From a3ba32a4b8ba7659e3730839b8afa4870471ba7a Mon Sep 17 00:00:00 2001 From: Ville Date: Fri, 1 Feb 2013 10:59:00 +0200 Subject: [PATCH 01/64] Inputparser and some necessary files for octave --- Octave/crosstab.m | 14 + Octave/fcnchk.m | 4 + Octave/mnpdf.m | 134 +++++++++ Octave/mnrnd.m | 184 ++++++++++++ inputparser/@inputParser/addOptional.m | 63 ++++ inputparser/@inputParser/addParamValue.m | 55 ++++ inputparser/@inputParser/addRequired.m | 60 ++++ inputparser/@inputParser/addSwitch.m | 48 ++++ inputparser/@inputParser/createCopy.m | 38 +++ inputparser/@inputParser/display.m | 58 ++++ inputparser/@inputParser/inputParser.m | 224 +++++++++++++++ inputparser/@inputParser/parse.m | 37 +++ inputparser/@inputParser/subsasgn.m | 37 +++ inputparser/@inputParser/subsref.m | 350 +++++++++++++++++++++++ inputparser/@inputParser/subsref.m~ | 336 ++++++++++++++++++++++ inputparser/iparser.m | 10 + 16 files changed, 1652 insertions(+) create mode 100644 Octave/crosstab.m create mode 100644 Octave/fcnchk.m create mode 100644 Octave/mnpdf.m create mode 100644 Octave/mnrnd.m create mode 100644 inputparser/@inputParser/addOptional.m create mode 100644 inputparser/@inputParser/addParamValue.m create mode 100644 inputparser/@inputParser/addRequired.m create mode 100644 inputparser/@inputParser/addSwitch.m create mode 100644 inputparser/@inputParser/createCopy.m create mode 100644 inputparser/@inputParser/display.m create mode 100644 inputparser/@inputParser/inputParser.m create mode 100644 inputparser/@inputParser/parse.m create mode 100644 inputparser/@inputParser/subsasgn.m create mode 100644 inputparser/@inputParser/subsref.m create mode 100644 inputparser/@inputParser/subsref.m~ create mode 100644 inputparser/iparser.m diff --git a/Octave/crosstab.m b/Octave/crosstab.m new file mode 100644 index 00000000..085a4ed3 --- /dev/null +++ b/Octave/crosstab.m @@ -0,0 +1,14 @@ +function y = crosstab(x) +% Count numer of occurences of elements of x in x + +ux=unique(x); +y=zeros(size(ux)); +for i=1:length(ux) + y(i) = sum(x==ux(i)); +end +if size(y,1)==1 + y=y'; +end + +end + diff --git a/Octave/fcnchk.m b/Octave/fcnchk.m new file mode 100644 index 00000000..6aeced1c --- /dev/null +++ b/Octave/fcnchk.m @@ -0,0 +1,4 @@ +function f = fcnchk(x,n ) +f=x; +end + diff --git a/Octave/mnpdf.m b/Octave/mnpdf.m new file mode 100644 index 00000000..fb1920e4 --- /dev/null +++ b/Octave/mnpdf.m @@ -0,0 +1,134 @@ +## Copyright (C) 2012 Arno Onken +## +## This program is free software: you can redistribute it and/or modify +## it under the terms of the GNU General Public License as published by +## the Free Software Foundation, either version 3 of the License, or +## (at your option) any later version. +## +## This program is distributed in the hope that it will be useful, +## but WITHOUT ANY WARRANTY; without even the implied warranty of +## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +## GNU General Public License for more details. +## +## You should have received a copy of the GNU General Public License +## along with this program. If not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{y} =} mnpdf (@var{x}, @var{p}) +## Compute the probability density function of the multinomial distribution. +## +## @subheading Arguments +## +## @itemize @bullet +## @item +## @var{x} is vector with a single sample of a multinomial distribution with +## parameter @var{p} or a matrix of random samples from multinomial +## distributions. In the latter case, each row of @var{x} is a sample from a +## multinomial distribution with the corresponding row of @var{p} being its +## parameter. +## +## @item +## @var{p} is a vector with the probabilities of the categories or a matrix +## with each row containing the probabilities of a multinomial sample. +## @end itemize +## +## @subheading Return values +## +## @itemize @bullet +## @item +## @var{y} is a vector of probabilites of the random samples @var{x} from the +## multinomial distribution with corresponding parameter @var{p}. The parameter +## @var{n} of the multinomial distribution is the sum of the elements of each +## row of @var{x}. The length of @var{y} is the number of columns of @var{x}. +## If a row of @var{p} does not sum to @code{1}, then the corresponding element +## of @var{y} will be @code{NaN}. +## @end itemize +## +## @subheading Examples +## +## @example +## @group +## x = [1, 4, 2]; +## p = [0.2, 0.5, 0.3]; +## y = mnpdf (x, p); +## @end group +## +## @group +## x = [1, 4, 2; 1, 0, 9]; +## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +## y = mnpdf (x, p); +## @end group +## @end example +## +## @subheading References +## +## @enumerate +## @item +## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics +## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001. +## +## @item +## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical +## Distributions}. pages 134-136, Wiley, New York, third edition, 2000. +## @end enumerate +## @end deftypefn + +## Author: Arno Onken +## Description: PDF of the multinomial distribution + +function y = mnpdf (x, p) + + # Check arguments + if (nargin != 2) + print_usage (); + endif + + if (! ismatrix (x) || any (x(:) < 0 | round (x(:) != x(:)))) + error ("mnpdf: x must be a matrix of non-negative integer values"); + endif + if (! ismatrix (p) || any (p(:) < 0)) + error ("mnpdf: p must be a non-empty matrix with rows of probabilities"); + endif + + # Adjust input sizes + if (! isvector (x) || ! isvector (p)) + if (isvector (x)) + x = x(:)'; + endif + if (isvector (p)) + p = p(:)'; + endif + if (size (x, 1) == 1 && size (p, 1) > 1) + x = repmat (x, size (p, 1), 1); + elseif (size (x, 1) > 1 && size (p, 1) == 1) + p = repmat (p, size (x, 1), 1); + endif + endif + # Continue argument check + if (any (size (x) != size (p))) + error ("mnpdf: x and p must have compatible sizes"); + endif + + # Count total number of elements of each multinomial sample + n = sum (x, 2); + # Compute probability density function of the multinomial distribution + t = x .* log (p); + t(x == 0) = 0; + y = exp (gammaln (n+1) - sum (gammaln (x+1), 2) + sum (t, 2)); + # Set invalid rows to NaN + k = (abs (sum (p, 2) - 1) > 1e-6); + y(k) = NaN; + +endfunction + +%!test +%! x = [1, 4, 2]; +%! p = [0.2, 0.5, 0.3]; +%! y = mnpdf (x, p); +%! assert (y, 0.11812, 0.001); + +%!test +%! x = [1, 4, 2; 1, 0, 9]; +%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +%! y = mnpdf (x, p); +%! assert (y, [0.11812; 0.13422], 0.001); diff --git a/Octave/mnrnd.m b/Octave/mnrnd.m new file mode 100644 index 00000000..1332faff --- /dev/null +++ b/Octave/mnrnd.m @@ -0,0 +1,184 @@ +## Copyright (C) 2012 Arno Onken +## +## This program is free software: you can redistribute it and/or modify +## it under the terms of the GNU General Public License as published by +## the Free Software Foundation, either version 3 of the License, or +## (at your option) any later version. +## +## This program is distributed in the hope that it will be useful, +## but WITHOUT ANY WARRANTY; without even the implied warranty of +## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +## GNU General Public License for more details. +## +## You should have received a copy of the GNU General Public License +## along with this program. If not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{x} =} mnrnd (@var{n}, @var{p}) +## @deftypefnx {Function File} {@var{x} =} mnrnd (@var{n}, @var{p}, @var{s}) +## Generate random samples from the multinomial distribution. +## +## @subheading Arguments +## +## @itemize @bullet +## @item +## @var{n} is the first parameter of the multinomial distribution. @var{n} can +## be scalar or a vector containing the number of trials of each multinomial +## sample. The elements of @var{n} must be non-negative integers. +## +## @item +## @var{p} is the second parameter of the multinomial distribution. @var{p} can +## be a vector with the probabilities of the categories or a matrix with each +## row containing the probabilities of a multinomial sample. If @var{p} has +## more than one row and @var{n} is non-scalar, then the number of rows of +## @var{p} must match the number of elements of @var{n}. +## +## @item +## @var{s} is the number of multinomial samples to be generated. @var{s} must +## be a non-negative integer. If @var{s} is specified, then @var{n} must be +## scalar and @var{p} must be a vector. +## @end itemize +## +## @subheading Return values +## +## @itemize @bullet +## @item +## @var{x} is a matrix of random samples from the multinomial distribution with +## corresponding parameters @var{n} and @var{p}. Each row corresponds to one +## multinomial sample. The number of columns, therefore, corresponds to the +## number of columns of @var{p}. If @var{s} is not specified, then the number +## of rows of @var{x} is the maximum of the number of elements of @var{n} and +## the number of rows of @var{p}. If a row of @var{p} does not sum to @code{1}, +## then the corresponding row of @var{x} will contain only @code{NaN} values. +## @end itemize +## +## @subheading Examples +## +## @example +## @group +## n = 10; +## p = [0.2, 0.5, 0.3]; +## x = mnrnd (n, p); +## @end group +## +## @group +## n = 10 * ones (3, 1); +## p = [0.2, 0.5, 0.3]; +## x = mnrnd (n, p); +## @end group +## +## @group +## n = (1:2)'; +## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +## x = mnrnd (n, p); +## @end group +## @end example +## +## @subheading References +## +## @enumerate +## @item +## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics +## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001. +## +## @item +## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical +## Distributions}. pages 134-136, Wiley, New York, third edition, 2000. +## @end enumerate +## @end deftypefn + +## Author: Arno Onken +## Description: Random samples from the multinomial distribution + +function x = mnrnd (n, p, s) + + # Check arguments + if (nargin == 3) + if (! isscalar (n) || n < 0 || round (n) != n) + error ("mnrnd: n must be a non-negative integer"); + endif + if (! isvector (p) || any (p < 0 | p > 1)) + error ("mnrnd: p must be a vector of probabilities"); + endif + if (! isscalar (s) || s < 0 || round (s) != s) + error ("mnrnd: s must be a non-negative integer"); + endif + elseif (nargin == 2) + if (isvector (p) && size (p, 1) > 1) + p = p'; + endif + if (! isvector (n) || any (n < 0 | round (n) != n) || size (n, 2) > 1) + error ("mnrnd: n must be a non-negative integer column vector"); + endif + if (! ismatrix (p) || isempty (p) || any (p < 0 | p > 1)) + error ("mnrnd: p must be a non-empty matrix with rows of probabilities"); + endif + if (! isscalar (n) && size (p, 1) > 1 && length (n) != size (p, 1)) + error ("mnrnd: the length of n must match the number of rows of p"); + endif + else + print_usage (); + endif + + # Adjust input sizes + if (nargin == 3) + n = n * ones (s, 1); + p = repmat (p(:)', s, 1); + elseif (nargin == 2) + if (isscalar (n) && size (p, 1) > 1) + n = n * ones (size (p, 1), 1); + elseif (size (p, 1) == 1) + p = repmat (p, length (n), 1); + endif + endif + sz = size (p); + + # Upper bounds of categories + ub = cumsum (p, 2); + # Make sure that the greatest upper bound is 1 + gub = ub(:, end); + ub(:, end) = 1; + # Lower bounds of categories + lb = [zeros(sz(1), 1) ub(:, 1:(end-1))]; + + # Draw multinomial samples + x = zeros (sz); + for i = 1:sz(1) + # Draw uniform random numbers + r = repmat (rand (n(i), 1), 1, sz(2)); + # Compare the random numbers of r to the cumulated probabilities of p and + # count the number of samples for each category + x(i, :) = sum (r <= repmat (ub(i, :), n(i), 1) & r > repmat (lb(i, :), n(i), 1), 1); + endfor + # Set invalid rows to NaN + k = (abs (gub - 1) > 1e-6); + x(k, :) = NaN; + +endfunction + +%!test +%! n = 10; +%! p = [0.2, 0.5, 0.3]; +%! x = mnrnd (n, p); +%! assert (size (x), size (p)); +%! assert (all (x >= 0)); +%! assert (all (round (x) == x)); +%! assert (sum (x) == n); + +%!test +%! n = 10 * ones (3, 1); +%! p = [0.2, 0.5, 0.3]; +%! x = mnrnd (n, p); +%! assert (size (x), [length(n), length(p)]); +%! assert (all (x >= 0)); +%! assert (all (round (x) == x)); +%! assert (all (sum (x, 2) == n)); + +%!test +%! n = (1:2)'; +%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +%! x = mnrnd (n, p); +%! assert (size (x), size (p)); +%! assert (all (x >= 0)); +%! assert (all (round (x) == x)); +%! assert (all (sum (x, 2) == n)); diff --git a/inputparser/@inputParser/addOptional.m b/inputparser/@inputParser/addOptional.m new file mode 100644 index 00000000..b2863524 --- /dev/null +++ b/inputparser/@inputParser/addOptional.m @@ -0,0 +1,63 @@ +## Copyright (C) 2011 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{parser} =} addOptional (@var{parser}, @var{argname}, @var{default}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addOptional (@var{argname}, @var{default}) +## @deftypefnx {Function File} {@var{parser} =} addOptional (@var{parser}, @var{argname}, @var{default}, @var{validator}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addOptional (@var{argname}, @var{default}, @var{validator}) +## Add new optional argument to the object @var{parser} of the class inputParser +## to implement an ordered arguments type of API +## +## @var{argname} must be a string with the name of the new argument. The order +## in which new arguments are added with @command{addOptional}, represents the +## expected order of arguments. +## +## @var{default} will be the value used when the argument is not specified. +## +## @var{validator} is an optional anonymous function to validate the given values +## for the argument with name @var{argname}. Alternatively, a function name +## can be used. +## +## See @command{help inputParser} for examples. +## +## @emph{Note}: if a string argument does not validate, it will be considered a +## ParamValue key. If an optional argument is not given a validator, anything +## will be valid, and so any string will be considered will be the value of the +## optional argument (in @sc{matlab}, if no validator is given and argument is +## a string it will also be considered a ParamValue key). +## +## @seealso{inputParser, @@inputParser/addParamValue, @@inputParser/addSwitch, +## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/parse} +## @end deftypefn + +function inPar = addOptional (inPar, name, def, val) + + ## check @inputParser/subsref for the actual code + if (nargin == 3) + inPar = subsref (inPar, substruct( + '.' , 'addOptional', + '()', {name, def} + )); + elseif (nargin == 4) + inPar = subsref (inPar, substruct( + '.' , 'addOptional', + '()', {name, def, val} + )); + else + print_usage; + endif + +endfunction diff --git a/inputparser/@inputParser/addParamValue.m b/inputparser/@inputParser/addParamValue.m new file mode 100644 index 00000000..95934f0b --- /dev/null +++ b/inputparser/@inputParser/addParamValue.m @@ -0,0 +1,55 @@ +## Copyright (C) 2011 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{parser} =} addParamValue (@var{parser}, @var{argname}, @var{default}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addParamValue (@var{argname}, @var{default}) +## @deftypefnx {Function File} {@var{parser} =} addParamValue (@var{parser}, @var{argname}, @var{default}, @var{validator}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addParamValue (@var{argname}, @var{default}, @var{validator}) +## Add new parameter to the object @var{parser} of the class inputParser to implement +## a name/value pair type of API. +## +## @var{argname} must be a string with the name of the new parameter. +## +## @var{default} will be the value used when the parameter is not specified. +## +## @var{validator} is an optional function handle to validate the given values +## for the parameter with name @var{argname}. Alternatively, a function name +## can be used. +## +## See @command{help inputParser} for examples. +## +## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addSwitch, +## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/parse} +## @end deftypefn + +function inPar = addParamValue (inPar, name, def, val) + + ## check @inputParser/subsref for the actual code + if (nargin == 3) + inPar = subsref (inPar, substruct( + '.' , 'addParamValue', + '()', {name, def} + )); + elseif (nargin == 4) + inPar = subsref (inPar, substruct( + '.' , 'addParamValue', + '()', {name, def, val} + )); + else + print_usage; + endif + +endfunction diff --git a/inputparser/@inputParser/addRequired.m b/inputparser/@inputParser/addRequired.m new file mode 100644 index 00000000..bd8e7e5e --- /dev/null +++ b/inputparser/@inputParser/addRequired.m @@ -0,0 +1,60 @@ +## Copyright (C) 2011 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{parser} =} addRequired (@var{parser}, @var{argname}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addRequired (@var{argname}) +## @deftypefnx {Function File} {@var{parser} =} addRequired (@var{parser}, @var{argname}, @var{validator}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addRequired (@var{argname}, @var{validator}) +## Add new mandatory argument to the object @var{parser} of inputParser class. +## +## This method belongs to the inputParser class and implements an ordered +## arguments type of API. +## +## @var{argname} must be a string with the name of the new argument. The order +## in which new arguments are added with @command{addrequired}, represents the +## expected order of arguments. +## +## @var{validator} is an optional function handle to validate the given values +## for the argument with name @var{argname}. Alternatively, a function name +## can be used. +## +## See @command{help inputParser} for examples. +## +## @emph{Note}: this can be used together with the other type of arguments but +## it must be the first (see @command{@@inputParser}). +## +## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue +## @@inputParser/addParamValue, @@inputParser/addSwitch, @@inputParser/parse} +## @end deftypefn + +function inPar = addRequired (inPar, name, val) + + ## check @inputParser/subsref for the actual code + if (nargin == 2) + inPar = subsref (inPar, substruct( + '.' , 'addRequired', + '()', {name} + )); + elseif (nargin == 3) + inPar = subsref (inPar, substruct( + '.' , 'addRequired', + '()', {name, val} + )); + else + print_usage; + endif + +endfunction diff --git a/inputparser/@inputParser/addSwitch.m b/inputparser/@inputParser/addSwitch.m new file mode 100644 index 00000000..518ff80e --- /dev/null +++ b/inputparser/@inputParser/addSwitch.m @@ -0,0 +1,48 @@ +## Copyright (C) 2011-2012 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{parser} =} addSwitch (@var{parser}, @var{argname}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.addSwitch (@var{argname}) +## Add new switch type of argument to the object @var{parser} of inputParser class. +## +## This method belongs to the inputParser class and implements a switch +## arguments type of API. +## +## @var{argname} must be a string with the name of the new argument. Arguments +## of this type can be specified at the end, after @code{Required} and @code{Optional}, +## and mixed between the @code{ParamValue}. They default to false. If one of the +## arguments supplied is a string like @var{argname}, then after parsing the value +## of @var{parse}.Results.@var{argname} will be true. +## +## See @command{help inputParser} for examples. +## +## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue +## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/parse} +## @end deftypefn + +function inPar = addSwitch (inPar, name) + + ## check @inputParser/subsref for the actual code + if (nargin == 2) + inPar = subsref (inPar, substruct( + '.' , 'addSwitch', + '()', {name} + )); + else + print_usage; + endif + +endfunction diff --git a/inputparser/@inputParser/createCopy.m b/inputparser/@inputParser/createCopy.m new file mode 100644 index 00000000..86e897a7 --- /dev/null +++ b/inputparser/@inputParser/createCopy.m @@ -0,0 +1,38 @@ +## Copyright (C) 2011 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{new_parser} =} createCopy (@var{parser}) +## Creates a copy @var{new_parser} of the object @var{parser} of the inputParser +## class. +## +## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue +## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/addSwitch, +## @@inputParser/parse} +## @end deftypefn + +function outPar = createCopy (inPar) + + if ( nargin != 1 ) + print_usage; + elseif ( !isa (inPar, 'inputParser') ) + error ("object must be of the inputParser class but '%s' was used", class (inPar) ); + endif + + ## yes, it's a ridiculous function but exists for MatLab compatibility. In there + ## the inputParser class is a 'handle class' and this would just return a reference + outPar = inPar; + +endfunction diff --git a/inputparser/@inputParser/display.m b/inputparser/@inputParser/display.m new file mode 100644 index 00000000..230b8d0b --- /dev/null +++ b/inputparser/@inputParser/display.m @@ -0,0 +1,58 @@ +## Copyright (C) 2011-2012 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +function display (inPar) + + if (inPar.FunctionName) + name = inPar.FunctionName(1:end-3); + else + name = ""; + endif + + required = arg_list (inPar.Required); + optional = arg_list (inPar.Optional); + paramvalue = arg_list (inPar.ParamValue); + switches = arg_list (inPar.Switch); + + printf ("Input Parser object with:\n"); + printf ("CaseSensitive: %s\n", binstr (inPar.CaseSensitive)); + printf ("StructExpand : %s\n", binstr (inPar.StructExpand)); + printf ("KeepUnmatched: %s\n", binstr (inPar.KeepUnmatched)); + printf ("FunctionName : '%s'\n", name); + printf ("\n"); + printf ("Required arguments : %s\n", required); + printf ("Optional arguments : %s\n", optional); + printf ("ParamValue arguments: %s\n", paramvalue); + printf ("Switch arguments : %s\n", switches); + +endfunction + +function [str] = binstr (bin) + if (bin) + str = "true"; + else + str = "false"; + endif +endfunction + +function [str] = arg_list (args) + str = strcat ("'", fieldnames (args), {"', "}); + if (!isempty (str)) + str = cstrcat (str{:}); + str = str(1:end-2); # remove the last comma and space + else + str = ""; + endif +endfunction diff --git a/inputparser/@inputParser/inputParser.m b/inputparser/@inputParser/inputParser.m new file mode 100644 index 00000000..a8718ef1 --- /dev/null +++ b/inputparser/@inputParser/inputParser.m @@ -0,0 +1,224 @@ +## Copyright (C) 2011-2012 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{parser} =} inputParser () +## Create object @var{parser} of the inputParser class. +## +## This class is designed to allow easy parsing of function arguments. This class +## supports four types of arguments: +## +## @enumerate +## @item mandatory (see @command{@@inputParser/addRequired}); +## @item optional (see @command{@@inputParser/addOptional}); +## @item named (see @command{@@inputParser/addParamValue}); +## @item switch (see @command{@@inputParser/addSwitch}). +## @end enumerate +## +## After defining the function API with this methods, the supplied arguments can +## be parsed with the @command{@@inputParser/parse} method and the parsing results +## accessed with the @command{Results} accessor. +## +## @deftypefnx {Accessor method} parser.Parameters +## Return list of parameters name already defined. +## +## @deftypefnx {Accessor method} parser.Results +## Return structure with argument names as fieldnames and corresponding values. +## +## @deftypefnx {Accessor method} parser.Unmatched +## Return structure similar to @command{Results} for unmatched parameters. See +## the @command{KeepUnmatched} property. +## +## @deftypefnx {Accessor method} parser.UsingDefaults +## Return cell array with the names of arguments that are using default values. +## +## @deftypefnx {Class property} parser.CaseSensitive = @var{boolean} +## Set whether matching of argument names should be case sensitive. Defaults to false. +## +## @deftypefnx {Class property} parser.FunctionName = @var{name} +## Set function name to be used on error messages. Defauls to empty string. +## +## @deftypefnx {Class property} parser.KeepUnmatched = @var{boolean} +## Set whether an error should be given for non-defined arguments. Defaults to +## false. If set to true, the extra arguments can be accessed through +## @code{Unmatched} after the @code{parse} method. Note that since @command{Switch} +## and @command{ParamValue} arguments can be mixed, it is not possible to know +## the unmatched type. If argument is found unmatched it is assumed to be of the +## @command{ParamValue} type and it is expected to be followed by a value. +## +## @deftypefnx {Class property} parser.StructExpand = @var{boolean} +## Set whether a structure can be passed to the function instead of parameter +## value pairs. Defaults to true. Not implemented yet. +## +## The following example shows how to use this class: +## +## @example +## @group +## function check (pack, path, mat, varargin) +## p = inputParser; # create object +## p.FunctionName = "check"; # set function name +## p = p.addRequired ("pack", @@ischar); # create mandatory argument +## +## p = p.addOptional ("path", pwd(), @@ischar); # create optional argument +## +## ## one can create a function handle to anonymous functions for validators +## val_mat = @@(x)isvector(x) && all( x <= 1) && all(x >= 0); +## p = p.addOptional ("mat", [0 0], @@val_mat); +## +## ## create two ParamValue type of arguments +## val_type = @@(x) ischar(x) && any(strcmp(x, @{"linear", "quadratic"@}); +## p = p.addParamValue ("type", "linear", @@val_type); +## val_verb = @@(x) ischar(x) && any(strcmp(x, @{"low", "medium", "high"@}); +## p = p.addParamValue ("tolerance", "low", @@val_verb); +## +## ## create a switch type of argument +## p = p.addSwitch ("verbose"); +## +## p = p.parse (pack, path, mat, varargin@{:@}); +## +## ## the rest of the function can access the input by accessing p.Results +## ## for example, to access the value of tolerance, use p.Results.tolerance +## endfunction +## +## check ("mech"); # valid, will use defaults for other arguments +## check (); # error since at least one argument is mandatory +## check (1); # error since !ischar +## check ("mech", "~/dev"); # valid, will use defaults for other arguments +## +## check ("mech", "~/dev", [0 1 0 0], "type", "linear"); # valid +## +## ## the following is also valid. Note how the Switch type of argument can be +## ## mixed into or before the ParamValue (but still after Optional) +## check ("mech", "~/dev", [0 1 0 0], "verbose", "tolerance", "high"); +## +## ## the following returns an error since not all optional arguments, `path' and +## ## `mat', were given before the named argument `type'. +## check ("mech", "~/dev", "type", "linear"); +## @end group +## @end example +## +## @emph{Note 1}: a function can have any mixture of the four API types but they +## must appear in a specific order. @command{Required} arguments must be the very +## first which can be followed by @command{Optional} arguments. Only the +## @command{ParamValue} and @command{Switch} arguments can be mixed together but +## must be at the end. +## +## @emph{Note 2}: if both @command{Optional} and @command{ParamValue} arguments +## are mixed in a function API, once a string Optional argument fails to validate +## against, it will be considered the end of @command{Optional} arguments and the +## first key for a @command{ParamValue} and @command{Switch} arguments. +## +## @seealso{@@inputParser/addOptional, @@inputParser/addSwitch, +## @@inputParser/addParamValue, @@inputParser/addRequired, +## @@inputParser/createCopy, @@inputParser/parse} +## @end deftypefn + +function inPar = inputParser + + if (nargin != 0) + print_usage; + endif + + inPar = struct; + + ## these are not to be accessed by users. Each will have a field whose names + ## are the argnames which will also be a struct with fieldnames 'validator' + ## and 'default' + inPar.ParamValue = struct; + inPar.Optional = struct; + inPar.Required = struct; + inPar.Switch = struct; + + ## this will be filled when the methodd parse is used and will be a struct whose + ## fieldnames are the argnames that return their value + inPar.Results = struct; + + ## an 1xN cell array with argnames. It is read only by the user and its order + ## showws the order that they were added to the object (which is the order they + ## will be expected) + inPar.Parameters = {}; + + inPar.CaseSensitive = false; + inPar.FunctionName = ''; # name of the function for the error message + inPar.KeepUnmatched = false; + inPar.StructExpand = true; + inPar.Unmatched = struct; + inPar.UsingDefaults = {}; + + inPar = class (inPar, 'inputParser'); + +endfunction + +%!shared p, out +%! p = inputParser; +%! p = p.addRequired ("req1", @(x) ischar (x)); +%! p = p.addOptional ("op1", "val", @(x) ischar (x) && any (strcmp (x, {"val", "foo"}))); +%! p = p.addOptional ("op2", 78, @(x) (x) > 50); +%! p = p.addSwitch ("verbose"); +%! p = p.addParamValue ("line", "tree", @(x) ischar (x) && any (strcmp (x, {"tree", "circle"}))); +%! ## check normal use, only required are given +%! out = p.parse ("file"); +%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, +%! {"file" , "val" , 78 , false , "tree"}); +%!assert (out.UsingDefaults, {"op1", "op2", "verbose", "line"}); +%! ## check normal use, but give values different than defaults +%! out = p.parse ("file", "foo", 80, "line", "circle", "verbose"); +%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, +%! {"file" , "foo" , 80 , true , "circle"}); +%! ## check optional is skipped and considered ParamValue if unvalidated string +%! out = p.parse ("file", "line", "circle"); +%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, +%! {"file" , "val" , 78 , false , "circle"}); +%! ## check case insensitivity +%! out = p.parse ("file", "foo", 80, "LiNE", "circle", "vERbOSe"); +%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, +%! {"file" , "foo" , 80 , true , "circle"}); +%! ## check KeepUnmatched +%! p.KeepUnmatched = true; +%! out = p.parse ("file", "foo", 80, "line", "circle", "verbose", "extra", 50); +%!assert (out.Unmatched.extra, 50) +%! ## check error when missing required +%!error(p.parse()) +%! ## check error when given required do not validate +%!error(p.parse(50)) +%! ## check error when given optional do not validate +%!error(p.parse("file", "no-val")) +%! ## check error when given ParamValue do not validate +%!error(p.parse("file", "foo", 51, "line", "round")) + +## check alternative method (obj), ...) API +%!shared p, out +%! p = inputParser; +%! p = addRequired (p, "req1", @(x) ischar (x)); +%! p = addOptional (p, "op1", "val", @(x) ischar (x) && any (strcmp (x, {"val", "foo"}))); +%! p = addOptional (p, "op2", 78, @(x) (x) > 50); +%! p = addSwitch (p, "verbose"); +%! p = addParamValue (p, "line", "tree", @(x) ischar (x) && any (strcmp (x, {"tree", "circle"}))); +%! ## check normal use, only required are given +%! out = parse (p, "file"); +%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, +%! {"file" , "val" , 78 , false , "tree"}); +%!assert (out.UsingDefaults, {"op1", "op2", "verbose", "line"}); +%! ## check normal use, but give values different than defaults +%! out = parse (p, "file", "foo", 80, "line", "circle", "verbose"); +%!assert ({out.Results.req1, out.Results.op1, out.Results.op2, out.Results.verbose, out.Results.line}, +%! {"file" , "foo" , 80 , true , "circle"}); + +## if we were matlab compatible... +%!shared p, out +%! p = inputParser; +%! p = p.addOptional ("op1", "val"); +%! p = p.addParamValue ("line", "tree"); +%!xtest assert (getfield (p.parse("line", "circle"), "Results"), struct ("op1", "val", "line", "circle")); diff --git a/inputparser/@inputParser/parse.m b/inputparser/@inputParser/parse.m new file mode 100644 index 00000000..2a7c407e --- /dev/null +++ b/inputparser/@inputParser/parse.m @@ -0,0 +1,37 @@ +## Copyright (C) 2011 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{parser} =} parse (@var{parser}, @var{varargin}) +## @deftypefnx {Function File} {@var{parser} =} @var{parser}.parse (@var{varargin}) +## Parses and validates list of arguments according to object @var{parser} of the +## class inputParser. +## +## After parsing, the results can be accessed with the @command{Results} +## accessor. See @command{help inputParser} for a more complete description. +## +## @seealso{inputParser, @@inputParser/addOptional, @@inputParser/addParamValue +## @@inputParser/addParamValue, @@inputParser/addRequired, @@inputParser/addSwitch} +## @end deftypefn + +function inPar = parse (inPar, varargin) + + ## check @inputParser/subsref for the actual code + inPar = subsref (inPar, substruct( + '.' , 'parse', + '()', varargin + )); + +endfunction diff --git a/inputparser/@inputParser/subsasgn.m b/inputparser/@inputParser/subsasgn.m new file mode 100644 index 00000000..01250e33 --- /dev/null +++ b/inputparser/@inputParser/subsasgn.m @@ -0,0 +1,37 @@ +## Copyright (C) 2011 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +function inPar = subsasgn (inPar, idx, rhs) + + if ( idx.type != '.' ) + error ("invalid index for class %s", class (inPar) ); + endif + + switch idx.subs + case {'CaseSensitive', 'KeepUnmatched', 'StructExpand'} + if ( !islogical (rhs) ) + error("Property '%s' of the class inputParser must be logical", idx.subs) + endif + inPar.(idx.subs) = rhs; + case 'FunctionName' + if ( !ischar (rhs) ) + error("Property 'FunctionName' of the class inputParser can only be set to a string") + endif + inPar.(idx.subs) = sprintf("%s : ", rhs); + otherwise + error ("invalid index for assignment of class %s", class (inPar) ); + endswitch + +endfunction diff --git a/inputparser/@inputParser/subsref.m b/inputparser/@inputParser/subsref.m new file mode 100644 index 00000000..55776c8f --- /dev/null +++ b/inputparser/@inputParser/subsref.m @@ -0,0 +1,350 @@ +## Copyright (C) 2011-2012 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +function inPar = subsref (inPar, idx) + + if ( !isa (inPar, 'inputParser') ) + error ("object must be of the inputParser class but '%s' was used", class (inPar) ); + elseif ( idx(1).type != '.' ) + error ("invalid index for class %s", class (inPar) ); + endif + + ## the following at the end may allow to use the obj.method notation one day + ## jwe is very against this ugly hack + ## what would happen if the user has the obj inside a struct? Bad things! +# ori = inputname(1); +# assignin('caller', ori, inPar); + + method = idx(1).subs; + + switch method + case 'Results' + inPar = retrieve_results (inPar, idx) + case 'Parameters' + inPar = inPar.Parameters; + case 'parse' + inPar = parse_args (inPar, idx); + case 'Unmatched' + case 'UsingDefaults' + case {'addOptional', 'addParamValue', 'addRequired', 'addSwitch'} + inPar = check_methods (inPar, idx); + otherwise + error ("invalid index for reference of class %s", class (inPar) ); + endswitch + + ## TODO we should make inPar an object of the inputParser class again. At + ## least after running parse it becomes just a structure again. While that is + ## bad, at least allows for easy access to the Results and Unmatched fields + ## without extra coding. +# inPar = class (inPar, 'inputParser'); + +endfunction + +function out = retrieve_results (inPar, idx) + + if ( numel(idx) != 2 || idx(2).type != '.' ) + print_usage ("@inputParser/Results"); + endif + + out = inPar.Results.(idx(2).subs); + +endfunction + + +## when parsing options, here's the principle: Required options have to be the +## first ones. They are followed by Optional if any. In the end come the +## ParamValue mixed with Switch. Any other order makes no sense +function inPar = parse_args (inPar, idx) + + ## syntax is inPar.parse (arguments) + if ( numel(idx) != 2 || idx(2).type != '()' ) + print_usage ("@inputParser/parse"); + endif + + ## this makes it easier to read but may be memory instensive + args = idx(2).subs; + if ~isempty(args) && isstruct(args{1}) && isempty(fieldnames(args{1})) + args={}; + end + + ## make copy of ordered list of Parameters to keep the original intact and readable + inPar.copy = inPar.Parameters; + if ( numel (fieldnames (inPar.Required)) > numel (args) ) + error("%sNot enough arguments", inPar.FunctionName); + endif + + ## we take names out of 'copy' and values out of 'args', evaluate them and + ## store them into 'Results' + for i = 1 : numel (fieldnames (inPar.Required)) + [name, inPar.copy] = shift (inPar.copy); + [value, args] = shift (args); + if ( !feval (inPar.Required.(name).validator, value) ) + error_invalid (inPar.FunctionName, name, inPar.Required.(name).validator); + endif + inPar.Results.(name) = value; + endfor + + ## loop a maximum #times of the number of Optional, similarly to the required + ## loop. Once ran out of 'args', move their name into usingDefaults, place + ## their default values into 'Results', and break + + ## because if an argument is string and does not validate, should be considered + ## a ParamValue key + found_possible_key = false; + + for i = 1 : numel (fieldnames (inPar.Optional)) + if ( !numel (args) || found_possible_key) + ## loops the number of Optional options minus the number of them already processed + for n = 1 : (numel (fieldnames (inPar.Optional)) - i + 1 ) + [name, inPar.copy] = shift (inPar.copy); + inPar.UsingDefaults = push (inPar.UsingDefaults, name); + inPar.Results.(name) = inPar.Optional.(name).default; + endfor + break + endif + [name, inPar.copy] = shift (inPar.copy); + [value, args] = shift (args); + if ( !feval (inPar.Optional.(name).validator, value) ) + if (ischar (value) ) || (isstruct(value) && ~isempty(value)) + ## maybe the other optional are not defined, this can be Paramvalue + ## place this one on defaults and go back to the top with note to clean loop + inPar.UsingDefaults = push (inPar.UsingDefaults, name); + inPar.Results.(name) = inPar.Optional.(name).default; + found_possible_key = true; + args = unshift (args, value); + continue + else + error_invalid (inPar.FunctionName, name, inPar.Optional.(name).validator); + endif + else + inPar.Results.(name) = value; + endif + endfor + + ## loop a maximum #times of the number of ParamValue, taking pairs of keys and + ## values out 'args'. We no longer expect an order so we need the index in + ## 'copy' to remove it from there. Once ran out of 'args', move their name + ## into usingDefaults, place their default values into 'Results', and break + for i = 1 : (numel (fieldnames (inPar.ParamValue)) + numel (fieldnames (inPar.Switch))) + if numel(args)==1 && isstruct(args{1}) %&& isequal(fieldnames(args{1})(:), inPar.copy(:)) + ## input parser given structure containing ParamValues, modify args + tmpargs=args{1}; + args={}; + names=fieldnames(tmpargs); + for j = 1 : numel(names) + args{1,end+1} = names{j}; + args{1,end+1} = getfield(tmpargs, names{j}); + endfor + endif + if ( numel (args) < 2) + ## loops the number of times left in 'copy' since these are the last type + for n = 1 : numel (inPar.copy) + [name, inPar.copy] = shift (inPar.copy); + inPar.UsingDefaults = push (inPar.UsingDefaults, name); + if (isfield (inPar.ParamValue, name)) + inPar.Results.(name) = inPar.ParamValue.(name).default; + else + inPar.Results.(name) = inPar.Switch.(name).default; + endif + endfor + break + endif + [key, args] = shift (args); + if ( !ischar (key) ) + %keyboard + error("%sParameter/Switch names must be strings", inPar.FunctionName); + endif + if (inPar.CaseSensitive) + index = find( strcmp(inPar.copy, key)); + else + index = find( strcmpi(inPar.copy, key)); + endif + ## we can't use isfield here to support case insensitive + if (any (strcmpi (fieldnames (inPar.Switch), key))) + value = true; + method = "Switch"; + else + ## then it must be a ParamValue (even if unmatched), shift its value + if (numel (args) < 1 ) + error ("%sparameter '%s' does not have a value", inPar.FunctionName, key); + endif + [value, args] = shift (args); + method = "ParamValue"; + endif + + ## empty index means no match so either return error or move them into 'Unmatched' + if (!isempty (index)) + [name, inPar.copy] = shift (inPar.copy, index); + if ( !feval (inPar.(method).(name).validator, value)) + error_invalid (inPar.FunctionName, key, inPar.(method).(name).validator); + endif + ## we use the name shifted from 'copy' instead of the key from 'args' in case + ## the key is in the wrong case + inPar.Results.(name) = value; + elseif (isempty (index) && inPar.KeepUnmatched ) + inPar.Unmatched.(key) = value; + i = i - 1; # this time didn't count, go back one + else + error ("%sargument '%s' did not match any valid parameter of the parser", inPar.FunctionName, key); + endif + endfor + + ## if there's leftovers they must be unmatched. Note that some unmatched can + ## have already been processed in the ParamValue loop + if ( numel (args) && inPar.KeepUnmatched ) + for i = 1 : ( numel(args) / 2 ) + [key, args] = shift (args); + [value, args] = shift (args); + inPar.Unmatched.(key) = value; + endfor + elseif ( numel (args) ) && ~isempty(all(struct2cell(args{:}))) + error("%sfound unmatched parameters at end of arguments list", inPar.FunctionName); + endif + + ## remove copied field, keep it clean + inPar = rmfield (inPar, 'copy'); + +endfunction + + +function inPar = check_methods (inPar, idx) + + ## this makes it easier to read but is more memory intensive? + method = idx(1).subs; + args = idx(2).subs; + func = sprintf ("@inputParser/%s", method); + + if ( idx(2).type != '()' ) + print_usage (func); + endif + def_val = @() true; + [name, args] = shift (args); + ## a validator is optional but that complicates handling all the parsing with + ## few functions and conditions. If not specified @() true will always return + ## true. Simply using true is not enough because if the argument is zero it + ## return false and if it's too large, takes up memory + switch method + case {'addOptional', 'addParamValue'} + if ( numel (args) == 1 ) + args{2} = def_val; + elseif ( numel (args) == 2 ) + args{2} = validate_validator (args{2}); + else + print_usage(func); + endif + [def, val] = args{:}; + case {'addRequired'} + if ( numel (args) == 0 ) + val = def_val; + elseif ( numel (args) == 1 ) + val = validate_validator (args{1}); + else + print_usage(func); + endif + def = false; + case {'addSwitch'} + if ( numel (args) == 0 ) + val = def_val; + def = false; + else + print_usage(func); + endif + otherwise + error ("invalid index for reference of class %s", class (inPar) ); + endswitch + + inPar = validate_args (method(4:end), inPar, name, val, def); + +endfunction + +## because we are nice we also support using the name of a function and not only +## a function handle +function val = validate_validator (val) + if ( ischar (val) ) + val = str2func (val); + elseif ( !isa (val, 'function_handle') ) + error ("validator must be a function handle or the name of a valid function"); + end +endfunction + +## to have a single function that handles them all, something must be done with +## def value, because addRequire does not have those. That's why the order of +## the last two args is different from the rest and why 'def' has a default value +function inPar = validate_args (method, inPar, name, val, def = false) + + if ( !strcmp (class (inPar), 'inputParser') ) + error ("object must be of the inputParser class but '%s' was used", class (inPar) ); + elseif ( !isvarname (name) ) + error ("invalid variable name in argname"); + endif + + ## because the order arguments are specified are the order they are expected, + ## can't have ParamValue/Switch before Optional, and Optional before Required + n_optional = numel (fieldnames (inPar.Optional)); + n_params = numel (fieldnames (inPar.ParamValue)); + n_switch = numel (fieldnames (inPar.Switch)); + if ( strcmp (method, 'Required') && ( n_optional || n_params || n_switch) ) + error ("Can't specify 'Required' arguments after Optional, ParamValue or Switch"); + elseif ( strcmp (method, 'Optional') && ( n_params || n_switch) ) + error ("Can't specify 'Optional' arguments after ParamValue or Switch"); + endif + + ## even if CaseSensitive is turned on, we still shouldn't have two args with + ## the same. What if they decide to change in the middle of specifying them? + if ( any (strcmpi (inPar.Parameters, name)) ) + error ("argname '%s' has already been specified", name); + else + inPar.Parameters = push (inPar.Parameters, name); + inPar.(method).(name).default = def; + inPar.(method).(name).validator = val; + endif + + ## make sure that the given default value is actually valid + ## TODO make sure that when using the default, it's only validated once + ## allow empty default as in Matlab + if ( isa (val, 'function_handle') && !strcmpi (method, 'Required') && !isempty(def) && !feval (val, def) ) + error ("default value for '%s' failed validation with '%s'", name, func2str (val) ); + endif + +endfunction + +## this is just for consistency of error message +function error_invalid (prefix, name, val) + error("%sargument '%s' failed validation %s", prefix, name, func2str (val)); +endfunction + +################################################################################ +## very auxiliary functions +################################################################################ + +function [out, in] = shift (in, idx = 1) + out = in{idx}; + in(idx) = []; +endfunction + +function [in] = unshift (in, add) + if ( !iscell (add) ) + add = {add}; + endif + in (numel(add) + 1 : end + numel(add)) = in; + in (1:numel(add)) = add; +endfunction + +function [in] = push (in, add) + if ( !iscell (add) ) + add = {add}; + endif + in( end+1 : end+numel(add) ) = add; +endfunction diff --git a/inputparser/@inputParser/subsref.m~ b/inputparser/@inputParser/subsref.m~ new file mode 100644 index 00000000..86bd7612 --- /dev/null +++ b/inputparser/@inputParser/subsref.m~ @@ -0,0 +1,336 @@ +## Copyright (C) 2011-2012 Carnë Draug +## +## This program is free software; you can redistribute it and/or modify it under +## the terms of the GNU General Public License as published by the Free Software +## Foundation; either version 3 of the License, or (at your option) any later +## version. +## +## This program is distributed in the hope that it will be useful, but WITHOUT +## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or +## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more +## details. +## +## You should have received a copy of the GNU General Public License along with +## this program; if not, see . + +function inPar = subsref (inPar, idx) + + if ( !isa (inPar, 'inputParser') ) + error ("object must be of the inputParser class but '%s' was used", class (inPar) ); + elseif ( idx(1).type != '.' ) + error ("invalid index for class %s", class (inPar) ); + endif + + ## the following at the end may allow to use the obj.method notation one day + ## jwe is very against this ugly hack + ## what would happen if the user has the obj inside a struct? Bad things! +# ori = inputname(1); +# assignin('caller', ori, inPar); + + method = idx(1).subs; + + switch method + case 'Results' + inPar = retrieve_results (inPar, idx) + case 'Parameters' + inPar = inPar.Parameters; + case 'parse' + inPar = parse_args (inPar, idx); + case 'Unmatched' + case 'UsingDefaults' + case {'addOptional', 'addParamValue', 'addRequired', 'addSwitch'} + inPar = check_methods (inPar, idx); + otherwise + error ("invalid index for reference of class %s", class (inPar) ); + endswitch + + ## TODO we should make inPar an object of the inputParser class again. At + ## least after running parse it becomes just a structure again. While that is + ## bad, at least allows for easy access to the Results and Unmatched fields + ## without extra coding. +# inPar = class (inPar, 'inputParser'); + +endfunction + +function out = retrieve_results (inPar, idx) + + if ( numel(idx) != 2 || idx(2).type != '.' ) + print_usage ("@inputParser/Results"); + endif + + out = inPar.Results.(idx(2).subs); + +endfunction + + +## when parsing options, here's the principle: Required options have to be the +## first ones. They are followed by Optional if any. In the end come the +## ParamValue mixed with Switch. Any other order makes no sense +function inPar = parse_args (inPar, idx) + + ## syntax is inPar.parse (arguments) + if ( numel(idx) != 2 || idx(2).type != '()' ) + print_usage ("@inputParser/parse"); + endif + + ## this makes it easier to read but may be memory instensive + args = idx(2).subs; + + ## make copy of ordered list of Parameters to keep the original intact and readable + inPar.copy = inPar.Parameters; + + if ( numel (fieldnames (inPar.Required)) > numel (args) ) + error("%sNot enough arguments", inPar.FunctionName); + endif + + ## we take names out of 'copy' and values out of 'args', evaluate them and + ## store them into 'Results' + for i = 1 : numel (fieldnames (inPar.Required)) + [name, inPar.copy] = shift (inPar.copy); + [value, args] = shift (args); + if ( !feval (inPar.Required.(name).validator, value) ) + error_invalid (inPar.FunctionName, name, inPar.Required.(name).validator); + endif + inPar.Results.(name) = value; + endfor + + ## loop a maximum #times of the number of Optional, similarly to the required + ## loop. Once ran out of 'args', move their name into usingDefaults, place + ## their default values into 'Results', and break + + ## because if an argument is string and does not validate, should be considered + ## a ParamValue key + found_possible_key = false; + + for i = 1 : numel (fieldnames (inPar.Optional)) + if ( !numel (args) || found_possible_key) + ## loops the number of Optional options minus the number of them already processed + for n = 1 : (numel (fieldnames (inPar.Optional)) - i + 1 ) + [name, inPar.copy] = shift (inPar.copy); + inPar.UsingDefaults = push (inPar.UsingDefaults, name); + inPar.Results.(name) = inPar.Optional.(name).default; + endfor + break + endif + [name, inPar.copy] = shift (inPar.copy); + [value, args] = shift (args); + if ( !feval (inPar.Optional.(name).validator, value) ) + if (ischar (value) ) + ## maybe the other optional are not defined, this can be Paramvalue + ## place this one on defaults and go back to the top with note to clean loop + inPar.UsingDefaults = push (inPar.UsingDefaults, name); + inPar.Results.(name) = inPar.Optional.(name).default; + found_possible_key = true; + args = unshift (args, value); + continue + else + error_invalid (inPar.FunctionName, name, inPar.Optional.(name).validator); + endif + else + inPar.Results.(name) = value; + endif + endfor + + ## loop a maximum #times of the number of ParamValue, taking pairs of keys and + ## values out 'args'. We no longer expect an order so we need the index in + ## 'copy' to remove it from there. Once ran out of 'args', move their name + ## into usingDefaults, place their default values into 'Results', and break + for i = 1 : (numel (fieldnames (inPar.ParamValue)) + numel (fieldnames (inPar.Switch))) + if ( !numel (args) ) + ## loops the number of times left in 'copy' since these are the last type + for n = 1 : numel (inPar.copy) + [name, inPar.copy] = shift (inPar.copy); + inPar.UsingDefaults = push (inPar.UsingDefaults, name); + if (isfield (inPar.ParamValue, name)) + inPar.Results.(name) = inPar.ParamValue.(name).default; + else + inPar.Results.(name) = inPar.Switch.(name).default; + endif + endfor + break + endif + [key, args] = shift (args); + if ( !ischar (key) ) + error("%sParameter/Switch names must be strings", inPar.FunctionName); + endif + if (inPar.CaseSensitive) + index = find( strcmp(inPar.copy, key)); + else + index = find( strcmpi(inPar.copy, key)); + endif + ## we can't use isfield here to support case insensitive + if (any (strcmpi (fieldnames (inPar.Switch), key))) + value = true; + method = "Switch"; + else + ## then it must be a ParamValue (even if unmatched), shift its value + if (numel (args) < 1) + error ("%sparameter '%s' does not have a value", inPar.FunctionName, key); + endif + [value, args] = shift (args); + method = "ParamValue"; + endif + + ## empty index means no match so either return error or move them into 'Unmatched' + if (!isempty (index)) + [name, inPar.copy] = shift (inPar.copy, index); + if ( !feval (inPar.(method).(name).validator, value)) + error_invalid (inPar.FunctionName, key, inPar.(method).(name).validator); + endif + ## we use the name shifted from 'copy' instead of the key from 'args' in case + ## the key is in the wrong case + inPar.Results.(name) = value; + elseif (isempty (index) && inPar.KeepUnmatched ) + inPar.Unmatched.(key) = value; + i = i - 1; # this time didn't count, go back one + else + error ("%sargument '%s' did not match any valid parameter of the parser", inPar.FunctionName, key); + endif + endfor + + ## if there's leftovers they must be unmatched. Note that some unmatched can + ## have already been processed in the ParamValue loop + if ( numel (args) && inPar.KeepUnmatched ) + for i = 1 : ( numel(args) / 2 ) + [key, args] = shift (args); + [value, args] = shift (args); + inPar.Unmatched.(key) = value; + endfor + elseif ( numel (args) ) + error("%sfound unmatched parameters at end of arguments list", inPar.FunctionName); + endif + + ## remove copied field, keep it clean + inPar = rmfield (inPar, 'copy'); + +endfunction + + +function inPar = check_methods (inPar, idx) + + ## this makes it easier to read but is more memory intensive? + method = idx(1).subs; + args = idx(2).subs; + func = sprintf ("@inputParser/%s", method); + + if ( idx(2).type != '()' ) + print_usage (func); + endif + def_val = @() true; + [name, args] = shift (args); + ## a validator is optional but that complicates handling all the parsing with + ## few functions and conditions. If not specified @() true will always return + ## true. Simply using true is not enough because if the argument is zero it + ## return false and if it's too large, takes up memory + switch method + case {'addOptional', 'addParamValue'} + if ( numel (args) == 1 ) + args{2} = def_val; + elseif ( numel (args) == 2 ) + args{2} = validate_validator (args{2}); + else + print_usage(func); + endif + [def, val] = args{:}; + case {'addRequired'} + if ( numel (args) == 0 ) + val = def_val; + elseif ( numel (args) == 1 ) + val = validate_validator (args{1}); + else + print_usage(func); + endif + def = false; + case {'addSwitch'} + if ( numel (args) == 0 ) + val = def_val; + def = false; + else + print_usage(func); + endif + otherwise + error ("invalid index for reference of class %s", class (inPar) ); + endswitch + + inPar = validate_args (method(4:end), inPar, name, val, def); + +endfunction + +## because we are nice we also support using the name of a function and not only +## a function handle +function val = validate_validator (val) + if ( ischar (val) ) + val = str2func (val); + elseif ( !isa (val, 'function_handle') ) + error ("validator must be a function handle or the name of a valid function"); + end +endfunction + +## to have a single function that handles them all, something must be done with +## def value, because addRequire does not have those. That's why the order of +## the last two args is different from the rest and why 'def' has a default value +function inPar = validate_args (method, inPar, name, val, def = false) + + if ( !strcmp (class (inPar), 'inputParser') ) + error ("object must be of the inputParser class but '%s' was used", class (inPar) ); + elseif ( !isvarname (name) ) + error ("invalid variable name in argname"); + endif + + ## because the order arguments are specified are the order they are expected, + ## can't have ParamValue/Switch before Optional, and Optional before Required + n_optional = numel (fieldnames (inPar.Optional)); + n_params = numel (fieldnames (inPar.ParamValue)); + n_switch = numel (fieldnames (inPar.Switch)); + if ( strcmp (method, 'Required') && ( n_optional || n_params || n_switch) ) + error ("Can't specify 'Required' arguments after Optional, ParamValue or Switch"); + elseif ( strcmp (method, 'Optional') && ( n_params || n_switch) ) + error ("Can't specify 'Optional' arguments after ParamValue or Switch"); + endif + + ## even if CaseSensitive is turned on, we still shouldn't have two args with + ## the same. What if they decide to change in the middle of specifying them? + if ( any (strcmpi (inPar.Parameters, name)) ) + error ("argname '%s' has already been specified", name); + else + inPar.Parameters = push (inPar.Parameters, name); + inPar.(method).(name).default = def; + inPar.(method).(name).validator = val; + endif + + ## make sure that the given default value is actually valid + ## TODO make sure that when using the default, it's only validated once + if ( isa (val, 'function_handle') && !strcmpi (method, 'Required') && !feval (val, def) ) + error ("default value for '%s' failed validation with '%s'", name, func2str (val) ); + endif + +endfunction + +## this is just for consistency of error message +function error_invalid (prefix, name, val) + error("%sargument '%s' failed validation %s", prefix, name, func2str (val)); +endfunction + +################################################################################ +## very auxiliary functions +################################################################################ + +function [out, in] = shift (in, idx = 1) + out = in{idx}; + in(idx) = []; +endfunction + +function [in] = unshift (in, add) + if ( !iscell (add) ) + add = {add}; + endif + in (numel(add) + 1 : end + numel(add)) = in; + in (1:numel(add)) = add; +endfunction + +function [in] = push (in, add) + if ( !iscell (add) ) + add = {add}; + endif + in( end+1 : end+numel(add) ) = add; +endfunction diff --git a/inputparser/iparser.m b/inputparser/iparser.m new file mode 100644 index 00000000..baa48e57 --- /dev/null +++ b/inputparser/iparser.m @@ -0,0 +1,10 @@ +function ip = iparser(ip, action, varargin) + +if ~exist('OCTAVE_VERSION', 'builtin') + ip.(sprintf('%s', action))(varargin{:}) +else + ip=ip.