../RobotoSlab/roboto-slab-v7-regular.woff    | Bin 0 -> 86288 bytes
 .../RobotoSlab/roboto-slab-v7-regular.woff2   | Bin 0 -> 66444 bytes
 _static/js/html5shiv-printshiv.min.js         |   4 -
 _static/js/html5shiv.min.js                   |   4 -
 _static/js/versions.js                        | 228 +++++++++++++++++
 _static/pygments.css                          |  67 ++---
 api_reference/center_selector.html            |  40 ++-
 api_reference/gsc_losses.html                 |  41 ++-
 api_reference/hopt.html                       |  35 ++-
 api_reference/index.html                      |  31 +--
 api_reference/kernels.html                    | 242 ++++++++++++++----
 api_reference/mmv_ops.html                    |  63 +++--
 api_reference/models.html                     | 215 ++++++++++++++--
 api_reference/optimization.html               |  35 ++-
 api_reference/options.html                    |  31 +--
 api_reference/outofcore.html                  |  31 +--
 api_reference/preconditioner.html             |  35 ++-
 api_reference/sparse.html                     |  31 +--
 examples/custom_kernels.html                  | 129 +++++-----
 examples/examples.html                        |  31 +--
 examples/falkon_cv.html                       |  67 ++---
 examples/falkon_mnist.html                    |  47 ++--
 examples/falkon_regression_tutorial.html      |  56 ++--
 examples/hyperopt.html                        |  68 ++---
 examples/hyperopt.ipynb                       |   4 +-
 examples/logistic_falkon.html                 |  62 ++---
 genindex.html                                 |  63 +++--
 get_started.html                              |  39 ++-
 index.html                                    |  31 +--
 install.html                                  |  83 ++----
 objects.inv                                   | Bin 5555 -> 5715 bytes
 py-modindex.html                              |  29 +--
 search.html                                   |  31 +--
 searchindex.js                                |   2 +-
 62 files changed, 1229 insertions(+), 698 deletions(-)
 create mode 100644 _static/fonts/Lato/lato-bold.eot
 create mode 100644 _static/fonts/Lato/lato-bold.ttf
 create mode 100644 _static/fonts/Lato/lato-bold.woff
 create mode 100644 _static/fonts/Lato/lato-bold.woff2
 create mode 100644 _static/fonts/Lato/lato-bolditalic.eot
 create mode 100644 _static/fonts/Lato/lato-bolditalic.ttf
 create mode 100644 _static/fonts/Lato/lato-bolditalic.woff
 create mode 100644 _static/fonts/Lato/lato-bolditalic.woff2
 create mode 100644 _static/fonts/Lato/lato-italic.eot
 create mode 100644 _static/fonts/Lato/lato-italic.ttf
 create mode 100644 _static/fonts/Lato/lato-italic.woff
 create mode 100644 _static/fonts/Lato/lato-italic.woff2
 create mode 100644 _static/fonts/Lato/lato-regular.eot
 create mode 100644 _static/fonts/Lato/lato-regular.ttf
 create mode 100644 _static/fonts/Lato/lato-regular.woff
 create mode 100644 _static/fonts/Lato/lato-regular.woff2
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-bold.eot
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-bold.ttf
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-bold.woff
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-bold.woff2
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-regular.eot
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-regular.woff
 create mode 100644 _static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2
 delete mode 100644 _static/js/html5shiv-printshiv.min.js
 delete mode 100644 _static/js/html5shiv.min.js
 create mode 100644 _static/js/versions.js

diff --git a/.buildinfo b/.buildinfo
index 682a0128..18e6a8e9 100644
--- a/.buildinfo
+++ b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: f53041f638e13eeb8dbee54aa3fe71e7
+config: 178cc21d72334f72906eadb9ca34a7e8
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt
index 31a4631a..d245ee5b 100644
--- a/_sources/install.rst.txt
+++ b/_sources/install.rst.txt
@@ -45,9 +45,9 @@ There are three ways of installing Falkon:
 
     .. code-block:: bash
 
-      $ pip install git+https://github.com/falkonml/falkon.git
+      $ pip install --no-build-isolation git+https://github.com/falkonml/falkon.git
 
-2. **From pypi with JIT compilation** (the C++ extension will be compiled when the library is first used):
+2. **From pypi with JIT compilation** (the C++ extension will be compiled when the library is first used) **BROKEN!!**:
 
     .. code-block:: bash
 
@@ -55,25 +55,25 @@ There are three ways of installing Falkon:
 
 3. **From pre-built wheels** which are available for the following versions of PyTorch and CUDA:
 
-     ============== ========= ========= ========= ========= =========
-      Linux          `cu113`   `cu115`   `cu116`   `cu117`   `cu118`
-     ============== ========= ========= ========= ========= =========
-      torch 1.11.0   ✅         ✅
-      torch 1.13.0                       ✅         ✅
-      torch 2.0.0                                  ✅         ✅
-     ============== ========= ========= ========= ========= =========
+    ============== ========= ========= ========= ========= =========
+     Linux          `cu118`   `cu121`   `cu124`   `cu126`   `cu128`
+    ============== ========= ========= ========= ========= =========
+     torch 2.4.0     ✅        ✅        ✅
+     torch 2.5.0     ✅        ✅        ✅
+     torch 2.6.0     ✅                  ✅        ✅
+     torch 2.7.0     ✅                            ✅        ✅
 
-    As an example, if you **already have installed** PyTorch 1.13 and CUDA 11.7 on your system, you should run
+    As an example, if you **already have installed** PyTorch 2.7 and CUDA 11.8 on your system, you should run
 
     .. code-block:: bash
 
-      $ pip install falkon -f https://falkon.dibris.unige.it/torch-1.13.0_cu117.html
+      $ pip install falkon -f https://falkon.dibris.unige.it/torch-2.7.0_cu118.html
 
-    Similarly for CPU packages
+    Similarly for **CPU-only packages**
 
     .. code-block:: bash
 
-      $ pip install falkon -f https://falkon.dibris.unige.it/torch-2.0.0_cpu.html
+      $ pip install falkon -f https://falkon.dibris.unige.it/torch-2.7.0_cpu.html
 
     please check `here <https://falkon.dibris.unige.it/index.html>`__ for a list of supported wheels.
 
diff --git a/_static/basic.css b/_static/basic.css
index 7577acb1..cfc60b86 100644
--- a/_static/basic.css
+++ b/_static/basic.css
@@ -670,6 +670,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -738,6 +748,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css
index c718cee4..88ba55b9 100644
--- a/_static/css/badge_only.css
+++ b/_static/css/badge_only.css
@@ -1 +1 @@
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diff --git a/_static/css/theme.css b/_static/css/theme.css
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index 2b43bd06..00000000
--- a/_static/js/html5shiv-printshiv.min.js
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diff --git a/_static/js/html5shiv.min.js b/_static/js/html5shiv.min.js
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diff --git a/_static/js/versions.js b/_static/js/versions.js
new file mode 100644
index 00000000..4958195e
--- /dev/null
+++ b/_static/js/versions.js
@@ -0,0 +1,228 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = true;
+const themeLanguageSelector = true;
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    // Insert the current language to the options on the selector
+    let languages = config.projects.translations.concat(config.projects.current);
+    languages = languages.sort((a, b) => a.language.name.localeCompare(b.language.name));
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${languages
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/_static/pygments.css b/_static/pygments.css
index 691aeb82..5f2b0a25 100644
--- a/_static/pygments.css
+++ b/_static/pygments.css
@@ -6,25 +6,26 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
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 .highlight { background: #eeffcc; }
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-.highlight .err { border: 1px solid #FF0000 } /* Error */
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-.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .cs { color: #408090; background-color: #FFF0F0 } /* Comment.Special */
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 .highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #F00 } /* Generic.Error */
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-.highlight .go { color: #333333 } /* Generic.Output */
-.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .go { color: #333 } /* Generic.Output */
+.highlight .gp { color: #C65D09; font-weight: bold } /* Generic.Prompt */
 .highlight .gs { font-weight: bold } /* Generic.Strong */
 .highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .gt { color: #04D } /* Generic.Traceback */
 .highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
 .highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
 .highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
@@ -32,43 +33,43 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
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 .highlight .kt { color: #902000 } /* Keyword.Type */
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-.highlight .s { color: #4070a0 } /* Literal.String */
-.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .s { color: #4070A0 } /* Literal.String */
+.highlight .na { color: #4070A0 } /* Name.Attribute */
 .highlight .nb { color: #007020 } /* Name.Builtin */
-.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
-.highlight .no { color: #60add5 } /* Name.Constant */
-.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
-.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .nc { color: #0E84B5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60ADD5 } /* Name.Constant */
+.highlight .nd { color: #555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #D55537; font-weight: bold } /* Name.Entity */
 .highlight .ne { color: #007020 } /* Name.Exception */
-.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nf { color: #06287E } /* Name.Function */
 .highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
-.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nn { color: #0E84B5; font-weight: bold } /* Name.Namespace */
 .highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .nv { color: #BB60D5 } /* Name.Variable */
 .highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .w { color: #BBB } /* Text.Whitespace */
 .highlight .mb { color: #208050 } /* Literal.Number.Bin */
 .highlight .mf { color: #208050 } /* Literal.Number.Float */
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-.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
-.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sa { color: #4070A0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070A0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070A0 } /* Literal.String.Char */
+.highlight .dl { color: #4070A0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070A0; font-style: italic } /* Literal.String.Doc */
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+.highlight .se { color: #4070A0; font-weight: bold } /* Literal.String.Escape */
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diff --git a/api_reference/center_selector.html b/api_reference/center_selector.html
index c6d89f4e..68e4ee1d 100644
--- a/api_reference/center_selector.html
+++ b/api_reference/center_selector.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.center_selection &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.center_selection &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -35,9 +35,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -364,7 +361,7 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>X_centers</em> – If Y is None this is the only output: M centers selected from the data.</p></li>
-<li><p><em>Y_centers</em> – If Y is not empty, a set of label centers shall be returned as well.</p></li>
+<li><p><em>Y_centers</em> – If Y is not None, a set of label centers will be returned as well.</p></li>
 </ul>
 </p>
 </dd>
@@ -384,10 +381,9 @@ <h2>CenterSelector<a class="headerlink" href="#centerselector" title="Permalink
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
-<li><p><em>X_centers</em> – If Y is None this is the only output: M centers selected from the data.</p></li>
-<li><p><em>Y_centers</em> – If Y is not empty, a set of label centers shall be returned as well.</p></li>
-<li><p><em>indices</em> – The indices in X associated with the chosen centers. Subclasses may not implement
-this method, in which case a <cite>NotImplementedError</cite> will be raised.</p></li>
+<li><p><em>X_centers</em> – M centers selected from the data.</p></li>
+<li><p><em>Y_centers</em> – If Y is not None, a set of label centers will be returned as well.</p></li>
+<li><p><em>indices</em> – The indices in X (and optionally Y) associated with the chosen centers.</p></li>
 </ul>
 </p>
 </dd>
diff --git a/api_reference/gsc_losses.html b/api_reference/gsc_losses.html
index ffccf0f2..68425a3b 100644
--- a/api_reference/gsc_losses.html
+++ b/api_reference/gsc_losses.html
@@ -1,24 +1,24 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.gsc_losses &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.gsc_losses &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -37,9 +37,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -504,7 +501,8 @@ <h2>Logistic loss<a class="headerlink" href="#logistic-loss" title="Permalink to
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.LogisticLoss">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">LogisticLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.LogisticLoss" title="Permalink to this definition"></a></dt>
-<dd><p>Wrapper for the logistic loss, to be used in conjunction with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
+<dd><p>Wrapper for the logistic loss, to be used in conjunction with the
+<a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Usage of this loss assumes a binary classification problem with labels -1 and +1. For different
 choices of labels, see <a class="reference internal" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="falkon.gsc_losses.WeightedCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">WeightedCrossEntropyLoss</span></code></a>.</p>
 <dl class="field-list simple">
@@ -588,7 +586,8 @@ <h2>Weighted binary cross entropy loss<a class="headerlink" href="#weighted-bina
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.gsc_losses.WeightedCrossEntropyLoss">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.gsc_losses.</span></span><span class="sig-name descname"><span class="pre">WeightedCrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">neg_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.gsc_losses.WeightedCrossEntropyLoss" title="Permalink to this definition"></a></dt>
-<dd><p>Wrapper for the weighted binary cross-entropy loss, to be used with the <a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
+<dd><p>Wrapper for the weighted binary cross-entropy loss, to be used with the
+<a class="reference internal" href="models.html#falkon.models.LogisticFalkon" title="falkon.models.LogisticFalkon"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticFalkon</span></code></a> estimator.</p>
 <p>Using this loss assumes a binary classification problem with labels 0 and +1. Additionally,
 this loss allows to place a different weight to samples belonging to one of the two classes
 (see the <cite>neg_weight</cite> parameter).</p>
diff --git a/api_reference/hopt.html b/api_reference/hopt.html
index ff33ec0c..8d9c0563 100644
--- a/api_reference/hopt.html
+++ b/api_reference/hopt.html
@@ -1,24 +1,24 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.hopt &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.hopt &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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-        <script src="../_static/jquery.js"></script>
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-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -36,9 +36,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/api_reference/index.html b/api_reference/index.html
index a91939f4..4ed41948 100644
--- a/api_reference/index.html
+++ b/api_reference/index.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>API Reference &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>API Reference &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
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@@ -35,9 +35,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/api_reference/kernels.html b/api_reference/kernels.html
index 372ef60f..0ae635b1 100644
--- a/api_reference/kernels.html
+++ b/api_reference/kernels.html
@@ -1,24 +1,24 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.kernels &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.kernels &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
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-        <script src="../_static/jquery.js"></script>
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-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -37,9 +37,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -365,7 +362,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -379,6 +376,12 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -463,7 +466,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.compute">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.compute" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> - without regards for differentiability.</p>
 <p>The kernel matrix should be stored in <code class="docutils literal notranslate"><span class="pre">out</span></code> to ensure the correctness of allocatable
 memory computations.</p>
@@ -475,12 +478,17 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 <li><p><strong>out</strong> (<em>torch.Tensor</em>) – The output matrix into which implementing classes should store the kernel.</p></li>
 <li><p><strong>diag</strong> (<em>bool</em>) – If true, <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code> have the same shape, and only the diagonal of <code class="docutils literal notranslate"><span class="pre">k(X1,</span> <span class="pre">X2)</span></code>
 is to be computed and stored in <code class="docutils literal notranslate"><span class="pre">out</span></code>. Otherwise compute the full kernel matrix.</p></li>
+<li><p><strong>kwargs</strong> – Additional keyword arguments which may be used in computing the kernel values</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>Supporting the <cite>diag</cite> argument is <strong>optional</strong>. It’s only used with in the hyper-parameter
+optimization module, so if you’re not using that you don’t need to implement
+this function for <cite>diag=True</cite> (it will always be False).</p>
 </dd></dl>
 
