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Polish docs (blue-yonder#331)
* correct references to FCParameters in doc closes issue 323 * smoother formulation * correct underlines * refer to footnotes * another missing reference to footnote * sample entropy does not have tolerance parameter * correctly reference footnotes * another two footnote references * add header for script modules * correct docstring of run script * fixed module level references * not possible to link rubric references in feature calculators * need newlines between codeblocks * fix reference of tests
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docs/api/tests.integrations.rst

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tests.integrations package
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==========================
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Submodules
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----------
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tests.integrations.test_full_pipeline module
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--------------------------------------------
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.. automodule:: tests.integrations.test_full_pipeline
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:members:
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:undoc-members:
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:show-inheritance:
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tests.integrations.test_notebooks module
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----------------------------------------
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.. automodule:: tests.integrations.test_notebooks
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:members:
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:undoc-members:
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:show-inheritance:
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tests.integrations.test_relevant_feature_extraction module
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----------------------------------------------------------
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.. automodule:: tests.integrations.test_relevant_feature_extraction
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: tests.integrations
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:members:
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:undoc-members:
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:show-inheritance:

docs/api/tests.rst

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:undoc-members:
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tests.test_feature_significance module
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--------------------------------------
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.. automodule:: tests.test_feature_significance
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:members:
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:undoc-members:
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Module contents
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---------------

docs/api/tests.units.rst

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tests.units package
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===================
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Submodules
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----------
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tests.units.test_feature_significance module
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--------------------------------------------
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.. automodule:: tests.units.test_feature_significance
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: tests.units
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:members:
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:show-inheritance:

docs/api/tsfresh.feature_selection.rst

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:show-inheritance:
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benjamini_hochberg_test
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----------------
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-----------------------
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.. automodule:: tsfresh.feature_selection.benjamini_hochberg_test
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relevance
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----------------
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---------
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.. automodule:: tsfresh.feature_selection.relevance
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:members:

docs/api/tsfresh.scripts.rst

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tsfresh.scripts package
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=======================
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Submodules
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----------
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tsfresh.scripts.run_tsfresh module
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----------------------------------
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.. automodule:: tsfresh.scripts.run_tsfresh
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:members:
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:undoc-members:
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:show-inheritance:
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tsfresh.scripts.test_timing module
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----------------------------------
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.. automodule:: tsfresh.scripts.test_timing
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:members:
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:undoc-members:
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:show-inheritance:
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Module contents
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---------------
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.. automodule:: tsfresh.scripts
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:members:
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:undoc-members:
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:show-inheritance:
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.. currentmodule:: tsfresh.feature_extraction.feature_calculators
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.. autosummary::
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abs_energy
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absolute_sum_of_changes
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agg_autocorrelation
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agg_linear_trend
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approximate_entropy
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ar_coefficient
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augmented_dickey_fuller
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autocorrelation
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binned_entropy
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c3
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change_quantiles
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count_above_mean
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count_below_mean
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cwt_coefficients
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energy_ratio_by_chunks
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fft_coefficient
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first_location_of_maximum
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first_location_of_minimum
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friedrich_coefficients
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has_duplicate
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has_duplicate_max
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has_duplicate_min
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index_mass_quantile
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kurtosis
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large_standard_deviation
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last_location_of_maximum
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last_location_of_minimum
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length
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linear_trend
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longest_strike_above_mean
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longest_strike_below_mean
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max_langevin_fixed_point
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maximum
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mean
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mean_abs_change
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mean_change
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mean_second_derivate_central
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median
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minimum
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number_crossing_m
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number_cwt_peaks
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number_peaks
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partial_autocorrelation
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percentage_of_reoccurring_datapoints_to_all_datapoints
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percentage_of_reoccurring_values_to_all_values
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quantile
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range_count
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ratio_beyond_r_sigma
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ratio_value_number_to_time_series_length
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sample_entropy
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set_property
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skewness
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spkt_welch_density
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standard_deviation
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sum_of_reoccurring_data_points
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sum_of_reoccurring_values
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sum_values
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symmetry_looking
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time_reversal_asymmetry_statistic
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value_count
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variance
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variance_larger_than_standard_deviation
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docs/text/how_to_add_custom_feature.rst

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Finally, you have to add custom settings if your feature is a simple or combiner feature with parameters. To do so,
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just append your feature with sane default parameters to the ``name_to_param`` dictionary inside the
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:class:`tsfresh.ComprehensiveFCParameters` constructor:
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:class:`tsfresh.feature_extraction.settings.ComprehensiveFCParameters` constructor:
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.. code:: python
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That is it, tsfresh will calculate your feature the next time you run it.
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Please make sure, that the different feature extraction settings
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(e.g. :class:`tsfresh.feature_extraction.settings.EfficientCParameters`,
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(e.g. :class:`tsfresh.feature_extraction.settings.EfficientFCParameters`,
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:class:`tsfresh.feature_extraction.settings.MinimalFCParameters` or
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:class:`tsfresh.feature_extraction.settings.ComprehensiveFCParameters`) do include different sets of
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feature calculators to use. You can control, which feature extraction settings object will include your new
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--------------------------
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We would very happy if you contribute your implemented features to tsfresh. So make sure to create a pull request at our
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`github page <https://github.com/blue-yonder/tsfresh>`_. We happily accept partly implemented features that we can
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finalize.
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`github page <https://github.com/blue-yonder/tsfresh>`_. We happily accept partly implemented feature calculators, which
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we can finalize collaboratively.

tsfresh/examples/driftbif_simulation.py

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class velocity(object):
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"""
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Simulates the velocity of a dissipative soliton (kind of self organized particle).
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The equilibrium velocity without noise R=0 for $\tau>1.0/\kappa_3$ is $\kappa_3 \sqrt{(tau - 1.0/\kappa_3)/Q}. Before the drift-bifurcation $\tau \le 1.0/\kappa_3$ the velocity is zero.
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Simulates the velocity of a dissipative soliton (kind of self organized particle) [6]_.
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The equilibrium velocity without noise R=0 for
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$\tau>1.0/\kappa_3$ is $\kappa_3 \sqrt{(tau - 1.0/\kappa_3)/Q}.
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Before the drift-bifurcation $\tau \le 1.0/\kappa_3$ the velocity is zero.
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References
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tsfresh/examples/har_dataset.py

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# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016
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"""
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This module implements functions to download and load the Human Activity Recognition dataset [4].
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A description of the data set can be found in [5].
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This module implements functions to download and load the Human Activity Recognition dataset [4]_.
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A description of the data set can be found in [5]_.
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References
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----------
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.. [4] https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
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.. [5] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. (2013)
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A Public Domain Dataset for Human Activity Recognition Using Smartphones.
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21th European Symposium on Artificial Neural Networks,
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Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.
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A Public Domain Dataset for Human Activity Recognition Using Smartphones.
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21th European Symposium on Artificial Neural Networks,
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Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.
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"""
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tsfresh/examples/robot_execution_failures.py

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# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016
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"""
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This module implements functions to download the Robot Execution Failures LP1 Data Set[1] and load it as as DataFrame.
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This module implements functions to download the Robot Execution Failures LP1 Data Set [1]_, [2]_, [3]_ and load it as
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as DataFrame.
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"""
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Download the Robot Execution Failures LP1 Data Set[#1] from the UCI Machine Learning Repository [#2] and store it
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locally.
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Examples

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