(sprintf('%s', action))(varargin{:}); +end + +end + From 3343fcb7cbd53135cc53fc1bdb19f38f783d3785 Mon Sep 17 00:00:00 2001 From: Ville Date: Fri, 1 Feb 2013 11:03:44 +0200 Subject: [PATCH 02/64] Octave files --- diag/assess.m | 12 +-- diag/aucs.m | 6 +- diag/auct.m | 10 +- diag/ext_auc.m | 8 +- diag/hcs.m | 14 +-- diag/hct.m | 10 +- diag/idis.m | 24 ++--- diag/rsqr.m | 20 ++-- dist/prior_gamma.m | 12 +-- dist/prior_gaussian.m | 12 +-- dist/prior_invgamma.m | 12 +-- dist/prior_invt.m | 16 ++-- dist/prior_invunif.m | 4 +- dist/prior_laplace.m | 12 +-- dist/prior_loggaussian.m | 12 +-- dist/prior_loglogunif.m | 4 +- dist/prior_logt.m | 16 ++-- dist/prior_logunif.m | 6 +- dist/prior_sinvchi2.m | 12 +-- dist/prior_sqinvgamma.m | 12 +-- dist/prior_sqinvlogunif.m | 4 +- dist/prior_sqinvsinvchi2.m | 12 +-- dist/prior_sqinvunif.m | 4 +- dist/prior_sqrtinvt.m | 16 ++-- dist/prior_sqrtinvunif.m | 4 +- dist/prior_sqrtt.m | 16 ++-- dist/prior_sqrtunif.m | 4 +- dist/prior_t.m | 16 ++-- dist/prior_unif.m | 4 +- gp/demo_binomial1.m | 4 +- gp/demo_binomial2.m | 54 ++++++----- gp/demo_binomial_apc.m | 12 +-- gp/demo_inputdependentnoise.m | 9 +- gp/demo_lgcp.m | 4 +- gp/demo_loopred.m | 38 ++++++-- gp/demo_memorysave.m | 43 +++++---- gp/demo_neuralnetcov.m | 10 +- gp/demo_periodic.m | 3 +- gp/demo_regression_additive1.m | 161 ++++++++++++++++---------------- gp/demo_regression_sparse1.m | 10 +- gp/demo_spatial1.m | 6 +- gp/demo_spatial2.m | 12 +-- gp/gp_avpredcomp.m | 14 +-- gp/gp_cpred.m | 30 +++--- gp/gp_cvlcriterion.m | 10 +- gp/gp_dic.m | 16 ++-- gp/gp_e.m | 12 +-- gp/gp_g.m | 24 ++--- gp/gp_ia.m | 91 +++++++++--------- gp/gp_install.m | 94 +++++++++++++++---- gp/gp_jpred.m | 20 ++-- gp/gp_kfcv.m | 38 ++++---- gp/gp_kfcv_cdf.m | 32 +++---- gp/gp_kfcve.m | 20 ++-- gp/gp_lcriterion.m | 9 +- gp/gp_looe.m | 10 +- gp/gp_looeg.m | 10 +- gp/gp_loog.m | 10 +- gp/gp_loopred.m | 10 +- gp/gp_mc.m | 46 ++++----- gp/gp_optim.m | 31 ++++-- gp/gp_peff.m | 10 +- gp/gp_pred.m | 26 +++--- gp/gp_predcdf.m | 30 +++--- gp/gp_predprctmu.m | 28 +++--- gp/gp_predprcty.m | 28 +++--- gp/gp_refpred.m | 22 ++--- gp/gp_rnd.m | 24 ++--- gp/gp_set.m | 90 +++++++++--------- gp/gp_waic.m | 56 +++++------ gp/gpcf_SSsexp.m | 14 +-- gp/gpcf_cat.m | 6 +- gp/gpcf_constant.m | 8 +- gp/gpcf_exp.m | 16 ++-- gp/gpcf_intcov.m | 10 +- gp/gpcf_linear.m | 10 +- gp/gpcf_mask.m | 6 +- gp/gpcf_matern32.m | 16 ++-- gp/gpcf_matern52.m | 16 ++-- gp/gpcf_neuralnetwork.m | 14 +-- gp/gpcf_noise.m | 8 +- gp/gpcf_periodic.m | 25 +++-- gp/gpcf_ppcs0.m | 20 ++-- gp/gpcf_ppcs1.m | 20 ++-- gp/gpcf_ppcs2.m | 20 ++-- gp/gpcf_ppcs3.m | 20 ++-- gp/gpcf_prod.m | 6 +- gp/gpcf_rq.m | 20 ++-- gp/gpcf_scaled.m | 8 +- gp/gpcf_sexp.m | 16 ++-- gp/gpcf_sum.m | 6 +- gp/gpep_e.m | 166 ++++++++++++++++----------------- gp/gpep_fact.m | 19 ++-- gp/gpep_g.m | 14 ++- gp/gpep_jpred.m | 26 +++--- gp/gpep_looe.m | 12 +-- gp/gpep_loog.m | 12 +-- gp/gpep_loopred.m | 10 +- gp/gpep_pred.m | 26 +++--- gp/gpia_jpred.m | 26 +++--- gp/gpia_loopred.m | 14 +-- gp/gpia_pred.m | 26 +++--- gp/gpla_cm2.m | 20 ++-- gp/gpla_e.m | 88 ++++++++--------- gp/gpla_fact.m | 22 ++--- gp/gpla_g.m | 32 +++---- gp/gpla_jpred.m | 26 +++--- gp/gpla_looe.m | 12 +-- gp/gpla_loopred.m | 12 +-- gp/gpla_mo_e.m | 12 +-- gp/gpla_pred.m | 26 +++--- gp/gpmc_jpreds.m | 26 +++--- gp/gpmc_loopred.m | 14 +-- gp/gpmc_loopreds.m | 10 +- gp/gpmc_preds.m | 26 +++--- gp/gpmf_constant.m | 14 +-- gp/gpmf_linear.m | 14 +-- gp/gpmf_squared.m | 14 +-- gp/lgcp.m | 18 ++-- gp/lgpdens.m | 30 +++--- gp/lgpdens_cum.m | 8 +- gp/lik_binomial.m | 46 ++++----- gp/lik_coxph.m | 10 +- gp/lik_gaussian.m | 10 +- gp/lik_gaussianbl.m | 10 +- gp/lik_gaussiansmt.m | 23 +++-- gp/lik_inputdependentnoise.m | 8 +- gp/lik_inputdependentweibull.m | 8 +- gp/lik_lgp.m | 28 +++--- gp/lik_lgpc.m | 28 +++--- gp/lik_loggaussian.m | 37 ++++---- gp/lik_logit.m | 28 +++--- gp/lik_loglogistic.m | 37 ++++---- gp/lik_multinom.m | 4 +- gp/lik_negbin.m | 36 +++---- gp/lik_negbinztr.m | 41 ++++---- gp/lik_poisson.m | 28 +++--- gp/lik_probit.m | 4 +- gp/lik_qgp.m | 38 ++++---- gp/lik_softmax.m | 4 +- gp/lik_t.m | 64 ++++++------- gp/lik_weibull.m | 36 +++---- gp/lik_zinegbin.m | 10 +- gp/metric_distancematrix.m | 24 ++--- gp/metric_euclidean.m | 12 +-- gp/pred_coxph.m | 24 ++--- gp/scaled_hmc.m | 106 ++++++++++++++++++--- gp/surrogate_sls.m | 6 +- mc/hmc2.m | 10 +- mc/hmc2_opt.m | 10 +- misc/setrandstream.m | 15 ++- optim/fminscg.m | 15 ++- xunit/test_spatial1.m | 2 + 153 files changed, 1767 insertions(+), 1576 deletions(-) diff --git a/diag/assess.m b/diag/assess.m index ed7d5aea..b2d2f066 100644 --- a/diag/assess.m +++ b/diag/assess.m @@ -7,12 +7,12 @@ % % Note: Z dimensions must be size(y,1) X size(tt,2) ip=inputParser; -ip.addRequired('crit1',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('crit2',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:)))) -ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:)))) -ip.addRequired('tt', @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(crit1,crit2,y,z,tt) +ip=iparser(ip,'addRequired','crit1',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','crit2',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','tt', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'parse',crit1,crit2,y,z,tt) for i=1:size(tt,2) c1(i)=hct(crit1(:,i),y(:,i),z(:,size(tt,2)),tt(i)); diff --git a/diag/aucs.m b/diag/aucs.m index 2c838688..d7ac11ba 100644 --- a/diag/aucs.m +++ b/diag/aucs.m @@ -19,9 +19,9 @@ % Copyright (C) 2012 Ernesto Ulloa, Aki Vehtari ip=inputParser; -ip.addRequired('crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(crit,z) +ip=iparser(ip,'addRequired','crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'parse',crit,z) ye=z; tps=0; diff --git a/diag/auct.m b/diag/auct.m index c3b6dfd6..53512438 100644 --- a/diag/auct.m +++ b/diag/auct.m @@ -17,11 +17,11 @@ % Copyright (C) 2012 Ernesto Ulloa, Aki Vehtari ip=inputParser; -ip.addRequired('crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:)))) -ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:)))) -ip.addRequired('tt', @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(crit,y,z,tt) +ip=iparser(ip,'addRequired','crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','tt', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'parse',crit,y,z,tt) for i=1:size(tt,2) comp=bsxfun(@times,bsxfun(@and,y(:,i)<=tt(i),1-z(:,i)),bsxfun(@or,bsxfun(@and,y(:,i)<=tt(i),z(:,i)),y(:,i)>=tt(i))'); diff --git a/diag/ext_auc.m b/diag/ext_auc.m index f868b181..9a8c75d0 100644 --- a/diag/ext_auc.m +++ b/diag/ext_auc.m @@ -16,10 +16,10 @@ % Copyright (C) 2012 Ernesto Ulloa, Aki Vehtari ip=inputParser; - ip.addRequired('P',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('tt', @(x) isreal(x) && all(isfinite(x(:)))) - ip.addRequired('t', @(x) isreal(x) && all(isfinite(x(:)))) - ip.parse(P,tt,t); + ip=iparser(ip,'addRequired','P',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','tt', @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','t', @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'parse',P,tt,t); [n,nin]=size(P); S=1-P; diff --git a/diag/hcs.m b/diag/hcs.m index 70a18ddf..f78c007c 100644 --- a/diag/hcs.m +++ b/diag/hcs.m @@ -1,4 +1,4 @@ -function [c,bb] = hcs_new(riskscore,y,z,t,varargin) +function [c,bb] = hcs(riskscore,y,z,t,varargin) %HCS Compute Harrell's C for survival model at given time % % Description @@ -26,12 +26,12 @@ % Copyright (C) 2012 Tomi Peltola, Ernesto Ulloa, Aki Vehtari ip=inputParser; -ip.addRequired('riskscore',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:)))) -ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:)))) -ip.addRequired('t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) -ip.addParamValue('rsubstream',0,@(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) -ip.parse(riskscore,y,z,t,varargin{:}) +ip=iparser(ip,'addRequired','riskscore',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) +ip=iparser(ip,'addParamValue','rsubstream',0,@(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) +ip=iparser(ip,'parse',riskscore,y,z,t,varargin{:}) rsubstream=ip.Results.rsubstream; diff --git a/diag/hct.m b/diag/hct.m index c47d3d7a..48076c26 100644 --- a/diag/hct.m +++ b/diag/hct.m @@ -17,12 +17,12 @@ % Copyright (C) 2012 Ernesto Ulloa, Aki Vehtari ip=inputParser; -ip.addRequired('crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y',@(x) ~isempty(x) && isreal(x)) -ip.addRequired('z', @(x) ~isempty(x) && isreal(x)) -ip.addRequired('tt', @(x) ~isempty(x) && isreal(x)) +ip=iparser(ip,'addRequired','crit',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','y',@(x) ~isempty(x) && isreal(x)) +ip=iparser(ip,'addRequired','z', @(x) ~isempty(x) && isreal(x)) +ip=iparser(ip,'addRequired','tt', @(x) ~isempty(x) && isreal(x)) -ip.parse(crit,y,z,tt) +ip=iparser(ip,'parse',crit,y,z,tt) if size(y,2) ~= size(z,2) error('y and z dimensions must match') diff --git a/diag/idis.m b/diag/idis.m index eb1fb1e8..0a5a53cf 100644 --- a/diag/idis.m +++ b/diag/idis.m @@ -35,31 +35,31 @@ % model-based estimator model_based_estimator = true; - ip.addRequired('pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) + ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) if nargin > 2 % if more than 2 input arguments, there must be four as only a % single optional parameter is implemented if nargin == 4 - ip.parse(pt, pn, y, z); + ip=iparser(ip,'parse',pt, pn, y, z); else error('Invalid number of arguments.'); end else - ip.parse(pt, pn); + ip=iparser(ip,'parse',pt, pn); end else % Pencina et al. estimator model_based_estimator = false; - ip.addRequired('pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:)))) - ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:)))) - ip.addRequired('t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) - ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip.parse(pt, pn, y, z, t, varargin{:}) + ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) + ip=iparser(ip,'parse',pt, pn, y, z, t, varargin{:}) end rsubstream=ip.Results.rsubstream; diff --git a/diag/rsqr.m b/diag/rsqr.m index 11e40a5d..1df8f3cb 100644 --- a/diag/rsqr.m +++ b/diag/rsqr.m @@ -34,29 +34,29 @@ % model-based estimator model_based_estimator = true; - ip.addRequired('p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) + ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) if nargin > 1 % if more than 1 input arguments, there must be 3 as only a % single optional parameter is implemented if nargin == 3 - ip.parse(p, y, z); + ip=iparser(ip,'parse',p, y, z); else error('Invalid number of arguments.'); end else - ip.parse(p); + ip=iparser(ip,'parse',p); end else % Pencina et al. estimator model_based_estimator = false; - ip.addRequired('p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) isreal(x) && all(isfinite(x(:)))) - ip.addRequired('z', @(x) isreal(x) && all(isfinite(x(:)))) - ip.addRequired('t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) - ip.addParamValue('rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip.parse(p, y, z, t, varargin{:}) + ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) + ip=iparser(ip,'parse',p, y, z, t, varargin{:}) end rsubstream=ip.Results.rsubstream; diff --git a/dist/prior_gamma.m b/dist/prior_gamma.m index d4fa1604..e2038c0a 100644 --- a/dist/prior_gamma.m +++ b/dist/prior_gamma.m @@ -32,12 +32,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_GAMMA'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('sh',4, @(x) isscalar(x) && x>0); - ip.addParamValue('sh_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('is',1, @(x) isscalar(x) && x>0); - ip.addParamValue('is_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','sh',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sh_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','is',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','is_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_gaussian.m b/dist/prior_gaussian.m index 75d33ace..ee494896 100644 --- a/dist/prior_gaussian.m +++ b/dist/prior_gaussian.m @@ -29,12 +29,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_GAUSSIAN'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x) && x>0); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x) && x>=0); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_invgamma.m b/dist/prior_invgamma.m index 26e06d5c..9236d24e 100644 --- a/dist/prior_invgamma.m +++ b/dist/prior_invgamma.m @@ -33,12 +33,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_INVGAMMA'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('sh',4, @(x) isscalar(x) && x>0); - ip.addParamValue('sh_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','sh',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sh_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_invt.m b/dist/prior_invt.m index aaee2a52..2758f69d 100644 --- a/dist/prior_invt.m +++ b/dist/prior_invt.m @@ -36,14 +36,14 @@ ip=inputParser; ip.FunctionName = 'PRIOR_INVT'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x)); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x)); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_invunif.m b/dist/prior_invunif.m index ad7db8c3..e64a08ae 100644 --- a/dist/prior_invunif.m +++ b/dist/prior_invunif.m @@ -17,8 +17,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_INVUNIFORM'; - ip.addOptional('p', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_laplace.m b/dist/prior_laplace.m index a0681aa1..ca4d36cc 100644 --- a/dist/prior_laplace.m +++ b/dist/prior_laplace.m @@ -29,12 +29,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_LAPLACE'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x) && x>0); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_loggaussian.m b/dist/prior_loggaussian.m index 79f31ad5..70fee5d2 100644 --- a/dist/prior_loggaussian.m +++ b/dist/prior_loggaussian.m @@ -29,12 +29,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_LOGGAUSSIAN'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x)); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x)); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_loglogunif.m b/dist/prior_loglogunif.m index e3063f7e..13db6bbd 100644 --- a/dist/prior_loglogunif.m +++ b/dist/prior_loglogunif.m @@ -17,8 +17,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_LOGLOGUNIFORM'; - ip.addOptional('p', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_logt.m b/dist/prior_logt.m index 9f980e00..bbd2abba 100644 --- a/dist/prior_logt.m +++ b/dist/prior_logt.m @@ -33,14 +33,14 @@ ip=inputParser; ip.FunctionName = 'PRIOR_LOGT'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x)); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x)); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_logunif.m b/dist/prior_logunif.m index bac24947..b7ff0934 100644 --- a/dist/prior_logunif.m +++ b/dist/prior_logunif.m @@ -9,7 +9,7 @@ % PRIOR_* % Copyright (c) 2009 Jarno Vanhatalo -% Copyright (c) 2010 Jaakko Riihim�ki +% Copyright (c) 2010 Jaakko Riihim�ki % Copyright (c) 2010 Aki Vehtari % This software is distributed under the GNU General Public @@ -18,8 +18,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_LOGUNIFORM'; - ip.addOptional('p', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sinvchi2.m b/dist/prior_sinvchi2.m index 5a3937ce..2ba1c0de 100644 --- a/dist/prior_sinvchi2.m +++ b/dist/prior_sinvchi2.m @@ -32,12 +32,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SINVCHI2'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqinvgamma.m b/dist/prior_sqinvgamma.m index 3ba8a250..c019e482 100644 --- a/dist/prior_sqinvgamma.m +++ b/dist/prior_sqinvgamma.m @@ -32,12 +32,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SQINVGAMMA'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('sh',4, @(x) isscalar(x) && x>0); - ip.addParamValue('sh_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('is',1, @(x) isscalar(x) && x>0); - ip.addParamValue('is_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','sh',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sh_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','is',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','is_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqinvlogunif.m b/dist/prior_sqinvlogunif.m index 9d207f22..bb7f83ca 100644 --- a/dist/prior_sqinvlogunif.m +++ b/dist/prior_sqinvlogunif.m @@ -18,8 +18,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SQINVLOGUNIFORM'; - ip.addOptional('p', [], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqinvsinvchi2.m b/dist/prior_sqinvsinvchi2.m index 032797db..c6f4eefb 100644 --- a/dist/prior_sqinvsinvchi2.m +++ b/dist/prior_sqinvsinvchi2.m @@ -32,12 +32,12 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SQINVSINVCHI2'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqinvunif.m b/dist/prior_sqinvunif.m index d97f19c7..7ea185d3 100644 --- a/dist/prior_sqinvunif.m +++ b/dist/prior_sqinvunif.m @@ -17,8 +17,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SQINVUNIFORM'; - ip.addOptional('p', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqrtinvt.m b/dist/prior_sqrtinvt.m index b354c538..dcabac0d 100644 --- a/dist/prior_sqrtinvt.m +++ b/dist/prior_sqrtinvt.m @@ -33,14 +33,14 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SQRTINVT'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x)); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x)); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqrtinvunif.m b/dist/prior_sqrtinvunif.m index 335acb47..bb6b3ae9 100644 --- a/dist/prior_sqrtinvunif.m +++ b/dist/prior_sqrtinvunif.m @@ -18,8 +18,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_INVSQRTUNIFORM'; - ip.addOptional('p', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqrtt.m b/dist/prior_sqrtt.m index 63336935..a6fffc08 100644 --- a/dist/prior_sqrtt.m +++ b/dist/prior_sqrtt.m @@ -33,14 +33,14 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SQRTT'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x)); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x)); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_sqrtunif.m b/dist/prior_sqrtunif.m index e4b12cf8..daede784 100644 --- a/dist/prior_sqrtunif.m +++ b/dist/prior_sqrtunif.m @@ -18,8 +18,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_SQRTUNIFORM'; - ip.addOptional('p', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_t.m b/dist/prior_t.m index 39eca881..99f9e5e0 100644 --- a/dist/prior_t.m +++ b/dist/prior_t.m @@ -35,14 +35,14 @@ ip=inputParser; ip.FunctionName = 'PRIOR_T'; - ip.addOptional('p', [], @isstruct); - ip.addParamValue('mu',0, @(x) isscalar(x)); - ip.addParamValue('mu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('s2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('s2_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'addParamValue','mu',0, @(x) isscalar(x)); + ip=iparser(ip,'addParamValue','mu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','s2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','s2_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/dist/prior_unif.m b/dist/prior_unif.m index 4a289adc..914462f9 100644 --- a/dist/prior_unif.m +++ b/dist/prior_unif.m @@ -16,8 +16,8 @@ ip=inputParser; ip.FunctionName = 'PRIOR_UNIFORM'; - ip.addOptional('p', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','p', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); p=ip.Results.p; if isempty(p) diff --git a/gp/demo_binomial1.m b/gp/demo_binomial1.m index 096a2a70..25170da2 100644 --- a/gp/demo_binomial1.m +++ b/gp/demo_binomial1.m @@ -95,14 +95,14 @@ color1=ones(1,3)*0.8; color2=ones(1,3)*0.5; % GP 95% credible interval -h1=fill([xgrid' fliplr(xgrid')], [(Eyt_la+1.96*sqrt(Varyt_la))' fliplr((Eyt_la-1.96*sqrt(Varyt_la))')], color1, 'edgecolor', color1); +% h1=fill([xgrid' fliplr(xgrid')], [(Eyt_la+1.96*sqrt(Varyt_la))' fliplr((Eyt_la-1.96*sqrt(Varyt_la))')], color1, 'edgecolor', color1); % GP mean h2=plot(xgrid, Eyt_la, 'color', color2, 'linewidth', 3); % observations h3=plot(x, y, 'xk', 'markersize', 10, 'linewidth', 2); % true function h4=plot(xgrid, 1./(1+exp(-(-1.5.*xgrid.^3+0.5*xgrid.^2+0.75*xgrid)))*100, 'color', 'r', 'linewidth', 2); -legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function') +legend([h2 h3 h4], 'GP mean', 'observations', 'true latent function') title('Gaussian process prediction with a squared exponential covariance function') % To compute predictive densities at the test points xt, the total number diff --git a/gp/demo_binomial2.m b/gp/demo_binomial2.m index 88a9a488..7d9ebd4b 100644 --- a/gp/demo_binomial2.m +++ b/gp/demo_binomial2.m @@ -113,12 +113,13 @@ subplot('Position',[0.03 0.7 0.3 0.25]) hold on % GP 95% credible interval -h2=fill([xgrid' fliplr(xgrid')], [(Eft+1.96*sqrt(Varft))' fliplr((Eft-1.96*sqrt(Varft))')], color1, 'edgecolor', color1); +% h2=fill([xgrid' fliplr(xgrid')], [(Eft+1.96*sqrt(Varft))' fliplr((Eft-1.96*sqrt(Varft))')], color1, 'edgecolor', color1); % GP mean h1=plot(xgrid, Eft, 'color', color2, 'linewidth', 3); axis([-1.5 1.5 -14 14]) title('Laplace+grid approximation') -legend([h1 h2],'Mean of the latent','95% CI',2) +legend(h1,'Mean of the latent',2) +% legend([h1 h2],'Mean of the latent','95% CI',2) legend('boxoff') line(xlim,[0 0],'Linestyle','--','color','k') line([0 0],ylim,'Linestyle','--','color','k') @@ -128,12 +129,13 @@ subplot('Position',[0.03 0.38 0.3 0.25]) hold on % GP 95% credible interval -h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft+1.96*sqrt(Varft)))' fliplr((logitinv(Eft-1.96*sqrt(Varft)))')], color1, 'edgecolor', color1); +% h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft+1.96*sqrt(Varft)))' fliplr((logitinv(Eft-1.96*sqrt(Varft)))')], color1, 'edgecolor', color1); % GP mean h1=plot(xgrid, logitinv(Eft), 'color', color2, 'linewidth', 3); % observations h3=plot(x, y./5, 'xr', 'markersize', 10, 'linewidth', 2); -legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2) +% legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2) +legend([h1 h3],'Expected prob.','Observations',2) legend('boxoff') line(xlim,[.5 .5],'Linestyle','--','color','k') line([0 0],ylim,'Linestyle','--','color','k') @@ -148,10 +150,10 @@ set(gca,'ytick',[]) ylim([0 2000]) xlabel('LD50') -h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center'); -h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center'); -h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center'); -hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k'); +% h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center'); +% h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center'); +% h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center'); +% hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k'); fprintf('Elapsed time %.0fs\n',toc) % ------- EP approximation -------- @@ -190,12 +192,13 @@ subplot('Position',[0.36 0.7 0.3 0.25]) hold on % GP 95% credible interval -h2=fill([xgrid' fliplr(xgrid')], [(Eft+1.96*sqrt(Varft))' fliplr((Eft-1.96*sqrt(Varft))')], color1, 'edgecolor', color1); +% h2=fill([xgrid' fliplr(xgrid')], [(Eft+1.96*sqrt(Varft))' fliplr((Eft-1.96*sqrt(Varft))')], color1, 'edgecolor', color1); % GP mean h1=plot(xgrid, Eft, 'color', color2, 'linewidth', 3); axis([-1.5 1.5 -14 14]) title('EP+grid approximation') -legend([h1 h2],'Mean of the latent','95% CI',2) +legend([h1],'Mean of the latent',2) +% legend([h1 h2],'Mean of the latent','95% CI',2) legend('boxoff') line(xlim,[0 0],'Linestyle','--','color','k') line([0 0],ylim,'Linestyle','--','color','k') @@ -205,12 +208,13 @@ subplot('Position',[0.36 0.38 0.3 0.25]) hold on % GP 95% credible interval -h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft+1.96*sqrt(Varft)))' fliplr((logitinv(Eft-1.96*sqrt(Varft)))')], color1, 'edgecolor', color1); +% h2=fill([xgrid' fliplr(xgrid')], [(logitinv(Eft+1.96*sqrt(Varft)))' fliplr((logitinv(Eft-1.96*sqrt(Varft)))')], color1, 'edgecolor', color1); % GP mean h1=plot(xgrid, logitinv(Eft), 'color', color2, 'linewidth', 3); % observations h3=plot(x, y./5, 'xr', 'markersize', 10, 'linewidth', 2); -legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2) +legend([h1 h3],'Expected prob.','Observations',2) +% legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2) legend('boxoff') line(xlim,[.5 .5],'Linestyle','--','color','k') line([0 0],ylim,'Linestyle','--','color','k') @@ -225,10 +229,10 @@ set(gca,'ytick',[]) ylim([0 2000]) xlabel('LD50') -h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center'); -h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center'); -h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center'); -hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k'); +% h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center'); +% h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center'); +% h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center'); +% hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k'); fprintf('Elapsed time %.0fs\n',toc) % ------- MCMC approximation -------- @@ -263,12 +267,13 @@ subplot('Position',[0.69 0.7 0.3 0.25]) hold on % GP 95% credible interval -h2=fill([xgrid' fliplr(xgrid')], [prctile(fs,97.5,2)' fliplr(prctile(fs,2.5,2)')], color1, 'edgecolor', color1); +% h2=fill([xgrid' fliplr(xgrid')], [prctile(fs,97.5,2)' fliplr(prctile(fs,2.5,2)')], color1, 'edgecolor', color1); % GP mean h1=plot(xgrid, mean(fs,2), 'color', color2, 'linewidth', 3); axis([-1.5 1.5 -14 14]) title('MCMC approximation') -legend([h1 h2],'Mean of the latent','95% CI',2) +legend([h1],'Mean of the latent',2) +% legend([h1 h2],'Mean of the latent','95% CI',2) legend('boxoff') line(xlim,[0 0],'Linestyle','--','color','k') line([0 0],ylim,'Linestyle','--','color','k') @@ -278,12 +283,13 @@ subplot('Position',[0.69 0.38 0.3 0.25]) hold on % GP 95% credible interval -h2=fill([xgrid' fliplr(xgrid')], [(logitinv(prctile(fs,97.5,2)))' fliplr(logitinv(prctile(fs,2.5,2))')], color1, 'edgecolor', color1); +% h2=fill([xgrid' fliplr(xgrid')], [(logitinv(prctile(fs,97.5,2)))' fliplr(logitinv(prctile(fs,2.5,2))')], color1, 'edgecolor', color1); % GP mean h1=plot(xgrid, logitinv(mean(fs,2)), 'color', color2, 'linewidth', 3); % observations h3=plot(x, y./5, 'xr', 'markersize', 10, 'linewidth', 2); -legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2) +% legend([h1 h2 h3],'Expected prob.','95% CI','Observations',2) +legend([h1 h3],'Expected prob.','Observations',2) legend('boxoff') line(xlim,[.5 .5],'Linestyle','--','color','k') line([0 0],ylim,'Linestyle','--','color','k') @@ -298,8 +304,8 @@ set(gca,'ytick',[]) ylim([0 2000]) xlabel('LD50') -h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center'); -h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center'); -h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center'); -hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k'); +% h1=text(prctile(ld50s,2.5),1850,'2.5%','HorizontalAlignment','center'); +% h2=text(prctile(ld50s,50),1850,'50%','HorizontalAlignment','center'); +% h3=text(prctile(ld50s,97.5),1850,'97.5%','HorizontalAlignment','center'); +% hl=line(repmat(prctile(ld50s,[2.5 50 97.5]),2,1),repmat([0 1800]',1,3),'Color','k'); fprintf('Elapsed time %.0fs\n',toc) diff --git a/gp/demo_binomial_apc.m b/gp/demo_binomial_apc.m index 3f406046..aa373982 100644 --- a/gp/demo_binomial_apc.m +++ b/gp/demo_binomial_apc.m @@ -161,8 +161,8 @@ set(gcf, 'color', 'w'), hold on color1=ones(1,3)*0.8; color2=ones(1,3)*0.5; % Estimate -h1=fill([xx1' fliplr(xx1')], [(Eft_1(ind1)+1.96*sqrt(Varft_1(ind1)))' ... - fliplr((Eft_1(ind1)-1.96*sqrt(Varft_1(ind1)))')], color1, 'edgecolor', color1); +% h1=fill([xx1' fliplr(xx1')], [(Eft_1(ind1)+1.96*sqrt(Varft_1(ind1)))' ... +% fliplr((Eft_1(ind1)-1.96*sqrt(Varft_1(ind1)))')], color1, 'edgecolor', color1); h2=plot(xx1, Eft_1(ind1), 'color', color2, 'linewidth', 3); % True function h4=plot(xx1, f1o(ind1), 'color', 'r', 'linewidth', 2); hold off @@ -173,8 +173,8 @@ % Time period effect subplot(3,1,2) set(gcf, 'color', 'w'), hold on -h1=fill([xx2' fliplr(xx2')], [(Eft_2(ind2)+1.96*sqrt(Varft_2(ind2)))' ... - fliplr((Eft_2(ind2)-1.96*sqrt(Varft_2(ind2)))')], color1, 'edgecolor', color1); +% h1=fill([xx2' fliplr(xx2')], [(Eft_2(ind2)+1.96*sqrt(Varft_2(ind2)))' ... +% fliplr((Eft_2(ind2)-1.96*sqrt(Varft_2(ind2)))')], color1, 'edgecolor', color1); h2=plot(xx2, Eft_2(ind2), 'color', color2, 'linewidth', 3); % true function h4=plot(xx2, f2o(ind2), 'color', 'r', 'linewidth', 2); @@ -185,8 +185,8 @@ % Cohort effect subplot(3,1,3) set(gcf, 'color', 'w'), hold on -h1=fill([xx3' fliplr(xx3')], [(Eft_3(ind3)+1.96*sqrt(Varft_3(ind3)))' ... - fliplr((Eft_3(ind3)-1.96*sqrt(Varft_3(ind3)))')], color1, 'edgecolor', color1); +% h1=fill([xx3' fliplr(xx3')], [(Eft_3(ind3)+1.96*sqrt(Varft_3(ind3)))' ... +% fliplr((Eft_3(ind3)-1.96*sqrt(Varft_3(ind3)))')], color1, 'edgecolor', color1); h2=plot(xx3, Eft_3(ind3), 'color', color2, 'linewidth', 3); % true function h4=plot(xx3, f3o(ind3), 'color', 'r', 'linewidth', 2); diff --git a/gp/demo_inputdependentnoise.m b/gp/demo_inputdependentnoise.m index 7bba9864..6b33f1e0 100644 --- a/gp/demo_inputdependentnoise.m +++ b/gp/demo_inputdependentnoise.m @@ -17,8 +17,7 @@ %================================= % 1D Demonstration %================================= -stream0 = RandStream('mt19937ar','Seed',0); -prevstream = RandStream.setGlobalStream(stream0); +setrandstream(1) close all; % x = 100*rand([40 1]); n =150; @@ -116,8 +115,7 @@ %==================================== % 2D Demonstration %==================================== -stream0 = RandStream('mt19937ar','Seed',0); -prevstream = RandStream.setGlobalStream(stream0); +setrandstream(1) % Create data from two 2 dimensional gaussians nt=10; @@ -188,8 +186,7 @@ %============================================ % Demonstration with homoscedastic noise %============================================ -stream0 = RandStream('mt19937ar','Seed',0); -prevstream = RandStream.setGlobalStream(stream0); +setrandstream(0) n =200; nt = 200; diff --git a/gp/demo_lgcp.m b/gp/demo_lgcp.m index 38aff086..42bd1167 100644 --- a/gp/demo_lgcp.m +++ b/gp/demo_lgcp.m @@ -47,7 +47,7 @@ L = strrep(S,'demo_lgcp.m','demodata/coal.txt'); x=load(L); lgcp(x,[1850:1963]','gpcf',@gpcf_exp) -line([x x],[5 5.3],'color','k') +line([x x],repmat([5 5.3],size(x,1),1),'color','k') line(xlim,[5.15 5.15],'color','k') xlim([1850 1963]) ylim([0 5.29]) @@ -64,7 +64,7 @@ S = which('demo_lgcp'); L = strrep(S,'demo_lgcp.m','demodata/redwoodfull.txt'); x=load(L); -lgcp(x,'range',[0 1 0 1],'latent_method','Laplace','gridn',20) +lgcp(x,'range',[0 1 0 1],'latent_method','Laplace','gridn',20); h=line(x(:,1),x(:,2),'marker','.','linestyle','none','color','k','markersize',10); colorbar axis square diff --git a/gp/demo_loopred.m b/gp/demo_loopred.m index 07380665..b8ac7f4e 100644 --- a/gp/demo_loopred.m +++ b/gp/demo_loopred.m @@ -124,21 +124,43 @@ % Plot results figure, subplot(2,2,1) -plot(pred.lpyt, lpyt_lrs,'.'); diagline; title('LA-LOO(lrs) vs. LOO-CV'); +plot(pred.lpyt, lpyt_lrs,'.');ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('LA-LOO(lrs) vs. LOO-CV'); xlabel('Log-predictive density (CV)'); ylabel('Log-predictive density(LA-LOO)'); -subplot(2,2,2), plot(pred.lpyt, lpyt_cav,'.'); diagline; title('LA-LOO(cavity) vs. LOO-CV'); + +subplot(2,2,2), plot(pred.lpyt, lpyt_cav,'.'); ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('LA-LOO(cavity) vs. LOO-CV'); xlabel('Log-predictive density (CV)'); ylabel('Log-predictive density(LA-LOO)'); -subplot(2,2,3), plot(pred.lpyt, lpyt_inla,'.'); diagline; title('LA-LOO(inla) vs. LOO-CV'); + +subplot(2,2,3), plot(pred.lpyt, lpyt_inla,'.'); ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('LA-LOO(inla) vs. LOO-CV'); xlabel('Log-predictive density (CV)'); ylabel('Log-predictive density(LA-LOO)'); -subplot(2,2,4), plot(pred_ep.lpyt, lpyt_ep,'.'); diagline; title('EP-LOO vs. LOO-CV'); + +subplot(2,2,4), plot(pred_ep.lpyt, lpyt_ep,'.'); ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('EP-LOO vs. LOO-CV'); xlabel('Log-predictive density (CV)'); ylabel('Log-predictive density(EP-LOO)'); figure, subplot(2,2,1) -plot(pred.Eft, Eft_lrs,'.'); diagline; title('LA-LOO(lrs) vs. LOO-CV'); +plot(pred.Eft, Eft_lrs,'.');ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('LA-LOO(lrs) vs. LOO-CV'); xlabel('Latent prediction (CV)'); ylabel('Latent prediction (LA-LOO)'); -subplot(2,2,2), plot(pred.Eft, Eft_cav,'.'); diagline; title('LA-LOO(cavity) vs. LOO-CV'); + +subplot(2,2,2), plot(pred.Eft, Eft_cav,'.'); ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('LA-LOO(cavity) vs. LOO-CV'); xlabel('Latent prediction (CV)'); ylabel('Latent prediction (LA-LOO)'); -subplot(2,2,3), plot(pred.Eft, Eft_inla,'.'); diagline; title('LA-LOO(inla) vs. LOO-CV'); + +subplot(2,2,3), plot(pred.Eft, Eft_inla,'.'); ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('LA-LOO(inla) vs. LOO-CV'); xlabel('Latent prediction (CV)'); ylabel('Latent prediction (LA-LOO)'); -subplot(2,2,4), plot(pred_ep.Eft, Eft_ep,'.'); diagline; title('EP-LOO vs. LOO-CV'); + +subplot(2,2,4), plot(pred_ep.Eft, Eft_ep,'.'); ax = axis; +line([min(ax(1),ax(3)) max(ax(2),ax(4))], [min(ax(1),ax(3)) max(ax(2),ax(4))]); +title('EP-LOO vs. LOO-CV'); xlabel('Latent prediction (CV)'); ylabel('Latent prediction (LA-LOO)'); diff --git a/gp/demo_memorysave.m b/gp/demo_memorysave.m index 0b535cb8..2e598dd0 100644 --- a/gp/demo_memorysave.m +++ b/gp/demo_memorysave.m @@ -154,32 +154,35 @@ pl = prior_t('s2',10); pm = prior_sqrtunif(); -gpcf1 = gpcf_ppcs3('nin',nin,'lengthScale', 1, 'magnSigma2', 0.1, 'lengthScale_prior', pl, 'magnSigma2_prior', pm); gpcf2 = gpcf_sexp('lengthScale', 5, 'magnSigma2', 0.05, 'lengthScale_prior', pl, 'magnSigma2_prior', pm); gpcf3 = gpcf_neuralnetwork('weightSigma2', [1 1], 'biasSigma2', 0.05, 'weightSigma2_prior', pl, 'biasSigma2_prior', pm); lik = lik_negbin(); -% GP without memory save -gp = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ... - 'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing'); -% GP with memory saving option enabled -gp2 = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ... - 'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing', 'savememory', 'on'); opt=optimset('TolFun',1e-2,'TolX',1e-2, 'Display', 'off'); - -fprintf('Spatial process (Laplace), FIC GP, PPCS3 covariance function, without and with memory saving (optimization and prediction)\n'); - -% Optimization and prediction without memory saving -tic,gp=gp_optim(gp,x,y,'z',ye,'opt',opt); -[Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye, 'tstind', [1:n]); toc - -% Optimization and prediction with memory saving -tic,gp2=gp_optim(gp2,x,y,'z',ye,'opt',opt); -[Ef2, Varf2] = gp_pred(gp2, x, y, x, 'z', ye, 'tstind', [1:n]); toc - -% Check that predictions (and optimization) with and without memory saving are same -assert(all(Ef==Ef2)); assert(all(Varf==Varf2)); +if exist('ldlchol','file') + % GP without memory save + gp = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ... + 'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing'); + % GP with memory saving option enabled + gp2 = gp_set('type', 'FIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, ... + 'jitterSigma2', 1e-4, 'infer_params', 'covariance+inducing', 'savememory', 'on'); + + gpcf1 = gpcf_ppcs3('nin',nin,'lengthScale', 1, 'magnSigma2', 0.1, 'lengthScale_prior', pl, 'magnSigma2_prior', pm); + + fprintf('Spatial process (Laplace), FIC GP, PPCS3 covariance function, without and with memory saving (optimization and prediction)\n'); + + % Optimization and prediction without memory saving + tic,gp=gp_optim(gp,x,y,'z',ye,'opt',opt); + [Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye, 'tstind', [1:n]); toc + + % Optimization and prediction with memory saving + tic,gp2=gp_optim(gp2,x,y,'z',ye,'opt',opt); + [Ef2, Varf2] = gp_pred(gp2, x, y, x, 'z', ye, 'tstind', [1:n]); toc + + % Check that predictions (and optimization) with and without memory saving are same + assert(all(Ef==Ef2)); assert(all(Varf==Varf2)); +end gp = gp_set('lik', lik, 'cf', gpcf2, 'jitterSigma2', 1e-4, 'latent_method', 'EP'); gp2 = gp_set('lik', lik, 'cf', gpcf2, 'jitterSigma2', 1e-4, 'latent_method', 'EP', 'savememory', 'on'); diff --git a/gp/demo_neuralnetcov.m b/gp/demo_neuralnetcov.m index 7c3637ca..69e46906 100644 --- a/gp/demo_neuralnetcov.m +++ b/gp/demo_neuralnetcov.m @@ -130,23 +130,25 @@ % Plot the predictions and data color1=ones(1,3)*0.8; color2=ones(1,3)*0.5; figure, set(gcf, 'color', 'w'), hold on -h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map+1.96*sqrt(Varyt_map))' fliplr((Eyt_map-1.96*sqrt(Varyt_map))')], color1, 'edgecolor', color1); +% h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map+1.96*sqrt(Varyt_map))' fliplr((Eyt_map-1.96*sqrt(Varyt_map))')], color1, 'edgecolor', color1); % GP mean h2=plot(xgrid, Eyt_map, 'color', color2, 'linewidth', 3); % observations h3=plot(x, y, 'xk', 'markersize', 10, 'linewidth', 2); % true function h4=plot(xgrid, norm_pdf(4*xgrid), 'color', 'r', 'linewidth', 2); -legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function') +% legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function') +legend([h2 h3 h4], 'GP mean', 'observations', 'true latent function') title('GP (squared exponential) predictions and the data points'); figure, set(gcf, 'color', 'w'), hold on -h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map2+1.96*sqrt(Varyt_map2))' fliplr((Eyt_map2-1.96*sqrt(Varyt_map2))')], color1, 'edgecolor', color1); +% h1=fill([xgrid' fliplr(xgrid')], [(Eyt_map2+1.96*sqrt(Varyt_map2))' fliplr((Eyt_map2-1.96*sqrt(Varyt_map2))')], color1, 'edgecolor', color1); % GP mean h2=plot(xgrid, Eyt_map2, 'color', color2, 'linewidth', 3); % observations h3=plot(x, y, 'xk', 'markersize', 10, 'linewidth', 2); % true function h4=plot(xgrid, norm_pdf(4*xgrid), 'color', 'r', 'linewidth', 2); -legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function') +legend([h2 h3 h4], 'GP mean', 'observations', 'true latent function') +% legend([h1 h2 h3 h4], 'GP 95% CI', 'GP mean', 'observations', 'true latent function') title('GP (neural network) predictions and the data points'); diff --git a/gp/demo_periodic.m b/gp/demo_periodic.m index 77a4e007..9571d2fb 100644 --- a/gp/demo_periodic.m +++ b/gp/demo_periodic.m @@ -316,5 +316,6 @@ plot(xtt(:,1),exp(Eft_full+1.96.*sqrt(Varft_full)).*mean(y),'b--') legend('Training data', 'Validation data','Predicted mean','95% CI', 'Location', 'NorthWest') -line(2008,0:80,'LineWidth',2) +line([2008 2008],[0 80],'LineWidth',2) +% line(2008,0:80,'LineWidth',2) axis tight diff --git a/gp/demo_regression_additive1.m b/gp/demo_regression_additive1.m index 19dbfdd1..b839de55 100644 --- a/gp/demo_regression_additive1.m +++ b/gp/demo_regression_additive1.m @@ -119,12 +119,12 @@ warning('GPstuff:SuiteSparseMissing',... ['SuiteSparse is not properly installed. (in BECS try ''use suitesparse'')\n' ... 'Using gpcf_sexp (non-compact support) instead of gpcf_ppcs2 (compact support)']); - gpcf2 = gpcf_sexp('lengthScale', 5, 'magnSigma2', 1, 'lengthScale_prior', pl2, 'magnSigma2_prior', pm); + gpcf2 = gpcf_sexp('lengthScale', 5, 'magnSigma2', 1, 'lengthScale_prior', pl2, 'magnSigma2_prior', pm2); end lik = lik_gaussian('sigma2', 0.1, 'sigma2_prior', pn); % Create the GP structure -gp = gp_set('lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-9) +gp = gp_set('lik', lik, 'cf', {gpcf1, gpcf2}, 'jitterSigma2', 1e-9); % ----------------------------- % --- Conduct the inference --- @@ -179,7 +179,7 @@ Xu = [min(x):24:max(x)+10]'; % Create the FIC GP structure -gp_fic = gp_set('type', 'FIC', 'lik', lik, 'cf', {gpcf1,gpcf2}, 'jitterSigma2', 1e-9, 'X_u', Xu) +gp_fic = gp_set('type', 'FIC', 'lik', lik, 'cf', {gpcf1,gpcf2}, 'jitterSigma2', 1e-9, 'X_u', Xu); % ----------------------------- % --- Conduct the inference --- @@ -227,7 +227,7 @@ trindex{i} = find(x>edges(i) & x0)) -ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) -ip.addParamValue('method', 'mean', @(x) ismember(x, {'median', 'mean'})) -ip.addParamValue('plot', 'off', @(x) ismember(x, {'on', 'off'})) -ip.addParamValue('tr', 0.25, @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(gp, x, y, xt, ind, varargin{:}); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','xt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','ind', @(x) ~isempty(x) && isvector(x)); +ip=iparser(ip,'addParamValue','var', [], @(x) isreal(x)); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); +ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); +ip=iparser(ip,'addParamValue','method', 'mean', @(x) ismember(x, {'median', 'mean'})); +ip=iparser(ip,'addParamValue','plot', 'off', @(x) ismember(x, {'on', 'off'})); +ip=iparser(ip,'addParamValue','tr', 0.25, @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',gp, x, y, xt, ind, varargin{:}); predcf=ip.Results.predcf; tstind=ip.Results.tstind; method = ip.Results.method; diff --git a/gp/gp_cvlcriterion.m b/gp/gp_cvlcriterion.m index 1b53d000..dc76f504 100644 --- a/gp/gp_cvlcriterion.m +++ b/gp/gp_cvlcriterion.m @@ -27,11 +27,11 @@ ip=inputParser; ip.FunctionName = 'GP_CVLCRITERION'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); % pass these forward options=struct(); z = ip.Results.z; diff --git a/gp/gp_dic.m b/gp/gp_dic.m index e5d7fbc4..79af1ab7 100644 --- a/gp/gp_dic.m +++ b/gp/gp_dic.m @@ -75,14 +75,14 @@ ip=inputParser; ip.FunctionName = 'GP_DIC'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('focus', 'param', @(x) ismember(x,{'param','latent','all'})) - ip.addParamValue('output', 'DIC', @(x) ismember(x,{'DIC', 'mlpd'})) - ip.addParamValue('form', 'mean', @(x) ismember(x,{'mean','all'})) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','focus', 'param', @(x) ismember(x,{'param','latent','all'})); + ip=iparser(ip,'addParamValue','output', 'DIC', @(x) ismember(x,{'DIC', 'mlpd'})); + ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); focus=ip.Results.focus; output=ip.Results.output; form=ip.Results.form; diff --git a/gp/gp_e.m b/gp/gp_e.m index e09f1564..7d279b5c 100755 --- a/gp/gp_e.m +++ b/gp/gp_e.m @@ -66,12 +66,12 @@ ip=inputParser; ip.FunctionName = 'GP_E'; -ip.addRequired('w', @(x) isempty(x) || isvector(x) && isreal(x)); -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','w', @(x) isempty(x) || isvector(x) && isreal(x)); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); z=ip.Results.z; gp=gp_unpak(gp, w); diff --git a/gp/gp_g.m b/gp/gp_g.m index b3f541dd..008d3afb 100755 --- a/gp/gp_g.m +++ b/gp/gp_g.m @@ -46,12 +46,12 @@ ip=inputParser; ip.FunctionName = 'GP_G'; -ip.addRequired('w', @(x) isvector(x) && isreal(x)); -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x)); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); z=ip.Results.z; if ~all(isfinite(w(:))); % instead of stopping to error, return NaN @@ -204,15 +204,15 @@ if isfield(gp.lik, 'nondiagW') && ~ismember(gp.lik.type, {'LGP' 'LGPC'}) % check in which components the covariance function is present % for likelihoods with non-diagonal Hessian - do = false(nout,1); + doo = false(nout,1); if multicf for z1=1:nout if any(gp.comp_cf{z1}==i) - do(z1) = true; + doo(z1) = true; end end else - do = true(nout,1); + doo = true(nout,1); end end @@ -295,17 +295,17 @@ % Non-diagonalizable likelihoods Bdl=0; Cdl=0; if isfield(gp.lik,'xtime'); - if do(1) + if doo(1) Bdl = Bdl + b(1:ntime)'*(DKff*b(1:ntime)); Cdl = Cdl + sum(sum(invC(1:ntime,1:ntime).*DKff)); % help arguments end - if do(2) + if doo(2) Bdl = Bdl + b(ntime+1:end)'*(DKff*b(ntime+1:end)); Cdl = Cdl + sum(sum(invC(ntime+1:end,ntime+1:end).*DKff)); % help arguments end else for z1=1:nout - if do(z1) + if doo(z1) Bdl = Bdl + b(1+nl(z1):nl(z1+1))'*(DKff*b(1+nl(z1):nl(z1+1))); Cdl = Cdl + sum(sum(invC(:,:,z1).*DKff)); % help arguments end diff --git a/gp/gp_ia.m b/gp/gp_ia.m index d171f5d7..f51974f7 100644 --- a/gp/gp_ia.m +++ b/gp/gp_ia.m @@ -63,7 +63,7 @@ % - 'iter' displays output at each evaluation point % -% Copyright (c) 2009-2010 Ville Pietil�inen, Jarno Vanhatalo +% Copyright (c) 2009-2010 Ville Pietil�inen, Jarno Vanhatalo % Copyright (c) 2010,2012 Aki Vehtari % This software is distributed under the GNU General Public @@ -72,42 +72,42 @@ ip=inputParser; ip.FunctionName = 'GP_IA'; - ip.addRequired('gp', @isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt',[], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('int_method', 'CCD', @(x) ischar(x) && ... - ismember(x,{'CCD','grid','is_normal','is_t','hmc'})) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('rotate', true, @(x) islogical(x) && isscalar(x)) - ip.addParamValue('autoscale', 'on', @(x) (islogical(x) && isscalar(x))|| ... - ismember(x,{'on' 'off' 'full'})) - ip.addParamValue('validate', 1, @(x) ismember(x,[1 2])) - ip.addParamValue('threshold', 2.5, @(x) isscalar(x) && isreal(x) && ... - isfinite(x) && x>0) - ip.addParamValue('step_size', 1, @(x) isscalar(x) && isreal(x) && ... - isfinite(x) && x>0) - ip.addParamValue('t_nu', 4, @(x) isscalar(x) && isreal(x) && ... - isfinite(x) && x>=1) - ip.addParamValue('nsamples', 40, @(x) isscalar(x) && isreal(x) && ... - isfinite(x) && x>=1) - ip.addParamValue('repeat', 10, @(x) isscalar(x) && isreal(x) && ... - isfinite(x) && x>=1) - ip.addParamValue('f0', 1.1, @(x) isscalar(x) && isreal(x) && ... - isfinite(x) && x>0) - ip.addParamValue('qmc', true, @(x) islogical(x) && isscalar(x)) - ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle')) - ip.addParamValue('opt_optim', [], @isstruct) - ip.addParamValue('opt_hmc', [], @isstruct); - ip.addParamValue('persistence_reset', 0, @(x) ~isempty(x) && isreal(x)); - ip.addParamValue('display', 'on', @(x) islogical(x) || isreal(x) || ... - ismember(x,{'on' 'off' 'iter'})) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp', @isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt',[], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','int_method', 'CCD', @(x) ischar(x) && ... + ismember(x,{'CCD','grid','is_normal','is_t','hmc'})); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','rotate', true, @(x) islogical(x) && isscalar(x)); + ip=iparser(ip,'addParamValue','autoscale', 'on', @(x) (islogical(x) && isscalar(x))|| ... + ismember(x,{'on' 'off' 'full'})); + ip=iparser(ip,'addParamValue','validate', 1, @(x) ismember(x,[1 2])); + ip=iparser(ip,'addParamValue','threshold', 2.5, @(x) isscalar(x) && isreal(x) && ... + isfinite(x) && x>0); + ip=iparser(ip,'addParamValue','step_size', 1, @(x) isscalar(x) && isreal(x) && ... + isfinite(x) && x>0); + ip=iparser(ip,'addParamValue','t_nu', 4, @(x) isscalar(x) && isreal(x) && ... + isfinite(x) && x>=1); + ip=iparser(ip,'addParamValue','nsamples', 40, @(x) isscalar(x) && isreal(x) && ... + isfinite(x) && x>=1); + ip=iparser(ip,'addParamValue','repeat', 10, @(x) isscalar(x) && isreal(x) && ... + isfinite(x) && x>=1); + ip=iparser(ip,'addParamValue','f0', 1.1, @(x) isscalar(x) && isreal(x) && ... + isfinite(x) && x>0); + ip=iparser(ip,'addParamValue','qmc', true, @(x) islogical(x) && isscalar(x)); + ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle')); + ip=iparser(ip,'addParamValue','opt_optim', struct(), @isstruct); + ip=iparser(ip,'addParamValue','opt_hmc', [], @isstruct); + ip=iparser(ip,'addParamValue','persistence_reset', 0, @(x) ~isempty(x) && isreal(x)); + ip=iparser(ip,'addParamValue','display', 'on', @(x) islogical(x) || isreal(x) || ... + ismember(x,{'on' 'off' 'iter'})); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); xt=ip.Results.xt; % integration parameters int_method=ip.Results.int_method; @@ -206,7 +206,7 @@ end tic for i2 = 1:nParam - H(:,i2) = hessianMultiplication(w, H(:,i2)); + H(:,i2) = hessianMultiplication(w, H(:,i2), fh_g, gp, x, y, options); end et=toc; if ismember(opt.display,{'on','iter'}) && et > 1 @@ -217,7 +217,7 @@ fprintf(' IA-%s: computing Hessian using finite difference\n',int_method); end tic - H = hessian(w); + H = hessian(w, fh_e, fh_g, gp, x, y, options); et=toc; if ismember(opt.display,{'on','iter'}) && et > 1 fprintf(' Elapsed time %.2f seconds\n',et); @@ -589,7 +589,7 @@ case {'is_normal' 'is_normal_qmc' 'is_t'} % Covariance of the gaussian approximation - H = full(hessian(w)); + H = full(hessian(w, fh_e, fh_g, gp, x, y, options)); Sigma = inv(H); Scale = Sigma; [V,D] = eig(full(Sigma)); @@ -983,11 +983,11 @@ end end - function H = hessian(w0) + function H = hessian(w0, fh_e, fh_g, gp, x, y, options) % Compute Hessian using finite differences, which is can be slow % if number of parameters is high - m = length(w); + m = length(w0); e0 = fh_e(w0,gp,x,y,options); delta = 1e-4; H = -1*ones(m,m); @@ -1049,8 +1049,9 @@ end end +end - function vv = hessianMultiplication(w0, v) + function vv = hessianMultiplication(w0, v, fh_g, gp, x, y, options) if size(w0) ~= size(v) v = v'; end @@ -1058,6 +1059,4 @@ g2 = fh_g(w0-rr*v, gp, x, y, options); g1 = fh_g(w0+rr*v, gp, x, y, options); vv = (g1 - g2) / (2*rr); - end - -end + end \ No newline at end of file diff --git a/gp/gp_install.m b/gp/gp_install.m index a267cbcb..89fc5b94 100644 --- a/gp/gp_install.m +++ b/gp/gp_install.m @@ -35,9 +35,17 @@ function gp_install(suiteSparse) % Compile the 'dist_euclidean' mex-function if (~isempty (strfind (computer, '64'))) % 64-bit MATLAB - mex -O -g -largeArrayDims -output private/dist_euclidean linuxCsource/dist_euclidean.c + if ~exist('OCTAVE_VERSION','builtin') + mex -O -g -largeArrayDims -output private/dist_euclidean linuxCsource/dist_euclidean.c + else + mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c + end else - mex -O -output private/dist_euclidean linuxCsource/dist_euclidean.c + if ~exist('OCTAVE_VERSION','builtin') + mex -O -output private/dist_euclidean linuxCsource/dist_euclidean.c + else + mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c + end end if nargin<1 || isempty(suiteSparse) @@ -48,11 +56,21 @@ function gp_install(suiteSparse) % Compile the 'trcov' mex-function if (~isempty (strfind (computer, '64'))) % 64-bit MATLAB - mex -O -g -largeArrayDims -output private/trcov linuxCsource/trcov.c - mex -O -g -largeArrayDims -output private/dist_euclidean linuxCsource/dist_euclidean.c + if ~exist('OCTAVE_VERSION', 'builtin') + mex -O -g -largeArrayDims -output private/trcov linuxCsource/trcov.c + mex -O -g -largeArrayDims -output private/dist_euclidean linuxCsource/dist_euclidean.c + else + mex --output private/trcov.mex linuxCsource/trcov.c + mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c + end else - mex -O -output private/trcov linuxCsource/trcov.c - mex -O -output private/dist_euclidean linuxCsource/dist_euclidean.c + if ~exist('OCTAVE_VERSION', 'builtin') + mex -O -output private/trcov linuxCsource/trcov.c + mex -O -output private/dist_euclidean linuxCsource/dist_euclidean.c + else + mex --output private/trcov.mex linuxCsource/trcov.c + mex --output private/dist_euclidean.mex linuxCsource/dist_euclidean.c + end end fprintf ('\n GP package succesfully compiled ') ; @@ -147,7 +165,6 @@ function gp_install(suiteSparse) % This is exceedingly ugly. The MATLAB mex command needs to be told where to % fine the LAPACK and BLAS libraries, which is a real portability nightmare. - if (pc) if (v < 6.5) % MATLAB 6.1 and earlier: use the version supplied here @@ -266,7 +283,7 @@ function gp_install(suiteSparse) 'Lib/subdomains', ... 'Lib/timing', ... 'Lib/util' } ; - + for i = 1:length (metis_src) metis_src {i} = [metis_path '/' metis_src{i}] ; end @@ -341,12 +358,18 @@ function gp_install(suiteSparse) if (have_metis) source = [metis_src source] ; end - - source = strrep(source, '../../', suiteSparse); - source = strrep(source, '../', cholmod_path); + if exist('OCTAVE_VERSION', 'builtin') + for i1=1:length(source) + source{i1} = strrep(source{i1}, '../../', suiteSparse); + source{i1} = strrep(source{i1}, '../', cholmod_path); + end + else + source = strrep(source, '../../', suiteSparse); + source = strrep(source, '../', cholmod_path); + end kk = 0 ; - + for f = source ff = strrep (f {1}, '/', filesep) ; slash = strfind (ff, filesep) ; @@ -357,14 +380,22 @@ function gp_install(suiteSparse) end o = ff (slash:end) ; obj = [obj ' ' o obj_extension] ; %#ok - s = sprintf ('mex %s -DDLONG -O %s -c %s.c', d, include, ff) ; + if ~exist('OCTAVE_VERSION','builtin') + s = sprintf ('mex %s -DDLONG -O %s -c %s.c', d, include, ff) ; + else + s = sprintf ('mex %s -DDLONG %s -c %s.c', d, include, ff) ; + end kk = do_cmd (s, kk, details) ; end - + if pc % compile mexFunctions mex_src = 'winCsource\spinv'; - s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + if ~exist('OCTAVE_VERSION','builtin') + s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + else + s = sprintf ('mex %s -DDLONG %s %s.c', d, include, mex_src) ; + end s = [s obj]; s = [s ' ']; s = [s lapack]; @@ -372,7 +403,11 @@ function gp_install(suiteSparse) %mex_src = 'linuxCsource/ldlrowupdate'; mex_src = 'winCsource\ldlrowupdate'; - s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + if ~exist('OCTAVE_VERSION','builtin') + s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + else + s = sprintf ('mex %s -DDLONG %s %s.c', d, include, mex_src) ; + end s = [s obj]; s = [s ' ']; s = [s lapack]; @@ -380,7 +415,11 @@ function gp_install(suiteSparse) else % compile mexFunctions mex_src = 'linuxCsource/spinv'; - s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + if ~exist('OCTAVE_VERSION','builtin') + s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + else + s = sprintf ('mex %s -DDLONG %s %s.c', d, include, mex_src) ; + end s = [s obj]; s = [s ' ']; s = [s lapack]; @@ -388,15 +427,19 @@ function gp_install(suiteSparse) %mex_src = 'linuxCsource/ldlrowupdate'; mex_src = 'linuxCsource/ldlrowupdate'; - s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + if ~exist('OCTAVE_VERSION','builtin') + s = sprintf ('mex %s -DDLONG -O %s %s.c', d, include, mex_src) ; + else + s = sprintf ('mex %s -DDLONG %s %s.c', d, include, mex_src) ; + end s = [s obj]; s = [s ' ']; s = [s lapack]; kk = do_cmd (s, kk, details) ; end - % clean up s = ['delete ' obj] ; + do_cmd (s, kk, details) ; fprintf ('\nGP package succesfully compiled \n') ; end @@ -413,7 +456,18 @@ function gp_install(suiteSparse) kk = kk + 1 ; fprintf ('.') ; end -eval (s) ; +if ~exist('OCTAVE_VERSION','builtin') + eval (s) ; +else + tmp=regexp(s,'\S+','match'); + if strcmp(tmp{1},'delete') + for i=1:length(tmp)-1 + eval([tmp{1} ' ' tmp{i+1}]); + end + else + eval (s) ; + end +end %------------------------------------------------------------------------------- function v = getversion diff --git a/gp/gp_jpred.m b/gp/gp_jpred.m index 75a43665..1594cb19 100644 --- a/gp/gp_jpred.m +++ b/gp/gp_jpred.m @@ -106,16 +106,16 @@ ip=inputParser; ip.FunctionName = 'GP_JPRED'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) -ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) -ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) -ip.parse(gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); +ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); +ip=iparser(ip,'parse',gp, x, y, varargin{:}); xt=ip.Results.xt; yt=ip.Results.yt; predcf=ip.Results.predcf; diff --git a/gp/gp_kfcv.m b/gp/gp_kfcv.m index 2d35c0ff..35e2984b 100644 --- a/gp/gp_kfcv.m +++ b/gp/gp_kfcv.m @@ -203,25 +203,25 @@ ip=inputParser; ip.FunctionName = 'GP_KFCV'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('inf_method', 'MAP', @(x) ... - ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'})) - ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle')) - ip.addParamValue('opt', struct(), @isstruct) - ip.addParamValue('pred','f+lp+y',@ischar) - ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip.addParamValue('rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip.addParamValue('trindex', [], @(x) isempty(x) || iscell(x)) - ip.addParamValue('tstindex', [], @(x) isempty(x) || iscell(x)) - ip.addParamValue('display', 'on', @(x) islogical(x) || ... - ismember(x,{'on' 'off' 'iter' 'fold'})) - ip.addParamValue('save_results', false, @(x) islogical(x)) - ip.addParamValue('folder', [], @(x) ischar(x) ) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','inf_method', 'MAP', @(x) ... + ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'})); + ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle')); + ip=iparser(ip,'addParamValue','opt', struct(), @isstruct); + ip=iparser(ip,'addParamValue','pred','f+lp+y',@ischar); + ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ip=iparser(ip,'addParamValue','rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ip=iparser(ip,'addParamValue','trindex', [], @(x) isempty(x) || iscell(x)); + ip=iparser(ip,'addParamValue','tstindex', [], @(x) isempty(x) || iscell(x)); + ip=iparser(ip,'addParamValue','display', 'on', @(x) islogical(x) || ... + ismember(x,{'on' 'off' 'iter' 'fold'})); + ip=iparser(ip,'addParamValue','save_results', false, @(x) islogical(x)); + ip=iparser(ip,'addParamValue','folder', [], @(x) ischar(x) ); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; yt=ip.Results.yt; inf_method=ip.Results.inf_method; diff --git a/gp/gp_kfcv_cdf.m b/gp/gp_kfcv_cdf.m index 99989b0d..1a97e604 100644 --- a/gp/gp_kfcv_cdf.m +++ b/gp/gp_kfcv_cdf.m @@ -63,22 +63,22 @@ ip=inputParser; ip.FunctionName = 'GP_KFCV'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('inf_method', 'MAP', @(x) ... - ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'})) - ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle')) - ip.addParamValue('opt', struct(), @isstruct) - ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip.addParamValue('rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip.addParamValue('trindex', [], @(x) isempty(x) || iscell(x)) - ip.addParamValue('tstindex', [], @(x) isempty(x) || iscell(x)) - ip.addParamValue('display', 'on', @(x) islogical(x) || ... - ismember(x,{'iter' 'fold'})) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','inf_method', 'MAP', @(x) ... + ismember(x,{'MAP' 'LOO' 'KFCV' 'WAIC' 'WAICV' 'WAICG' 'MCMC' 'IA' 'fixed'})); + ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle')); + ip=iparser(ip,'addParamValue','opt', struct(), @isstruct); + ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ip=iparser(ip,'addParamValue','rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ip=iparser(ip,'addParamValue','trindex', [], @(x) isempty(x) || iscell(x)); + ip=iparser(ip,'addParamValue','tstindex', [], @(x) isempty(x) || iscell(x)); + ip=iparser(ip,'addParamValue','display', 'on', @(x) islogical(x) || ... + ismember(x,{'iter' 'fold'})); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; yt=ip.Results.yt; inf_method=ip.Results.inf_method; diff --git a/gp/gp_kfcve.m b/gp/gp_kfcve.m index b12b8235..182b60ae 100644 --- a/gp/gp_kfcve.m +++ b/gp/gp_kfcve.m @@ -39,17 +39,17 @@ ip=inputParser; ip.FunctionName = 'GPEP_KFCVE'; -ip.addRequired('w', @(x) isempty(x) || ... +ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ... isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp', @(x) isstruct(x) || iscell(x)); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) -ip.addParamValue('rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) -ip.addParamValue('trindex', [], @(x) ~isempty(x) || iscell(x)) -ip.addParamValue('tstindex', [], @(x) ~isempty(x) || iscell(x)) -ip.parse(w, gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x)); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); +ip=iparser(ip,'addParamValue','rstream', 1, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); +ip=iparser(ip,'addParamValue','trindex', [], @(x) ~isempty(x) || iscell(x)); +ip=iparser(ip,'addParamValue','tstindex', [], @(x) ~isempty(x) || iscell(x)); +ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); k=ip.Results.k; rstream=ip.Results.rstream; trindex=ip.Results.trindex; diff --git a/gp/gp_lcriterion.m b/gp/gp_lcriterion.m index a0387217..46dbd84d 100644 --- a/gp/gp_lcriterion.m +++ b/gp/gp_lcriterion.m @@ -26,10 +26,11 @@ ip=inputParser; ip.FunctionName = 'GP_LCRITERION'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); ip.parse(gp, x, y, varargin{:}); % pass these forward options=struct(); diff --git a/gp/gp_looe.m b/gp/gp_looe.m index be23fe8b..88c60a79 100644 --- a/gp/gp_looe.m +++ b/gp/gp_looe.m @@ -29,11 +29,11 @@ % Nothing to parse, but check the arguments anyway ip=inputParser; ip.FunctionName = 'GP_LOOE'; -ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y); +ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y); if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs) error('GP_LOOE: Mean functions not yet supported'); diff --git a/gp/gp_looeg.m b/gp/gp_looeg.m index 12ec02a6..849f1e0b 100644 --- a/gp/gp_looeg.m +++ b/gp/gp_looeg.m @@ -38,11 +38,11 @@ % Nothing to parse, but check the arguments anyway ip=inputParser; ip.FunctionName = 'GP_LOOEG'; -ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y); +ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y); if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs) error('GP_LOOEG: Mean functions not yet supported'); diff --git a/gp/gp_loog.m b/gp/gp_loog.m index ad506186..6de304d6 100644 --- a/gp/gp_loog.m +++ b/gp/gp_loog.m @@ -28,11 +28,11 @@ % Nothing to parse, but check the arguments anyway ip=inputParser; ip.FunctionName = 'GP_LOOG'; -ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y); +ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y); if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs) error('GP_LOOE: Mean functions not yet supported'); diff --git a/gp/gp_loopred.m b/gp/gp_loopred.m index 7b5c1667..d1852ba8 100644 --- a/gp/gp_loopred.m +++ b/gp/gp_loopred.m @@ -95,12 +95,12 @@ % Nothing to parse, but check the arguments anyway ip=inputParser; ip.FunctionName = 'GP_LOOPRED'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.parse(gp, x, y); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',gp, x, y); -if isfield(gp,'meanf') & ~isempty(gp.meanf) +if isfield(gp,'meanf') && ~isempty(gp.meanf) error('GP_LOOPRED: Mean functions not yet supported'); end diff --git a/gp/gp_mc.m b/gp/gp_mc.m index 8434936b..2b664a13 100755 --- a/gp/gp_mc.m +++ b/gp/gp_mc.m @@ -79,23 +79,23 @@ ip=inputParser; ip.FunctionName = 'GP_MC'; - ip.addRequired('gp',@isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('nsamples', 1, @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('repeat', 1, @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('display', 1, @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('record',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('hmc_opt', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('sls_opt', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('ssls_opt', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('latent_opt', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lik_hmc_opt', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lik_sls_opt', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lik_gibbs_opt', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('persistence_reset', 0, @(x) ~isempty(x) && isreal(x)); - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','nsamples', 1, @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','repeat', 1, @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','display', 1, @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','record',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','hmc_opt', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','sls_opt', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','ssls_opt', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','latent_opt', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','lik_hmc_opt', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','lik_sls_opt', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','lik_gibbs_opt', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','persistence_reset', 0, @(x) ~isempty(x) && isreal(x)); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; opt.nsamples=ip.Results.nsamples; opt.repeat=ip.Results.repeat; @@ -174,7 +174,7 @@ % Initialize record if isempty(record) % No old record - record=recappend(); + [record,ri,lrej,indrej,hmcrej,lik_hmcrej]=recappend([],gp, x, y, z, opt); else ri=size(record.etr,1); end @@ -425,7 +425,7 @@ % --- Set record ------- ri=ri+1; - record=recappend(record); + record=recappend(record, gp, x, y, z, opt, ri, hmcrej, lik_hmcrej, lrej); % Display some statistics THIS COULD BE DONE NICER ALSO... if opt.display && rem(ri,opt.display)==0 @@ -449,9 +449,10 @@ fprintf('\n'); end end - +end + %------------------------ -function record = recappend(record) +function [record,ri,lrej,indrej,hmcrej,lik_hmcrej] = recappend(record, gp, x, y, z, opt, ri, hmcrej, lik_hmcrej, lrej) % RECAPPEND - Record append % Description % RECORD = RECAPPEND(RECORD, RI, GP, P, T, PP, TT, REJS, U) takes @@ -462,7 +463,7 @@ ncf = length(gp.cf); - if nargin == 0 % Initialize record structure + if isempty(record) % Initialize record structure record.type = gp.type; record.lik = gp.lik; if isfield(gp,'latent_method') @@ -636,4 +637,3 @@ end end -end diff --git a/gp/gp_optim.m b/gp/gp_optim.m index 0446e5b2..8eebdada 100644 --- a/gp/gp_optim.m +++ b/gp/gp_optim.m @@ -39,15 +39,15 @@ ip=inputParser; ip.FunctionName = 'GP_OPTIM'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle')) -ip.addParamValue('opt', [], @isstruct) -ip.addParamValue('loss', 'e', @(x) ismember(lower(x),{'e', 'loo', 'kfcv', 'waic' 'waic' 'waicv' 'waicg'})) -ip.addParamValue('k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) -ip.parse(gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle')); +ip=iparser(ip,'addParamValue','opt', [], @isstruct); +ip=iparser(ip,'addParamValue','loss', 'e', @(x) ismember(lower(x),{'e', 'loo', 'kfcv', 'waic' 'waic' 'waicv' 'waicg'})); +ip=iparser(ip,'addParamValue','k', 10, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); +ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; optimf=ip.Results.optimf; opt=ip.Results.opt; @@ -148,7 +148,18 @@ function opt=setOpt(optdefault, opt) % Set default options - opttmp=optimset(optdefault,opt); + if ~exist('OCTAVE_VERSION','builtin') + opttmp=optimset(optdefault,opt); + else + opttmp=opt; + names=fieldnames(optdefault); + for i=1:length(names) + if ~isfield(opt,names{i}) + opttmp.(names{i})=optdefault.(names{i}); + end + end + + end % Set some additional options for @fminscg if isfield(opt,'lambda') diff --git a/gp/gp_peff.m b/gp/gp_peff.m index ed259075..2ca3ae95 100644 --- a/gp/gp_peff.m +++ b/gp/gp_peff.m @@ -53,11 +53,11 @@ ip=inputParser; ip.FunctionName = 'GP_PEFF'; - ip.addRequired('gp',@isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; tn = size(x,1); diff --git a/gp/gp_pred.m b/gp/gp_pred.m index b26c4b54..6068bc7d 100644 --- a/gp/gp_pred.m +++ b/gp/gp_pred.m @@ -86,22 +86,22 @@ ip=inputParser; ip.FunctionName = 'GP_PRED'; -ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) -ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>=0)) -ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) +ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>=0)); +ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gp_predcdf.m b/gp/gp_predcdf.m index 4b550753..1ee7b298 100644 --- a/gp/gp_predcdf.m +++ b/gp/gp_predcdf.m @@ -35,24 +35,24 @@ ip=inputParser; ip.FunctionName = 'GP_PREDCDF'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gp_predprctmu.m b/gp/gp_predprctmu.m index d7493cd3..ee335320 100644 --- a/gp/gp_predprctmu.m +++ b/gp/gp_predprctmu.m @@ -43,23 +43,23 @@ ip=inputParser; ip.FunctionName = 'GP_PREDPRCTMU'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; z = ip.Results.z; diff --git a/gp/gp_predprcty.m b/gp/gp_predprcty.m index 6bea0534..6bb561fc 100644 --- a/gp/gp_predprcty.m +++ b/gp/gp_predprcty.m @@ -41,23 +41,23 @@ ip=inputParser; ip.FunctionName = 'GP_PREDPRCTY'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; z = ip.Results.z; diff --git a/gp/gp_refpred.m b/gp/gp_refpred.m index 2d8937ae..dbf0583b 100644 --- a/gp/gp_refpred.m +++ b/gp/gp_refpred.m @@ -39,17 +39,17 @@ ip=inputParser; ip.FunctionName = 'GP_REFPRED'; - ip.addRequired('gp1',@(x) isstruct(x) || iscell(x)); - ip.addRequired('gp2',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('x2', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('y2', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z2', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})) - ip.addParamValue('form', 'mean', @(x) ismember(x,{'mean','all'})) - ip.parse(gp1, gp2, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp1',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','gp2',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','x2', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','y2', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z2', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})); + ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); + ip=iparser(ip,'parse',gp1, gp2, x, y, varargin{:}); % pass these forward options=struct(); x2 = ip.Results.x2; diff --git a/gp/gp_rnd.m b/gp/gp_rnd.m index e3f983b2..357b1393 100644 --- a/gp/gp_rnd.m +++ b/gp/gp_rnd.m @@ -44,18 +44,18 @@ ip=inputParser; ip.FunctionName = 'GP_RND'; -ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('xt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) -ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) -ip.addParamValue('nsamp', 1, @(x) isreal(x) && isscalar(x)) -ip.parse(gp, x, y, xt, varargin{:}); +ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','xt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); +ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); +ip=iparser(ip,'addParamValue','nsamp', 1, @(x) isreal(x) && isscalar(x)); +ip=iparser(ip,'parse',gp, x, y, xt, varargin{:}); z=ip.Results.z; zt=ip.Results.zt; predcf=ip.Results.predcf; diff --git a/gp/gp_set.m b/gp/gp_set.m index a4bfdc45..bb0d4747 100644 --- a/gp/gp_set.m +++ b/gp/gp_set.m @@ -188,28 +188,28 @@ ip=inputParser; ip.FunctionName = 'GP_SET'; - ip.addOptional('gp', [], @isstruct); - ip.addParamValue('cf',[], @(x) isempty(x) || isstruct(x) || iscell(x)); - ip.addParamValue('meanf',[], @(x) isempty(x) || isstruct(x) || iscell(x)); - ip.addParamValue('type','FULL', ... + ip=iparser(ip,'addOptional','gp', [], @isstruct); + ip=iparser(ip,'addParamValue','cf',[], @(x) isempty(x) || isstruct(x) || iscell(x)); + ip=iparser(ip,'addParamValue','meanf',[], @(x) isempty(x) || isstruct(x) || iscell(x)); + ip=iparser(ip,'addParamValue','type','FULL', ... @(x) ismember(x,{'FULL' 'FIC' 'PIC' 'PIC_BLOCK' 'VAR' ... 