 <dl class="py method">
@@ -500,21 +508,21 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 the keyword arguments passed by the <a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="falkon.mmv_ops.fmmv.sparse_mmv_run_thread"><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.mmv_ops.fmmv.sparse_mmv_run_thread()</span></code></a>
 and <a class="reference internal" href="mmv_ops.html#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="falkon.mmv_ops.fmm.sparse_mm_run_thread"><code class="xref py py-func docutils literal notranslate"><span class="pre">falkon.mmv_ops.fmm.sparse_mm_run_thread()</span></code></a> functions are:</p>
 <ul>
-<li><p>X1_csr : the X1 matrix in CSR format</p></li>
-<li><p>X2_csr : the X2 matrix in CSR format</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">X1_csr</span></code> : the <code class="docutils literal notranslate"><span class="pre">X1</span></code> matrix in CSR format</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">X2_csr</span></code> : the <code class="docutils literal notranslate"><span class="pre">X2</span></code> matrix in CSR format</p></li>
 </ul>
 </p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <cite>out</cite>.</p>
+<dd class="field-even"><p><strong>out</strong> (<em>torch.Tensor</em>) – The kernel matrix. Should use the same underlying storage as the parameter <code class="docutils literal notranslate"><span class="pre">out</span></code>.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.dmmv">
-<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">dmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.dmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute double matrix-vector multiplications where the matrix is the current kernel.</p>
 <p>The general form of <cite>dmmv</cite> operations is: <cite>Kernel(X2, X1) &#64; (Kernel(X1, X2) &#64; v + w)</cite>
 where if <cite>v</cite> is None, then we simply have <cite>Kernel(X2, X1) &#64; w</cite> and if <cite>w</cite> is None
@@ -535,6 +543,12 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -542,7 +556,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
@@ -591,7 +605,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.kernel.Kernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.kernel.Kernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -606,6 +620,12 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -613,7 +633,7 @@ <h2>Kernel<a class="headerlink" href="#kernel" title="Permalink to this heading"
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
@@ -666,7 +686,7 @@ <h2>DiffKernel<a class="headerlink" href="#diffkernel" title="Permalink to this
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.diff_kernel.DiffKernel.compute_diff">
-<span class="sig-name descname"><span class="pre">compute_diff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.compute_diff" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">compute_diff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.diff_kernel.DiffKernel.compute_diff" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix of <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code>. The output should be differentiable with
 respect to <cite>X1</cite>, <cite>X2</cite>, and all kernel parameters returned by the <a class="reference internal" href="#falkon.kernels.diff_kernel.DiffKernel.diff_params" title="falkon.kernels.diff_kernel.DiffKernel.diff_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">diff_params()</span></code></a> method.</p>
 <dl class="field-list simple">
@@ -754,7 +774,7 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">keops_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">keops_mmv_impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl" title="Permalink to this definition"></a></dt>
 <dd><p>Implementation of the KeOps formula to compute a kernel-vector product.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -770,6 +790,12 @@ <h2>KeopsKernelMixin<a class="headerlink" href="#keopskernelmixin" title="Permal
 be created.</p></li>
 <li><p><strong>opt</strong> (<a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a>) – Options to be used for computing the operation. Useful are the memory size options,
 CUDA options and KeOps options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -842,7 +868,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 norm.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.GaussianKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -856,6 +882,12 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -887,6 +919,12 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -894,7 +932,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
@@ -909,7 +947,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.GaussianKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.GaussianKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -924,6 +962,12 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -931,7 +975,7 @@ <h3>Gaussian kernel<a class="headerlink" href="#gaussian-kernel" title="Permalin
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
@@ -965,7 +1009,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 \[k(x, x') = \exp{-\frac{\lVert x - x' \rVert}{\sigma}}\]</div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -979,6 +1023,12 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1010,6 +1060,12 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1017,7 +1073,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
@@ -1032,7 +1088,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LaplacianKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LaplacianKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1047,6 +1103,12 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1054,7 +1116,7 @@ <h3>Laplacian kernel<a class="headerlink" href="#laplacian-kernel" title="Permal
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
@@ -1097,7 +1159,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 may be computationally more efficient.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.MaternKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1111,6 +1173,12 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1142,6 +1210,12 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1149,7 +1223,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
@@ -1164,7 +1238,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.MaternKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.MaternKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1179,6 +1253,12 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1186,7 +1266,7 @@ <h3>Matern kernel<a class="headerlink" href="#matern-kernel" title="Permalink to
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
@@ -1226,7 +1306,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1240,6 +1320,12 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1271,6 +1357,12 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1278,7 +1370,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
@@ -1293,7 +1385,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.PolynomialKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.PolynomialKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1308,6 +1400,12 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1315,7 +1413,7 @@ <h3>Polynomial kernel<a class="headerlink" href="#polynomial-kernel" title="Perm
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
@@ -1358,7 +1456,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.LinearKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1372,6 +1470,12 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1403,6 +1507,12 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1410,7 +1520,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
@@ -1425,7 +1535,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.LinearKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.LinearKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1440,6 +1550,12 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1447,7 +1563,7 @@ <h3>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
@@ -1482,7 +1598,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.__call__" title="Permalink to this definition"></a></dt>
 <dd><p>Compute the kernel matrix between <code class="docutils literal notranslate"><span class="pre">X1</span></code> and <code class="docutils literal notranslate"><span class="pre">X2</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1496,6 +1612,12 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1527,6 +1649,12 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">X2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">X2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1534,7 +1662,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># N is 100, D is 3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># M is 150</span>
@@ -1549,7 +1677,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.kernels.SigmoidKernel.mmv">
-<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">mmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.kernels.SigmoidKernel.mmv" title="Permalink to this definition"></a></dt>
 <dd><p>Compute matrix-vector multiplications where the matrix is the current kernel.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1564,6 +1692,12 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 be created.</p></li>
 <li><p><strong>opt</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><em>FalkonOptions</em></a><em>]</em>) – Options to be used for computing the operation. Useful are the memory size options
 and CUDA options.</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1571,7 +1705,7 @@ <h3>Sigmoid kernel<a class="headerlink" href="#sigmoid-kernel" title="Permalink
 </dd>
 </dl>
 <p class="rubric">Examples</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">falkon</span><span class="o">,</span> <span class="nn">torch</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span><span class="o">,</span><span class="w"> </span><span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">k</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">GaussianKernel</span><span class="p">(</span><span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># You can substitute the Gaussian kernel by any other.</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
diff --git a/api_reference/mmv_ops.html b/api_reference/mmv_ops.html
index b0da287c..edc4236f 100644
--- a/api_reference/mmv_ops.html
+++ b/api_reference/mmv_ops.html
@@ -1,24 +1,24 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.mmv_ops &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.mmv_ops &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
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-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -37,9 +37,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -368,7 +365,7 @@ <h2>fmm<a class="headerlink" href="#fmm" title="Permalink to this heading"></
 <p>Block-wise kernel calculation. If the inputs require gradient, this function uses a differentiable implementation.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmm.fmm">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">fmm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.fmm" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">fmm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmm.fmm" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -377,7 +374,7 @@ <h2>fmmv<a class="headerlink" href="#fmmv" title="Permalink to this heading">
 <p>Block-wise kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fmmv" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 </section>
@@ -386,7 +383,7 @@ <h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this heading">
 <p>Block-wise double kernel-vector products.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.fdmmv">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fdmmv" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">fdmmv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">X2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseOptions</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.mmv_ops.fmmv.fdmmv" title="Permalink to this definition"></a></dt>
 <dd><p>Double kernel-vector product</p>
 <p>Computes kernel <span class="math notranslate nohighlight">\(K = k(X_1, X_2)\)</span> and then the double kernel-vector
 product <span class="math notranslate nohighlight">\(K^{\top} (K v + w)\)</span>.</p>
@@ -403,6 +400,12 @@ <h2>fdmmv<a class="headerlink" href="#fdmmv" title="Permalink to this heading">
 <li><p><strong>differentiable</strong> – Whether the inputs are intended to be differentiated with. Currently setting this
 to <code class="docutils literal notranslate"><span class="pre">True</span></code> results in a <code class="code docutils literal notranslate"><span class="pre">NotImplementedError</span></code>.</p></li>
 <li><p><strong>opt</strong> – Options to be used for this operation</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -435,7 +438,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 They are specialized for different input types.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmm.sparse_mm_run_thread">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">sparse_mm_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comp_dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmm.</span></span><span class="sig-name descname"><span class="pre">sparse_mm_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comp_dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmm.sparse_mm_run_thread" title="Permalink to this definition"></a></dt>
 <dd><p>Inner loop to compute (part of) a kernel matrix for two sparse input tensors</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -451,6 +454,12 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 of <cite>m1</cite> or <cite>m2</cite>).</p></li>
 <li><p><strong>dev</strong> – Device on which to run the calculations</p></li>
 <li><p><strong>tid</strong> – Thread ID. If on the main thread this will be -1</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -461,7 +470,7 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="falkon.mmv_ops.fmmv.sparse_mmv_run_thread">
-<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">sparse_mmv_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mem_needed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">falkon.mmv_ops.fmmv.</span></span><span class="sig-name descname"><span class="pre">sparse_mmv_run_thread</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sparse.html#falkon.sparse.sparse_tensor.SparseTensor" title="falkon.sparse.sparse_tensor.SparseTensor"><span class="pre">SparseTensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blk_m</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mem_needed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dev</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_m2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.mmv_ops.fmmv.sparse_mmv_run_thread" title="Permalink to this definition"></a></dt>
 <dd><p>Inner loop to compute (part of) a kernel-vector product for sparse input matrices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -478,6 +487,12 @@ <h2>Low-level functions<a class="headerlink" href="#low-level-functions" title="
 <li><p><strong>mem_needed</strong> – Memory needed for pre-allocations</p></li>
 <li><p><strong>dev</strong> – Device on which to run the calculations</p></li>
 <li><p><strong>tid</strong> – Thread ID or -1 if on main thread</p></li>
+<li><p><strong>kwargs_m1</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m1</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m1</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
+<li><p><strong>kwargs_m2</strong> – Keyword arguments containing tensors which should be split along with <code class="docutils literal notranslate"><span class="pre">m2</span></code>.
+For example this could be a set of indices corresponding to <code class="docutils literal notranslate"><span class="pre">m2</span></code>, which are then
+correctly split and available in the kernel computation.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
diff --git a/api_reference/models.html b/api_reference/models.html
index a723c07c..cf22bfae 100644
--- a/api_reference/models.html
+++ b/api_reference/models.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.models &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.models &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -35,9 +35,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -343,7 +340,7 @@
 <span id="falkon-model"></span><h2>Falkon<a class="headerlink" href="#falkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.Falkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">Falkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">Falkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon" title="Permalink to this definition"></a></dt>
 <dd><p>Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
 projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
@@ -456,6 +453,20 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.Falkon.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.get_metadata_routing" title="Permalink to this definition"></a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>routing</strong> (<em>MetadataRequest</em>) – A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.Falkon.get_params">
 <span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.get_params" title="Permalink to this definition"></a></dt>
@@ -471,6 +482,14 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.Falkon.init_kernel_matrix">
+<span class="sig-name descname"><span class="pre">init_kernel_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ny_pts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></span><a class="headerlink" href="#falkon.models.Falkon.init_kernel_matrix" title="Permalink to this definition"></a></dt>
+<dd><p>Decide whether to store the full kernel. If dimensions are such that it is convenient
+to precompute it, it is saved in a <code class="xref py py-class docutils literal notranslate"><span class="pre">PrecomputedKernel</span></code> which is used for
+subsequent computations. Otherwise return the original kernel..</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.Falkon.predict">
 <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.Falkon.predict" title="Permalink to this definition"></a></dt>
@@ -486,6 +505,47 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.Falkon.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.models.Falkon" title="falkon.models.falkon.Falkon"><span class="pre">Falkon</span></a></span></span><a class="headerlink" href="#falkon.models.Falkon.set_fit_request" title="Permalink to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Xts</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">Xts</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>Yts</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">Yts</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>warm_start</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> (<em>object</em>) – The updated object.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.Falkon.set_params">
 <span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.Falkon.set_params" title="Permalink to this definition"></a></dt>
@@ -622,6 +682,20 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.LogisticFalkon.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.get_metadata_routing" title="Permalink to this definition"></a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>routing</strong> (<em>MetadataRequest</em>) – A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.LogisticFalkon.get_params">
 <span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.get_params" title="Permalink to this definition"></a></dt>
@@ -652,6 +726,46 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.LogisticFalkon.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.models.LogisticFalkon" title="falkon.models.logistic_falkon.LogisticFalkon"><span class="pre">LogisticFalkon</span></a></span></span><a class="headerlink" href="#falkon.models.LogisticFalkon.set_fit_request" title="Permalink to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Xts</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">Xts</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>Yts</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">Yts</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> (<em>object</em>) – The updated object.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.LogisticFalkon.set_params">
 <span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.LogisticFalkon.set_params" title="Permalink to this definition"></a></dt>
@@ -677,7 +791,7 @@
 <span id="ic-falkon-model"></span><h2>InCoreFalkon<a class="headerlink" href="#incorefalkon" title="Permalink to this heading"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">InCoreFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">falkon.models.</span></span><span class="sig-name descname"><span class="pre">InCoreFalkon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalty</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_selection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="center_selector.html#falkon.center_selection.CenterSelector" title="falkon.center_selection.CenterSelector"><span class="pre">CenterSelector</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxiter</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_every</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_fn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><span class="pre">FalkonOptions</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon" title="Permalink to this definition"></a></dt>
 <dd><p>In GPU core Falkon Kernel Ridge Regression solver.</p>
 <p>This estimator object solves approximate kernel ridge regression problems with Nystroem
 projections and a fast optimization algorithm as described in <a class="reference internal" href="../index.html#flk-1"><span class="std std-ref">[1]</span></a>, <a class="reference internal" href="../index.html#flk-2"><span class="std std-ref">[2]</span></a>.</p>
@@ -797,6 +911,20 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.get_metadata_routing" title="Permalink to this definition"></a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>routing</strong> (<em>MetadataRequest</em>) – A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon.get_params">
 <span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.get_params" title="Permalink to this definition"></a></dt>
@@ -812,6 +940,14 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon.init_kernel_matrix">
+<span class="sig-name descname"><span class="pre">init_kernel_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ny_pts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="kernels.html#falkon.kernels.kernel.Kernel" title="falkon.kernels.kernel.Kernel"><span class="pre">Kernel</span></a></span></span><a class="headerlink" href="#falkon.models.InCoreFalkon.init_kernel_matrix" title="Permalink to this definition"></a></dt>
+<dd><p>Decide whether to store the full kernel. If dimensions are such that it is convenient
+to precompute it, it is saved in a <code class="xref py py-class docutils literal notranslate"><span class="pre">PrecomputedKernel</span></code> which is used for
+subsequent computations. Otherwise return the original kernel..</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon.predict">
 <span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="headerlink" href="#falkon.models.InCoreFalkon.predict" title="Permalink to this definition"></a></dt>
@@ -827,6 +963,47 @@
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="falkon.models.InCoreFalkon.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Yts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#falkon.models.InCoreFalkon" title="falkon.models.incore_falkon.InCoreFalkon"><span class="pre">InCoreFalkon</span></a></span></span><a class="headerlink" href="#falkon.models.InCoreFalkon.set_fit_request" title="Permalink to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Xts</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">Xts</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>Yts</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">Yts</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>warm_start</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> (<em>object</em>) – The updated object.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="falkon.models.InCoreFalkon.set_params">
 <span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#falkon.models.InCoreFalkon.set_params" title="Permalink to this definition"></a></dt>
diff --git a/api_reference/optimization.html b/api_reference/optimization.html
index b20f24fb..7bde9786 100644
--- a/api_reference/optimization.html
+++ b/api_reference/optimization.html
@@ -1,24 +1,24 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.optim &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.optim &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -37,9 +37,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/api_reference/options.html b/api_reference/options.html
index 0d088130..5bd65d95 100644
--- a/api_reference/options.html
+++ b/api_reference/options.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.options &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.options &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -35,9 +35,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/api_reference/outofcore.html b/api_reference/outofcore.html
index 1fa0089b..37497271 100644
--- a/api_reference/outofcore.html
+++ b/api_reference/outofcore.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.ooc_ops &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.ooc_ops &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -35,9 +35,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/api_reference/preconditioner.html b/api_reference/preconditioner.html
index d0482d2b..1e2e7ba3 100644
--- a/api_reference/preconditioner.html
+++ b/api_reference/preconditioner.html
@@ -1,24 +1,24 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.preconditioner &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.preconditioner &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -37,9 +37,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/api_reference/sparse.html b/api_reference/sparse.html
index ac43f214..d3d1a295 100644
--- a/api_reference/sparse.html
+++ b/api_reference/sparse.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>falkon.sparse &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>falkon.sparse &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -35,9 +35,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/examples/custom_kernels.html b/examples/custom_kernels.html
index ed7df3d2..6820f95c 100644
--- a/examples/custom_kernels.html
+++ b/examples/custom_kernels.html
@@ -1,25 +1,25 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Implementing A Custom Kernel &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Implementing A Custom Kernel &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,9 +38,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
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   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
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@@ -354,16 +351,16 @@ <h1>Implementing A Custom Kernel<a class="headerlink" href="#Implementing-A-Cust
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 
-<span class="kn">import</span> <span class="nn">falkon</span>
-<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
-<span class="kn">from</span> <span class="nn">falkon.kernels</span> <span class="kn">import</span> <span class="n">Kernel</span><span class="p">,</span> <span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">falkon</span><span class="w"> </span><span class="kn">import</span> <span class="n">FalkonOptions</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">falkon.kernels</span><span class="w"> </span><span class="kn">import</span> <span class="n">Kernel</span><span class="p">,</span> <span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span>
 </pre></div>
 </div>
 </div>
@@ -411,10 +408,10 @@ <h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simpl
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
 