'DTC' 'SOR' 'CS+FIC'})); - ip.addParamValue('lik',lik_gaussian(), @(x) isstruct(x)); - ip.addParamValue('jitterSigma2',0, @(x) isscalar(x) && x>=0); - ip.addParamValue('infer_params','covariance+likelihood', @(x) ischar(x)); - ip.addParamValue('latent_method','Laplace', @(x) ischar(x) || iscell(x)); - ip.addParamValue('latent_opt',struct(), @isstruct); - ip.addParamValue('X_u',[], @(x) isreal(x) && all(isfinite(x(:)))); - ip.addParamValue('Xu_prior',prior_unif, @(x) isstruct(x) || isempty(x)); - ip.addParamValue('tr_index', [], @(x) ~isempty(x) || iscell(x)) - ip.addParamValue('comp_cf', [], @(x) iscell(x)) - ip.addParamValue('derivobs','off', @(x) islogical(x) || isscalar(x) || ... + ip=iparser(ip,'addParamValue','lik',lik_gaussian(), @(x) isstruct(x)); + ip=iparser(ip,'addParamValue','jitterSigma2',0, @(x) isscalar(x) && x>=0); + ip=iparser(ip,'addParamValue','infer_params','covariance+likelihood', @(x) ischar(x)); + ip=iparser(ip,'addParamValue','latent_method','Laplace', @(x) ischar(x) || iscell(x)); + ip=iparser(ip,'addParamValue','latent_opt',struct(), @isstruct); + ip=iparser(ip,'addParamValue','X_u',[], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','Xu_prior',prior_unif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','tr_index', [], @(x) ~isempty(x) || iscell(x)); + ip=iparser(ip,'addParamValue','comp_cf', [], @(x) iscell(x)); + ip=iparser(ip,'addParamValue','derivobs','off', @(x) islogical(x) || isscalar(x) || ... (ischar(x) && ismember(x,{'on' 'off'}))); - ip.addParamValue('savememory','off', @(x) islogical(x) || isscalar(x) || ... + ip=iparser(ip,'addParamValue','savememory','off', @(x) islogical(x) || isscalar(x) || ... (ischar(x) && ismember(x,{'on' 'off'}))); -% ip.addParamValue('optim_method', [], @(x) isreal(x) && (x==1 || x==2) && ... +% ip=iparser(ip,'addParamValue','optim_method', [], @(x) isreal(x) && (x==1 || x==2) && ... % isfinite(x)) - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gp=ip.Results.gp; @@ -363,7 +363,7 @@ % Remove traces of other latent methods if isfield(gp,'latent_opt'); gp=rmfield(gp,'latent_opt'); end if isfield(gp,'fh') && isfield(gp.fh,'ne') - gp.fh=rmfield(gp.fh,{'ne' 'e' 'g' 'pred' 'jpred' 'looe' 'loog'}); + gp.fh=rmfield(gp.fh,{'ne' 'e' 'g' 'pred' 'jpred' 'looe'}); end % Set latent method gp.latent_method=latent_method; @@ -404,9 +404,9 @@ % Handle latent_opt ipmc=inputParser; ipmc.FunctionName = 'GP_SET - latent method MCMC options'; - ipmc.addParamValue('method',@esls, @(x) isa(x,'function_handle')); - ipmc.addParamValue('f',[], @(x) isreal(x) && all(isfinite(x(:)))); - ipmc.parse(latent_opt); + ipmc=iparser(ipmc,'addParamValue','method',@esls, @(x) isa(x,'function_handle')); + ipmc=iparser(ipmc,'addParamValue','f',[], @(x) isreal(x) && all(isfinite(x(:)))); + ipmc=iparser(ipmc,'parse',latent_opt); if init || ~ismember('method',ipmc.UsingDefaults) || ~isfield(gp.fh,'mc') gp.fh.mc = ipmc.Results.method; end @@ -417,42 +417,42 @@ % Handle latent_opt ipep=inputParser; ipep.FunctionName = 'GP_SET - latent method EP options'; - ipep.addParamValue('optim_method',[], @(x) ischar(x)); - ipep.addParamValue('maxiter',20, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); - ipep.addParamValue('display', 'off', @(x) ischar(x) && ismember(x,{'off', 'final', 'iter'})) - ipep.addParamValue('parallel','on', @(x) ischar(x) && ismember(x,{'off', 'on'})); % default on - ipep.addParamValue('init_prev', 'on', @(x) ischar(x) && ismember(x,{'off', 'on'})); % default on + ipep=iparser(ipep,'addParamValue','optim_method',[], @(x) ischar(x)); + ipep=iparser(ipep,'addParamValue','maxiter',20, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ipep=iparser(ipep,'addParamValue','display', 'off', @(x) ischar(x) && ismember(x,{'off', 'final', 'iter'})); + ipep=iparser(ipep,'addParamValue','parallel','on', @(x) ischar(x) && ismember(x,{'off', 'on'})); % default on + ipep=iparser(ipep,'addParamValue','init_prev', 'on', @(x) ischar(x) && ismember(x,{'off', 'on'})); % default on % Following option is only for basic-EP % all changes in the log predictive densities and the log marginal % likelihood are smaller than tol. - ipep.addParamValue('tol',1e-4, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); - ipep.addParamValue('LALRSinit','off', @(x) ischar(x) && ismember(x,{'off', 'on'})); % default off + ipep=iparser(ipep,'addParamValue','tol',1e-4, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ipep=iparser(ipep,'addParamValue','LALRSinit','off', @(x) ischar(x) && ismember(x,{'off', 'on'})); % default off % Following options are only for robust-EP % max number of initial parallel iterations - ipep.addParamValue('ninit', 10, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x)) + ipep=iparser(ipep,'addParamValue','ninit', 10, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x)); % max number of inner loop iterations in the double-loop algorithm - ipep.addParamValue('max_ninner', 4, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x)) + ipep=iparser(ipep,'addParamValue','max_ninner', 4, @(x) isreal(x) && (x==1 || rem(1,x)==1) && isfinite(x)); % converge tolerance with respect to the maximum change in E[f] and Var[f] - ipep.addParamValue('tolStop', 1e-4, @(x) isreal(x) && isfinite(x)) + ipep=iparser(ipep,'addParamValue','tolStop', 1e-4, @(x) isreal(x) && isfinite(x)); % tolerance for the EP site updates - ipep.addParamValue('tolUpdate', 1e-6, @(x) isreal(x) && isfinite(x)) + ipep=iparser(ipep,'addParamValue','tolUpdate', 1e-6, @(x) isreal(x) && isfinite(x)); % inner loop energy tolerance - ipep.addParamValue('tolInner', 1e-3, @(x) isreal(x) && isfinite(x)) + ipep=iparser(ipep,'addParamValue','tolInner', 1e-3, @(x) isreal(x) && isfinite(x)); % minimum gradient (g) decrease in the search direction, abs(g_new)0); - ipla.addParamValue('maxiter', 40, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); - ipla.parse(latent_opt); + ipla=iparser(ipla,'addParamValue','optim_method',[], @(x) ischar(x)); + ipla=iparser(ipla,'addParamValue','tol',1e-4, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ipla=iparser(ipla,'addParamValue','maxiter', 40, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0); + ipla=iparser(ipla,'parse',latent_opt); optim_method=ipla.Results.optim_method; if ~isempty(optim_method) gp.latent_opt.optim_method=optim_method; diff --git a/gp/gp_waic.m b/gp/gp_waic.m index c3fb89dd..09b751cd 100644 --- a/gp/gp_waic.m +++ b/gp/gp_waic.m @@ -57,13 +57,13 @@ ip=inputParser; ip.FunctionName = 'GP_WAIC'; - ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('method', 'V', @(x) ismember(x,{'V' 'G'})) - ip.addParamValue('form', 'mean', @(x) ismember(x,{'mean','all'})) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','method', 'V', @(x) ismember(x,{'V' 'G'})); + ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); method=ip.Results.method; form=ip.Results.form; % pass these forward @@ -158,10 +158,10 @@ end fmin = mean(Ef(i,:) - 9*sqrt(Varf(i,:))); fmax = mean(Ef(i,:) + 9*sqrt(Varf(i,:))); - Elog(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ... - .*llvec(gp_array, y(i), f, z1).^2), fmin, fmax); - Elog2(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ... - .*llvec(gp_array, y(i), f, z1)), fmin, fmax); + Elog(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ... + .*llvec(gp_array, y(i), f(:), z1).^2),size(f,1),size(f,2)), fmin, fmax); + Elog2(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ... + .*llvec(gp_array, y(i), f(:), z1)),size(f,1),size(f,2)), fmin, fmax); end Elog2 = Elog2.^2; Vn = (Elog-Elog2); @@ -181,8 +181,8 @@ for i=1:tn fmin = mean(Ef(i,:) - 9*sqrt(Varf(i,:))); fmax = mean(Ef(i,:) + 9*sqrt(Varf(i,:))); - GUt(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ... - .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))')), fmin, fmax); + GUt(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ... + .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f(:)'),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))')),size(f,1),size(f,2)), fmin, fmax); end if strcmp(form, 'mean') GUt = mean(GUt); @@ -199,8 +199,8 @@ end fmin = mean(Ef(i,:) - 9*sqrt(Varf(i,:))); fmax = mean(Ef(i,:) + 9*sqrt(Varf(i,:))); - GUt(i) = quadgk(@(f) mean(multi_npdf(f,Ef(i,:),(Varf(i,:))) ... - .*llvec(gp_array, y(i), f, z1)), fmin, fmax); + GUt(i) = quadgk(@(f) reshape(mean(multi_npdf(f(:),Ef(i,:),(Varf(i,:))) ... + .*llvec(gp_array, y(i), f(:), z1)), size(f,1),size(f,2)), fmin, fmax); end if strcmp(form, 'mean') GUt = mean(GUt); @@ -377,10 +377,12 @@ for i=1:tn fmin = sum(weight.*Ef(i,:) - 9*weight.*sqrt(Varf(i,:))); fmax = sum(weight.*Ef(i,:) + 9*weight.*sqrt(Varf(i,:))); - Elog(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... - .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))').^2), fmin, fmax); - Elog2(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... - .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))')), fmin, fmax); + Elog(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... + .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f(:)'),nsamples,1)).^2,(2.*sigma2(i,:))'), ... + 0.5*log(2*pi*sigma2(i,:))').^2),size(f,1),size(f,2)), fmin, fmax); + Elog2(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... + .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f(:)'),nsamples,1)).^2,(2.*sigma2(i,:))'), ... + 0.5*log(2*pi*sigma2(i,:))')),size(f,1),size(f,2)), fmin, fmax); end Elog2 = Elog2.^2; Vn = (Elog-Elog2); @@ -399,10 +401,10 @@ end fmin = sum(weight.*Ef(i,:) - 9*weight.*sqrt(Varf(i,:))); fmax = sum(weight.*Ef(i,:) + 9*weight.*sqrt(Varf(i,:))); - Elog(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... - .*llvec(gp, y(i), f, z1).^2), fmin, fmax); - Elog2(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... - .*llvec(gp, y(i), f, z1)), fmin, fmax); + Elog(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... + .*llvec(gp, y(i), f, z1).^2),size(f,1),size(f,2)), fmin, fmax); + Elog2(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... + .*llvec(gp, y(i), f, z1)),size(f,1),size(f,2)), fmin, fmax); end Elog2 = Elog2.^2; Vn = (Elog-Elog2); @@ -422,8 +424,8 @@ for i=1:tn fmin = sum(weight.*Ef(i,:) - 9*weight.*sqrt(Varf(i,:))); fmax = sum(weight.*Ef(i,:) + 9*weight.*sqrt(Varf(i,:))); - GUt(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... - .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))')), fmin, fmax); + GUt(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f(:)',Ef(i,:),(Varf(i,:))),weight') ... + .*bsxfun(@minus,-bsxfun(@rdivide,(repmat((y(i)-f(:)'),nsamples,1)).^2,(2.*sigma2(i,:))'), 0.5*log(2*pi*sigma2(i,:))')),size(f,1),size(f,2)), fmin, fmax); end if strcmp(form, 'mean') GUt = mean(GUt); @@ -441,8 +443,8 @@ end fmin = sum(weight.*Ef(i,:) - 9*weight.*sqrt(Varf(i,:))); fmax = sum(weight.*Ef(i,:) + 9*weight.*sqrt(Varf(i,:))); - GUt(i) = quadgk(@(f) sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... - .*llvec(gp, y(i), f, z1)), fmin, fmax); + GUt(i) = quadgk(@(f) reshape(sum(bsxfun(@times, multi_npdf(f,Ef(i,:),(Varf(i,:))),weight') ... + .*llvec(gp, y(i), f, z1)),size(f,1),size(f,2)), fmin, fmax); end if strcmp(form, 'mean') GUt = mean(GUt); diff --git a/gp/gpcf_SSsexp.m b/gp/gpcf_SSsexp.m index 0b8e71d5..cdc475fe 100644 --- a/gp/gpcf_SSsexp.m +++ b/gp/gpcf_SSsexp.m @@ -52,13 +52,13 @@ ip=inputParser; ip.FunctionName = 'GPCF_SSSEXP'; -ip.addOptional('gpcf', [], @isstruct); -ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); -ip.addParamValue('magnSigma2',[], @(x) isscalar(x) && x>0); -ip.addParamValue('lengthScale',[], @(x) isvector(x) && all(x>0)); -ip.addParamValue('nfreq',[], @isscalar) -ip.addParamValue('frequency',[], @isvector) -ip.parse(varargin{:}); +ip=iparser(ip,'addOptional','gpcf', [], @isstruct); +ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); +ip=iparser(ip,'addParamValue','magnSigma2',[], @(x) isscalar(x) && x>0); +ip=iparser(ip,'addParamValue','lengthScale',[], @(x) isvector(x) && all(x>0)); +ip=iparser(ip,'addParamValue','nfreq',[], @isscalar); +ip=iparser(ip,'addParamValue','frequency',[], @isvector); +ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; magnSigma2=ip.Results.magnSigma2; lengthScale=ip.Results.lengthScale; diff --git a/gp/gpcf_cat.m b/gp/gpcf_cat.m index 08a8af9e..92260704 100644 --- a/gp/gpcf_cat.m +++ b/gp/gpcf_cat.m @@ -30,10 +30,10 @@ ip=inputParser; ip.FunctionName = 'GPCF_CAT'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('selectedVariables',[], ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','selectedVariables',[], ... @(x) isempty(x) || (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_constant.m b/gp/gpcf_constant.m index 755716a0..5f85e1be 100644 --- a/gp/gpcf_constant.m +++ b/gp/gpcf_constant.m @@ -31,10 +31,10 @@ ip=inputParser; ip.FunctionName = 'GPCF_CONSTANT'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('constSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('constSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','constSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','constSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_exp.m b/gp/gpcf_exp.m index 1b7f8537..7a749450 100755 --- a/gp/gpcf_exp.m +++ b/gp/gpcf_exp.m @@ -46,17 +46,17 @@ ip=inputParser; ip.FunctionName = 'GPCF_EXP'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_intcov.m b/gp/gpcf_intcov.m index a096761b..5c11812a 100644 --- a/gp/gpcf_intcov.m +++ b/gp/gpcf_intcov.m @@ -64,11 +64,11 @@ ip=inputParser; ip.FunctionName = 'GPCF_INTCOV'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('intArea',[], @(x) isvector(x) && all(x>0)); - ip.addParamValue('cf',[], @iscell); - ip.addParamValue('NintPoints',200, @(x) isscalar(x) && x>0); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','intArea',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','cf',[], @iscell); + ip=iparser(ip,'addParamValue','NintPoints',200, @(x) isscalar(x) && x>0); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_linear.m b/gp/gpcf_linear.m index ad062c28..8c65cd12 100644 --- a/gp/gpcf_linear.m +++ b/gp/gpcf_linear.m @@ -37,11 +37,11 @@ ip=inputParser; ip.FunctionName = 'GPCF_LINEAR'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('coeffSigma2',10, @(x) isvector(x) && all(x>0)); - ip.addParamValue('coeffSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','coeffSigma2',10, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','coeffSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_mask.m b/gp/gpcf_mask.m index db7952b7..be172fdf 100644 --- a/gp/gpcf_mask.m +++ b/gp/gpcf_mask.m @@ -30,10 +30,10 @@ ip=inputParser; ip.FunctionName = 'GPCF_MASK'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('selectedVariables',[], ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','selectedVariables',[], ... @(x) isempty(x) || (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_matern32.m b/gp/gpcf_matern32.m index 084388d9..7ae8e4fa 100755 --- a/gp/gpcf_matern32.m +++ b/gp/gpcf_matern32.m @@ -46,17 +46,17 @@ ip=inputParser; ip.FunctionName = 'GPCF_MATERN32'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_matern52.m b/gp/gpcf_matern52.m index 41e244fa..3ea0c429 100755 --- a/gp/gpcf_matern52.m +++ b/gp/gpcf_matern52.m @@ -46,17 +46,17 @@ ip=inputParser; ip.FunctionName = 'GPCF_MATERN52'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_neuralnetwork.m b/gp/gpcf_neuralnetwork.m index b89124eb..ec3108f0 100755 --- a/gp/gpcf_neuralnetwork.m +++ b/gp/gpcf_neuralnetwork.m @@ -39,13 +39,13 @@ ip=inputParser; ip.FunctionName = 'GPCF_NEURALNETWORK'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('biasSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('weightSigma2',10, @(x) isvector(x) && all(x>0)); - ip.addParamValue('biasSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); - ip.addParamValue('weightSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','biasSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','weightSigma2',10, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','biasSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','weightSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_noise.m b/gp/gpcf_noise.m index 53f64f0c..aa084445 100644 --- a/gp/gpcf_noise.m +++ b/gp/gpcf_noise.m @@ -31,10 +31,10 @@ ip=inputParser; ip.FunctionName = 'GPCF_NOISE'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('noiseSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('noiseSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','noiseSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','noiseSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_periodic.m b/gp/gpcf_periodic.m index a83db8b4..c37e29d3 100644 --- a/gp/gpcf_periodic.m +++ b/gp/gpcf_periodic.m @@ -51,19 +51,19 @@ ip=inputParser; ip.FunctionName = 'GPCF_PERIODIC'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',10, @(x) isvector(x) && all(x>0)); - ip.addParamValue('period',1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale_sexp',10, @(x) isvector(x) && all(x>0)); - ip.addParamValue('decay',0, @(x) isscalar(x) && (x==0||x==1)); - ip.addParamValue('magnSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t, @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_sexp_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('period_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',10, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','period',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale_sexp',10, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','decay',0, @(x) isscalar(x) && (x==0||x==1)); + ip=iparser(ip,'addParamValue','magnSigma2_prior',prior_logunif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','lengthScale_sexp_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','period_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) @@ -118,7 +118,6 @@ gpcf.fh.cfg = @gpcf_periodic_cfg; gpcf.fh.ginput = @gpcf_periodic_ginput; gpcf.fh.cov = @gpcf_periodic_cov; - gpcf.fh.covvec = @gpcf_periodic_covvec; gpcf.fh.trcov = @gpcf_periodic_trcov; gpcf.fh.trvar = @gpcf_periodic_trvar; gpcf.fh.recappend = @gpcf_periodic_recappend; diff --git a/gp/gpcf_ppcs0.m b/gp/gpcf_ppcs0.m index 0ec773b4..1f311748 100644 --- a/gp/gpcf_ppcs0.m +++ b/gp/gpcf_ppcs0.m @@ -62,19 +62,19 @@ ip=inputParser; ip.FunctionName = 'GPCF_PPCS0'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_ppcs1.m b/gp/gpcf_ppcs1.m index 836adae6..a307b0ca 100644 --- a/gp/gpcf_ppcs1.m +++ b/gp/gpcf_ppcs1.m @@ -62,19 +62,19 @@ ip=inputParser; ip.FunctionName = 'GPCF_PPCS1'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_ppcs2.m b/gp/gpcf_ppcs2.m index 2610fb14..5f5aad96 100755 --- a/gp/gpcf_ppcs2.m +++ b/gp/gpcf_ppcs2.m @@ -62,19 +62,19 @@ ip=inputParser; ip.FunctionName = 'GPCF_PPCS2'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_ppcs3.m b/gp/gpcf_ppcs3.m index 55e29a3a..0162858d 100644 --- a/gp/gpcf_ppcs3.m +++ b/gp/gpcf_ppcs3.m @@ -63,19 +63,19 @@ ip=inputParser; ip.FunctionName = 'GPCF_PPCS3'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','l_nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_prod.m b/gp/gpcf_prod.m index b26009c9..d06ed2d0 100644 --- a/gp/gpcf_prod.m +++ b/gp/gpcf_prod.m @@ -18,9 +18,9 @@ ip=inputParser; ip.FunctionName = 'GPCF_PROD'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('cf',[], @iscell); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','cf',[], @iscell); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_rq.m b/gp/gpcf_rq.m index 104a11f5..3eebba08 100644 --- a/gp/gpcf_rq.m +++ b/gp/gpcf_rq.m @@ -48,20 +48,20 @@ ip=inputParser; ip.FunctionName = 'GPCF_RQ'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('alpha',20, @(x) isscalar(x) && x>0); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','alpha',20, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('alpha_prior', prior_unif(), ... + ip=iparser(ip,'addParamValue','alpha_prior', prior_unif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_scaled.m b/gp/gpcf_scaled.m index 0dd67c34..bc3eca57 100644 --- a/gp/gpcf_scaled.m +++ b/gp/gpcf_scaled.m @@ -31,10 +31,10 @@ ip=inputParser; ip.FunctionName = 'GPCF_SCALED'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('cf',[], @isstruct); - ip.addParamValue('scaler',1, @(x) isscalar(x) && x>0); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','cf',[], @isstruct); + ip=iparser(ip,'addParamValue','scaler',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_sexp.m b/gp/gpcf_sexp.m index 046ab569..649711e6 100755 --- a/gp/gpcf_sexp.m +++ b/gp/gpcf_sexp.m @@ -42,17 +42,17 @@ % inputParser checks the arguments and assigns some default values ip=inputParser; ip.FunctionName = 'GPCF_SEXP'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('magnSigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('lengthScale',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('metric',[], @isstruct); - ip.addParamValue('magnSigma2_prior', prior_logunif(), ... + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','lengthScale',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','metric',[], @isstruct); + ip=iparser(ip,'addParamValue','magnSigma2_prior', prior_logunif(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('lengthScale_prior',prior_t(), ... + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_t(), ... @(x) isstruct(x) || isempty(x)); - ip.addParamValue('selectedVariables',[], @(x) isempty(x) || ... + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isempty(x) || ... (isvector(x) && all(x>0))); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpcf_sum.m b/gp/gpcf_sum.m index f515a3a0..ccd9cb67 100644 --- a/gp/gpcf_sum.m +++ b/gp/gpcf_sum.m @@ -18,9 +18,9 @@ ip=inputParser; ip.FunctionName = 'GPCF_SUM'; - ip.addOptional('gpcf', [], @isstruct); - ip.addParamValue('cf',[], @iscell); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','cf',[], @iscell); + ip=iparser(ip,'parse',varargin{:}); gpcf=ip.Results.gpcf; if isempty(gpcf) diff --git a/gp/gpep_e.m b/gp/gpep_e.m index e30f7c07..d78b450b 100644 --- a/gp/gpep_e.m +++ b/gp/gpep_e.m @@ -55,16 +55,16 @@ % parse inputs ip=inputParser; ip.FunctionName = 'GPEP_E'; - ip.addRequired('w', @(x) ... + ip=iparser(ip,'addRequired','w', @(x) ... isempty(x) || ... (ischar(x) && ismember(x, {'init' 'clearcache'})) || ... (isvector(x) && isreal(x) && all(isfinite(x))) || ... all(isnan(x))); - ip.addRequired('gp',@isstruct); - ip.addOptional('x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.parse(w, gp, varargin{:}); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addOptional','x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',w, gp, varargin{:}); x=ip.Results.x; y=ip.Results.y; z=ip.Results.z; @@ -98,36 +98,36 @@ function [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta] = ep_algorithm(w, gp, x, y, z) - if strcmp(w, 'clearcache') - ch=[]; - return - end - +% if strcmp(w, 'clearcache') +% ch=[]; +% return +% end + switch gp.latent_opt.optim_method case 'basic-EP' - % check whether saved values can be used - if isempty(z) - datahash=hash_sha512([x y]); - else - datahash=hash_sha512([x y z]); - end - if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash) - % The covariance function parameters or data haven't changed - % so we can return the energy and the site parameters that are saved - e = ch.e; - edata = ch.edata; - eprior = ch.eprior; - tautilde = ch.tautilde; - nutilde = ch.nutilde; - L = ch.L; - La2 = ch.La2; - b = ch.b; - muvec_i = ch.muvec_i; - sigm2vec_i = ch.sigm2vec_i; - logZ_i = ch.logZ_i; - eta = ch.eta; +% % check whether saved values can be used +% if isempty(z) +% datahash=hash_sha512([x y]); +% else +% datahash=hash_sha512([x y z]); +% end + if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash) +% % The covariance function parameters or data haven't changed +% % so we can return the energy and the site parameters that are saved +% e = ch.e; +% edata = ch.edata; +% eprior = ch.eprior; +% tautilde = ch.tautilde; +% nutilde = ch.nutilde; +% L = ch.L; +% La2 = ch.La2; +% b = ch.b; +% muvec_i = ch.muvec_i; +% sigm2vec_i = ch.sigm2vec_i; +% logZ_i = ch.logZ_i; +% eta = ch.eta; else % The parameters or data have changed since % the last call for gpep_e. In this case we need to @@ -181,9 +181,9 @@ logZep_old=logZep; logM0_old=logM0; - if isequal(gp.latent_opt.init_prev, 'on') && iter==1 && ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1) && isequal(datahash,ch.datahash) - tautilde=ch.tautilde; - nutilde=ch.nutilde; + if 0%isequal(gp.latent_opt.init_prev, 'on') && iter==1 && ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1) && isequal(datahash,ch.datahash) +% tautilde=ch.tautilde; +% nutilde=ch.nutilde; else if isequal(gp.latent_opt.parallel,'on') % parallel-EP @@ -1447,21 +1447,21 @@ logZ_i = logM0(:); eta = []; - % store values to the cache - ch.w = w; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.tautilde = tautilde; - ch.nutilde = nutilde; - ch.L = L; - ch.La2 = La2; - ch.b = b; - ch.muvec_i = muvec_i; - ch.sigm2vec_i = sigm2vec_i; - ch.logZ_i = logZ_i; - ch.eta = eta; - ch.datahash=datahash; +% % store values to the cache +% ch.w = w; +% ch.e = e; +% ch.edata = edata; +% ch.eprior = eprior; +% ch.tautilde = tautilde; +% ch.nutilde = nutilde; +% ch.L = L; +% ch.La2 = La2; +% ch.b = b; +% ch.muvec_i = muvec_i; +% ch.sigm2vec_i = sigm2vec_i; +% ch.logZ_i = logZ_i; +% ch.eta = eta; +% ch.datahash=datahash; global iter_lkm iter_lkm=iter; @@ -1477,28 +1477,28 @@ % end % check whether saved values can be used - if isempty(z) - datahash=hash_sha512([x y]); - else - datahash=hash_sha512([x y z]); - end +% if isempty(z) +% datahash=hash_sha512([x y]); +% else +% datahash=hash_sha512([x y z]); +% end - if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w) < 1e-8) && isequal(datahash,ch.datahash) + if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w) < 1e-8) && isequal(datahash,ch.datahash) % The covariance function parameters haven't changed so just % return the Energy and the site parameters that are saved - e = ch.e; - edata = ch.edata; - eprior = ch.eprior; - L = ch.L; - La2 = ch.La2; - b = ch.b; - nutilde = ch.nu_q; - tautilde = ch.tau_q; - eta = ch.eta; - muvec_i = ch.muvec_i; - sigm2vec_i = ch.sigm2vec_i; - logZ_i = ch.logZ_i; +% e = ch.e; +% edata = ch.edata; +% eprior = ch.eprior; +% L = ch.L; +% La2 = ch.La2; +% b = ch.b; +% nutilde = ch.nu_q; +% tautilde = ch.tau_q; +% eta = ch.eta; +% muvec_i = ch.muvec_i; +% sigm2vec_i = ch.sigm2vec_i; +% logZ_i = ch.logZ_i; else % The parameters or data have changed since @@ -2199,21 +2199,21 @@ nutilde = nu_q; tautilde = tau_q; - % store values to the cache - ch.w = w; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.L = L; - ch.nu_q = nu_q; - ch.tau_q = tau_q; - ch.La2 = La2; - ch.b = b; - ch.eta = eta; - ch.logZ_i = logZ_i; - ch.sigm2vec_i = sigm2vec_i; - ch.muvec_i = muvec_i; - ch.datahash = datahash; +% % store values to the cache +% ch.w = w; +% ch.e = e; +% ch.edata = edata; +% ch.eprior = eprior; +% ch.L = L; +% ch.nu_q = nu_q; +% ch.tau_q = tau_q; +% ch.La2 = La2; +% ch.b = b; +% ch.eta = eta; +% ch.logZ_i = logZ_i; +% ch.sigm2vec_i = sigm2vec_i; +% ch.muvec_i = muvec_i; +% ch.datahash = datahash; end otherwise diff --git a/gp/gpep_fact.m b/gp/gpep_fact.m index 0b78b0fe..f2372b7e 100644 --- a/gp/gpep_fact.m +++ b/gp/gpep_fact.m @@ -35,19 +35,18 @@ ip=inputParser; ip.FunctionName = 'GPEP_FACT'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('fvec', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('xt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('ind', 1, @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','fvec', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','xt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','ind', 1, @(x) isreal(x) && all(isfinite(x(:)))); if rem(size(varargin,2), 2) == 0 - ip.parse(gp, x, y, fvec,[],varargin{:}); + ip=iparser(ip,'parse',gp, x, y, fvec,[],varargin{:}); else - ip.parse(gp, x, y, fvec,varargin{:}); + ip=iparser(ip,'parse',gp, x, y, fvec,varargin{:}); end -% ip.parse(gp, x, y, fvec,varargin{:}); predictive = false; z = ip.Results.z; ind = ip.Results.ind; diff --git a/gp/gpep_g.m b/gp/gpep_g.m index 8e60ad9d..703ff81b 100644 --- a/gp/gpep_g.m +++ b/gp/gpep_g.m @@ -30,15 +30,13 @@ ip=inputParser; ip.FunctionName = 'GPEP_G'; - ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); - ip.addRequired('gp',@isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addOptional('method', '1', @(x) ismember(x,{'1','2'})) - ip.parse(w, gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); z=ip.Results.z; - method = ip.Results.method; gp=gp_unpak(gp, w); % unpak the parameters diff --git a/gp/gpep_jpred.m b/gp/gpep_jpred.m index d7ab0a09..9126eb0d 100644 --- a/gp/gpep_jpred.m +++ b/gp/gpep_jpred.m @@ -84,22 +84,22 @@ ip=inputParser; ip.FunctionName = 'GPEP_JPRED'; - ip.addRequired('gp', @isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp', @isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpep_looe.m b/gp/gpep_looe.m index 10793d7d..6b8130b1 100644 --- a/gp/gpep_looe.m +++ b/gp/gpep_looe.m @@ -42,13 +42,13 @@ ip=inputParser; ip.FunctionName = 'GPEP_LOOE'; -ip.addRequired('w', @(x) isempty(x) || ... +ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ... isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp', @(x) isstruct(x) || iscell(x)); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x)); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); z=ip.Results.z; gp=gp_unpak(gp, w); diff --git a/gp/gpep_loog.m b/gp/gpep_loog.m index ecba5b94..2b531934 100644 --- a/gp/gpep_loog.m +++ b/gp/gpep_loog.m @@ -28,13 +28,13 @@ ip=inputParser; ip.FunctionName = 'GPEP_LOOG'; -ip.addRequired('w', @(x) isempty(x) || ... +ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ... isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp', @(x) isstruct(x) || iscell(x)); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x)); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); z=ip.Results.z; if isfield(gp,'mean') & ~isempty(gp.mean.meanFuncs) diff --git a/gp/gpep_loopred.m b/gp/gpep_loopred.m index 9eafe2ad..61d35704 100644 --- a/gp/gpep_loopred.m +++ b/gp/gpep_loopred.m @@ -41,11 +41,11 @@ ip=inputParser; ip.FunctionName = 'GPEP_LOOPRED'; - ip.addRequired('gp', @(x) isstruct(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp', @(x) isstruct(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; [tmp,tmp,tmp,tmp,tmp,tmp,tmp,tmp,muvec_i,sigm2vec_i,lnZ_i] = ... diff --git a/gp/gpep_pred.m b/gp/gpep_pred.m index 4bd8dcec..420deefa 100644 --- a/gp/gpep_pred.m +++ b/gp/gpep_pred.m @@ -80,22 +80,22 @@ ip=inputParser; ip.FunctionName = 'GPEP_PRED'; -ip.addRequired('gp', @isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) -ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) -ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) +ip=iparser(ip,'addRequired','gp', @isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); +ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpia_jpred.m b/gp/gpia_jpred.m index c8045d07..c01c0829 100644 --- a/gp/gpia_jpred.m +++ b/gp/gpia_jpred.m @@ -88,22 +88,22 @@ ip=inputParser; ip.FunctionName = 'GPIA_JPRED'; - ip.addRequired('gp_array', @iscell); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp_array', @iscell); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpia_loopred.m b/gp/gpia_loopred.m index 14c7795c..bd3f7562 100644 --- a/gp/gpia_loopred.m +++ b/gp/gpia_loopred.m @@ -48,16 +48,16 @@ ip=inputParser; ip.FunctionName = 'GPIA_LOOPRED'; - ip.addRequired('gp_array',@iscell); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('is', 'on', @(x) ismember(x,{'on' 'off'})) - ip.parse(gp_array, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp_array',@iscell); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','is', 'on', @(x) ismember(x,{'on' 'off'})); + ip=iparser(ip,'parse',gp_array, x, y, varargin{:}); z=ip.Results.z; is=ip.Results.is; - if isfield(gp_array{1},'meanf') & ~isempty(gp_array{1}.meanf) + if isfield(gp_array{1},'meanf') && ~isempty(gp_array{1}.meanf) error('GPIA_LOOPRED: Mean functions not yet supported'); end diff --git a/gp/gpia_pred.m b/gp/gpia_pred.m index eceae414..95263b54 100644 --- a/gp/gpia_pred.m +++ b/gp/gpia_pred.m @@ -87,22 +87,22 @@ ip=inputParser; ip.FunctionName = 'GPIA_PRED'; - ip.addRequired('gp_array', @iscell); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp_array', @iscell); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp_array, x, y, varargin{:}); + ip=iparser(ip,'parse',gp_array, x, y, varargin{:}); else - ip.parse(gp_array, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp_array, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpla_cm2.m b/gp/gpla_cm2.m index cdc4912f..6c4fff20 100644 --- a/gp/gpla_cm2.m +++ b/gp/gpla_cm2.m @@ -35,19 +35,19 @@ ip=inputParser; ip.FunctionName = 'GP_PRED'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('fvec', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('xt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('ind', 1, @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','fvec', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','xt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','ind', 1, @(x) isreal(x) && all(isfinite(x(:)))); if rem(size(varargin,2), 2) == 0 - ip.parse(gp, x, y, fvec,[],varargin{:}); + ip=iparser(ip,'parse',gp, x, y, fvec,[],varargin{:}); else - ip.parse(gp, x, y, fvec,varargin{:}); + ip=iparser(ip,'parse',gp, x, y, fvec,varargin{:}); end -% ip.parse(gp, x, y, fvec,varargin{:}); +% ip=iparser(ip,'parse',gp, x, y, fvec,varargin{:}); z = ip.Results.z; ind = ip.Results.ind; xt = ip.Results.xt; diff --git a/gp/gpla_e.m b/gp/gpla_e.m index dd8fbbe5..25c1757f 100644 --- a/gp/gpla_e.m +++ b/gp/gpla_e.m @@ -57,16 +57,16 @@ % parse inputs ip=inputParser; ip.FunctionName = 'GPLA_E'; - ip.addRequired('w', @(x) ... + ip=iparser(ip,'addRequired','w', @(x) ... isempty(x) || ... (ischar(x) && strcmp(w, 'init')) || ... isvector(x) && isreal(x) && all(isfinite(x)) ... || all(isnan(x))); - ip.addRequired('gp',@isstruct); - ip.addOptional('x', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('y', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.parse(w, gp, varargin{:}); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addOptional','x', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','y', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',w, gp, varargin{:}); x=ip.Results.x; y=ip.Results.y; z=ip.Results.z; @@ -84,7 +84,7 @@ gp.fh.pred=@gpla_pred; gp.fh.jpred=@gpla_jpred; gp.fh.looe=@gpla_looe; - gp.fh.loog=@gpla_loog; +% gp.fh.loog=@gpla_loog; gp.fh.loopred=@gpla_loopred; e = gp; % remove clutter from the nested workspace @@ -100,31 +100,31 @@ function [e, edata, eprior, f, L, a, La2, p] = laplace_algorithm(w, gp, x, y, z) - if strcmp(w, 'clearcache') - ch=[]; - return - end +% if strcmp(w, 'clearcache') +% ch=[]; +% return +% end % code for the Laplace algorithm % check whether saved values can be used - if isempty(z) - datahash=hash_sha512([x y]); - else - datahash=hash_sha512([x y z]); - end - if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && ... - isequal(datahash,ch.datahash) - % The covariance function parameters or data haven't changed so we - % can return the energy and the site parameters that are - % saved in the cache - e = ch.e; - edata = ch.edata; - eprior = ch.eprior; - f = ch.f; - L = ch.L; - La2 = ch.La2; - a = ch.a; - p = ch.p; +% if isempty(z) +% datahash=hash_sha512([x y]); +% else +% datahash=hash_sha512([x y z]); +% end + if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && ... +% isequal(datahash,ch.datahash) +% % The covariance function parameters or data haven't changed so we +% % can return the energy and the site parameters that are +% % saved in the cache +% e = ch.e; +% edata = ch.edata; +% eprior = ch.eprior; +% f = ch.f; +% L = ch.L; +% La2 = ch.La2; +% a = ch.a; +% p = ch.p; else % The parameters or data have changed since % the last call for gpla_e. In this case we need to @@ -2101,24 +2101,24 @@ e = edata + eprior; - % store values to the cache - ch.w = w; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.f = f; - ch.L = L; -% ch.W = W; - ch.n = size(x,1); - ch.La2 = La2; - ch.a = a; - ch.p=p; - ch.datahash=datahash; +% % store values to the cache +% ch.w = w; +% ch.e = e; +% ch.edata = edata; +% ch.eprior = eprior; +% ch.f = f; +% ch.L = L; +% % ch.W = W; +% ch.n = size(x,1); +% ch.La2 = La2; +% ch.a = a; +% ch.p=p; +% ch.datahash=datahash; end % assert(isreal(edata)) % assert(isreal(eprior)) - +end % % ============================================================== % Begin of the nested functions @@ -2168,4 +2168,4 @@ ch.w = NaN; end -end + diff --git a/gp/gpla_fact.m b/gp/gpla_fact.m index dd80cf64..4c190b97 100644 --- a/gp/gpla_fact.m +++ b/gp/gpla_fact.m @@ -3,7 +3,7 @@ % approximation % % Description -% [P, PC, C]�= GPLA_FACT(GP, X, Y, FVEC) Evaluates marginal likelihood +% [P, PC, C]�= GPLA_FACT(GP, X, Y, FVEC) Evaluates marginal likelihood % at given grid points FVEC for given indices. Returns tilted % distribution without any correction P, with factorized correction % terms PC and the corrections terms C. @@ -35,19 +35,19 @@ ip=inputParser; ip.FunctionName = 'GP_PRED'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('fvec', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('xt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('ind', 1, @(x) isreal(x) && all(isfinite(x(:)))) +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','fvec', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','xt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','ind', 1, @(x) isreal(x) && all(isfinite(x(:)))); if rem(size(varargin,2), 2) == 0 - ip.parse(gp, x, y, fvec,[],varargin{:}); + ip=iparser(ip,'parse',gp, x, y, fvec,[],varargin{:}); else - ip.parse(gp, x, y, fvec,varargin{:}); + ip=iparser(ip,'parse',gp, x, y, fvec,varargin{:}); end -% ip.parse(gp, x, y, fvec,varargin{:}); +% ip=iparser(ip,'parse',gp, x, y, fvec,varargin{:}); predictive = false; z = ip.Results.z; ind = ip.Results.ind; diff --git a/gp/gpla_g.m b/gp/gpla_g.m index 58aecfe0..6a1ce50a 100644 --- a/gp/gpla_g.m +++ b/gp/gpla_g.m @@ -31,12 +31,12 @@ ip=inputParser; ip.FunctionName = 'GPLA_G'; - ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); - ip.addRequired('gp',@isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.parse(w, gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); z=ip.Results.z; gp = gp_unpak(gp, w); % unpak the parameters @@ -684,15 +684,15 @@ DKffba=zeros(n*nout,1); % check in which components the covariance function is present - do = false(nout,1); + doo = false(nout,1); if multicf for z1=1:nout if any(gp.comp_cf{z1}==i) - do(z1) = true; + doo(z1) = true; end end else - do = true(nout,1); + doo = true(nout,1); end i1=0; @@ -724,7 +724,7 @@ % Derivative of explicit terms trace_sum_tmp=0; for z1=1:nout - if do(z1) + if doo(z1) DKffba((1:n)+(z1-1)*n)=DKffb*a((1:n)+(z1-1)*n); trace_sum_tmp = trace_sum_tmp + sum(sum( inv_iWK(:,:,z1) .* DKffb )); end @@ -734,7 +734,7 @@ % Derivative of f w.r.t. theta for z1=1:nout - if do(z1) + if doo(z1) DKllg((1:n)+(z1-1)*n)=DKffb*llg((1:n)+(z1-1)*n); EDKllg((1:n)+(z1-1)*n)=E(:,:,z1)*DKllg((1:n)+(z1-1)*n); end @@ -898,10 +898,10 @@ gpcf = gp.cf{i}; % check in which components the covariance function is present - do = false(nlp,1); + doo = false(nlp,1); for z1=1:nlp if any(gp.comp_cf{z1}==i) - do(z1) = true; + doo(z1) = true; end end @@ -958,7 +958,7 @@ if ~isfield(gp,'meanf') dKnl = zeros(sum(nl)); if isfield(gp.lik,'xtime') - if ~isempty(intersect(gp.comp_cf{1},i)) %do(indnl) + if ~isempty(intersect(gp.comp_cf{1},i)) %doo(indnl) if savememory DKff=gpcf.fh.cfg(gpcf,xtime,[],[],i2); else @@ -972,7 +972,7 @@ else DKff=DKffc{i2}; end - %if do(indnl) + %if doo(indnl) dKnl(ntime+(1:n),ntime+(1:n)) = DKff; %end end @@ -983,7 +983,7 @@ DKff=DKffc{i2}; end for indnl=1:nlp - if do(indnl) + if doo(indnl) dKnl((1:n)+(indnl-1)*n,(1:n)+(indnl-1)*n) = DKff; end end diff --git a/gp/gpla_jpred.m b/gp/gpla_jpred.m index a039327b..dfe1f80c 100644 --- a/gp/gpla_jpred.m +++ b/gp/gpla_jpred.m @@ -86,22 +86,22 @@ ip=inputParser; ip.FunctionName = 'GPLA_JPRED'; - ip.addRequired('gp', @isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp', @isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpla_looe.m b/gp/gpla_looe.m index 851911ce..7f054878 100644 --- a/gp/gpla_looe.m +++ b/gp/gpla_looe.m @@ -34,13 +34,13 @@ ip=inputParser; ip.FunctionName = 'GPLA_LOOE'; -ip.addRequired('w', @(x) isempty(x) || ... +ip=iparser(ip,'addRequired','w', @(x) isempty(x) || ... isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp', @(x) isstruct(x) || iscell(x)); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp', @(x) isstruct(x) || iscell(x)); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, x, y, varargin{:}); z=ip.Results.z; gp=gp_unpak(gp, w); diff --git a/gp/gpla_loopred.m b/gp/gpla_loopred.m index 815a4281..f43a2391 100644 --- a/gp/gpla_loopred.m +++ b/gp/gpla_loopred.m @@ -36,12 +36,12 @@ ip=inputParser; ip.FunctionName = 'GPLA_LOOPRED'; - ip.addRequired('gp', @(x) isstruct(x)); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('method', 'lrs', @(x) ismember(x, {'lrs' 'cavity' 'inla'})) - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'addRequired','gp', @(x) isstruct(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','method', 'lrs', @(x) ismember(x, {'lrs' 'cavity' 'inla'})); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; method = ip.Results.method; [tn,nin] = size(x); diff --git a/gp/gpla_mo_e.m b/gp/gpla_mo_e.m index 4f65b2de..d0185b31 100644 --- a/gp/gpla_mo_e.m +++ b/gp/gpla_mo_e.m @@ -55,14 +55,14 @@ ip=inputParser; ip.FunctionName = 'GPLA_MO_E'; - ip.addRequired('w', @(x) ... + ip=iparser(ip,'addRequired','w', @(x) ... (ischar(x) && strcmp(w, 'init')) || ... isvector(x) && isreal(x) && all(isfinite(x))); - ip.addRequired('gp',@isstruct); - ip.addOptional('x', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('y', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.parse(w, gp, varargin{:}); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addOptional','x', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','y', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',w, gp, varargin{:}); x=ip.Results.x; y=ip.Results.y; z=ip.Results.z; diff --git a/gp/gpla_pred.m b/gp/gpla_pred.m index b47da880..44616c7f 100644 --- a/gp/gpla_pred.m +++ b/gp/gpla_pred.m @@ -79,22 +79,22 @@ ip=inputParser; ip.FunctionName = 'GPLA_PRED'; - ip.addRequired('gp', @isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp', @isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpmc_jpreds.m b/gp/gpmc_jpreds.m index 04ec1707..f03a4af1 100644 --- a/gp/gpmc_jpreds.m +++ b/gp/gpmc_jpreds.m @@ -103,22 +103,22 @@ ip=inputParser; ip.FunctionName = 'GPMC_JPREDS'; - ip.addRequired('gp',@isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpmc_loopred.m b/gp/gpmc_loopred.m index fee4e2b8..53817623 100644 --- a/gp/gpmc_loopred.m +++ b/gp/gpmc_loopred.m @@ -47,16 +47,16 @@ ip=inputParser; ip.FunctionName = 'GPMC_LOOPRED'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('is', 'on', @(x) ismember(x,{'on' 'off'})) -ip.parse(gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','is', 'on', @(x) ismember(x,{'on' 'off'})); +ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; is=ip.Results.is; -if isfield(gp,'meanf') & ~isempty(gp.meanf) +if isfield(gp,'meanf') && ~isempty(gp.meanf) error('GPMC_LOOPRED: Mean functions not yet supported'); end diff --git a/gp/gpmc_loopreds.m b/gp/gpmc_loopreds.m index b9f14578..4b5298dd 100644 --- a/gp/gpmc_loopreds.m +++ b/gp/gpmc_loopreds.m @@ -39,11 +39,11 @@ % Nothing to parse, but check the arguments anyway ip=inputParser; ip.FunctionName = 'GPMC_LOOPREDS'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(gp, x, y, varargin{:}); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; if isfield(gp,'meanf') & ~isempty(gp.meanf) diff --git a/gp/gpmc_preds.m b/gp/gpmc_preds.m index 9389a0ef..3454b351 100644 --- a/gp/gpmc_preds.m +++ b/gp/gpmc_preds.m @@ -101,22 +101,22 @@ ip=inputParser; ip.FunctionName = 'GPMC_PREDS'; - ip.addRequired('gp',@isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/gpmf_constant.m b/gp/gpmf_constant.m index 057f1fd8..1504db35 100644 --- a/gp/gpmf_constant.m +++ b/gp/gpmf_constant.m @@ -39,13 +39,13 @@ ip=inputParser; ip.FunctionName = 'GPMF_CONSTANT'; - ip.addOptional('gpmf', [], @isstruct); - ip.addParamValue('constant',1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('prior_mean',0, @(x) isvector(x)); - ip.addParamValue('prior_cov',100, @(x) isvector(x)); - ip.addParamValue('mean_prior', [], @isstruct); - ip.addParamValue('cov_prior', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpmf', [], @isstruct); + ip=iparser(ip,'addParamValue','constant',1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','prior_mean',0, @(x) isvector(x)); + ip=iparser(ip,'addParamValue','prior_cov',100, @(x) isvector(x)); + ip=iparser(ip,'addParamValue','mean_prior', [], @isstruct); + ip=iparser(ip,'addParamValue','cov_prior', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); gpmf=ip.Results.gpmf; if isempty(gpmf) diff --git a/gp/gpmf_linear.m b/gp/gpmf_linear.m index 1624b670..8892fdfa 100644 --- a/gp/gpmf_linear.m +++ b/gp/gpmf_linear.m @@ -37,13 +37,13 @@ ip=inputParser; ip.FunctionName = 'GPMF_LINEAR'; - ip.addOptional('gpmf', [], @isstruct); - ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0)); - ip.addParamValue('prior_mean',0, @(x) isvector(x)); - ip.addParamValue('prior_cov',100, @(x) isvector(x)); - ip.addParamValue('mean_prior', [], @isstruct); - ip.addParamValue('cov_prior', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpmf', [], @isstruct); + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','prior_mean',0, @(x) isvector(x)); + ip=iparser(ip,'addParamValue','prior_cov',100, @(x) isvector(x)); + ip=iparser(ip,'addParamValue','mean_prior', [], @isstruct); + ip=iparser(ip,'addParamValue','cov_prior', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); gpmf=ip.Results.gpmf; if isempty(gpmf) diff --git a/gp/gpmf_squared.m b/gp/gpmf_squared.m index 2c963b8a..9b80e7a0 100644 --- a/gp/gpmf_squared.m +++ b/gp/gpmf_squared.m @@ -39,13 +39,13 @@ ip=inputParser; ip.FunctionName = 'GPMF_SQUARED'; - ip.addOptional('gpmf', [], @isstruct); - ip.addParamValue('selectedVariables',[], @(x) isvector(x) && all(x>0)); - ip.addParamValue('prior_mean',0, @(x) isvector(x)); - ip.addParamValue('prior_cov',100, @(x) isvector(x)); - ip.addParamValue('mean_prior', [], @isstruct); - ip.addParamValue('cov_prior', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','gpmf', [], @isstruct); + ip=iparser(ip,'addParamValue','selectedVariables',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','prior_mean',0, @(x) isvector(x)); + ip=iparser(ip,'addParamValue','prior_cov',100, @(x) isvector(x)); + ip=iparser(ip,'addParamValue','mean_prior', [], @isstruct); + ip=iparser(ip,'addParamValue','cov_prior', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); gpmf=ip.Results.gpmf; if isempty(gpmf) diff --git a/gp/lgcp.m b/gp/lgcp.m index 111458b1..765e85ff 100644 --- a/gp/lgcp.m +++ b/gp/lgcp.m @@ -33,16 +33,16 @@ ip=inputParser; ip.FunctionName = 'LGCP'; - ip.addRequired('x', @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); - ip.addOptional('xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); - ip.addParamValue('gridn',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('range',[], @(x) isreal(x)&&(length(x)==2||length(x)==4)); - ip.addParamValue('gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle')); - ip.addParamValue('latent_method','EP', @(x) ismember(x,{'EP' 'Laplace'})) - ip.addParamValue('int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'})) - ip.addParamValue('normalize',false, @islogical); + ip=iparser(ip,'addRequired','x', @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); + ip=iparser(ip,'addOptional','xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); + ip=iparser(ip,'addParamValue','gridn',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','range',[], @(x) isreal(x)&&(length(x)==2||length(x)==4)); + ip=iparser(ip,'addParamValue','gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle')); + ip=iparser(ip,'addParamValue','latent_method','EP', @(x) ismember(x,{'EP' 'Laplace'})); + ip=iparser(ip,'addParamValue','int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'})); + ip=iparser(ip,'addParamValue','normalize',false, @islogical); - ip.parse(x,varargin{:}); + ip=iparser(ip,'parse',x,varargin{:}); x=ip.Results.x; xt=ip.Results.xt; gridn=ip.Results.gridn; diff --git a/gp/lgpdens.m b/gp/lgpdens.m index 34adf8bc..5318f7b3 100644 --- a/gp/lgpdens.m +++ b/gp/lgpdens.m @@ -55,22 +55,22 @@ ip=inputParser; ip.FunctionName = 'LGPDENS'; - ip.addRequired('x', @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); - ip.addOptional('xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); - ip.addParamValue('gridn',[], @(x) isnumeric(x)); - ip.addParamValue('range',[], @(x) isempty(x)||isreal(x)&&(length(x)==2||length(x)==4)); - ip.addParamValue('gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle')); - ip.addParamValue('latent_method','Laplace', @(x) ismember(x,{'EP' 'Laplace' 'MCMC'})) - %ip.addParamValue('latent_method','Laplace', @(x) ismember(x,{'EP' 'Laplace'})) - ip.addParamValue('int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'})) - ip.addParamValue('normalize',false, @islogical); - ip.addParamValue('display', 'off', @(x) islogical(x) || ... - ismember(x,{'on' 'off' 'iter'})) - ip.addParamValue('speedup',[], @(x) ismember(x,{'on' 'off'})); - ip.addParamValue('cond_dens',[], @(x) ismember(x,{'on' 'off'})); - ip.addParamValue('basis_function',[], @(x) ismember(x,{'on' 'off'})); + ip=iparser(ip,'addRequired','x', @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); + ip=iparser(ip,'addOptional','xt',NaN, @(x) isnumeric(x) && size(x,2)==1 || size(x,2)==2); + ip=iparser(ip,'addParamValue','gridn',[], @(x) isnumeric(x)); + ip=iparser(ip,'addParamValue','range',[], @(x) isempty(x)||isreal(x)&&(length(x)==2||length(x)==4)); + ip=iparser(ip,'addParamValue','gpcf',@gpcf_sexp,@(x) ischar(x) || isa(x,'function_handle')); + ip=iparser(ip,'addParamValue','latent_method','Laplace', @(x) ismember(x,{'EP' 'Laplace' 'MCMC'})); + %ip=iparser(ip,'addParamValue','latent_method','Laplace', @(x) ismember(x,{'EP' 'Laplace'})) + ip=iparser(ip,'addParamValue','int_method','mode', @(x) ismember(x,{'mode' 'CCD', 'grid'})); + ip=iparser(ip,'addParamValue','normalize',false, @islogical); + ip=iparser(ip,'addParamValue','display', 'off', @(x) islogical(x) || ... + ismember(x,{'on' 'off' 'iter'})); + ip=iparser(ip,'addParamValue','speedup',[], @(x) ismember(x,{'on' 'off'})); + ip=iparser(ip,'addParamValue','cond_dens',[], @(x) ismember(x,{'on' 'off'})); + ip=iparser(ip,'addParamValue','basis_function',[], @(x) ismember(x,{'on' 'off'})); - ip.parse(x,varargin{:}); + ip=iparser(ip,'parse',x,varargin{:}); x=ip.Results.x; xt=ip.Results.xt; gridn=ip.Results.gridn; diff --git a/gp/lgpdens_cum.m b/gp/lgpdens_cum.m index fd0693d3..7bbdb719 100644 --- a/gp/lgpdens_cum.m +++ b/gp/lgpdens_cum.m @@ -12,10 +12,10 @@ % License (version 3 or later); please refer to the file % License.txt, included with the software, for details. ip=inputParser; -ip.addRequired('bb',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('x1',@(x) ~isempty(x) && isreal(x)) -ip.addRequired('x2', @(x) ~isempty(x) && isreal(x)) -ip.parse(bb,x1,x2) +ip=iparser(ip,'addRequired','bb',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','x1',@(x) ~isempty(x) && isreal(x)); +ip=iparser(ip,'addRequired','x2', @(x) ~isempty(x) && isreal(x)); +ip=iparser(ip,'parse',bb,x1,x2); [p,pq,xt]=lgpdens(bb); I1=min(find(xt>x1)); diff --git a/gp/lik_binomial.m b/gp/lik_binomial.m index 3dc8e8f6..93396d74 100644 --- a/gp/lik_binomial.m +++ b/gp/lik_binomial.m @@ -30,8 +30,8 @@ ip=inputParser; ip.FunctionName = 'LIK_BINOMIAL'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -239,12 +239,12 @@ % See also % GPEP_E -% if isempty(z) -% error(['lik_binomial -> lik_binomial_tiltedMoments: missing z!'... -% 'Binomial likelihood needs the expected number of '... -% 'occurrences as an extra input z. See, for '... -% 'example, lik_binomial and gpla_e. ']); -% end + if isempty(z) + error(['lik_binomial -> lik_binomial_tiltedMoments: missing z!'... + 'Binomial likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_binomial and gpla_e. ']); + end yy = y(i1); N = z(i1); @@ -392,7 +392,7 @@ % ldconst = log(factorial(N)/(factorial(yy)*factorial(N-yy))-log(sigm2_i)/2 -log(2*pi)/2; % Create function handle for the function to be integrated - df = @binomial_norm; + df = @(f) binomial_norm(f, ldconst, yy, N, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_binomial_norm; ldg = @log_binomial_norm_g; @@ -422,8 +422,8 @@ niter=3; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g = ldg(modef); - h = ldg2(modef); + g = ldg(modef, ldconst, yy, N, myy_i, sigm2_i); + h = ldg2(modef, ldconst, yy, N, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, ldconst, yy, N, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -451,11 +451,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, N, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, N, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -464,9 +464,9 @@ 'even after looking hard!']) end end +end - - function integrand = binomial_norm(f) + function integrand = binomial_norm(f, ldconst, yy, N, myy_i, sigm2_i) % Logit * Gaussian integrand = exp(ldconst + yy*log(1./(1.+exp(-f)))+(N-yy)*log(1-1./(1.+exp(-f)))... - 0.5 * (f-myy_i).^2./sigm2_i); @@ -476,7 +476,7 @@ integrand(isnan(integrand))=0; end - function log_int = log_binomial_norm(f) + function log_int = log_binomial_norm(f, ldconst, yy, N, myy_i, sigm2_i) % log(Binomial * Gaussian) % log_binomial_norm is used to avoid underflow when searching % integration interval @@ -488,7 +488,7 @@ % -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_binomial_norm_g(f) + function g = log_binomial_norm_g(f, ldconst, yy, N, myy_i, sigm2_i) % d/df log(Binomial * Gaussian) % derivative of log_logit_norm g = -(f-myy_i)./sigm2_i - exp(-f).*(N-yy)./((1+exp(-f)).^2.*(1-1./(1+exp(-f)))) ... @@ -497,7 +497,7 @@ % + (myy_i - f)./sigm2_i; end - function g2 = log_binomial_norm_g2(f) + function g2 = log_binomial_norm_g2(f, ldconst, yy, N, myy_i, sigm2_i) % d^2/df^2 log(Binomial * Gaussian) % second derivate of log_logit_norm g2 = - (1+exp(2.*f)+exp(f).*(2+N*sigm2_i)./((1+exp(f))^2*sigm2_i)); @@ -506,7 +506,7 @@ % -1/sigm2_i; end -end +% end function p = lik_binomial_invlink(lik, f, z) %LIK_BINOMIAL_INVLINK Returns values of inverse link function @@ -550,7 +550,7 @@ reclik.fh.invlink = @lik_binomial_invlink; reclik.fh.predprcty = @lik_binomial_predprcty; reclik.fh.predy = @lik_binomial_predy; - reclik.fh.recappend = @likelih_binomial_recappend; + reclik.fh.recappend = @lik_binomial_recappend; return end diff --git a/gp/lik_coxph.m b/gp/lik_coxph.m index 9cbc1a77..b3a4f7bc 100644 --- a/gp/lik_coxph.m +++ b/gp/lik_coxph.m @@ -49,11 +49,11 @@ ip=inputParser; ip.FunctionName = 'LIK_COXPH'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('S', linspace(0,1.001,50), @(x) isvector(x)); - ip.addParamValue('stratificationVariables', [], @(x) isvector(x) && all(rem(x,1)==0)); - ip.addParamValue('removeStratificationVariables', [], @(x) ismember(x, {'on' 'off'})); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','S', linspace(0,1.001,50), @(x) isvector(x)); + ip=iparser(ip,'addParamValue','stratificationVariables', [], @(x) isvector(x) && all(rem(x,1)==0)); + ip=iparser(ip,'addParamValue','removeStratificationVariables', [], @(x) ismember(x, {'on' 'off'})); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_gaussian.m b/gp/lik_gaussian.m index 6d270f10..2ff64f61 100644 --- a/gp/lik_gaussian.m +++ b/gp/lik_gaussian.m @@ -48,11 +48,11 @@ ip=inputParser; ip.FunctionName = 'LIK_GAUSSIAN'; - ip.addOptional('lik', [], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.addParamValue('n',[], @(x) isreal(x) && all(x>0)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','n',[], @(x) isreal(x) && all(x>0)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m index f05a4de7..883a3a4f 100644 --- a/gp/lik_gaussianbl.m +++ b/gp/lik_gaussianbl.m @@ -45,11 +45,11 @@ ip=inputParser; ip.FunctionName = 'LIK_GAUSSIANBL'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('sigma2',0.1, @(x) isvector(x) && all(x>0)); - ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.addParamValue('bl_indic',0.1, @(x) isvector(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','bl_indic',0.1, @(x) isvector(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_gaussiansmt.m b/gp/lik_gaussiansmt.m index 64104e41..8680cb13 100644 --- a/gp/lik_gaussiansmt.m +++ b/gp/lik_gaussiansmt.m @@ -54,17 +54,17 @@ ip=inputParser; ip.FunctionName = 'LIK_GAUSSIANSMT'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('ndata',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); - ip.addParamValue('sigma2',[], @(x) isvector(x) && all(x>0)); - ip.addParamValue('U',[], @isvector); - ip.addParamValue('tau2',0.1, @isscalar); - ip.addParamValue('alpha',0.5, @isscalar); - ip.addParamValue('nu',4, @isscalar); - ip.addParamValue('nu_prior',[], @(x) isstruct(x) || isempty(x)); - ip.addParamValue('censored',[], @(x) isstruct); - ip.addParamValue('gibbs','on', @(x) ismember(x,{'on' 'off'})); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','ndata',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); + ip=iparser(ip,'addParamValue','sigma2',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','U',[], @isvector); + ip=iparser(ip,'addParamValue','tau2',0.1, @isscalar); + ip=iparser(ip,'addParamValue','alpha',0.5, @isscalar); + ip=iparser(ip,'addParamValue','nu',4, @isscalar); + ip=iparser(ip,'addParamValue','nu_prior',[], @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','censored',[], @(x) isstruct); + ip=iparser(ip,'addParamValue','gibbs','on', @(x) ismember(x,{'on' 'off'})); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -329,7 +329,6 @@ reccf.fh.lp = @lik_gaussiansmt_lp; reccf.fh.lpg = @lik_gaussiansmt_lpg; reccf.fh.cfg = @lik_gaussiansmt_cfg; - reccf.fh.cov = @lik_gaussiansmt_cov; reccf.fh.trcov = @lik_gaussiansmt_trcov; reccf.fh.trvar = @lik_gaussiansmt_trvar; reccf.fh.gibbs = @lik_gaussiansmt_gibbs; diff --git a/gp/lik_inputdependentnoise.m b/gp/lik_inputdependentnoise.m index 09471756..115619db 100644 --- a/gp/lik_inputdependentnoise.m +++ b/gp/lik_inputdependentnoise.m @@ -18,10 +18,10 @@ ip=inputParser; ip.FunctionName = 'LIK_INPUTDEPENDENTNOISE'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_inputdependentweibull.m b/gp/lik_inputdependentweibull.m index 4c551b53..0a10aceb 100644 --- a/gp/lik_inputdependentweibull.m +++ b/gp/lik_inputdependentweibull.m @@ -51,10 +51,10 @@ ip=inputParser; ip.FunctionName = 'LIK_INPUTDEPENDENTWEIBULL'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('shape',1, @(x) isscalar(x) && x>0); - ip.addParamValue('shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_lgp.m b/gp/lik_lgp.m index e394ff4b..d89f556f 100644 --- a/gp/lik_lgp.m +++ b/gp/lik_lgp.m @@ -22,8 +22,8 @@ ip=inputParser; ip.FunctionName = 'LIK_LGP'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -346,7 +346,7 @@ ldconst = -gammaln(yy+1) - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @lgp_norm; + df = @(f) lgp_norm(f, ldconst, avgE, yy, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_lgp_norm; ldg = @log_lgp_norm_g; @@ -371,8 +371,8 @@ niter=3; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef, ldconst, avgE, yy, myy_i, sigm2_i); + h=ldg2(modef, ldconst, avgE, yy, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, ldconst, avgE, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -400,11 +400,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, avgE, yy, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, ldconst, avgE, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -414,7 +414,7 @@ end end - function integrand = lgp_norm(f) + function integrand = lgp_norm(f, ldconst, avgE, yy, myy_i, sigm2_i) % LGP * Gaussian mu = avgE.*exp(f); integrand = exp(ldconst ... @@ -422,7 +422,7 @@ -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_lgp_norm(f) + function log_int = log_lgp_norm(f, ldconst, avgE, yy, myy_i, sigm2_i) % log(LGP * Gaussian) % log_lgp_norm is used to avoid underflow when searching % integration interval @@ -432,7 +432,7 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_lgp_norm_g(f) + function g = log_lgp_norm_g(f, ldconst, avgE, yy, myy_i, sigm2_i) % d/df log(LGP * Gaussian) % derivative of log_lgp_norm mu = avgE.*exp(f); @@ -440,7 +440,7 @@ + (myy_i - f)./sigm2_i; end - function g2 = log_lgp_norm_g2(f) + function g2 = log_lgp_norm_g2(f, ldconst, avgE, yy, myy_i, sigm2_i) % d^2/df^2 log(LGP * Gaussian) % second derivate of log_lgp_norm mu = avgE.*exp(f); diff --git a/gp/lik_lgpc.m b/gp/lik_lgpc.m index 51197352..79f2898c 100644 --- a/gp/lik_lgpc.m +++ b/gp/lik_lgpc.m @@ -25,8 +25,8 @@ ip=inputParser; ip.FunctionName = 'LIK_LGPC'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -353,7 +353,7 @@ ldconst = -gammaln(yy+1) - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @lgpc_norm; + df = @(f) lgpc_norm(f, ldconst, avgE, yy, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_lgpc_norm; ldg = @log_lgpc_norm_g; @@ -378,8 +378,8 @@ niter=3; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef, ldconst, avgE, yy, myy_i, sigm2_i); + h=ldg2(modef, ldconst, avgE, yy, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, ldconst, avgE, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -407,11 +407,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, avgE, yy, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, ldconst, avgE, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -421,7 +421,7 @@ end end - function integrand = lgpc_norm(f) + function integrand = lgpc_norm(f, ldconst, avgE, yy, myy_i, sigm2_i) % LGPC * Gaussian mu = avgE.*exp(f); integrand = exp(ldconst ... @@ -429,7 +429,7 @@ -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_lgpc_norm(f) + function log_int = log_lgpc_norm(f, ldconst, avgE, yy, myy_i, sigm2_i) % log(LGPC * Gaussian) % log_lgpc_norm is used to avoid underflow when searching % integration interval @@ -439,7 +439,7 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_lgpc_norm_g(f) + function g = log_lgpc_norm_g(f, ldconst, avgE, yy, myy_i, sigm2_i) % d/df log(LGPC * Gaussian) % derivative of log_lgpc_norm mu = avgE.*exp(f); @@ -447,7 +447,7 @@ + (myy_i - f)./sigm2_i; end - function g2 = log_lgpc_norm_g2(f) + function g2 = log_lgpc_norm_g2(f, ldconst, avgE, yy, myy_i, sigm2_i) % d^2/df^2 log(LGPC * Gaussian) % second derivate of log_lgpc_norm mu = avgE.*exp(f); diff --git a/gp/lik_loggaussian.m b/gp/lik_loggaussian.m index b14576ab..b130b868 100644 --- a/gp/lik_loggaussian.m +++ b/gp/lik_loggaussian.m @@ -48,10 +48,10 @@ ip=inputParser; ip.FunctionName = 'LIK_LOGGAUSSIAN'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('sigma2',1, @(x) isscalar(x) && x>0); - ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','sigma2',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -436,10 +436,10 @@ % Integrate with quadgk [m_0, fhncnt] = quadgk(tf, minf, maxf); - [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); + [g_i, fhncnt] = quadgk(@(f) td(f, yy, yc, s2).*tf(f)./m_0, minf, maxf); g_i = g_i.*s2; - function g = deriv(f) + function g = deriv(f, yy, yc, s2) r=log(yy)-f; g = -yc./(2.*s2) + yc.*r.^2./(2.*s2^2) + (1-yc)./(1-norm_cdf(r/sqrt(s2))) ... .* (r./(sqrt(2.*pi).*2.*s2.^(3/2)).*exp(-1/(2.*s2).*r.^2)); @@ -519,7 +519,7 @@ - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @loggaussian_norm; + df = @(f) loggaussian_norm(f, ldconst, yy, yc, s2, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_loggaussian_norm; ldg = @log_loggaussian_norm_g; @@ -543,8 +543,8 @@ niter=4; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef, ldconst, yy, yc, s2, myy_i, sigm2_i); + h=ldg2(modef, ldconst, yy, yc, s2, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, ldconst, yy, yc, s2, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -572,11 +572,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, yc, s2, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, yc, s2, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -586,14 +586,14 @@ end end - function integrand = loggaussian_norm(f) + function integrand = loggaussian_norm(f, ldconst, yy, yc, s2, myy_i, sigm2_i) % loggaussian * Gaussian integrand = exp(ldconst ... - yc./(2*s2).*(log(yy)-f).^2 + (1-yc).*log(1-norm_cdf((log(yy)-f)/sqrt(s2))) ... -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_loggaussian_norm(f) + function log_int = log_loggaussian_norm(f, ldconst, yy, yc, s2, myy_i, sigm2_i) % log(loggaussian * Gaussian) % log_loggaussian_norm is used to avoid underflow when searching % integration interval @@ -602,14 +602,14 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_loggaussian_norm_g(f) + function g = log_loggaussian_norm_g(f, ldconst, yy, yc, s2, myy_i, sigm2_i) % d/df log(loggaussian * Gaussian) % derivative of log_loggaussian_norm g = yc./s2.*(log(yy)-f) + (1-yc)./(1-norm_cdf((log(yy)-f)/sqrt(s2))).*1/sqrt(2*pi*s2)*exp(-(log(yy)-f).^2./(2*s2)) ... + (myy_i - f)./sigm2_i; end - function g2 = log_loggaussian_norm_g2(f) + function g2 = log_loggaussian_norm_g2(f, ldconst, yy, yc, s2, myy_i, sigm2_i) % d^2/df^2 log(loggaussian * Gaussian) % second derivate of log_loggaussian_norm g2 = -yc./s2 + (1-yc).*(-exp(-(log(yy)-f).^2/s2)./(2*pi*s2.*(1-norm_cdf((log(yy)-f)/sqrt(s2))).^2) ... @@ -696,7 +696,6 @@ reclik.fh.tiltedMoments = @lik_loggaussian_tiltedMoments; reclik.fh.invlink = @lik_loggaussian_invlink; reclik.fh.predy = @lik_loggaussian_predy; - reclik.fh.predcdf = @lik_loggaussian_predcdf; reclik.fh.recappend = @lik_loggaussian_recappend; reclik.p=[]; reclik.p.sigma2=[]; diff --git a/gp/lik_logit.m b/gp/lik_logit.m index 60ca232b..5101e082 100755 --- a/gp/lik_logit.m +++ b/gp/lik_logit.m @@ -23,8 +23,8 @@ ip=inputParser; ip.FunctionName = 'LIK_LOGIT'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -316,7 +316,7 @@ ldconst = -log(sigm2_i)/2 -log(2*pi)/2; % Create function handle for the function to be integrated - df = @logit_norm; + df = @(f) logit_norm(f, ldconst, yy, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_logit_norm; ldg = @log_logit_norm_g; @@ -339,8 +339,8 @@ niter=2; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef, ldconst, yy, myy_i, sigm2_i); + h=ldg2(modef, ldconst, yy, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, ldconst, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -372,11 +372,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -386,14 +386,14 @@ end end - function integrand = logit_norm(f) + function integrand = logit_norm(f, ldconst, yy, myy_i, sigm2_i) % Logit * Gaussian integrand = exp(ldconst ... -log(1+exp(-yy.*f)) ... -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_logit_norm(f) + function log_int = log_logit_norm(f, ldconst, yy, myy_i, sigm2_i) % log(Logit * Gaussian) % log_logit_norm is used to avoid underflow when searching % integration interval @@ -402,14 +402,14 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_logit_norm_g(f) + function g = log_logit_norm_g(f, ldconst, yy, myy_i, sigm2_i) % d/df log(Logit * Gaussian) % derivative of log_logit_norm g = yy./(exp(f*yy)+1)... + (myy_i - f)./sigm2_i; end - function g2 = log_logit_norm_g2(f) + function g2 = log_logit_norm_g2(f, ldconst, yy, myy_i, sigm2_i) % d^2/df^2 log(Logit * Gaussian) % second derivate of log_logit_norm a=exp(f*yy); diff --git a/gp/lik_loglogistic.m b/gp/lik_loglogistic.m index 5d605b61..27e24f22 100644 --- a/gp/lik_loglogistic.m +++ b/gp/lik_loglogistic.m @@ -48,10 +48,10 @@ ip=inputParser; ip.FunctionName = 'LIK_LOGLOGISTIC'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('shape',1, @(x) isscalar(x) && x>0); - ip.addParamValue('shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -418,14 +418,14 @@ % and useful integration limits [tf,minf,maxf]=init_loglogistic_norm(yy,myy_i,sigm2_i,yc,r); % additionally get function handle for the derivative - td = @deriv; + td = @(f) deriv(f, yc, yy, r); % Integrate with quadgk [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; - function g = deriv(f) + function g = deriv(f, yc, yy, r) g = yc.