-<span class="k">def</span> <span class="nf">learn_with_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">):</span>
+<span class="k">def</span><span class="w"> </span><span class="nf">learn_with_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">):</span>
     <span class="n">flk_opt</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">use_cpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">model</span> <span class="o">=</span> <span class="n">falkon</span><span class="o">.</span><span class="n">Falkon</span><span class="p">(</span>
         <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">flk_opt</span><span class="p">,</span>
@@ -428,11 +425,11 @@ <h2>Setup a simple problem for testing<a class="headerlink" href="#Setup-a-simpl
 </section>
 <section id="Basic-Kernel-Implementation">
 <h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Implementation" title="Permalink to this heading"></a></h2>
-<p>We must inherit from the <code class="docutils literal notranslate"><span class="pre">falkon.kernels.Kernel</span></code> class, and implement: - <code class="docutils literal notranslate"><span class="pre">compute</span></code> method: the core of the kernel implementation. Given two input matrices (of size <span class="math notranslate nohighlight">\(n\times d\)</span> and <span class="math notranslate nohighlight">\(m\times d\)</span>), and an output matrix (of size <span class="math notranslate nohighlight">\(n\times m\)</span>), compute the kernel function between the two inputs and store it in the output.</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>The additional `diag` parameter is a boolean flag. It indicates that a) $n$ is equal to $m$, b) only the diagonal of the kernel matrix should be computed.
-</pre></div>
-</div>
-<ul class="simple">
+<p>We must inherit from the <code class="docutils literal notranslate"><span class="pre">falkon.kernels.Kernel</span></code> class, and implement:</p>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">compute</span></code> method: the core of the kernel implementation. Given two input matrices (of size <span class="math notranslate nohighlight">\(n\times d\)</span> and <span class="math notranslate nohighlight">\(m\times d\)</span>), and an output matrix (of size <span class="math notranslate nohighlight">\(n\times m\)</span>), compute the kernel function between the two inputs and store it in the output.</p>
+<p>The additional <code class="docutils literal notranslate"><span class="pre">diag</span></code> parameter is a boolean flag. It indicates that a) <span class="math notranslate nohighlight">\(n\)</span> is equal to <span class="math notranslate nohighlight">\(m\)</span>, b) only the diagonal of the kernel matrix should be computed.</p>
+</li>
 <li><p><code class="docutils literal notranslate"><span class="pre">compute_sparse</span></code> method: this should be used if you want your kernel to support sparse data. We will implement it in a later section.</p></li>
 </ul>
 <p>We will implement a <strong>linear</strong> kernel:</p>
@@ -443,15 +440,15 @@ <h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Impleme
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">BasicLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">BasicLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
         <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
         <span class="c1"># an instance of `FalkonOptions`.</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;basic_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
 
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
         <span class="c1"># To support different devices/data types, you must make sure</span>
         <span class="c1"># the lengthscale is compatible with the data.</span>
         <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
@@ -467,7 +464,7 @@ <h2>Basic Kernel Implementation<a class="headerlink" href="#Basic-Kernel-Impleme
 
         <span class="k">return</span> <span class="n">out</span>
 
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -590,16 +587,23 @@ <h3>Test the basic kernel<a class="headerlink" href="#Test-the-basic-kernel" tit
 <section id="Differentiable-Kernel">
 <h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" title="Permalink to this heading"></a></h2>
 <p>A differentiable kernel is needed for automatic hyperparameter optimization (see the <a class="reference internal" href="hyperopt.html"><span class="doc">notebook</span></a>).</p>
-<p>It requires inheriting from <code class="docutils literal notranslate"><span class="pre">falkon.kernels.DiffKernel</span></code>. In addition to the methods already discussed, we must implement: - <code class="docutils literal notranslate"><span class="pre">compute_diff</span></code>, which works similarly to the <code class="docutils literal notranslate"><span class="pre">compute</span></code> method but it does not have an <code class="docutils literal notranslate"><span class="pre">out</span></code> parameter. The implementation should be fully differentiable with respect to its inputs, and to the kernel hyperparameters. - <code class="docutils literal notranslate"><span class="pre">detach</span></code>, which essentially clones the kernel with the parameters <em>detached</em> from the computational graph.</p>
-<p>Another important difference from the basic kernel is the call to the <em>constructor</em>, which must include - All kernel hyperparameters as keyword arguments. These will be available as attributes on the class. Hyperparameters do not need to be tensors.</p>
+<p>It requires inheriting from <code class="docutils literal notranslate"><span class="pre">falkon.kernels.DiffKernel</span></code>. In addition to the methods already discussed, we must implement:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">compute_diff</span></code>, which works similarly to the <code class="docutils literal notranslate"><span class="pre">compute</span></code> method but it does not have an <code class="docutils literal notranslate"><span class="pre">out</span></code> parameter. The implementation should be fully differentiable with respect to its inputs, and to the kernel hyperparameters.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">detach</span></code>, which essentially clones the kernel with the parameters <em>detached</em> from the computational graph.</p></li>
+</ul>
+<p>Another important difference from the basic kernel is the call to the <em>constructor</em>, which must include</p>
+<ul class="simple">
+<li><p>All kernel hyperparameters as keyword arguments. These will be available as attributes on the class. Hyperparameters do not need to be tensors.</p></li>
+</ul>
 <p><strong>``core_fn`` parameter (optional)</strong></p>
 <p>The constructor can also <em>optionally</em> contain a <code class="docutils literal notranslate"><span class="pre">core_fn</span></code> parameter which can simplify implementation by uniting the <code class="docutils literal notranslate"><span class="pre">compute</span></code> and <code class="docutils literal notranslate"><span class="pre">compute_diff</span></code> implementations. Have a look at the implementation of kernels in <code class="docutils literal notranslate"><span class="pre">falkon.kernels.dot_prod_kernel.py</span></code> and <code class="docutils literal notranslate"><span class="pre">falkon.kernels.distance_kernel.py</span></code> for how to use the <code class="docutils literal notranslate"><span class="pre">core_fn</span></code> parameter.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DiffLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">DiffLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
         <span class="c1"># Super-class constructor call. We do not specify core_fn</span>
         <span class="c1"># but we must specify the hyperparameter of this kernel (lengthscale)</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;diff_linear&quot;</span><span class="p">,</span>
@@ -607,7 +611,7 @@ <h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" tit
                          <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                          <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
         <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
         <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
         <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
@@ -617,7 +621,7 @@ <h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" tit
 