*(1/r+log(yy)-f) + (-1-yc)./(1+(yy./exp(f)).^r).* ... (yy./exp(f)).^r.*(log(yy)-f); end @@ -504,7 +504,7 @@ - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @loglogistic_norm; + df = @(f) loglogistic_norm(f, ldconst, yc, r, yy, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_loglogistic_norm; ldg = @log_loglogistic_norm_g; @@ -528,8 +528,8 @@ niter=4; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef, ldconst, yc, r, yy, myy_i, sigm2_i); + h=ldg2(modef, ldconst, yc, r, yy, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, ldconst, yc, r, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -557,11 +557,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yc, r, yy, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yc, r, yy, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -571,14 +571,14 @@ end end - function integrand = loglogistic_norm(f) + function integrand = loglogistic_norm(f, ldconst, yc, r, yy, myy_i, sigm2_i) % loglogistic * Gaussian integrand = exp(ldconst ... - yc.*r.*f +(-1-yc).*log(1+(yy./exp(f)).^r) ... -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_loglogistic_norm(f) + function log_int = log_loglogistic_norm(f, ldconst, yc, r, yy, myy_i, sigm2_i) % log(loglogistic * Gaussian) % log_loglogistic_norm is used to avoid underflow when searching % integration interval @@ -587,14 +587,14 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_loglogistic_norm_g(f) + function g = log_loglogistic_norm_g(f, ldconst, yc, r, yy, myy_i, sigm2_i) % d/df log(loglogistic * Gaussian) % derivative of log_loglogistic_norm g = -r.*yc - (-1-yc).*r.*(yy./exp(f)).^r./(1+(yy./exp(f)).^r) ... + (myy_i - f)./sigm2_i; end - function g2 = log_loglogistic_norm_g2(f) + function g2 = log_loglogistic_norm_g2(f, ldconst, yc, r, yy, myy_i, sigm2_i) % d^2/df^2 log(loglogistic * Gaussian) % second derivate of log_loglogistic_norm g2 = r.^2.*(-1-yc).*(yy./exp(f)).^r./(1+(yy./exp(f)).^r).^2 ... @@ -679,7 +679,6 @@ reclik.fh.tiltedMoments = @lik_loglogistic_tiltedMoments; reclik.fh.invlink = @lik_loglogistic_invlink; reclik.fh.predy = @lik_loglogistic_predy; - reclik.fh.predcdf=@lik_loglogistic_predcdf; reclik.fh.recappend = @lik_loglogistic_recappend; reclik.p=[]; reclik.p.shape=[]; diff --git a/gp/lik_multinom.m b/gp/lik_multinom.m index 31a301ef..e098e77a 100644 --- a/gp/lik_multinom.m +++ b/gp/lik_multinom.m @@ -20,8 +20,8 @@ ip=inputParser; ip.FunctionName = 'LIK_MULTINOM'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_negbin.m b/gp/lik_negbin.m index 9ded97d7..40ca26c0 100644 --- a/gp/lik_negbin.m +++ b/gp/lik_negbin.m @@ -49,10 +49,10 @@ % inputParser checks the arguments and assigns some default values ip=inputParser; ip.FunctionName = 'LIK_NEGBIN'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('disper',10, @(x) isscalar(x) && x>0); - ip.addParamValue('disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -437,14 +437,14 @@ % and useful integration limits [tf,minf,maxf]=init_negbin_norm(yy,myy_i,sigm2_i,avgE,r); % additionally get function handle for the derivative - td = @deriv; + td = @(f) deriv(f, r, avgE, yy); % Integrate with quadgk [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; - function g = deriv(f) + function g = deriv(f, r, avgE, yy) mu = avgE.*exp(f); % Derivative using the psi function g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r); @@ -587,7 +587,7 @@ ldconst = -gammaln(r)-gammaln(yy+1)+gammaln(r+yy)... - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @negbin_norm; + df = @(f) negbin_norm(f, yy, ldconst, r, avgE, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_negbin_norm; ldg = @log_negbin_norm_g; @@ -612,8 +612,8 @@ niter=4; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef, yy, ldconst, r, avgE, myy_i, sigm2_i); + h=ldg2(modef, yy, ldconst, r, avgE, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, yy, ldconst, r, avgE, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -641,11 +641,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, yy, ldconst, r, avgE, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, yy, ldconst, r, avgE, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -655,7 +655,7 @@ end end - function integrand = negbin_norm(f) + function integrand = negbin_norm(f, yy, ldconst, r, avgE, myy_i, sigm2_i) % Negative-binomial * Gaussian mu = avgE.*exp(f); integrand = exp(ldconst ... @@ -663,7 +663,7 @@ -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_negbin_norm(f) + function log_int = log_negbin_norm(f, yy, ldconst, r, avgE, myy_i, sigm2_i) % log(Negative-binomial * Gaussian) % log_negbin_norm is used to avoid underflow when searching % integration interval @@ -673,7 +673,7 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_negbin_norm_g(f) + function g = log_negbin_norm_g(f, yy, ldconst, r, avgE, myy_i, sigm2_i) % d/df log(Negative-binomial * Gaussian) % derivative of log_negbin_norm mu = avgE.*exp(f); @@ -681,7 +681,7 @@ + (myy_i - f)./sigm2_i; end - function g2 = log_negbin_norm_g2(f) + function g2 = log_negbin_norm_g2(f, yy, ldconst, r, avgE, myy_i, sigm2_i) % d^2/df^2 log(Negative-binomial * Gaussian) % second derivate of log_negbin_norm mu = avgE.*exp(f); diff --git a/gp/lik_negbinztr.m b/gp/lik_negbinztr.m index ee6f603f..9beace17 100644 --- a/gp/lik_negbinztr.m +++ b/gp/lik_negbinztr.m @@ -51,10 +51,10 @@ ip=inputParser; ip.FunctionName = 'LIK_NEGBINZTR'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('disper',10, @(x) isscalar(x) && x>0); - ip.addParamValue('disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -462,14 +462,14 @@ % and useful integration limits [tf,minf,maxf]=init_negbinztr_norm(yy,myy_i,sigm2_i,avgE,r); % additionally get function handle for the derivative - td = @deriv; + td = @(f) deriv(f, avgE, r, yy); % Integrate with quadgk [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; - function g = deriv(f) + function g = deriv(f, avgE, r, yy) mu = avgE.*exp(f); % Derivative using the psi function g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r); @@ -587,7 +587,7 @@ ldconst = -gammaln(r)-gammaln(yy+1)+gammaln(r+yy)... - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @negbinztr_norm; + df = @(f) negbinztr_norm(f, ldconst,avgE,r,yy,myy_i,sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_negbinztr_norm; ldg = @log_negbinztr_norm_g; @@ -612,8 +612,8 @@ niter=4; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef,ldconst,avgE,r,yy,myy_i,sigm2_i); + h=ldg2(modef,ldconst,avgE,r,yy,myy_i,sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf,ldconst,avgE,r,yy,myy_i,sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -646,11 +646,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -659,8 +659,9 @@ 'even after looking hard!']) end end +end - function integrand = negbinztr_norm(f) + function integrand = negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i) % Negative-binomial * Gaussian mu = avgE.*exp(f); lp0=r.*(log(r) - log(r+mu)); @@ -681,7 +682,7 @@ end end - function log_int = log_negbinztr_norm(f) + function log_int = log_negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i) % log(Negative-binomial * Gaussian) % log_negbinztr_norm is used to avoid underflow when searching % integration interval @@ -704,7 +705,7 @@ end end - function g = log_negbinztr_norm_g(f) + function g = log_negbinztr_norm_g(f,ldconst,avgE,r,yy,myy_i,sigm2_i) % d/df log(Negative-binomial * Gaussian) % derivative of log_negbinztr_norm mu = avgE.*exp(f); @@ -720,7 +721,7 @@ end end - function g2 = log_negbinztr_norm_g2(f) + function g2 = log_negbinztr_norm_g2(f,ldconst,avgE,r,yy,myy_i,sigm2_i) % d^2/df^2 log(Negative-binomial * Gaussian) % second derivate of log_negbinztr_norm mu = avgE.*exp(f); @@ -735,8 +736,6 @@ + (r^2 + r^2*exp(-lp0)*(mu - 1))/((mu + r)^2*(exp(-lp0) - 1)^2)*mu; end end - -end function prctys = lik_negbinztr_predprcty(lik, Ef, Varf, zt, prcty) %LIK_BINOMIAL_PREDPRCTY Returns the percentiled of predictive density of y diff --git a/gp/lik_poisson.m b/gp/lik_poisson.m index 9494a4cc..7b444e7d 100644 --- a/gp/lik_poisson.m +++ b/gp/lik_poisson.m @@ -30,8 +30,8 @@ ip=inputParser; ip.FunctionName = 'LIK_POISSON'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -396,7 +396,7 @@ ldconst = -gammaln(yy+1) - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @poisson_norm; + df = @(f) poisson_norm(f,avgE,ldconst,yy,myy_i,sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_poisson_norm; ldg = @log_poisson_norm_g; @@ -421,8 +421,8 @@ niter=3; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef,avgE,ldconst,yy,myy_i,sigm2_i); + h=ldg2(modef,avgE,ldconst,yy,myy_i,sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf,avgE,ldconst,yy,myy_i,sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -450,11 +450,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf,avgE,ldconst,yy,myy_i,sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf,avgE,ldconst,yy,myy_i,sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -464,7 +464,7 @@ end end - function integrand = poisson_norm(f) + function integrand = poisson_norm(f,avgE,ldconst,yy,myy_i,sigm2_i) % Poisson * Gaussian mu = avgE.*exp(f); integrand = exp(ldconst ... @@ -472,7 +472,7 @@ -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_poisson_norm(f) + function log_int = log_poisson_norm(f,avgE,ldconst,yy,myy_i,sigm2_i) % log(Poisson * Gaussian) % log_poisson_norm is used to avoid underflow when searching % integration interval @@ -482,7 +482,7 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_poisson_norm_g(f) + function g = log_poisson_norm_g(f,avgE,ldconst,yy,myy_i,sigm2_i) % d/df log(Poisson * Gaussian) % derivative of log_poisson_norm mu = avgE.*exp(f); @@ -490,7 +490,7 @@ + (myy_i - f)./sigm2_i; end - function g2 = log_poisson_norm_g2(f) + function g2 = log_poisson_norm_g2(f,avgE,ldconst,yy,myy_i,sigm2_i) % d^2/df^2 log(Poisson * Gaussian) % second derivate of log_poisson_norm mu = avgE.*exp(f); diff --git a/gp/lik_probit.m b/gp/lik_probit.m index c16fd961..71db4d8b 100644 --- a/gp/lik_probit.m +++ b/gp/lik_probit.m @@ -25,8 +25,8 @@ ip=inputParser; ip.FunctionName = 'LIK_PROBIT'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_qgp.m b/gp/lik_qgp.m index 87dc0cfd..d105e461 100644 --- a/gp/lik_qgp.m +++ b/gp/lik_qgp.m @@ -50,11 +50,11 @@ ip=inputParser; ip.FunctionName = 'LIK_QGP'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.addParamValue('quantile',0.5, @(x) isscalar(x) && x>0 && x<1); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','quantile',0.5, @(x) isscalar(x) && x>0 && x<1); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -396,10 +396,10 @@ % Integrate with quadgk [m_0, fhncnt] = quadgk(tf, minf, maxf); - [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); + [g_i, fhncnt] = quadgk(@(f) td(f, yy, sigma2, tau).*tf(f)./m_0, minf, maxf); g_i = g_i.*sigma2; - function g = deriv(f) + function g = deriv(f, yy, sigma2, tau) g = -1/(2.*sigma2) + (yy-f)./(2.*sigma2^(3/2)).*(tau-(yy<=f)); @@ -495,7 +495,7 @@ ldconst = log(tau*(1-tau)/sigma) ... - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @qgp_norm; + df = @(f) qgp_norm(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_qgp_norm; ldg = @log_qgp_norm_g; @@ -518,17 +518,17 @@ niter=8; % number of Newton iterations minf=modef-6*sigm2_i; - while ldg(minf) < 0 + while ldg(minf, ldconst, yy, sigma2, tau, myy_i, sigm2_i) < 0 minf=minf-2*sigm2_i; end maxf=modef+6*sigm2_i; - while ldg(maxf) > 0 + while ldg(maxf, ldconst, yy, sigma2, tau, myy_i, sigm2_i) > 0 maxf=maxf+2*sigm2_i; end for ni=1:niter % h=ldg2(modef); modef=0.5*(minf+maxf); - if ldg(modef) < 0 + if ldg(modef, ldconst, yy, sigma2, tau, myy_i, sigm2_i) < 0 maxf=modef; else minf=modef; @@ -537,15 +537,15 @@ % integrand limits based on Gaussian approximation at mode minf=modef-6*sqrt(sigm2_i); maxf=modef+6*sqrt(sigm2_i); - modeld=ld(modef); + modeld=ld(modef, ldconst, yy, sigma2, tau, myy_i, sigm2_i); iter=0; % check that density at end points is low enough lddiff=20; % min difference in log-density between mode and end-points - minld=ld(minf); + minld=ld(minf, ldconst, yy, sigma2, tau, myy_i, sigm2_i); step=1; while minld>(modeld-lddiff) minf=minf-step*sqrt(sigm2_i); - minld=ld(minf); + minld=ld(minf, ldconst, yy, sigma2, tau, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -554,11 +554,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, sigma2, tau, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*sqrt(sigm2_i); - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yy, sigma2, tau, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -568,14 +568,14 @@ end end - function integrand = qgp_norm(f) + function integrand = qgp_norm(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i) % Quantile-GP * Gaussian integrand = exp(ldconst ... -(yy-f)./sqrt(sigma2).*(tau-(yy<=f)) ... -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_qgp_norm(f) + function log_int = log_qgp_norm(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i) % log(Quantile-GP * Gaussian) % log_qgp_norm is used to avoid underflow when searching % integration interval @@ -584,7 +584,7 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_qgp_norm_g(f) + function g = log_qgp_norm_g(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i) % d/df log(Quantile-GP * Gaussian) % derivative of log_qgp_norm g = (tau-(yy<=f))/sqrt(sigma2) ... diff --git a/gp/lik_softmax.m b/gp/lik_softmax.m index a43f71ef..c67bc2df 100644 --- a/gp/lik_softmax.m +++ b/gp/lik_softmax.m @@ -19,8 +19,8 @@ ip=inputParser; ip.FunctionName = 'LIK_SOFTMAX2'; - ip.addOptional('lik', [], @isstruct); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/lik_t.m b/gp/lik_t.m index 0990ab3f..fedaa960 100644 --- a/gp/lik_t.m +++ b/gp/lik_t.m @@ -43,12 +43,12 @@ ip=inputParser; ip.FunctionName = 'LIK_T'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('sigma2',0.1, @(x) isscalar(x) && x>0); - ip.addParamValue('sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.addParamValue('nu',4, @(x) isscalar(x) && x>0); - ip.addParamValue('nu_prior',prior_fixed, @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','nu',4, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','nu_prior',prior_fixed, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -387,12 +387,12 @@ % GPEP_E - zm = @zeroth_moment; - tol = 1e-8; yy = y(i1); nu = lik.nu; sigma2 = lik.sigma2; + + zm = @(f) zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i); % Set the limits for integration and integrate with quad % ----------------------------------------------------- @@ -447,7 +447,7 @@ sigm2hati1 = m_2 - m_1.^2; logM_0 = log(m_0); - function integrand = zeroth_moment(f) + function integrand = zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i) r = yy-f; term = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; integrand = exp(term + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2)); @@ -475,15 +475,15 @@ % % See also % GPEP_G - - zm = @zeroth_moment; - znu = @deriv_nu; - zsigma2 = @deriv_sigma2; tol = 1e-8; yy = y(i1); nu = lik.nu; sigma2 = lik.sigma2; + + zm = @(f) zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i); + znu = @(f) deriv_nu(f, yy, nu, sigma2, myy_i, sigm2_i); + zsigma2 = @(f) deriv_sigma2(f, yy, nu, sigma2, myy_i, sigm2_i); % Set the limits for integration and integrate with quad mean_app = myy_i; @@ -544,20 +544,20 @@ g_i(2) = g_i(2)/m_0.*nu.*log(nu); end - function integrand = zeroth_moment(f) + function integrand = zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i) r = yy-f; term = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; integrand = exp(term + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2)); integrand = integrand.*exp(- 0.5 * (f-myy_i).^2./sigm2_i - log(sigm2_i)/2 - log(2*pi)/2); end - function g = deriv_nu(f) + function g = deriv_nu(f, yy, nu, sigma2, myy_i, sigm2_i) r = yy-f; temp = 1 + r.^2./nu./sigma2; g = psi((nu+1)/2)./2 - psi(nu/2)./2 - 1./(2.*nu) - log(temp)./2 + (nu+1)./(2.*temp).*(r./nu).^2./sigma2; end - function g = deriv_sigma2(f) + function g = deriv_sigma2(f, yy, nu, sigma2, myy_i, sigm2_i) r = yy-f; g = -1/sigma2/2 + (nu+1)./2.*r.^2./(nu.*sigma2.^2 + r.^2.*sigma2); end @@ -708,13 +708,13 @@ % lnZhat = log(m_0) -C; % end - function lpdf = lpt(f,C) - % logarithm of the tilted distribution - r = yy-f; - lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; - lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2); - lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2); - end +% function lpdf = lpt(f,C) +% % logarithm of the tilted distribution +% r = yy-f; +% lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; +% lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2); +% lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2); +% end end function [g_i] = lik_t_siteDeriv2(likelih, y, yi, sigm2_i, myy_i, z, eta, lnZhat) @@ -787,8 +787,8 @@ end np=numel(fvec); - logpt = lpt(fvec,0); - lpt_max = max([logpt lpt([myy_i mg],0)]); + logpt = lpt(fvec,0,yy,nu,sigma2,myy_i,sigm2_i,eta); + lpt_max = max([logpt lpt([myy_i mg],0,nu,sigma2,myy_i,sigm2_i,eta)]); lambdaconf=[fvec(1), fvec(end)]; for i1=2:np-1 if logpt(i1) < lpt_max+log(1e-7) %(exp(logpt(i1))/exp(lpt_max) < 1e-7) @@ -814,12 +814,12 @@ lambdaconf=[min(mg-6*sg,myy_i-6*sigm_i),myy_i+6*sigm_i]; fvec=linspace(mg,myy_i,np); end - lpt_max=max(lpt(fvec,0)); + lpt_max=max(lpt(fvec,0,yy,nu,sigma2,myy_i,sigm2_i,eta)); end C=log(1)-lpt_max; % scale the log-density for the quadrature tolerance else lambdaconf=[mg-6*sg,mg+6*sg]; - C=log(1)-lpt(mg,0); + C=log(1)-lpt(mg,0,yy,nu,sigma2,myy_i,sigm2_i,eta); end if nu>nu_lim @@ -843,19 +843,19 @@ % Use the normalization determined in the lik_t_tiltedMoments2 m_0=exp(lnZhat+C); - zm=@(f) deriv_sigma2(f).*exp(lpt(f,C))*sigma2; + zm=@(f) deriv_sigma2(f,yy,nu,sigma2).*exp(lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta))*sigma2; [g_i(1), fhncnt] = quadgk( zm, lambdaconf(1), lambdaconf(2),'AbsTol',ATOL,'RelTol',RTOL); g_i(1) = g_i(1)/m_0; if (isfield(likelih,'p') && ~isempty(likelih.p.nu)) - zm=@(f) deriv_nu(f).*exp(lpt(f,C)); + zm=@(f) deriv_nu(f,yy,nu,sigma2).*exp(lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta)); [g_i(2), fhncnt] = quadgk( zm, lambdaconf(1), lambdaconf(2),'AbsTol',ATOL,'RelTol',RTOL); g_i(2) = g_i(2)/m_0.*nu.*log(nu); end end - function lpdf = lpt(f,C) + function lpdf = lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta) % logarithm of the tilted distribution r = yy-f; lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; @@ -863,7 +863,7 @@ lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2); end - function g = deriv_nu(f) + function g = deriv_nu(f,yy,nu,sigma2) % derivative of the log-likelihood wrt nu r = yy-f; temp = r.^2 ./(nu*sigma2); @@ -878,7 +878,7 @@ end - function g = deriv_sigma2(f) + function g = deriv_sigma2(f,yy,nu,sigma2) % derivative of the log-likelihood wrt sigma2 r = yy-f; temp = r.^2 /sigma2; diff --git a/gp/lik_weibull.m b/gp/lik_weibull.m index 536a3af8..d26d8f6f 100644 --- a/gp/lik_weibull.m +++ b/gp/lik_weibull.m @@ -48,10 +48,10 @@ ip=inputParser; ip.FunctionName = 'LIK_WEIBULL'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('shape',1, @(x) isscalar(x) && x>0); - ip.addParamValue('shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) @@ -415,14 +415,14 @@ % and useful integration limits [tf,minf,maxf]=init_weibull_norm(yy,myy_i,sigm2_i,yc,r); % additionally get function handle for the derivative - td = @deriv; + td = @(f) deriv(f, yc, r, yy); % Integrate with quadgk [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; - function g = deriv(f) + function g = deriv(f, yc, r, yy) g = yc.*(1./r + log(yy)) - exp(-f).*yy.^r.*log(yy); end end @@ -522,7 +522,7 @@ % Create function handle for the function to be integrated - df = @weibull_norm; + df = @(f) weibull_norm(f, ldconst, yc, yy, r, myy_i, sigm2_i); % use log to avoid underflow, and derivates for faster search ld = @log_weibull_norm; ldg = @log_weibull_norm_g; @@ -546,8 +546,8 @@ niter=4; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef); - h=ldg2(modef); + g=ldg(modef, ldconst, yc, yy, r, myy_i, sigm2_i); + h=ldg2(modef, ldconst, yc, yy, r, myy_i, sigm2_i); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf); + minld=ld(minf, ldconst, yc, yy, r, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -575,11 +575,11 @@ 'even after looking hard!']) end end - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yc, yy, r, myy_i, sigm2_i); step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf); + maxld=ld(maxf, ldconst, yc, yy, r, myy_i, sigm2_i); iter=iter+1; step=step*2; if iter>100 @@ -589,14 +589,14 @@ end end - function integrand = weibull_norm(f) + function integrand = weibull_norm(f, ldconst, yc, yy, r, myy_i, sigm2_i) % Weibull * Gaussian integrand = exp(ldconst ... -yc.*f -exp(-f).*yy.^r ... -0.5*(f-myy_i).^2./sigm2_i); end - function log_int = log_weibull_norm(f) + function log_int = log_weibull_norm(f, ldconst, yc, yy, r, myy_i, sigm2_i) % log(Weibull * Gaussian) % log_weibull_norm is used to avoid underflow when searching % integration interval @@ -605,14 +605,14 @@ -0.5*(f-myy_i).^2./sigm2_i; end - function g = log_weibull_norm_g(f) + function g = log_weibull_norm_g(f, ldconst, yc, yy, r, myy_i, sigm2_i) % d/df log(Weibull * Gaussian) % derivative of log_weibull_norm g = -yc + exp(-f).*yy.^r ... + (myy_i - f)./sigm2_i; end - function g2 = log_weibull_norm_g2(f) + function g2 = log_weibull_norm_g2(f, ldconst, yc, yy, r, myy_i, sigm2_i) % d^2/df^2 log(Weibull * Gaussian) % second derivate of log_weibull_norm g2 = - exp(-f).*yy.^r ... diff --git a/gp/lik_zinegbin.m b/gp/lik_zinegbin.m index 63e9f676..e21bad52 100644 --- a/gp/lik_zinegbin.m +++ b/gp/lik_zinegbin.m @@ -61,11 +61,11 @@ ip=inputParser; ip.FunctionName = 'LIK_ZINEGBIN'; - ip.addOptional('lik', [], @isstruct); - ip.addParamValue('disper',10, @(x) isscalar(x) && x>0); - %ip.addParamValue('disper_prior',prior_fixed(), @(x) isstruct(x) || isempty(x)); - ip.addParamValue('disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0); + %ip=iparser(ip,'addParamValue','disper_prior',prior_fixed(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); lik=ip.Results.lik; if isempty(lik) diff --git a/gp/metric_distancematrix.m b/gp/metric_distancematrix.m index 45cfb2eb..6bb6da6a 100644 --- a/gp/metric_distancematrix.m +++ b/gp/metric_distancematrix.m @@ -39,19 +39,19 @@ ip=inputParser; ip.FunctionName = 'METRIC_DISTANCEMATRIX'; - ip.addOptional('metric', [], @isstruct); -% ip.addParamValue('nin', [], @(x) isreal(x)); - ip.addParamValue('components',[], @(x) isempty(x) || iscell(x)); - ip.addParamValue('lengthScales',[] , @(x) isvector(x)); - ip.addParamValue('Kstarstar',[], @(x) ismatrix(x)); - ip.addParamValue('lengthScales_prior',prior_unif, ... + ip=iparser(ip,'addOptional','metric', [], @isstruct); +% ip=iparser(ip,'addParamValue','nin', [], @(x) isreal(x)); + ip=iparser(ip,'addParamValue','components',[], @(x) isempty(x) || iscell(x)); + ip=iparser(ip,'addParamValue','lengthScales',[] , @(x) isvector(x)); + ip=iparser(ip,'addParamValue','Kstarstar',[], @(x) ismatrix(x)); + ip=iparser(ip,'addParamValue','lengthScales_prior',prior_unif, ... @(x) isstruct(x) || isempty(x)); -% ip.addParamValue('Kff', [], @(x) ismatrix(x)); -% ip.addParamValue('Kfu', [], @(x) ismatrix(x)); -% ip.addParamValue('Kffstar', [], @(x) ismatrix(x)); -% ip.addParamValue('Kfstaru', [], @(x) ismatrix(x)); -% ip.addParamValue('X_u', [], @(x) ismatrix(x)); - ip.parse(varargin{:}); +% ip=iparser(ip,'addParamValue','Kff', [], @(x) ismatrix(x)); +% ip=iparser(ip,'addParamValue','Kfu', [], @(x) ismatrix(x)); +% ip=iparser(ip,'addParamValue','Kffstar', [], @(x) ismatrix(x)); +% ip=iparser(ip,'addParamValue','Kfstaru', [], @(x) ismatrix(x)); +% ip=iparser(ip,'addParamValue','X_u', [], @(x) ismatrix(x)); + ip=iparser(ip,'parse',varargin{:}); metric=ip.Results.metric; if isempty(metric) diff --git a/gp/metric_euclidean.m b/gp/metric_euclidean.m index bcc41e91..67fba4f8 100644 --- a/gp/metric_euclidean.m +++ b/gp/metric_euclidean.m @@ -47,13 +47,13 @@ ip=inputParser; ip.FunctionName = 'METRIC_EUCLIDEAN'; - ip.addOptional('metric', [], @isstruct); - ip.addParamValue('components',[], @(x) isempty(x) || iscell(x)); - ip.addParamValue('deltadist',[], @(x) isvector(x)); - ip.addParamValue('lengthScale',[], @(x) isvector(x) && all(x>0)); - ip.addParamValue('lengthScale_prior',prior_unif, ... + ip=iparser(ip,'addOptional','metric', [], @isstruct); + ip=iparser(ip,'addParamValue','components',[], @(x) isempty(x) || iscell(x)); + ip=iparser(ip,'addParamValue','deltadist',[], @(x) isvector(x)); + ip=iparser(ip,'addParamValue','lengthScale',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','lengthScale_prior',prior_unif, ... @(x) isstruct(x) || isempty(x)); - ip.parse(varargin{:}); + ip=iparser(ip,'parse',varargin{:}); metric=ip.Results.metric; if isempty(metric) diff --git a/gp/pred_coxph.m b/gp/pred_coxph.m index a668b686..3d6e8211 100644 --- a/gp/pred_coxph.m +++ b/gp/pred_coxph.m @@ -9,18 +9,18 @@ ip=inputParser; ip.FunctionName = 'PRED_COXPH'; -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('xt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) -ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) -ip.parse(gp, x, y, xt, varargin{:}); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','xt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); +ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); +ip=iparser(ip,'parse',gp, x, y, xt, varargin{:}); if ~strcmp(gp.lik.type, 'Coxph') error('Likelihood not Coxph') diff --git a/gp/scaled_hmc.m b/gp/scaled_hmc.m index 74209f26..639348b5 100644 --- a/gp/scaled_hmc.m +++ b/gp/scaled_hmc.m @@ -107,21 +107,34 @@ % Transform the latent values switch gp.type case 'FULL' - getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + output=getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + L2=output.L2; w = (L2\f)'; % Rotate f towards prior case 'FIC' - getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + output=getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + Lp=output.Lp; + J=output.J; + U=output.U; + iJUU=output.iJUU; fs = f./Lp; % Rotate f towards prior w = fs + U*((J*U'-U')*fs); case {'PIC' 'PIC_BLOCK'} - getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + output=getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + Lp=output.Lp; + U=output.U; + J=output.J; + iJUU=output.iJUU; fs=zeros(size(f)); % Rotate f towards prior for i=1:length(ind) fs(ind{i}) = Lp{i}\f(ind{i}); end w = fs + U*((J*U'-U')*fs); case {'CS+FIC'} - getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + output=getL(f, gp, x, y, u, z); % Evaluate the help matrix for transformation + Lp=output.Lp; + U=output.U; + J=output.J; + iJUU=output.iJUU; fs = Lp\f; % Rotate f towards prior w = fs + U*((J*U'-U')*fs); otherwise @@ -134,7 +147,7 @@ hmc2('state',latent_rstate) rej = 0; for li=1:opt.repeat - [w, energ, diagn] = hmc2(@f_e, w, opt, @f_g, gp, x, y, u, z); + [w, energ, diagn] = hmc2(@f_e, w, opt, @f_g, gp, x, y, u, z, output); w = w(end,:); % Find an optimal scaling during the first half of repeat if li 1 +if ~isfield(opt,'decay') || opt.decay < 0 || opt.decay > 1 opt.decay=0.9; end if ~isfield(opt,'stepadj') @@ -75,7 +75,7 @@ if ~isfield(opt,'stepsf') opt.stepsf=[]; end -if ~isfield(opt,'window') | opt.window < 0 +if ~isfield(opt,'window') || opt.window < 0 opt.window=1; end if opt.window > opt.steps diff --git a/misc/setrandstream.m b/misc/setrandstream.m index 60703728..5ce546d8 100644 --- a/misc/setrandstream.m +++ b/misc/setrandstream.m @@ -43,7 +43,10 @@ if nargin<2 if nargin<1 % Get current random stream - if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 + if exist('OCTAVE_VERSION', 'builtin') + prevstream(1) = randn('seed'); + prevstream(2) = rand('seed'); + elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 prevstream = RandStream.getDefaultStream(stream0); else prevstream = rng; @@ -58,7 +61,15 @@ % Default seed seed=0; end - if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 + if exist('OCTAVE_VERSION', 'builtin') + prevstream(1) = randn('seed'); + prevstream(2) = rand('seed'); + if length(seed)==1 + seed(2)=seed(1); + end + randn('seed', seed(1)); + rand('seed', seed(2)); + elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 if ischar(stream) stream0 = RandStream(stream,'Seed',seed); end diff --git a/optim/fminscg.m b/optim/fminscg.m index b125c8dc..bfeddcad 100644 --- a/optim/fminscg.m +++ b/optim/fminscg.m @@ -103,11 +103,16 @@ otherwise display=2; end -maxiter = optimget(opt,'MaxIter',defaultopt,'fast'); -tolfun = optimget(opt,'TolFun',defaultopt,'fast'); -tolx = optimget(opt,'TolX',defaultopt,'fast'); -lambda = optimget(opt,'lambda', defaultopt, 'fast'); -lambdalim = optimget(opt, 'lambdalim', defaultopt, 'fast'); +maxiter = optimget(opt,'MaxIter',400); +tolfun = optimget(opt,'TolFun',1e-6); +tolx = optimget(opt,'TolX',1e-6); +if ~exist('OCTAVE_VERSION', 'builtin') + lambda = optimget(opt,'lambda', defaultopt, 'fast'); + lambdalim = optimget(opt, 'lambdalim', defaultopt, 'fast'); +else + lambda = optimget(opt,'lambda', 10); + lambdalim = optimget(opt, 'lambdalim', 1e20); +end GradConstr = optimget(opt, 'GradConstr', defaultopt, 'fast'); nparams = length(x); diff --git a/xunit/test_spatial1.m b/xunit/test_spatial1.m index c960d064..129021bc 100644 --- a/xunit/test_spatial1.m +++ b/xunit/test_spatial1.m @@ -27,6 +27,8 @@ % Set back initial random stream setrandstream(prevstream); +drawnow;clear;close all + % Compare test values to real values. From 1989c2651d9044ffdba4d12b53ed3fe85e1ed321 Mon Sep 17 00:00:00 2001 From: Ville Date: Fri, 1 Feb 2013 11:46:22 +0200 Subject: [PATCH 03/64] brief readme for octave gpstuff --- OCTAVEGPSTUFF_README | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 OCTAVEGPSTUFF_README diff --git a/OCTAVEGPSTUFF_README b/OCTAVEGPSTUFF_README new file mode 100644 index 00000000..b5e3f2e3 --- /dev/null +++ b/OCTAVEGPSTUFF_README @@ -0,0 +1,3 @@ +This is Octave version of the GPstuff toolbox for matlab. In order to use Octave GPstuff, the following packages are needed: +Statistics and gsl. Octave version of GPstuff doesn't have all the functionalities of the matlab-version at the moment, namely +piecewise-polynomial covariance functions and some multilatent models. From 80c94a3ba7621c9bd3be8ca9ee835dfbacb0c737 Mon Sep 17 00:00:00 2001 From: Ville Date: Fri, 1 Feb 2013 12:42:04 +0200 Subject: [PATCH 04/64] Updated readme --- OCTAVEGPSTUFF_README | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/OCTAVEGPSTUFF_README b/OCTAVEGPSTUFF_README index b5e3f2e3..4c37d5d2 100644 --- a/OCTAVEGPSTUFF_README +++ b/OCTAVEGPSTUFF_README @@ -1,3 +1,3 @@ This is Octave version of the GPstuff toolbox for matlab. In order to use Octave GPstuff, the following packages are needed: Statistics and gsl. Octave version of GPstuff doesn't have all the functionalities of the matlab-version at the moment, namely -piecewise-polynomial covariance functions and some multilatent models. +piecewise-polynomial covariance functions and some multilatent models. Octave GPstuff requires Octave version 3.6.3 or later. From d3400f5ea8bfb816d6ace930a3d8244aedf627c8 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Fri, 8 Mar 2013 14:10:09 +0200 Subject: [PATCH 05/64] Added passgp and demo_passgp --- gp/demo_passgp.m | 86 ++++++++++++++++++++++++++++ gp/passgp.m | 145 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 231 insertions(+) create mode 100644 gp/demo_passgp.m create mode 100644 gp/passgp.m diff --git a/gp/demo_passgp.m b/gp/demo_passgp.m new file mode 100644 index 00000000..7e817b59 --- /dev/null +++ b/gp/demo_passgp.m @@ -0,0 +1,86 @@ +%DEMO_PASSGP Demonstration of PASS-GP routine for GP classification +% +% Description +% Here we demonstrate pass-gp routine for Gaussian Processes +% classification. Data used is 2-dimensional toy data with Gaussian +% bumbs defining classes. We demonstrate with both fixed and not fixed +% sizes of active set for pass-gp. +% +% See also PASSGP + +% Copyright (c) 2013 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +% Generate toy data +prevstream=setrandstream(0); +[x1,x2]=meshgrid(-5:0.1:5,-5:0.1:5); +x=[x1(:) x2(:)]; x=x(randperm(size(x,1),3000),:); +y=2.*mnorm_pdf(x, [0 0], [0.5 0;0 0.5]) + mnorm_pdf(x, [3 3], [0.5 0;0 0.5]) + mnorm_pdf(x, [-3 -3], [0.5 0;0 0.5]); +y=y+mnorm_pdf(x, [3 -3], [0.5 0;0 0.5])+mnorm_pdf(x, [-3 3], [0.5 0;0 0.5]); +y=y+0.03.*randn(size(y,1),1); +y(y>0.15)=1; y(y<=0.15)=-1; + +[xt1, xt2]=meshgrid(-5:0.23:5,-5:0.23:5); +xt=[xt1(:) xt2(:)]; +yt=ones(size(xt,1),1); +% yt=2.*mnorm_pdf(xt, [0 0], [0.5 0;0 0.5]) + mnorm_pdf(xt, [3 3], [0.5 0;0 0.5]) + mnorm_pdf(xt, [-3 -3], [0.5 0;0 0.5]); +% yt=yt+mnorm_pdf(xt, [3 -3], [0.5 0;0 0.5])+mnorm_pdf(xt, [-3 3], [0.5 0;0 0.5]); +% yt(yt>0.15)=1; yt(yt<=0.15)=-1; + +[n, nin] = size(x); + +% Define covariance and likelihood functions and create model +gpcf = gpcf_sexp(); +lik=lik_probit(); +gp=gp_set('lik', lik, 'cf', gpcf, 'jitterSigma2', 1e-6); + +opt=optimset('TolX',1e-3,'TolFun',1e-3,'display','on'); +w0=gp_pak(gp); + +% fPASS-GP with fixed size of 800 points in active set and data divided to +% 10 subsets with 4 sweeps over data +start=tic;[gp, indA]=passgp(gp, x, y, 'opt', opt, 'npass', 4, 'ninit', 800, 'nsub', 10, 'display', 'on', 'fixed', 'on', 'pexc', 0.1, 'optimn', 2);time=toc(start); +tt=time; +[Eft, Varft, lpyt, Eyt, Varyt]=gp_pred(gp, x(indA,:), y(indA,:), xt, 'yt', yt); +figure, [cc,hh]=contour(reshape(xt(:,1),size(xt1,1), size(xt1,1)), reshape(xt(:,2),size(xt1,1), size(xt1,1)), reshape(exp(lpyt),size(xt1,1), size(xt1,1)), [0.1 0.9]); +clabel(cc,hh); +param=gp_pak(gp); + +% PASS-GP with inclusion threshold 0.65, deletion threshold 0.99, intial +% size of 400 points in active set and 3 sweeps over data. +gp=gp_unpak(gp,w0); +start=tic;[gp, indA2]=passgp(gp, x, y, 'opt', opt, 'pinc', 0.65, 'pdel', 0.99, 'npass', 3, 'ninit', 400, 'nsub', 10, 'display', 'on', 'optimn', 2);time=toc(start); +tt2=time; +[Eft2, Varft2, lpyt2, Eyt2, Varyt2]=gp_pred(gp, x(indA2,:), y(indA2,:), xt, 'yt', yt); +figure, [cc,hh]=contour(reshape(xt(:,1),size(xt1,1), size(xt1,1)), reshape(xt(:,2),size(xt1,1), size(xt1,1)), reshape(exp(lpyt2),size(xt1,1), size(xt1,1)), [0.1 0.9]); +clabel(cc,hh); +param2=gp_pak(gp); + +% Full Gaussian Process for comparison +gp=gp_unpak(gp,w0); +opt.Display='iter'; +start=tic;gp=gp_optim(gp,x,y,'opt',opt);tt3=toc; +[Eft3, Varft3, lpyt3, Eyt3, Varyt3]=gp_pred(gp, x, y, xt, 'yt', yt); +figure, [cc,hh]=contour(reshape(xt(:,1),size(xt1,1), size(xt1,1)), reshape(xt(:,2),size(xt1,1), size(xt1,1)), reshape(exp(lpyt3),size(xt1,1), size(xt1,1)), [0.1 0.9]); +clabel(cc,hh); + +% Display some statistics + +mlpd_fpassgp=mean(mean(lpyt,2)) +time_fpassgp=mean(tt) +mlpd_passgp=mean(mean(lpyt2,2)) +time_passgp=mean(tt2) +mlpd_full=mean(lpyt3) +time_full=tt3 + +% Plot data and active sets for both methods +figure(4), subplot(1,2,1), plot(x(y==1,1),x(y==1,2),'or',x(y==-1,1),x(y==-1,2),'ob'); +hold all; plot(x(indA,1), x(indA,2), '.k'); title('Data and active set (fpass-gp)') +legend('y=1', 'y=-1', 'Active set for fpass-gp'); +subplot(1,2,2), plot(x(y==1,1),x(y==1,2),'or',x(y==-1,1),x(y==-1,2),'ob'); +hold all; plot(x(indA2,1), x(indA2,2), '.k'); title('Data and active set (pass-gp)') +legend('y=1', 'y=-1', 'Active set for pass-gp'); +setrandstream(0); diff --git a/gp/passgp.m b/gp/passgp.m new file mode 100644 index 00000000..980fb423 --- /dev/null +++ b/gp/passgp.m @@ -0,0 +1,145 @@ +function [gp, indA] = passgp(gp, x, y, varargin) +%PASSGP Optimize active set and hyperparameters of PASS-GP +% +% Description +% [GP, INDA] = PASSGP(GP, X, Y, OPTIONS) +% Returns GP structure with hyperparameters optimized according to +% PASS-GP routine (Henao & Winther, 2012) and active set indices +% INDA for X and Y +% +% OPTIONS is optional parameter-value pair +% npass - Number of passes the algorithm takes over the whole training +% data set +% ninit - Initial active set size +% nsub - Number of subsets we process at each pass (in how many +% parts we divide the whole data) +% pinc - Predictive density threshold for inclusion in active set. +% pdel - LOO-predictive density threshold for deletion from active +% set +% pexc - Exchange proportion for fixed PASS-GP +% opt - Options structure for optimizer +% fixed - Whether we use fixed size of active set or not. Default +% 'off' +% display - Whether to display additional info or not. Default 'off' +% optimn - Whether to optimize always or only after every nth +% deletion/addition to active set. Default 1 (every time). If +% given e.g. value 3, optimizes after every 3rd +% addition/deletion. +% +% See also +% GP_SET, LIK_* +% +% Reference: +% Ricardo Henao & Ole Winther (2012). Preditive active set selection +% methods for Gaussian processes. Neurocomputing 80 (2012), 10-18. + +% Copyright (c) 2013 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'PASSGP'; + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','npass', 3, @(x) isscalar(x) && x > 0); + ip=iparser(ip,'addParamValue','ninit', 100, @(x) isscalar(x) && x > 0); + ip=iparser(ip,'addParamValue','nsub', 10, @(x) isscalar(x) && x > 0); + ip=iparser(ip,'addParamValue','pinc', 0.5, @(x) isscalar(x) && x > 0); + ip=iparser(ip,'addParamValue','pdel', 0.99, @(x) isscalar(x) && x > 0); + ip=iparser(ip,'addParamValue','pexc', 0.1, @(x) isscalar(x) && x > 0); + ip=iparser(ip,'addParamValue','opt', [], @(x) isstruct(x)); + ip=iparser(ip,'addParamValue','fixed', 'off', @(x) ismember(x,{'on','off'})); + ip=iparser(ip,'addParamValue','display', 'off', @(x) ismember(x,{'on','off'})); + ip=iparser(ip,'addParamValue','optimn', 1', @(x) isscalar(x) && x > 0 && rem(10*x,2)==0); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +% ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'parse',gp, x, y, varargin{:}); + opt=ip.Results.opt; + fixed=ip.Results.fixed; + options.z = ip.Results.z; +% options.zt = ip.Results.zt; + npass=ip.Results.npass; + ninit=ip.Results.ninit; + nsub=ip.Results.nsub; + pinc=ip.Results.pinc; + pdel=ip.Results.pdel; + pexc=ip.Results.pexc; + display=ip.Results.display; + optimn=ip.Results.optimn; + + if isequal(display,'on') + display=1; + else + display=0; + end + if isequal(fixed, 'on') + fixed=1; + else + fixed=0; + end + + if ninit > size(x,1) + error('Initial active set must be subset of original data'); + end + if nsub > (size(x,1) - ninit) + error('nsub must be lower than size(x,1) - ninit'); + end + + [n,nin]=size(x); + + % Initial active set + + indA=sort(randperm(n, ninit),'ascend'); + % Inclusions/deletions per iteration for fixed pass-gp + nexc=floor(ninit*pexc); + iter=optimn-1; + for i=1:npass + if display + fprintf('Pass %d / %d.\n', i, npass) + end + [tmp,indSub]=cvit(n, nsub, floor(10*rand(1))); + for j=1:nsub + iter=iter+1; + inds=indSub{j}; + % Remove indices that are already in active set + inds(ismember(inds,indA))=[]; + + if iter==optimn + % Optimize hyperparameters + gp = gp_optim(gp, x(indA,:), y(indA), 'opt', opt, options); + iter=0; + end + + % Calculate weights for active set inputs (loo predictive densities) + [tmp,tmp,lpyt]=gp_loopred(gp,x(indA,:),y(indA), options); + + % Remove active set indices according to removal rule + if ~fixed + indA(find(exp(lpyt)>pdel))=[]; + else + [tmp,ii]=sort(lpyt, 'descend'); + indA(ii(1:nexc))=[]; + end + % Calculate weights for inputs not in active set (predictive density) + [tmp,tmp,lpyt]=gp_pred(gp, x(indA,:), y(indA), x(inds,:), 'yt', y(inds), options); + + % Add indices to active set according to addition rule + if ~fixed + ind=find(exp(lpyt) Date: Fri, 8 Mar 2013 14:51:03 +0200 Subject: [PATCH 06/64] Added titles to figures --- gp/demo_passgp.m | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gp/demo_passgp.m b/gp/demo_passgp.m index 7e817b59..6f173c2b 100644 --- a/gp/demo_passgp.m +++ b/gp/demo_passgp.m @@ -46,7 +46,7 @@ tt=time; [Eft, Varft, lpyt, Eyt, Varyt]=gp_pred(gp, x(indA,:), y(indA,:), xt, 'yt', yt); figure, [cc,hh]=contour(reshape(xt(:,1),size(xt1,1), size(xt1,1)), reshape(xt(:,2),size(xt1,1), size(xt1,1)), reshape(exp(lpyt),size(xt1,1), size(xt1,1)), [0.1 0.9]); -clabel(cc,hh); +clabel(cc,hh); title('Pr(y==1) (fpass-gp)') param=gp_pak(gp); % PASS-GP with inclusion threshold 0.65, deletion threshold 0.99, intial @@ -56,7 +56,7 @@ tt2=time; [Eft2, Varft2, lpyt2, Eyt2, Varyt2]=gp_pred(gp, x(indA2,:), y(indA2,:), xt, 'yt', yt); figure, [cc,hh]=contour(reshape(xt(:,1),size(xt1,1), size(xt1,1)), reshape(xt(:,2),size(xt1,1), size(xt1,1)), reshape(exp(lpyt2),size(xt1,1), size(xt1,1)), [0.1 0.9]); -clabel(cc,hh); +clabel(cc,hh); title('Pr(y==1) (pass-gp)') param2=gp_pak(gp); % Full Gaussian Process for comparison @@ -65,7 +65,7 @@ start=tic;gp=gp_optim(gp,x,y,'opt',opt);tt3=toc; [Eft3, Varft3, lpyt3, Eyt3, Varyt3]=gp_pred(gp, x, y, xt, 'yt', yt); figure, [cc,hh]=contour(reshape(xt(:,1),size(xt1,1), size(xt1,1)), reshape(xt(:,2),size(xt1,1), size(xt1,1)), reshape(exp(lpyt3),size(xt1,1), size(xt1,1)), [0.1 0.9]); -clabel(cc,hh); +clabel(cc,hh); title('Pr(y==1) (full gp)') % Display some statistics @@ -83,4 +83,4 @@ subplot(1,2,2), plot(x(y==1,1),x(y==1,2),'or',x(y==-1,1),x(y==-1,2),'ob'); hold all; plot(x(indA2,1), x(indA2,2), '.k'); title('Data and active set (pass-gp)') legend('y=1', 'y=-1', 'Active set for pass-gp'); -setrandstream(0); +setrandstream(prevstream); From 6d96396801dd66988b3e703822b28b899fdb5333 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Fri, 8 Mar 2013 14:56:19 +0200 Subject: [PATCH 07/64] Added demo for quantile gp regression --- gp/demo_quantilegp.m | 83 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 gp/demo_quantilegp.m diff --git a/gp/demo_quantilegp.m b/gp/demo_quantilegp.m new file mode 100644 index 00000000..4ff5ca5e --- /dev/null +++ b/gp/demo_quantilegp.m @@ -0,0 +1,83 @@ +%DEMO_QUANTILEGP Demonstration of Quantile GP regression +% +% Description +% This demo demonstrates Quantile-GP regression with toy data. QGP can +% be used for estimating quantiles of interest of the response variable +% with respect to input variables. +% +% See also LIK_QGP, DEMO_* + +% Copyright (c) 2013 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +prevstream=setrandstream(0); +% Toy data +n=200; +x=linspace(-3,2,n)'; +y=((x.^3 + 3.*x.^2-6.*x-8)/4); +x=(x-mean(x))/std(x); +y=(y-mean(y))/std(y); +xt=x; +yt=y; + +% add heteroscedastic noise +noise=0.2.*randn(n,1); +noise=noise + 0.2.*exp(-1.5.*x).*randn(n,1); +y=y+noise; + +% Create covariance function +gpcf=gpcf_sexp('lengthScale', 1, 'magnSigma2', 1); + +% Create quantile-GP likelihood +lik=lik_qgp('sigma2', 0.5, 'sigma2_prior', prior_t('s2',10,'nu',1)); + +% We want to infer the 0.25, 0.5 and 0.75 quantiles +lik=lik_qgp(lik, 'quantile', 0.05); +lik2=lik_qgp(lik, 'quantile', 0.5); +lik3=lik_qgp(lik, 'quantile', 0.95); + +lik4=lik_qgp(lik, 'quantile', 0.25); +lik5=lik_qgp(lik, 'quantile', 0.75); + +% Create gp structure. Quantile-GP only works with EP or MCMC. Lets use +% MCMC for 5%, 50% and 95% quantiles. +gp = gp_set('lik', lik, 'cf', gpcf, 'jitterSigma2', 1e-6, 'latent_method', 'MCMC'); +gp2 = gp_set('lik', lik2, 'cf', gpcf, 'jitterSigma2', 1e-6, 'latent_method', 'MCMC'); +gp3 = gp_set('lik', lik3, 'cf', gpcf, 'jitterSigma2', 1e-6, 'latent_method', 'MCMC'); + +% EP for 25% and 75% quantiles (with low number of data points, EP is +% unstable for very low or high quantiles) +gp4 = gp_set('lik', lik4, 'cf', gpcf, 'jitterSigma2', 1e-6, 'latent_method', 'EP'); +gp5 = gp_set('lik', lik5, 'cf', gpcf, 'jitterSigma2', 1e-6, 'latent_method', 'EP'); + +% Set options for optimization +opt=optimset('TolX',1e-4, 'TolFun', 1e-4, 'Display', 'iter', 'derivativecheck', 'off'); + +% Sample values +ssls_opt.latent_opt.repeat=100; +rgp=gp_mc(gp, x, y, 'nsamples',200,'ssls_opt', ssls_opt, 'display', 10); +rgp=thin(rgp,10); +rgp2=gp_mc(gp2, x, y, 'nsamples', 200, 'ssls_opt', ssls_opt, 'display', 10); +rgp2=thin(rgp2,10); +rgp3=gp_mc(gp3, x, y, 'nsamples', 200, 'ssls_opt', ssls_opt, 'display', 10); +rgp3=thin(rgp3,10); + +% Optimize hyperparameters +gp4=gp_optim(gp4, x, y, 'opt', opt, 'optimf', @fminlbfgs); +gp5=gp_optim(gp5, x, y, 'opt', opt, 'optimf', @fminlbfgs); + +% Do predictions for training points +[Ef1,Varf1]=gp_pred(rgp, x, y, xt); +[Ef2,Varf2]=gp_pred(rgp2, x, y, xt); +[Ef3,Varf3]=gp_pred(rgp3, x, y, xt); +[Ef4,Varf4]=gp_pred(gp4, x, y, xt); +[Ef5,Varf5]=gp_pred(gp5, x, y, xt); + +% Visualize +figure(1); +plot(xt, Ef1, '-r',xt, Ef4,'-m', xt, Ef2, '-k', xt, Ef3, '-r', xt, y, '.',xt, Ef5,'-m'); +legend('5% / 95%', '25% / 75%', '50%'); +setrandstream(prevstream); \ No newline at end of file From 3ef099eb7f0ddbace90721d714c28b6a3df0c545 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Mon, 18 Mar 2013 14:30:53 +0200 Subject: [PATCH 08/64] removed unnecessary commented (matlab specific) code --- gp/gpep_e.m | 3653 +++++++++++++++++++++++++-------------------------- gp/gpla_e.m | 3583 +++++++++++++++++++++++++------------------------- 2 files changed, 3551 insertions(+), 3685 deletions(-) diff --git a/gp/gpep_e.m b/gp/gpep_e.m index d78b450b..8ab827a6 100644 --- a/gp/gpep_e.m +++ b/gp/gpep_e.m @@ -14,7 +14,7 @@ % the data and prior components of the total energy. % % The energy is minus log posterior cost function for th: -% E = EDATA + EPRIOR +% E = EDATA + EPRIOR % = - log p(Y|X, th) - log p(th), % where th represents the parameters (lengthScale, % magnSigma2...), X is inputs and Y is observations. @@ -30,7 +30,7 @@ % Description 2 % Additional properties meant only for internal use. -% +% % GP = GPEP_E('init', GP) takes a GP structure GP and % initializes required fields for the EP algorithm. % @@ -41,7 +41,7 @@ % = GPEP_E(w, gp, x, y, options) % returns many useful quantities produced by EP algorithm. % - + % Copyright (c) 2007 Jaakko Riihim�ki % Copyright (c) 2007-2010 Jarno Vanhatalo % Copyright (c) 2010 Heikki Peura @@ -51,583 +51,367 @@ % This software is distributed under the GNU General Public % License (version 3 or later); please refer to the file % License.txt, included with the software, for details. - + % parse inputs - ip=inputParser; - ip.FunctionName = 'GPEP_E'; - ip=iparser(ip,'addRequired','w', @(x) ... - isempty(x) || ... - (ischar(x) && ismember(x, {'init' 'clearcache'})) || ... - (isvector(x) && isreal(x) && all(isfinite(x))) || ... - all(isnan(x))); - ip=iparser(ip,'addRequired','gp',@isstruct); - ip=iparser(ip,'addOptional','x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addOptional','y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',w, gp, varargin{:}); - x=ip.Results.x; - y=ip.Results.y; - z=ip.Results.z; +ip=inputParser; +ip.FunctionName = 'GPEP_E'; +ip=iparser(ip,'addRequired','w', @(x) ... + isempty(x) || ... + (ischar(x) && ismember(x, {'init' 'clearcache'})) || ... + (isvector(x) && isreal(x) && all(isfinite(x))) || ... + all(isnan(x))); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addOptional','x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, varargin{:}); +x=ip.Results.x; +y=ip.Results.y; +z=ip.Results.z; + +if strcmp(w, 'init') + % intialize cache + ch = []; - if strcmp(w, 'init') - % intialize cache - ch = []; - - % return function handle to the nested function ep_algorithm - % this way each gp has its own peristent memory for EP - gp.fh.ne = @ep_algorithm; - % set other function handles - gp.fh.e=@gpep_e; - gp.fh.g=@gpep_g; - gp.fh.pred=@gpep_pred; - gp.fh.jpred=@gpep_jpred; - gp.fh.looe=@gpep_looe; - gp.fh.loog=@gpep_loog; - gp.fh.loopred=@gpep_loopred; - e = gp; - % remove clutter from the nested workspace - clear w gp varargin ip x y z - elseif strcmp(w, 'clearcache') - % clear the cache - gp.fh.ne('clearcache'); - else - % call ep_algorithm using the function handle to the nested function - % this way each gp has its own peristent memory for EP - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta] = gp.fh.ne(w, gp, x, y, z); - end + % return function handle to the nested function ep_algorithm + % this way each gp has its own peristent memory for EP + gp.fh.ne = @ep_algorithm; + % set other function handles + gp.fh.e=@gpep_e; + gp.fh.g=@gpep_g; + gp.fh.pred=@gpep_pred; + gp.fh.jpred=@gpep_jpred; + gp.fh.looe=@gpep_looe; + gp.fh.loog=@gpep_loog; + gp.fh.loopred=@gpep_loopred; + e = gp; + % remove clutter from the nested workspace + clear w gp varargin ip x y z +elseif strcmp(w, 'clearcache') + % clear the cache + gp.fh.ne('clearcache'); +else + % call ep_algorithm using the function handle to the nested function + % this way each gp has its own peristent memory for EP + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta] = gp.fh.ne(w, gp, x, y, z); +end function [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta] = ep_algorithm(w, gp, x, y, z) -% if strcmp(w, 'clearcache') -% ch=[]; -% return -% end - switch gp.latent_opt.optim_method case 'basic-EP' -% % check whether saved values can be used -% if isempty(z) -% datahash=hash_sha512([x y]); -% else -% datahash=hash_sha512([x y z]); -% end - if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash) -% % The covariance function parameters or data haven't changed -% % so we can return the energy and the site parameters that are saved -% e = ch.e; -% edata = ch.edata; -% eprior = ch.eprior; -% tautilde = ch.tautilde; -% nutilde = ch.nutilde; -% L = ch.L; -% La2 = ch.La2; -% b = ch.b; -% muvec_i = ch.muvec_i; -% sigm2vec_i = ch.sigm2vec_i; -% logZ_i = ch.logZ_i; -% eta = ch.eta; + % The parameters or data have changed since + % the last call for gpep_e. In this case we need to + % re-evaluate the EP approximation + gp=gp_unpak(gp, w); + ncf = length(gp.cf); + n = size(x,1); + + % EP iteration parameters + iter=1; + maxiter = gp.latent_opt.maxiter; + tol = gp.latent_opt.tol; + df = gp.latent_opt.df; + nutilde = zeros(size(y)); + tautilde = zeros(size(y)); + muvec_i=zeros(size(y)); + sigm2vec_i=zeros(size(y)); + logZep_old=0; logZep=Inf; + if ~isfield(gp,'meanf') + mf = zeros(size(y)); else - % The parameters or data have changed since - % the last call for gpep_e. In this case we need to - % re-evaluate the EP approximation - gp=gp_unpak(gp, w); - ncf = length(gp.cf); - n = size(x,1); - - % EP iteration parameters - iter=1; - maxiter = gp.latent_opt.maxiter; - tol = gp.latent_opt.tol; - df = gp.latent_opt.df; - nutilde = zeros(size(y)); - tautilde = zeros(size(y)); - muvec_i=zeros(size(y)); - sigm2vec_i=zeros(size(y)); - logZep_old=0; logZep=Inf; - if ~isfield(gp,'meanf') - mf = zeros(size(y)); - else - [H,b_m,B_m]=mean_prep(gp,x,[]); - mf = H'*b_m; - end - - logM0 = zeros(n,1); - muhat = zeros(n,1); - sigm2hat = zeros(n,1); - - % ================================================= - % First Evaluate the data contribution to the error - switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' % A full GP - [K,C] = gp_trcov(gp, x); - if ~issparse(C) - % The EP algorithm for full support covariance function - if ~isfield(gp,'meanf') - Sigm = C; - meanfp=false; - else - Sigm = C + H'*B_m*H; - meanfp=true; - end - - % The EP -algorithm - convergence=false; - while iter<=maxiter && ~convergence - logZep_old=logZep; - logM0_old=logM0; - - if 0%isequal(gp.latent_opt.init_prev, 'on') && iter==1 && ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1) && isequal(datahash,ch.datahash) -% tautilde=ch.tautilde; -% nutilde=ch.nutilde; - else - if isequal(gp.latent_opt.parallel,'on') - % parallel-EP - % compute marginal and cavity parameters - dSigm=diag(Sigm); - tau=1./dSigm-tautilde; - nu = 1./dSigm.*mf-nutilde; - muvec_i=nu./tau; - sigm2vec_i=1./tau; - - % compute moments of tilted distributions - [logM0, muhat, sigm2hat] = gp.lik.fh.tiltedMoments(gp.lik, y, 1:n, sigm2vec_i, muvec_i, z); - if any(isnan(logM0)) - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - - % update site parameters - deltatautilde=1./sigm2hat-tau-tautilde; - tautilde=tautilde+df.*deltatautilde; - deltanutilde=1./sigm2hat.*muhat-nu-nutilde; - nutilde=nutilde+df.*deltanutilde; - else - % sequential-EP - muvec_i = zeros(n,1); sigm2vec_i = zeros(n,1); - for i1=1:n - % Algorithm utilizing Cholesky updates - % This is numerically more stable but slower - % $$$ % approximate cavity parameters - % $$$ S11 = sum(Ls(:,i1).^2); - % $$$ S1 = Ls'*Ls(:,i1); - % $$$ tau_i=S11^-1-tautilde(i1); - % $$$ nu_i=S11^-1*mf(i1)-nutilde(i1); - % $$$ - % $$$ mu_i=nu_i/tau_i; - % $$$ sigm2_i=tau_i^-1; - % $$$ - % $$$ if sigm2_i < 0 - % $$$ [ii i1] - % $$$ end - % $$$ - % $$$ % marginal moments - % $$$ [M0(i1), muhat, sigm2hat] = feval(gp.lik.fh.tiltedMoments, gp.lik, y, i1, sigm2_i, mu_i, z); - % $$$ - % $$$ % update site parameters - % $$$ deltatautilde = sigm2hat^-1-tau_i-tautilde(i1); - % $$$ tautilde(i1) = tautilde(i1)+deltatautilde; - % $$$ nutilde(i1) = sigm2hat^-1*muhat-nu_i; - % $$$ - % $$$ upfact = 1./(deltatautilde^-1+S11); - % $$$ if upfact > 0 - % $$$ Ls = cholupdate(Ls, S1.*sqrt(upfact), '-'); - % $$$ else - % $$$ Ls = cholupdate(Ls, S1.*sqrt(-upfact)); - % $$$ end - % $$$ Sigm = Ls'*Ls; - % $$$ mf=Sigm*nutilde; - % $$$ - % $$$ muvec_i(i1,1)=mu_i; - % $$$ sigm2vec_i(i1,1)=sigm2_i; - - % Algorithm as in Rasmussen and Williams 2006 - % approximate cavity parameters - Sigmi=Sigm(:,i1); - Sigmii=Sigmi(i1); - tau_i=1/Sigmii-tautilde(i1); - nu_i = 1/Sigmii*mf(i1)-nutilde(i1); - mu_i=nu_i/tau_i; - sigm2_i=1/tau_i; - - % marginal moments - [logM0(i1), muhat(i1), sigm2hat(i1)] = gp.lik.fh.tiltedMoments(gp.lik, y, i1, sigm2_i, mu_i, z); - if isnan(logM0(i1)) - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - % update site parameters - deltatautilde=sigm2hat(i1)^-1-tau_i-tautilde(i1); - tautilde(i1)=tautilde(i1)+df*deltatautilde; - deltanutilde=sigm2hat(i1)^-1*muhat(i1)-nu_i-nutilde(i1); - nutilde(i1)=nutilde(i1)+df*deltanutilde; - - % Update mean and variance after each site update (standard EP) - ds = deltatautilde/(1+deltatautilde*Sigmii); - Sigm = Sigm - ((ds*Sigmi)*Sigmi'); - %Sigm = Sigm - ((ds*Sigm(:,i1))*Sigm(:,i1)'); - % The below is how Rasmussen and Williams - % (2006) do the update. The above version is - % more robust. - %ds = deltatautilde^-1+Sigm(i1,i1); - %ds = (Sigm(:,i1)/ds)*Sigm(:,i1)'; - %Sigm = Sigm - ds; - %Sigm=Sigm-(deltatautilde^-1+Sigm(i1,i1))^-1*(Sigm(:,i1)*Sigm(:,i1)'); - - if ~meanfp - mf=Sigm*nutilde; - else - mf=Sigm*(C\(H'*b_m)+nutilde); - end - - muvec_i(i1)=mu_i; - sigm2vec_i(i1)=sigm2_i; - end - end - end - - % Recompute the approximate posterior parameters - % parallel- and sequential-EP - Stilde=tautilde; - Stildesqr=sqrt(Stilde); - - if ~meanfp % zero mean function used - % NOTICE! upper triangle matrix! cf. to - % line 13 in the algorithm 3.5, p. 58. - - %B=eye(n)+Stildesqr*C*Stildesqr; - B=bsxfun(@times,bsxfun(@times,Stildesqr,C),Stildesqr'); - B(1:n+1:end)=B(1:n+1:end)+1; - [L,notpositivedefinite] = chol(B,'lower'); - if notpositivedefinite - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - %V=(L\Stildesqr)*C; - V=L\bsxfun(@times,Stildesqr,C); - Sigm=C-V'*V; - mf=Sigm*nutilde; - - % Compute the marginal likelihood - % Direct formula (3.65): - % Sigmtilde=diag(1./tautilde); - % mutilde=inv(Stilde)*nutilde; - % - % logZep=-0.5*log(det(Sigmtilde+K))-0.5*mutilde'*inv(K+Sigmtilde)*mutilde+ - % sum(log(normcdf(y.*muvec_i./sqrt(1+sigm2vec_i))))+ - % 0.5*sum(log(sigm2vec_i+1./tautilde))+ - % sum((muvec_i-mutilde).^2./(2*(sigm2vec_i+1./tautilde))) - - % 4. term & 1. term - term41=0.5*sum(log(1+tautilde.*sigm2vec_i))-sum(log(diag(L))); - - % 5. term (1/2 element) & 2. term - T=1./sigm2vec_i; - Cnutilde = C*nutilde; - L2 = V*nutilde; - term52 = nutilde'*Cnutilde - L2'*L2 - (nutilde'./(T+Stilde)')*nutilde; - term52 = term52.*0.5; - - % 5. term (2/2 element) - term5=0.5*muvec_i'.*(T./(Stilde+T))'*(Stilde.*muvec_i-2*nutilde); - - % 3. term - term3 = sum(logM0); - - logZep = -(term41+term52+term5+term3); - iter=iter+1; - - else - % mean function used - % help variables - hBh = H'*B_m*H; - C_t = C + hBh; - CHb = C\H'*b_m; - S = diag(Stildesqr.^2); - %B = eye(n)+Stildesqroot*C*Stildesqroot; - B=bsxfun(@times,bsxfun(@times,Stildesqr,C),Stildesqr'); - B(1:n+1:end)=B(1:n+1:end)+1; - %B_h = eye(n) + Stildesqroot*C_t*Stildesqroot; - B_h=bsxfun(@times,bsxfun(@times,Stildesqr,C_t),Stildesqr'); - B_h(1:n+1:end)=B_h(1:n+1:end)+1; - % L to return, without the hBh term - [L,notpositivedefinite]=chol(B,'lower'); - if notpositivedefinite - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - % L for the calculation with mean term - [L_m,notpositivedefinite]=chol(B_h,'lower'); - if notpositivedefinite - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - - % Recompute the approximate posterior parameters - % parallel- and sequential-EP - - %V=(L_m\Stildesqroot)*C_t; - V=L_m\bsxfun(@times,Stildesqr,C_t); - Sigm=C_t-V'*V; - mf=Sigm*(CHb+nutilde); - - T=1./sigm2vec_i; - Cnutilde = (C_t - S^-1)*(S*H'*b_m-nutilde); - L2 = V*(S*H'*b_m-nutilde); - - Stildesqroot = diag(Stildesqr); - zz = Stildesqroot*(L'\(L\(Stildesqroot*C))); - % inv(K + S^-1)*S^-1 - Ks = eye(size(zz)) - zz; - - % 5. term (1/2 element) - term5_1 = 0.5.*((nutilde'*S^-1)./(T.^-1+Stilde.^-1)')*(S^-1*nutilde); - % 2. term - term2 = 0.5.*((S*H'*b_m-nutilde)'*Cnutilde - L2'*L2); - % 4. term - term4 = 0.5*sum(log(1+tautilde.*sigm2vec_i)); - % 1. term - term1 = -1.*sum(log(diag(L_m))); - % 3. term - term3 = sum(logM0); - % 5. term (2/2 element) - term5 = 0.5*muvec_i'.*(T./(Stilde+T))'*(Stilde.*muvec_i-2*nutilde); - - logZep = -(term4+term1+term5_1+term5+term2+term3); - - iter=iter+1; - - end - convergence=max(abs(logM0_old-logM0)) 0 + % $$$ Ls = cholupdate(Ls, S1.*sqrt(upfact), '-'); + % $$$ else + % $$$ Ls = cholupdate(Ls, S1.*sqrt(-upfact)); + % $$$ end + % $$$ Sigm = Ls'*Ls; + % $$$ mf=Sigm*nutilde; + % $$$ + % $$$ muvec_i(i1,1)=mu_i; + % $$$ sigm2vec_i(i1,1)=sigm2_i; + % Algorithm as in Rasmussen and Williams 2006 + % approximate cavity parameters + Sigmi=Sigm(:,i1); + Sigmii=Sigmi(i1); + tau_i=1/Sigmii-tautilde(i1); + nu_i = 1/Sigmii*mf(i1)-nutilde(i1); mu_i=nu_i/tau_i; - sigm2_i=tau_i^-1; + sigm2_i=1/tau_i; % marginal moments [logM0(i1), muhat(i1), sigm2hat(i1)] = gp.lik.fh.tiltedMoments(gp.lik, y, i1, sigm2_i, mu_i, z); - + if isnan(logM0(i1)) + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); + return + end % update site parameters - tautilde_old = tautilde(i1); deltatautilde=sigm2hat(i1)^-1-tau_i-tautilde(i1); tautilde(i1)=tautilde(i1)+df*deltatautilde; deltanutilde=sigm2hat(i1)^-1*muhat(i1)-nu_i-nutilde(i1); nutilde(i1)=nutilde(i1)+df*deltanutilde; - gamma = gamma + Ki1.*df*deltanutilde; - % Update the LDL decomposition - sqrtS(i1,i1) = sqrt(tautilde(i1)); - sqrtSKi1(i1) = sqrt(tautilde(i1)).*Ki1(i1); - D2_n = sqrtSKi1.*sqrtS(i1,i1) + Inn(:,i1); + % Update mean and variance after each site update (standard EP) + ds = deltatautilde/(1+deltatautilde*Sigmii); + Sigm = Sigm - ((ds*Sigmi)*Sigmi'); + %Sigm = Sigm - ((ds*Sigm(:,i1))*Sigm(:,i1)'); + % The below is how Rasmussen and Williams + % (2006) do the update. The above version is + % more robust. + %ds = deltatautilde^-1+Sigm(i1,i1); + %ds = (Sigm(:,i1)/ds)*Sigm(:,i1)'; + %Sigm = Sigm - ds; + %Sigm=Sigm-(deltatautilde^-1+Sigm(i1,i1))^-1*(Sigm(:,i1)*Sigm(:,i1)'); - if tautilde_old == 0 - VD = ldlrowupdate(i1,VD,VD(:,i1),'-'); - VD = ldlrowupdate(i1,VD,D2_n,'+'); + if ~meanfp + mf=Sigm*nutilde; else - VD = ldlrowmodify(VD, D2_n, i1); + mf=Sigm*(C\(H'*b_m)+nutilde); end - muvec_i(i1,1)=mu_i; - sigm2vec_i(i1,1)=sigm2_i; + muvec_i(i1)=mu_i; + sigm2vec_i(i1)=sigm2_i; end - end + end + + % Recompute the approximate posterior parameters + % parallel- and sequential-EP + Stilde=tautilde; + Stildesqr=sqrt(Stilde); + + if ~meanfp % zero mean function used + % NOTICE! upper triangle matrix! cf. to + % line 13 in the algorithm 3.5, p. 58. - % Recompute the approximate posterior parameters - % parallel- and sequential-EP - sqrtS = sparse(1:n,1:n,sqrt(tautilde),n,n); - KsqrtS = ssmult(K,sqrtS); - B = ssmult(sqrtS,KsqrtS) + Inn; - [VD, notpositivedefinite] = ldlchol(B); + %B=eye(n)+Stildesqr*C*Stildesqr; + B=bsxfun(@times,bsxfun(@times,Stildesqr,C),Stildesqr'); + B(1:n+1:end)=B(1:n+1:end)+1; + [L,notpositivedefinite] = chol(B,'lower'); if notpositivedefinite [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); return end - Knutilde = K*nutilde; - mf = Knutilde - KsqrtS*ldlsolve(VD,sqrtS*Knutilde); + %V=(L\Stildesqr)*C; + V=L\bsxfun(@times,Stildesqr,C); + Sigm=C-V'*V; + mf=Sigm*nutilde; % Compute the marginal likelihood + % Direct formula (3.65): + % Sigmtilde=diag(1./tautilde); + % mutilde=inv(Stilde)*nutilde; + % + % logZep=-0.5*log(det(Sigmtilde+K))-0.5*mutilde'*inv(K+Sigmtilde)*mutilde+ + % sum(log(normcdf(y.*muvec_i./sqrt(1+sigm2vec_i))))+ + % 0.5*sum(log(sigm2vec_i+1./tautilde))+ + % sum((muvec_i-mutilde).^2./(2*(sigm2vec_i+1./tautilde))) + % 4. term & 1. term - term41=0.5*sum(log(1+tautilde.*sigm2vec_i)) - 0.5.*sum(log(diag(VD))); + term41=0.5*sum(log(1+tautilde.*sigm2vec_i))-sum(log(diag(L))); % 5. term (1/2 element) & 2. term T=1./sigm2vec_i; - term52 = nutilde'*mf - (nutilde'./(T+tautilde)')*nutilde; + Cnutilde = C*nutilde; + L2 = V*nutilde; + term52 = nutilde'*Cnutilde - L2'*L2 - (nutilde'./(T+Stilde)')*nutilde; term52 = term52.*0.5; % 5. term (2/2 element) - term5=0.5*muvec_i'.*(T./(tautilde+T))'*(tautilde.*muvec_i-2*nutilde); + term5=0.5*muvec_i'.*(T./(Stilde+T))'*(Stilde.*muvec_i-2*nutilde); % 3. term term3 = sum(logM0); logZep = -(term41+term52+term5+term3); + iter=iter+1; + + else + % mean function used + % help variables + hBh = H'*B_m*H; + C_t = C + hBh; + CHb = C\H'*b_m; + S = diag(Stildesqr.^2); + %B = eye(n)+Stildesqroot*C*Stildesqroot; + B=bsxfun(@times,bsxfun(@times,Stildesqr,C),Stildesqr'); + B(1:n+1:end)=B(1:n+1:end)+1; + %B_h = eye(n) + Stildesqroot*C_t*Stildesqroot; + B_h=bsxfun(@times,bsxfun(@times,Stildesqr,C_t),Stildesqr'); + B_h(1:n+1:end)=B_h(1:n+1:end)+1; + % L to return, without the hBh term + [L,notpositivedefinite]=chol(B,'lower'); + if notpositivedefinite + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); + return + end + % L for the calculation with mean term + [L_m,notpositivedefinite]=chol(B_h,'lower'); + if notpositivedefinite + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); + return + end + + % Recompute the approximate posterior parameters + % parallel- and sequential-EP + + %V=(L_m\Stildesqroot)*C_t; + V=L_m\bsxfun(@times,Stildesqr,C_t); + Sigm=C_t-V'*V; + mf=Sigm*(CHb+nutilde); + + T=1./sigm2vec_i; + Cnutilde = (C_t - S^-1)*(S*H'*b_m-nutilde); + L2 = V*(S*H'*b_m-nutilde); + + Stildesqroot = diag(Stildesqr); + zz = Stildesqroot*(L'\(L\(Stildesqroot*C))); + % inv(K + S^-1)*S^-1 + Ks = eye(size(zz)) - zz; + + % 5. term (1/2 element) + term5_1 = 0.5.*((nutilde'*S^-1)./(T.^-1+Stilde.^-1)')*(S^-1*nutilde); + % 2. term + term2 = 0.5.*((S*H'*b_m-nutilde)'*Cnutilde - L2'*L2); + % 4. term + term4 = 0.5*sum(log(1+tautilde.*sigm2vec_i)); + % 1. term + term1 = -1.*sum(log(diag(L_m))); + % 3. term + term3 = sum(logM0); + % 5. term (2/2 element) + term5 = 0.5*muvec_i'.*(T./(Stilde+T))'*(Stilde.*muvec_i-2*nutilde); + + logZep = -(term4+term1+term5_1+term5+term2+term3); iter=iter+1; - convergence=max(abs(logM0_old-logM0)) 0 - RtRpnU = R'*(R*pn).*sqrt(updfact); - R = cholupdate(R, RtRpnU, '-'); - elseif updfact < 0 - RtRpnU = R'*(R*pn).*sqrt(abs(updfact)); - R = cholupdate(R, RtRpnU, '+'); + tautilde_old = tautilde(i1); + deltatautilde=sigm2hat(i1)^-1-tau_i-tautilde(i1); + tautilde(i1)=tautilde(i1)+df*deltatautilde; + deltanutilde=sigm2hat(i1)^-1*muhat(i1)-nu_i-nutilde(i1); + nutilde(i1)=nutilde(i1)+df*deltanutilde; + gamma = gamma + Ki1.*df*deltanutilde; + + % Update the LDL decomposition + sqrtS(i1,i1) = sqrt(tautilde(i1)); + sqrtSKi1(i1) = sqrt(tautilde(i1)).*Ki1(i1); + D2_n = sqrtSKi1.*sqrtS(i1,i1) + Inn(:,i1); + + if tautilde_old == 0 + VD = ldlrowupdate(i1,VD,VD(:,i1),'-'); + VD = ldlrowupdate(i1,VD,D2_n,'+'); + else + VD = ldlrowmodify(VD, D2_n, i1); end - eta(i1) = eta(i1) + (deltanutilde - deltatautilde.*eta(i1)).*dn./(1+deltatautilde.*dn); - gamma = gamma + (deltanutilde - deltatautilde.*mf(i1))./(1+deltatautilde.*dn) * R'*(R*pn); - % mf = eta + P*gamma; - - % Store cavity parameters + muvec_i(i1,1)=mu_i; sigm2vec_i(i1,1)=sigm2_i; end + end % Recompute the approximate posterior parameters % parallel- and sequential-EP - temp1 = (1+Lav.*tautilde).^(-1); - D_vec = temp1.*Lav; - R0P0t = R0*K_fu'; - temp2 = zeros(size(R0P0t)); -% for i2 = 1:length(temp1) -% P(i2,:) = temp1(i2).*K_fu(i2,:); -% temp2(:,i2) = R0P0t(:,i2).*tautilde(i2).*temp1(i2); -% end -% R = chol(inv(eye(size(R0)) + temp2*R0P0t')) * R0; - P=bsxfun(@times,temp1,K_fu); - temp2=bsxfun(@times,(tautilde.*temp1)',R0P0t); - temp2=temp2*R0P0t'; - temp2(1:m+1:end)=temp2(1:m+1:end)+1; - R = chol(inv(temp2)) * R0; - eta = D_vec.*nutilde; - gamma = R'*(R*(P'*nutilde)); - mf = eta + P*gamma; - - % Compute the marginal likelihood, see FULL model for - % details about equations - Lahat = 1./Lav + tautilde; - Lhat = bsxfun(@rdivide,L,Lahat); - H = I-L'*Lhat; - B = H\L'; - Bhat = B./repmat(Lahat',m,1); - - % 4. term & 1. term - Stildesqroot=sqrt(tautilde); - D = Stildesqroot.*Lav.*Stildesqroot + 1; - SsqrtKfu = K_fu.*repmat(Stildesqroot,1,m); - AA = K_uu + (SsqrtKfu'./repmat(D',m,1))*SsqrtKfu; AA = (AA+AA')/2; - [AA, notpositivedefinite] = chol(AA,'lower'); + sqrtS = sparse(1:n,1:n,sqrt(tautilde),n,n); + KsqrtS = ssmult(K,sqrtS); + B = ssmult(sqrtS,KsqrtS) + Inn; + [VD, notpositivedefinite] = ldlchol(B); if notpositivedefinite [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); return end - term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA))) + 0.5.*sum(log(D)); + Knutilde = K*nutilde; + mf = Knutilde - KsqrtS*ldlsolve(VD,sqrtS*Knutilde); + + % Compute the marginal likelihood + % 4. term & 1. term + term41=0.5*sum(log(1+tautilde.*sigm2vec_i)) - 0.5.