         <span class="k">return</span> <span class="n">out</span>
 
-    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
         <span class="c1"># The implementation here is similar to `compute` without in-place operations.</span>
         <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
         <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
@@ -627,14 +631,14 @@ <h2>Differentiable Kernel<a class="headerlink" href="#Differentiable-Kernel" tit
 
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="c1"># Clones the class with detached hyperparameters</span>
         <span class="k">return</span> <span class="n">DiffLinearKernel</span><span class="p">(</span>
             <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
             <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
         <span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">diag</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -769,21 +773,26 @@ <h3>Test the differentiable kernel<a class="headerlink" href="#Test-the-differen
 <h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title="Permalink to this heading"></a></h2>
 <p>We must inherit from <code class="docutils literal notranslate"><span class="pre">falkon.kernels.KeopsKernelMixin</span></code> and implement the method <code class="docutils literal notranslate"><span class="pre">keops_mmv_impl</span></code>.</p>
 <p>KeOps-enabled kernels will still use the implementation in the <code class="docutils literal notranslate"><span class="pre">compute</span></code> function for computing the kernel matrix itself, but will use KeOps to compute kernel-vector products (if the data dimension is small enough).</p>
-<p>This method is responsible for kernel-vector products, and it should contain: 1. A formula definition (see <a class="reference external" href="https://www.kernel-operations.io/keops/api/math-operations.html">https://www.kernel-operations.io/keops/api/math-operations.html</a> for the appropriate syntax) 2. A definition of all variables (again have a look at the KeOps documentation, or the implementation of other kernels within Falkon) 3. A call to the <code class="docutils literal notranslate"><span class="pre">keops_mmv</span></code> method of the <code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code> class, responsible for calling into the KeOps formula.</p>
+<p>This method is responsible for kernel-vector products, and it should contain:</p>
+<ol class="arabic simple">
+<li><p>A formula definition (see <a class="reference external" href="https://www.kernel-operations.io/keops/api/math-operations.html">https://www.kernel-operations.io/keops/api/math-operations.html</a> for the appropriate syntax)</p></li>
+<li><p>A definition of all variables (again have a look at the KeOps documentation, or the implementation of other kernels within Falkon)</p></li>
+<li><p>A call to the <code class="docutils literal notranslate"><span class="pre">keops_mmv</span></code> method of the <code class="docutils literal notranslate"><span class="pre">KeopsKernelMixin</span></code> class, responsible for calling into the KeOps formula.</p></li>
+</ol>
 <p>For our kernel we will use the <code class="docutils literal notranslate"><span class="pre">(X</span> <span class="pre">|</span> <span class="pre">Y)</span></code> syntax for the dot-product between samples, and then multiplication with the vector <code class="docutils literal notranslate"><span class="pre">v</span></code>. The aliases list maps the symbols used in the formula with the KeOps variable types.</p>
 <p>For more examples check the <a class="reference external" href="https://www.kernel-operations.io">KeOps documentatiaon</a> or the implementation of existing kernels.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">KeopsLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">KeopsLinearKernel</span><span class="p">(</span><span class="n">DiffKernel</span><span class="p">,</span> <span class="n">KeopsKernelMixin</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;my-keops-linear&quot;</span><span class="p">,</span>
                          <span class="n">options</span><span class="p">,</span>
                          <span class="n">core_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                          <span class="n">lengthscale</span><span class="o">=</span><span class="n">lengthscale</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
         <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
         <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
 
@@ -794,7 +803,7 @@ <h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title
 
         <span class="k">return</span> <span class="n">out</span>
 
-    <span class="k">def</span> <span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute_diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
         <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span>
 
         <span class="k">if</span> <span class="n">diag</span><span class="p">:</span>
@@ -802,13 +811,13 @@ <h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title
 
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">scaled_X1</span><span class="p">,</span> <span class="n">X2</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">KeopsLinearKernel</span><span class="p">(</span>
             <span class="n">lengthscale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span>
             <span class="n">options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span>
         <span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">keops_mmv_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">opt</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">keops_mmv_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">opt</span><span class="p">):</span>
         <span class="c1"># Keops formula for kernel-vector.</span>
         <span class="n">formula</span> <span class="o">=</span> <span class="s1">&#39;(scale * (X | Y)) * v&#39;</span>
         <span class="n">aliases</span> <span class="o">=</span> <span class="p">[</span>
@@ -824,7 +833,7 @@ <h2>Adding KeOps Support<a class="headerlink" href="#Adding-KeOps-Support" title
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">keops_mmv</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">formula</span><span class="p">,</span> <span class="n">aliases</span><span class="p">,</span> <span class="n">other_vars</span><span class="p">,</span> <span class="n">opt</span><span class="p">)</span>
 
 
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Sparse not implemented&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -924,8 +933,8 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">SparseTensor</span>
-<span class="kn">from</span> <span class="nn">falkon.sparse</span> <span class="kn">import</span> <span class="n">sparse_matmul</span><span class="p">,</span> <span class="n">bdot</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">falkon.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">SparseTensor</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">falkon.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">sparse_matmul</span><span class="p">,</span> <span class="n">bdot</span>
 </pre></div>
 </div>
 </div>
@@ -933,15 +942,15 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">SparseLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">SparseLinearKernel</span><span class="p">(</span><span class="n">Kernel</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lengthscale</span><span class="p">,</span> <span class="n">options</span><span class="p">):</span>
         <span class="c1"># The base class takes as inputs a name for the kernel, and</span>
         <span class="c1"># an instance of `FalkonOptions`.</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;sparse_linear&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span> <span class="o">=</span> <span class="n">lengthscale</span>
 
-    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">X2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">diag</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
         <span class="n">lengthscale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengthscale</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
 
         <span class="n">scaled_X1</span> <span class="o">=</span> <span class="n">X1</span> <span class="o">*</span> <span class="n">lengthscale</span>
@@ -955,7 +964,7 @@ <h2>Supporting Sparse Data<a class="headerlink" href="#Supporting-Sparse-Data" t
 
         <span class="k">return</span> <span class="n">out</span>
 
-    <span class="k">def</span> <span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">compute_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                        <span class="n">X1</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
                        <span class="n">X2</span><span class="p">:</span> <span class="n">SparseTensor</span><span class="p">,</span>
                        <span class="n">out</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
diff --git a/examples/examples.html b/examples/examples.html
index 17b0c279..593128ad 100644
--- a/examples/examples.html
+++ b/examples/examples.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Examples &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Examples &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -35,9 +35,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/examples/falkon_cv.html b/examples/falkon_cv.html
index ddbd542c..c0e18e79 100644
--- a/examples/falkon_cv.html
+++ b/examples/falkon_cv.html
@@ -1,25 +1,25 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Hyperparameter Tuning with Falkon &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Hyperparameter Tuning with Falkon &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,9 +38,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -351,13 +348,13 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span><span class="p">,</span> <span class="n">metrics</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span><span class="p">,</span> <span class="n">metrics</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -422,7 +419,11 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 </section>
 <section id="Data-Preprocessing">
 <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this heading"></a></h2>
-<p>As always with Falkon we must: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32)</p>
+<p>As always with Falkon we must:</p>
+<ol class="arabic simple">
+<li><p>Convert from numpy arrays to torch tensors</p></li>
+<li><p>Convert data and labels to the same data-type (in this case float32)</p></li>
+</ol>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
@@ -480,7 +481,7 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
     <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
@@ -488,8 +489,12 @@ <h2>Search for the optimal parameters<a class="headerlink" href="#Search-for-the
 </pre></div>
 </div>
 </div>
-<p>The parameter settings which will be explored by the grid-search are: - four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data) - four different regularization values - two different values for M: the number of inducing points. As we will see, a larger <code class="docutils literal notranslate"><span class="pre">M</span></code> is almost always better than a smaller one (but it leads to longer training times). Of course this is not the case if the dataset is easy to overfit, since reducing <code class="docutils literal notranslate"><span class="pre">M</span></code> may
-also provide additional regularization.</p>
+<p>The parameter settings which will be explored by the grid-search are:</p>
+<ul class="simple">
+<li><p>four different kernel length-scales (varying around small positive numbers, which are usually good for normalized data)</p></li>
+<li><p>four different regularization values</p></li>
+<li><p>two different values for M: the number of inducing points. As we will see, a larger <code class="docutils literal notranslate"><span class="pre">M</span></code> is almost always better than a smaller one (but it leads to longer training times). Of course this is not the case if the dataset is easy to overfit, since reducing <code class="docutils literal notranslate"><span class="pre">M</span></code> may also provide additional regularization.</p></li>
+</ul>
 <p>When we create the estimator we pass it additional parameters via the <code class="docutils literal notranslate"><span class="pre">FalkonOptions</span></code> class. In our case we want to ensure that the model runs on the CPU by setting <code class="docutils literal notranslate"><span class="pre">use_cpu=True</span></code>.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -596,7 +601,7 @@ <h2>Plot grid-search results<a class="headerlink" href="#Plot-grid-search-result
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_heatmap</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">plot_heatmap</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">xlabel</span><span class="p">,</span> <span class="n">ylabel</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
     <span class="n">piv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">ylabel</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
     <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;coolwarm_r&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
diff --git a/examples/falkon_mnist.html b/examples/falkon_mnist.html
index 19119540..0ee823d0 100644
--- a/examples/falkon_mnist.html
+++ b/examples/falkon_mnist.html
@@ -1,25 +1,25 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>MNIST Classification with Falkon &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>MNIST Classification with Falkon &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,9 +38,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -347,13 +344,13 @@ <h1>MNIST Classification with Falkon<a class="headerlink" href="#MNIST-Classific
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torchvision</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">torchvision</span>
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -455,7 +452,7 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">classif_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">classif_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
     <span class="n">y_true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">y_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">err</span> <span class="o">=</span> <span class="n">y_true</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="o">!=</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
diff --git a/examples/falkon_regression_tutorial.html b/examples/falkon_regression_tutorial.html
index 8d77dbc4..5dfc0ead 100644
--- a/examples/falkon_regression_tutorial.html
+++ b/examples/falkon_regression_tutorial.html
@@ -1,25 +1,25 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Falkon Regression Tutorial &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Falkon Regression Tutorial &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,9 +38,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -351,13 +348,13 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -434,7 +431,12 @@ <h2>Pre-process the data<a class="headerlink" href="#Pre-process-the-data" title
 <h2>Create the Falkon model<a class="headerlink" href="#Create-the-Falkon-model" title="Permalink to this heading"></a></h2>
 <p>The Falkon object is the main API of this library. It is similar in spirit to the fit-transform API of scikit-learn, while supporting some additional features such as monitoring of validation error.</p>
 <p>While Falkon models have many options, most are related to performance fine-tuning which becomes useful with much larger datasets. Here we only showcase some of the more basic options.</p>
-<p>Mandatory parameters are: - the kernel function (here we use a linear kernel) - the amount of regularization, which we set to some small positive value - the number of inducing points M. We set M to 5000, which is a sizable portion of the dataset.</p>
+<p>Mandatory parameters are:</p>
+<ul class="simple">
+<li><p>the kernel function (here we use a linear kernel)</p></li>
+<li><p>the amount of regularization, which we set to some small positive value</p></li>
+<li><p>the number of inducing points M. We set M to 5000, which is a sizable portion of the dataset.</p></li>
+</ul>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
@@ -491,7 +493,7 @@ <h2>Evaluating model performance<a class="headerlink" href="#Evaluating-model-pe
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtrain</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
 <span class="n">test_pred</span> <span class="o">=</span> <span class="n">flk</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">Xtest</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span>
 