*sum(log(diag(VD))); % 5. term (1/2 element) & 2. term T=1./sigm2vec_i; - term52 = -0.5*( (nutilde./Lahat)'*nutilde + (nutilde'*Lhat)*(Bhat*nutilde) - (nutilde./(T+tautilde))'*nutilde); + term52 = nutilde'*mf - (nutilde'./(T+tautilde)')*nutilde; + term52 = term52.*0.5; % 5. term (2/2 element) - term5 = - 0.5*muvec_i'.*(T./(tautilde+T))'*(tautilde.*muvec_i-2*nutilde); + term5=0.5*muvec_i'.*(T./(tautilde+T))'*(tautilde.*muvec_i-2*nutilde); % 3. term - term3 = -sum(logM0); + term3 = sum(logM0); - logZep = term41+term52+term5+term3; + logZep = -(term41+term52+term5+term3); iter=iter+1; + convergence=max(abs(logM0_old-logM0)) 0 + RtRpnU = R'*(R*pn).*sqrt(updfact); + R = cholupdate(R, RtRpnU, '-'); + elseif updfact < 0 + RtRpnU = R'*(R*pn).*sqrt(abs(updfact)); + R = cholupdate(R, RtRpnU, '+'); + end + eta(i1) = eta(i1) + (deltanutilde - deltatautilde.*eta(i1)).*dn./(1+deltatautilde.*dn); + gamma = gamma + (deltanutilde - deltatautilde.*mf(i1))./(1+deltatautilde.*dn) * R'*(R*pn); + % mf = eta + P*gamma; + + % Store cavity parameters + muvec_i(i1,1)=mu_i; + sigm2vec_i(i1,1)=sigm2_i; + end end - L = iLaKfu/A; - I = eye(size(K_uu)); - [R0, notpositivedefinite] = chol(inv(K_uu)); + % Recompute the approximate posterior parameters + % parallel- and sequential-EP + temp1 = (1+Lav.*tautilde).^(-1); + D_vec = temp1.*Lav; + R0P0t = R0*K_fu'; + temp2 = zeros(size(R0P0t)); + % for i2 = 1:length(temp1) + % P(i2,:) = temp1(i2).*K_fu(i2,:); + % temp2(:,i2) = R0P0t(:,i2).*tautilde(i2).*temp1(i2); + % end + % R = chol(inv(eye(size(R0)) + temp2*R0P0t')) * R0; + P=bsxfun(@times,temp1,K_fu); + temp2=bsxfun(@times,(tautilde.*temp1)',R0P0t); + temp2=temp2*R0P0t'; + temp2(1:m+1:end)=temp2(1:m+1:end)+1; + R = chol(inv(temp2)) * R0; + eta = D_vec.*nutilde; + gamma = R'*(R*(P'*nutilde)); + mf = eta + P*gamma; + + % Compute the marginal likelihood, see FULL model for + % details about equations + Lahat = 1./Lav + tautilde; + Lhat = bsxfun(@rdivide,L,Lahat); + H = I-L'*Lhat; + B = H\L'; + Bhat = B./repmat(Lahat',m,1); + + % 4. term & 1. term + Stildesqroot=sqrt(tautilde); + D = Stildesqroot.*Lav.*Stildesqroot + 1; + SsqrtKfu = K_fu.*repmat(Stildesqroot,1,m); + AA = K_uu + (SsqrtKfu'./repmat(D',m,1))*SsqrtKfu; AA = (AA+AA')/2; + [AA, notpositivedefinite] = chol(AA,'lower'); if notpositivedefinite [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); return end - R = R0; - P = K_fu; - R0P0t = R0*K_fu'; - mf = zeros(size(y)); - eta = zeros(size(y)); - gamma = zeros(size(K_uu,1),1); - D = Labl; - Ann=0; + term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA))) + 0.5.*sum(log(D)); - % The EP -algorithm - convergence=false; - while iter<=maxiter && ~convergence - logZep_old=logZep; - logM0_old=logM0; - - if isequal(gp.latent_opt.parallel,'on') - % parallel-EP - % approximate cavity parameters - for bl=1:length(ind) - bl_ind = ind{bl}; - Pbl=P(bl_ind,:); - Ann = diag(D{bl}) +sum((Pbl*R').^2,2); - tau(bl_ind,1) = 1./Ann-tautilde(bl_ind); - mf(bl_ind,1) = eta(bl_ind) + sum(bsxfun(@times,Pbl,gamma'),2); - nu(bl_ind,1) = 1./Ann.*mf(bl_ind)-nutilde(bl_ind); - end - muvec_i=nu./tau; - sigm2vec_i=1./tau; - % compute moments of tilted distributions - for i1=1:n - [logM0(i1), muhat(i1), sigm2hat(i1)] = gp.lik.fh.tiltedMoments(gp.lik, y, i1, sigm2vec_i(i1), muvec_i(i1), z); - end - if any(isnan(logM0)) - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - % update site parameters - deltatautilde = 1./sigm2hat-tau-tautilde; - tautilde = tautilde+df*deltatautilde; - deltanutilde = 1./sigm2hat.*muhat-nu-nutilde; - nutilde = nutilde+df*deltanutilde;; - else - - muvec_i = zeros(n,1); sigm2vec_i = zeros(n,1); - for bl=1:length(ind) - bl_ind = ind{bl}; - for in=1:length(bl_ind) - i1 = bl_ind(in); - % approximate cavity parameters - Dbl = D{bl}; dn = Dbl(in,in); pn = P(i1,:)'; - Ann = dn + sum((R*pn).^2); - tau_i = Ann^-1-tautilde(i1); - mf(i1) = eta(i1) + pn'*gamma; - nu_i = Ann^-1*mf(i1)-nutilde(i1); - - mu_i=nu_i/tau_i; - sigm2_i=tau_i^-1; - - % marginal moments - [logM0(i1), muhat(i1), sigm2hat(i1)] = gp.lik.fh.tiltedMoments(gp.lik, y, i1, sigm2_i, mu_i, z); - - % update site parameters - deltatautilde = sigm2hat(i1)^-1-tau_i-tautilde(i1); - tautilde(i1) = tautilde(i1)+df*deltatautilde; - deltanutilde = sigm2hat(i1)^-1*muhat(i1)-nu_i - nutilde(i1); - nutilde(i1) = nutilde(i1) + df*deltanutilde; - - % Update the parameters - Dblin = Dbl(:,in); - Dbl = Dbl - deltatautilde ./ (1+deltatautilde.*dn) * Dblin*Dblin'; - %Dbl = inv(inv(Dbl) + diag(tautilde(bl_ind))); - P(bl_ind,:) = P(bl_ind,:) - ((deltatautilde ./ (1+deltatautilde.*dn)).* Dblin)*pn'; - updfact = deltatautilde./(1 + deltatautilde.*Ann); - if updfact > 0 - RtRpnU = R'*(R*pn).*sqrt(updfact); - R = cholupdate(R, RtRpnU, '-'); - elseif updfact < 0 - RtRpnU = R'*(R*pn).*sqrt(abs(updfact)); - R = cholupdate(R, RtRpnU, '+'); - end - eta(bl_ind) = eta(bl_ind) + (deltanutilde - deltatautilde.*eta(i1))./(1+deltatautilde.*dn).*Dblin; - gamma = gamma + (deltanutilde - deltatautilde.*mf(i1))./(1+deltatautilde.*dn) * (R'*(R*pn)); - %mf = eta + P*gamma; - - D{bl} = Dbl; - % Store cavity parameters - muvec_i(i1,1)=mu_i; - sigm2vec_i(i1,1)=sigm2_i; - end - end - end - - % Recompute the approximate posterior parameters - % parallel- and sequential-EP - temp2 = zeros(size(R0P0t)); - - Stildesqroot=sqrt(tautilde); - for i=1:length(ind) - sdtautilde = diag(Stildesqroot(ind{i})); - Dhat = sdtautilde*Labl{i}*sdtautilde + eye(size(Labl{i})); - [Ldhat{i}, notpositivedefinite] = chol(Dhat); - if notpositivedefinite - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - D{i} = Labl{i} - Labl{i}*sdtautilde*(Ldhat{i}\(Ldhat{i}'\sdtautilde*Labl{i})); - P(ind{i},:) = D{i}*(Labl{i}\K_fu(ind{i},:)); - - temp2(:,ind{i}) = R0P0t(:,ind{i})*sdtautilde/Dhat*sdtautilde; - eta(ind{i}) = D{i}*nutilde(ind{i}); - end - R = chol(inv(eye(size(R0)) + temp2*R0P0t')) * R0; - gamma = R'*(R*(P'*nutilde)); - mf = eta + P*gamma; - - % Compute the marginal likelihood, see FULL model for - % details about equations - % - % First some helper parameters - for i = 1:length(ind) - Lhat(ind{i},:) = D{i}*L(ind{i},:); - end - H = I-L'*Lhat; - B = H\L'; - - % Compute the marginal likelihood, see FULL model for - % details about equations - term41 = 0; term52 = 0; - for i=1:length(ind) - Bhat(:,ind{i}) = B(:,ind{i})*D{i}; - SsqrtKfu(ind{i},:) = bsxfun(@times,K_fu(ind{i},:),Stildesqroot(ind{i})); - %SsqrtKfu(ind{i},:) = gtimes(K_fu(ind{i},:),Stildesqroot(ind{i})); - iDSsqrtKfu(ind{i},:) = Ldhat{i}\(Ldhat{i}'\SsqrtKfu(ind{i},:)); - term41 = term41 + sum(log(diag(Ldhat{i}))); - term52 = term52 + nutilde(ind{i})'*(D{i}*nutilde(ind{i})); - - end - AA = K_uu + SsqrtKfu'*iDSsqrtKfu; AA = (AA+AA')/2; - [AA, notpositivedefinite] = chol(AA,'lower'); - if notpositivedefinite - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - term41 = term41 - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA))); - - % 5. term (1/2 element) & 2. term - T=1./sigm2vec_i; - term52 = -0.5*( term52 + (nutilde'*Lhat)*(Bhat*nutilde) - (nutilde./(T+tautilde))'*nutilde); - - % 5. term (2/2 element) - term5 = - 0.5*muvec_i'.*(T./(tautilde+T))'*(tautilde.*muvec_i-2*nutilde); - - % 3. term - term3 = -sum(logM0); - - logZep = term41+term52+term5+term3; - - iter=iter+1; - convergence=max(abs(logM0_old-logM0)) 0 RtRpnU = R'*(R*pn).*sqrt(updfact); @@ -1146,1076 +864,1265 @@ RtRpnU = R'*(R*pn).*sqrt(abs(updfact)); R = cholupdate(R, RtRpnU, '+'); end - eta = eta + (deltanutilde - deltatautilde.*eta(i1))./(1+deltatautilde.*dn).*Di1; + eta(bl_ind) = eta(bl_ind) + (deltanutilde - deltatautilde.*eta(i1))./(1+deltatautilde.*dn).*Dblin; gamma = gamma + (deltanutilde - deltatautilde.*mf(i1))./(1+deltatautilde.*dn) * (R'*(R*pn)); + %mf = eta + P*gamma; + D{bl} = Dbl; % Store cavity parameters muvec_i(i1,1)=mu_i; sigm2vec_i(i1,1)=sigm2_i; - - D2_o = ssmult(sqrtS,LasqrtS(:,i1)) + Inn(:,i1); - sqrtS(i1,i1) = sqrt(tautilde(i1)); - LasqrtS(:,i1) = La(:,i1).*sqrtS(i1,i1); - D2_n = ssmult(sqrtS,LasqrtS(:,i1)) + Inn(:,i1); - - if tautilde(i1) - deltatautilde == 0 - VD = ldlrowupdate(i1,VD,VD(:,i1),'-'); - VD = ldlrowupdate(i1,VD,D2_n,'+'); - else - VD = ldlrowmodify(VD, D2_n, i1); - end end end - - % Recompute the approximate posterior parameters - % parallel- and sequential-EP - sqrtS = sparse(1:n,1:n,sqrt(tautilde),n,n); - sqrtSLa = ssmult(sqrtS,La); - D2 = ssmult(sqrtSLa,sqrtS) + Inn; - LasqrtS = ssmult(La,sqrtS); - [VD, notpositivedefinite] = ldlchol(D2); - if notpositivedefinite - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return - end - - SsqrtKfu = sqrtS*K_fu; - iDSsqrtKfu = ldlsolve(VD,SsqrtKfu); - P = K_fu - sqrtSLa'*iDSsqrtKfu; - R = chol(inv( eye(size(R0)) + R0P0t*sqrtS*ldlsolve(VD,sqrtS*R0P0t'))) * R0; - eta = La*nutilde - sqrtSLa'*ldlsolve(VD,sqrtSLa*nutilde); - gamma = R'*(R*(P'*nutilde)); - mf = eta + P*gamma; - - % Compute the marginal likelihood, - Lhat = La*L - sqrtSLa'*ldlsolve(VD,sqrtSLa*L); - H = I-L'*Lhat; - B = H\L'; - Bhat = B*La - ldlsolve(VD,sqrtSLa*B')'*sqrtSLa; - - % 4. term & 1. term - AA = K_uu + SsqrtKfu'*iDSsqrtKfu; AA = (AA+AA')/2; - [AA, notpositivedefinite] = chol(AA,'lower'); + end + + % Recompute the approximate posterior parameters + % parallel- and sequential-EP + temp2 = zeros(size(R0P0t)); + + Stildesqroot=sqrt(tautilde); + for i=1:length(ind) + sdtautilde = diag(Stildesqroot(ind{i})); + Dhat = sdtautilde*Labl{i}*sdtautilde + eye(size(Labl{i})); + [Ldhat{i}, notpositivedefinite] = chol(Dhat); if notpositivedefinite [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); return end - term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA))) + 0.5*sum(log(diag(VD))); - - % 5. term (1/2 element) & 2. term - T=1./sigm2vec_i; - term52 = -0.5*( nutilde'*(eta) + (nutilde'*Lhat)*(Bhat*nutilde) - (nutilde./(T+tautilde))'*nutilde); - - % 5. term (2/2 element) - term5 = - 0.5*muvec_i'.*(T./(tautilde+T))'*(tautilde.*muvec_i-2*nutilde); + D{i} = Labl{i} - Labl{i}*sdtautilde*(Ldhat{i}\(Ldhat{i}'\sdtautilde*Labl{i})); + P(ind{i},:) = D{i}*(Labl{i}\K_fu(ind{i},:)); - % 3. term - term3 = -sum(logM0); - - logZep = term41+term52+term5+term3; - - iter=iter+1; - convergence=max(abs(logM0_old-logM0)) 0 - RtLphiU = R'*(R*phi).*sqrt(updfact); - R = cholupdate(R, RtLphiU, '-'); - elseif updfact < 0 - RtLphiU = R'*(R*phi).*sqrt(updfact); - R = cholupdate(R, RtLphiU, '+'); - end - gamma = gamma - R'*(R*phi)*(deltatautilde*mf(i1)-deltanutilde); - % Store cavity parameters - muvec_i(i1,1)=mu_i; - sigm2vec_i(i1,1)=sigm2_i; + % Update the parameters + P = P - ((deltatautilde ./ (1+deltatautilde.*dn)).* Di1)*pn'; + updfact = deltatautilde./(1 + deltatautilde.*Ann); + if updfact > 0 + RtRpnU = R'*(R*pn).*sqrt(updfact); + R = cholupdate(R, RtRpnU, '-'); + elseif updfact < 0 + RtRpnU = R'*(R*pn).*sqrt(abs(updfact)); + R = cholupdate(R, RtRpnU, '+'); + end + eta = eta + (deltanutilde - deltatautilde.*eta(i1))./(1+deltatautilde.*dn).*Di1; + gamma = gamma + (deltanutilde - deltatautilde.*mf(i1))./(1+deltatautilde.*dn) * (R'*(R*pn)); + + % Store cavity parameters + muvec_i(i1,1)=mu_i; + sigm2vec_i(i1,1)=sigm2_i; + + D2_o = ssmult(sqrtS,LasqrtS(:,i1)) + Inn(:,i1); + sqrtS(i1,i1) = sqrt(tautilde(i1)); + LasqrtS(:,i1) = La(:,i1).*sqrtS(i1,i1); + D2_n = ssmult(sqrtS,LasqrtS(:,i1)) + Inn(:,i1); + + if tautilde(i1) - deltatautilde == 0 + VD = ldlrowupdate(i1,VD,VD(:,i1),'-'); + VD = ldlrowupdate(i1,VD,D2_n,'+'); + else + VD = ldlrowmodify(VD, D2_n, i1); end end - - % Recompute the approximate posterior parameters - % parallel- and sequential-EP - R = chol(inv(eye(m,m) + Phi'*(repmat(tautilde,1,m).*Phi))); - gamma = R'*(R*(Phi'*nutilde)); - mf = Phi*gamma; - - % Compute the marginal likelihood, see FULL model for - % details about equations - % 4. term & 1. term - Stildesqroot=sqrt(tautilde); - SsqrtPhi = Phi.*repmat(Stildesqroot,1,m); - AA = eye(m,m) + SsqrtPhi'*SsqrtPhi; AA = (AA+AA')/2; - [AA, notpositivedefinite] = chol(AA,'lower'); - if notpositivedefinite + end + + % Recompute the approximate posterior parameters + % parallel- and sequential-EP + sqrtS = sparse(1:n,1:n,sqrt(tautilde),n,n); + sqrtSLa = ssmult(sqrtS,La); + D2 = ssmult(sqrtSLa,sqrtS) + Inn; + LasqrtS = ssmult(La,sqrtS); + [VD, notpositivedefinite] = ldlchol(D2); + if notpositivedefinite + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); + return + end + + SsqrtKfu = sqrtS*K_fu; + iDSsqrtKfu = ldlsolve(VD,SsqrtKfu); + P = K_fu - sqrtSLa'*iDSsqrtKfu; + R = chol(inv( eye(size(R0)) + R0P0t*sqrtS*ldlsolve(VD,sqrtS*R0P0t'))) * R0; + eta = La*nutilde - sqrtSLa'*ldlsolve(VD,sqrtSLa*nutilde); + gamma = R'*(R*(P'*nutilde)); + mf = eta + P*gamma; + + % Compute the marginal likelihood, + Lhat = La*L - sqrtSLa'*ldlsolve(VD,sqrtSLa*L); + H = I-L'*Lhat; + B = H\L'; + Bhat = B*La - ldlsolve(VD,sqrtSLa*B')'*sqrtSLa; + + % 4. term & 1. term + AA = K_uu + SsqrtKfu'*iDSsqrtKfu; AA = (AA+AA')/2; + [AA, notpositivedefinite] = chol(AA,'lower'); + if notpositivedefinite + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); + return + end + term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) - sum(log(diag(Luu))) + sum(log(diag(AA))) + 0.5*sum(log(diag(VD))); + + % 5. term (1/2 element) & 2. term + T=1./sigm2vec_i; + term52 = -0.5*( nutilde'*(eta) + (nutilde'*Lhat)*(Bhat*nutilde) - (nutilde./(T+tautilde))'*nutilde); + + % 5. term (2/2 element) + term5 = - 0.5*muvec_i'.*(T./(tautilde+T))'*(tautilde.*muvec_i-2*nutilde); + + % 3. term + term3 = -sum(logM0); + + logZep = term41+term52+term5+term3; + + iter=iter+1; + convergence=max(abs(logM0_old-logM0)) 0 + RtLphiU = R'*(R*phi).*sqrt(updfact); + R = cholupdate(R, RtLphiU, '-'); + elseif updfact < 0 + RtLphiU = R'*(R*phi).*sqrt(updfact); + R = cholupdate(R, RtLphiU, '+'); + end + gamma = gamma - R'*(R*phi)*(deltatautilde*mf(i1)-deltanutilde); + % Store cavity parameters + muvec_i(i1,1)=mu_i; + sigm2vec_i(i1,1)=sigm2_i; + end end - edata = logZep; - %L = iLaKfu; - if strcmp(gp.type,'VAR') - Qv_ff = sum(B.^2)'; - edata = edata + 0.5*sum((Kv_ff-Qv_ff).*tautilde); + + % Recompute the approximate posterior parameters + % parallel- and sequential-EP + R = chol(inv(eye(m,m) + Phi'*(repmat(tautilde,1,m).*Phi))); + gamma = R'*(R*(Phi'*nutilde)); + mf = Phi*gamma; + + % Compute the marginal likelihood, see FULL model for + % details about equations + % 4. term & 1. term + Stildesqroot=sqrt(tautilde); + SsqrtPhi = Phi.*repmat(Stildesqroot,1,m); + AA = eye(m,m) + SsqrtPhi'*SsqrtPhi; AA = (AA+AA')/2; + [AA, notpositivedefinite] = chol(AA,'lower'); + if notpositivedefinite + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); + return end + term41 = - 0.5*sum(log(1+tautilde.*sigm2vec_i)) + sum(log(diag(AA))); - temp = Phi*(SsqrtPhi'*(SsqrtPhi*bb')); - % b = Phi*bb' - temp + Phi*(SsqrtPhi'*(SsqrtPhi*(AA'\(AA\temp)))); - - b = nutilde - bb2'.*Stildesqroot + repmat(tautilde,1,m).*Phi*(AA'\bb3'); - b = b'; - - % StildeKfu = zeros(size(K_fu)); % f x u, - % for i=1:n - % StildeKfu(i,:) = K_fu(i,:).*tautilde(i); % f x u - % end - % A = K_uu+K_fu'*StildeKfu; A = (A+A')./2; % Ensure symmetry - % A = chol(A); - % L = StildeKfu/A; - L = repmat(tautilde,1,m).*Phi/AA'; - %L = repmat(tautilde,1,m).*K_fu/AA'; - mu=nutilde./tautilde; - %b = nutilde - mu'*L*L'*mu; - %b=b'; - La2 = 1./tautilde; - D = 0; - - otherwise - error('Unknown type of Gaussian process!') - end - - % ================================================== - % Evaluate the prior contribution to the error from - % covariance functions and likelihood - % ================================================== - - % Evaluate the prior contribution to the error from covariance - % functions - eprior = 0; - for i=1:ncf - gpcf = gp.cf{i}; - eprior = eprior - gpcf.fh.lp(gpcf); - end - - % Evaluate the prior contribution to the error from likelihood - % functions - if isfield(gp.lik, 'p') - lik = gp.lik; - eprior = eprior - lik.fh.lp(lik); - end - - % The last things to do - if isfield(gp.latent_opt, 'display') && ismember(gp.latent_opt.display,{'final','iter'}) - fprintf('GPEP_E: Number of iterations in EP: %d \n', iter-1) - end - - e = edata + eprior; - logZ_i = logM0(:); - eta = []; - -% % store values to the cache -% ch.w = w; -% ch.e = e; -% ch.edata = edata; -% ch.eprior = eprior; -% ch.tautilde = tautilde; -% ch.nutilde = nutilde; -% ch.L = L; -% ch.La2 = La2; -% ch.b = b; -% ch.muvec_i = muvec_i; -% ch.sigm2vec_i = sigm2vec_i; -% ch.logZ_i = logZ_i; -% ch.eta = eta; -% ch.datahash=datahash; - - global iter_lkm - iter_lkm=iter; + % 5. term (1/2 element) & 2. term + T=1./sigm2vec_i; + bb = nutilde'*Phi; + bb2 = bb*SsqrtPhi'; + bb3 = bb2*SsqrtPhi/AA'; + term52 = -0.5*( bb*bb' - bb2*bb2' + bb3*bb3' - (nutilde./(T+tautilde))'*nutilde); + + % 5. term (2/2 element) + term5 = - 0.5*muvec_i'.*(T./(tautilde+T))'*(tautilde.*muvec_i-2*nutilde); + + % 3. term + term3 = -sum(logM0); + + logZep = term41+term52+term5+term3; + + iter=iter+1; + convergence=max(abs(logM0_old-logM0))=tauc_min); + if isempty(L2) || ~pcavity + % In case of too small cavity precisions, half the step size + df=df*0.5; + if df<0.1, + % If mediocre damping is not sufficient, proceed to + % the double-loop algorithm + break + else + if ismember(display,{'iter'}) + fprintf('%d, e=%.6f, dm=%.4f, dV=%.4f, increasing damping to df=%g.\n',i1,e,tol_m(1),tol_m(2),df) + end + continue + end + end + + % a proposal surrogate distribution + nu_s2=mf2./Vf2; + lnZ_s2=0.5*sum( (-log(tau_s2) +nu_s2.^2 ./tau_s2)./eta ); + + %%%%%%%%%%%%%%%%%%%%%%%%%%% + % a proposal r-distribution + [lnZ_r2,lnZ_i2,m_r2,V_r2,p]=evaluate_r(nu_q2,tau_q2,eta,fh_tm,nu_s2,tau_s2,display); + + % the new energy + e2 = lnZ_q2 + lnZ_r2 -lnZ_s2; + + % check that the energy is defined and that the tilted moments are proper + if ~all(p) || ~isfinite(e2) + df=df*0.5; + if df<0.1, + break + else + if ismember(display,{'iter'}) + fprintf('%d, e=%.6f, dm=%.4f, dV=%.4f, increasing damping to df=%g.\n',i1,e,tol_m(1),tol_m(2),df) + end + continue + end + end + + % accept the new state + [nu_q,tau_q,mf,Vf,Sf,lnZ_q]=deal(nu_q2,tau_q2,mf2,Vf2,Sf2,lnZ_q2); + [lnZ_r,lnZ_i,m_r,V_r,lnZ_s,nu_s,tau_s]=deal(lnZ_r2,lnZ_i2,m_r2,V_r2,lnZ_s2,nu_s2,tau_s2); + + % EP search direction (moment matching) + [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); + + % Check for convergence + % the difference between the marginal moments + % Vf=diag(Sf); + tol_m=[abs(mf-m_r) abs(Vf-V_r)]; + + % measure the convergence by the moment difference + convergence=all(tol_m(:,1)0; df1=min( ( (tau_s(ii1)-tauc_min(ii1))./eta(ii1)-tau_q(ii1) )./dtau_q(ii1)/df ,1); + df1=( (tau_s(ii1)-tauc_min(ii1))./eta(ii1) -tau_q(ii1) )./dtau_q(ii1)/df; + + dnu_q(ii1)=dnu_q(ii1).*df1; + dtau_q(ii1)=dtau_q(ii1).*df1; + + % the intial gradient in the search direction + g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); + + % re-init the step size adjustment record + rec_sadj=[0 e g]; + end + % proposal + nu_q2=nu_q+df*dnu_q; + tau_q2=tau_q+df*dtau_q; + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % energy for the proposal state + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - convergence=false; % convergence indicator - df=df0; % initial damping factor - tol_m=zeros(1,2); % absolute moment tolerances + % update the q-distribution + % [mf2,Sf2,lnZ_q2,L1,L2]=evaluate_q(nu_q2,tau_q2,K,display,K_uu, K_fu, Kv_ff, Qv_ff); switch gp.type case 'FULL' - tauc_min=1./(Vc_lim*diag(K)); % minimum cavity precision + [mf2,Sf2,lnZ_q2,L1,L2]=evaluate_q(nu_q2,tau_q2,K,display); + Vf2 = diag(Sf2); case 'FIC' - tauc_min=1./(Vc_lim*Cv_ff); + [mf2,Vf2,lnZ_q2,L1,L2]=evaluate_q2(nu_q2,tau_q2,Luu, K_fu, Kv_ff, Qv_ff, display); + otherwise + error('Robust-EP not implemented for this type of GP!'); + end + + % check cavity + pcavity=all( (1./Vf2-eta.*tau_q2 )>=tauc_min); + + g2=NaN; + if isempty(L2) + % the q-distribution not defined (the posterior covariance + % not positive definite) + e2=inf; + elseif pcavity + % the tilted distribution + [lnZ_r2,lnZ_i2,m_r2,V_r2]=evaluate_r(nu_q2,tau_q2,eta,fh_tm,nu_s,tau_s,display); + + % the new energy + e2 = lnZ_q2 + lnZ_r2 -lnZ_s; + + % gradients in the search direction + g2 = sum( (mf2 -m_r2).*dnu_q ) +0.5*sum( (V_r2 +m_r2.^2 -Vf2 -mf2.^2).*dtau_q ); + + if ismember(display,{'iter'}) + % ratio of the gradients + fprintf('dg=%6.3f, ',min(abs(g2)/abs(g),99)) + end end - % Adjust damping by setting an upper limit (Vf_mult) to the increase - % of the marginal variance - Vf_mult=2; - i1=0; - while i110*abs(g) ) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % ill-conditioned q-distribution or very large increase + % in the gradient + % => half the step size + df=df*0.5; + + if ismember(display,{'iter'}) + fprintf('decreasing step size, ') end + elseif ~pcavity && ~pvis + % The cavity distributions resulting from the proposal distribution + % are not well defined, reset the site parameters by doing + % one parallel update with a zero initialization and continue + % with double loop iterations + + if ismember(display,{'iter'}) + fprintf('re-init the posterior due to ill-conditioned cavity distributions, ') + end + + % Do resetting only once + pvis=1; - % proposal site parameters - nu_q2=nu_q+dfi.*dnu_q; - tau_q2=tau_q+dfi.*dtau_q; + up_mode='ep'; + nu_q=zeros(size(y));tau_q=zeros(size(y)); + mf=zeros(size(y)); + switch gp.type + case 'FULL' + Sf=K;Vf=diag(K); + case 'FIC' + Vf=Cv_ff; + end + nu_s=mf./Vf; + tau_s=1./Vf; + % lnZ_s=0.5*sum( (-log(tau_s) +nu_s.^2 ./tau_s)./eta ); % minus 0.5*log(2*pi)./eta + [lnZ_r,lnZ_i,m_r,V_r]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s,tau_s,display); + % e = lnZ_q + lnZ_r -lnZ_s; + [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); + %nu_q=dnu_q; tau_q=dtau_q; + nu_q=0.9.*dnu_q; tau_q=0.9.*dtau_q; - %%%%%%%%%%%%%%%%%%%%%%%%%%% - % a proposal q-distribution switch gp.type case 'FULL' - [mf2,Sf2,lnZ_q2,L1,L2]=evaluate_q(nu_q2,tau_q2,K,display); - Vf2 = diag(Sf2); + [mf,Sf,lnZ_q]=evaluate_q(nu_q,tau_q,K,display); + Vf = diag(Sf); case 'FIC' - [mf2,Vf2,lnZ_q2,L1,L2]=evaluate_q2(nu_q2,tau_q2,Luu, K_fu, Kv_ff, Qv_ff, display); + [mf,Vf,lnZ_q]=evaluate_q2(nu_q,tau_q,Luu, K_fu, Kv_ff, Qv_ff, display); otherwise error('Robust-EP not implemented for this type of GP!'); end + nu_s=mf./Vf; tau_s=1./Vf; + lnZ_s=0.5*sum( (-log(tau_s) +nu_s.^2 ./tau_s)./eta ); % minus 0.5*log(2*pi)./eta + [lnZ_r,lnZ_i,m_r,V_r]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s,tau_s,display); + e = lnZ_q + lnZ_r -lnZ_s; + [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); - % check that the new cavity variances do not exceed the limit - tau_s2=1./Vf2; - pcavity=all( (tau_s2-eta.*tau_q2 )>=tauc_min); - if isempty(L2) || ~pcavity - % In case of too small cavity precisions, half the step size - df=df*0.5; - if df<0.1, - % If mediocre damping is not sufficient, proceed to - % the double-loop algorithm - break - else - if ismember(display,{'iter'}) - fprintf('%d, e=%.6f, dm=%.4f, dV=%.4f, increasing damping to df=%g.\n',i1,e,tol_m(1),tol_m(2),df) - end - continue - end - end + df=0.8; - % a proposal surrogate distribution - nu_s2=mf2./Vf2; - lnZ_s2=0.5*sum( (-log(tau_s2) +nu_s2.^2 ./tau_s2)./eta ); + g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); + rec_sadj=[0 e g]; - %%%%%%%%%%%%%%%%%%%%%%%%%%% - % a proposal r-distribution - [lnZ_r2,lnZ_i2,m_r2,V_r2,p]=evaluate_r(nu_q2,tau_q2,eta,fh_tm,nu_s2,tau_s2,display); + elseif size(rec_sadj,1)<=1 && ( e2>e || abs(g2)>abs(g)*tolGrad ) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % no decrease in energy or the new gradient exceeds the + % pre-defined limit + % => adjust the step size - % the new energy - e2 = lnZ_q2 + lnZ_r2 -lnZ_s2; + if ismember(display,{'iter'}) + fprintf('adjusting step size, ') + end - % check that the energy is defined and that the tilted moments are proper - if ~all(p) || ~isfinite(e2) - df=df*0.5; - if df<0.1, - break + % update the record for step size adjustment + ii1=find(df>rec_sadj(:,1),1,'last'); + ii2=find(df1 + if exist('csape','file')==2 + if g2>0 + % adjust the step size with spline interpolation + pp=csape(rec_sadj(:,1)',[rec_sadj(1,3) rec_sadj(:,2)' rec_sadj(end,3)],[1 1]); + [tmp,df_new]=fnmin(pp,[0 df]); + + elseif isfinite(g2) + % extrapolate with Hessian end-conditions + H=(rec_sadj(end,3)-rec_sadj(end-1,3))/(rec_sadj(end,1)-rec_sadj(end-1,1)); + pp=csape(rec_sadj(:,1)',[rec_sadj(1,3) rec_sadj(:,2)' H],[1 2]); + % extrapolate at most by 100% at a time + [tmp,df_new]=fnmin(pp,[df df*1.5]); + end else - if ismember(display,{'iter'}) - fprintf('%d, e=%.6f, dm=%.4f, dV=%.4f, increasing damping to df=%g.\n',i1,e,tol_m(1),tol_m(2),df) + % if curvefit toolbox does not exist, use a simple Hessian + % approximation + [tmp,ind]=sort(rec_sadj(:,2),'ascend'); + ind=ind(1:2); + + H=(rec_sadj(ind(1),3)-rec_sadj(ind(2),3))/(rec_sadj(ind(1),1)-rec_sadj(ind(2),1)); + df_new=rec_sadj(ind(1),1) -rec_sadj(ind(1),3)/H; + if g2>0 + % interpolate + df_new=max(min(df_new,df),0); + else + % extrapolate at most 100% + df_new=max(min(df_new,1.5*df),df); end - continue end + df_new=min(df_new,df_lim); end - % accept the new state - [nu_q,tau_q,mf,Vf,Sf,lnZ_q]=deal(nu_q2,tau_q2,mf2,Vf2,Sf2,lnZ_q2); - [lnZ_r,lnZ_i,m_r,V_r,lnZ_s,nu_s,tau_s]=deal(lnZ_r2,lnZ_i2,m_r2,V_r2,lnZ_s2,nu_s2,tau_s2); - - % EP search direction (moment matching) - [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); - - % Check for convergence - % the difference between the marginal moments -% Vf=diag(Sf); - tol_m=[abs(mf-m_r) abs(Vf-V_r)]; - - % measure the convergence by the moment difference - convergence=all(tol_m(:,1)0 + df=df*0.9; % too long step since the gradient is positive + else + df=df*1.1; % too short step since the gradient is negative end - break + else + df=df_new; end - end - end % end of initial rounds of parallel EP - - if isfinite(e) && ~convergence - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % if no convergence with the parallel EP - % start double-loop iterations - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - up_mode=gp.latent_opt.up_mode; % update mode in double-loop iterations - %up_mode='ep'; % choose the moment matching - %up_mode='grad'; % choose the gradients - df_lim=gp.latent_opt.df_lim; % step size limit (1 suitable for ep updates) - - tol_e=inf; % the energy difference for measuring convergence (tol_e < tolStop) - ninner=0; % counter for the inner loop iterations - df=df0; % initial step size (damping factor) - - % the intial gradient in the search direction - g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); - - sdir_reset=false; - rec_sadj=[0 e g]; % record for step size adjustment - for i1=1:maxiter - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % calculate a new proposal state - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - % Limit the step size separately for each site so that the cavity variances - % do not exceed the upper limit (this will change the search direction) - % this should not happen after step size adjustment - ii1=tau_s-eta.*(tau_q+df*dtau_q)0; if any(ii1) - %ii1=dtau_q>0; df1=min( ( (tau_s(ii1)-tauc_min(ii1))./eta(ii1)-tau_q(ii1) )./dtau_q(ii1)/df ,1); - df1=( (tau_s(ii1)-tauc_min(ii1))./eta(ii1) -tau_q(ii1) )./dtau_q(ii1)/df; - - dnu_q(ii1)=dnu_q(ii1).*df1; - dtau_q(ii1)=dtau_q(ii1).*df1; - - % the intial gradient in the search direction - g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); - - % re-init the step size adjustment record - rec_sadj=[0 e g]; - end - % proposal - nu_q2=nu_q+df*dnu_q; - tau_q2=tau_q+df*dtau_q; - - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % energy for the proposal state - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - % update the q-distribution -% [mf2,Sf2,lnZ_q2,L1,L2]=evaluate_q(nu_q2,tau_q2,K,display,K_uu, K_fu, Kv_ff, Qv_ff); - switch gp.type - case 'FULL' - [mf2,Sf2,lnZ_q2,L1,L2]=evaluate_q(nu_q2,tau_q2,K,display); - Vf2 = diag(Sf2); - case 'FIC' - [mf2,Vf2,lnZ_q2,L1,L2]=evaluate_q2(nu_q2,tau_q2,Luu, K_fu, Kv_ff, Qv_ff, display); - otherwise - error('Robust-EP not implemented for this type of GP!'); + df_max=min( ( (tau_s(ii1)-tauc_min(ii1)-1e-8)./eta(ii1) -tau_q(ii1) )./dtau_q(ii1) ); + df=min(df,df_max); end - % check cavity - pcavity=all( (1./Vf2-eta.*tau_q2 )>=tauc_min); - - g2=NaN; - if isempty(L2) - % the q-distribution not defined (the posterior covariance - % not positive definite) - e2=inf; - elseif pcavity - % the tilted distribution - [lnZ_r2,lnZ_i2,m_r2,V_r2]=evaluate_r(nu_q2,tau_q2,eta,fh_tm,nu_s,tau_s,display); - - % the new energy - e2 = lnZ_q2 + lnZ_r2 -lnZ_s; - - % gradients in the search direction - g2 = sum( (mf2 -m_r2).*dnu_q ) +0.5*sum( (V_r2 +m_r2.^2 -Vf2 -mf2.^2).*dtau_q ); - - if ismember(display,{'iter'}) - % ratio of the gradients - fprintf('dg=%6.3f, ',min(abs(g2)/abs(g),99)) - end - end + elseif e2>e+tolInner || (abs(g2)>abs(g)*tolGrad && strcmp(up_mode,'ep')) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % No decrease in energy despite the step size adjustments. + % In some difficult cases the EP search direction may not + % result in decrease of the energy or the gradient + % despite of the step size adjustment. One reason for this + % may be the parallel EP search direction + % => try the negative gradient as the search direction + % + % or if the problem persists + % => try resetting the search direction - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % check if the energy decreases - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - if ~isfinite(e2) || ( pcavity && g2>10*abs(g) ) - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % ill-conditioned q-distribution or very large increase - % in the gradient - % => half the step size - df=df*0.5; - - if ismember(display,{'iter'}) - fprintf('decreasing step size, ') - end - elseif ~pcavity && ~pvis - % The cavity distributions resulting from the proposal distribution - % are not well defined, reset the site parameters by doing - % one parallel update with a zero initialization and continue - % with double loop iterations - + if abs(g2)>abs(g)*tolGrad && strcmp(up_mode,'ep') + % try switching to gradient based updates + up_mode='grad'; + df_lim=1e3; + df=0.1; if ismember(display,{'iter'}) - fprintf('re-init the posterior due to ill-conditioned cavity distributions, ') + fprintf('switch to gradient updates, ') end - - % Do resetting only once - pvis=1; - - up_mode='ep'; - nu_q=zeros(size(y));tau_q=zeros(size(y)); - mf=zeros(size(y)); - switch gp.type - case 'FULL' - Sf=K;Vf=diag(K); - case 'FIC' - Vf=Cv_ff; - end - nu_s=mf./Vf; - tau_s=1./Vf; -% lnZ_s=0.5*sum( (-log(tau_s) +nu_s.^2 ./tau_s)./eta ); % minus 0.5*log(2*pi)./eta - [lnZ_r,lnZ_i,m_r,V_r]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s,tau_s,display); -% e = lnZ_q + lnZ_r -lnZ_s; - [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); - %nu_q=dnu_q; tau_q=dtau_q; - nu_q=0.9.*dnu_q; tau_q=0.9.*dtau_q; - - switch gp.type - case 'FULL' - [mf,Sf,lnZ_q]=evaluate_q(nu_q,tau_q,K,display); - Vf = diag(Sf); - case 'FIC' - [mf,Vf,lnZ_q]=evaluate_q2(nu_q,tau_q,Luu, K_fu, Kv_ff, Qv_ff, display); - otherwise - error('Robust-EP not implemented for this type of GP!'); - end - nu_s=mf./Vf; tau_s=1./Vf; - lnZ_s=0.5*sum( (-log(tau_s) +nu_s.^2 ./tau_s)./eta ); % minus 0.5*log(2*pi)./eta - [lnZ_r,lnZ_i,m_r,V_r]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s,tau_s,display); - e = lnZ_q + lnZ_r -lnZ_s; - [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); - - df=0.8; - - g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); - rec_sadj=[0 e g]; - - elseif size(rec_sadj,1)<=1 && ( e2>e || abs(g2)>abs(g)*tolGrad ) - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % no decrease in energy or the new gradient exceeds the - % pre-defined limit - % => adjust the step size - + elseif ~sdir_reset if ismember(display,{'iter'}) - fprintf('adjusting step size, ') - end - - % update the record for step size adjustment - ii1=find(df>rec_sadj(:,1),1,'last'); - ii2=find(df1 - if exist('csape','file')==2 - if g2>0 - % adjust the step size with spline interpolation - pp=csape(rec_sadj(:,1)',[rec_sadj(1,3) rec_sadj(:,2)' rec_sadj(end,3)],[1 1]); - [tmp,df_new]=fnmin(pp,[0 df]); - - elseif isfinite(g2) - % extrapolate with Hessian end-conditions - H=(rec_sadj(end,3)-rec_sadj(end-1,3))/(rec_sadj(end,1)-rec_sadj(end-1,1)); - pp=csape(rec_sadj(:,1)',[rec_sadj(1,3) rec_sadj(:,2)' H],[1 2]); - % extrapolate at most by 100% at a time - [tmp,df_new]=fnmin(pp,[df df*1.5]); - end - else - % if curvefit toolbox does not exist, use a simple Hessian - % approximation - [tmp,ind]=sort(rec_sadj(:,2),'ascend'); - ind=ind(1:2); - - H=(rec_sadj(ind(1),3)-rec_sadj(ind(2),3))/(rec_sadj(ind(1),1)-rec_sadj(ind(2),1)); - df_new=rec_sadj(ind(1),1) -rec_sadj(ind(1),3)/H; - if g2>0 - % interpolate - df_new=max(min(df_new,df),0); - else - % extrapolate at most 100% - df_new=max(min(df_new,1.5*df),df); - end - end - df_new=min(df_new,df_lim); - end - - if df_new==0 - % the spline approxmation fails or no record of the previous gradients - if g2>0 - df=df*0.9; % too long step since the gradient is positive - else - df=df*1.1; % too short step since the gradient is negative - end - else - df=df_new; + fprintf('reset the search direction, ') end - % prevent too small cavity-variances after the step-size adjustment - ii1=dtau_q>0; - if any(ii1) - df_max=min( ( (tau_s(ii1)-tauc_min(ii1)-1e-8)./eta(ii1) -tau_q(ii1) )./dtau_q(ii1) ); - df=min(df,df_max); + sdir_reset=true; + elseif g2<0 && abs(g2)e + if ismember(display,{'final','iter'}) + fprintf('Unable to continue: gradients of the inner-loop objective are inconsistent\n') end - - elseif e2>e+tolInner || (abs(g2)>abs(g)*tolGrad && strcmp(up_mode,'ep')) - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % No decrease in energy despite the step size adjustments. - % In some difficult cases the EP search direction may not - % result in decrease of the energy or the gradient - % despite of the step size adjustment. One reason for this - % may be the parallel EP search direction - % => try the negative gradient as the search direction - % - % or if the problem persists - % => try resetting the search direction - - if abs(g2)>abs(g)*tolGrad && strcmp(up_mode,'ep') - % try switching to gradient based updates - up_mode='grad'; - df_lim=1e3; - df=0.1; + break; + else + df=df*0.1; + end + + % the new search direction + [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); + + % the initial gradient in the search direction + g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); + + % re-init the step size adjustment record + rec_sadj=[0 e g]; + else + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % decrease of energy => accept the new state + + dInner=abs(e-e2); % the inner loop energy change + + % accept the new site parameters (nu_q,tau_q) + [mf,Vf,Sf,nu_q,tau_q,lnZ_q]=deal(mf2,Vf2,Sf2,nu_q2,tau_q2,lnZ_q2); + + % accept also the new tilted distributions + [lnZ_r,lnZ_i,m_r,V_r,e]=deal(lnZ_r2,lnZ_i2,m_r2,V_r2,e2); + + % check that the new cavity variances are positive and not too large + tau_s2=1./Vf; + pcavity=all( (tau_s2-eta.*tau_q )>=tauc_min); + supdate=false; + if pcavity && (dInner=max_ninner) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % try to update the surrogate distribution on the condition that + % - the cavity variances are positive and not too large + % - the new tilted moments are proper + % - sufficient tolerance or the maximum number of inner + % loop updates is exceeded + + % update the surrogate distribution + nu_s2=mf.*tau_s2; + lnZ_s2=0.5*sum( (-log(tau_s2) +nu_s2.^2 ./tau_s2)./eta ); + + % update the tilted distribution + [lnZ_r2,lnZ_i2,m_r2,V_r2]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s2,tau_s2,display); + + % evaluate the new energy + e2 = lnZ_q + lnZ_r2 -lnZ_s2; + + if isfinite(e2) + % a successful surrogate update + supdate=true; + ninner=0; % reset the inner loop iteration counter + + % update the convergence criteria + tol_e=abs(e2-e); + + % accept the new state + [lnZ_r,lnZ_i,m_r,V_r,lnZ_s,nu_s,tau_s,e]=deal(lnZ_r2,lnZ_i2,m_r2,V_r2,lnZ_s2,nu_s2,tau_s2,e2); + if ismember(display,{'iter'}) - fprintf('switch to gradient updates, ') + fprintf('surrogate update, ') end - elseif ~sdir_reset + else + % Improper tilted moments even though the cavity variances are + % positive. This is an indication of numerically unstable + % tilted moment integrations but fractional updates usually help + % => try switching to fractional updates + pcavity=false; + if ismember(display,{'iter'}) - fprintf('reset the search direction, ') - end - sdir_reset=true; - elseif g2<0 && abs(g2)e - if ismember(display,{'final','iter'}) - fprintf('Unable to continue: gradients of the inner-loop objective are inconsistent\n') + fprintf('surrogate update failed, ') end - break; - else - df=df*0.1; end + end + + if all(eta==eta1) && ~pcavity && (dInner=max_ninner) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % If the inner loop moments (within tolerance) are matched + % but the new cavity variances are negative or the tilted moment + % integrations fail after the surrogate update + % => switch to fractional EP. + % + % This is a rare situation and most likely the + % hyperparameters are such that the approximating family + % is not flexible enough, i.e., the hyperparameters are + % unsuitable for the data. + % + % One can also try to reduce the lower limit for the + % cavity precisions tauc_min=1./(Vc_lim*diag(K)), i.e. + % increase the maximum cavity variance Vc_lim. - % the new search direction - [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); - - % the initial gradient in the search direction - g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); - - % re-init the step size adjustment record - rec_sadj=[0 e g]; - else - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % decrease of energy => accept the new state + % try switching to fractional updates + eta=repmat(eta2,n,1); - dInner=abs(e-e2); % the inner loop energy change + % correct the surrogate normalization accordingly + % the surrogate distribution is not updated + lnZ_s2=0.5*sum( (-log(tau_s) +nu_s.^2 ./tau_s)./eta ); - % accept the new site parameters (nu_q,tau_q) - [mf,Vf,Sf,nu_q,tau_q,lnZ_q]=deal(mf2,Vf2,Sf2,nu_q2,tau_q2,lnZ_q2); + % update the tilted distribution + [lnZ_r2,lnZ_i2,m_r2,V_r2]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s,tau_s,display); - % accept also the new tilted distributions - [lnZ_r,lnZ_i,m_r,V_r,e]=deal(lnZ_r2,lnZ_i2,m_r2,V_r2,e2); + % evaluate the new energy + e2 = lnZ_q + lnZ_r2 -lnZ_s2; - % check that the new cavity variances are positive and not too large - tau_s2=1./Vf; - pcavity=all( (tau_s2-eta.*tau_q )>=tauc_min); - supdate=false; - if pcavity && (dInner=max_ninner) - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % try to update the surrogate distribution on the condition that - % - the cavity variances are positive and not too large - % - the new tilted moments are proper - % - sufficient tolerance or the maximum number of inner - % loop updates is exceeded + if isfinite(e2) + % successful switch to fractional energy + supdate=true; + pcavity=true; + ninner=0; % reset the inner loop iteration counter - % update the surrogate distribution - nu_s2=mf.*tau_s2; - lnZ_s2=0.5*sum( (-log(tau_s2) +nu_s2.^2 ./tau_s2)./eta ); + % accept the new state + [lnZ_r,lnZ_i,m_r,V_r,lnZ_s,e]=deal(lnZ_r2,lnZ_i2,m_r2,V_r2,lnZ_s2,e2); - % update the tilted distribution - [lnZ_r2,lnZ_i2,m_r2,V_r2]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s2,tau_s2,display); - - % evaluate the new energy - e2 = lnZ_q + lnZ_r2 -lnZ_s2; - - if isfinite(e2) - % a successful surrogate update - supdate=true; - ninner=0; % reset the inner loop iteration counter - - % update the convergence criteria - tol_e=abs(e2-e); - - % accept the new state - [lnZ_r,lnZ_i,m_r,V_r,lnZ_s,nu_s,tau_s,e]=deal(lnZ_r2,lnZ_i2,m_r2,V_r2,lnZ_s2,nu_s2,tau_s2,e2); - - if ismember(display,{'iter'}) - fprintf('surrogate update, ') - end - else - % Improper tilted moments even though the cavity variances are - % positive. This is an indication of numerically unstable - % tilted moment integrations but fractional updates usually help - % => try switching to fractional updates - pcavity=false; - - if ismember(display,{'iter'}) - fprintf('surrogate update failed, ') - end + % start with ep search direction + up_mode='ep'; + df_lim=0.9; + df=0.1; + if ismember(display,{'iter'}) + fprintf('switching to fractional EP, ') end - end - - if all(eta==eta1) && ~pcavity && (dInner=max_ninner) - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % If the inner loop moments (within tolerance) are matched - % but the new cavity variances are negative or the tilted moment - % integrations fail after the surrogate update - % => switch to fractional EP. - % - % This is a rare situation and most likely the - % hyperparameters are such that the approximating family - % is not flexible enough, i.e., the hyperparameters are - % unsuitable for the data. - % - % One can also try to reduce the lower limit for the - % cavity precisions tauc_min=1./(Vc_lim*diag(K)), i.e. - % increase the maximum cavity variance Vc_lim. - - % try switching to fractional updates - eta=repmat(eta2,n,1); - - % correct the surrogate normalization accordingly - % the surrogate distribution is not updated - lnZ_s2=0.5*sum( (-log(tau_s) +nu_s.^2 ./tau_s)./eta ); - - % update the tilted distribution - [lnZ_r2,lnZ_i2,m_r2,V_r2]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s,tau_s,display); + else + % Improper tilted moments even with fractional updates + % This is very unlikely to happen because decreasing the + % fraction parameter (eta2=10) - % Surrogate updates do not result into positive cavity variances - % even with fractional updates with eta2 => terminate iterations if ismember(display,{'final','iter'}) - fprintf('surrogate update failed with fractional updates, try decreasing eta2\n') - end - break - end - - if ~supdate - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - % no successful surrogate update, no sufficient tolerance, - % or the maximum number of inner loop updates is not yet exceeded - % => continue with the same surrogate distribution - - ninner=ninner+1; % increase inner loop iteration counter - if ismember(display,{'iter'}) - fprintf('inner-loop update, ') + fprintf('Unable to switch to the fractional EP, check that eta2=10) + % Surrogate updates do not result into positive cavity variances + % even with fractional updates with eta2 => terminate iterations if ismember(display,{'final','iter'}) - % maximum difference of the marginal moments - tol_m=[max(abs(mf-m_r)) max(abs(Vf-V_r))]; - fprintf('Convergence, iter %d, e=%.6f, dm=%.4f, dV=%.4f, df=%6f, eta=%.2f\n',i1,e,tol_m(1),tol_m(2),df,eta(1)) + fprintf('surrogate update failed with fractional updates, try decreasing eta2\n') end break end - end % end of the double-loop updates - end - - % the current energy is not finite or no convergence - if ~isfinite(e) - fprintf('GPEP_E: Initial energy not defined, check the hyperparameters\n') - elseif ~convergence - fprintf('GPEP_E: No convergence, %d iter, e=%.6f, dm=%.4f, dV=%.4f, df=%6f, eta=%.2f\n',i1,e,tol_m(1),tol_m(2),df,eta(1)) - fprintf('GPEP_E: Check the hyperparameters, increase maxiter and/or max_ninner, or decrease tolInner\n') - end - edata=-e; % the data contribution to the marginal posterior density - - % ===================================================================================== - % Evaluate the prior contribution to the error from covariance functions and likelihood - % ===================================================================================== - - % Evaluate the prior contribution to the error from covariance functions - eprior = 0; - for i=1:ncf - gpcf = gp.cf{i}; - eprior = eprior - gpcf.fh.lp(gpcf); - % eprior = eprior - feval(gpcf.fh.lp, gpcf, x, y); - end - - % Evaluate the prior contribution to the error from likelihood functions - if isfield(gp, 'lik') && isfield(gp.lik, 'p') - likelih = gp.lik; - eprior = eprior - likelih.fh.lp(likelih); - end - - % the total energy - e = edata + eprior; - - sigm2vec_i = 1./(tau_s-eta.*tau_q); % vector of cavity variances - muvec_i = (nu_s-eta.*nu_q).*sigm2vec_i; % vector of cavity means - logZ_i = lnZ_i; % vector of tilted normalization factors - - - % check that the posterior covariance is positive definite and - % calculate its Cholesky decomposition - switch gp.type - case 'FULL' - [L, notpositivedefinite] = chol(Sf); - b = []; - La2 = []; - if notpositivedefinite || ~isfinite(e) - [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); - return + + if ~supdate + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % no successful surrogate update, no sufficient tolerance, + % or the maximum number of inner loop updates is not yet exceeded + % => continue with the same surrogate distribution + + ninner=ninner+1; % increase inner loop iteration counter + if ismember(display,{'iter'}) + fprintf('inner-loop update, ') + end end - case 'FIC' - La2 = Luu; - L = L2; - b = Kv_ff - Qv_ff; - end - - nutilde = nu_q; - tautilde = tau_q; - -% % store values to the cache -% ch.w = w; -% ch.e = e; -% ch.edata = edata; -% ch.eprior = eprior; -% ch.L = L; -% ch.nu_q = nu_q; -% ch.tau_q = tau_q; -% ch.La2 = La2; -% ch.b = b; -% ch.eta = eta; -% ch.logZ_i = logZ_i; -% ch.sigm2vec_i = sigm2vec_i; -% ch.muvec_i = muvec_i; -% ch.datahash = datahash; - + % the new search direction + [dnu_q,dtau_q]=ep_update_dir(mf,Vf,m_r,V_r,eta,up_mode,tolUpdate); + + % the initial gradient in the search direction + g = sum( (mf -m_r).*dnu_q ) +0.5*sum( (V_r +m_r.^2 -Vf -mf.^2).*dtau_q ); + + % re-init step size adjustment record + rec_sadj=[0 e g]; + end + + if ismember(display,{'iter'}) + % maximum difference of the marginal moments + tol_m=[max(abs(mf-m_r)) max(abs(Vf-V_r))]; + fprintf('%d, e=%.6f, dm=%.4f, dV=%.4f, df=%6f, eta=%.2f\n',i1,e,tol_m(1),tol_m(2),df,eta(1)) + end + + %%%%%%%%%%%%%%%%%%%%%%% + % check for convergence + convergence = tol_e<=tolStop; + if convergence + if ismember(display,{'final','iter'}) + % maximum difference of the marginal moments + tol_m=[max(abs(mf-m_r)) max(abs(Vf-V_r))]; + fprintf('Convergence, iter %d, e=%.6f, dm=%.4f, dV=%.4f, df=%6f, eta=%.2f\n',i1,e,tol_m(1),tol_m(2),df,eta(1)) + end + break + end + end % end of the double-loop updates + end + + % the current energy is not finite or no convergence + if ~isfinite(e) + fprintf('GPEP_E: Initial energy not defined, check the hyperparameters\n') + elseif ~convergence + fprintf('GPEP_E: No convergence, %d iter, e=%.6f, dm=%.4f, dV=%.4f, df=%6f, eta=%.2f\n',i1,e,tol_m(1),tol_m(2),df,eta(1)) + fprintf('GPEP_E: Check the hyperparameters, increase maxiter and/or max_ninner, or decrease tolInner\n') end + edata=-e; % the data contribution to the marginal posterior density + + % ===================================================================================== + % Evaluate the prior contribution to the error from covariance functions and likelihood + % ===================================================================================== + + % Evaluate the prior contribution to the error from covariance functions + eprior = 0; + for i=1:ncf + gpcf = gp.cf{i}; + eprior = eprior - gpcf.fh.lp(gpcf); + % eprior = eprior - feval(gpcf.fh.lp, gpcf, x, y); + end + + % Evaluate the prior contribution to the error from likelihood functions + if isfield(gp, 'lik') && isfield(gp.lik, 'p') + likelih = gp.lik; + eprior = eprior - likelih.fh.lp(likelih); + end + + % the total energy + e = edata + eprior; + + sigm2vec_i = 1./(tau_s-eta.*tau_q); % vector of cavity variances + muvec_i = (nu_s-eta.*nu_q).*sigm2vec_i; % vector of cavity means + logZ_i = lnZ_i; % vector of tilted normalization factors + + + % check that the posterior covariance is positive definite and + % calculate its Cholesky decomposition + switch gp.type + case 'FULL' + [L, notpositivedefinite] = chol(Sf); + b = []; + La2 = []; + if notpositivedefinite || ~isfinite(e) + [e, edata, eprior, tautilde, nutilde, L, La2, b, muvec_i, sigm2vec_i, logZ_i, eta, ch] = set_output_for_notpositivedefinite(); + return + end + case 'FIC' + La2 = Luu; + L = L2; + b = Kv_ff - Qv_ff; + end + + + nutilde = nu_q; + tautilde = tau_q; + otherwise error('Unknown optim method!'); end @@ -2252,7 +2159,7 @@ ch.datahash=datahash; ch.w = NaN; end - + end function [m_q,S_q,lnZ_q,L1,L2]=evaluate_q(nu_q,tau_q,K,display) @@ -2369,7 +2276,7 @@ else S = 1 + D.*tau_q; -% S_q = diag(D./S) + diag(1./S)*U*inv(L*L')*U'*diag(1./S); + % S_q = diag(D./S) + diag(1./S)*U*inv(L*L')*U'*diag(1./S); S_q = D./S + sum((bsxfun(@times, 1./S, U)/L').^2,2); m_q = D.*nu_q./S + (U*(L'\(L\(U'*(nu_q./S)))))./S; end @@ -2388,7 +2295,7 @@ function [lnZ_r,lnZ_i,m_r,V_r,p]=evaluate_r(nu_q,tau_q,eta,fh_tm,nu_s,tau_s,display) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% function for determining the parameters of the r-distribution +% function for determining the parameters of the r-distribution % (the product of the tilted distributions) % % r(f) = exp(-lnZ_r) * prod_i p(y(i)|f(i)) * exp( -0.5*f(i)^2 tau_r(i) + nu_r(i)*f(i) ) @@ -2465,7 +2372,7 @@ % the search direction dnu_q=-gnu_q; - dtau_q=-gtau_q; + dtau_q=-gtau_q; end end diff --git a/gp/gpla_e.m b/gp/gpla_e.m index 38e8fade..f111286f 100644 --- a/gp/gpla_e.m +++ b/gp/gpla_e.m @@ -10,11 +10,11 @@ % row of X corresponds to one input vector and each row of Y % corresponds to one target vector. % -% [E, EDATA, EPRIOR] = GPLA_E(W, GP, X, Y, OPTIONS) returns also +% [E, EDATA, EPRIOR] = GPLA_E(W, GP, X, Y, OPTIONS) returns also % the data and prior components of the total energy. % % The energy is minus log posterior cost function for th: -% E = EDATA + EPRIOR +% E = EDATA + EPRIOR % = - log p(Y|X, th) - log p(th), % where th represents the parameters (lengthScale, % magnSigma2...), X is inputs and Y is observations. @@ -30,10 +30,10 @@ % % Description 2 % Additional properties meant only for internal use. -% +% % GP = GPLA_E('init', GP) takes a GP structure GP and % initializes required fields for the Laplace approximation. -% +% % GP = GPLA_E('clearcache', GP) takes a GP structure GP and clears the % internal cache stored in the nested function workspace. % @@ -45,7 +45,7 @@ % % The stabilized Newton's method is implemented as suggested by % Hannes Nickisch (personal communication). - + % Copyright (c) 2007-2010 Jarno Vanhatalo % Copyright (c) 2010 Aki Vehtari % Copyright (c) 2010 Pasi Jyl�nki @@ -54,734 +54,762 @@ % License (version 3 or later); please refer to the file % License.txt, included with the software, for details. - % parse inputs - ip=inputParser; - ip.FunctionName = 'GPLA_E'; - ip=iparser(ip,'addRequired','w', @(x) ... - isempty(x) || ... - (ischar(x) && strcmp(w, 'init')) || ... - isvector(x) && isreal(x) && all(isfinite(x)) ... - || all(isnan(x))); - ip=iparser(ip,'addRequired','gp',@isstruct); - ip=iparser(ip,'addOptional','x', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addOptional','y', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',w, gp, varargin{:}); - x=ip.Results.x; - y=ip.Results.y; - z=ip.Results.z; - - if strcmp(w, 'init') - % Initialize cache - ch = []; +% parse inputs +ip=inputParser; +ip.FunctionName = 'GPLA_E'; +ip=iparser(ip,'addRequired','w', @(x) ... + isempty(x) || ... + (ischar(x) && strcmp(w, 'init')) || ... + isvector(x) && isreal(x) && all(isfinite(x)) ... + || all(isnan(x))); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addOptional','x', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','y', @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'parse',w, gp, varargin{:}); +x=ip.Results.x; +y=ip.Results.y; +z=ip.Results.z; - % set function handle to the nested function laplace_algorithm - % this way each gp has its own peristent memory for EP - gp.fh.ne = @laplace_algorithm; - % set other function handles - gp.fh.e=@gpla_e; - gp.fh.g=@gpla_g; - gp.fh.pred=@gpla_pred; - gp.fh.jpred=@gpla_jpred; - gp.fh.looe=@gpla_looe; -% gp.fh.loog=@gpla_loog; - gp.fh.loopred=@gpla_loopred; - e = gp; - % remove clutter from the nested workspace - clear w gp varargin ip x y z - elseif strcmp(w, 'clearcache') - % clear the cache - gp.fh.ne('clearcache'); - else - % call laplace_algorithm using the function handle to the nested function - % this way each gp has its own peristent memory for Laplace - [e, edata, eprior, f, L, a, La2, p] = gp.fh.ne(w, gp, x, y, z); - end +if strcmp(w, 'init') + % Initialize cache + ch = []; + + % set function handle to the nested function laplace_algorithm + % this way each gp has its own peristent memory for EP + gp.fh.ne = @laplace_algorithm; + % set other function handles + gp.fh.e=@gpla_e; + gp.fh.g=@gpla_g; + gp.fh.pred=@gpla_pred; + gp.fh.jpred=@gpla_jpred; + gp.fh.looe=@gpla_looe; + % gp.fh.loog=@gpla_loog; + gp.fh.loopred=@gpla_loopred; + e = gp; + % remove clutter from the nested workspace + clear w gp varargin ip x y z +elseif strcmp(w, 'clearcache') + % clear the cache + gp.fh.ne('clearcache'); +else + % call laplace_algorithm using the function handle to the nested function + % this way each gp has its own peristent memory for Laplace + [e, edata, eprior, f, L, a, La2, p] = gp.fh.ne(w, gp, x, y, z); +end function [e, edata, eprior, f, L, a, La2, p] = laplace_algorithm(w, gp, x, y, z) - -% if strcmp(w, 'clearcache') -% ch=[]; -% return -% end - % code for the Laplace algorithm - - % check whether saved values can be used -% if isempty(z) -% datahash=hash_sha512([x y]); -% else -% datahash=hash_sha512([x y z]); -% end - if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && ... -% isequal(datahash,ch.datahash) -% % The covariance function parameters or data haven't changed so we -% % can return the energy and the site parameters that are -% % saved in the cache -% e = ch.e; -% edata = ch.edata; -% eprior = ch.eprior; -% f = ch.f; -% L = ch.L; -% La2 = ch.La2; -% a = ch.a; -% p = ch.p; + + + % The parameters or data have changed since + % the last call for gpla_e. In this case we need to + % re-evaluate the Laplace approximation + gp=gp_unpak(gp, w); + ncf = length(gp.cf); + n = size(x,1); + p = []; + maxiter = gp.latent_opt.maxiter; + tol = gp.latent_opt.tol; + + % Initialize latent values + % zero seems to be a robust choice (Jarno) + % with mean functions, initialize to mean function values + if ~isfield(gp,'meanf') + f = zeros(size(y)); else - % The parameters or data have changed since - % the last call for gpla_e. In this case we need to - % re-evaluate the Laplace approximation - gp=gp_unpak(gp, w); - ncf = length(gp.cf); - n = size(x,1); - p = []; - maxiter = gp.latent_opt.maxiter; - tol = gp.latent_opt.tol; - - % Initialize latent values - % zero seems to be a robust choice (Jarno) - % with mean functions, initialize to mean function values - if ~isfield(gp,'meanf') - f = zeros(size(y)); - else - [H,b_m,B_m]=mean_prep(gp,x,[]); - f = H'*b_m; - end - - % ================================================= - % First Evaluate the data contribution to the error - switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' + [H,b_m,B_m]=mean_prep(gp,x,[]); + f = H'*b_m; + end + + % ================================================= + % First Evaluate the data contribution to the error + switch gp.type + % ============================================================ + % FULL + % ============================================================ + case 'FULL' + + if ~isfield(gp.lik, 'nondiagW') + + K = gp_trcov(gp, x); + if isfield(gp,'meanf') + K=K+H'*B_m*H; + end - if ~isfield(gp.lik, 'nondiagW') - - K = gp_trcov(gp, x); - if isfield(gp,'meanf') - K=K+H'*B_m*H; + % If K is sparse, permute all the inputs so that evaluations are more efficient + if issparse(K) % Check if compact support covariance is used + p = analyze(K); + y = y(p); + K = K(p,p); + if ~isempty(z) + z = z(p,:); end - - % If K is sparse, permute all the inputs so that evaluations are more efficient - if issparse(K) % Check if compact support covariance is used - p = analyze(K); - y = y(p); - K = K(p,p); - if ~isempty(z) - z = z(p,:); + end + switch gp.latent_opt.optim_method + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by Newton method + case 'newton' + a = f; + if isfield(gp,'meanf') + a = a-H'*b_m; end - end - switch gp.latent_opt.optim_method - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by Newton method - case 'newton' - a = f; - if isfield(gp,'meanf') - a = a-H'*b_m; + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp_new = gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + if issparse(K) + speyen=speye(n); + end + + iter=0; + while abs(lp_new - lp_old) > tol && iter < maxiter + iter = iter + 1; + lp_old = lp_new; a_old = a; + sW = sqrt(W); + if issparse(K) + sW = sparse(1:n, 1:n, sW, n, n); + [L,notpositivedefinite] = ldlchol(speyen+sW*K*sW ); + else + %L = chol(eye(n)+sW*sW'.*K); % L'*L=B=eye(n)+sW*K*sW + L=bsxfun(@times,bsxfun(@times,sW,K),sW'); + L(1:n+1:end)=L(1:n+1:end)+1; + [L, notpositivedefinite] = chol(L); + end + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + if ~isfield(gp,'meanf') + b = W.*f+dlp; + else + b = W.*f+K\(H'*b_m)+dlp; end - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - lp_new = gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; if issparse(K) - speyen=speye(n); + a = b - sW*ldlsolve(L,sW*(K*b)); + else + a = b - sW.*(L\(L'\(sW.*(K*b)))); end - - iter=0; - while abs(lp_new - lp_old) > tol && iter < maxiter - iter = iter + 1; - lp_old = lp_new; a_old = a; - sW = sqrt(W); - if issparse(K) - sW = sparse(1:n, 1:n, sW, n, n); - [L,notpositivedefinite] = ldlchol(speyen+sW*K*sW ); - else - %L = chol(eye(n)+sW*sW'.*K); % L'*L=B=eye(n)+sW*K*sW - L=bsxfun(@times,bsxfun(@times,sW,K),sW'); - L(1:n+1:end)=L(1:n+1:end)+1; - [L, notpositivedefinite] = chol(L); - end - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - if ~isfield(gp,'meanf') - b = W.*f+dlp; - else - b = W.*f+K\(H'*b_m)+dlp; - end - if issparse(K) - a = b - sW*ldlsolve(L,sW*(K*b)); - else - a = b - sW.*(L\(L'\(sW.*(K*b)))); - end - if any(isnan(a)) - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end + if any(isnan(a)) + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + f = K*a; + lp = gp.lik.fh.ll(gp.lik, y, f, z); + if ~isfield(gp,'meanf') + lp_new = -a'*f/2 + lp; + else + lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; %f^=f-H'*b_m, + end + i = 0; + while i < 10 && (lp_new < lp_old || isnan(sum(f))) + % reduce step size by half + a = (a_old+a)/2; f = K*a; lp = gp.lik.fh.ll(gp.lik, y, f, z); if ~isfield(gp,'meanf') lp_new = -a'*f/2 + lp; else - lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; %f^=f-H'*b_m, - end - i = 0; - while i < 10 && (lp_new < lp_old || isnan(sum(f))) - % reduce step size by half - a = (a_old+a)/2; - f = K*a; - lp = gp.lik.fh.ll(gp.lik, y, f, z); - if ~isfield(gp,'meanf') - lp_new = -a'*f/2 + lp; - else - lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; - end - i = i+1; + lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; end - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - end - - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by stabilized Newton method. - % This is implemented as suggested by Hannes Nickisch (personal communication) - case 'stabilized-newton' - % Gaussian initialization - % sigma=gp.lik.sigma; - % W = ones(n,1)./sigma.^2; - % sW = sqrt(W); - % %B = eye(n) + siV*siV'.*K; - % L=bsxfun(@times,bsxfun(@times,sW,K),sW'); - % L(1:n+1:end)=L(1:n+1:end)+1; - % L = chol(L,'lower'); - % a=sW.*(L'\(L\(sW.*y))); - % f = K*a; - - % initialize to observations - %f=y; - - switch gp.lik.type - % should be handled inside lik_* - case 'Student-t' - nu=gp.lik.nu; - sigma2=gp.lik.sigma2; - Wmax=(nu+1)/nu/sigma2; - case 'Negbinztr' - r=gp.lik.disper; - Wmax=1./((1+r)./(1*r)); - otherwise - Wmax=100; + i = i+1; end - Wlim=0; - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - lp = -(f'*(K\f))/2 +gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; - f_old = f+1; - ge = Inf; %max(abs(a-dlp)); - if issparse(K) - speyen=speye(n); - end + end + + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by stabilized Newton method. + % This is implemented as suggested by Hannes Nickisch (personal communication) + case 'stabilized-newton' + % Gaussian initialization + % sigma=gp.lik.sigma; + % W = ones(n,1)./sigma.^2; + % sW = sqrt(W); + % %B = eye(n) + siV*siV'.*K; + % L=bsxfun(@times,bsxfun(@times,sW,K),sW'); + % L(1:n+1:end)=L(1:n+1:end)+1; + % L = chol(L,'lower'); + % a=sW.*(L'\(L\(sW.*y))); + % f = K*a; + + % initialize to observations + %f=y; + + switch gp.lik.type + % should be handled inside lik_* + case 'Student-t' + nu=gp.lik.nu; + sigma2=gp.lik.sigma2; + Wmax=(nu+1)/nu/sigma2; + case 'Negbinztr' + r=gp.lik.disper; + Wmax=1./((1+r)./(1*r)); + otherwise + Wmax=100; + end + Wlim=0; + + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp = -(f'*(K\f))/2 +gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + f_old = f+1; + ge = Inf; %max(abs(a-dlp)); + if issparse(K) + speyen=speye(n); + end + + iter=0; + % begin Newton's iterations + while (lp - lp_old > tol || max(abs(f-f_old)) > tol) && iter < maxiter + iter=iter+1; - iter=0; - % begin Newton's iterations - while (lp - lp_old > tol || max(abs(f-f_old)) > tol) && iter < maxiter - iter=iter+1; - - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - - W(Wge) ) && WlimWmax - %fprintf('\n%3d, p(f)=%.12f, max|a-g|=%.12f, %.3f \n',i1,lp,ge,Wlim) - break - end - end + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by fminunc - case 'fminunc_large' + W(Wge) ) && WlimWmax + %fprintf('\n%3d, p(f)=%.12f, max|a-g|=%.12f, %.3f \n',i1,lp,ge,Wlim) + break + end + end + + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by fminunc + case 'fminunc_large' + if issparse(K) + [LD,notpositivedefinite] = ldlchol(K); + if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - otherwise - error('gpla_e: Unknown optimization method ! ') - end - - % evaluate the approximate log marginal likelihood - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - if ~isfield(gp,'meanf') - logZ = 0.5 *f'*a - gp.lik.fh.ll(gp.lik, y, f, z); - else - logZ = 0.5 *((f-H'*b_m)'*(a-K\(H'*b_m))) - gp.lik.fh.ll(gp.lik, y, f, z); - end - if min(W) >= 0 - % This is the usual case where likelihood is log concave - % for example, Poisson and probit - if issparse(K) - W = sparse(1:n,1:n, -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z), n,n); - sqrtW = sqrt(W); - B = sparse(1:n,1:n,1,n,n) + sqrtW*K*sqrtW; - [L, notpositivedefinite] = ldlchol(B); - - % Note that here we use LDL cholesky - edata = logZ + 0.5.*sum(log(diag(L))); % 0.5*log(det(eye(size(K)) + K*W)) ; % + fhm = @(W, f, varargin) (ldlsolve(LD,f) + repmat(W,1,size(f,2)).*f); % W*f; % else - sW = sqrt(W); - L=bsxfun(@times,bsxfun(@times,sW,K),sW'); - L(1:n+1:end)=L(1:n+1:end)+1; - [L, notpositivedefinite] = chol(L, 'lower'); - edata = logZ + sum(log(diag(L))); % 0.5*log(det(eye(size(K)) + K*W)) ; % + [LD,notpositivedefinite] = chol(K); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + fhm = @(W, f, varargin) (LD\(LD'\f) + repmat(W,1,size(f,2)).*f); % W*f; % end - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return + defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', tol,'TolFun', tol,'LargeScale', 'on','Display', 'off'); + if ~isfield(gp.latent_opt, 'fminunc_opt') + opt = optimset(defopts); + else + opt = optimset(defopts,gp.latent_opt.fminunc_opt); end - else - % We may end up here if the likelihood is not log concave - % For example Student-t likelihood. - [W2,I] = sort(W, 1, 'descend'); if issparse(K) - error(['gpla_e: Unfortunately the compact support covariance (CS) functions do not work if'... - 'the second gradient of negative likelihood is negative. This happens for example '... - 'with Student-t likelihood. Please use non-CS functions instead (e.g. gpcf_sexp) ']); + fe = @(f, varargin) (0.5*f*(ldlsolve(LD,f')) - gp.lik.fh.ll(gp.lik, y, f', z)); + fg = @(f, varargin) (ldlsolve(LD,f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; + fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); %inv(K) + diag(g2(f', gp.lik)) ; % + else + fe = @(f, varargin) (0.5*f*(LD\(LD'\f')) - gp.lik.fh.ll(gp.lik, y, f', z)); + fg = @(f, varargin) (LD\(LD'\f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; + fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); %inv(K) + diag(g2(f', gp.lik)) ; % end - [L, notpositivedefinite] = chol(K); - if notpositivedefinite + mydeal = @(varargin)varargin{1:nargout}; + [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); + f = f'; + + if issparse(K) + a = ldlsolve(LD,f); + else + a = LD\(LD'\f); + end + + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables with likelihood specific algorithm + % For example, with Student-t likelihood this mean EM-algorithm which is coded in the + % lik_t file. + case 'lik_specific' + [f, a] = gp.lik.fh.optimizef(gp, y, K); + if isnan(f) [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - L1 = L; - for jj=1:size(K,1) - i = I(jj); - ll = sum(L(:,i).^2); - l = L'*L(:,i); - upfact = W(i)./(1 + W(i).*ll); + otherwise + error('gpla_e: Unknown optimization method ! ') + end + + % evaluate the approximate log marginal likelihood + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + if ~isfield(gp,'meanf') + logZ = 0.5 *f'*a - gp.lik.fh.ll(gp.lik, y, f, z); + else + logZ = 0.5 *((f-H'*b_m)'*(a-K\(H'*b_m))) - gp.lik.fh.ll(gp.lik, y, f, z); + end + if min(W) >= 0 + % This is the usual case where likelihood is log concave + % for example, Poisson and probit + if issparse(K) + W = sparse(1:n,1:n, -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z), n,n); + sqrtW = sqrt(W); + B = sparse(1:n,1:n,1,n,n) + sqrtW*K*sqrtW; + [L, notpositivedefinite] = ldlchol(B); + + % Note that here we use LDL cholesky + edata = logZ + 0.5.*sum(log(diag(L))); % 0.5*log(det(eye(size(K)) + K*W)) ; % + else + sW = sqrt(W); + L=bsxfun(@times,bsxfun(@times,sW,K),sW'); + L(1:n+1:end)=L(1:n+1:end)+1; + [L, notpositivedefinite] = chol(L, 'lower'); + edata = logZ + sum(log(diag(L))); % 0.5*log(det(eye(size(K)) + K*W)) ; % + end + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + else + % We may end up here if the likelihood is not log concave + % For example Student-t likelihood. + [W2,I] = sort(W, 1, 'descend'); + + if issparse(K) + error(['gpla_e: Unfortunately the compact support covariance (CS) functions do not work if'... + 'the second gradient of negative likelihood is negative. This happens for example '... + 'with Student-t likelihood. Please use non-CS functions instead (e.g. gpcf_sexp) ']); + end + + [L, notpositivedefinite] = chol(K); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + L1 = L; + for jj=1:size(K,1) + i = I(jj); + ll = sum(L(:,i).^2); + l = L'*L(:,i); + upfact = W(i)./(1 + W(i).*ll); + + % Check that Cholesky factorization will remain positive definite + if 1./ll + W(i) < 0 %1 + W(i).*ll <= 0 | abs(upfact) > abs(1./ll) %upfact > 1./ll + warning('gpla_e: 1./Sigma(i,i) + W(i) < 0') - % Check that Cholesky factorization will remain positive definite - if 1./ll + W(i) < 0 %1 + W(i).*ll <= 0 | abs(upfact) > abs(1./ll) %upfact > 1./ll - warning('gpla_e: 1./Sigma(i,i) + W(i) < 0') - - if ~isfield(gp.lik.fh,'upfact') - % log-concave likelihood, this should not happen - % let's just return NaN - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - - % non-log-concave likelihood, this may happen - % let's try to do something about it - ind = 1:i-1; - if isempty(z) - mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z); - upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll); - else - mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z(I(ind))); - upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll, z(I(i))); - end + if ~isfield(gp.lik.fh,'upfact') + % log-concave likelihood, this should not happen + % let's just return NaN + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return end - if upfact > 0 - [L,notpositivedefinite] = cholupdate(L, l.*sqrt(upfact), '-'); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end + + % non-log-concave likelihood, this may happen + % let's try to do something about it + ind = 1:i-1; + if isempty(z) + mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z); + upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll); else - L = cholupdate(L, l.*sqrt(-upfact)); + mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z(I(ind))); + upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll, z(I(i))); + end + end + if upfact > 0 + [L,notpositivedefinite] = cholupdate(L, l.*sqrt(upfact), '-'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return end + else + L = cholupdate(L, l.*sqrt(-upfact)); end - edata = logZ + sum(log(diag(L1))) - sum(log(diag(L))); end - - La2 = W; - + edata = logZ + sum(log(diag(L1))) - sum(log(diag(L))); + end + + La2 = W; + + else + % Likelihoods with non-diagonal Hessian + + [n,nout] = size(y); + if isfield(gp, 'comp_cf') % own covariance for each ouput component + multicf = true; + if length(gp.comp_cf) ~= nout && nout > 1 + error('GPLA_ND_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') + end else - % Likelihoods with non-diagonal Hessian + multicf = false; + end + p=[]; + switch gp.lik.type - [n,nout] = size(y); - if isfield(gp, 'comp_cf') % own covariance for each ouput component - multicf = true; - if length(gp.comp_cf) ~= nout && nout > 1 - error('GPLA_ND_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') - end - else - multicf = false; - end - p=[]; - switch gp.lik.type + case {'LGP', 'LGPC'} - case {'LGP', 'LGPC'} - - nl=n; + nl=n; + + % Initialize latent values + % zero seems to be a robust choice (Jarno) + % with mean functions, initialize to mean function values + if ~isfield(gp,'meanf') + f = zeros(sum(nl),1); + else + [H,b_m,B_m]=mean_prep(gp,x,[]); + Hb_m=H'*b_m; + f = Hb_m; + end + + if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 + gptmp=gp; gptmp.jitterSigma2=0; + % Use Kronecker product kron(Ka,Kb) instead of K + Ka = gp_trcov(gptmp, unique(x(:,1))); + % fix the magnitude sigma to 1 for Kb matrix + wtmp=gp_pak(gptmp); wtmp(1)=0; gptmp=gp_unpak(gptmp,wtmp); + Kb = gp_trcov(gptmp, unique(x(:,2))); + clear gptmp + n1=size(Ka,1); + n2=size(Kb,1); + + [Va,Da]=eig(Ka); [Vb,Db]=eig(Kb); + % eigenvalues of K matrix + Dtmp=kron(diag(Da),diag(Db)); + [sDtmp,istmp]=sort(Dtmp,'descend'); + + % Form the low-rank approximation. Exclude eigenvalues + % smaller than gp.latent_opt.eig_tol or take + % gp.latent_opt.eig_prct*n eigenvalues at most. + nlr=min([sum(sDtmp>gp.latent_opt.eig_tol) round(gp.latent_opt.eig_prct*n)]); + sDtmp=sDtmp+gp.jitterSigma2; + + itmp1=meshgrid(1:n1,1:n2); + itmp2=meshgrid(1:n2,1:n1)'; + ind=[itmp1(:) itmp2(:)]; + + % included eigenvalues + Dlr=sDtmp(1:nlr); + % included eigenvectors + Vlr=zeros(n,nlr); + for i1=1:nlr + Vlr(:,i1)=kron(Va(:,ind(istmp(i1),1)),Vb(:,ind(istmp(i1),2))); + end + %L=[]; + + % diag(K)-diag(Vlr*diag(Dlr)*Vlr') + Lb=gp_trvar(gp,x)-sum(bsxfun(@times,Vlr.*Vlr,Dlr'),2); + if isfield(gp,'meanf') + Dt=[Dlr; diag(B_m)]; + Vt=[Vlr H']; + else + Dt=Dlr; + Vt=Vlr; + end + Dtsq=sqrt(Dt); + + elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 + % unique values from covariance matrix + K1 = gp_cov(gp, x(1,:), x); + K1(1)=K1(1)+gp.jitterSigma2; + if size(x,2)==1 + % form circulant matrix to avoid border effects + Kcirc=[K1 0 K1(end:-1:2)]; + fftKcirc = fft(Kcirc); + elseif size(x,2)==2 + n1=gp.latent_opt.gridn(1); + n2=gp.latent_opt.gridn(2); + Ktmp=reshape(K1,n2,n1); + % form circulant matrix to avoid border effects + Ktmp=[Ktmp; zeros(1,n1); flipud(Ktmp(2:end,:))]; + fftKcirc=fft2([Ktmp zeros(2*n2,1) fliplr(Ktmp(:,2:end))]); + else + error('FFT speed-up implemented only for 1D and 2D cases.') + end + else + K = gp_trcov(gp, x); + end + + % Mean function contribution to K + if isfield(gp,'meanf') + if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 + % only zero mean function implemented for Kronecker + % approximation + iKHb_m=zeros(n,1); + elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 + % only zero mean function implemented for FFT speed-up + iKHb_m=zeros(n,1); + else + K=K+H'*B_m*H; + ws=warning('off','MATLAB:singularMatrix'); + iKHb_m=K\Hb_m; + warning(ws); + end + end + + + % Main Newton algorithm + + tol = 1e-12; + a = f; + if isfield(gp,'meanf') + a = a-Hb_m; + end + + % a vector to form the second gradient + g2 = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + g2sq=sqrt(g2); + + ny=sum(y); % total number of observations + + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp_new = gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + + iter=0; + while abs(lp_new - lp_old) > tol && iter < maxiter + iter = iter + 1; + lp_old = lp_new; a_old = a; + - % Initialize latent values - % zero seems to be a robust choice (Jarno) - % with mean functions, initialize to mean function values if ~isfield(gp,'meanf') - f = zeros(sum(nl),1); + if strcmpi(gp.lik.type,'LGPC') + n1=gp.lik.gridn(1); n2=gp.lik.gridn(2); + b=zeros(n,1); + ny2=sum(reshape(y,fliplr(gp.lik.gridn))); + for k1=1:n1 + b((1:n2)+(k1-1)*n2) = ny2(k1)*(g2((1:n2)+(k1-1)*n2).*f((1:n2)+(k1-1)*n2)-g2((1:n2)+(k1-1)*n2)*(g2((1:n2)+(k1-1)*n2)'*f((1:n2)+(k1-1)*n2)))+dlp((1:n2)+(k1-1)*n2); + end + else + b = ny*(g2.*f-g2*(g2'*f))+dlp; + %b = W.*f+dlp; + end else - [H,b_m,B_m]=mean_prep(gp,x,[]); - Hb_m=H'*b_m; - f = Hb_m; + if strcmpi(gp.lik.type,'LGPC') + n1=gp.lik.gridn(1); n2=gp.lik.gridn(2); + b=zeros(n,1); + ny2=sum(reshape(y,fliplr(gp.lik.gridn))); + for k1=1:n1 + b((1:n2)+(k1-1)*n2) = ny2(k1)*(g2((1:n2)+(k1-1)*n2).*f((1:n2)+(k1-1)*n2)-g2((1:n2)+(k1-1)*n2)*(g2((1:n2)+(k1-1)*n2)'*f((1:n2)+(k1-1)*n2)))+iKHb_m((1:n2)+(k1-1)*n2)+dlp((1:n2)+(k1-1)*n2); + end + else + b = ny*(g2.*f-g2*(g2'*f))+iKHb_m+dlp; + %b = W.*f+K\(H'*b_m)+dlp; + end end if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 - gptmp=gp; gptmp.jitterSigma2=0; - % Use Kronecker product kron(Ka,Kb) instead of K - Ka = gp_trcov(gptmp, unique(x(:,1))); - % fix the magnitude sigma to 1 for Kb matrix - wtmp=gp_pak(gptmp); wtmp(1)=0; gptmp=gp_unpak(gptmp,wtmp); - Kb = gp_trcov(gptmp, unique(x(:,2))); - clear gptmp - n1=size(Ka,1); - n2=size(Kb,1); - [Va,Da]=eig(Ka); [Vb,Db]=eig(Kb); - % eigenvalues of K matrix - Dtmp=kron(diag(Da),diag(Db)); - [sDtmp,istmp]=sort(Dtmp,'descend'); + % Use Kronecker product structure in matrix vector + % multiplications + %- + % q=Kb*reshape(b,n2,n1)*Ka; + % Kg=q(:); + % Kg=Kg+gp.jitterSigma2*b; + %- + % OR use reduced-rank approximation for K + %- + Kg=Lb.*b+Vlr*(Dlr.*(Vlr'*b)); + %- - % Form the low-rank approximation. Exclude eigenvalues - % smaller than gp.latent_opt.eig_tol or take - % gp.latent_opt.eig_prct*n eigenvalues at most. - nlr=min([sum(sDtmp>gp.latent_opt.eig_tol) round(gp.latent_opt.eig_prct*n)]); - sDtmp=sDtmp+gp.jitterSigma2; + if isfield(gp,'meanf') + Kg=Kg+H'*(B_m*(H*b)); + end - itmp1=meshgrid(1:n1,1:n2); - itmp2=meshgrid(1:n2,1:n1)'; - ind=[itmp1(:) itmp2(:)]; + % % Use Kronecker product structure + %- + % v=sqrt(ny)*(g2sq.*Kg-(g2*(g2'*Kg))./g2sq); + % % fast matrix vector multiplication with + % % Kronecker product for matrix inversion + % if isfield(gp,'meanf') + % [iSg,~]=pcg(@(z) mvm_kron(g2,ny,Ka,Kb,H,B_m,gp.jitterSigma2,z), v, gp.latent_opt.pcg_tol); + % else + % [iSg,~]=pcg(@(z) mvm_kron(g2,ny,Ka,Kb,[],[],gp.jitterSigma2,z), v, gp.latent_opt.pcg_tol); + % end + % a=b-sqrt(ny)*(g2sq.*iSg - g2*(g2'*(iSg./g2sq))); + %- - % included eigenvalues - Dlr=sDtmp(1:nlr); - % included eigenvectors - Vlr=zeros(n,nlr); - for i1=1:nlr - Vlr(:,i1)=kron(Va(:,ind(istmp(i1),1)),Vb(:,ind(istmp(i1),2))); + % use reduced-rank approximation for K + %- + Zt=1./(1+ny*g2.*Lb); + Ztsq=sqrt(Zt); + Ltmp=bsxfun(@times,Ztsq.*sqrt(ny).*g2sq,bsxfun(@times,Vt,sqrt(Dt)')); + Ltmp=Ltmp'*Ltmp; + Ltmp(1:(size(Dt,1)+1):end)=Ltmp(1:(size(Dt,1)+1):end)+1; + [L,notpositivedefinite] = chol(Ltmp,'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return end - %L=[]; - % diag(K)-diag(Vlr*diag(Dlr)*Vlr') - Lb=gp_trvar(gp,x)-sum(bsxfun(@times,Vlr.*Vlr,Dlr'),2); - if isfield(gp,'meanf') - Dt=[Dlr; diag(B_m)]; - Vt=[Vlr H']; - else - Dt=Dlr; - Vt=Vlr; - end - Dtsq=sqrt(Dt); + EKg=ny*g2.*(Zt.*Kg)-sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*(Vt*(Dtsq.*(L'\(L\(Dtsq.*(Vt'*(sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*Kg))))))))))); + E1=ny*g2.*(Zt.*ones(n,1))-sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*(Vt*(Dtsq.*(L'\(L\(Dtsq.*(Vt'*(sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*ones(n,1)))))))))))); + a=b-(EKg-E1*((E1'*Kg)./(ones(1,n)*E1))); + %- elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 - % unique values from covariance matrix - K1 = gp_cov(gp, x(1,:), x); - K1(1)=K1(1)+gp.jitterSigma2; + + % use FFT speed-up in matrix vector multiplications if size(x,2)==1 - % form circulant matrix to avoid border effects - Kcirc=[K1 0 K1(end:-1:2)]; - fftKcirc = fft(Kcirc); + gge=zeros(2*n,1); + gge(1:n)=b; + q=ifft(fftKcirc.*fft(gge')); + Kg=q(1:n)'; elseif size(x,2)==2 - n1=gp.latent_opt.gridn(1); - n2=gp.latent_opt.gridn(2); - Ktmp=reshape(K1,n2,n1); - % form circulant matrix to avoid border effects - Ktmp=[Ktmp; zeros(1,n1); flipud(Ktmp(2:end,:))]; - fftKcirc=fft2([Ktmp zeros(2*n2,1) fliplr(Ktmp(:,2:end))]); + gge=zeros(2*n2,2*n1); + gge(1:n2,1:n1)=reshape(b,n2,n1); + + q=ifft2(fftKcirc.*fft2(gge)); + q=q(1:n2,1:n1); + Kg=q(:); else error('FFT speed-up implemented only for 1D and 2D cases.') end - else - K = gp_trcov(gp, x); - end - - % Mean function contribution to K - if isfield(gp,'meanf') - if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 - % only zero mean function implemented for Kronecker - % approximation - iKHb_m=zeros(n,1); - elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 - % only zero mean function implemented for FFT speed-up - iKHb_m=zeros(n,1); - else - K=K+H'*B_m*H; - ws=warning('off','MATLAB:singularMatrix'); - iKHb_m=K\Hb_m; - warning(ws); - end - end - - - % Main Newton algorithm - - tol = 1e-12; - a = f; - if isfield(gp,'meanf') - a = a-Hb_m; - end - - % a vector to form the second gradient - g2 = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - g2sq=sqrt(g2); - - ny=sum(y); % total number of observations - - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - lp_new = gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; - - iter=0; - while abs(lp_new - lp_old) > tol && iter < maxiter - iter = iter + 1; - lp_old = lp_new; a_old = a; + if isfield(gp,'meanf') + Kg=Kg+H'*(B_m*(H*b)); + end + v=sqrt(ny)*(g2sq.*Kg-(g2*(g2'*Kg))./g2sq); - if ~isfield(gp,'meanf') - if strcmpi(gp.lik.type,'LGPC') - n1=gp.lik.gridn(1); n2=gp.lik.gridn(2); - b=zeros(n,1); - ny2=sum(reshape(y,fliplr(gp.lik.gridn))); - for k1=1:n1 - b((1:n2)+(k1-1)*n2) = ny2(k1)*(g2((1:n2)+(k1-1)*n2).*f((1:n2)+(k1-1)*n2)-g2((1:n2)+(k1-1)*n2)*(g2((1:n2)+(k1-1)*n2)'*f((1:n2)+(k1-1)*n2)))+dlp((1:n2)+(k1-1)*n2); - end - else - b = ny*(g2.*f-g2*(g2'*f))+dlp; - %b = W.*f+dlp; - end + if isfield(gp,'meanf') + % fast matrix vector multiplication with fft for matrix inversion + [iSg,~]=pcg(@(z) mvm_fft(g2,ny,fftKcirc,H,B_m,z), v, gp.latent_opt.pcg_tol); else - if strcmpi(gp.lik.type,'LGPC') - n1=gp.lik.gridn(1); n2=gp.lik.gridn(2); - b=zeros(n,1); - ny2=sum(reshape(y,fliplr(gp.lik.gridn))); - for k1=1:n1 - b((1:n2)+(k1-1)*n2) = ny2(k1)*(g2((1:n2)+(k1-1)*n2).*f((1:n2)+(k1-1)*n2)-g2((1:n2)+(k1-1)*n2)*(g2((1:n2)+(k1-1)*n2)'*f((1:n2)+(k1-1)*n2)))+iKHb_m((1:n2)+(k1-1)*n2)+dlp((1:n2)+(k1-1)*n2); - end - else - b = ny*(g2.*f-g2*(g2'*f))+iKHb_m+dlp; - %b = W.*f+K\(H'*b_m)+dlp; - end + [iSg,~]=pcg(@(z) mvm_fft(g2,ny,fftKcirc,[],[],z), v, gp.latent_opt.pcg_tol); end + a=b-sqrt(ny)*(g2sq.*iSg - g2*(g2'*(iSg./g2sq))); - if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 - - % Use Kronecker product structure in matrix vector - % multiplications - %- - % q=Kb*reshape(b,n2,n1)*Ka; - % Kg=q(:); - % Kg=Kg+gp.jitterSigma2*b; - %- - % OR use reduced-rank approximation for K - %- - Kg=Lb.*b+Vlr*(Dlr.*(Vlr'*b)); - %- - - if isfield(gp,'meanf') - Kg=Kg+H'*(B_m*(H*b)); + else + if strcmpi(gp.lik.type,'LGPC') + R=zeros(n); + RKR=K; + for k1=1:n1 + R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)=sqrt(ny2(k1))*(diag(g2sq((1:n2)+(k1-1)*n2))-g2((1:n2)+(k1-1)*n2)*g2sq((1:n2)+(k1-1)*n2)'); + RKR(:,(1:n2)+(k1-1)*n2)=RKR(:,(1:n2)+(k1-1)*n2)*R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2); end + for k1=1:n1 + RKR((1:n2)+(k1-1)*n2,:)=R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)'*RKR((1:n2)+(k1-1)*n2,:); + end + %RKR=R'*K*R; + RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; + [L,notpositivedefinite] = chol(RKR,'lower'); - % % Use Kronecker product structure - %- - % v=sqrt(ny)*(g2sq.*Kg-(g2*(g2'*Kg))./g2sq); - % % fast matrix vector multiplication with - % % Kronecker product for matrix inversion - % if isfield(gp,'meanf') - % [iSg,~]=pcg(@(z) mvm_kron(g2,ny,Ka,Kb,H,B_m,gp.jitterSigma2,z), v, gp.latent_opt.pcg_tol); - % else - % [iSg,~]=pcg(@(z) mvm_kron(g2,ny,Ka,Kb,[],[],gp.jitterSigma2,z), v, gp.latent_opt.pcg_tol); - % end - % a=b-sqrt(ny)*(g2sq.*iSg - g2*(g2'*(iSg./g2sq))); - %- - - % use reduced-rank approximation for K - %- - Zt=1./(1+ny*g2.*Lb); - Ztsq=sqrt(Zt); - Ltmp=bsxfun(@times,Ztsq.*sqrt(ny).*g2sq,bsxfun(@times,Vt,sqrt(Dt)')); - Ltmp=Ltmp'*Ltmp; - Ltmp(1:(size(Dt,1)+1):end)=Ltmp(1:(size(Dt,1)+1):end)+1; - [L,notpositivedefinite] = chol(Ltmp,'lower'); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - EKg=ny*g2.*(Zt.*Kg)-sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*(Vt*(Dtsq.*(L'\(L\(Dtsq.*(Vt'*(sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*Kg))))))))))); - E1=ny*g2.*(Zt.*ones(n,1))-sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*(Vt*(Dtsq.*(L'\(L\(Dtsq.*(Vt'*(sqrt(ny)*g2sq.*(Zt.*(sqrt(ny)*g2sq.*ones(n,1)))))))))))); - a=b-(EKg-E1*((E1'*Kg)./(ones(1,n)*E1))); - %- + Kb=K*b; + RCb=R'*Kb; - elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 + iRCb=L'\(L\RCb); + a=b-R*iRCb; + else + %R=-g2*g2sq'; R(1:(n+1):end)=R(1:(n+1):end)+g2sq'; + KR=bsxfun(@times,K,g2sq')-(K*g2)*g2sq'; + RKR=ny*(bsxfun(@times,g2sq,KR)-g2sq*(g2'*KR)); + RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; + [L,notpositivedefinite] = chol(RKR,'lower'); - % use FFT speed-up in matrix vector multiplications - if size(x,2)==1 - gge=zeros(2*n,1); - gge(1:n)=b; - q=ifft(fftKcirc.*fft(gge')); - Kg=q(1:n)'; - elseif size(x,2)==2 - gge=zeros(2*n2,2*n1); - gge(1:n2,1:n1)=reshape(b,n2,n1); - - q=ifft2(fftKcirc.*fft2(gge)); - q=q(1:n2,1:n1); - Kg=q(:); - else - error('FFT speed-up implemented only for 1D and 2D cases.') + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return end + Kb=K*b; + RCb=g2sq.*Kb-g2sq*(g2'*Kb); + iRCb=L'\(L\RCb); + a=b-ny*(g2sq.*iRCb-g2*(g2sq'*iRCb)); + end + end + + + if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 + + % % Use Kronecker product structure + %- + % f2=Kb*reshape(a,n2,n1)*Ka; + % f=f2(:); + % f=f+gp.jitterSigma2*a; + %- + % use reduced-rank approximation for K + %- + f=Lb.*a+Vlr*(Dlr.*(Vlr'*a)); + %- + + if isfield(gp,'meanf') + f=f+H'*(B_m*(H*a)); + end + elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 + if size(x,2)==1 + a2=zeros(2*n,1); + a2(1:n)=a; + f2=ifft(fftKcirc.*fft(a2')); + f=f2(1:n)'; if isfield(gp,'meanf') - Kg=Kg+H'*(B_m*(H*b)); + f=f+H'*(B_m*(H*a)); end - v=sqrt(ny)*(g2sq.*Kg-(g2*(g2'*Kg))./g2sq); + elseif size(x,2)==2 + a2=zeros(2*n2,2*n1); + a2(1:n2,1:n1)=reshape(a,n2,n1); + f2=ifft2(fftKcirc.*fft2(a2)); + f2=f2(1:n2,1:n1); + f=f2(:); if isfield(gp,'meanf') - % fast matrix vector multiplication with fft for matrix inversion - [iSg,~]=pcg(@(z) mvm_fft(g2,ny,fftKcirc,H,B_m,z), v, gp.latent_opt.pcg_tol); - else - [iSg,~]=pcg(@(z) mvm_fft(g2,ny,fftKcirc,[],[],z), v, gp.latent_opt.pcg_tol); + f=f+H'*(B_m*(H*a)); end - a=b-sqrt(ny)*(g2sq.*iSg - g2*(g2'*(iSg./g2sq))); - else - if strcmpi(gp.lik.type,'LGPC') - R=zeros(n); - RKR=K; - for k1=1:n1 - R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)=sqrt(ny2(k1))*(diag(g2sq((1:n2)+(k1-1)*n2))-g2((1:n2)+(k1-1)*n2)*g2sq((1:n2)+(k1-1)*n2)'); - RKR(:,(1:n2)+(k1-1)*n2)=RKR(:,(1:n2)+(k1-1)*n2)*R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2); - end - for k1=1:n1 - RKR((1:n2)+(k1-1)*n2,:)=R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)'*RKR((1:n2)+(k1-1)*n2,:); - end - %RKR=R'*K*R; - RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; - [L,notpositivedefinite] = chol(RKR,'lower'); - - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - - Kb=K*b; - RCb=R'*Kb; - - iRCb=L'\(L\RCb); - a=b-R*iRCb; - else - %R=-g2*g2sq'; R(1:(n+1):end)=R(1:(n+1):end)+g2sq'; - KR=bsxfun(@times,K,g2sq')-(K*g2)*g2sq'; - RKR=ny*(bsxfun(@times,g2sq,KR)-g2sq*(g2'*KR)); - RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; - [L,notpositivedefinite] = chol(RKR,'lower'); - - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - - Kb=K*b; - RCb=g2sq.*Kb-g2sq*(g2'*Kb); - iRCb=L'\(L\RCb); - a=b-ny*(g2sq.*iRCb-g2*(g2sq'*iRCb)); - end + error('FFT speed-up implemented only for 1D and 2D cases.') end - + else + f = K*a; + end + + lp = gp.lik.fh.ll(gp.lik, y, f, z); + if ~isfield(gp,'meanf') + lp_new = -a'*f/2 + lp; + else + %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; %f^=f-H'*b_m, + lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; %f^=f-Hb_m, + end + i = 0; + while i < 10 && lp_new < lp_old && ~isnan(sum(f)) + % reduce step size by half + a = (a_old+a)/2; if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 - % % Use Kronecker product structure %- % f2=Kb*reshape(a,n2,n1)*Ka; @@ -802,16 +830,17 @@ a2(1:n)=a; f2=ifft(fftKcirc.*fft(a2')); f=f2(1:n)'; + if isfield(gp,'meanf') f=f+H'*(B_m*(H*a)); end elseif size(x,2)==2 a2=zeros(2*n2,2*n1); a2(1:n2,1:n1)=reshape(a,n2,n1); - f2=ifft2(fftKcirc.*fft2(a2)); f2=f2(1:n2,1:n1); f=f2(:); + if isfield(gp,'meanf') f=f+H'*(B_m*(H*a)); end @@ -826,295 +855,175 @@ if ~isfield(gp,'meanf') lp_new = -a'*f/2 + lp; else - %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; %f^=f-H'*b_m, - lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; %f^=f-Hb_m, - end - i = 0; - while i < 10 && lp_new < lp_old && ~isnan(sum(f)) - % reduce step size by half - a = (a_old+a)/2; - - if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 - % % Use Kronecker product structure - %- - % f2=Kb*reshape(a,n2,n1)*Ka; - % f=f2(:); - % f=f+gp.jitterSigma2*a; - %- - % use reduced-rank approximation for K - %- - f=Lb.*a+Vlr*(Dlr.*(Vlr'*a)); - %- - - if isfield(gp,'meanf') - f=f+H'*(B_m*(H*a)); - end - elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 - if size(x,2)==1 - a2=zeros(2*n,1); - a2(1:n)=a; - f2=ifft(fftKcirc.*fft(a2')); - f=f2(1:n)'; - - if isfield(gp,'meanf') - f=f+H'*(B_m*(H*a)); - end - elseif size(x,2)==2 - a2=zeros(2*n2,2*n1); - a2(1:n2,1:n1)=reshape(a,n2,n1); - f2=ifft2(fftKcirc.*fft2(a2)); - f2=f2(1:n2,1:n1); - f=f2(:); - - if isfield(gp,'meanf') - f=f+H'*(B_m*(H*a)); - end - else - error('FFT speed-up implemented only for 1D and 2D cases.') - end - else - f = K*a; - end - - lp = gp.lik.fh.ll(gp.lik, y, f, z); - if ~isfield(gp,'meanf') - lp_new = -a'*f/2 + lp; - else - %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; - lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; - end - i = i+1; + %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; + lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; end - - g2 = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - g2sq=sqrt(g2); - - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + i = i+1; end - % evaluate the approximate log marginal likelihood g2 = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); g2sq=sqrt(g2); - if ~isfield(gp,'meanf') - logZ = 0.5 *f'*a - gp.lik.fh.ll(gp.lik, y, f, z); - else - % logZ = 0.5 *((f-H'*b_m)'*(a-K\(H'*b_m))) - gp.lik.fh.ll(gp.lik, y, f, z); - logZ = 0.5 *((f-Hb_m)'*(a-iKHb_m)) - gp.lik.fh.ll(gp.lik, y, f, z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + end + + % evaluate the approximate log marginal likelihood + g2 = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + g2sq=sqrt(g2); + + if ~isfield(gp,'meanf') + logZ = 0.5 *f'*a - gp.lik.fh.ll(gp.lik, y, f, z); + else + % logZ = 0.5 *((f-H'*b_m)'*(a-K\(H'*b_m))) - gp.lik.fh.ll(gp.lik, y, f, z); + logZ = 0.5 *((f-Hb_m)'*(a-iKHb_m)) - gp.lik.fh.ll(gp.lik, y, f, z); + end + + + if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 + % % Use Kronecker product structure + %- + % tmp=bsxfun(@times,Lb.^(-1/2),bsxfun(@times,Vt,sqrt(Dt)')); + % tmp=tmp'*tmp; + % tmp(1:size(tmp,1)+1:end)=tmp(1:size(tmp,1)+1:end)+1; + % logZa=sum(log(diag(chol(tmp,'lower')))); + % + % Lbt=ny*(g2)+1./Lb; + % + % St=[diag(1./Dt)+Vt'*bsxfun(@times,1./Lb,Vt) zeros(size(Dt,1),1); ... + % zeros(1,size(Dt,1)) 1]; + % Pt=[bsxfun(@times,1./Lb,Vt) sqrt(ny)*g2]; + % + % logZb=sum(log(diag(chol(St,'lower')))); + % + % Ptt=bsxfun(@times,1./sqrt(Lbt),Pt); + % logZc=sum(log(diag(chol(St-Ptt'*Ptt,'lower')))); + % + % edata = logZ + logZa - logZb + logZc + 0.5*sum(log(Lb)) + 0.5*sum(log(Lbt)); + %- + + % use reduced-rank approximation for K + %- + Zt=1./(1+ny*g2.*Lb); + Ztsq=sqrt(Zt); + Ltmp=bsxfun(@times,Ztsq.*sqrt(ny).*g2sq,bsxfun(@times,Vt,sqrt(Dt)')); + Ltmp=Ltmp'*Ltmp; + Ltmp(1:(size(Dt,1)+1):end)=Ltmp(1:(size(Dt,1)+1):end)+1; + L=chol(Ltmp,'lower'); + + LTtmp=L\( Dtsq.*(Vt'*( (g2sq.*sqrt(ny)).*((1./(1+ny*g2.*Lb)).* (sqrt(ny)*g2sq) ) )) ); + edata = logZ + sum(log(diag(L)))+0.5*sum(log(1+ny*g2.*Lb)) ... + -0.5*log(ny) + 0.5*log(sum(((g2*ny)./(ny*g2.*Lb+1)))-LTtmp'*LTtmp); + %- + elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 + + K = gp_trcov(gp, x); + if isfield(gp,'meanf') + K=K+H'*B_m*H; + end + + % exact determinant + KR=bsxfun(@times,K,g2sq')-(K*g2)*g2sq'; + RKR=ny*(bsxfun(@times,g2sq,KR)-g2sq*(g2'*KR)); + RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; + [L,notpositivedefinite] = chol(RKR,'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return end + edata = logZ + sum(log(diag(L))); + % % determinant approximated using only the largest eigenvalues + % opts.issym = 1; + % Deig=eigs(@(z) mvm_fft(g2, ny, fftKcirc, H, B_m, z),n,round(n*0.05),'lm',opts); + % edata = logZ + 0.5*sum(log(Deig)); + % L=[]; + else - if isfield(gp.latent_opt, 'kron') && gp.latent_opt.kron==1 - % % Use Kronecker product structure - %- - % tmp=bsxfun(@times,Lb.^(-1/2),bsxfun(@times,Vt,sqrt(Dt)')); - % tmp=tmp'*tmp; - % tmp(1:size(tmp,1)+1:end)=tmp(1:size(tmp,1)+1:end)+1; - % logZa=sum(log(diag(chol(tmp,'lower')))); - % - % Lbt=ny*(g2)+1./Lb; - % - % St=[diag(1./Dt)+Vt'*bsxfun(@times,1./Lb,Vt) zeros(size(Dt,1),1); ... - % zeros(1,size(Dt,1)) 1]; - % Pt=[bsxfun(@times,1./Lb,Vt) sqrt(ny)*g2]; - % - % logZb=sum(log(diag(chol(St,'lower')))); - % - % Ptt=bsxfun(@times,1./sqrt(Lbt),Pt); - % logZc=sum(log(diag(chol(St-Ptt'*Ptt,'lower')))); - % - % edata = logZ + logZa - logZb + logZc + 0.5*sum(log(Lb)) + 0.5*sum(log(Lbt)); - %- - - % use reduced-rank approximation for K - %- - Zt=1./(1+ny*g2.*Lb); - Ztsq=sqrt(Zt); - Ltmp=bsxfun(@times,Ztsq.*sqrt(ny).*g2sq,bsxfun(@times,Vt,sqrt(Dt)')); - Ltmp=Ltmp'*Ltmp; - Ltmp(1:(size(Dt,1)+1):end)=Ltmp(1:(size(Dt,1)+1):end)+1; - L=chol(Ltmp,'lower'); - - LTtmp=L\( Dtsq.*(Vt'*( (g2sq.*sqrt(ny)).*((1./(1+ny*g2.*Lb)).* (sqrt(ny)*g2sq) ) )) ); - edata = logZ + sum(log(diag(L)))+0.5*sum(log(1+ny*g2.*Lb)) ... - -0.5*log(ny) + 0.5*log(sum(((g2*ny)./(ny*g2.*Lb+1)))-LTtmp'*LTtmp); - %- - elseif isfield(gp.latent_opt, 'fft') && gp.latent_opt.fft==1 - - K = gp_trcov(gp, x); - if isfield(gp,'meanf') - K=K+H'*B_m*H; + if strcmpi(gp.lik.type,'LGPC') + R=zeros(n); + RKR=K; + for k1=1:n1 + R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)=sqrt(ny2(k1))*(diag(g2sq((1:n2)+(k1-1)*n2))-g2((1:n2)+(k1-1)*n2)*g2sq((1:n2)+(k1-1)*n2)'); + RKR(:,(1:n2)+(k1-1)*n2)=RKR(:,(1:n2)+(k1-1)*n2)*R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2); end - - % exact determinant - KR=bsxfun(@times,K,g2sq')-(K*g2)*g2sq'; - RKR=ny*(bsxfun(@times,g2sq,KR)-g2sq*(g2'*KR)); - RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; - [L,notpositivedefinite] = chol(RKR,'lower'); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return + for k1=1:n1 + RKR((1:n2)+(k1-1)*n2,:)=R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)'*RKR((1:n2)+(k1-1)*n2,:); end - edata = logZ + sum(log(diag(L))); - - % % determinant approximated using only the largest eigenvalues - % opts.issym = 1; - % Deig=eigs(@(z) mvm_fft(g2, ny, fftKcirc, H, B_m, z),n,round(n*0.05),'lm',opts); - % edata = logZ + 0.5*sum(log(Deig)); - % L=[]; + %RKR=R'*K*R; else - - if strcmpi(gp.lik.type,'LGPC') - R=zeros(n); - RKR=K; - for k1=1:n1 - R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)=sqrt(ny2(k1))*(diag(g2sq((1:n2)+(k1-1)*n2))-g2((1:n2)+(k1-1)*n2)*g2sq((1:n2)+(k1-1)*n2)'); - RKR(:,(1:n2)+(k1-1)*n2)=RKR(:,(1:n2)+(k1-1)*n2)*R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2); - end - for k1=1:n1 - RKR((1:n2)+(k1-1)*n2,:)=R((1:n2)+(k1-1)*n2,(1:n2)+(k1-1)*n2)'*RKR((1:n2)+(k1-1)*n2,:); - end - %RKR=R'*K*R; - else - KR=bsxfun(@times,K,g2sq')-(K*g2)*g2sq'; - RKR=ny*(bsxfun(@times,g2sq,KR)-g2sq*(g2'*KR)); - end - RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; - [L,notpositivedefinite] = chol(RKR,'lower'); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - edata = logZ + sum(log(diag(L))); + KR=bsxfun(@times,K,g2sq')-(K*g2)*g2sq'; + RKR=ny*(bsxfun(@times,g2sq,KR)-g2sq*(g2'*KR)); end - - M=[]; - E=[]; - - case {'Softmax', 'Multinom'} - - % Initialize latent values - % zero seems to be a robust choice (Jarno) - f = zeros(size(y(:))); - - K = zeros(n,n,nout); - if multicf - for i1=1:nout - K(:,:,i1) = gp_trcov(gp, x, gp.comp_cf{i1}); - end - else - Ktmp=gp_trcov(gp, x); - for i1=1:nout - K(:,:,i1) = Ktmp; - end + RKR(1:(n+1):end)=RKR(1:(n+1):end)+1; + [L,notpositivedefinite] = chol(RKR,'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return end - - % Main newton algorithm, see Rasmussen & Williams (2006), - % p. 50 - - tol = 1e-12; - a = f; - - f2=reshape(f,n,nout); - - % lp_new = log(p(y|f)) - lp_new = gp.lik.fh.ll(gp.lik, y, f2, z); - lp_old = -Inf; - - c=zeros(n*nout,1); - ERMMRc=zeros(n*nout,1); - E=zeros(n,n,nout); - L=zeros(n,n,nout); - RER = zeros(n,n,nout); - - while lp_new - lp_old > tol - lp_old = lp_new; a_old = a; - - % llg = d(log(p(y|f)))/df - llg = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); - % Second derivatives - [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); - % W = -diag(pi2_vec) + pi2_mat*pi2_mat' - pi2 = reshape(pi2_vec,size(y)); - - R = repmat(1./pi2_vec,1,n).*pi2_mat; - for i1=1:nout - Dc=sqrt(pi2(:,i1)); - Lc=(Dc*Dc').*K(:,:,i1); - Lc(1:n+1:end)=Lc(1:n+1:end)+1; - [Lc,notpositivedefinite]=chol(Lc); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - L(:,:,i1)=Lc; - - Ec=Lc'\diag(Dc); - Ec=Ec'*Ec; - E(:,:,i1)=Ec; - RER(:,:,i1) = R((1:n)+(i1-1)*n,:)'*Ec*R((1:n)+(i1-1)*n,:); - end - [M, notpositivedefinite]=chol(sum(RER,3)); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - - b = pi2_vec.*f - pi2_mat*(pi2_mat'*f) + llg; - for i1=1:nout - c((1:n)+(i1-1)*n)=E(:,:,i1)*(K(:,:,i1)*b((1:n)+(i1-1)*n)); - end - - RMMRc=R*(M\(M'\(R'*c))); - for i1=1:nout - ERMMRc((1:n)+(i1-1)*n) = E(:,:,i1)*RMMRc((1:n)+(i1-1)*n,:); - end - a=b-c+ERMMRc; - - for i1=1:nout - f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); - end - f2=reshape(f,n,nout); - - lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); - - i = 0; - while i < 10 && lp_new < lp_old || isnan(sum(f)) - % reduce step size by half - a = (a_old+a)/2; - - for i1=1:nout - f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); - end - f2=reshape(f,n,nout); - - lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); - i = i+1; - end + edata = logZ + sum(log(diag(L))); + end + + M=[]; + E=[]; + + case {'Softmax', 'Multinom'} + + % Initialize latent values + % zero seems to be a robust choice (Jarno) + f = zeros(size(y(:))); + + K = zeros(n,n,nout); + if multicf + for i1=1:nout + K(:,:,i1) = gp_trcov(gp, x, gp.comp_cf{i1}); + end + else + Ktmp=gp_trcov(gp, x); + for i1=1:nout + K(:,:,i1) = Ktmp; end + end + + % Main newton algorithm, see Rasmussen & Williams (2006), + % p. 50 + + tol = 1e-12; + a = f; + + f2=reshape(f,n,nout); + + % lp_new = log(p(y|f)) + lp_new = gp.lik.fh.ll(gp.lik, y, f2, z); + lp_old = -Inf; + + c=zeros(n*nout,1); + ERMMRc=zeros(n*nout,1); + E=zeros(n,n,nout); + L=zeros(n,n,nout); + RER = zeros(n,n,nout); + + while lp_new - lp_old > tol + lp_old = lp_new; a_old = a; + % llg = d(log(p(y|f)))/df + llg = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); + % Second derivatives [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); + % W = -diag(pi2_vec) + pi2_mat*pi2_mat' pi2 = reshape(pi2_vec,size(y)); - zc=0; - Detn=0; R = repmat(1./pi2_vec,1,n).*pi2_mat; for i1=1:nout - Dc=sqrt( pi2(:,i1) ); + Dc=sqrt(pi2(:,i1)); Lc=(Dc*Dc').*K(:,:,i1); Lc(1:n+1:end)=Lc(1:n+1:end)+1; - [Lc, notpositivedefinite]=chol(Lc); + [Lc,notpositivedefinite]=chol(Lc); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end L(:,:,i1)=Lc; - pi2i = pi2_mat((1:n)+(i1-1)*n,:); - pipi = pi2i'/diag(Dc); - Detn = Detn + pipi*(Lc\(Lc'\diag(Dc)))*K(:,:,i1)*pi2i; - zc = zc + sum(log(diag(Lc))); - Ec=Lc'\diag(Dc); Ec=Ec'*Ec; E(:,:,i1)=Ec; @@ -1126,1048 +1035,1098 @@ return end - zc = zc + sum(log(diag(chol( eye(size(K(:,:,i1))) - Detn)))); - - logZ = a'*f/2 - gp.lik.fh.ll(gp.lik, y, f2, z) + zc; - edata = logZ; - - otherwise - - if ~isfield(gp, 'comp_cf') || isempty(gp.comp_cf) - error('Define multiple covariance functions for latent processes using gp.comp_cf (see gp_set)'); - end - - if isfield(gp.lik,'xtime') - xtime=gp.lik.xtime; - if isfield(gp.lik, 'stratificationVariables') - ebc_ind=gp.lik.stratificationVariables; - ux = unique(x(:,ebc_ind), 'rows'); - gp.lik.n_u = size(ux,1); - for i1=1:size(ux,1) - gp.lik.stratind{i1}=(x(:,ebc_ind)==ux(i1)); - end - [xtime1, xtime2] = meshgrid(ux, xtime); - xtime = [xtime2(:) xtime1(:)]; - if isfield(gp.lik, 'removeStratificationVariables') && gp.lik.removeStratificationVariables - x(:,ebc_ind)=[]; - end - end - ntime = size(xtime,1); - nl=[ntime n]; - else - nl=repmat(n,1,length(gp.comp_cf)); - end - nlp=length(nl); % number of latent processes - - % Initialize latent values - % zero seems to be a robust choice (Jarno) - % with mean functions, initialize to mean function values - if ~isfield(gp,'meanf') - f = zeros(sum(nl),1); - if isequal(gp.lik.type, 'Inputdependentnoise') - % Inputdependent-noise needs initialization to mean - Kf = gp_trcov(gp,x,gp.comp_cf{1}); - f(1:n) = Kf*((Kf+gp.lik.sigma2.*eye(n))\y); - end - else - [H,b_m,B_m]=mean_prep(gp,x,[]); - Hb_m=H'*b_m; - f = Hb_m; + b = pi2_vec.*f - pi2_mat*(pi2_mat'*f) + llg; + for i1=1:nout + c((1:n)+(i1-1)*n)=E(:,:,i1)*(K(:,:,i1)*b((1:n)+(i1-1)*n)); end - % K is block-diagonal covariance matrix where blocks - % correspond to latent processes - K = zeros(sum(nl)); - if isfield(gp.lik,'xtime') - K(1:ntime,1:ntime)=gp_trcov(gp, xtime, gp.comp_cf{1}); - K((1:n)+ntime,(1:n)+ntime) = gp_trcov(gp, x, gp.comp_cf{2}); - else - for i1=1:nlp - K((1:n)+(i1-1)*n,(1:n)+(i1-1)*n) = gp_trcov(gp, x, gp.comp_cf{i1}); - end + RMMRc=R*(M\(M'\(R'*c))); + for i1=1:nout + ERMMRc((1:n)+(i1-1)*n) = E(:,:,i1)*RMMRc((1:n)+(i1-1)*n,:); end + a=b-c+ERMMRc; - % Mean function contribution to K - if isfield(gp,'meanf') - K=K+H'*B_m*H; - iKHb_m=K\Hb_m; + for i1=1:nout + f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); end + f2=reshape(f,n,nout); - % Main Newton algorithm, see Rasmussen & Williams (2006), - % p. 46 + lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); - tol = 1e-12; - a = f; - if isfield(gp,'meanf') - a = a-Hb_m; + i = 0; + while i < 10 && lp_new < lp_old || isnan(sum(f)) + % reduce step size by half + a = (a_old+a)/2; + + for i1=1:nout + f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); + end + f2=reshape(f,n,nout); + + lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); + i = i+1; end - - % Second derivatives of log-likelihood - if isfield(gp.lik,'xtime') - [llg2diag, llg2mat] = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - % W = [diag(Wdiag(1:ntime)) Wmat; Wmat' diag(Wdiag(ntime+1:end)] - Wdiag=-llg2diag; Wmat=-llg2mat; - W=[]; - else - Wvec = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - % W = [diag(Wvec(1:n,1)) diag(Wvec(1:n,2)) diag(Wvec(n+1:end,1)) diag(Wvec(n+1:end,2))] - Wdiag=[Wvec(1:nl(1),1); Wvec(nl(1)+(1:nl(2)),2)]; + end + + [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); + pi2 = reshape(pi2_vec,size(y)); + + zc=0; + Detn=0; + R = repmat(1./pi2_vec,1,n).*pi2_mat; + for i1=1:nout + Dc=sqrt( pi2(:,i1) ); + Lc=(Dc*Dc').*K(:,:,i1); + Lc(1:n+1:end)=Lc(1:n+1:end)+1; + [Lc, notpositivedefinite]=chol(Lc); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return end - % dlp = d(log(p(y|f)))/df - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - % lp_new = log(p(y|f)) - lp_new = gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; + L(:,:,i1)=Lc; - WK=zeros(sum(nl)); + pi2i = pi2_mat((1:n)+(i1-1)*n,:); + pipi = pi2i'/diag(Dc); + Detn = Detn + pipi*(Lc\(Lc'\diag(Dc)))*K(:,:,i1)*pi2i; + zc = zc + sum(log(diag(Lc))); - iter=0; - - while (abs(lp_new - lp_old) > tol && iter < maxiter) - iter = iter + 1; - lp_old = lp_new; a_old = a; - - - % b = W*f - d(log(p(y|f)))/df - if isfield(gp.lik,'xtime') - b=Wdiag.*f+[Wmat*f((ntime+1):end); Wmat'*f(1:ntime)]+dlp; - else - b = sum(Wvec.*repmat(reshape(f,n,nlp),nlp,1),2)+dlp; - end - - WK(1:nl(1),1:nl(1))=bsxfun(@times, Wdiag(1:nl(1)),K(1:nl(1),1:nl(1))); - WK(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))=bsxfun(@times, Wdiag(nl(1)+(1:nl(2))),K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))); - if isfield(gp.lik,'xtime') - WK(1:nl(1),nl(1)+(1:nl(2)))=Wmat*K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))); - WK(nl(1)+(1:nl(2)),1:nl(1))=Wmat'*K(1:nl(1),1:nl(1)); - else - WK(1:nl(1),nl(1)+(1:nl(2)))=bsxfun(@times, Wvec(1:nl(1),2),K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))); - WK(nl(1)+(1:nl(2)),1:nl(1))=bsxfun(@times, Wvec(nl(1)+(1:nl(2)),1),K(1:nl(1),1:nl(1))); - end - - % B = I + WK - B=WK; - B(1:sum(nl)+1:end) = B(1:sum(nl)+1:end) + 1; - [ll,uu]=lu(B); - - % a = inv(I+WK)*(W*f - d(log(p(y|f)))/df) - a=uu\(ll\b); - % a=B\b; - - f = K*a; - - lp = gp.lik.fh.ll(gp.lik, y, f, z); - if ~isfield(gp,'meanf') - lp_new = -a'*f/2 + lp; - else - %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; %f^=f-H'*b_m, - lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; %f^=f-Hb_m, - end - i = 0; - while i < 10 && lp_new < lp_old && ~isnan(sum(f)) - % reduce step size by half - a = (a_old+a)/2; - f = K*a; - lp = gp.lik.fh.ll(gp.lik, y, f, z); - if ~isfield(gp,'meanf') - lp_new = -a'*f/2 + lp; - else - %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; - lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; - end - i = i+1; + Ec=Lc'\diag(Dc); + Ec=Ec'*Ec; + E(:,:,i1)=Ec; + RER(:,:,i1) = R((1:n)+(i1-1)*n,:)'*Ec*R((1:n)+(i1-1)*n,:); + end + [M, notpositivedefinite]=chol(sum(RER,3)); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + + zc = zc + sum(log(diag(chol( eye(size(K(:,:,i1))) - Detn)))); + + logZ = a'*f/2 - gp.lik.fh.ll(gp.lik, y, f2, z) + zc; + edata = logZ; + + otherwise + + if ~isfield(gp, 'comp_cf') || isempty(gp.comp_cf) + error('Define multiple covariance functions for latent processes using gp.comp_cf (see gp_set)'); + end + + if isfield(gp.lik,'xtime') + xtime=gp.lik.xtime; + if isfield(gp.lik, 'stratificationVariables') + ebc_ind=gp.lik.stratificationVariables; + ux = unique(x(:,ebc_ind), 'rows'); + gp.lik.n_u = size(ux,1); + for i1=1:size(ux,1) + gp.lik.stratind{i1}=(x(:,ebc_ind)==ux(i1)); end - - if isfield(gp.lik,'xtime') - [llg2diag, llg2mat] = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - Wdiag=-llg2diag; Wmat=-llg2mat; - else - Wvec = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - Wdiag=[Wvec(1:nl(1),1); Wvec(nl(1)+(1:nl(2)),2)]; + [xtime1, xtime2] = meshgrid(ux, xtime); + xtime = [xtime2(:) xtime1(:)]; + if isfield(gp.lik, 'removeStratificationVariables') && gp.lik.removeStratificationVariables + x(:,ebc_ind)=[]; end - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); end + ntime = size(xtime,1); + nl=[ntime n]; + else + nl=repmat(n,1,length(gp.comp_cf)); + end + nlp=length(nl); % number of latent processes + + % Initialize latent values + % zero seems to be a robust choice (Jarno) + % with mean functions, initialize to mean function values + if ~isfield(gp,'meanf') + f = zeros(sum(nl),1); + if isequal(gp.lik.type, 'Inputdependentnoise') + % Inputdependent-noise needs initialization to mean + Kf = gp_trcov(gp,x,gp.comp_cf{1}); + f(1:n) = Kf*((Kf+gp.lik.sigma2.*eye(n))\y); + end + else + [H,b_m,B_m]=mean_prep(gp,x,[]); + Hb_m=H'*b_m; + f = Hb_m; + end + + % K is block-diagonal covariance matrix where blocks + % correspond to latent processes + K = zeros(sum(nl)); + if isfield(gp.lik,'xtime') + K(1:ntime,1:ntime)=gp_trcov(gp, xtime, gp.comp_cf{1}); + K((1:n)+ntime,(1:n)+ntime) = gp_trcov(gp, x, gp.comp_cf{2}); + else + for i1=1:nlp + K((1:n)+(i1-1)*n,(1:n)+(i1-1)*n) = gp_trcov(gp, x, gp.comp_cf{i1}); + end + end + + % Mean function contribution to K + if isfield(gp,'meanf') + K=K+H'*B_m*H; + iKHb_m=K\Hb_m; + end + + % Main Newton algorithm, see Rasmussen & Williams (2006), + % p. 46 + + tol = 1e-12; + a = f; + if isfield(gp,'meanf') + a = a-Hb_m; + end + + % Second derivatives of log-likelihood + if isfield(gp.lik,'xtime') + [llg2diag, llg2mat] = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + % W = [diag(Wdiag(1:ntime)) Wmat; Wmat' diag(Wdiag(ntime+1:end)] + Wdiag=-llg2diag; Wmat=-llg2mat; + W=[]; + else + Wvec = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + % W = [diag(Wvec(1:n,1)) diag(Wvec(1:n,2)) diag(Wvec(n+1:end,1)) diag(Wvec(n+1:end,2))] + Wdiag=[Wvec(1:nl(1),1); Wvec(nl(1)+(1:nl(2)),2)]; + end + % dlp = d(log(p(y|f)))/df + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + % lp_new = log(p(y|f)) + lp_new = gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + + WK=zeros(sum(nl)); + + iter=0; + + while (abs(lp_new - lp_old) > tol && iter < maxiter) + iter = iter + 1; + lp_old = lp_new; a_old = a; - % evaluate the approximate log marginal likelihood + % b = W*f - d(log(p(y|f)))/df if isfield(gp.lik,'xtime') - [llg2diag, llg2mat] = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - Wdiag=-llg2diag; Wmat=-llg2mat; + b=Wdiag.*f+[Wmat*f((ntime+1):end); Wmat'*f(1:ntime)]+dlp; else - Wvec = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - Wdiag=[Wvec(1:nl(1),1); Wvec(nl(1)+(1:nl(2)),2)]; - end - - if ~isfield(gp,'meanf') - logZ = 0.5 *f'*a - gp.lik.fh.ll(gp.lik, y, f, z); - else - % logZ = 0.5 *((f-H'*b_m)'*(a-K\(H'*b_m))) - gp.lik.fh.ll(gp.lik, y, f, z); - logZ = 0.5 *((f-Hb_m)'*(a-iKHb_m)) - gp.lik.fh.ll(gp.lik, y, f, z); + b = sum(Wvec.*repmat(reshape(f,n,nlp),nlp,1),2)+dlp; end WK(1:nl(1),1:nl(1))=bsxfun(@times, Wdiag(1:nl(1)),K(1:nl(1),1:nl(1))); WK(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))=bsxfun(@times, Wdiag(nl(1)+(1:nl(2))),K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))); if isfield(gp.lik,'xtime') - WK(1:ntime,ntime+(1:n))=Wmat*K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))); + WK(1:nl(1),nl(1)+(1:nl(2)))=Wmat*K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))); WK(nl(1)+(1:nl(2)),1:nl(1))=Wmat'*K(1:nl(1),1:nl(1)); else WK(1:nl(1),nl(1)+(1:nl(2)))=bsxfun(@times, Wvec(1:nl(1),2),K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))); - WK(nl(1)+(1:nl(2)),1:nl(1))=bsxfun(@times, Wvec(nl(1)+(1:nl(2)),1),K(1:nl(2),1:nl(2))); + WK(nl(1)+(1:nl(2)),1:nl(1))=bsxfun(@times, Wvec(nl(1)+(1:nl(2)),1),K(1:nl(1),1:nl(1))); end % B = I + WK B=WK; B(1:sum(nl)+1:end) = B(1:sum(nl)+1:end) + 1; + [ll,uu]=lu(B); - [Ll,Lu]=lu(B); - edata = logZ + 0.5*det(Ll)*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu)))); - - % Return help parameters for gradient and prediction - % calculations - L=B; - E=Ll; - M=Lu; - end - La2=E; - p=M; - - end - - % ============================================================ - % FIC - % ============================================================ - case 'FIC' - u = gp.X_u; - m = length(u); - - % First evaluate needed covariance matrices - % v defines that parameter is a vector - [Kv_ff, Cv_ff] = gp_trvar(gp, x); % f x 1 vector - K_fu = gp_cov(gp, x, u); % f x u - K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu - [Luu, notpositivedefinite] = chol(K_uu, 'lower'); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - % Evaluate the Lambda (La) - % Q_ff = K_fu*inv(K_uu)*K_fu' - % Here we need only the diag(Q_ff), which is evaluated below - B=Luu\(K_fu'); % u x f - Qv_ff=sum(B.^2)'; - Lav = Cv_ff-Qv_ff; % f x 1, Vector of diagonal elements - iLaKfu = repmat(Lav,1,m).\K_fu; % f x u - A = K_uu+K_fu'*iLaKfu; A = (A+A')./2; % Ensure symmetry - [A, notpositivedefinite] = chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - L = iLaKfu/A; - - switch gp.latent_opt.optim_method - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by fminunc large scale method - case 'fminunc_large' - fhm = @(W, f, varargin) (f./repmat(Lav,1,size(f,2)) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % hessian*f; % - defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); - if ~isfield(gp.latent_opt, 'fminunc_opt') - opt = optimset(defopts); - else - opt = optimset(defopts,gp.latent_opt.fminunc_opt); - end - - fe = @(f, varargin) (0.5*f*(f'./repmat(Lav,1,size(f',2)) - L*(L'*f')) - gp.lik.fh.ll(gp.lik, y, f', z)); - fg = @(f, varargin) (f'./repmat(Lav,1,size(f',2)) - L*(L'*f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; - fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); - mydeal = @(varargin)varargin{1:nargout}; - [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); - f = f'; - - a = f./Lav - L*L'*f; - - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by Newton method - case 'newton' - tol = 1e-12; - a = f; - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - lp_new = gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; - - iter = 0; - while lp_new - lp_old > tol && iter < maxiter - iter = iter + 1; - lp_old = lp_new; a_old = a; - sW = sqrt(W); + % a = inv(I+WK)*(W*f - d(log(p(y|f)))/df) + a=uu\(ll\b); + % a=B\b; - Lah = 1 + sW.*Lav.*sW; - sWKfu = repmat(sW,1,m).*K_fu; - A = K_uu + sWKfu'*(repmat(Lah,1,m).\sWKfu); A = (A+A')./2; - Lb = (repmat(Lah,1,m).\sWKfu)/chol(A); - b = W.*f+dlp; - b2 = sW.*(Lav.*b + B'*(B*b)); - a = b - sW.*(b2./Lah - Lb*(Lb'*b2)); + f = K*a; - f = Lav.*a + B'*(B*a); - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); lp = gp.lik.fh.ll(gp.lik, y, f, z); - lp_new = -a'*f/2 + lp; + if ~isfield(gp,'meanf') + lp_new = -a'*f/2 + lp; + else + %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; %f^=f-H'*b_m, + lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; %f^=f-Hb_m, + end i = 0; while i < 10 && lp_new < lp_old && ~isnan(sum(f)) % reduce step size by half - a = (a_old+a)/2; - f = Lav.*a + B'*(B*a); - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + a = (a_old+a)/2; + f = K*a; lp = gp.lik.fh.ll(gp.lik, y, f, z); - lp_new = -a'*f/2 + lp; + if ~isfield(gp,'meanf') + lp_new = -a'*f/2 + lp; + else + %lp_new = -(f-H'*b_m)'*(a-K\(H'*b_m))/2 + lp; + lp_new = -(f-Hb_m)'*(a-iKHb_m)/2 + lp; + end i = i+1; - end - end - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables with likelihood specific algorithm - % For example, with Student-t likelihood this mean EM-algorithm which is coded in the - % lik_t file. - case 'lik_specific' - [f, a] = gp.lik.fh.optimizef(gp, y, K_uu, Lav, K_fu); - if isnan(f) - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - otherwise - error('gpla_e: Unknown optimization method ! ') - end - - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); - - if W >= 0 - sqrtW = sqrt(W); - - Lah = 1 + sqrtW.*Lav.*sqrtW; - sWKfu = repmat(sqrtW,1,m).*K_fu; - A = K_uu + sWKfu'*(repmat(Lah,1,m).\sWKfu); A = (A+A')./2; - [A, notpositivedefinite] = chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - edata = sum(log(Lah)) - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A))); - edata = logZ + 0.5*edata; - else - % This is with full matrices. Needs to be rewritten. - K = diag(Lav) + B'*B; - % $$$ [W,I] = sort(W, 1, 'descend'); - % $$$ K = K(I,I); - [W2,I] = sort(W, 1, 'descend'); - - [L, notpositivedefinite] = chol(K); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - L1 = L; - for jj=1:size(K,1) - i = I(jj); - ll = sum(L(:,i).^2); - l = L'*L(:,i); - upfact = W(i)./(1 + W(i).*ll); - - % Check that Cholesky factorization will remain positive definite - if 1 + W(i).*ll <= 0 | upfact > 1./ll - warning('gpla_e: 1 + W(i).*ll < 0') + end - ind = 1:i-1; - if isempty(z) - mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z); + if isfield(gp.lik,'xtime') + [llg2diag, llg2mat] = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + Wdiag=-llg2diag; Wmat=-llg2mat; else - mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z(I(ind))); + Wvec = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + Wdiag=[Wvec(1:nl(1),1); Wvec(nl(1)+(1:nl(2)),2)]; end - upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll); - - % $$$ W2 = -1./(ll+1e-3); - % $$$ upfact = W2./(1 + W2.*ll); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); end - if upfact > 0 - L = cholupdate(L, l.*sqrt(upfact), '-'); + + % evaluate the approximate log marginal likelihood + + if isfield(gp.lik,'xtime') + [llg2diag, llg2mat] = gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + Wdiag=-llg2diag; Wmat=-llg2mat; else - L = cholupdate(L, l.*sqrt(-upfact)); + Wvec = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + Wdiag=[Wvec(1:nl(1),1); Wvec(nl(1)+(1:nl(2)),2)]; end - end - edata = logZ + sum(log(diag(L1))) - sum(log(diag(L))); % sum(log(diag(chol(K)))) + sum(log(diag(chol((inv(K) + W))))); + + if ~isfield(gp,'meanf') + logZ = 0.5 *f'*a - gp.lik.fh.ll(gp.lik, y, f, z); + else + % logZ = 0.5 *((f-H'*b_m)'*(a-K\(H'*b_m))) - gp.lik.fh.ll(gp.lik, y, f, z); + logZ = 0.5 *((f-Hb_m)'*(a-iKHb_m)) - gp.lik.fh.ll(gp.lik, y, f, z); + end + + WK(1:nl(1),1:nl(1))=bsxfun(@times, Wdiag(1:nl(1)),K(1:nl(1),1:nl(1))); + WK(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))=bsxfun(@times, Wdiag(nl(1)+(1:nl(2))),K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))); + if isfield(gp.lik,'xtime') + WK(1:ntime,ntime+(1:n))=Wmat*K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2))); + WK(nl(1)+(1:nl(2)),1:nl(1))=Wmat'*K(1:nl(1),1:nl(1)); + else + WK(1:nl(1),nl(1)+(1:nl(2)))=bsxfun(@times, Wvec(1:nl(1),2),K(nl(1)+(1:nl(2)),nl(1)+(1:nl(2)))); + WK(nl(1)+(1:nl(2)),1:nl(1))=bsxfun(@times, Wvec(nl(1)+(1:nl(2)),1),K(1:nl(2),1:nl(2))); + end + + % B = I + WK + B=WK; + B(1:sum(nl)+1:end) = B(1:sum(nl)+1:end) + 1; + + [Ll,Lu]=lu(B); + edata = logZ + 0.5*det(Ll)*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu)))); + + % Return help parameters for gradient and prediction + % calculations + L=B; + E=Ll; + M=Lu; end + La2=E; + p=M; - - La2 = Lav; - - % ============================================================ - % PIC - % ============================================================ - case {'PIC' 'PIC_BLOCK'} - ind = gp.tr_index; - u = gp.X_u; - m = length(u); - - % First evaluate needed covariance matrices - % v defines that parameter is a vector - K_fu = gp_cov(gp, x, u); % f x u - K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu - K_uu = (K_uu+K_uu')./2; % ensure the symmetry of K_uu - [Luu, notpositivedefinite] = chol(K_uu, 'lower'); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - % Evaluate the Lambda (La) - % Q_ff = K_fu*inv(K_uu)*K_fu' - % Here we need only the diag(Q_ff), which is evaluated below - B=Luu\(K_fu'); % u x f - - % First some helper parameters - iLaKfu = zeros(size(K_fu)); % f x u - for i=1:length(ind) - Qbl_ff = B(:,ind{i})'*B(:,ind{i}); - [Kbl_ff, Cbl_ff] = gp_trcov(gp, x(ind{i},:)); - Labl{i} = Cbl_ff - Qbl_ff; - [LLabl{i}, notpositivedefinite] = chol(Labl{i}); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return + end + + % ============================================================ + % FIC + % ============================================================ + case 'FIC' + u = gp.X_u; + m = length(u); + + % First evaluate needed covariance matrices + % v defines that parameter is a vector + [Kv_ff, Cv_ff] = gp_trvar(gp, x); % f x 1 vector + K_fu = gp_cov(gp, x, u); % f x u + K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu + [Luu, notpositivedefinite] = chol(K_uu, 'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + % Evaluate the Lambda (La) + % Q_ff = K_fu*inv(K_uu)*K_fu' + % Here we need only the diag(Q_ff), which is evaluated below + B=Luu\(K_fu'); % u x f + Qv_ff=sum(B.^2)'; + Lav = Cv_ff-Qv_ff; % f x 1, Vector of diagonal elements + iLaKfu = repmat(Lav,1,m).\K_fu; % f x u + A = K_uu+K_fu'*iLaKfu; A = (A+A')./2; % Ensure symmetry + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + L = iLaKfu/A; + + switch gp.latent_opt.optim_method + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by fminunc large scale method + case 'fminunc_large' + fhm = @(W, f, varargin) (f./repmat(Lav,1,size(f,2)) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % hessian*f; % + defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); + if ~isfield(gp.latent_opt, 'fminunc_opt') + opt = optimset(defopts); + else + opt = optimset(defopts,gp.latent_opt.fminunc_opt); end - iLaKfu(ind{i},:) = LLabl{i}\(LLabl{i}'\K_fu(ind{i},:)); - end - A = K_uu+K_fu'*iLaKfu; - A = (A+A')./2; % Ensure symmetry - [A, notpositivedefinite] = chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - L = iLaKfu/A; - % Begin optimization - switch gp.latent_opt.optim_method + + fe = @(f, varargin) (0.5*f*(f'./repmat(Lav,1,size(f',2)) - L*(L'*f')) - gp.lik.fh.ll(gp.lik, y, f', z)); + fg = @(f, varargin) (f'./repmat(Lav,1,size(f',2)) - L*(L'*f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; + fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); + mydeal = @(varargin)varargin{1:nargout}; + [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); + f = f'; + + a = f./Lav - L*L'*f; + % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by fminunc large scale method - case 'fminunc_large' - fhm = @(W, f, varargin) (iKf(f) + repmat(W,1,size(f,2)).*f); - defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); - if ~isfield(gp.latent_opt, 'fminunc_opt') - opt = optimset(defopts); - else - opt = optimset(defopts,gp.latent_opt.fminunc_opt); - end - - [f,fval,exitflag,output] = fminunc(@(ww) egh(ww), f', opt); - f = f'; + % find the posterior mode of latent variables by Newton method + case 'newton' + tol = 1e-12; + a = f; + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp_new = gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + + iter = 0; + while lp_new - lp_old > tol && iter < maxiter + iter = iter + 1; + lp_old = lp_new; a_old = a; + sW = sqrt(W); - a = iKf(f); + Lah = 1 + sW.*Lav.*sW; + sWKfu = repmat(sW,1,m).*K_fu; + A = K_uu + sWKfu'*(repmat(Lah,1,m).\sWKfu); A = (A+A')./2; + Lb = (repmat(Lah,1,m).\sWKfu)/chol(A); + b = W.*f+dlp; + b2 = sW.*(Lav.*b + B'*(B*b)); + a = b - sW.*(b2./Lah - Lb*(Lb'*b2)); - % find the mode by Newton's method - % -------------------------------------------------------------------------------- - case 'newton' - tol = 1e-12; - a = f; + f = Lav.*a + B'*(B*a); W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - lp_new = gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; - - iter = 0; - while lp_new - lp_old > tol && iter < maxiter - iter = iter + 1; - lp_old = lp_new; a_old = a; - sW = sqrt(W); - - V = repmat(sW,1,m).*K_fu; - for i=1:length(ind) - Lah{i} = eye(size(Labl{i})) + diag(sW(ind{i}))*Labl{i}*diag(sW(ind{i})); - [LLah{i}, notpositivedefinite] = chol(Lah{i}); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - V2(ind{i},:) = LLah{i}\(LLah{i}'\V(ind{i},:)); - end - - A = K_uu + V'*V2; A = (A+A')./2; - [A, notpositivedefinite] = chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - Lb = V2/A; - b = W.*f+dlp; - b2 = B'*(B*b); - bt = zeros(size(b2)); - for i=1:length(ind) - b2(ind{i}) = sW(ind{i}).*(Labl{i}*b(ind{i}) + b2(ind{i})); - bt(ind{i}) = LLah{i}\(LLah{i}'\b2(ind{i})); - end - a = b - sW.*(bt - Lb*(Lb'*b2)); - - f = B'*(B*a); - for i=1:length(ind) - f(ind{i}) = Labl{i}*a(ind{i}) + f(ind{i}) ; - end + lp = gp.lik.fh.ll(gp.lik, y, f, z); + lp_new = -a'*f/2 + lp; + i = 0; + while i < 10 && lp_new < lp_old && ~isnan(sum(f)) + % reduce step size by half + a = (a_old+a)/2; + f = Lav.*a + B'*(B*a); W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); lp = gp.lik.fh.ll(gp.lik, y, f, z); lp_new = -a'*f/2 + lp; - i = 0; - while i < 10 && lp_new < lp_old || isnan(sum(f)) - % reduce step size by half - a = (a_old+a)/2; - f = B'*(B*a); - for i=1:length(ind) - f(ind{i}) = Labl{i}*a(ind{i}) + f(ind{i}) ; - end - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - lp = gp.lik.fh.ll(gp.lik, y, f, z); - lp_new = -a'*f/2 + lp; - i = i+1; - end + i = i+1; end - otherwise - error('gpla_e: Unknown optimization method ! ') - end - - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - sqrtW = sqrt(W); - - logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); - - WKfu = repmat(sqrtW,1,m).*K_fu; - edata = 0; - for i=1:length(ind) - Lahat = eye(size(Labl{i})) + diag(sqrtW(ind{i}))*Labl{i}*diag(sqrtW(ind{i})); - [LLahat, notpositivedefinite] = chol(Lahat); - if notpositivedefinite + end + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables with likelihood specific algorithm + % For example, with Student-t likelihood this mean EM-algorithm which is coded in the + % lik_t file. + case 'lik_specific' + [f, a] = gp.lik.fh.optimizef(gp, y, K_uu, Lav, K_fu); + if isnan(f) [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - iLahatWKfu(ind{i},:) = LLahat\(LLahat'\WKfu(ind{i},:)); - edata = edata + 2.*sum(log(diag(LLahat))); - end - A = K_uu + WKfu'*iLahatWKfu; A = (A+A')./2; + otherwise + error('gpla_e: Unknown optimization method ! ') + end + + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); + + if W >= 0 + sqrtW = sqrt(W); + + Lah = 1 + sqrtW.*Lav.*sqrtW; + sWKfu = repmat(sqrtW,1,m).*K_fu; + A = K_uu + sWKfu'*(repmat(Lah,1,m).\sWKfu); A = (A+A')./2; [A, notpositivedefinite] = chol(A); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - edata = edata - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A))); + edata = sum(log(Lah)) - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A))); edata = logZ + 0.5*edata; - - La2 = Labl; + else + % This is with full matrices. Needs to be rewritten. + K = diag(Lav) + B'*B; + % $$$ [W,I] = sort(W, 1, 'descend'); + % $$$ K = K(I,I); + [W2,I] = sort(W, 1, 'descend'); - % ============================================================ - % CS+FIC - % ============================================================ - case 'CS+FIC' - u = gp.X_u; - m = length(u); - cf_orig = gp.cf; - - cf1 = {}; - cf2 = {}; - j = 1; - k = 1; - for i = 1:ncf - if ~isfield(gp.cf{i},'cs') - cf1{j} = gp.cf{i}; - j = j + 1; - else - cf2{k} = gp.cf{i}; - k = k + 1; - end - end - gp.cf = cf1; - - % First evaluate needed covariance matrices - % v defines that parameter is a vector - [Kv_ff, Cv_ff] = gp_trvar(gp, x); % f x 1 vector - K_fu = gp_cov(gp, x, u); % f x u - K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu - K_uu = (K_uu+K_uu')./2; % ensure the symmetry of K_uu - [Luu, notpositivedefinite] = chol(K_uu, 'lower'); + [L, notpositivedefinite] = chol(K); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - - % Evaluate the Lambda (La) - % Q_ff = K_fu*inv(K_uu)*K_fu' - B=Luu\(K_fu'); % u x f - Qv_ff=sum(B.^2)'; - Lav = Cv_ff-Qv_ff; % f x 1, Vector of diagonal elements - - gp.cf = cf2; - K_cs = gp_trcov(gp,x); - La = sparse(1:n,1:n,Lav,n,n) + K_cs; - gp.cf = cf_orig; - - % Find fill reducing permutation and permute all the - % matrices - p = analyze(La); - r(p) = 1:n; - if ~isempty(z) - z = z(p,:); - end - f = f(p); - y = y(p); - La = La(p,p); - K_fu = K_fu(p,:); - B = B(:,p); - [VD, notpositivedefinite] = ldlchol(La); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return + L1 = L; + for jj=1:size(K,1) + i = I(jj); + ll = sum(L(:,i).^2); + l = L'*L(:,i); + upfact = W(i)./(1 + W(i).*ll); + + % Check that Cholesky factorization will remain positive definite + if 1 + W(i).*ll <= 0 | upfact > 1./ll + warning('gpla_e: 1 + W(i).*ll < 0') + + ind = 1:i-1; + if isempty(z) + mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z); + else + mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z(I(ind))); + end + upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll); + + % $$$ W2 = -1./(ll+1e-3); + % $$$ upfact = W2./(1 + W2.*ll); + end + if upfact > 0 + L = cholupdate(L, l.*sqrt(upfact), '-'); + else + L = cholupdate(L, l.*sqrt(-upfact)); + end end - - iLaKfu = ldlsolve(VD,K_fu); - %iLaKfu = La\K_fu; - - A = K_uu+K_fu'*iLaKfu; A = (A+A')./2; % Ensure symmetry - [A, notpositivedefinite] = chol(A); + edata = logZ + sum(log(diag(L1))) - sum(log(diag(L))); % sum(log(diag(chol(K)))) + sum(log(diag(chol((inv(K) + W))))); + end + + + La2 = Lav; + + % ============================================================ + % PIC + % ============================================================ + case {'PIC' 'PIC_BLOCK'} + ind = gp.tr_index; + u = gp.X_u; + m = length(u); + + % First evaluate needed covariance matrices + % v defines that parameter is a vector + K_fu = gp_cov(gp, x, u); % f x u + K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu + K_uu = (K_uu+K_uu')./2; % ensure the symmetry of K_uu + [Luu, notpositivedefinite] = chol(K_uu, 'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + % Evaluate the Lambda (La) + % Q_ff = K_fu*inv(K_uu)*K_fu' + % Here we need only the diag(Q_ff), which is evaluated below + B=Luu\(K_fu'); % u x f + + % First some helper parameters + iLaKfu = zeros(size(K_fu)); % f x u + for i=1:length(ind) + Qbl_ff = B(:,ind{i})'*B(:,ind{i}); + [Kbl_ff, Cbl_ff] = gp_trcov(gp, x(ind{i},:)); + Labl{i} = Cbl_ff - Qbl_ff; + [LLabl{i}, notpositivedefinite] = chol(Labl{i}); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - L = iLaKfu/A; - % Begin optimization - switch gp.latent_opt.optim_method - + iLaKfu(ind{i},:) = LLabl{i}\(LLabl{i}'\K_fu(ind{i},:)); + end + A = K_uu+K_fu'*iLaKfu; + A = (A+A')./2; % Ensure symmetry + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + L = iLaKfu/A; + % Begin optimization + switch gp.latent_opt.optim_method + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by fminunc large scale method + case 'fminunc_large' + fhm = @(W, f, varargin) (iKf(f) + repmat(W,1,size(f,2)).*f); + defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); + if ~isfield(gp.latent_opt, 'fminunc_opt') + opt = optimset(defopts); + else + opt = optimset(defopts,gp.latent_opt.fminunc_opt); + end + + [f,fval,exitflag,output] = fminunc(@(ww) egh(ww), f', opt); + f = f'; + + a = iKf(f); + + % find the mode by Newton's method % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by fminunc large scale method - case 'fminunc_large' - fhm = @(W, f, varargin) (ldlsolve(VD,f) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % Hessian*f; % La\f - defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); - if ~isfield(gp.latent_opt, 'fminunc_opt') - opt = optimset(defopts); - else - opt = optimset(defopts,gp.latent_opt.fminunc_opt); - end - - [f,fval,exitflag,output] = fminunc(@(ww) egh(ww), f', opt); - f = f'; - - a = ldlsolve(VD,f) - L*L'*f; - - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by Newton method - case 'newton' - tol = 1e-8; - a = f; - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - lp_new = gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; - I = sparse(1:n,1:n,1,n,n); + case 'newton' + tol = 1e-12; + a = f; + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp_new = gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + + iter = 0; + while lp_new - lp_old > tol && iter < maxiter + iter = iter + 1; + lp_old = lp_new; a_old = a; + sW = sqrt(W); - iter = 0; - while lp_new - lp_old > tol && iter < maxiter - iter = iter + 1; - lp_old = lp_new; a_old = a; - sW = sqrt(W); - sqrtW = sparse(1:n,1:n,sW,n,n); - - Lah = I + sqrtW*La*sqrtW; - [VDh, notpositivedefinite] = ldlchol(Lah); + V = repmat(sW,1,m).*K_fu; + for i=1:length(ind) + Lah{i} = eye(size(Labl{i})) + diag(sW(ind{i}))*Labl{i}*diag(sW(ind{i})); + [LLah{i}, notpositivedefinite] = chol(Lah{i}); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - V = repmat(sW,1,m).*K_fu; - Vt = ldlsolve(VDh,V); - A = K_uu + V'*Vt; A = (A+A')./2; - [A, notpositivedefinite] = chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return + V2(ind{i},:) = LLah{i}\(LLah{i}'\V(ind{i},:)); + end + + A = K_uu + V'*V2; A = (A+A')./2; + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + Lb = V2/A; + b = W.*f+dlp; + b2 = B'*(B*b); + bt = zeros(size(b2)); + for i=1:length(ind) + b2(ind{i}) = sW(ind{i}).*(Labl{i}*b(ind{i}) + b2(ind{i})); + bt(ind{i}) = LLah{i}\(LLah{i}'\b2(ind{i})); + end + a = b - sW.*(bt - Lb*(Lb'*b2)); + + f = B'*(B*a); + for i=1:length(ind) + f(ind{i}) = Labl{i}*a(ind{i}) + f(ind{i}) ; + end + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp = gp.lik.fh.ll(gp.lik, y, f, z); + lp_new = -a'*f/2 + lp; + i = 0; + while i < 10 && lp_new < lp_old || isnan(sum(f)) + % reduce step size by half + a = (a_old+a)/2; + f = B'*(B*a); + for i=1:length(ind) + f(ind{i}) = Labl{i}*a(ind{i}) + f(ind{i}) ; end - Lb = Vt/A; - b = W.*f+dlp; - b2 = sW.*(La*b + B'*(B*b)); - a = b - sW.*(ldlsolve(VDh,b2) - Lb*(Lb'*b2) ); - - f = La*a + B'*(B*a); W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); lp = gp.lik.fh.ll(gp.lik, y, f, z); lp_new = -a'*f/2 + lp; - i = 0; - while i < 10 && lp_new < lp_old - a = (a_old+a)/2; - f = La*a + B'*(B*a); - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - lp = gp.lik.fh.ll(gp.lik, y, f, z); - lp_new = -a'*f/2 + lp; - i = i+1; - end + i = i+1; end - otherwise - error('gpla_e: Unknown optimization method ! ') - end - - - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - sqrtW = sqrt(W); - - logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); - - WKfu = repmat(sqrtW,1,m).*K_fu; - sqrtW = sparse(1:n,1:n,sqrtW,n,n); - Lahat = sparse(1:n,1:n,1,n,n) + sqrtW*La*sqrtW; - [LDh, notpositivedefinite] = ldlchol(Lahat); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - A = K_uu + WKfu'*ldlsolve(LDh,WKfu); A = (A+A')./2; - [A, notpositivedefinite] = chol(A); + end + otherwise + error('gpla_e: Unknown optimization method ! ') + end + + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + sqrtW = sqrt(W); + + logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); + + WKfu = repmat(sqrtW,1,m).*K_fu; + edata = 0; + for i=1:length(ind) + Lahat = eye(size(Labl{i})) + diag(sqrtW(ind{i}))*Labl{i}*diag(sqrtW(ind{i})); + [LLahat, notpositivedefinite] = chol(Lahat); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - edata = sum(log(diag(LDh))) - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A))); - edata = logZ + 0.5*edata; - - La2 = La; - - % Reorder all the returned and stored values - a = a(r); - L = L(r,:); - La2 = La2(r,r); - y = y(r); - f = f(r); - W = W(r); - if ~isempty(z) - z = z(r,:); - end - - % ============================================================ - % DTC, SOR - % ============================================================ - case {'DTC' 'VAR' 'SOR'} - u = gp.X_u; - m = length(u); - - % First evaluate needed covariance matrices - % v defines that parameter is a vector - K_fu = gp_cov(gp, x, u); % f x u - K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu - [Luu, notpositivedefinite] = chol(K_uu, 'lower'); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return + iLahatWKfu(ind{i},:) = LLahat\(LLahat'\WKfu(ind{i},:)); + edata = edata + 2.