-<span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<span class="k">def</span><span class="w"> </span><span class="nf">rmse</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">pred</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training RMSE: </span><span class="si">%.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">train_pred</span><span class="p">,</span> <span class="n">Ytrain</span><span class="p">)))</span>
diff --git a/examples/hyperopt.html b/examples/hyperopt.html
index 1754577e..c2560648 100644
--- a/examples/hyperopt.html
+++ b/examples/hyperopt.html
@@ -1,25 +1,25 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Automatic Hyperparameter Optimization &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Automatic Hyperparameter Optimization &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,9 +38,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -347,11 +344,11 @@ <h1>Automatic Hyperparameter Optimization<a class="headerlink" href="#Automatic-
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
 <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -379,7 +376,7 @@ <h2>Load the data<a class="headerlink" href="#Load-the-data" title="Permalink to
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">plot_digit</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Label: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">y</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
@@ -415,7 +412,11 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 </section>
 <section id="Data-Preprocessing">
 <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this heading"></a></h2>
-<p>As always with Falkon we must: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32)</p>
+<p>As always with Falkon we must:</p>
+<ol class="arabic simple">
+<li><p>Convert from numpy arrays to torch tensors</p></li>
+<li><p>Convert data and labels to the same data-type (in this case float32)</p></li>
+</ol>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
@@ -468,7 +469,12 @@ <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Pe
 </section>
 <section id="Hyperparameter-Optimization">
 <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optimization" title="Permalink to this heading"></a></h2>
-<p>The Falkon algorithm has three main kinds of hyper-parameters: 1. The kernel parameters. Most commonly when using the Gaussian kernel these are the length-scales for each dimension in the data 2. The amount of regularization <span class="math notranslate nohighlight">\(\lambda\)</span> (the penalty term, which helps prevent overfitting). 3. The Nystrom centers. These are sometimes not considered hyper parameters, and they are chosen uniformly at random from the training set. However, it is possible to find better centers!</p>
+<p>The Falkon algorithm has three main kinds of hyper-parameters:</p>
+<ol class="arabic simple">
+<li><p>The kernel parameters. Most commonly when using the Gaussian kernel these are the length-scales for each dimension in the data</p></li>
+<li><p>The amount of regularization <span class="math notranslate nohighlight">\(\lambda\)</span> (the penalty term, which helps prevent overfitting).</p></li>
+<li><p>The Nystrom centers. These are sometimes not considered hyper parameters, and they are chosen uniformly at random from the training set. However, it is possible to find better centers!</p></li>
+</ol>
 <p>In the <a class="reference internal" href="falkon_cv.html"><span class="doc">grid-search</span></a> notebook all three types of hyperparameters are considered, but only with a small number of options in each category.</p>
 <p>With automatic hyperparameter optimization, which is based on a gradient descent-type procedure, we can instead define a much larger search space for the hyperparameters.</p>
 <p>In particular, we will optimize the kernel length-scale (one for each dimension in the data), the regularization and the Nystrom centers. Optimizing the Nystrom centers is especially useful since it allows to reduce their number, thus speeding up the whole training and inference process!</p>
@@ -476,7 +482,7 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">mclass_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
     <span class="n">true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
@@ -489,9 +495,9 @@ <h2>Hyperparameter Optimization<a class="headerlink" href="#Hyperparameter-Optim
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">falkon.hopt</span>
-<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span>
-<span class="kn">from</span> <span class="nn">falkon.hopt.objectives</span> <span class="kn">import</span> <span class="n">NystromCompReg</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">falkon.hopt</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">falkon</span><span class="w"> </span><span class="kn">import</span> <span class="n">FalkonOptions</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">falkon.hopt.objectives</span><span class="w"> </span><span class="kn">import</span> <span class="n">NystromCompReg</span>
 </pre></div>
 </div>
 </div>
diff --git a/examples/hyperopt.ipynb b/examples/hyperopt.ipynb
index 26837d5a..379f8c68 100644
--- a/examples/hyperopt.ipynb
+++ b/examples/hyperopt.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "702cd386",
+   "id": "2f610d86",
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7364c275",
+   "id": "f0da3f85",
    "metadata": {
     "collapsed": false,
     "pycharm": {
diff --git a/examples/logistic_falkon.html b/examples/logistic_falkon.html
index 57daf7af..8a57191d 100644
--- a/examples/logistic_falkon.html
+++ b/examples/logistic_falkon.html
@@ -1,25 +1,25 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Introducing Logistic Falkon &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Introducing Logistic Falkon &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
   
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="../_static/doctools.js?v=888ff710"></script>
+      <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,9 +38,6 @@
           <a href="../index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -351,12 +348,12 @@ <h2>Introduction<a class="headerlink" href="#Introduction" title="Permalink to t
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">model_selection</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
 
-<span class="kn">import</span> <span class="nn">falkon</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">falkon</span>
 </pre></div>
 </div>
 </div>
@@ -415,12 +412,17 @@ <h2>Split into training and test sets<a class="headerlink" href="#Split-into-tra
 </section>
 <section id="Data-Preprocessing">
 <h2>Data Preprocessing<a class="headerlink" href="#Data-Preprocessing" title="Permalink to this heading"></a></h2>
-<p>The minimal preprocessing steps are: 1. Convert from numpy arrays to torch tensors 2. Convert data and labels to the same data-type (in this case float32) 3. Reshape the labels so that they have two dimensions. This is not strictly necessary, but Falkon internally works with 2D tensors, so the output of the <code class="docutils literal notranslate"><span class="pre">Falkon.predict</span></code> method will always be 2D. 4. Change the labels from 0, 1 to -1, 1. Note that Logistic Falkon uses the following formula for the logistic loss:</p>
+<p>The minimal preprocessing steps are:</p>
+<ol class="arabic">
+<li><p>Convert from numpy arrays to torch tensors</p></li>
+<li><p>Convert data and labels to the same data-type (in this case float32)</p></li>
+<li><p>Reshape the labels so that they have two dimensions. This is not strictly necessary, but Falkon internally works with 2D tensors, so the output of the <code class="docutils literal notranslate"><span class="pre">Falkon.predict</span></code> method will always be 2D.</p></li>
+<li><p>Change the labels from 0, 1 to -1, 1. Note that Logistic Falkon uses the following formula for the logistic loss:</p>
 <div class="math notranslate nohighlight">
 \[\log(1 + e^{-y_1 y_2})\]</div>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>where $y_1$ and $y_2$ are labels and predictions respectively, which only makes sense if the labels are -1, 1.
-</pre></div>
-</div>
+<p>where <span class="math notranslate nohighlight">\(y_1\)</span> and <span class="math notranslate nohighlight">\(y_2\)</span> are labels and predictions respectively, which only makes sense if the labels are -1, 1.</p>
+</li>
+</ol>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
@@ -449,7 +451,7 @@ <h2>Define the Falkon model<a class="headerlink" href="#Define-the-Falkon-model"
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">binary_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">binary_loss</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">true</span> <span class="o">!=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
 </pre></div>
 </div>
@@ -604,7 +606,7 @@ <h1>Plot predictions<a class="headerlink" href="#Plot-predictions" title="Permal
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">plot_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span>
                <span class="n">X_test</span><span class="p">[((</span><span class="n">Y_test</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span> <span class="o">==</span> <span class="n">Y_test</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">],</span>
                <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;correct cls 0&quot;</span><span class="p">)</span>
diff --git a/genindex.html b/genindex.html
index f2f7f833..b998b131 100644
--- a/genindex.html
+++ b/genindex.html
@@ -1,21 +1,21 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Index &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Index &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="_static/doctools.js?v=888ff710"></script>
+      <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" /> 
@@ -32,9 +32,6 @@
           <a href="index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -603,6 +600,16 @@ <h2 id="G">G</h2>
 </li>
       <li><a href="api_reference/hopt.html#falkon.hopt.objectives.GCV">GCV (class in falkon.hopt.objectives)</a>
 </li>
+      <li><a href="api_reference/models.html#falkon.models.Falkon.get_metadata_routing">get_metadata_routing() (falkon.models.Falkon method)</a>
+
+      <ul>
+        <li><a href="api_reference/models.html#falkon.models.InCoreFalkon.get_metadata_routing">(falkon.models.InCoreFalkon method)</a>
+</li>
+        <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.get_metadata_routing">(falkon.models.LogisticFalkon method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_reference/models.html#falkon.models.Falkon.get_params">get_params() (falkon.models.Falkon method)</a>
 
       <ul>
@@ -611,8 +618,6 @@ <h2 id="G">G</h2>
         <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.get_params">(falkon.models.LogisticFalkon method)</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_reference/outofcore.html#falkon.ooc_ops.gpu_cholesky">gpu_cholesky() (in module falkon.ooc_ops)</a>
 </li>
       <li><a href="api_reference/outofcore.html#falkon.ooc_ops.gpu_lauum">gpu_lauum() (in module falkon.ooc_ops)</a>
@@ -647,14 +652,20 @@ <h2 id="I">I</h2>
         <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.init">(falkon.preconditioner.LogisticPreconditioner method)</a>
 </li>
       </ul></li>
-      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.invA">invA() (falkon.preconditioner.FalkonPreconditioner method)</a>
+      <li><a href="api_reference/models.html#falkon.models.Falkon.init_kernel_matrix">init_kernel_matrix() (falkon.models.Falkon method)</a>
 
       <ul>
-        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.invA">(falkon.preconditioner.LogisticPreconditioner method)</a>
+        <li><a href="api_reference/models.html#falkon.models.InCoreFalkon.init_kernel_matrix">(falkon.models.InCoreFalkon method)</a>
 </li>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.invA">invA() (falkon.preconditioner.FalkonPreconditioner method)</a>
+
+      <ul>
+        <li><a href="api_reference/preconditioner.html#falkon.preconditioner.LogisticPreconditioner.invA">(falkon.preconditioner.LogisticPreconditioner method)</a>
+</li>
+      </ul></li>
       <li><a href="api_reference/preconditioner.html#falkon.preconditioner.FalkonPreconditioner.invAt">invAt() (falkon.preconditioner.FalkonPreconditioner method)</a>
 
       <ul>
@@ -834,6 +845,14 @@ <h2 id="S">S</h2>
         <li><a href="api_reference/center_selector.html#falkon.center_selection.FixedSelector.select_indices">(falkon.center_selection.FixedSelector method)</a>
 </li>
         <li><a href="api_reference/center_selector.html#falkon.center_selection.UniformSelector.select_indices">(falkon.center_selection.UniformSelector method)</a>
+</li>
+      </ul></li>
+      <li><a href="api_reference/models.html#falkon.models.Falkon.set_fit_request">set_fit_request() (falkon.models.Falkon method)</a>
+
+      <ul>
+        <li><a href="api_reference/models.html#falkon.models.InCoreFalkon.set_fit_request">(falkon.models.InCoreFalkon method)</a>
+</li>
+        <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.set_fit_request">(falkon.models.LogisticFalkon method)</a>
 </li>
       </ul></li>
       <li><a href="api_reference/models.html#falkon.models.Falkon.set_params">set_params() (falkon.models.Falkon method)</a>
@@ -844,10 +863,10 @@ <h2 id="S">S</h2>
         <li><a href="api_reference/models.html#falkon.models.LogisticFalkon.set_params">(falkon.models.LogisticFalkon method)</a>
 </li>
       </ul></li>
-      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.SGPR">SGPR (class in falkon.hopt.objectives)</a>
-</li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_reference/hopt.html#falkon.hopt.objectives.SGPR">SGPR (class in falkon.hopt.objectives)</a>
+</li>
       <li><a href="api_reference/kernels.html#falkon.kernels.SigmoidKernel">SigmoidKernel (class in falkon.kernels)</a>
 </li>
       <li><a href="api_reference/optimization.html#falkon.optim.ConjugateGradient.solve">solve() (falkon.optim.ConjugateGradient method)</a>
diff --git a/get_started.html b/get_started.html
index ae769a25..86288e43 100644
--- a/get_started.html
+++ b/get_started.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Getting Started &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Getting Started &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="_static/doctools.js?v=888ff710"></script>
+      <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,9 +35,6 @@
           <a href="index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -340,9 +337,9 @@
 <span id="get-started"></span><h1>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading"></a></h1>
 <p>Once Falkon is installed, getting started is easy. The basic setup to use the <cite>Falkon</cite> estimator only requires
 few lines of code:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_boston</span>
-<span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">Falkon</span><span class="p">,</span> <span class="n">kernels</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_boston</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">falkon</span><span class="w"> </span><span class="kn">import</span> <span class="n">Falkon</span><span class="p">,</span> <span class="n">kernels</span>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">load_boston</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
@@ -363,7 +360,7 @@ <h2>Passing Options<a class="headerlink" href="#passing-options" title="Permalin
 <p>A number of different options exist for both the <a class="reference internal" href="api_reference/models.html#falkon-model"><span class="std std-ref">Falkon</span></a> and <a class="reference internal" href="api_reference/models.html#log-falkon-model"><span class="std std-ref">LogisticFalkon</span></a>
 estimators (see <a class="reference internal" href="api_reference/options.html#api-options"><span class="std std-ref">falkon.FalkonOptions</span></a>).
 All options can be passed to the estimator through the <a class="reference internal" href="api_reference/options.html#falkon.options.FalkonOptions" title="falkon.options.FalkonOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">FalkonOptions</span></code></a> class, like so:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">falkon</span> <span class="kn">import</span> <span class="n">FalkonOptions</span><span class="p">,</span> <span class="n">Falkon</span><span class="p">,</span> <span class="n">kernels</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">falkon</span><span class="w"> </span><span class="kn">import</span> <span class="n">FalkonOptions</span><span class="p">,</span> <span class="n">Falkon</span><span class="p">,</span> <span class="n">kernels</span>
 