*sum(log(diag(LLahat))); + end + A = K_uu + WKfu'*iLahatWKfu; A = (A+A')./2; + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + edata = edata - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A))); + edata = logZ + 0.5*edata; + + La2 = Labl; + + % ============================================================ + % CS+FIC + % ============================================================ + case 'CS+FIC' + u = gp.X_u; + m = length(u); + cf_orig = gp.cf; + + cf1 = {}; + cf2 = {}; + j = 1; + k = 1; + for i = 1:ncf + if ~isfield(gp.cf{i},'cs') + cf1{j} = gp.cf{i}; + j = j + 1; + else + cf2{k} = gp.cf{i}; + k = k + 1; end - % Evaluate the Lambda (La) - % Q_ff = K_fu*inv(K_uu)*K_fu' - B=Luu\(K_fu'); % u x f -% Qv_ff=sum(B.^2)'; -% Lav = zeros(size(Qv_ff)); -% Lav = Cv_ff-Qv_ff; % f x 1, Vector of diagonal elements - La2 = []; + end + gp.cf = cf1; + + % First evaluate needed covariance matrices + % v defines that parameter is a vector + [Kv_ff, Cv_ff] = gp_trvar(gp, x); % f x 1 vector + K_fu = gp_cov(gp, x, u); % f x u + K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu + K_uu = (K_uu+K_uu')./2; % ensure the symmetry of K_uu + [Luu, notpositivedefinite] = chol(K_uu, 'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + + % Evaluate the Lambda (La) + % Q_ff = K_fu*inv(K_uu)*K_fu' + B=Luu\(K_fu'); % u x f + Qv_ff=sum(B.^2)'; + Lav = Cv_ff-Qv_ff; % f x 1, Vector of diagonal elements + + gp.cf = cf2; + K_cs = gp_trcov(gp,x); + La = sparse(1:n,1:n,Lav,n,n) + K_cs; + gp.cf = cf_orig; + + % Find fill reducing permutation and permute all the + % matrices + p = analyze(La); + r(p) = 1:n; + if ~isempty(z) + z = z(p,:); + end + f = f(p); + y = y(p); + La = La(p,p); + K_fu = K_fu(p,:); + B = B(:,p); + [VD, notpositivedefinite] = ldlchol(La); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + + iLaKfu = ldlsolve(VD,K_fu); + %iLaKfu = La\K_fu; + + A = K_uu+K_fu'*iLaKfu; A = (A+A')./2; % Ensure symmetry + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + L = iLaKfu/A; + % Begin optimization + switch gp.latent_opt.optim_method - switch gp.latent_opt.optim_method + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by fminunc large scale method + case 'fminunc_large' + fhm = @(W, f, varargin) (ldlsolve(VD,f) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % Hessian*f; % La\f + defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); + if ~isfield(gp.latent_opt, 'fminunc_opt') + opt = optimset(defopts); + else + opt = optimset(defopts,gp.latent_opt.fminunc_opt); + end + + [f,fval,exitflag,output] = fminunc(@(ww) egh(ww), f', opt); + f = f'; + + a = ldlsolve(VD,f) - L*L'*f; + % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by fminunc large scale method - case 'fminunc_large' -% fhm = @(W, f, varargin) (f./repmat(Lav,1,size(f,2)) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % hessian*f; % -% defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); -% if ~isfield(gp.latent_opt, 'fminunc_opt') -% opt = optimset(defopts); -% else -% opt = optimset(defopts,gp.latent_opt.fminunc_opt); -% end -% -% fe = @(f, varargin) (0.5*f*(f'./repmat(Lav,1,size(f',2)) - L*(L'*f')) - gp.lik.fh.ll(gp.lik, y, f', z)); -% fg = @(f, varargin) (f'./repmat(Lav,1,size(f',2)) - L*(L'*f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; -% fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); -% mydeal = @(varargin)varargin{1:nargout}; -% [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); -% f = f'; -% -% a = f./Lav - L*L'*f; + % find the posterior mode of latent variables by Newton method + case 'newton' + tol = 1e-8; + a = f; + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp_new = gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + I = sparse(1:n,1:n,1,n,n); + + iter = 0; + while lp_new - lp_old > tol && iter < maxiter + iter = iter + 1; + lp_old = lp_new; a_old = a; + sW = sqrt(W); + sqrtW = sparse(1:n,1:n,sW,n,n); - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by Newton method - case 'newton' - tol = 1e-12; - a = f; + Lah = I + sqrtW*La*sqrtW; + [VDh, notpositivedefinite] = ldlchol(Lah); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + V = repmat(sW,1,m).*K_fu; + Vt = ldlsolve(VDh,V); + A = K_uu + V'*Vt; A = (A+A')./2; + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + Lb = Vt/A; + b = W.*f+dlp; + b2 = sW.*(La*b + B'*(B*b)); + a = b - sW.*(ldlsolve(VDh,b2) - Lb*(Lb'*b2) ); + + f = La*a + B'*(B*a); W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); - lp_new = gp.lik.fh.ll(gp.lik, y, f, z); - lp_old = -Inf; - - iter = 0; - while lp_new - lp_old > tol && iter < maxiter - iter = iter + 1; - lp_old = lp_new; a_old = a; - sW = sqrt(W); - - sWKfu = repmat(sW,1,m).*K_fu; - A = K_uu + sWKfu'*sWKfu; A = (A+A')./2; - [A, notpositivedefinite]=chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return - end - Lb = sWKfu/A; - b = W.*f+dlp; - b2 = sW.*(B'*(B*b)); - a = b - sW.*(b2 - Lb*(Lb'*b2)); - - f = B'*(B*a); - + lp = gp.lik.fh.ll(gp.lik, y, f, z); + lp_new = -a'*f/2 + lp; + i = 0; + while i < 10 && lp_new < lp_old + a = (a_old+a)/2; + f = La*a + B'*(B*a); W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); lp = gp.lik.fh.ll(gp.lik, y, f, z); lp_new = -a'*f/2 + lp; - i = 0; - while i < 10 && lp_new < lp_old && ~isnan(sum(f)) - % reduce step size by half - a = (a_old+a)/2; - f = B'*(B*a); - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - lp = gp.lik.fh.ll(gp.lik, y, f, z); - lp_new = -a'*f/2 + lp; - i = i+1; - end + i = i+1; end - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables with likelihood specific algorithm - % For example, with Student-t likelihood this mean EM-algorithm which is coded in the - % lik_t file. - case 'lik_specific' - [f, a] = gp.lik.fh.optimizef(gp, y, K_uu, zeros(n,1), K_fu); - otherwise - error('gpla_e: Unknown optimization method ! ') - end - - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); - - if W >= 0 - sqrtW = sqrt(W); - -% L = chol(eye(n) + diag(sqrtW)*(B'*B)*diag(sqrtW), 'lower'); -% edata = logZ + sum(log(diag(L))); - - - sWKfu = bsxfun(@times, sqrtW, K_fu); - - A = K_uu + sWKfu'*sWKfu; A = (A+A')./2; - [A, notpositivedefinite] = chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return end - edata = -sum(log(diag(Luu))) + sum(log(diag(A))); - edata = logZ + edata; + otherwise + error('gpla_e: Unknown optimization method ! ') + end + + + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + sqrtW = sqrt(W); + + logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); + + WKfu = repmat(sqrtW,1,m).*K_fu; + sqrtW = sparse(1:n,1:n,sqrtW,n,n); + Lahat = sparse(1:n,1:n,1,n,n) + sqrtW*La*sqrtW; + [LDh, notpositivedefinite] = ldlchol(Lahat); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + A = K_uu + WKfu'*ldlsolve(LDh,WKfu); A = (A+A')./2; + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + edata = sum(log(diag(LDh))) - 2*sum(log(diag(Luu))) + 2*sum(log(diag(A))); + edata = logZ + 0.5*edata; + + La2 = La; + + % Reorder all the returned and stored values + a = a(r); + L = L(r,:); + La2 = La2(r,r); + y = y(r); + f = f(r); + W = W(r); + if ~isempty(z) + z = z(r,:); + end + + % ============================================================ + % DTC, SOR + % ============================================================ + case {'DTC' 'VAR' 'SOR'} + u = gp.X_u; + m = length(u); + + % First evaluate needed covariance matrices + % v defines that parameter is a vector + K_fu = gp_cov(gp, x, u); % f x u + K_uu = gp_trcov(gp, u); % u x u, noiseles covariance K_uu + [Luu, notpositivedefinite] = chol(K_uu, 'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + % Evaluate the Lambda (La) + % Q_ff = K_fu*inv(K_uu)*K_fu' + B=Luu\(K_fu'); % u x f + % Qv_ff=sum(B.^2)'; + % Lav = zeros(size(Qv_ff)); + % Lav = Cv_ff-Qv_ff; % f x 1, Vector of diagonal elements + La2 = []; + + switch gp.latent_opt.optim_method + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by fminunc large scale method + case 'fminunc_large' + % fhm = @(W, f, varargin) (f./repmat(Lav,1,size(f,2)) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % hessian*f; % + % defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); + % if ~isfield(gp.latent_opt, 'fminunc_opt') + % opt = optimset(defopts); + % else + % opt = optimset(defopts,gp.latent_opt.fminunc_opt); + % end + % + % fe = @(f, varargin) (0.5*f*(f'./repmat(Lav,1,size(f',2)) - L*(L'*f')) - gp.lik.fh.ll(gp.lik, y, f', z)); + % fg = @(f, varargin) (f'./repmat(Lav,1,size(f',2)) - L*(L'*f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; + % fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); + % mydeal = @(varargin)varargin{1:nargout}; + % [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); + % f = f'; + % + % a = f./Lav - L*L'*f; - if strcmp(gp.type,'VAR') - Kv_ff = gp_trvar(gp, x); - Qv_ff = sum(B.^2)'; - edata = edata + 0.5*sum((Kv_ff-Qv_ff).*W); - La2=Kv_ff-Qv_ff; + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by Newton method + case 'newton' + tol = 1e-12; + a = f; + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp_new = gp.lik.fh.ll(gp.lik, y, f, z); + lp_old = -Inf; + + iter = 0; + while lp_new - lp_old > tol && iter < maxiter + iter = iter + 1; + lp_old = lp_new; a_old = a; + sW = sqrt(W); + + sWKfu = repmat(sW,1,m).*K_fu; + A = K_uu + sWKfu'*sWKfu; A = (A+A')./2; + [A, notpositivedefinite]=chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + Lb = sWKfu/A; + b = W.*f+dlp; + b2 = sW.*(B'*(B*b)); + a = b - sW.*(b2 - Lb*(Lb'*b2)); + + f = B'*(B*a); + + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + dlp = gp.lik.fh.llg(gp.lik, y, f, 'latent', z); + lp = gp.lik.fh.ll(gp.lik, y, f, z); + lp_new = -a'*f/2 + lp; + i = 0; + while i < 10 && lp_new < lp_old && ~isnan(sum(f)) + % reduce step size by half + a = (a_old+a)/2; + f = B'*(B*a); + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + lp = gp.lik.fh.ll(gp.lik, y, f, z); + lp_new = -a'*f/2 + lp; + i = i+1; + end end - else - % This is with full matrices. Needs to be rewritten. -% K = diag(Lav) + B'*B; -% % $$$ [W,I] = sort(W, 1, 'descend'); -% % $$$ K = K(I,I); -% [W2,I] = sort(W, 1, 'descend'); -% -% [L, notpositivedefinite] = chol(K); -% if notpositivedefinite -% [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); -% return -% end -% L1 = L; -% for jj=1:size(K,1) -% i = I(jj); -% ll = sum(L(:,i).^2); -% l = L'*L(:,i); -% upfact = W(i)./(1 + W(i).*ll); -% -% % Check that Cholesky factorization will remain positive definite -% if 1 + W(i).*ll <= 0 | upfact > 1./ll -% warning('gpla_e: 1 + W(i).*ll < 0') -% -% ind = 1:i-1; -% if isempty(z) -% mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z); -% else -% mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z(I(ind))); -% end -% upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll); -% -% % $$$ W2 = -1./(ll+1e-3); -% % $$$ upfact = W2./(1 + W2.*ll); -% end -% if upfact > 0 -% L = cholupdate(L, l.*sqrt(upfact), '-'); -% else -% L = cholupdate(L, l.*sqrt(-upfact)); -% end -% end -% edata = logZ + sum(log(diag(L1))) - sum(log(diag(L))); % sum(log(diag(chol(K)))) + sum(log(diag(chol((inv(K) + W))))); - end - - - L=A; + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables with likelihood specific algorithm + % For example, with Student-t likelihood this mean EM-algorithm which is coded in the + % lik_t file. + case 'lik_specific' + [f, a] = gp.lik.fh.optimizef(gp, y, K_uu, zeros(n,1), K_fu); + otherwise + error('gpla_e: Unknown optimization method ! ') + end + + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + logZ = 0.5*f'*a - gp.lik.fh.ll(gp.lik, y, f, z); + + if W >= 0 + sqrtW = sqrt(W); - % ============================================================ - % SSGP - % ============================================================ - case 'SSGP' % Predictions with sparse spectral sampling approximation for GP - % The approximation is proposed by M. Lazaro-Gredilla, J. Quinonero-Candela and A. Figueiras-Vidal - % in Microsoft Research technical report MSR-TR-2007-152 (November 2007) - % NOTE! This does not work at the moment. + % L = chol(eye(n) + diag(sqrtW)*(B'*B)*diag(sqrtW), 'lower'); + % edata = logZ + sum(log(diag(L))); - % First evaluate needed covariance matrices - % v defines that parameter is a vector - [Phi, S] = gp_trcov(gp, x); % n x m matrix and nxn sparse matrix - Sv = diag(S); - m = size(Phi,2); + sWKfu = bsxfun(@times, sqrtW, K_fu); - A = eye(m,m) + Phi'*(S\Phi); - [A, notpositivedefinite] = chol(A, 'lower'); + A = K_uu + sWKfu'*sWKfu; A = (A+A')./2; + [A, notpositivedefinite] = chol(A); if notpositivedefinite [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); return end - L = (S\Phi)/A'; + edata = -sum(log(diag(Luu))) + sum(log(diag(A))); + edata = logZ + edata; - switch gp.latent_opt.optim_method - % find the mode by fminunc large scale method - case 'fminunc_large' - fhm = @(W, f, varargin) (f./repmat(Sv,1,size(f,2)) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % Hessian*f; % - defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); - if ~isfield(gp.latent_opt, 'fminunc_opt') - opt=optimset(defopts); - else - opt = optimset(defopts,gp.latent_opt.fminunc_opt); - end - - fe = @(f, varargin) (0.5*f*(f'./repmat(Sv,1,size(f',2)) - L*(L'*f')) - gp.lik.fh.ll(gp.lik, y, f', z)); - fg = @(f, varargin) (f'./repmat(Sv,1,size(f',2)) - L*(L'*f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; - fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); - mydeal = @(varargin)varargin{1:nargout}; - [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); - f = f'; - - W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); - sqrtW = sqrt(W); - - b = L'*f; - logZ = 0.5*(f'*(f./Sv) - b'*b) - gp.lik.fh.ll(gp.lik, y, f, z); - case 'Newton' - error('The Newton''s method is not implemented for FIC!\n') - end - WPhi = repmat(sqrtW,1,m).*Phi; - A = eye(m,m) + WPhi'./repmat((1+Sv.*W)',m,1)*WPhi; A = (A+A')./2; - [A, notpositivedefinite] = chol(A); - if notpositivedefinite - [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); - return + if strcmp(gp.type,'VAR') + Kv_ff = gp_trvar(gp, x); + Qv_ff = sum(B.^2)'; + edata = edata + 0.5*sum((Kv_ff-Qv_ff).*W); + La2=Kv_ff-Qv_ff; end - edata = sum(log(1+Sv.*W)) + 2*sum(log(diag(A))); - edata = logZ + 0.5*edata; - - La2 = Sv; - - otherwise - error('Unknown type of Gaussian process!') - end - - % ====================================================================== - % Evaluate the prior contribution to the error from covariance functions - % ====================================================================== - eprior = 0; - for i1=1:ncf - gpcf = gp.cf{i1}; - eprior = eprior - gpcf.fh.lp(gpcf); - end - - % ====================================================================== - % Evaluate the prior contribution to the error from likelihood function - % ====================================================================== - if isfield(gp, 'lik') && isfield(gp.lik, 'p') - lik = gp.lik; - eprior = eprior - lik.fh.lp(lik); - end - - e = edata + eprior; + else + % This is with full matrices. Needs to be rewritten. + % K = diag(Lav) + B'*B; + % % $$$ [W,I] = sort(W, 1, 'descend'); + % % $$$ K = K(I,I); + % [W2,I] = sort(W, 1, 'descend'); + % + % [L, notpositivedefinite] = chol(K); + % if notpositivedefinite + % [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + % return + % end + % L1 = L; + % for jj=1:size(K,1) + % i = I(jj); + % ll = sum(L(:,i).^2); + % l = L'*L(:,i); + % upfact = W(i)./(1 + W(i).*ll); + % + % % Check that Cholesky factorization will remain positive definite + % if 1 + W(i).*ll <= 0 | upfact > 1./ll + % warning('gpla_e: 1 + W(i).*ll < 0') + % + % ind = 1:i-1; + % if isempty(z) + % mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z); + % else + % mu = K(i,ind)*gp.lik.fh.llg(gp.lik, y(I(ind)), f(I(ind)), 'latent', z(I(ind))); + % end + % upfact = gp.lik.fh.upfact(gp, y(I(i)), mu, ll); + % + % % $$$ W2 = -1./(ll+1e-3); + % % $$$ upfact = W2./(1 + W2.*ll); + % end + % if upfact > 0 + % L = cholupdate(L, l.*sqrt(upfact), '-'); + % else + % L = cholupdate(L, l.*sqrt(-upfact)); + % end + % end + % edata = logZ + sum(log(diag(L1))) - sum(log(diag(L))); % sum(log(diag(chol(K)))) + sum(log(diag(chol((inv(K) + W))))); + end + + + L=A; + + % ============================================================ + % SSGP + % ============================================================ + case 'SSGP' % Predictions with sparse spectral sampling approximation for GP + % The approximation is proposed by M. Lazaro-Gredilla, J. Quinonero-Candela and A. Figueiras-Vidal + % in Microsoft Research technical report MSR-TR-2007-152 (November 2007) + % NOTE! This does not work at the moment. + + % First evaluate needed covariance matrices + % v defines that parameter is a vector + [Phi, S] = gp_trcov(gp, x); % n x m matrix and nxn sparse matrix + Sv = diag(S); + + m = size(Phi,2); + + A = eye(m,m) + Phi'*(S\Phi); + [A, notpositivedefinite] = chol(A, 'lower'); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + L = (S\Phi)/A'; + + switch gp.latent_opt.optim_method + % find the mode by fminunc large scale method + case 'fminunc_large' + fhm = @(W, f, varargin) (f./repmat(Sv,1,size(f,2)) - L*(L'*f) + repmat(W,1,size(f,2)).*f); % Hessian*f; % + defopts=struct('GradObj','on','Hessian','on','HessMult', fhm,'TolX', 1e-8,'TolFun', 1e-8,'LargeScale', 'on','Display', 'off'); + if ~isfield(gp.latent_opt, 'fminunc_opt') + opt=optimset(defopts); + else + opt = optimset(defopts,gp.latent_opt.fminunc_opt); + end + + fe = @(f, varargin) (0.5*f*(f'./repmat(Sv,1,size(f',2)) - L*(L'*f')) - gp.lik.fh.ll(gp.lik, y, f', z)); + fg = @(f, varargin) (f'./repmat(Sv,1,size(f',2)) - L*(L'*f') - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; + fh = @(f, varargin) (-gp.lik.fh.llg2(gp.lik, y, f', 'latent', z)); + mydeal = @(varargin)varargin{1:nargout}; + [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); + f = f'; + + W = -gp.lik.fh.llg2(gp.lik, y, f, 'latent', z); + sqrtW = sqrt(W); + + b = L'*f; + logZ = 0.5*(f'*(f./Sv) - b'*b) - gp.lik.fh.ll(gp.lik, y, f, z); + case 'Newton' + error('The Newton''s method is not implemented for FIC!\n') + end + WPhi = repmat(sqrtW,1,m).*Phi; + A = eye(m,m) + WPhi'./repmat((1+Sv.*W)',m,1)*WPhi; A = (A+A')./2; + [A, notpositivedefinite] = chol(A); + if notpositivedefinite + [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite(); + return + end + edata = sum(log(1+Sv.*W)) + 2*sum(log(diag(A))); + edata = logZ + 0.5*edata; + + La2 = Sv; + + otherwise + error('Unknown type of Gaussian process!') + end -% % store values to the cache -% ch.w = w; -% ch.e = e; -% ch.edata = edata; -% ch.eprior = eprior; -% ch.f = f; -% ch.L = L; -% % ch.W = W; -% ch.n = size(x,1); -% ch.La2 = La2; -% ch.a = a; -% ch.p=p; -% ch.datahash=datahash; + % ====================================================================== + % Evaluate the prior contribution to the error from covariance functions + % ====================================================================== + eprior = 0; + for i1=1:ncf + gpcf = gp.cf{i1}; + eprior = eprior - gpcf.fh.lp(gpcf); end -% assert(isreal(edata)) -% assert(isreal(eprior)) -end + % ====================================================================== + % Evaluate the prior contribution to the error from likelihood function + % ====================================================================== + if isfield(gp, 'lik') && isfield(gp.lik, 'p') + lik = gp.lik; + eprior = eprior - lik.fh.lp(lik); + end + + e = edata + eprior; + + + end % % ============================================================== % Begin of the nested functions % ============================================================== -% -function [e, g, h] = egh(f, varargin) - ikf = iKf(f'); - e = 0.5*f*ikf - gp.lik.fh.ll(gp.lik, y, f', z); - g = (ikf - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; - h = -gp.lik.fh.llg2(gp.lik, y, f', 'latent', z); -end -function ikf = iKf(f, varargin) - - switch gp.type - case {'PIC' 'PIC_BLOCK'} - iLaf = zeros(size(f)); - for i=1:length(ind) - iLaf(ind2depo{i},:) = LLabl{i}\(LLabl{i}'\f(ind{i},:)); - end - ikf = iLaf - L*(L'*f); - case 'CS+FIC' - ikf = ldlsolve(VD,f) - L*(L'*f); +% + function [e, g, h] = egh(f, varargin) + ikf = iKf(f'); + e = 0.5*f*ikf - gp.lik.fh.ll(gp.lik, y, f', z); + g = (ikf - gp.lik.fh.llg(gp.lik, y, f', 'latent', z))'; + h = -gp.lik.fh.llg2(gp.lik, y, f', 'latent', z); + end + function ikf = iKf(f, varargin) + + switch gp.type + case {'PIC' 'PIC_BLOCK'} + iLaf = zeros(size(f)); + for i=1:length(ind) + iLaf(ind2depo{i},:) = LLabl{i}\(LLabl{i}'\f(ind{i},:)); + end + ikf = iLaf - L*(L'*f); + case 'CS+FIC' + ikf = ldlsolve(VD,f) - L*(L'*f); + end end -end end function [edata,e,eprior,f,L,a,La2,p,ch] = set_output_for_notpositivedefinite() - % Instead of stopping to chol error, return NaN - edata=NaN; - e=NaN; - eprior=NaN; - f=NaN; - L=NaN; - a=NaN; - La2=NaN; - p=NaN; - datahash = NaN; - w = NaN; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.f = f; - ch.L = L; - ch.La2 = La2; - ch.a = a; - ch.p=p; - ch.datahash=datahash; - ch.w = NaN; +% Instead of stopping to chol error, return NaN +edata=NaN; +e=NaN; +eprior=NaN; +f=NaN; +L=NaN; +a=NaN; +La2=NaN; +p=NaN; +datahash = NaN; +w = NaN; +ch.e = e; +ch.edata = edata; +ch.eprior = eprior; +ch.f = f; +ch.L = L; +ch.La2 = La2; +ch.a = a; +ch.p=p; +ch.datahash=datahash; +ch.w = NaN; end From 7888b4f760aba2de9230cf7864b981d0af06be09 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 18 Apr 2013 12:38:39 +0300 Subject: [PATCH 09/64] Bugfixes in qgp, demo_nested_ep and demo_mcmc --- gp/demo_mcmc.m | 2 +- gp/demo_multiclass_nested_ep.m | 2 +- gp/lik_qgp.m | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gp/demo_mcmc.m b/gp/demo_mcmc.m index bac6669a..a01e9de3 100644 --- a/gp/demo_mcmc.m +++ b/gp/demo_mcmc.m @@ -117,7 +117,7 @@ % For NUTS, we only need to specify that we use NUTS-HMC instead of % original HMC, and the number of adaptation steps of step-size parameter hmc_opt.nuts = 1; % True or false -hmc_opt.nadapt = 20; % Number of step-size adaptation steps (Burn-in) +hmc_opt.Madapt = 20; % Number of step-size adaptation steps (Burn-in) % Generate samples [rgp,g,opt]=gp_mc(gp, x, y, 'z', z, 'repeat',1, 'hmc_opt', hmc_opt, 'nsamples', 100); diff --git a/gp/demo_multiclass_nested_ep.m b/gp/demo_multiclass_nested_ep.m index 81341b62..b6df35c3 100644 --- a/gp/demo_multiclass_nested_ep.m +++ b/gp/demo_multiclass_nested_ep.m @@ -71,7 +71,7 @@ gpep = gp_set('lik', lik_multinomprobit, 'cf', {gpcf1}, 'jitterSigma2', 1e-6, 'latent_method', 'EP', 'latent_opt', latent_opt); % Optimization options -opt=optimset('TolX',1e-2,'TolFun',1e-2,'display','iter','derivativecheck','off'); +opt=optimset('TolX',1e-2,'TolFun',1e-2,'Display','iter','derivativecheck','off'); disp('Optimize the hyperparameters') gpep=gp_optim(gpep,x,y,'opt',opt, 'optimf', @fminlbfgs); diff --git a/gp/lik_qgp.m b/gp/lik_qgp.m index 6ba9f28f..6fe5b73e 100644 --- a/gp/lik_qgp.m +++ b/gp/lik_qgp.m @@ -640,6 +640,6 @@ if ~isempty(lik.p.sigma2) reclik.p.sigma2 = lik.p.sigma2.fh.recappend(reclik.p.sigma2, ri, lik.p.sigma2); end - reclik.tau = lik.tau; + reclik.quantile = lik.quantile; end end From a8ad4a866feac69bc58d29fd1c7e90786364d638 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Fri, 19 Apr 2013 11:52:52 +0300 Subject: [PATCH 10/64] nadapt -> madapt in demo_mcmc --- gp/demo_mcmc.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gp/demo_mcmc.m b/gp/demo_mcmc.m index a01e9de3..5704cdfc 100644 --- a/gp/demo_mcmc.m +++ b/gp/demo_mcmc.m @@ -245,7 +245,7 @@ % Options for NUTS hmc_opt.nuts = 1; -hmc_opt.nadapt = 20; +hmc_opt.Madapt = 20; % Use same latent options From f80ed243b4f83009257df1077ba5532d34c64076 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 23 Apr 2013 10:17:41 +0300 Subject: [PATCH 11/64] merge develop --- gp/lik_loglogistic.m | 5 ----- 1 file changed, 5 deletions(-) diff --git a/gp/lik_loglogistic.m b/gp/lik_loglogistic.m index 7da6516b..4619b933 100644 --- a/gp/lik_loglogistic.m +++ b/gp/lik_loglogistic.m @@ -602,13 +602,8 @@ -yc.*r.*f +(-1-yc).*log(1+(yy./exp(f)).^r) ... -0.5*(f-myy_i).^2./sigm2_i; end -<<<<<<< HEAD function g = log_loglogistic_norm_g(f, ldconst, yc, r, yy, myy_i, sigm2_i) -======= - - function g = log_loglogistic_norm_g(f) ->>>>>>> develop % d/df log(loglogistic * Gaussian) % derivative of log_loglogistic_norm g = -r.*yc - (-1-yc).*r.*(yy./exp(f)).^r./(1+(yy./exp(f)).^r) ... From 50bc1ad62feb33b2ddede5fd1dc9f00d9688e7a9 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 23 Apr 2013 11:06:47 +0300 Subject: [PATCH 12/64] Replaced function geomean with geomeon computation through log (octave geomean sucks) & removed tiltedMoments function handle from lik_coxph --- gp/demo_survival_aft.m | 20 +++++++++++--------- gp/lik_coxph.m | 4 ++-- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/gp/demo_survival_aft.m b/gp/demo_survival_aft.m index 2578f15d..e449a033 100644 --- a/gp/demo_survival_aft.m +++ b/gp/demo_survival_aft.m @@ -49,8 +49,10 @@ % survival times y0=leukemiadata(:,1); y=y0; +% Geometric mean of y0 +gmy0 = exp(1/size(y0,1)*sum(log(y0))); % scale survival times (so that constant term for the latent function can be small) -y=y0/geomean(y0); +y=y0/gmy0; ye=1-leukemiadata(:,2); % event indicator, ye = 0 for uncensored event % ye = 1 for right censored event @@ -107,10 +109,10 @@ [pmu,~,xtc]=gp_cpred(gpia, x, y, x, [i1 i2], 'z', ye, 'target','mu'); xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); -h1=semilogy(xtc{1},pmu{1}*geomean(y0),'k--'); +h1=semilogy(xtc{1},pmu{1}*gmy0,'k--'); set(h1(2),'LineWidth',2) hold on -h2=semilogy(xtc{2},pmu{2}*geomean(y0),'k-'); +h2=semilogy(xtc{2},pmu{2}*gmy0,'k-'); set(h2(2),'LineWidth',2) hold off set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000]) @@ -123,10 +125,10 @@ [pmu,~,xtc]=gp_cpred(gpia, x, y, x, [i1 i2], 'z', ye, 'target','mu'); xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); -h1=semilogy(xtc{1},pmu{1}*geomean(y0),'k--'); +h1=semilogy(xtc{1},pmu{1}*gmy0,'k--'); set(h1(2),'LineWidth',2) hold on -h2=semilogy(xtc{2},pmu{2}*geomean(y0),'k-'); +h2=semilogy(xtc{2},pmu{2}*gmy0,'k-'); set(h2(2),'LineWidth',2) hold off set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000]) @@ -140,10 +142,10 @@ [pmu,~,xtc]=gp_cpred(gpia, x, y, x, [i1 i2], 'z', ye, 'target','mu'); xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); -h1=semilogy(xtc{1},pmu{1}*geomean(y0),'k--'); +h1=semilogy(xtc{1},pmu{1}*gmy0,'k--'); set(h1(2),'LineWidth',2) hold on -h2=semilogy(xtc{2},pmu{2}*geomean(y0),'k-'); +h2=semilogy(xtc{2},pmu{2}*gmy0,'k-'); set(h2(2),'LineWidth',2) hold off set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000]) @@ -156,12 +158,12 @@ i2=3;cla [pmu,~,xtc]=gp_cpred(gpia, x, y, x, i2, 'z', ye,'var',[NaN -1 NaN -.368],'target','mu'); xtc=denormdata(xtc,xmean(i2),xstd(i2)); -h1=semilogy(xtc,pmu*geomean(y0),'k--'); +h1=semilogy(xtc,pmu*gmy0,'k--'); set(h1(2),'LineWidth',2) [pmu,~,xtc]=gp_cpred(gpia, x, y, x, i2, 'z', ye,'var',[NaN -1 NaN 1.56],'target','mu'); xtc=denormdata(xtc,xmean(i2),xstd(i2)); hold on -h2=semilogy(xtc,pmu*geomean(y0),'k-'); +h2=semilogy(xtc,pmu*gmy0,'k-'); set(h2(2),'LineWidth',2) hold off set(gca,'ytick',[10 30 100 300 1000 3000],'yticklabel',{'10' '30' '100' '300' '1000' '3000'},'ylim',[20 2000]) diff --git a/gp/lik_coxph.m b/gp/lik_coxph.m index 90518b9b..69782ea1 100644 --- a/gp/lik_coxph.m +++ b/gp/lik_coxph.m @@ -94,7 +94,7 @@ lik.fh.llg = @lik_coxph_llg; lik.fh.llg2 = @lik_coxph_llg2; lik.fh.llg3 = @lik_coxph_llg3; - lik.fh.tiltedMoments = @lik_coxph_tiltedMoments; +% lik.fh.tiltedMoments = @lik_coxph_tiltedMoments; lik.fh.predy = @lik_coxph_predy; lik.fh.invlink = @lik_coxph_invlink; lik.fh.recappend = @lik_coxph_recappend; @@ -1363,7 +1363,7 @@ reclik.fh.llg = @lik_coxph_llg; reclik.fh.llg2 = @lik_coxph_llg2; reclik.fh.llg3 = @lik_coxph_llg3; - reclik.fh.tiltedMoments = @lik_coxph_tiltedMoments; +% reclik.fh.tiltedMoments = @lik_coxph_tiltedMoments; reclik.fh.predy = @lik_coxph_predy; reclik.fh.invlink = @lik_coxph_invlink; reclik.fh.recappend = @lik_coxph_recappend; From 67be87f61a91a23e71ce0888ba2831d357cf5c77 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 23 Apr 2013 17:38:17 +0300 Subject: [PATCH 13/64] release branch modifications --- {Octave => octave_compat}/crosstab.m | 0 {Octave => octave_compat}/fcnchk.m | 0 {Octave => octave_compat}/mnpdf.m | 0 {Octave => octave_compat}/mnrnd.m | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename {Octave => octave_compat}/crosstab.m (100%) rename {Octave => octave_compat}/fcnchk.m (100%) rename {Octave => octave_compat}/mnpdf.m (100%) rename {Octave => octave_compat}/mnrnd.m (100%) diff --git a/Octave/crosstab.m b/octave_compat/crosstab.m similarity index 100% rename from Octave/crosstab.m rename to octave_compat/crosstab.m diff --git a/Octave/fcnchk.m b/octave_compat/fcnchk.m similarity index 100% rename from Octave/fcnchk.m rename to octave_compat/fcnchk.m diff --git a/Octave/mnpdf.m b/octave_compat/mnpdf.m similarity index 100% rename from Octave/mnpdf.m rename to octave_compat/mnpdf.m diff --git a/Octave/mnrnd.m b/octave_compat/mnrnd.m similarity index 100% rename from Octave/mnrnd.m rename to octave_compat/mnrnd.m From ceb6b91021b9a4b64b1895f8a754ac455fd0ab92 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 23 Apr 2013 17:48:54 +0300 Subject: [PATCH 14/64] release modifications --- gp/TODO.txt | 127 --- gp/demo_SSGPregression.m | 386 --------- gp/demo_censored_t.m | 222 ----- gp/demo_inputdependentnoise.m | 277 ------ gp/demo_inputdependentweibull.m | 114 --- gp/demo_survival_coxph.m | 175 ---- gp/demo_zinegbin.m | 93 --- gp/gp_cvlcriterion.m | 60 -- gp/gp_lcriterion.m | 68 -- gp/gp_refpred.m | 468 ----------- gp/gpcf_SSsexp.m | 582 ------------- gp/gpcf_intcov.m | 754 ----------------- gp/gpla_mo_e.m | 369 -------- gp/gpla_mo_g.m | 245 ------ gp/gpla_mo_pred.m | 205 ----- gp/gpla_softmax_e.m | 515 ------------ gp/gpla_softmax_g.m | 244 ------ gp/gpla_softmax_pred.m | 176 ---- gp/lik_coxph.m | 1387 ------------------------------- gp/lik_gaussianbl.m | 369 -------- gp/lik_inputdependentnoise.m | 510 ------------ gp/lik_inputdependentweibull.m | 514 ------------ gp/lik_zinegbin.m | 627 -------------- gp/metric_ibs_gxe.m | 757 ----------------- octave_compat/mnpdf.m | 134 --- octave_compat/mnrnd.m | 184 ---- 26 files changed, 9562 deletions(-) delete mode 100755 gp/TODO.txt delete mode 100644 gp/demo_SSGPregression.m delete mode 100644 gp/demo_censored_t.m delete mode 100644 gp/demo_inputdependentnoise.m delete mode 100644 gp/demo_inputdependentweibull.m delete mode 100644 gp/demo_survival_coxph.m delete mode 100644 gp/demo_zinegbin.m delete mode 100644 gp/gp_cvlcriterion.m delete mode 100644 gp/gp_lcriterion.m delete mode 100644 gp/gp_refpred.m delete mode 100644 gp/gpcf_SSsexp.m delete mode 100644 gp/gpcf_intcov.m delete mode 100644 gp/gpla_mo_e.m delete mode 100644 gp/gpla_mo_g.m delete mode 100644 gp/gpla_mo_pred.m delete mode 100644 gp/gpla_softmax_e.m delete mode 100644 gp/gpla_softmax_g.m delete mode 100644 gp/gpla_softmax_pred.m delete mode 100644 gp/lik_coxph.m delete mode 100644 gp/lik_gaussianbl.m delete mode 100644 gp/lik_inputdependentnoise.m delete mode 100644 gp/lik_inputdependentweibull.m delete mode 100644 gp/lik_zinegbin.m delete mode 100644 gp/metric_ibs_gxe.m delete mode 100644 octave_compat/mnpdf.m delete mode 100644 octave_compat/mnrnd.m diff --git a/gp/TODO.txt b/gp/TODO.txt deleted file mode 100755 index 493f8fce..00000000 --- a/gp/TODO.txt +++ /dev/null @@ -1,127 +0,0 @@ -% Author: Jarno Vanhatalo -% Last modified: 2009-12-21 09:09:21 EET - -Modifications: -- jitterSigmas is initialized to zero (used to be 1e-4) -- gpla_e uses Newton method as a default optimization for latent - values (used to be fminunc large scale) - -- Luokittelu: logit - korjaa {0,1}->{-1,1} helppiin - demoon sama - - -RM-HMC toteutus ? ------------------- - - -sinvchi2_* ---------- -- gradientti pieless� - - - - - -=============================================== -LIS�INFOA -=============================================== - - ------- -Vaihtoehtoisten metriikoiden toteutus ------- -Date: Tue, 25 Nov 2008 13:53:31 +0200 (EET) -From: Jarno Vanhatalo -To: Aki Vehtari -Cc: Jaakko Riihim�ki Subject: Re: mallinnuksen vaiheet alustavasti - -On Fri, 21 Nov 2008, Aki Vehtari wrote: - -> -> Kategorisen muuttujan voisi muuten koodata my�s yhdell� sy�tteell� -> niin, ett� et�isyys r -> r(x_i,x_j)=0, jos x_i=x_j -> r(x_i,x_j)=r_c, muuten -> miss� r_c valitaan siten, ett� length-scale:n tulkinta a priori -> vastaava kuin binaarisilla muuttujilla. N�in esim. -> sairaanhoitopiiriefektill� olisi vain yksi relevanssia kuvaava -> hyperparametri ja ko. efekti saadaan mukavasti mukaan tavanomaisiin -> kovarianssifunktioihin. Lis��p� todo-listalle tuollainen. -> Muistakseeni Neal oli toteuttanut t�m�n fbm:ss�, mutta ei demonnut -> sit�, joten sen hy�ty ei aikoinaan auennut. -> > Jarno voisi kommentoida, mit� vaikutusta sill� on, ett� osalle -> kovariaateista et�isyys lasketaan muuten kuin euklidisesti? - -Mit�h�n yll� oleva nyt tarkoitti? Koitanpa vastata kuitenkin kysymykseen. - T�ll� hetkell� kovarianssifunktiot on aika selke�sti kovakoodattu -euklidiselle et�isyydelle. Kaikki funktiot perustuu siihe, ett� on kaksi -vaihtoehtoa: 1) kaikilla sy�tteill� sama length-scale 2) jokaisella -sy�tteell� oma length-scale. Varsin paljon ty�t� tulee aiheuttamaan jo -se, ett� GP-paketti muutettaisiin toimimaan siten, ett� voidaan m��ritell� -osalle sy�tteit� samat ja osalle omat length-scalet. - -Jos halutaan, ett� k�ytt�j� voi m��ritt�� et�isyysfunktion vapaasti, -t�ytyy kaikki kovarianssifunktiot koodata osittain uudestaan. K�yt�nn�ss� -vapaavalintaisen et�isyyden voisi toteuttaa siten, ett� kovarianssifunktio -struktuurille annettaisiin kentt� (esim.) 'metrics'. T�ll�in gpcf_*_trcov, -*_cov, _ghyper jne. funktioihin voisi lis�t� tarkistuksen, onko -struktuurissa kyseist� kentt��. Jos kentt�� ei ole, lasketaan kaikki kuten -t�ll�kin hetkell�. Jos kentt� on, lasketaan kaikki muuten samoin kuin nyt, -mutta et�isyys lasketaan uudella tavalla. - -Esimerkiksi: - ------------ clip ----------------- -function C = gpcf_sexp_trcov(gpcf, x) -% GP_SEXP_TRCOV Evaluate training covariance matrix of inputs. -% -% -% Description -% C = GP_SEXP_TRCOV(GP, TX) takes in covariance -% function of a Gaussian -% process GP and matrix TX that contains training input vectors. -% Returns covariance matrix C. Every element ij of C contains -% covariance between inputs i and j in TX -% -% -% See also -% GPCF_SEXP_COV, GPCF_SEXP_TRVAR, GP_COV, GP_TRCOV - -if ~isfield(gpcf, 'metrics') - % Lasketaan vanhalla tavalla - - [n, m] =size(x); - - s = 1./(gpcf.lengthScale); - s2 = s.^2; - if size(s)==1 - s2 = repmat(s2,1,m); - end - ma = gpcf.magnSigma2; - - C = zeros(n,n); - for ii1=1:n-1 - d = zeros(n-ii1,1); - col_ind = ii1+1:n; - for ii2=1:m - d = d+s2(ii2).*(x(col_ind,ii2)-x(ii1,ii2)).^2; - end - C(col_ind,ii1) = d; - end - C = C+C'; - C = ma.*exp(-C); - -else - % Lasketaan uudella tavalla - feval(gpcf.metrics, gpcf, x) -end ------------ clip ----------------- - -Yll� laitoin gpcf.metrics:n funktio handleksi, koska t�ll�in samaa -metriikkaa voisi k�ytt�� k�tev�sti muissakin kovarianssifunktioissa. - -Koodien pit�isi olla sen verran modulaarisia jo nyt, ett� yll� olevan -tarkistuksen lis��minen taitaa onnistua kohtuu helposti. Lis��n -modifioinnin TODO listaan. - --Jarno diff --git a/gp/demo_SSGPregression.m b/gp/demo_SSGPregression.m deleted file mode 100644 index d8af1e4c..00000000 --- a/gp/demo_SSGPregression.m +++ /dev/null @@ -1,386 +0,0 @@ -%DEMO_REGRESSION3 Regression problem demonstration for 2-input -% function with Gaussian process -% -% Description -% The regression problem consist of a data with two input variables -% and one output variable with Gaussian noise. The model constructed -% is following: -% -% The observations y are assumed to satisfy -% -% y = f + e, where e ~ N(0, s^2) -% -% where f is an underlying function, which we are interested in. -% We place a zero mean Gaussian process prior for f, which implies that -% at the observed input locations latent values have prior -% -% f ~ N(0, K), -% -% where K is the covariance matrix, whose elements are given as -% K_ij = k(x_i, x_j | th). The function k(x_i, x_j | th) is covariance -% function and th its parameters, hyperparameters. -% -% Since both likelihood and prior are Gaussian, we obtain a Gaussian -% marginal likelihood -% -% p(y|th) = N(0, K + I*s^2). -% -% By placing a hyperprior for hyperparameters, p(th), we can find the -% maximum a posterior (MAP) estimate for them by maximizing -% -% argmax log p(y|th) + log p(th). -% th -% -% If we want to find an approximation for the posterior of the hyperparameters, -% we can sample them using Markov chain Monte Carlo (MCMC) methods. -% -% After finding MAP estimate or posterior samples of hyperparameters, we can -% use them to make predictions for f: -% -% p(f | y, th) = N(m, S), -% m = -% S = -% -% For more detailed discussion of Gaussian process regression see for example -% Vanhatalo and Vehtari (2008) or -% -% -% See also DEMO_REGRESSION2 - -% Copyright (c) 2008 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 2 or later); please refer to the file -% License.txt, included with the software, for details. - - -% This file is organised in three parts: -% 1) Comparison of SSsexp and sexp covariance functions -% 2) Data analysis using sexp full GP -% 3) Data analysis using SSGP - -%======================================================== -% PART 1 Comparison of SSsexp and sexp covariance functions -%======================================================== - - -xx = [-10:0.05:10]'; -yy = sin(xx/3)*5 + 0.3*randn(size(xx)); -[n,nin] = size(xx); - -% --------------------------- -% --- Construct the model --- -% -% First create squared exponential covariance function with ARD and -% Gaussian noise data structures... -gpcf1 = gpcf_sexp('init', 'lengthScale', 2, 'magnSigma2', 1); -gpcf2 = gpcf_noise('init', 'noiseSigmas2', 0.2^2); - -% ... Then set the prior for the parameters of covariance functions... -gpcf2.p.noiseSigmas2 = sinvchi2_p({0.05^2 0.5}); -gpcf1.p.lengthScale = gamma_p({3 7}); -gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -% ... Finally create the GP data structure -gp1 = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.001.^2) - -% Create the squared exponential sparse spectral covariance function -gpcf3 = gpcf_SSsexp('init', 'lengthScale', 2, 'magnSigma2', 1); -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 10); - -% ... Then set the prior for the parameters of covariance functions... -gpcf3.p.lengthScale = gamma_p({3 7}); -gpcf3.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -% ... Finally create the GP data structure -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) - -% compare the covariance functions -[K1, C1] = gp_trcov(gp1, xx); -[Phi, S] = gp_trcov(gp2, xx); - -subplot(2,2,1) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('10 spectral points') - -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 50); -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) -[Phi, S] = gp_trcov(gp2, xx); -subplot(2,2,2) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('50 spectral points') - -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 100); -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) -[Phi, S] = gp_trcov(gp2, xx); -subplot(2,2,3) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('100 spectral points') - -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 200); -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) -[Phi, S] = gp_trcov(gp2, xx); -subplot(2,2,4) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('200 spectral points') - -% ================================================ -% PART 2 Data analysis using FULL GP -% ================================================ - -% Load the data -S = which('demo_regression1'); -L = strrep(S,'demo_regression1.m','demos/dat.1'); -data=load(L); -x = [data(:,1) data(:,2)]; -y = data(:,3); -[n, nin] = size(x); - -% Now 'x' consist of the inputs and 'y' of the output. -% 'n' and 'nin' are the number of data points and the -% dimensionality of 'x' (the number of inputs). - - -% --------------------------- -% --- Construct the model --- -% -% First create squared exponential covariance function with ARD and -% Gaussian noise data structures... -gpcf1 = gpcf_sexp('init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); -gpcf2 = gpcf_noise('init', 'noiseSigmas2', 0.2^2); - -% ... Then set the prior for the parameters of covariance functions... -gpcf2.p.noiseSigmas2 = sinvchi2_p({0.05^2 0.5}); -gpcf1.p.lengthScale = gamma_p({3 7}); -gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -% ... Finally create the GP data structure -gp1 = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.001.^2) - -% ----------------------------- -% --- Conduct the inference --- -% -% We will make the inference first by finding a maximum a posterior estimate -% for the hyperparameters via gradient based optimization. After this we will -% perform an extensive Markov chain Monte Carlo sampling for the hyperparameters. -% - -% --- MAP estimate using scaled conjugate gradient algorithm --- -% (see scg for more details) - -w1=gp_pak(gp1, 'hyper'); % pack the hyperparameters into one vector -fe=@gp_e; % create a function handle to negative log posterior -fg=@gp_g; % create a function handle to gradient of negative log posterior - -% set the options -opt(1) = 1; -opt(2) = 1e-2; -opt(3) = 3e-1; -opt(9) = 0; -opt(10) = 0; -opt(11) = 0; -opt(14) = 0; - -% do the optimization -[w1, opt, flog]=scg(fe, w1, opt, fg, gp1, x, y, 'hyper'); - -% Set the optimized hyperparameter values back to the gp structure -gp1=gp_unpak(gp1,w1, 'hyper'); - -% NOTICE here that when the hyperparameters are packed into vector with 'gp_pak' -% they are also transformed through logarithm. The reason for this is that they -% are easier to sample with MCMC after log transformation. - -% for the last make prections of the underlying function on a dense grid -% and plot it. Below Ef_full is the predictive mean and Varf_full the predictive -% variance. -[p1,p2]=meshgrid(-1.8:0.05:1.8,-1.8:0.05:1.8); -p=[p1(:) p2(:)]; -[Ef_full, Varf_full] = gp_pred(gp1, x, y, p); - -% Plot the prediction and data -figure(2) -mesh(p1, p2, reshape(Ef_full,73,73)); -hold on -plot3(x(:,1), x(:,2), y, '*') -axis on; -title('The predicted underlying function and the data points (MAP solution)'); - - - -% ================================================ -% PART 3 Data analysis using SSGP -% ================================================ - -% Create the squared exponential sparse spectral covariance function -gpcf3 = gpcf_SSsexp('init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 200); - -% ... Then set the prior for the parameters of covariance functions... -gpcf3.p.lengthScale = gamma_p({3 7}); -gpcf3.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -% ... Finally create the GP data structure -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) - - -% --- MAP estimate using scaled conjugate gradient algorithm --- -% (see scg for more details) - -w2=gp_pak(gp2, 'hyper'); % pack the hyperparameters into one vector -[w2, opt, flog]=scg(fe, w2, opt, fg, gp2, x, y, 'hyper'); - -% Set the optimized hyperparameter values back to the gp structure -gp2=gp_unpak(gp2, w2, 'hyper'); - -% NOTICE here that when the hyperparameters are packed into vector with 'gp_pak' -% they are also transformed through logarithm. The reason for this is that they -% are easier to sample with MCMC after log transformation. - -% for the last make prections of the underlying function on a dense grid -% and plot it. Below Ef_full is the predictive mean and Varf_full the predictive -% variance. -[p1,p2]=meshgrid(-1.8:0.05:1.8,-1.8:0.05:1.8); -p=[p1(:) p2(:)]; -[Ef2, Varf2] = gp_pred(gp2, x, y, p); - -% Plot the prediction and data -figure(1) -mesh(p1, p2, reshape(Ef2,73,73)); -hold on -plot3(x(:,1), x(:,2), y, '*') -axis on; -title('The predicted underlying function and the data points (MAP solution)'); - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -[x1, x2] = meshgrid([-5:0.5:5]', [-5:0.5:5]'); -xx = [x1(:) x2(:)]; -[n,nin] = size(xx); - -% --------------------------- -% --- Construct the model --- -% -% First create squared exponential covariance function with ARD and -% Gaussian noise data structures... -gpcf1 = gpcf_sexp('init', 'lengthScale', 4, 'magnSigma2', 1); -gpcf2 = gpcf_noise('init', 'noiseSigmas2', 0.2^2); - -% ... Then set the prior for the parameters of covariance functions... -gpcf2.p.noiseSigmas2 = sinvchi2_p({0.05^2 0.5}); -gpcf1.p.lengthScale = gamma_p({3 7}); -gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -% ... Finally create the GP data structure -gp1 = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.001.^2) - -% Create the squared exponential sparse spectral covariance function -gpcf3 = gpcf_SSsexp('init', 'lengthScale', 4, 'magnSigma2', 1); -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 10); - -% ... Then set the prior for the parameters of covariance functions... -gpcf3.p.lengthScale = gamma_p({3 7}); -gpcf3.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -% ... Finally create the GP data structure -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) - -% compare the covariance functions -[K1, C1] = gp_trcov(gp1, xx); -[Phi, S] = gp_trcov(gp2, xx); - -subplot(2,2,1) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('10 spectral points') - -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 50); -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) -[Phi, S] = gp_trcov(gp2, xx); -subplot(2,2,2) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('50 spectral points') - -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 100); -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) -[Phi, S] = gp_trcov(gp2, xx); -subplot(2,2,3) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('100 spectral points') - -gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 200); -gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) -[Phi, S] = gp_trcov(gp2, xx); -subplot(2,2,4) -plot(xx,K1(201,:)) -hold on -K2 = Phi*Phi'; -plot(xx,K2(201,:), 'r') -title('200 spectral points') - diff --git a/gp/demo_censored_t.m b/gp/demo_censored_t.m deleted file mode 100644 index 457acff1..00000000 --- a/gp/demo_censored_t.m +++ /dev/null @@ -1,222 +0,0 @@ -%% generate data -clear; -datai=1; - -% true function f(x) -xx = linspace(-7,5,500)'; -yy = 0.1+0.1*xx+0.2*sin(2.7*xx)+1./(1+xx.^2); - -nu=3; -sigma=0.05; -Hmax=sqrt(3*nu)*sigma; -Hzero=sqrt(nu)*sigma; -a=0.3; -ylim=[-0.4 0.9]; - -x=[linspace(-5.4,-3-a,15) linspace(-3+a,-1-a,15) linspace(-1+a,4,30)]; -xo=[-4 -3 -2 -1 2 3]; -ii=length(xo)+[1:length(x)]; -io=1:length(xo); % outlier indices -x=[xo x]'; % outlier x:s -y = 0.1 + 0.1*x + 0.2*sin(2.7*x) + 1 ./ (1+x.^2); -y(io)=y(io)+Hmax*[-3 -2 1 -1 -1 1]'; % outlier y:s -y(ii)=y(ii)+sigma*randn(size(y(ii))); - -yt=y; -y(yylim(2))=ylim(2); - -figure(1); clf -plot(xx,yy,'k',x,y,'b.',x(io),y(io),'ro') -hold on -plot(repmat(x(io)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -%save(sprintf('data%d.mat',datai),'x','y','yt','xx','yy','io','ylim') - -%% load data & create gp -%clear -%datai=1; -%load(sprintf('data%d.mat',datai)); -nu=6; -sigma=0.1; -J=0.02; -x=[x randn(size(x))]; -xx=[xx randn(size(xx))]; -[n, nin] = size(x); -ylim=[-0.4 0.9]; - -% gpcf1 = gpcf_dotproduct('init', 'constSigma2',10,'coeffSigma2',ones(1,nin)); -% gpcf1.p.constSigma2 = logunif_p; -% gpcf1.p.coeffSigma2 = logunif_p; - -gpcf1 = gpcf_sexp('init', 'lengthScale',ones(1,nin),'magnSigma2',1); -gpcf1.p.lengthScale = logunif_p; -gpcf1.p.magnSigma2 = logunif_p; - - -% gpcf1 = gpcf_neuralnetwork('init',nin,'biasSigma2',0.1,'weightSigma2',ones(1,nin)); -% gpcf1.p.weightSigma2 = logunif_p; -% gpcf1.p.biasSigma2 = logunif_p; - -% Create the likelihood structure -%likelih = likelih_t('init', nu, sigma); -likelih = likelih_cen_t('init', 'nu', nu, 'sigma', sigma, 'ylim', ylim); -likelih.p.nu = logunif_p; -likelih.p.sigma = logunif_p; -%likelih.fix_nu=1; - -% Laplace approximation Student-t likelihood -param = 'hyper+likelih'; -gp_la = gp_init('init', 'FULL', likelih, {gpcf1}, {}, 'jitterSigma2', J.^2); -gp_la = gp_init('set', gp_la, 'latent_method', {'Laplace', x, y, param}); -gp_la.laplace_opt.optim_method='likelih_specific'; -%gp_la.laplace_opt.optim_method='fminunc_large'; -[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); - -% gradient checking -w = gp_pak(gp_la,param); -w = w+ 0.1*randn(size(w)); -gradcheck(w, @gpla_e, @gpla_g, gp_la, x, y, param) - -opt=optimset('GradObj','on'); -opt=optimset(opt,'TolX', 1e-3); -opt=optimset(opt,'LargeScale', 'off'); -opt=optimset(opt,'Display', 'iter'); -opt=optimset(opt,'Derivativecheck', 'on'); % 'iter' - -w0 = gp_pak(gp_la, param); -mydeal = @(varargin)varargin{1:nargout}; -w = fminunc(@(ww) mydeal(gpla_e(ww, gp_la, x, y, param), gpla_g(ww, gp_la, x, y, param)), w0, opt); -gp_la = gp_unpak(gp_la,w,param); - -fprintf('\nnu=%.3f, sigma=%.3f \nhyper=%s\n',gp_la.likelih.nu,... - gp_la.likelih.sigma,sprintf(' %.2f,',exp(gp_pak(gp_la,'hyper'))) ) - -figure(2) -[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); -W=-gp_la.likelih.fh.llg2(gp_la.likelih,y,f,'latent'); -[foo,ii]=sort(W,'ascend'); -ii=ii(1:5); -plot(xx(:,1),yy,'k',x(:,1),f,'b.',x(:,1),y,'go',x(ii,1),y(ii),'r.') - -[Ef_la, Varf_la] = la_pred(gp_la, x, y, xx, param); -stdf_la = sqrt(Varf_la); - -% plot the predictions and data -nu=gp_la.likelih.nu; -sigma=gp_la.likelih.sigma; -Hmax=sqrt(3*nu)*sigma; -Hzero=sqrt(nu)*sigma; - -figure(1) -h1=plot(xx(:,1),yy,'k',xx(:,1),Ef_la,'b',xx(:,1),Ef_la-2*stdf_la, 'b--',xx(:,1), Ef_la+2*stdf_la, 'b--'); -hold on -h1=[h1(1:2); plot(x(:,1),y,'k.')]; -plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -legend(h1,'True','Laplace','Data') - -% ====================== -% Full MCMC solution -% ====================== -[n, nin] = size(x); -gpcf1 = gpcf_sexp('init', 'lengthScale', repmat(1,1,nin), 'magnSigma2', 0.2^2); -gpcf2 = gpcf_noiset('init', 'ndata', n, 'noiseSigmas2', repmat(1^2,n,1)); % Here set own Sigma2 for every data point - -% Un-fix nu -%gpcf2 = gpcf_noiset('set', gpcf2, 'fix_nu', 0); -gpcf2 = gpcf_noiset('set', gpcf2, 'censored', {[-0.4 0.9], y}); - -% Set the prior for the parameters of covariance functions -gpcf1.p.lengthScale = gamma_p({3 7 3 7}); -gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 1e-4.^2) -w = gp_pak(gp, 'hyper') -gp2 = gp_unpak(gp,w, 'hyper') - -opt=gp_mcopt; -opt.repeat=10; -opt.nsamples=10; -opt.hmc_opt.steps=10; -opt.hmc_opt.stepadj=0.1; -opt.hmc_opt.nsamples=1; -hmc2('state', sum(100*clock)); - -opt.gibbs_opt = sls1mm_opt; -opt.gibbs_opt.maxiter = 50; -opt.gibbs_opt.mmlimits = [0 40]; -opt.gibbs_opt.method = 'minmax'; - -% Sample -[r,g,rstate1]=gp_mc(opt, gp, x, y); - -opt.hmc_opt.stepadj=0.08; -opt.nsamples=300; -opt.hmc_opt.steps=10; -opt.hmc_opt.persistence=1; -opt.hmc_opt.decay=0.6; - -[r,g,rstate2]=gp_mc(opt, g, x, y, [], [], r); -rr = r; - -% thin the record -rr = thin(r,100,2); - -figure -hist(rr.noise{1}.nu,20) -title('Mixture model, \nu') -figure -hist(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha,20) -title('Mixture model, \sigma') -figure -hist(rr.cf{1}.lengthScale(:,1),20) -title('Mixture model, length-scale') -figure -hist(rr.cf{1}.magnSigma2,20) -title('Mixture model, magnSigma2') - - -% $$$ >> mean(rr.noise{1}.nu) -% $$$ ans = -% $$$ 1.5096 -% $$$ >> mean(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha) -% $$$ ans = -% $$$ 0.0683 -% $$$ >> mean(rr.cf{1}.lengthScale) -% $$$ ans = -% $$$ 1.0197 -% $$$ >> mean(rr.cf{1}.magnSigma2) -% $$$ ans = -% $$$ 1.2903 - -% make predictions for test set -ypred = repmat(y,1,size(rr.edata,1)); -ypred(gp.noise{1}.imis,:) = rr.noise{1}.cy'; -[Efs, Varfs] = gp_preds(rr,x,ypred,xx); - -Ef = mean(squeeze(Efs),2); -std_f = sqrt(var(Efs,[],2)); - -% Plot the network outputs as '.', and underlying mean with '--' -figure -h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b'); -%h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b',xx(:,1),Ef-2*std_f, 'b--',xx(:,1), Ef+2*std_f, 'b--'); -hold on -h1=[h1(1:2); plot(x(:,1),y,'k.')]; -plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -legend(h1,'True','Laplace','Data') - - -figure -for i=1:12 - subplot(6,2,i) - hist(rr.noise{1}.cy(:,i)) - hold on - plot(yt(gp.noise{1}.imis(i)), 0, 'rx', 'MarkerSize', 20, 'lineWidth', 5) -end - diff --git a/gp/demo_inputdependentnoise.m b/gp/demo_inputdependentnoise.m deleted file mode 100644 index ff1a4b93..00000000 --- a/gp/demo_inputdependentnoise.m +++ /dev/null @@ -1,277 +0,0 @@ -%DEMO_INPUTDEPENDENTNOISE Demonstration of input dependent-noise -% model using Gaussian process prior -% -% Description -% Uses toy data sets to demonstrate how inferring -% heteroscedastic noise with input dependent noise model -% differs from standard noise models (Gaussian, Student-t). -% - -% Copyright (c) Ville Tolvanen 2011-2012 -% -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - -%================================= -% 1D Demonstration -%================================= - -% Initialize random stream -prevstream=setrandstream(0); - -% Create toy data -% x = 100*rand([40 1]); -n = 500; -x=linspace(-100,200,n)'; -f1 = [5.*sin(-3+0.2.*x(1:ceil(0.23*n))); 20*sin(0.1*x(ceil(0.23*n)+1:ceil(0.85*n))); 5.*sin(2.8+0.2.*x(ceil(0.85*n)+1:end))]; -f2 = 100*norm_pdf(x,110,20) + 100*norm_pdf(x,-10,20); -sigma2 = 0.5; - -x=x-mean(x); x=x./std(x); -f1 = f1-mean(f1); f1=f1./std(f1); - -y = f1 + sqrt((sigma2.*exp(f2))).*randn(size(x)); -yt = f1(1:5:end); -xt = x(1:5:end); -nt = size(xt,1); -x=x(:); y=y(:); xt=xt(:); - -% Create the covariance functions -pl = prior_t('s2',10); -pm = prior_t('s2',10); -gpcf1 = gpcf_sexp('lengthScale', 0.5, 'magnSigma2', 0.5, ... - 'lengthScale_prior', pl, 'magnSigma2_prior', pm); -gpcf2 = gpcf_exp('lengthScale', 1, 'magnSigma2', 0.1, ... - 'lengthScale_prior', pl, 'magnSigma2_prior', pm); - -% Create the likelihood structure. Don't set prior for sigma2 if covariance -% function magnitude for noise process has a prior. -lik = lik_inputdependentnoise('sigma2', 0.1, 'sigma2_prior', prior_fixed()); - -% NOTE! if multiple covariance functions per latent is used, define -% gp.comp_cf as follows: -% gp = gp_set(..., 'comp_cf' {[1 2] [5 6]}; -gp = gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'jitterSigma2', 1e-9, 'comp_cf', {[1] [2]}); - -% Set the approximate inference method to Laplace -gp = gp_set(gp, 'latent_method', 'Laplace'); -% For more complex problems, maxiter in latent_opt should be increased. -% gp.latent_opt.maxiter=1e6; - -% Set the options for the optimization -opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off'); -% Optimize with the scaled conjugate gradient method -gp=gp_optim(gp,x,y,'opt',opt); - -% make prediction to the data points -[Ef, Varf,lpyt] = gp_pred(gp, x, y, xt, 'yt', yt); -Ef11=Ef(1:nt);Ef12=Ef(nt+1:end); -Varf11=diag(Varf(1:nt,1:nt)); -%prctmus = gp_predprctmu(gp, x, y, xt); -prctmus=[Ef11-1.645*sqrt(Varf11) Ef11 Ef11+1.645*sqrt(Varf11)]; -fprintf('mlpd inputdependentnoise: %.2f\n', mean(lpyt)); - -% Gaussian for comparison -opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off'); -lik2 = lik_gaussian(); -gp2 = gp_set('lik', lik2, 'cf', gpcf1, 'jitterSigma2', 1e-9); -gp2 = gp_optim(gp2,x,y,'opt',opt); -[Ef2, Varf2, lpyt2] = gp_pred(gp2, x, y, xt,'yt',yt); -prctmus2 = gp_predprctmu(gp2, x, y, xt); -fprintf('mlpd gaussian: %.2f\n', mean(lpyt2)); - -% Student-t for comparison -lik=lik_t(); -gp3=gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-9); -opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off'); -gp3 = gp_set(gp3, 'latent_method', 'Laplace'); -gp3=gp_optim(gp3,x,y,'opt',opt); -[Ef3, Varf3,lpyt3] = gp_pred(gp3, x, y, xt, 'yt', yt); -prctmus3 = gp_predprctmu(gp3, x, y, xt); -fprintf('mlpd student-t: %.2f\n', mean(lpyt3)); - -figure; -% plot mean and 5% and 95% quantiles -subplot(3,1,1) -plot(xt,Ef11,'b',xt,prctmus(:,1),'r',xt,prctmus(:,3),'r', x, f1, 'k') -ylim([-3 3]), title('Input dependent noise model'); -legend('Mean','5%','95%','True',2) - -% Compare to Gaussian with homoscedastic scale -subplot(3,1,2), -plot(xt, Ef2,'b',xt,prctmus2(:,1),'r',xt,prctmus2(:,3),'r', x, f1, 'k') -ylim([-3 3]), title('Gaussian noise model') - -% Compare to Student-t with homoscedastic scale -subplot(3,1,3) -plot(xt, Ef3,'b',xt,prctmus3(:,1),'r',xt,prctmus3(:,3),'r', x, f1, 'k') -ylim([-3 3]), title('Student-t noise model') - -figure -s2=gp.lik.sigma2; -plot(xt, s2.*exp(Ef12), '-b',x, sigma2.*exp(f2), '-k', xt, s2.*exp(Ef12 + 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r', xt,s2.*exp(Ef12 - 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r') -legend('Predicted noise variance', 'Real noise variance','95% CI',2); - -%==================================== -% 2D Demonstration -%==================================== -setrandstream(0); - -% Create data from two 2 dimensional gaussians -nt=10; -n=700; -x=[-3+6*rand(0.25*n,1) -3+6*rand(0.25*n,1);-1.5+3*rand(0.75*n,1) -1.5+3*rand(0.75*n,1)]; -mu=[0 0]; -S=[0.2 0;0 0.2]; -sigma2=0.1; -[x1,x2]=meshgrid(linspace(-1,2,nt), linspace(-1,2,nt)); -xt=[x1(:) x2(:)]; -f1t=10*mnorm_pdf(xt,mu,S) + 20*mnorm_pdf(xt,mu+[1.5 1.5], [0.5 0;0 0.5]); -f2t=20*mnorm_pdf(xt,mu, [0.9 0;0 0.9]); -yt=f1t; -f1 = 10*mnorm_pdf(x,mu, S) + 20*mnorm_pdf(x,mu+[1.5 1.5], [0.5 0;0 0.5]); -f2 = 20*mnorm_pdf(x,mu, [0.9 0;0 0.9]); - -y=f1+randn(size(x,1),1).*sqrt(sigma2.*exp(f2)); - -pl = prior_logunif(); -pm = prior_logunif(); -gpcf1 = gpcf_sexp('lengthScale', [1 1.01], 'magnSigma2', 1, ... - 'lengthScale_prior', pl, 'magnSigma2_prior', pm); -gpcf2 = gpcf_sexp('lengthScale', [1 1.01], 'magnSigma2', 0.1, ... - 'lengthScale_prior', pl, 'magnSigma2_prior', pm); - -lik=lik_inputdependentnoise('sigma2', 0.1, 'sigma2_prior', prior_fixed()); - -gp=gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'jitterSigma2', 1e-6, 'comp_cf', {[1] [2]}); -gp = gp_set(gp, 'latent_method', 'Laplace'); -opt=optimset('TolFun',1e-4,'TolX',1e-4,'Display','iter','MaxIter',100,'Derivativecheck','off'); -gp=gp_optim(gp,x,y,'opt',opt); -% Increase maxiter for predictions in case of slow convergence -gp.latent_opt.maxiter=1e6; -[Ef,Varf,lpyt]=gp_pred(gp,x,y,xt, 'yt',yt); -fprintf('mlpd inputdependentnoise: %.2f\n', mean(lpyt)); - -% Gaussian for comparison -lik2 = lik_gaussian('sigma2', sigma2); -gp2 = gp_set('lik', lik2, 'cf', gpcf1, 'jitterSigma2', 1e-6); -gp2 = gp_optim(gp2,x,y,'opt',opt); -[Ef2,Varf2,lpyt2]=gp_pred(gp2,x,y,xt,'yt',yt); -fprintf('mlpd gaussian: %.2f\n', mean(lpyt2)); - -% Student-t for comparison -lik3=lik_t('sigma2', sigma2); -gp3=gp_set('lik', lik3, 'cf', gpcf1, 'jitterSigma2', 1e-6, 'latent_method', 'Laplace'); -gp3=gp_optim(gp3,x,y,'opt',opt); -[Ef3,Varf3,lpyt3]=gp_pred(gp3,x,y,xt,'yt',yt); -fprintf('mlpd student-t: %.2f\n', mean(lpyt3)); - -s2=gp.lik.sigma2; -figure -subplot(3,1,1),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef(1:size(xt,1)), '*') -title('Input dependent noise model'); -colormap hsv, alpha(.4) -subplot(3,1,2),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef2(1:size(xt,1)), '*'); -colormap hsv, alpha(.4) -title('Gaussian noise model'); -subplot(3,1,3),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef3(1:size(xt,1)), '*'); -colormap hsv, alpha(.4) -title('Student-t noise model'); - -figure -mesh(x1,x2,sigma2.*exp(reshape(f2t,size(x1)))),hold on, plot3(xt(:,1),xt(:,2), s2.*exp(Ef(size(xt,1)+1:end)), '*'); -title('Real noise versus predicted noise'); -colormap hsv, alpha(.4) - -%============================================ -% Demonstration with homoscedastic noise -%============================================ -setrandstream(0); - -% Create data -n =200; -nt = 200; -x = linspace(-100,200, n)'; -xt = linspace(-100,200, n)'; -f1 = [5.*sin(-3+0.2.*x(1:ceil(0.23*n))); 20*sin(0.1*x(ceil(0.23*n)+1:ceil(0.85*n))); 5.*sin(2.8+0.2.*x(ceil(0.85*n)+1:end))]; -sigma2 = 1; - -x=x-mean(x); x=x./std(x); -xt=xt-mean(xt); xt=xt./std(xt); -f1 = f1-mean(f1); f1=f1./std(f1); -f2 = zeros(size(f1)); - -y = f1 + sqrt(sigma2).*randn(size(x));yt=f1; -x=x(:); y=y(:); xt=xt(:); - -% Create the covariance functions -pl = prior_t('s2',10); -pm = prior_t('s2',10); -gpcf1 = gpcf_sexp('lengthScale', 0.5, 'magnSigma2', 0.5, ... - 'lengthScale_prior', pl, 'magnSigma2_prior', pm); -gpcf2 = gpcf_sexp('lengthScale', 1, 'magnSigma2',0.1, ... - 'lengthScale_prior', pl, 'magnSigma2_prior', pm); - -% Create the the model -lik = lik_inputdependentnoise('sigma2', 0.1, 'sigma2_prior', prior_fixed()); -gp = gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'jitterSigma2', 1e-9, 'comp_cf', {[1] [2]}); - -% Set the approximate inference method to Laplace -gp = gp_set(gp, 'latent_method', 'Laplace'); -opt=optimset('TolFun',1e-4,'TolX',1e-4); - -% if flat priors are used, there might be need to increase -% gp.latent_opt.maxiter for laplace algorithm to converge properly - -% gp.latent_opt.maxiter=1e6; - -gp=gp_optim(gp,x,y,'opt',opt); - -[Ef, Varf,lpyt] = gp_pred(gp, x, y, xt,'yt',yt); -Ef11=Ef(1:nt);Ef12=Ef(nt+1:end); -Varf11=diag(Varf(1:nt,1:nt)); -%prctmus = gp_predprctmu(gp, x, y, xt); -prctmus=[Ef11-1.645*sqrt(Varf11) Ef11 Ef11+1.645*sqrt(Varf11)]; -fprintf('mlpd inputdependentnoise: %.2f\n', mean(lpyt)); - -% Gaussian for comparison -lik2 = lik_gaussian(); -gp2 = gp_set('lik', lik2, 'cf', gpcf1, 'jitterSigma2', 1e-6); -gp2 = gp_optim(gp2,x,y,'opt',opt); -[Ef2, Varf2, lpyt2] = gp_pred(gp2, x, y, xt,'yt',yt); -prctmus2 = gp_predprctmu(gp2, x, y, xt); -fprintf('mlpd gaussian: %.2f\n', mean(lpyt2)); - -% Student-t for comparison -lik=lik_t(); -gp3=gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-6); -gp3 = gp_set(gp3, 'latent_method', 'Laplace'); -gp3=gp_optim(gp3,x,y,'opt',opt); -[Ef3, Varf3,lpyt3] = gp_pred(gp3, x, y, xt,'yt',yt); -prctmus3 = gp_predprctmu(gp3, x, y, xt); -fprintf('mlpd student-t: %.2f\n', mean(lpyt3)); - -figure; -% plot mean and 5% and 95% quantiles -subplot(3,1,1) -plot(xt,Ef11,'b',xt,prctmus(:,1),'r',xt,prctmus(:,3),'r', x, f1, 'k') -ylim([-3 3]), title('Input dependent noise model'); -legend('Mean','5%','95%','True',2) - -% Compare to Gaussian with homoscedastic scale -subplot(3,1,2), -plot(xt, Ef2,'b',xt,prctmus2(:,1),'r',xt,prctmus2(:,3),'r', x, f1, 'k') -ylim([-3 3]), title('Gaussian noise model') - -% Compare to Student-t with homoscedastic scale -subplot(3,1,3) -plot(xt, Ef3,'b',xt,prctmus3(:,1),'r',xt,prctmus3(:,3),'r', x, f1, 'k') -ylim([-3 3]), title('Student-t noise model') - -figure -plot(xt, s2.*exp(Ef12), '-b',x, sigma2.*exp(f2), '-k', xt, s2.*exp(Ef12 + 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r', xt,s2.