 <span class="c1"># Options to: increase the amount of output information; avoid using the KeOps library</span>
 <span class="n">options</span> <span class="o">=</span> <span class="n">FalkonOptions</span><span class="p">(</span><span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">keops_active</span><span class="o">=</span><span class="s2">&quot;no&quot;</span><span class="p">)</span>
diff --git a/index.html b/index.html
index 6d3fa228..0d44b1cc 100644
--- a/index.html
+++ b/index.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Falkon &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Falkon &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="_static/doctools.js?v=888ff710"></script>
+      <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -34,9 +34,6 @@
           <a href="#" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
diff --git a/install.html b/install.html
index ca957a74..b2362af4 100644
--- a/install.html
+++ b/install.html
@@ -1,22 +1,22 @@
+
+
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Install &mdash; falkon 0.8.2 documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
+  <title>Install &mdash; falkon 0.9.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+
   
-        <script src="_static/jquery.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=1bc1ca25"></script>
+      <script src="_static/doctools.js?v=888ff710"></script>
+      <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -35,9 +35,6 @@
           <a href="index.html" class="icon icon-home">
             falkon
           </a>
-              <div class="version">
-                0.8.2
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -353,7 +350,7 @@ <h3>PyTorch and CUDA<a class="headerlink" href="#pytorch-and-cuda" title="Permal
 alongside PyTorch.
 PyTorch can be installed in several ways by following the instructions at <a class="reference external" href="https://pytorch.org/get-started/locally/">Install PyTorch</a>.
 If GPU support is desired, make sure that PyTorch CUDA bindings are working:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
 <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
 </pre></div>
 </div>
@@ -371,12 +368,12 @@ <h2>Installing<a class="headerlink" href="#installing" title="Permalink to this
 <ol class="arabic">
 <li><p><strong>From source</strong> by running</p>
 <blockquote>
-<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/falkonml/falkon.git
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--no-build-isolation<span class="w"> </span>git+https://github.com/falkonml/falkon.git
 </pre></div>
 </div>
 </div></blockquote>
 </li>
-<li><p><strong>From pypi with JIT compilation</strong> (the C++ extension will be compiled when the library is first used):</p>
+<li><p><strong>From pypi with JIT compilation</strong> (the C++ extension will be compiled when the library is first used) <strong>BROKEN!!</strong>:</p>
 <blockquote>
 <div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon
 </pre></div>
@@ -386,47 +383,17 @@ <h2>Installing<a class="headerlink" href="#installing" title="Permalink to this
 <li><p><strong>From pre-built wheels</strong> which are available for the following versions of PyTorch and CUDA:</p>
 <blockquote>
 <div><blockquote>
-<div><table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Linux</p></th>
-<th class="head"><p><cite>cu113</cite></p></th>
-<th class="head"><p><cite>cu115</cite></p></th>
-<th class="head"><p><cite>cu116</cite></p></th>
-<th class="head"><p><cite>cu117</cite></p></th>
-<th class="head"><p><cite>cu118</cite></p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>torch 1.11.0</p></td>
-<td><p>✅</p></td>
-<td><p>✅</p></td>
-<td></td>
-<td></td>
-<td></td>
-</tr>
-<tr class="row-odd"><td><p>torch 1.13.0</p></td>
-<td></td>
-<td></td>
-<td><p>✅</p></td>
-<td><p>✅</p></td>
-<td></td>
-</tr>
-<tr class="row-even"><td><p>torch 2.0.0</p></td>
-<td></td>
-<td></td>
-<td></td>
-<td><p>✅</p></td>
-<td><p>✅</p></td>
-</tr>
-</tbody>
-</table>
+<div><p>torch 2.4.0     ✅        ✅        ✅
+torch 2.5.0     ✅        ✅        ✅
+torch 2.6.0     ✅                  ✅        ✅
+torch 2.7.0     ✅                            ✅        ✅</p>
 </div></blockquote>
-<p>As an example, if you <strong>already have installed</strong> PyTorch 1.13 and CUDA 11.7 on your system, you should run</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon<span class="w"> </span>-f<span class="w"> </span>https://falkon.dibris.unige.it/torch-1.13.0_cu117.html
+<p>As an example, if you <strong>already have installed</strong> PyTorch 2.7 and CUDA 11.8 on your system, you should run</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon<span class="w"> </span>-f<span class="w"> </span>https://falkon.dibris.unige.it/torch-2.7.0_cu118.html
 </pre></div>
 </div>
-<p>Similarly for CPU packages</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon<span class="w"> </span>-f<span class="w"> </span>https://falkon.dibris.unige.it/torch-2.0.0_cpu.html
+<p>Similarly for <strong>CPU-only packages</strong></p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon<span class="w"> </span>-f<span class="w"> </span>https://falkon.dibris.unige.it/torch-2.7.0_cpu.html
 </pre></div>
 </div>
 <p>please check <a class="reference external" href="https://falkon.dibris.unige.it/index.html">here</a> for a list of supported wheels.</p>
diff --git a/objects.inv b/objects.inv
index 6614a67ff99df1e7607be53765b1dfd07f35fac4..71026f2725ea135688b3baf9a35eebe4113984c8 100644
GIT binary patch
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\ No newline at end of file
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"sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx": 58}, "alltitles": {"falkon.center_selection": [[0, "module-falkon.center_selection"]], "CenterSelector": [[0, "centerselector"]], "UniformSelector": [[0, "uniformselector"]], "FixedSelector": [[0, "fixedselector"]], "falkon.gsc_losses": [[1, "module-falkon"]], "Loss": [[1, "loss"]], "Logistic loss": [[1, "logistic-loss"]], "Weighted binary cross entropy loss": [[1, "weighted-binary-cross-entropy-loss"]], "falkon.hopt": [[2, "module-falkon.hopt"]], "Objectives": [[2, "objectives"]], "Nystrom Complexity Regularization": [[2, "nystrom-complexity-regularization"]], "Stochastic Nystrom Computational Regularization": [[2, "stochastic-nystrom-computational-regularization"]], "Complexity Regularization": [[2, "complexity-regularization"]], "Generalized Cross Validation": [[2, "generalized-cross-validation"]], "Hold Out Cross Validation": [[2, "hold-out-cross-validation"]], "Leave One Out Cross Validation": [[2, "leave-one-out-cross-validation"]], "SGPR": [[2, "sgpr"]], "API Reference": [[3, "api-reference"]], "falkon.kernels": [[4, "module-falkon.kernels"]], "Kernel": [[4, "kernel"]], "DiffKernel": [[4, "diffkernel"]], "KeopsKernelMixin": [[4, "keopskernelmixin"]], "Radial kernels": [[4, "radial-kernels"]], "Gaussian kernel": [[4, "gaussian-kernel"]], "Laplacian kernel": [[4, "laplacian-kernel"]], "Matern kernel": [[4, "matern-kernel"]], "Dot-Product kernels": [[4, "dot-product-kernels"]], "Polynomial kernel": [[4, "polynomial-kernel"]], "Linear kernel": [[4, "linear-kernel"]], "Sigmoid kernel": [[4, "sigmoid-kernel"]], "falkon.mmv_ops": [[5, "falkon-mmv-ops"]], "run_keops_mmv": [[5, "run-keops-mmv"]], "fmm": [[5, "fmm"]], "fmmv": [[5, "fmmv"]], "fdmmv": [[5, "fdmmv"]], "incore_fmmv": [[5, "incore-fmmv"]], "incore_fdmmv": [[5, "incore-fdmmv"]], "Low-level functions": [[5, "low-level-functions"]], "falkon.models": [[6, "module-falkon.models"]], "Falkon": [[6, "falkon"], [20, "falkon"]], "LogisticFalkon": [[6, "logisticfalkon"]], "InCoreFalkon": [[6, "incorefalkon"]], "falkon.optim": [[7, "module-falkon.optim"]], "Optimizer": [[7, "optimizer"]], "Conjugate gradient methods": [[7, "conjugate-gradient-methods"]], "ConjugateGradient": [[7, "conjugategradient"]], "FalkonConjugateGradient": [[7, "falkonconjugategradient"]], "falkon.options": [[8, "module-falkon.options"]], "FalkonOptions": [[8, "falkonoptions"]], "falkon.ooc_ops": [[9, "falkon-ooc-ops"]], "gpu_cholesky": [[9, "gpu-cholesky"]], "gpu_lauum": [[9, "gpu-lauum"]], "falkon.preconditioner": [[10, "module-falkon.preconditioner"]], "Preconditioner": [[10, "preconditioner"]], "Cholesky preconditioners": [[10, "cholesky-preconditioners"]], "FalkonPreconditioner": [[10, "falkonpreconditioner"]], "LogisticPreconditioner": [[10, "logisticpreconditioner"]], "falkon.sparse": [[11, "module-falkon.sparse"]], "SparseTensor": [[11, "sparsetensor"]], "Sparse operations": [[11, "sparse-operations"]], "Implementing A Custom Kernel": [[12, "Implementing-A-Custom-Kernel"]], "Setup a simple problem for testing": [[12, "Setup-a-simple-problem-for-testing"]], "Basic Kernel Implementation": [[12, "Basic-Kernel-Implementation"]], "Test the basic kernel": [[12, "Test-the-basic-kernel"]], "Differentiable Kernel": [[12, "Differentiable-Kernel"]], "Test the differentiable kernel": [[12, "Test-the-differentiable-kernel"]], "Adding KeOps Support": [[12, "Adding-KeOps-Support"]], "Test the KeOps kernel": [[12, "Test-the-KeOps-kernel"]], "Supporting Sparse Data": [[12, "Supporting-Sparse-Data"]], "Testing sparse support": [[12, "Testing-sparse-support"]], "Examples": [[13, "examples"]], "Hyperparameter Tuning with Falkon": [[14, "Hyperparameter-Tuning-with-Falkon"]], "Introduction": [[14, "Introduction"], [16, "Introduction"], [18, "Introduction"]], "Load the data": [[14, "Load-the-data"], [16, "Load-the-data"], [17, "Load-the-data"], [18, "Load-the-data"]], "Split into training and test sets": [[14, "Split-into-training-and-test-sets"], [17, "Split-into-training-and-test-sets"], [18, "Split-into-training-and-test-sets"]], "Data Preprocessing": [[14, "Data-Preprocessing"], [15, "Data-Preprocessing"], [17, "Data-Preprocessing"], [18, "Data-Preprocessing"]], "Search for the optimal parameters": [[14, "Search-for-the-optimal-parameters"]], "Evaluating the model": [[14, "Evaluating-the-model"]], "Plot grid-search results": [[14, "Plot-grid-search-results"]], "MNIST Classification with Falkon": [[15, "MNIST-Classification-with-Falkon"]], "Download the MNIST dataset & load it in memory": [[15, "Download-the-MNIST-dataset-&-load-it-in-memory"]], "Run Falkon": [[15, "Run-Falkon"]], "Falkon Regression Tutorial": [[16, "Falkon-Regression-Tutorial"]], "Pre-process the data": [[16, "Pre-process-the-data"]], "Create the Falkon model": [[16, "Create-the-Falkon-model"]], "Training the model": [[16, "Training-the-model"]], "Evaluating model performance": [[16, "Evaluating-model-performance"]], "Automatic Hyperparameter Optimization": [[17, "Automatic-Hyperparameter-Optimization"]], "Hyperparameter Optimization": [[17, "Hyperparameter-Optimization"]], "Introducing Logistic Falkon": [[18, "Introducing-Logistic-Falkon"]], "Define the Falkon model": [[18, "Define-the-Falkon-model"]], "Define Logistic Falkon model": [[18, "Define-Logistic-Falkon-model"]], "Train both models": [[18, "Train-both-models"]], "Testing": [[18, "Testing"]], "Plot predictions": [[18, "Plot-predictions"]], "Getting Started": [[19, "getting-started"]], "Passing Options": [[19, "passing-options"]], "More Examples": [[19, "more-examples"]], "Contents": [[20, "contents"]], "References": [[20, "references"]], "Indices and tables": [[20, "indices-and-tables"]], "Install": [[21, "install"]], "Supported Platforms": [[21, "supported-platforms"]], "Prerequisites": [[21, "prerequisites"]], "PyTorch and CUDA": [[21, "pytorch-and-cuda"]], "Intel MKL": [[21, "intel-mkl"]], "Installing": [[21, "installing"]], "Testing the installation": [[21, "testing-the-installation"]], "Development": [[21, "development"]]}, "indexentries": {"centerselector (class in falkon.center_selection)": [[0, "falkon.center_selection.CenterSelector"]], "fixedselector (class in falkon.center_selection)": [[0, "falkon.center_selection.FixedSelector"]], "uniformselector (class in falkon.center_selection)": [[0, "falkon.center_selection.UniformSelector"]], "falkon.center_selection": [[0, "module-falkon.center_selection"]], "module": [[0, "module-falkon.center_selection"], [1, "module-falkon"], [2, "module-falkon.hopt"], [4, "module-falkon.kernels"], [5, "module-falkon.mmv_ops"], [6, "module-falkon.models"], [7, "module-falkon.optim"], [8, "module-falkon.options"], [9, "module-falkon.ooc_ops"], [10, "module-falkon.preconditioner"], [11, "module-falkon.sparse"]], "select() (falkon.center_selection.centerselector method)": [[0, "falkon.center_selection.CenterSelector.select"]], "select() 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\ No newline at end of file

From da9b107e25efbbd60a3a201cadda3f134d84516b Mon Sep 17 00:00:00 2001
From: Giodiro <Giodiro@users.noreply.github.com>
Date: Wed, 16 Jul 2025 15:04:02 +0000
Subject: [PATCH 7/7] deploy: d232e7cf2ce55f1eb239aab1d2e92c507142a70b

---
 _sources/install.rst.txt |  1 +
 examples/hyperopt.ipynb  |  4 ++--
 install.html             | 47 +++++++++++++++++++++++++++++++++++-----
 searchindex.js           |  2 +-
 4 files changed, 45 insertions(+), 9 deletions(-)

diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt
index d245ee5b..aaa57354 100644
--- a/_sources/install.rst.txt
+++ b/_sources/install.rst.txt
@@ -62,6 +62,7 @@ There are three ways of installing Falkon:
      torch 2.5.0     ✅        ✅        ✅
      torch 2.6.0     ✅                  ✅        ✅
      torch 2.7.0     ✅                            ✅        ✅
+    ============== ========= ========= ========= ========= =========
 
     As an example, if you **already have installed** PyTorch 2.7 and CUDA 11.8 on your system, you should run
 
diff --git a/examples/hyperopt.ipynb b/examples/hyperopt.ipynb
index 379f8c68..639eaae3 100644
--- a/examples/hyperopt.ipynb
+++ b/examples/hyperopt.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2f610d86",
+   "id": "d237711a",
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -16,7 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "f0da3f85",
+   "id": "73b68133",
    "metadata": {
     "collapsed": false,
     "pycharm": {
diff --git a/install.html b/install.html
index b2362af4..6786253b 100644
--- a/install.html
+++ b/install.html
@@ -382,12 +382,47 @@ <h2>Installing<a class="headerlink" href="#installing" title="Permalink to this
 </li>
 <li><p><strong>From pre-built wheels</strong> which are available for the following versions of PyTorch and CUDA:</p>
 <blockquote>
-<div><blockquote>
-<div><p>torch 2.4.0     ✅        ✅        ✅
-torch 2.5.0     ✅        ✅        ✅
-torch 2.6.0     ✅                  ✅        ✅
-torch 2.7.0     ✅                            ✅        ✅</p>
-</div></blockquote>
+<div><table class="docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Linux</p></th>
+<th class="head"><p><cite>cu118</cite></p></th>
+<th class="head"><p><cite>cu121</cite></p></th>
+<th class="head"><p><cite>cu124</cite></p></th>
+<th class="head"><p><cite>cu126</cite></p></th>
+<th class="head"><p><cite>cu128</cite></p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>torch 2.4.0</p></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p>torch 2.5.0</p></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><p>torch 2.6.0</p></td>
+<td><p>✅</p></td>
+<td></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><p>torch 2.7.0</p></td>
+<td><p>✅</p></td>
+<td></td>
+<td></td>
+<td><p>✅</p></td>
+<td><p>✅</p></td>
+</tr>
+</tbody>
+</table>
 <p>As an example, if you <strong>already have installed</strong> PyTorch 2.7 and CUDA 11.8 on your system, you should run</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>falkon<span class="w"> </span>-f<span class="w"> </span>https://falkon.dibris.unige.it/torch-2.7.0_cu118.html
 </pre></div>
diff --git a/searchindex.js b/searchindex.js
index 175e6fad..401a7dcc 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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[[14, "Split-into-training-and-test-sets"], [17, "Split-into-training-and-test-sets"], [18, "Split-into-training-and-test-sets"]], "Data Preprocessing": [[14, "Data-Preprocessing"], [15, "Data-Preprocessing"], [17, "Data-Preprocessing"], [18, "Data-Preprocessing"]], "Search for the optimal parameters": [[14, "Search-for-the-optimal-parameters"]], "Evaluating the model": [[14, "Evaluating-the-model"]], "Plot grid-search results": [[14, "Plot-grid-search-results"]], "MNIST Classification with Falkon": [[15, "MNIST-Classification-with-Falkon"]], "Download the MNIST dataset & load it in memory": [[15, "Download-the-MNIST-dataset-&-load-it-in-memory"]], "Run Falkon": [[15, "Run-Falkon"]], "Falkon Regression Tutorial": [[16, "Falkon-Regression-Tutorial"]], "Pre-process the data": [[16, "Pre-process-the-data"]], "Create the Falkon model": [[16, "Create-the-Falkon-model"]], "Training the model": [[16, "Training-the-model"]], "Evaluating model performance": [[16, "Evaluating-model-performance"]], "Automatic Hyperparameter Optimization": [[17, "Automatic-Hyperparameter-Optimization"]], "Hyperparameter Optimization": [[17, "Hyperparameter-Optimization"]], "Introducing Logistic Falkon": [[18, "Introducing-Logistic-Falkon"]], "Define the Falkon model": [[18, "Define-the-Falkon-model"]], "Define Logistic Falkon model": [[18, "Define-Logistic-Falkon-model"]], "Train both models": [[18, "Train-both-models"]], "Testing": [[18, "Testing"]], "Plot predictions": [[18, "Plot-predictions"]], "Getting Started": [[19, "getting-started"]], "Passing Options": [[19, "passing-options"]], "More Examples": [[19, "more-examples"]], "Contents": [[20, "contents"]], "References": [[20, "references"]], "Indices and tables": [[20, "indices-and-tables"]], "Install": [[21, "install"]], "Supported Platforms": [[21, "supported-platforms"]], "Prerequisites": [[21, "prerequisites"]], "PyTorch and CUDA": [[21, "pytorch-and-cuda"]], "Intel MKL": [[21, "intel-mkl"]], "Installing": [[21, "installing"]], "Testing the installation": [[21, "testing-the-installation"]], "Development": [[21, "development"]]}, "indexentries": {"centerselector (class in falkon.center_selection)": [[0, "falkon.center_selection.CenterSelector"]], "fixedselector (class in falkon.center_selection)": [[0, "falkon.center_selection.FixedSelector"]], "uniformselector (class in falkon.center_selection)": [[0, "falkon.center_selection.UniformSelector"]], "falkon.center_selection": [[0, "module-falkon.center_selection"]], "module": [[0, "module-falkon.center_selection"], [1, "module-falkon"], [2, "module-falkon.hopt"], [4, "module-falkon.kernels"], [5, "module-falkon.mmv_ops"], [6, "module-falkon.models"], [7, "module-falkon.optim"], [8, "module-falkon.options"], [9, "module-falkon.ooc_ops"], [10, "module-falkon.preconditioner"], [11, "module-falkon.sparse"]], "select() (falkon.center_selection.centerselector method)": [[0, "falkon.center_selection.CenterSelector.select"]], "select() (falkon.center_selection.fixedselector method)": [[0, "falkon.center_selection.FixedSelector.select"]], "select() (falkon.center_selection.uniformselector method)": [[0, "falkon.center_selection.UniformSelector.select"]], "select_indices() (falkon.center_selection.centerselector method)": [[0, "falkon.center_selection.CenterSelector.select_indices"]], "select_indices() (falkon.center_selection.fixedselector method)": [[0, "falkon.center_selection.FixedSelector.select_indices"]], "select_indices() (falkon.center_selection.uniformselector method)": [[0, "falkon.center_selection.UniformSelector.select_indices"]], "logisticloss (class in falkon.gsc_losses)": [[1, "falkon.gsc_losses.LogisticLoss"]], "loss (class in falkon.gsc_losses)": [[1, "falkon.gsc_losses.Loss"]], "weightedcrossentropyloss (class in falkon.gsc_losses)": [[1, "falkon.gsc_losses.WeightedCrossEntropyLoss"]], "__call__() (falkon.gsc_losses.logisticloss method)": [[1, "falkon.gsc_losses.LogisticLoss.__call__"]], "__call__() (falkon.gsc_losses.loss method)": [[1, "falkon.gsc_losses.Loss.__call__"]], "__call__() (falkon.gsc_losses.weightedcrossentropyloss method)": [[1, "falkon.gsc_losses.WeightedCrossEntropyLoss.__call__"]], "ddf() (falkon.gsc_losses.logisticloss method)": [[1, "falkon.gsc_losses.LogisticLoss.ddf"]], "ddf() (falkon.gsc_losses.loss method)": [[1, "falkon.gsc_losses.Loss.ddf"]], "ddf() (falkon.gsc_losses.weightedcrossentropyloss method)": [[1, "falkon.gsc_losses.WeightedCrossEntropyLoss.ddf"]], "df() (falkon.gsc_losses.logisticloss method)": [[1, "falkon.gsc_losses.LogisticLoss.df"]], "df() (falkon.gsc_losses.loss method)": [[1, "falkon.gsc_losses.Loss.df"]], "df() (falkon.gsc_losses.weightedcrossentropyloss method)": [[1, "falkon.gsc_losses.WeightedCrossEntropyLoss.df"]], "falkon": [[1, "module-falkon"]], "knmp_grad() (falkon.gsc_losses.loss method)": [[1, "falkon.gsc_losses.Loss.knmp_grad"]], "knmp_hess() (falkon.gsc_losses.loss method)": [[1, "falkon.gsc_losses.Loss.knmp_hess"]], "compreg (class in falkon.hopt.objectives)": [[2, "falkon.hopt.objectives.CompReg"]], "gcv (class in falkon.hopt.objectives)": [[2, "falkon.hopt.objectives.GCV"]], "holdout (class in falkon.hopt.objectives)": [[2, "falkon.hopt.objectives.HoldOut"]], "hyperoptobjective (class in falkon.hopt.objectives.objectives)": [[2, "falkon.hopt.objectives.objectives.HyperoptObjective"]], "loocv (class in falkon.hopt.objectives)": [[2, "falkon.hopt.objectives.LOOCV"]], "nystromcompreg (class in falkon.hopt.objectives)": [[2, "falkon.hopt.objectives.NystromCompReg"]], "sgpr (class in falkon.hopt.objectives)": [[2, "falkon.hopt.objectives.SGPR"]], "stochasticnystromcompreg (class in falkon.hopt.objectives)": [[2, "falkon.hopt.objectives.StochasticNystromCompReg"]], "falkon.hopt": [[2, "module-falkon.hopt"]], "diffkernel (class in falkon.kernels.diff_kernel)": [[4, "falkon.kernels.diff_kernel.DiffKernel"]], "gaussiankernel (class in falkon.kernels)": [[4, "falkon.kernels.GaussianKernel"]], "keopskernelmixin (class in falkon.kernels.keops_helpers)": [[4, "falkon.kernels.keops_helpers.KeopsKernelMixin"]], "kernel (class in falkon.kernels.kernel)": [[4, "falkon.kernels.kernel.Kernel"]], "laplaciankernel (class in falkon.kernels)": [[4, "falkon.kernels.LaplacianKernel"]], "linearkernel (class in falkon.kernels)": [[4, "falkon.kernels.LinearKernel"]], "maternkernel (class in falkon.kernels)": [[4, "falkon.kernels.MaternKernel"]], "polynomialkernel (class in falkon.kernels)": [[4, "falkon.kernels.PolynomialKernel"]], "sigmoidkernel (class in falkon.kernels)": [[4, "falkon.kernels.SigmoidKernel"]], "__call__() (falkon.kernels.gaussiankernel method)": [[4, "falkon.kernels.GaussianKernel.__call__"]], "__call__() (falkon.kernels.laplaciankernel method)": [[4, "falkon.kernels.LaplacianKernel.__call__"]], "__call__() (falkon.kernels.linearkernel method)": [[4, "falkon.kernels.LinearKernel.__call__"]], "__call__() (falkon.kernels.maternkernel method)": [[4, "falkon.kernels.MaternKernel.__call__"]], "__call__() (falkon.kernels.polynomialkernel method)": [[4, "falkon.kernels.PolynomialKernel.__call__"]], "__call__() (falkon.kernels.sigmoidkernel method)": [[4, "falkon.kernels.SigmoidKernel.__call__"]], "__call__() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.__call__"]], "_decide_dmmv_impl() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel._decide_dmmv_impl"]], "_decide_mm_impl() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel._decide_mm_impl"]], "_decide_mmv_impl() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel._decide_mmv_impl"]], "compute() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.compute"]], "compute_diff() (falkon.kernels.diff_kernel.diffkernel method)": [[4, "falkon.kernels.diff_kernel.DiffKernel.compute_diff"]], "compute_sparse() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.compute_sparse"]], "detach() (falkon.kernels.diff_kernel.diffkernel method)": [[4, "falkon.kernels.diff_kernel.DiffKernel.detach"]], "diff_params (falkon.kernels.diff_kernel.diffkernel property)": [[4, "falkon.kernels.diff_kernel.DiffKernel.diff_params"]], "dmmv() (falkon.kernels.gaussiankernel method)": [[4, "falkon.kernels.GaussianKernel.dmmv"]], "dmmv() (falkon.kernels.laplaciankernel method)": [[4, "falkon.kernels.LaplacianKernel.dmmv"]], "dmmv() (falkon.kernels.linearkernel method)": [[4, "falkon.kernels.LinearKernel.dmmv"]], "dmmv() (falkon.kernels.maternkernel method)": [[4, "falkon.kernels.MaternKernel.dmmv"]], "dmmv() (falkon.kernels.polynomialkernel method)": [[4, "falkon.kernels.PolynomialKernel.dmmv"]], "dmmv() (falkon.kernels.sigmoidkernel method)": [[4, "falkon.kernels.SigmoidKernel.dmmv"]], "dmmv() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.dmmv"]], "extra_mem() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.extra_mem"]], "falkon.kernels": [[4, "module-falkon.kernels"]], "keops_mmv() (falkon.kernels.keops_helpers.keopskernelmixin method)": [[4, "falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv"]], "keops_mmv_impl() (falkon.kernels.keops_helpers.keopskernelmixin method)": [[4, "falkon.kernels.keops_helpers.KeopsKernelMixin.keops_mmv_impl"]], "mmv() (falkon.kernels.gaussiankernel method)": [[4, "falkon.kernels.GaussianKernel.mmv"]], "mmv() (falkon.kernels.laplaciankernel method)": [[4, "falkon.kernels.LaplacianKernel.mmv"]], "mmv() (falkon.kernels.linearkernel method)": [[4, "falkon.kernels.LinearKernel.mmv"]], "mmv() (falkon.kernels.maternkernel method)": [[4, "falkon.kernels.MaternKernel.mmv"]], "mmv() (falkon.kernels.polynomialkernel method)": [[4, "falkon.kernels.PolynomialKernel.mmv"]], "mmv() (falkon.kernels.sigmoidkernel method)": [[4, "falkon.kernels.SigmoidKernel.mmv"]], "mmv() (falkon.kernels.kernel.kernel method)": [[4, "falkon.kernels.kernel.Kernel.mmv"]], "falkon.mmv_ops": [[5, "module-falkon.mmv_ops"]], "fdmmv() (in module falkon.mmv_ops.fmmv)": [[5, "falkon.mmv_ops.fmmv.fdmmv"]], "fmm() (in module falkon.mmv_ops.fmm)": [[5, "falkon.mmv_ops.fmm.fmm"]], "fmmv() (in module falkon.mmv_ops.fmmv)": [[5, "falkon.mmv_ops.fmmv.fmmv"]], "incore_fdmmv() (in module falkon.mmv_ops.fmmv_incore)": [[5, "falkon.mmv_ops.fmmv_incore.incore_fdmmv"]], "incore_fmmv() (in module falkon.mmv_ops.fmmv_incore)": [[5, "falkon.mmv_ops.fmmv_incore.incore_fmmv"]], "run_keops_mmv() (in module falkon.mmv_ops.keops)": [[5, "falkon.mmv_ops.keops.run_keops_mmv"]], "sparse_mm_run_thread() (in module falkon.mmv_ops.fmm)": [[5, "falkon.mmv_ops.fmm.sparse_mm_run_thread"]], "sparse_mmv_run_thread() (in module falkon.mmv_ops.fmmv)": [[5, "falkon.mmv_ops.fmmv.sparse_mmv_run_thread"]], "falkon (class in falkon.models)": [[6, "falkon.models.Falkon"]], "incorefalkon (class in falkon.models)": [[6, "falkon.models.InCoreFalkon"]], "logisticfalkon (class in falkon.models)": [[6, 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(falkon.models.falkon method)": [[6, "falkon.models.Falkon.init_kernel_matrix"]], "init_kernel_matrix() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.init_kernel_matrix"]], "predict() (falkon.models.falkon method)": [[6, "falkon.models.Falkon.predict"]], "predict() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.predict"]], "predict() (falkon.models.logisticfalkon method)": [[6, "falkon.models.LogisticFalkon.predict"]], "set_fit_request() (falkon.models.falkon method)": [[6, "falkon.models.Falkon.set_fit_request"]], "set_fit_request() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.set_fit_request"]], "set_fit_request() (falkon.models.logisticfalkon method)": [[6, "falkon.models.LogisticFalkon.set_fit_request"]], "set_params() (falkon.models.falkon method)": [[6, "falkon.models.Falkon.set_params"]], "set_params() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.set_params"]], "set_params() 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\ No newline at end of file
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"SparseTensor": [[11, "sparsetensor"]], "Sparse operations": [[11, "sparse-operations"]], "Implementing A Custom Kernel": [[12, "Implementing-A-Custom-Kernel"]], "Setup a simple problem for testing": [[12, "Setup-a-simple-problem-for-testing"]], "Basic Kernel Implementation": [[12, "Basic-Kernel-Implementation"]], "Test the basic kernel": [[12, "Test-the-basic-kernel"]], "Differentiable Kernel": [[12, "Differentiable-Kernel"]], "Test the differentiable kernel": [[12, "Test-the-differentiable-kernel"]], "Adding KeOps Support": [[12, "Adding-KeOps-Support"]], "Test the KeOps kernel": [[12, "Test-the-KeOps-kernel"]], "Supporting Sparse Data": [[12, "Supporting-Sparse-Data"]], "Testing sparse support": [[12, "Testing-sparse-support"]], "Examples": [[13, "examples"]], "Hyperparameter Tuning with Falkon": [[14, "Hyperparameter-Tuning-with-Falkon"]], "Introduction": [[14, "Introduction"], [16, "Introduction"], [18, "Introduction"]], "Load the data": [[14, "Load-the-data"], [16, "Load-the-data"], [17, "Load-the-data"], [18, "Load-the-data"]], "Split into training and test sets": [[14, "Split-into-training-and-test-sets"], [17, "Split-into-training-and-test-sets"], [18, "Split-into-training-and-test-sets"]], "Data Preprocessing": [[14, "Data-Preprocessing"], [15, "Data-Preprocessing"], [17, "Data-Preprocessing"], [18, "Data-Preprocessing"]], "Search for the optimal parameters": [[14, "Search-for-the-optimal-parameters"]], "Evaluating the model": [[14, "Evaluating-the-model"]], "Plot grid-search results": [[14, "Plot-grid-search-results"]], "MNIST Classification with Falkon": [[15, "MNIST-Classification-with-Falkon"]], "Download the MNIST dataset & load it in memory": [[15, "Download-the-MNIST-dataset-&-load-it-in-memory"]], "Run Falkon": [[15, "Run-Falkon"]], "Falkon Regression Tutorial": [[16, "Falkon-Regression-Tutorial"]], "Pre-process the data": [[16, "Pre-process-the-data"]], "Create the Falkon model": [[16, "Create-the-Falkon-model"]], "Training the model": [[16, "Training-the-model"]], "Evaluating model performance": [[16, "Evaluating-model-performance"]], "Automatic Hyperparameter Optimization": [[17, "Automatic-Hyperparameter-Optimization"]], "Hyperparameter Optimization": [[17, "Hyperparameter-Optimization"]], "Introducing Logistic Falkon": [[18, "Introducing-Logistic-Falkon"]], "Define the Falkon model": [[18, "Define-the-Falkon-model"]], "Define Logistic Falkon model": [[18, "Define-Logistic-Falkon-model"]], "Train both models": [[18, "Train-both-models"]], "Testing": [[18, "Testing"]], "Plot predictions": [[18, "Plot-predictions"]], "Getting Started": [[19, "getting-started"]], "Passing Options": [[19, "passing-options"]], "More Examples": [[19, "more-examples"]], "Contents": [[20, "contents"]], "References": [[20, "references"]], "Indices and tables": [[20, "indices-and-tables"]], "Install": [[21, "install"]], "Supported Platforms": [[21, "supported-platforms"]], "Prerequisites": [[21, "prerequisites"]], "PyTorch and 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(falkon.models.falkon method)": [[6, "falkon.models.Falkon.set_params"]], "set_params() (falkon.models.incorefalkon method)": [[6, "falkon.models.InCoreFalkon.set_params"]], "set_params() (falkon.models.logisticfalkon method)": [[6, "falkon.models.LogisticFalkon.set_params"]], "conjugategradient (class in falkon.optim)": [[7, "falkon.optim.ConjugateGradient"]], "falkonconjugategradient (class in falkon.optim)": [[7, "falkon.optim.FalkonConjugateGradient"]], "optimizer (class in falkon.optim)": [[7, "falkon.optim.Optimizer"]], "falkon.optim": [[7, "module-falkon.optim"]], "solve() (falkon.optim.conjugategradient method)": [[7, "falkon.optim.ConjugateGradient.solve"]], "falkonoptions (class in falkon.options)": [[8, "falkon.options.FalkonOptions"]], "falkon.options": [[8, "module-falkon.options"]], "falkon.ooc_ops": [[9, "module-falkon.ooc_ops"]], "gpu_cholesky() (in module falkon.ooc_ops)": [[9, "falkon.ooc_ops.gpu_cholesky"]], "gpu_lauum() (in module falkon.ooc_ops)": [[9, "falkon.ooc_ops.gpu_lauum"]], "falkonpreconditioner (class in falkon.preconditioner)": [[10, "falkon.preconditioner.FalkonPreconditioner"]], "logisticpreconditioner (class in falkon.preconditioner)": [[10, "falkon.preconditioner.LogisticPreconditioner"]], "preconditioner (class in falkon.preconditioner.preconditioner)": [[10, "falkon.preconditioner.preconditioner.Preconditioner"]], "apply() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.apply"]], "apply() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.apply"]], "apply_t() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.apply_t"]], "apply_t() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.apply_t"]], "falkon.preconditioner": [[10, "module-falkon.preconditioner"]], "init() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.init"]], "init() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.init"]], "inva() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invA"]], "inva() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invA"]], "invat() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invAt"]], "invat() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invAt"]], "invt() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invT"]], "invt() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invT"]], "invtt() (falkon.preconditioner.falkonpreconditioner method)": [[10, "falkon.preconditioner.FalkonPreconditioner.invTt"]], "invtt() (falkon.preconditioner.logisticpreconditioner method)": [[10, "falkon.preconditioner.LogisticPreconditioner.invTt"]], "sparsetensor (class in falkon.sparse.sparse_tensor)": [[11, "falkon.sparse.sparse_tensor.SparseTensor"]], "sparsetype (class in falkon.sparse.sparse_tensor)": [[11, "falkon.sparse.sparse_tensor.SparseType"]], "falkon.sparse": [[11, "module-falkon.sparse"]], "narrow_rows() (falkon.sparse.sparse_tensor.sparsetensor method)": [[11, "falkon.sparse.sparse_tensor.SparseTensor.narrow_rows"]], "sparse_matmul() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_matmul"]], "sparse_norm() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_norm"]], "sparse_square_norm() (in module falkon.sparse)": [[11, "falkon.sparse.sparse_square_norm"]]}})
\ No newline at end of file