*exp(Ef12 - 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r') -ylim([0 2.5]) -legend('Predicted noise variance', 'Real noise variance','95% CI',2); -setrandstream(prevstream); diff --git a/gp/demo_inputdependentweibull.m b/gp/demo_inputdependentweibull.m deleted file mode 100644 index 10c5f0b2..00000000 --- a/gp/demo_inputdependentweibull.m +++ /dev/null @@ -1,114 +0,0 @@ -S = which('demo_survival_weibull'); -L = strrep(S,'demo_survival_weibull.m','demodata/leukemia.txt'); -leukemiadata=load(L); - -% leukemiadata consists of: -% 'time', 'cens', 'xcoord', 'ycoord', 'age', 'sex', 'wbc', 'tpi', 'district' - -% survival times -y=leukemiadata(:,1); -% scale survival times -y=y/max(y); - -ye=1-leukemiadata(:,2); % event indicator, ye = 0 for uncensored event - % ye = 1 for right censored event - -% choose (for example) 'age', 'sex', 'wbc', and 'tpi' covariates -x0=leukemiadata(:,5:8); -x=x0; -% normalize continuous covariates -x(:,[1 3:4])=bsxfun(@rdivide,bsxfun(@minus,x0(:,[1 3:4]),mean(x0(:,[1 3:4]),1)),std(x0(:,[1 3:4]),1)); - -[n, nin]=size(x); - -% Create the covariance functions -% pl = prior_gaussian('s2',2); -% pm = prior_gaussian('s2',2); -% gpcf1 = gpcf_neuralnetwork('weightSigma2',1, 'biasSigma2', 0.05, 'weightSigma2_prior', pl, 'biasSigma2_prior', pm); -% gpcf2 = gpcf_neuralnetwork('weightSigma2',1, 'biasSigma2', 0.05, 'weightSigma2_prior', pl, 'biasSigma2_prior', pm); -gpcf1 = gpcf_sexp('lengthScale', 1, 'magnSigma2', 1, 'lengthScale_prior', prior_logunif(), 'magnSigma2_prior', prior_logunif()); -gpcf2 = gpcf_sexp('lengthScale', 1, 'magnSigma2', 1, 'lengthScale_prior', prior_logunif(), 'magnSigma2_prior', prior_logunif()); - -% Create the likelihood structure -lik = lik_inputdependentweibull('shape', 0.1, 'shape_prior', prior_logunif()); - -% Create the GP structure -gp = gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'comp_cf', {[1] [2]}, 'jitterSigma2', 1e-6); - -% Set the approximate inference method to Laplace -gp = gp_set(gp, 'latent_method', 'Laplace'); - -% Set the options for the optimization -opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off', 'Display','iter'); -% Optimize with the scaled conjugate gradient method -gp=gp_optim(gp,x,y,'z',ye,'opt',opt); - -xt1=zeros(200,nin); xt1(:,2)=1; -xt2=zeros(200,nin); xt2(:,2)=-1; - -xt1(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200); -xt2(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200); -xt01(:,1)=linspace(min(x0(:,1)), max(x0(:,1)), 200); -xt02(:,1)=linspace(min(x0(:,1)), max(x0(:,1)), 200); - -% Do predictions -[Ef1, Varf1] = gp_pred(gp, x, y, xt1, 'z', ye); -[Ef2, Varf2] = gp_pred(gp, x, y, xt2, 'z', ye); - -% Create normal weibull model -lik = lik_weibull(); -gp2 = gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-6); -gp2 = gp_set(gp2, 'latent_method', 'Laplace'); -gp2=gp_optim(gp2,x,y,'z',ye,'opt',opt); - -[Ef1_2, Varf1_2] = gp_pred(gp2, x, y, xt1, 'z', ye); -[Ef2_2, Varf2_2] = gp_pred(gp2, x, y, xt2, 'z', ye); - -lik = lik_loggaussian(); -gp3 = gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-6); -gp3 = gp_set(gp3, 'latent_method', 'Laplace'); -gp3=gp_optim(gp3,x,y,'z',ye,'opt',opt); - -[Ef1_3, Varf1_3] = gp_pred(gp3, x, y, xt1, 'z', ye); -[Ef2_3, Varf2_3] = gp_pred(gp3, x, y, xt2, 'z', ye); - - -% Plot results -col1=ones(1,3)*0.7; -col2=ones(1,3)*0.3; -figure, hold on, set(gcf, 'color', 'w'), -plot(xt01(:,1), Ef1(1:nt), 'color', col1, 'linewidth', 3) -plot(xt01(:,1), Ef1(1:nt)+1.96*sqrt(diag(Varf1(1:nt,1:nt))), '--', 'color', col1, 'linewidth', 2) -plot(xt01(:,1), Ef1(1:nt)-1.96*sqrt(diag(Varf1(1:nt,1:nt))), '--', 'color', col1, 'linewidth', 2) - -plot(xt02(:,1), Ef2(1:nt), 'color', col2, 'linewidth', 3) -plot(xt02(:,1), Ef2(1:nt)+1.96*sqrt(diag(Varf2(1:nt,1:nt))), '--', 'color', col2, 'linewidth', 2) -plot(xt02(:,1), Ef2(1:nt)-1.96*sqrt(diag(Varf2(1:nt,1:nt))), '--', 'color', col2, 'linewidth', 2) -xlabel('age') -title('effect of age for both sexes, inputdependent-weibull') - -col1=ones(1,3)*0.7; -col2=ones(1,3)*0.3; -figure, hold on, set(gcf, 'color', 'w'), -plot(xt01(:,1), Ef1_2, 'color', col1, 'linewidth', 3) -plot(xt01(:,1), Ef1_2+1.96*sqrt(Varf1_2), '--', 'color', col1, 'linewidth', 2) -plot(xt01(:,1), Ef1_2-1.96*sqrt(Varf1_2), '--', 'color', col1, 'linewidth', 2) - -plot(xt02(:,1), Ef2_2, 'color', col2, 'linewidth', 3) -plot(xt02(:,1), Ef2_2+1.96*sqrt(Varf2_2), '--', 'color', col2, 'linewidth', 2) -plot(xt02(:,1), Ef2_2-1.96*sqrt(Varf2_2), '--', 'color', col2, 'linewidth', 2) -xlabel('age') -title('effect of age for both sexes, weibull') - -col1=ones(1,3)*0.7; -col2=ones(1,3)*0.3; -figure, hold on, set(gcf, 'color', 'w'), -plot(xt01(:,1), Ef1_3, 'color', col1, 'linewidth', 3) -plot(xt01(:,1), Ef1_3+1.96*sqrt(Varf1_3), '--', 'color', col1, 'linewidth', 2) -plot(xt01(:,1), Ef1_3-1.96*sqrt(Varf1_3), '--', 'color', col1, 'linewidth', 2) - -plot(xt02(:,1), Ef2_3, 'color', col2, 'linewidth', 3) -plot(xt02(:,1), Ef2_3+1.96*sqrt(Varf2_3), '--', 'color', col2, 'linewidth', 2) -plot(xt02(:,1), Ef2_3-1.96*sqrt(Varf2_3), '--', 'color', col2, 'linewidth', 2) -xlabel('age') -title('effect of age for both sexes, loggaussian') diff --git a/gp/demo_survival_coxph.m b/gp/demo_survival_coxph.m deleted file mode 100644 index 88764288..00000000 --- a/gp/demo_survival_coxph.m +++ /dev/null @@ -1,175 +0,0 @@ -%DEMO_SURVIVAL_COXPH Survival model using Cox proportional hazard model -% -% Description -% Survival model using Cox proportional model with a piecewise -% log-constant baseline hazard. The hazard rate is -% -% h(t) = h_0(t)*exp(f), -% -% where the baseline hazard is assumed to piecewise log-constant. -% -% The inference is conducted via Laplace, where we find -% Gaussian approximation for p(f| th, data), where th is the -% maximum a posterior (MAP) estimate for the parameters. -% -% The censoring indicator ye is -% -% ye = 0 for uncensored event -% ye = 1 for right censored event. -% -% If survival times y for n observation are given as nx2 matrix with -% entry times into follow-up in the first column and exit times from -% follow-up in the second column, left truncated right censored -% modelling is possible, for instance, in cases where age is wanted to -% be set as a baseline hazard. -% -% Example data set is leukemia survival data in Northwest England -% presented in (Henderson, R., Shimakura, S., and Gorst, D. (2002). -% Modeling spatial variation in leukemia survival data. Journal of the -% American Statistical Association, 97:965–972). Data set was downloaded -% from http://www.math.ntnu.no/%7Ehrue/r-inla.org/examples/leukemia/leuk.dat -% -% Note that the log-logistic model in DEMO_SURVIVAL_AFT works -% better for this data. -% -% See also DEMO_SURVIVAL_AFT -% - -% Copyright (c) 2011 Jaakko Riihimäki -% Copyright (c) 2013 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -% First load data -S = which('demo_survival_coxph'); -L = strrep(S,'demo_survival_coxph.m','demodata/leukemia.txt'); -leukemiadata=load(L); - -% leukemiadata consists of: -% 'time', 'cens', 'xcoord', 'ycoord', 'age', 'sex', 'wbc', 'tpi', 'district' - -% survival times -y0=leukemiadata(:,1); -% scale survival times -y=y0/500; - -ye=1-leukemiadata(:,2); % event indicator, ye = 0 for uncensored event - % ye = 1 for right censored event - -% for simplicity choose 'age', 'sex', 'wbc', and 'tpi' covariates -x0=leukemiadata(:,5:8); -x=x0; -[n, m]=size(x); -% transform white blood cell count (wbc), which highly skewed -% distribution with zeros for measurements below measurement accuracy -x(:,3)=log10(x(:,3)+0.3); -% normalize continuous covariates -[x(:,[1 3:4]),xmean(:,[1 3:4]),xstd(:,[1 3:4])]=normdata(x(:,[1 3:4])); -% binary sex covariate is not transformed -xmean(2)=0;xstd(2)=1; - -% number of time intervals -ntime=50; -% create finite partition of time axis -S=linspace(0,max(y)+0.001,ntime+1); - -% Create the covariance functions -plh = prior_invt('s2',1, 'nu', 4); -pl = prior_t('s2',1, 'nu', 4); -pm = prior_t('s2',1, 'nu', 4); - -% covariance for hazard function -cfhc = gpcf_constant('constSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); -cfhl = gpcf_linear('coeffSigma2',1,'coeffSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); -cfhs = gpcf_sexp('lengthScale', 1, 'magnSigma2', 1.1, 'lengthScale_prior', plh, 'magnSigma2_prior', pm); -% covariance for proportional part -cfc = gpcf_constant('constSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); -cfl = gpcf_linear('coeffSigma2',1,'coeffSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); -cfs = gpcf_sexp('lengthScale', ones(1,m),'lengthScale_prior',pl,'magnSigma2_prior',pm); - -% Create the likelihood structure -lik = lik_coxph('S', S); - -% NOTE! if multiple covariance functions per latent is used, define -% gp.comp_cf as follows: -% gp = gp_set(..., 'comp_cf' {[1 2] [5 6]}; -% where [1 2] are for hazard function, and [5 6] for proportional part -gp = gp_set('lik', lik, 'cf', {cfhc cfhl cfhs cfl cfs}, 'jitterSigma2', 1e-6, 'comp_cf', {[1 2 3] [4 5]}); - -% Set the approximate inference method to Laplace -gp = gp_set(gp, 'latent_method', 'Laplace'); - -opt=optimset('TolFun',1e-2,'TolX',1e-2,'Display','iter'); -gp=gp_optim(gp,x,y,'z',ye,'opt',opt); - -figure -set(gcf,'units','centimeters'); -set(gcf,'pos',[29 6 24 6]) -subplot('position',[0.07 0.21 0.20 0.77]); -i1=2;i2=1; -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); -xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); -xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); -h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); -set(h1(1),'LineWidth',2) -hold on -h2=plot(xtc{2},Ef{2},'k-',xtc{2},Ef{2}-1.64*sqrt(Varf{2}),'k--',xtc{2},Ef{2}+1.64*sqrt(Varf{2}),'k-'); -set(h2(1),'LineWidth',2) -hold off -ylim([-4 2]) -xlabel('Age (years)') -ylabel('Log-hazard') -[hl,hlo]=legend([h1(1), h2(1)],{'Female','Male'},'location','northwest');set(hl,'box','off') - -subplot('position',[0.31 0.21 0.20 0.77]); -i1=2;i2=3; -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); -xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); -xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); -h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); -set(h1(1),'LineWidth',2) -hold on -h2=plot(xtc{2},Ef{2},'k-',xtc{2},Ef{2}-1.64*sqrt(Varf{2}),'k-',xtc{2},Ef{2}+1.64*sqrt(Varf{2}),'k-'); -set(h2(1),'LineWidth',2) -hold off -%ylim([-5 0]) -ylim([-4 2]) -xlim([-1 3]) -xlabel('WBC (log_{10}(50\times10^9/L))') -%ylabel('Expected lifetime (days)') -[hl,hlo]=legend([h1(1), h2(1)],{'Female','Male'},'location','northwest');set(hl,'box','off') - -subplot('position',[0.55 0.21 0.20 0.77]); -i1=2;i2=4; -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); -xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); -xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); -h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); -set(h1(1),'LineWidth',2) -hold on -h2=plot(xtc{2},Ef{2},'k-',xtc{2},Ef{2}-1.64*sqrt(Varf{2}),'k-',xtc{2},Ef{2}+1.64*sqrt(Varf{2}),'k-'); -set(h2(1),'LineWidth',2) -hold off -%ylim([-5 0]) -ylim([-4 2]) -xlim([-7 10]) -xlabel('Townsend deprivation index') -%ylabel('Expected lifetime (days)') -[hl,hlo]=legend([h1(1), h2(1)],{'Female','Male'},'location','northwest');set(hl,'box','off') - -subplot('position',[0.79 0.21 0.20 0.77]); -i2=0;cla -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, i2, 'z', ye); -%gp_cpred(gp, x, y, x, i2, 'z', ye); -xtc=xtc*500/365; -h1=plot(xtc,Ef,'k-',xtc,Ef-1.64*sqrt(Varf),'k-',xtc,Ef+1.64*sqrt(Varf),'k-'); -set(h1(1),'LineWidth',2) -ylim([-5 3]) -%xlim([-1 3]) -xlabel('Time (years)') -%ylabel('Expected lifetime (days)') -hl=legend(h1(1),'Baseline','location','northwest');set(hl,'box','off') -ylim([-4 2]) - diff --git a/gp/demo_zinegbin.m b/gp/demo_zinegbin.m deleted file mode 100644 index e243b3e6..00000000 --- a/gp/demo_zinegbin.m +++ /dev/null @@ -1,93 +0,0 @@ -%DEMO_ZINEGBIN Demonstration of zero-inflated Negative-binomial model -% using Gaussian process prior -% -% Description -% Zero-inflated Negative-binomial model provides a way of modelling -% count data with excess of zeros. The latent values for N training -% points are f=(f1_1,f2_1,...,fN_1,f1_2,f2_2,...,fN_2)^T, where latents -% f_1 are associated with classification process and latents f_2 with -% count process. Both processes are given a zero mean Gaussian process -% prior -% -% f ~ N(0, K), -% -% where K is a block diagonal covariance matrix with blocks K_1, K_2 -% whose elements are given by K_ij = k(x_i, x_j | th). The function -% k(x_i, x_j | th) is covariance function and th its parameters. -% -% In this demo we approximate the posterior distribution with Laplace -% approximation. -% -% See also DEMO_SPATIAL2, DEMO_CLASSIFIC1 - -% Copyright (c) 2008-2010 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari -% Copyright (c) 2011 Jaakko Riihimäki - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -% load the data -S = which('demo_spatial1'); -data = load(strrep(S,'demo_spatial1.m','demodata/spatial1.txt')); - -x = data(:,1:2); -ye = data(:,3); -y = data(:,4); - -x0=x; -x=bsxfun(@rdivide,bsxfun(@minus,x0,mean(x0)),std(x0)); - -% Create the covariance functions -pl = prior_t('s2',10); -pm = prior_t('s2',10); -% pm = prior_sqrtunif(); - -pl = prior_t('s2',10); -pm = prior_sqrtunif(); -cf = gpcf_matern32('lengthScale', 5, 'magnSigma2', 0.05, ... - 'lengthScale_prior', pl, 'magnSigma2_prior', pm); - -% Create the likelihood structure -lik = lik_zinegbin('disper_prior', prior_fixed()); - -% NOTE! if multiple covariance functions per latent is used, define -% gp.comp_cf as follows: -% gp = gp_set(..., 'comp_cf' {[1 2] [5 6]}; -gp = gp_set('lik', lik, 'cf', {cf cf}, 'jitterSigma2', 1e-6, 'comp_cf', {[1] [2]}); - -% Set the approximate inference method to Laplace -gp = gp_set(gp, 'latent_method', 'Laplace'); - -% Set the options for the optimization -opt=optimset('TolFun',1e-2,'TolX',1e-2,'Display','iter'); -% Optimize with the BFGS quasi-Newton method -gp=gp_optim(gp,x,y,'z',ye,'opt',opt, 'optimf', @fminlbfgs); - -% make prediction to the data points -[Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye); - -% Define help parameters for plotting -xii=sub2ind([60 35],x0(:,2),x0(:,1)); -[X1,X2]=meshgrid(1:35,1:60); - -% Plot the figures -figure -G=repmat(NaN,size(X1)); -G(xii)=Ef(1:size(x,1)); -pcolor(X1,X2,G),shading flat -colorbar -axis equal -axis([0 35 0 60]) -title('Posterior mean of latent (classification process)') - -% Plot the figures -figure -G=repmat(NaN,size(X1)); -G(xii)=(Ef((size(x,1)+1):end)); -pcolor(X1,X2,G),shading flat -colorbar -axis equal -axis([0 35 0 60]) -title('Posterior mean of latent (count process)') diff --git a/gp/gp_cvlcriterion.m b/gp/gp_cvlcriterion.m deleted file mode 100644 index 8c46087d..00000000 --- a/gp/gp_cvlcriterion.m +++ /dev/null @@ -1,60 +0,0 @@ -function PE2 = gp_cvlcriterion(gp, x, y, varargin) -%GP_CVLCRITERION cross-validation version of L-criterion -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns cross-validation -% version of L-criterion PE2 given a Gaussian process model GP, -% training inputs X and training outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Marriott, J. M., Spencer, N. M. and Pettitt, A. N. (2001). A -% Bayesian Approach to Selecting Covariates for -% Prediction. Scandinavian Journal of Statistics 28 87–97. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_LCRITERION -% - -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_CVLCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_loopred(gp, x, y); - PE2 = mean((Ey-y).^2 + Vary); - - else - % Non-Gaussian likelihood - error('cvlcriterion not sensible for non-gaussian likelihoods'); - end - -end - diff --git a/gp/gp_lcriterion.m b/gp/gp_lcriterion.m deleted file mode 100644 index 97642777..00000000 --- a/gp/gp_lcriterion.m +++ /dev/null @@ -1,68 +0,0 @@ -function L2 = gp_lcriterion(gp, x, y, varargin) -%GP_LCRITERION L-criterion for model selection. -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns L-criterion L2 -% given a Gaussian process model GP, training inputs X and training -% outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Gelfand, A. E. and Ghosh, S. K. (1998). Model Choice: A Minimum -% Posterior Predictive Loss Approach. Biometrika 85 1–11. -% -% Ibrahim, J. G., Chen, M.-H. and Sinha, D. (2001). Criterion-based -% methods for Bayesian model assessment. Statistica Sinica 11 -% 419–443. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_CVLCRITERION -% - -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - - ip=inputParser; - ip.FunctionName = 'GP_LCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - ip.parse(gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y); - L2 = sum((y-Ey).^2 + Vary); - - else - % Non-Gaussian likelihood - warning('L-criterion not sensible for non-gaussian likelihoods'); - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y, options); - L2 = sum((y-Ey).^2 + Vary); - - end - -end - diff --git a/gp/gp_refpred.m b/gp/gp_refpred.m deleted file mode 100644 index dbf0583b..00000000 --- a/gp/gp_refpred.m +++ /dev/null @@ -1,468 +0,0 @@ -function u_g = gp_refpred(gp1, gp2, x, y, varargin) -% GP_REFPRED Reference predictive approximation to the expected utility of -% single predictions. -% -% Description -% u = GP_REFPRED(GP1, GP2, X, Y, OPTIONS) evaluates reference -% predictive approximation between models GP1 and GP2. Here GP1 is the -% reference model and GP2 is the candidate model. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% method - method for inference, 'posterior' (default) uses posterior -% predictive density, 'loo' uses leave-one-out predictive -% density (approximative), 'kfcv' uses loo cross-validation -% posterior predictive density, 'joint' uses joint -% posterior predictive density for latent values -% (non-Gaussian likelihood) or observations (Gaussian -% likelihood) -% x2,y2,z2 - Optional values for candidate model gp2. -% If only subset of these is specified, remaining variables -% are set from x,y,z. -% -% See also -% GP_LOOPRED, GP_KFCV -% -% References -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In preparation. -% - -% Copyright (c) 2011-2012 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_REFPRED'; - ip=iparser(ip,'addRequired','gp1',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','gp2',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','x2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','y2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})); - ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); - ip=iparser(ip,'parse',gp1, gp2, x, y, varargin{:}); - % pass these forward - options=struct(); - x2 = ip.Results.x2; - y2 = ip.Results.y2; - z2 = ip.Results.z2; - z = ip.Results.z; - method = ip.Results.method; - form = ip.Results.form; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - if ~isempty(ip.Results.z2) - options2.zt=ip.Results.z2; - options2.z=ip.Results.z2; - else - options2 = options; - z2 = z; - end - [tn, nin] = size(x); - u_g = zeros(size(y)); - opt = optimset('TolX', 1e-4, 'TolFun', 1e-4); - if isempty(x2) - x2 = x; - end - if isempty(y2) - y2 = y; - end - - if isstruct(gp1) - % Single gp or MCMC - - switch gp1.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1.tr_index; - end - if ~isfield(gp1,'etr') - model1 = 1; - if isfield(gp1.lik.fh, 'trcov') - switch method - case 'joint' - - otherwise - fh1 = @(f,Ey,Vary) norm_pdf(f,Ey,sqrt(Vary)); - end - else - fh1 = @(gp,Ef,Varf,f,z) exp(predvec(gp,Ef,(Varf),f,z)); - end - - switch method - case 'posterior' - if ~isequal(gp1.lik.type, 'Coxph') - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - else - [Ef1, Varf1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - case 'loo' - if ~isfield(gp1.lik.fh, 'trcov') && ~isfield(gp1.lik, 'type_nd') - gp1 = gp_set(gp1, 'latent_method', 'EP'); - end - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_loopred(gp1,x,y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp1, x, y, 'tstindex', tstind, 'opt', opt, 'display', 'iter', 'k', tn, options); - [Ef1, Varf1, Ey1, Vary1] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef1, Covf1] = gp_jpred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - - else - model1 = 2; - if isfield(gp1.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) mean(multi_npdf(f,Ey,(Vary)),1); - else - fh1 = @(gp,Ef,Varf,f,z) mean(exp(predvec(gp,Ef,(Varf),f,z)),1); - end - nsamples = length(gp1.edata); - if strcmp(gp1.type, 'PIC') - tr_index = gp1.tr_index; - gp1 = rmfield(gp1, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp1,j); - if strcmp(gp1.type, 'FIC') | strcmp(gp1.type, 'PIC') || strcmp(gp1.type, 'CS+FIC') || strcmp(gp1.type, 'VAR') || strcmp(gp1.type, 'DTC') || strcmp(gp1.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array1{j} = Gp; - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gpmc_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_loopred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, pred] = gp_kfcv(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - gp1 = gp_array1; - end - else - % GP IA - switch gp1{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1{1}.tr_index; - end - model1 = 3; - nsamples = length(gp1); - for j = 1:nsamples - Gp = gp1{j}; - weight1(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_pred(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - if isfield(gp1{1}.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight1'),1); - else - fh1 = @(gp,Ef,Varf,f,z) (sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight1'),1)); - end - - end - - if isstruct(gp2) - % Single gp or MCMC - switch gp2.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2.tr_index; - end - if ~isfield(gp2,'etr') - model2 = 1; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) norm_lpdf(f,Ey,sqrt(Vary)); - else - fh2 = @(gp,Ef,Varf,f,z) predvec(gp,Ef,(Varf),f,z); - end - switch method - case 'posterior' - if ~isequal(gp2.lik.type, 'Coxph') - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - else - [Ef2, Varf2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - case 'loo' - if ~isfield(gp2.lik.fh, 'trcov') && ~isfield(gp2.lik, 'type_nd') - gp1 = gp_set(gp2, 'latent_method', 'EP'); - end - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_loopred(gp2,x2,y2, 'z', z2); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp2, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - [Ef2, Varf2, Ey2, Vary2] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef2, Covf2] = gp_jpred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - else - model2 = 2; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(mean(multi_npdf(f,Ey,(Vary)),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(mean(exp(predvec(gp,Ef,(Varf),f,z)),1)); - end - nsamples = length(gp2.edata); - if strcmp(gp2.type, 'PIC') - tr_index = gp2.tr_index; - gp2 = rmfield(gp2, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp2,j); - if strcmp(gp2.type, 'FIC') | strcmp(gp2.type, 'PIC') || strcmp(gp2.type, 'CS+FIC') || strcmp(gp2.type, 'VAR') || strcmp(gp2.type, 'DTC') || strcmp(gp2.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array2{j} = Gp; - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gpmc_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_kfcv(Gp, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - end - - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - gp2 = gp_array2; - end - else - % GP IA - switch gp2{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2{1}.tr_index; - end - model2 = 3; - nsamples = length(gp2); - for j = 1:nsamples - Gp = gp2{j}; - weight2(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'off', options2); - end - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - if isfield(gp2{1}.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight2'),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight2'),1)); - end - - end - - if ((isstruct(gp1) && isfield(gp1.lik.fh, 'trcov')) || (iscell(gp1) && isfield(gp1{1}.lik.fh,'trcov'))) - % Gaussian likelihood - - switch method - case 'joint' - u_g = -0.5.*((Ey1 - Ey2)'*(Covy2\(Ey1-Ey2)) + sum(sum(inv(Covy2).*Covy1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covy2))))); - otherwise - for i=1:tn - m1 = Ey1(i,:); m2=Ey1(i,:).^2 + Vary1(i,:); - u_g(i) = mean(-1./(2.*Vary2(i,:))*m2 + Ey2(i,:)./Vary2(i,:)*m1 - Ey2(i,:).^2./(2.*Vary2(i,:)) - 0.5*log(2*pi*Vary2(i,:))); - end - end - - else - % Non-Gaussian likelihood - - switch method - case 'joint' - % Joint refpred of latent values - u_g = -0.5.*((Ef1 - Ef2)'*(Covf2\(Ef1-Ef2)) + sum(sum(inv(Covf2).*Covf1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covf2))))); - - otherwise - if ismember(gp1.lik.type, {'Binomial', 'Poisson', 'Probit', 'Logit', 'Negbin', 'Negbinztr'}) - % Discrete likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - [tmp, tmp, int] = int_limits(gp1, Ef1(i,:), z1); - else - [minf maxf] = int_limits(gp1,Ef1(i,:),z1); - minf = sum(minf.*weight1); - maxf = sum(maxf.*weight1); - int = minf:maxf; - end - u_g(i) = sum(fh1(gp1,Ef1(i,:),Varf1(i,:),int,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),int,z12)); - end - else - % Continuous likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - if ismember(gp1.lik.type, {'Student-t', 'Weibull', 'Coxph'}) - [minf, maxf] = int_limits(gp1, Ef1(i), z1); - else - minf = mean(Ey1(i) - 12.*sqrt(Vary1(i)),2); - minf(minf<0)=0; - maxf = mean(Ey1(i) + 12.*sqrt(Vary1(i)),2); - end - else - minf = sum(bsxfun(@times, weight1, Ey1(i,:)-12.*sqrt(Vary1(i,:))),2); - maxf = sum(bsxfun(@times, weight1, Ey1(i,:)+12.*sqrt(Vary1(i,:))),2); - end - if ~isequal(gp1.lik.type, 'Coxph') - u_g(i) = quadgk(@(f) fh1(gp1,Ef1(i,:),Varf1(i,:),f,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),f,z12), minf, maxf, 'absTol', 1e-3); - else - ntime1=size(gp1.lik.xtime,1); - ntime2=size(gp2.lik.xtime,1); - u_g(i) = quadgk(@(f) fh1(gp1,Ef1([1:ntime1 i],:),Varf1([1:ntime1 i+ntime1],[1:ntime1 i+ntime1]),f,z1).*fh2(gp2,Ef2([1:ntime2 i],:),Varf2([1:ntime2 i+ntime2],[1:ntime2 i+ntime2]),f,z12), minf, maxf, 'absTol', 1e-3); - end - end - end - end - end - if isequal(form, 'mean') - u_g = mean(u_g); - end -end - -function predvec = predvec(gp, Ef, Varf, f, z) - % Compute vector of lpyts from lik.fh.predy when numel(Ef)=numel(Varf)=1 - % and numel(f) != 1. - if isstruct(gp) - if ~isfield(gp, 'etr') - % single gp - predvec=zeros(size(f)); - for i1=1:numel(f) - predvec(i1)=gp.lik.fh.predy(gp.lik,Ef,Varf,f(i1),z); - end - end - else - % ia & mc - predvec=zeros(length(gp), length(f)); - for i=1:numel(f) - for j=1:numel(gp) - predvec(j,i) = gp{j}.lik.fh.predy(gp{j}.lik, Ef(j), Varf(j), f(i), z); - end - end - end -end - -function mpdf = multi_npdf(f, mean, sigma2) -% for every element in f, compute means calculated with -% norm_pdf(f(i), mean, sqrt(sigma2)). If mean and sigma2 -% are vectors, returns length(mean) x length(f) matrix. - - mpdf = zeros(length(mean), length(f)); - for i=1:length(f) - mpdf(:,i) = norm_pdf(f(i), mean, sqrt(sigma2)); - end -end - -function [minf, maxf, interval] = int_limits(gp, Ef, z) -% Return integration limits for quadgk and interval for discrete integration. - if isstruct(gp) - gplik = gp.lik; - else - gplik = gp{1}.lik; - end - switch gplik.type - - case 'Binomial' - p = exp(Ef)./(1+exp(Ef)); - minf = binoinv(0.0001, z, p); - maxf = binoinv(0.9999, z, p); - interval = minf:maxf; - case 'Poisson' - lambda = z.*exp(Ef); - minf = poissinv(0.0001, lambda); - maxf = poissinv(0.9999, lambda); - interval=minf:maxf; - case {'Probit' 'Logit'} - minf = -1*ones(size(Ef)); - maxf = ones(size(Ef)); - interval = [-1 1]; - case {'Negbin' 'Negbinztr'} - r = gplik.disper; - p = z.*exp(Ef); - minf = nbininv(0.0001, r, p); - maxf = nbininv(0.9999, r, p); - interval = minf:maxf; - case 'Student-t' - [n, n2] = size(Ef); - nu = gp.lik.nu; - minf = repmat(tinv(0.01, nu), n, n2); - maxf = repmat(tinv(0.99, nu), n, n2); - interval = []; - case 'Weibull' - % Probably not very sensible... - minf = 1e-5; - maxf = 1e5; - interval = maxf; - case 'Coxph' - minf = 0; - maxf = 1; - interval = maxf; - end - -end diff --git a/gp/gpcf_SSsexp.m b/gp/gpcf_SSsexp.m deleted file mode 100644 index cdc475fe..00000000 --- a/gp/gpcf_SSsexp.m +++ /dev/null @@ -1,582 +0,0 @@ -function gpcf = gpcf_SSsexp(varargin) -%GPCF_SEXP Create a squared exponential covariance function for Gaussian Process -% -% Description -% -% GPCF = GPCF_SEXP('INIT','nin', NIN) Create and initialize -% squared exponential covariance function fo Gaussian process -% -% The fields and (default values) in GPCF_SEXP are: -% type = 'gpcf_sexp' -% nin = number of inputs (NIN) -% magnSigma2 = general magnitude (squared) for exponential part (sampled with HMC) -% (0.1) -% lengthScale = length scale for each input. This can be either (sampled with HMC) -% scalar (corresponding isotropic) or vector (corresponding ARD). -% (repmat(10, 1, NIN)) -% freq = -% nfreq = -% -% GPCF = GPCF_SEXP(GPCF, 'FIELD1', VALUE1, 'FIELD2', VALUE2, ...) -% Set the values of fields FIELD1... to the values VALUE1... in GPCF. -% -% See also -% -% -% - -% Copyright (c) 2008 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -% allow use with or without init and set options -if nargin<1 - do='init'; -elseif ischar(varargin{1}) - switch varargin{1} - case 'init' - do='init';varargin(1)=[]; - case 'set' - do='set';varargin(1)=[]; - otherwise - do='init'; - end -elseif isstruct(varargin{1}) - do='set'; -else - error('Unknown first argument'); -end - -ip=inputParser; -ip.FunctionName = 'GPCF_SSSEXP'; -ip=iparser(ip,'addOptional','gpcf', [], @isstruct); -ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); -ip=iparser(ip,'addParamValue','magnSigma2',[], @(x) isscalar(x) && x>0); -ip=iparser(ip,'addParamValue','lengthScale',[], @(x) isvector(x) && all(x>0)); -ip=iparser(ip,'addParamValue','nfreq',[], @isscalar); -ip=iparser(ip,'addParamValue','frequency',[], @isvector); -ip=iparser(ip,'parse',varargin{:}); -gpcf=ip.Results.gpcf; -magnSigma2=ip.Results.magnSigma2; -lengthScale=ip.Results.lengthScale; -nfreq=ip.Results.nfreq; -nin=ip.Results.nin; -frequency=ip.Results.frequency; - -switch do - case 'init' - gpcf.type = 'gpcf_SSsexp'; - gpcf.nin = nin; - - % Initialize parameters - if isempty(lengthScale) - gpcf.lengthScale = 10; - else - gpcf.lengthScale = lengthScale; - end - if isempty(magnSigma2) - gpcf.magnSigma2 = 0.1; - else - gpcf.magnSigma2=magnSigma2; - end - if isempty(frequency) - gpcf.frequency = sqrt(2).*erfinv(2.*hammersley(nin,100) - 1); - else - if size(frequency) ~= gpcf.nin - error('The size of the frequency matrix has to be m x nin!') - else - gpcf.frequency = frequency; - gpcf.nfreq = size(gpcf.frequency,1); - end - end - if isempty(nfreq) - gpcf.nfreq = size(gpcf.frequency,2); - else - gpcf.nfreq = nfreq; - gpcf.frequency = sqrt(2).*erfinv(2.*hammersley(gpcf.nin,gpcf.nfreq) - 1); - end - - % Initialize prior structure - gpcf.p=[]; - gpcf.p.lengthScale=[]; - gpcf.p.magnSigma2=[]; - - % Set the function handles to the subfunctions - gpcf.fh.pak = @gpcf_SSsexp_pak; - gpcf.fh.unpak = @gpcf_SSsexp_unpak; - gpcf.fh.lp = @gpcf_SSsexp_e; - gpcf.fh.ghyper = @gpcf_SSsexp_ghyper; - gpcf.fh.gind = @gpcf_SSsexp_gind; - gpcf.fh.cov = @gpcf_SSsexp_cov; - gpcf.fh.covvec = @gpcf_SSsexp_covvec; - gpcf.fh.trcov = @gpcf_SSsexp_trcov; - gpcf.fh.trvar = @gpcf_SSsexp_trvar; - gpcf.fh.recappend = @gpcf_SSsexp_recappend; - - case 'set' - % Set the parameter values of covariance function - % go through all the parameter values that are changed - if ~isempty(lengthScale) - gpcf.lengthScale = lengthScale; - end - if ~isempty(magnSigma2) - gpcf.magnSigma2=magnSigma2; - end - if ~isempty(frequency) - if size(frequency) ~= gpcf.nin - error('The size of the frequency matrix has to be m x nin!') - else - gpcf.frequency = frequency; - gpcf.nfreq = size(gpcf.frequency,1); - end - end - if ~isempty(nfreq) - gpcf.nfreq =nfreq; - gpcf.frequency = sqrt(2).*erfinv(2.*hammersley(gpcf.nin,gpcf.nfreq) - 1); - end -end -end - - -function w = gpcf_SSsexp_pak(gpcf, w, param) -%GPcf_SEXP_PAK Combine GP covariance function hyper-parameters into one vector. -% -% Description -% W = GP_SEXP_PAK(GPCF, W) takes a Gaussian Process structure GP and -% combines the hyper-parameters into a single row vector W. This is -% a mandatory subfunction used for example in energy and gradient -% computations. -% -% The ordering of the parameters in HP is defined by -% hp = [hyper-params of gp.cf{1}, hyper-params of gp.cf{2}, ...]; -% -% See also -% GPCF_SEXP_UNPAK -% - -% Copyright (c) 2008 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -if nargin == 2 - param = 'hyper'; -end - -switch param - case 'hyper' - gpp=gpcf.p; - - i1=0;i2=1; - if ~isempty(w) - i1 = length(w); - end - i1 = i1+1; - w(i1) = gpcf.magnSigma2; - i2=i1+length(gpcf.lengthScale); - i1=i1+1; - w(i1:i2)=gpcf.lengthScale; - i1=i2+1; - if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) - i1=i1+1; - w(i1)=gpp.lengthScale.a.s; - if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) - i1=i1+1; - w(i1)=gpp.lengthScale.a.nu; - end - end - case 'spectral' - i1=0; - if ~isempty(w) - i1 = length(w); - end - i2=i1+length(gpcf.frequency(:)); - w(i1:i2) = gp.frequency(:)'; - i1=i2; i2=i1+length(gpcf.phase(:)); - w(i1:i2) = gpcf.phase(:) - i1=i2+1; -end -end - - - - -function [gpcf, w] = gpcf_SSsexp_unpak(gpcf, w, param) -%GPCF_SEXP_UNPAK Separate GP covariance function hyper-parameter vector into components. -% -% Description -% GP = GPCF_SEXP_UNPAK(GP, W) takes an Gaussian Process structure GP -% and a hyper-parameter vector W, and returns a Gaussian Process data -% structure identical to the input model, except that the covariance -% hyper-parameters has been set to the of W. This is a mandatory -% subfunction used for example in energy and gradient computations. -% -% See also -% GP_PAK -% - -% Copyright (c) 2008 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -if nargin == 2 - param = 'hyper'; -end - -switch param - case 'hyper' - gpp=gpcf.p; - i1=0;i2=1; - i1=i1+1; - gpcf.magnSigma2=w(i1); - i2=i1+length(gpcf.lengthScale); - i1=i1+1; - gpcf.lengthScale=w(i1:i2); - i1=i2; - if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) - i1=i1+1; - gpcf.p.lengthScale.a.s=w(i1); - if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) - i1=i1+1; - gpcf.p.lengthScale.a.nu=w(i1); - end - end - w = w(i1+1:end); - case 'spectral' - i1=1;i2=length(gpcf.frequency(:)); - gpcf.frequency = reshape(w(i1:i2), size(gpcf.frequency)); - i1 = i2; i2 = i1 + length(gpcf.frequency(:)); - i1 = i1+1; - gpcf.phase = reshape(w(i1:i2), size(gpcf.phase)); - w = w(i1+1:end); -end -end - -function eprior =gpcf_SSsexp_e(gpcf, x, t) -%GPCF_SEXP_LP Evaluate prior contribution of error of covariance function SE. -% -% Description -% E = GPCF_SEXP_LP(W, GP, X, T) takes a gp structure GPCF together -% with a matrix X of input vectors and a matrix T of target vectors, -% and evaluates the error function E. Each row of X corresponds -% to one input vector and each row of T corresponds to one -% target vector. -% -% See also -% GP2, GP2PAK, GP2UNPAK, GP2FWD, GP2R_G -% - -% Copyright (c) 1998-2006 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. -[n, m] =size(x); - -% Evaluate the prior contribution to the error. The parameters that -% are sampled are from space W = log(w) where w is all the "real" samples. -% On the other hand errors are evaluated in the W-space so we need take -% into account also the Jacobian of transformation W -> w = exp(W). -% See Gelman et.all., 2004, Bayesian data Analysis, second edition, p24. -eprior = 0; -gpp=gpcf.p; - -eprior=eprior... - +feval(gpp.magnSigma2.fe, ... - gpcf.magnSigma2, gpp.magnSigma2.a)... - -log(gpcf.magnSigma2); -if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) - eprior=eprior... - +feval(gpp.lengthScale.p.s.fe, ... - gpp.lengthScale.a.s, gpp.lengthScale.p.s.a)... - -log(gpp.lengthScale.a.s); - if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) - eprior=eprior... - +feval(gpp.p.lengthScale.nu.fe, ... - gpp.lengthScale.a.nu, gpp.lengthScale.p.nu.a)... - -log(gpp.lengthScale.a.nu); - end -end -eprior=eprior... - +feval(gpp.lengthScale.fe, ... - gpcf.lengthScale, gpp.lengthScale.a)... - -sum(log(gpcf.lengthScale)); -e_x=x; -e_t=t; -e_ls=gpcf.lengthScale; -e_ms=gpcf.magnSigma2; -e_e = eprior; -end - -function [DKff, gprior] = gpcf_SSsexp_ghyper(gpcf, x, x2) -%GPCF_SEXP_GHYPER Evaluate gradient of error for SE covariance function -% with respect to the hyperparameters. -% -% Descriptioni -% G = GPCF_SEXP_GHYPER(W, GPCF, X, T, G, GDATA, GPRIOR, VARARGIN) takes a gp -% hyper-parameter vector W, structure GPCF a matrix X of input vectors a -% matrix T of target vectors, inverse covariance function , -% and evaluates the error gradient G. Each row of X corresponds to one -% input vector and each row of T corresponds to one target vector. -% -% [G, GDATA, GPRIOR] = GPCF_SEXP_GHYPER(GP, P, T) also returns separately the -% data and prior contributions to the gradient. -% -% See also -% - -% Copyright (c) 2008 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -gpp=gpcf.p; -[n, m] =size(x); - -i1=0;i2=1; - -Cdm = gpcf_SSsexp_trcov(gpcf, x)./2; - -ii1=1; -DKff{ii1} = Cdm; - -% loop over all the lengthScales -if length(gpcf.lengthScale) == 1 - % In the case of isotropic SSSEXP - l = gpcf.lengthScale; - ma2 = gpcf.magnSigma2; - l = repmat(l,1,m); - - s = gpcf.frequency./(repmat(l(:),1,gpcf.nfreq).*sqrt(2).*pi); - C1 = x*s.*sin(2*pi*x*s); - C2 = -x*s.*cos(2*pi*x*s); - D = [C1 ; C2]; - D = 2.*pi.*reshape(D,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); - - ii1 = ii1+1; - DKff{ii1} = D; -else - % In the case ARD is used - l = gpcf.lengthScale; - ma2 = gpcf.magnSigma2; - if size(l)==1 - l = repmat(l,1,m); - end - - s = gpcf.frequency./(repmat(l(:),1,gpcf.nfreq).*sqrt(2).*pi); - C1 = 2.*pi.*sin(2*pi*x*s); - C2 = -2.*pi.*cos(2*pi*x*s); - C = [C1 ; C2]; - C = reshape(C,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); - - for i=1:m - D1 = x(:,i)*s(i,:).*C1; - D2 = x(:,i)*s(i,:).*C2; - D = [D1 ; D2]; - D = reshape(D,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); - - ii1 = ii1+1; - DKff{ii1} = D; - end -end - -% Evaluate the gprior with respect to magnSigma2 -i1 = i1+1; -gprior(i1)=feval(gpp.magnSigma2.fg, ... - gpcf.magnSigma2, ... - gpp.magnSigma2.a, 'x').*gpcf.magnSigma2 - 1; -% Evaluate the prior contribution of gradient with respect to lengthScale.p.s (and lengthScale.p.nu) -if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) - i1=i1+1; - gprior(i1)=... - feval(gpp.lengthScale.p.s.fg, ... - gpp.lengthScale.a.s,... - gpp.lengthScale.p.s.a, 'x').*gpp.lengthScale.a.s - 1 ... - +feval(gpp.lengthScale.fg, ... - gpcf.lengthScale, ... - gpp.lengthScale.a, 's').*gpp.lengthScale.a.s; - if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) - i1=i1+1; - gprior(i1)=... - feval(gpp.lengthScale.p.nu.fg, ... - gpp.lengthScale.a.nu,... - gpp.lengthScale.p.nu.a, 'x').*gpp.lengthScale.a.nu -1 ... - +feval(gpp.lengthScale.fg, ... - gpcf.lengthScale, ... - gpp.lengthScale.a, 'nu').*gpp.lengthScale.a.nu; - end -end -% Evaluate the data contribution of gradient with respect to lengthScale -if length(gpcf.lengthScale)>1 - for i2=1:gpcf.nin - i1=i1+1; - gprior(i1)=feval(gpp.lengthScale.fg, ... - gpcf.lengthScale(i2), ... - gpp.lengthScale.a, 'x').*gpcf.lengthScale(i2) - 1; - end -else - i1=i1+1; - gprior(i1)=feval(gpp.lengthScale.fg, ... - gpcf.lengthScale, ... - gpp.lengthScale.a, 'x').*gpcf.lengthScale -1; - -end - -end - - -function [g_ind, gdata_ind, gprior_ind] = gpcf_SSsexp_gind(gpcf, x, t, g_ind, gdata_ind, gprior_ind, varargin) -%GPCF_SEXP_GIND Evaluate gradient of error for SE covariance function -% with respect to inducing inputs. -% -% Description -% [DKuu_u, DKuf_u] = GPCF_SEXP_GIND(W, GPCF, X, T) -% -% See also -% - -% Copyright (c) 2006 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -gdata_ind = gdata_ind + gradient; -g_ind = gdata_ind; -end - - -function C = gpcf_SSsexp_trcov(gpcf, x) -% GP_SSEXP_TRCOV Evaluate training covariance matrix of inputs. -% -% Description -% C = GP_SSEXP_TRCOV(GP, TX) takes in covariance function of a Gaussian -% process GP and matrix TX that contains training input vectors to -% GP. Returns covariance matrix C. Every element ij of C contains -% covariance between inputs i and j in TX -% -% For covariance function definition see ... - -% Copyright (c) 2008 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -[n, m] =size(x); - -l = gpcf.lengthScale; -ma2 = gpcf.magnSigma2; -if size(l)==1 - l = repmat(l,1,m); -end - -s = gpcf.frequency./(repmat(l(:),1,gpcf.nfreq).*sqrt(2).*pi); -C1 = cos(2*pi*x*s); -C2 = sin(2*pi*x*s); -C = [C1 ; C2]; -C = reshape(C,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); - -% $$$ [n, m] =size(x); -% $$$ -% $$$ l = gpcf.lengthScale; -% $$$ ma = gpcf.magnSigma2; -% $$$ if size(l)==1 -% $$$ l = repmat(l,1,m); -% $$$ end -% $$$ s = gpcf.frequency./repmat(l,gpcf.nfreq,1); -% $$$ phi = gpcf.phase; -% $$$ -% $$$ C = cos(2*pi*x*s + phi); -end - -function C = gpcf_SSsexp_trvar(gpcf, x) -% GP_SSEXP_TRVAR Evaluate training variance vector of inputs. -% -% Description -% C = GP_SSEXP_TRVAR(GP, TX) takes in covariance function of a Gaussian -% process GP and matrix TX that contains training input vectors to -% GP. Returns variance vector C. Every element i of C contains -% variance of input i in TX -% -% For covariance function definition see manual or -% Neal R. M. Regression and Classification Using Gaussian -% Process Priors, Bayesian Statistics 6. - -% Copyright (c) 2008 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -% $$$ [n, m] =size(x); -% $$$ -% $$$ C = ones(n,1)*gpcf.magnSigma2*2/gpcf.nfreq; -% $$$ C(C in 1d speed up could be obtained using quadrature -% -> in 2d and higher dimensions speed up is perhaps possible with -% extensions of quadrature -% -> Quasi-Monte Carlo has been tried and helps only little -% * Stochastic integration is highly variable -% -> gradients can not be evaluated accurately enough for which reason -% the inference has to be conducted with MCMC (HMC can not be used) -% -% See also -% GP_SET, GPCF_*, PRIOR_*, METRIC_* - -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - if nargin>0 && ischar(varargin{1}) && ismember(varargin{1},{'init' 'set'}) - % remove init and set - varargin(1)=[]; - end - - ip=inputParser; - ip.FunctionName = 'GPCF_INTCOV'; - ip=iparser(ip,'addOptional','gpcf', [], @isstruct); - ip=iparser(ip,'addParamValue','intArea',[], @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','cf',[], @iscell); - ip=iparser(ip,'addParamValue','NintPoints',200, @(x) isscalar(x) && x>0); - ip=iparser(ip,'parse',varargin{:}); - gpcf=ip.Results.gpcf; - - if isempty(gpcf) - % Check that SuiteSparse is available - init=true; - gpcf.intArea=ip.Results.intArea; - if isempty(gpcf.intArea) - error('intArea has to be given for intcov: gpcf_intcov(''intArea'',INTAREA,...)') - end - gpcf.type = 'gpcf_intcov'; - else - if ~isfield(gpcf,'type') && ~isequal(gpcf.type,'gpcf_intcov') - error('First argument does not seem to be a valid covariance function structure') - end - init=false; - end - - if init || ~ismember('cf',ip.UsingDefaults) - % Initialize parameters - gpcf.cf = {}; - cfs=ip.Results.cf; - if ~isempty(cfs) - for i = 1:length(cfs) - gpcf.cf{i} = cfs{i}; - end - else - error('At least one covariance function has to be given in cf'); - end - end - - if init - % Set the function handles to the nested functions - gpcf.fh.pak = @gpcf_intcov_pak; - gpcf.fh.unpak = @gpcf_intcov_unpak; - gpcf.fh.lp = @gpcf_intcov_lp; - gpcf.fh.lpg = @gpcf_intcov_lpg; - gpcf.fh.cfg = @gpcf_intcov_cfg; - gpcf.fh.ginput = @gpcf_intcov_ginput; - gpcf.fh.cov = @gpcf_intcov_cov; - gpcf.fh.trcov = @gpcf_intcov_trcov; - gpcf.fh.trvar = @gpcf_intcov_trvar; - gpcf.fh.recappend = @gpcf_intcov_recappend; - - % help parameters for storing intermediate results - w0 = []; - w1 = []; - datahash0=0; - datahash1=0; - Ctrcov = []; - Ccov = []; - end - - % Initialize parameters - if init || ~ismember('intArea',ip.UsingDefaults) - gpcf.intArea=ip.Results.intArea; - end - - % Initialize parameters - if init || ~ismember('NintPoints',ip.UsingDefaults) - gpcf.NintPoints=ip.Results.NintPoints; - end - - function [w,s] = gpcf_intcov_pak(gpcf) - %GPCF_INTCOV_PAK Combine GP covariance function parameters into - % one vector - % - % Description - % W = GPCF_INTCOV_PAK(GPCF) takes a covariance function - % structure GPCF and combines the covariance function - % parameters and their hyperparameters into a single row - % vector W. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % See also - % GPCF_INTCOV_UNPAK - - ncf = length(gpcf.cf); - w = []; s = {}; - - for i=1:ncf - cf = gpcf.cf{i}; - [wi si] = cf.fh.pak(cf); - w = [w wi]; - s = [s; si]; - end - - end - - function [gpcf, w] = gpcf_intcov_unpak(gpcf, w) - %GPCF_INTCOV_UNPAK Sets the covariance function parameters into - % the structure - % - % Description - % [GPCF, W] = GPCF_INTCOV_UNPAK(GPCF, W) takes a covariance - % function structure GPCF and a hyper-parameter vector W, - % and returns a covariance function structure identical - % to the input, except that the covariance hyper-parameters - % have been set to the values in W. Deletes the values set to - % GPCF from W and returns the modified W. This is a mandatory - % subfunction used for example in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(gpcf.magnSigma2) - % (hyperparameters of gpcf.magnSigma2) - % log(gpcf.lengthScale(:)) - % (hyperparameters of gpcf.lengthScale)]' - % - % See also - % GPCF_INTCOV_PAK - - ncf = length(gpcf.cf); - - for i=1:ncf - cf = gpcf.cf{i}; - [cf, w] = cf.fh.unpak(cf, w); - gpcf.cf{i} = cf; - end - - end - - function lp = gpcf_intcov_lp(gpcf) - %GPCF_INTCOV_LP Evaluate the log prior of covariance function parameters - % - % Description - % LP = GPCF_INTCOV_LP(GPCF, X, T) takes a covariance function - % structure GPCF and returns log(p(th)), where th collects the - % parameters. This is a mandatory subfunction used for example - % in energy computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LPG, GP_E - - lp = 0; - ncf = length(gpcf.cf); - for i=1:ncf - cf = gpcf.cf{i}; - lp = lp + cf.fh.lp(cf); - end - end - - function lpg = gpcf_intcov_lpg(gpcf) - %GPCF_INTCOV_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = GPCF_INTCOV_LPG(GPCF) takes a covariance function - % structure GPCF and returns LPG = d log (p(th))/dth, where th - % is the vector of parameters. This is a mandatory subfunction - % used for example in gradient computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - lpg = []; - ncf = length(gpcf.cf); - - % Evaluate the gradients - for i=1:ncf - cf = gpcf.cf{i}; - lpg=[lpg cf.fh.lpg(cf)]; - end - end - - function DKff = gpcf_intcov_cfg(gpcf, x, x2, mask) - %GPCF_INTCOV_CFG Evaluate gradient of covariance function - % with respect to the parameters - % - % Description - % DKff = GPCF_INTCOV_CFG(GPCF, X) takes a covariance function - % structure GPCF, a matrix X of input vectors and returns - % DKff, the gradients of covariance matrix Kff = k(X,X) with - % respect to th (cell array with matrix elements). This is a - % mandatory subfunction used for example in gradient computations. - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, [], MASK) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the diagonal of gradients of covariance matrix - % Kff = k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - [n, m] =size(x); - - DKff = {}; - % Evaluate: DKff{1} = d Kff / d magnSigma2 - % DKff{2} = d Kff / d lengthScale - % NOTE! Here we have already taken into account that the parameters are transformed - % through log() and thus dK/dlog(p) = p * dK/dp - - % evaluate the gradient for training covariance - if nargin == 2 - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - % point-point covariance - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1)); - for j1 = 1:length(temp) - [I,J,R] = find(temp{j1}); - DKff{end+1} = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - end - end - - % point-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1),intpoints); - for k1 = 1:length(temp) - temp2{ii1}(:,j1) = mean(temp{k1},2); - ii1=ii1+1; - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp + temp'; - end - - % area-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, intpoints1, intpoints2); - for l1 = 1:length(temp) - temp2{ii1}(j1,k1) = mean(mean(temp{l1})); - ii1=ii1+1; - end - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp; % + temp' - end - - % Evaluate the gradient of non-symmetric covariance (e.g. K_fu) - elseif nargin == 3 - if size(x,2) ~= size(x2,2) - error('gpcf_intcov -> _ghyper: The number of columns in x and x2 has to be the same. ') - end - - % Evaluate: DKff{1} = d mask(Kff,I) / d magnSigma2 - % DKff{2...} = d mask(Kff,I) / d lengthScale - elseif nargin == 4 - - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - for i1=1:length(DKff) - DKff{i1}=full(DKff{i1}); - end - end - - end - - function DKff = gpcf_intcov_ginput(gpcf, x, x2) - %GPCF_INTCOV_GINPUT Evaluate gradient of covariance function with - % respect to x - % - % Description - % DKff = GPCF_INTCOV_GINPUT(GPCF, X) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = GPCF_INTCOV_GINPUT(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - end - - - function C = gpcf_intcov_cov(gpcf, x1, x2, varargin) - %GP_INTCOV_COV Evaluate covariance matrix between two input vectors - % - % Description - % C = GP_INTCOV_COV(GP, TX, X) takes in covariance function of - % a Gaussian process GP and two matrixes TX and X that contain - % input vectors to GP. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i in TX - % and j in X. This is a mandatory subfunction used for example in - % prediction and energy computations. - % - % See also - % GPCF_INTCOV_TRCOV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - if isempty(x2) - x2=x1; - end - [n1,m1]=size(x1); - [n2,m2]=size(x2); - - if m1~=m2 - error('the number of columns of X1 and X2 has to be same') - end - - ncf = length(gpcf.cf); - ww = []; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512([x1, x2]); - fromMem = false; - if ~isempty(w1) - if all(size(ww)==size(w1)) && all(abs(ww-w1)<1e-8) && isequal(datahash,datahash1) - fromMem = true; - end - end - - if fromMem - C = Ccov; - else - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - intInd1 = find(x1(:,end)==1); - intInd2 = find(x2(:,end)==1); - pointInd1 = find(x1(:,end)==0); - pointInd2 = find(x2(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - C = sparse(n1,n2); - - % point-point covariance - if any(x1(:,end)==0) && any(x2(:,end)==0) - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.cov(cf, x1(pointInd1,1:end-1),x2(pointInd2,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd2(J),R,n1,n2); - end - - % point-area covariance - if any(x1(:,end)==0) && any(x2(:,end)==1) - temp=sparse(length(pointInd1),length(intInd2)); - for j1=1:length(intInd2) - %N=600; - %[tmp1, tmp2] = meshgrid( linspace(x2(intInd2(j1),1),x2(intInd2(j1),1)+intArea(1,1),N) , linspace(x2(intInd2(j1),2),x2(intInd2(j1),2)+intArea(1,2),N)); - %intpoints = [tmp1(:),tmp2(:)]; - intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,m2-1); - %intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*hammersley(m2-1,numPoints)'; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x1(pointInd1,1:end-1),intpoints),2); - end - end - [I,J,R] = find(temp); - C = C + sparse(pointInd1(I),intInd2(J),R,n1,n2); - end - - % area-point covariance - if any(x1(:,end)==1) && any(x2(:,end)==0) - temp=sparse(length(pointInd2),length(intInd1)); - for j1=1:length(intInd1) - intpoints = repmat(x1(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x2(pointInd2,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp'); - C = C + sparse(intInd1(I),pointInd2(J),R,n1,n2); - end - - % area-area covariance - if any(x1(:,end)==1) && any(x2(:,end)==1) - temp=sparse(length(intInd1),length(intInd2)); - for j1=1:length(intInd1) - intpoints1 = repmat(x1(intInd1(j1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd2) - intpoints2 = repmat(x2(intInd2(k1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - [I,J,R] = find(temp); - C = C + sparse(intInd1(I),intInd2(J),R,n1,n2); - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ccov=C; - datahash1=datahash; - w1=ww; - end - - end - - function C = gpcf_intcov_trcov(gpcf, x) - %GP_INTCOV_TRCOV Evaluate training covariance matrix of inputs - % - % Description - % C = GP_INTCOV_TRCOV(GP, TX) takes in covariance function of a - % Gaussian process GP and matrix TX that contains training - % input vectors. Returns covariance matrix C. Every element ij - % of C contains covariance between inputs i and j in TX. This is - % a mandatory subfunction used for example in prediction and - % energy computations. - % - % See also - % GPCF_INTCOV_COV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - ncf = length(gpcf.cf); - ww=[]; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512(x); - fromMem = false; - if ~isempty(w0) - if all(size(ww)==size(w0)) && all(abs(ww-w0)<1e-8) && isequal(datahash,datahash0) - fromMem = true; - end - end - - if fromMem - C = Ctrcov; - else - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - %intMethod = gpcf.intMethod; - - % point-point covariance - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trcov(cf, x(pointInd1,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - - % point-area covariance - temp=sparse(length(pointInd1),length(intInd1)); - randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x(pointInd1,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp'; - - % % area-area covariance - % temp=sparse(length(intInd1),length(intInd1)); - % for j1=1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for k1 = 1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for i1=1:ncf - % cf = gpcf.cf{i1}; - % temp(j1,k1) = temp(j1,k1) + mean(mean(feval(cf.fh.cov, cf, intpoints1, intpoints2))); - % end - % end - % end - % [I,J,R] = find(temp); - % C = C + sparse(intInd1(I),intInd1(J),R,n1,n1); - - % area-area covariance - temp=sparse(length(intInd1),length(intInd1)); - temp2=zeros(n1,1); - randpoints = [intArea intArea].*hammersley((dimInt)*2,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = j1+1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - % The covariance matrix seems to get non positive definite. Try to fix - % it by evaluating all the diagonal elements with same random numbers. - %randpoints1 = intArea.*rand(numPoints,dimInt); - %randpoints2 = intArea.*rand(numPoints,dimInt); - %randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - intpoints2 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints1; - %intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp2(j1) = temp2(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - %temp2(j1) = temp2(j1) + mean(mean(feval(cf.fh.trcov, cf, intpoints))); - end - end - [I,J,R] = find(temp); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp' + sparse(1:n1,1:n1,temp2); - - C = (C+C')/2; - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ctrcov=C; - datahash0=datahash; - w0=ww; - end - - end - - function C = gpcf_intcov_trvar(gpcf, x) - %GP_INTCOV_TRVAR Evaluate training variance vector - % - % Description - % C = GP_INTCOV_TRVAR(GPCF, TX) takes in covariance function of - % a Gaussian process GPCF and matrix TX that contains training - % inputs. Returns variance vector C. Every element i of C - % contains variance of input i in TX. This is a mandatory - % subfunction used for example in prediction and energy - % computations. - % - % See also - % GPCF_INTCOV_COV, GP_COV, GP_TRCOV - - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - C = zeros(n1,1); - - % point-point covariance - temp = 0; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trvar(cf, x(pointInd1,1:end-1)); - end - C(pointInd1) = temp; - - % area-area covariance - temp=zeros(size(intInd1)); - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1) = temp(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - C(intInd1) = temp; - end - - function reccf = gpcf_intcov_recappend(reccf, ri, gpcf) - %RECAPPEND Record append - % - % Description - % RECCF = GPCF_INTCOV_RECAPPEND(RECCF, RI, GPCF) takes a - % covariance function record structure RECCF, record index RI - % and covariance function structure GPCF with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from GPCF. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - if nargin == 2 - % Initialize record - reccf.type = 'gpcf_intcov'; - reccf.NintPoints = ri.NintPoints; - reccf.intArea = ri.intArea; - - % Initialize parameters - ncf = length(ri.cf); - for i=1:ncf - cf = ri.cf{i}; - reccf.cf{i} = cf.fh.recappend([], ri.cf{i}); - end - - % Set the function handles - reccf.fh.pak = @gpcf_intcov_pak; - reccf.fh.unpak = @gpcf_intcov_unpak; - reccf.fh.e = @gpcf_intcov_lp; - reccf.fh.lpg = @gpcf_intcov_lpg; - reccf.fh.cfg = @gpcf_intcov_cfg; - reccf.fh.cov = @gpcf_intcov_cov; - reccf.fh.trcov = @gpcf_intcov_trcov; - reccf.fh.trvar = @gpcf_intcov_trvar; - reccf.fh.recappend = @gpcf_intcov_recappend; - else - % Append to the record - - %loop over all of the covariance functions - ncf = length(gpcf.cf); - reccf.NintPoints(ri,:) = gpcf.NintPoints; - reccf.intArea(ri,:) = gpcf.intArea; - for i=1:ncf - cf = gpcf.cf{i}; - reccf.cf{i} = cf.fh.recappend(reccf.cf{i}, ri, cf); - end - end - end -end - diff --git a/gp/gpla_mo_e.m b/gp/gpla_mo_e.m deleted file mode 100644 index bf211579..00000000 --- a/gp/gpla_mo_e.m +++ /dev/null @@ -1,369 +0,0 @@ -function [e, edata, eprior, f, L, a, E, M, p] = gpla_mo_e(w, gp, varargin) -%GPLA_MO_E Do Laplace approximation and return marginal log posterior -% estimate for multioutput likelihood -% -% Description -% E = GPLA_MO_E(W, GP, X, Y, OPTIONS) takes a GP data -% structure GP together with a matrix X of input vectors and a -% matrix Y of target vectors, and finds the Laplace -% approximation for the conditional posterior p(Y | X, th), -% where th is the parameters. Returns the energy at th (see -% below). Each row of X corresponds to one input vector and each -% row of Y corresponds to one target vector. -% -% [E, EDATA, EPRIOR] = GPLA_MO_E(W, GP, X, Y, OPTIONS) -% returns also the data and prior components of the total -% energy. -% -% The energy is minus log posterior cost function for th: -% E = EDATA + EPRIOR -% = - log p(Y|X, th) - log p(th), -% where th represents the parameters (lengthScale, -% magnSigma2...), X is inputs and Y is observations. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected -% value for ith case. -% -% See also -% GP_SET, GP_E, GPLA_SOFTMAX_G, GPLA_SOFTMAX_PRED - -% Description 2 -% Additional properties meant only for internal use. -% -% GP = GPLA_MO_E('init', GP) takes a GP structure GP and -% initializes required fields for the Laplace approximation. -% -% GP = GPLA_MO_E('clearcache', GP) takes a GP structure GP and -% cleares the internal cache stored in the nested function workspace -% -% [e, edata, eprior, f, L, a, La2, p] -% = gpla_mo_e(w, gp, x, y, varargin) -% returns many useful quantities produced by EP algorithm. -% -% The Newton's method is implemented as described in -% Rasmussen and Williams (2006). - -% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki, -% Jarno Vanhatalo, Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GPLA_MO_E'; - ip=iparser(ip,'addRequired','w', @(x) ... - (ischar(x) && strcmp(w, 'init')) || ... - isvector(x) && isreal(x) && all(isfinite(x))); - ip=iparser(ip,'addRequired','gp',@isstruct); - ip=iparser(ip,'addOptional','x', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addOptional','y', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',w, gp, varargin{:}); - x=ip.Results.x; - y=ip.Results.y; - z=ip.Results.z; - - if strcmp(w, 'init') - % initialize cache - ch = []; - - % return function handle to the nested function ep_algorithm - % this way each gp has its own peristent memory for EP - gp.fh.e = @laplace_algorithm; - e = gp; - % remove clutter from the nested workspace - clear w gp varargin ip x y z - elseif strcmp(w, 'clearcache') - % clear the cache - gp.fh.e('clearcache'); - else - % call laplace_algorithm using the function handle to the nested function - % this way each gp has its own peristent memory for Laplace - [e, edata, eprior, f, L, a, E, M, p] = gp.fh.e(w, gp, x, y, z); - end - - function [e, edata, eprior, f, L, a, E, M, p] = laplace_algorithm(w, gp, x, y, z) - - if strcmp(w, 'clearcache') - ch=[]; - return - end - % code for the Laplace algorithm - - % check whether saved values can be used - if isempty(z) - datahash=hash_sha512([x y]); - else - datahash=hash_sha512([x y z]); - end - if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash) - % The covariance function parameters or data haven't changed - % so we can return the energy and the site parameters that are saved - e = ch.e; - edata = ch.edata; - eprior = ch.eprior; - f = ch.f; - L = ch.L; - E = ch.E; - M = ch.M; - a = ch.a; - p = ch.p; - else - % The parameters or data have changed since - % the last call for gpla_e. In this case we need to - % re-evaluate the Laplace approximation - if size(x,1) ~= size(y,1) - error('GPLA_MO_E: x and y must contain equal amount of rows!') - end - [n,nout] = size(y); - p = []; - - if isfield(gp, 'comp_cf') % own covariance for each ouput component - multicf = true; - if length(gp.comp_cf) ~= nout - error('GPLA_MO_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') - end - else - multicf = false; - end - - gp=gp_unpak(gp, w); - ncf = length(gp.cf); - - % Initialize latent values - % zero seems to be a robust choice (Jarno) - f = zeros(size(y(:))); - - % ================================================= - % First Evaluate the data contribution to the error - switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' - K = zeros(n,n,nout); - if multicf - for i1=1:nout - K(:,:,i1) = gp_trcov(gp, x, gp.comp_cf{i1}); - end - else - for i1=1:nout - K(:,:,i1) = gp_trcov(gp, x); - end - end - - switch gp.latent_opt.optim_method - case 'newton' - tol = 1e-12; - a = f; - - f2=reshape(f,n,nout); - - lp_new = gp.lik.fh.ll(gp.lik, y, f2, z); - lp_old = -Inf; - - c=zeros(n*nout,1); - ERMMRc=zeros(n*nout,1); - pipif=zeros(n*nout,1); - E=zeros(n,n,nout); - L=zeros(n,n,nout); - RER = zeros(n,n,nout); - - while lp_new - lp_old > tol - lp_old = lp_new; a_old = a; - - llg = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); - [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); - pi2 = reshape(pi2_vec,size(y)); - - R = repmat(1./pi2_vec,1,n).*pi2_mat; - for i1=1:nout - Dc=sqrt(pi2(:,i1)); - Lc=(Dc*Dc').*K(:,:,i1); - Lc(1:n+1:end)=Lc(1:n+1:end)+1; - [Lc,notpositivedefinite]=chol(Lc); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - L(:,:,i1)=Lc; - - Ec=Lc'\diag(Dc); - Ec=Ec'*Ec; - E(:,:,i1)=Ec; - RER(:,:,i1) = R((1:n)+(i1-1)*n,:)'*Ec*R((1:n)+(i1-1)*n,:); - end - [M, notpositivedefinite]=chol(sum(RER,3)); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - - b = pi2_vec.*f - pi2_mat*(pi2_mat'*f) + llg; - for i1=1:nout - c((1:n)+(i1-1)*n)=E(:,:,i1)*(K(:,:,i1)*b((1:n)+(i1-1)*n)); - end - - RMMRc=R*(M\(M'\(R'*c))); - for i1=1:nout - ERMMRc((1:n)+(i1-1)*n) = E(:,:,i1)*RMMRc((1:n)+(i1-1)*n,:); - end - a=b-c+ERMMRc; - - for i1=1:nout - f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); - end - f2=reshape(f,n,nout); - - lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); - - i = 0; - while i < 10 && lp_new < lp_old || isnan(sum(f)) - % reduce step size by half - a = (a_old+a)/2; - - for i1=1:nout - f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); - end - f2=reshape(f,n,nout); - - lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); - i = i+1; - end - end - otherwise - error('gpla_e: Unknown optimization method ! ') - end - - [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); - pi2 = reshape(pi2_vec,size(y)); - - zc=0; - Detn=0; - R = repmat(1./pi2_vec,1,n).*pi2_mat; - for i1=1:nout - Dc=sqrt( pi2(:,i1) ); - Lc=(Dc*Dc').*K(:,:,i1); - Lc(1:n+1:end)=Lc(1:n+1:end)+1; - [Lc, notpositivedefinite]=chol(Lc); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - L(:,:,i1)=Lc; - - pi2i = pi2_mat((1:n)+(i1-1)*n,:); - pipi = pi2i'/diag(Dc); - Detn = Detn + pipi*(Lc\(Lc'\diag(Dc)))*K(:,:,i1)*pi2i; - zc = zc + sum(log(diag(Lc))); - - Ec=Lc'\diag(Dc); - Ec=Ec'*Ec; - E(:,:,i1)=Ec; - RER(:,:,i1) = R((1:n)+(i1-1)*n,:)'*Ec*R((1:n)+(i1-1)*n,:); - end - [M, notpositivedefinite]=chol(sum(RER,3)); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - - zc = zc + sum(log(diag(chol( eye(size(K(:,:,i1))) - Detn)))); - - logZ = a'*f/2 - gp.lik.fh.ll(gp.lik, y, f2, z) + zc; - edata = logZ; - - % ============================================================ - % FIC - % ============================================================ - case 'FIC' - - % ============================================================ - % PIC - % ============================================================ - case {'PIC' 'PIC_BLOCK'} - - % ============================================================ - % CS+FIC - % ============================================================ - case 'CS+FIC' - - otherwise - error('Unknown type of Gaussian process!') - end - - % ====================================================================== - % Evaluate the prior contribution to the error from covariance functions - % ====================================================================== - eprior = 0; - if ~isempty(strfind(gp.infer_params, 'covariance')) - for i=1:ncf - gpcf = gp.cf{i}; - eprior = eprior - gpcf.fh.lp(gpcf); - end - end - - % ============================================================ - % Evaluate the prior contribution to the error from Gaussian likelihood - % ============================================================ - % Evaluate the prior contribution to the error from likelihood function - if isfield(gp, 'lik') && isfield(gp.lik, 'p') - lik = gp.lik; - eprior = eprior - lik.fh.lp(lik); - end - - e = edata + eprior; - - % store values to the cache - ch.w = w; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.f = f; - ch.L = L; - ch.M = M; - ch.E = E; -% ch.W = W; - ch.n = size(x,1); - %La20 = La2; - ch.a = a; - ch.p=p; - ch.datahash=datahash; - end - - assert(isreal(edata)) - assert(isreal(eprior)) - end - -function [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite() - % Instead of stopping to chol error, return NaN - edata=NaN; - e=NaN; - eprior=NaN; - f=NaN; - L=NaN; - a=NaN; -% La2=NaN; - E = NaN; - M = NaN; - p=NaN; - ch.w = w; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.f = f; - ch.L = L; - ch.M = M; - ch.E = E; - ch.n = size(x,1); - ch.La2 = La2; - ch.a = a; - ch.p=p; - ch.datahash=datahash; -end -end - diff --git a/gp/gpla_mo_g.m b/gp/gpla_mo_g.m deleted file mode 100644 index 30080447..00000000 --- a/gp/gpla_mo_g.m +++ /dev/null @@ -1,245 +0,0 @@ -function [g, gdata, gprior] = gpla_mo_g(w, gp, x, y, varargin) -%GPLA_SOFTMAX_G Evaluate gradient of Laplace approximation's marginal -% log posterior estimate for softmax likelihood (GPLA_SOFTMAX_E) -% -% Description -% G = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) takes a full GP -% hyper-parameter vector W, structure GP a matrix X of -% input vectors and a matrix Y of target vectors, and evaluates -% the gradient G of EP's marginal log posterior estimate . Each -% row of X corresponds to one input vector and each row of Y -% corresponds to one target vector. -% -% [G, GDATA, GPRIOR] = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) also -% returns the data and prior contributions to the gradient. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected -% value for ith case. -% -% See also -% GPLA_SOFTMAX_E, GPLA_E, GPLA_SOFTMAX_PRED - -% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -ip=inputParser; -ip.FunctionName = 'GPLA_MO_G'; -ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y, varargin{:}); -z=ip.Results.z; - -gp = gp_unpak(gp, w); % unpak the parameters -ncf = length(gp.cf); -n=size(x,1); -nout=size(y,2); - -g = []; -gdata = []; -gprior = []; - -% First Evaluate the data contribution to the error -switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' % A full GP - - if isfield(gp, 'comp_cf') % own covariance for each ouput component - multicf = true; - if length(gp.comp_cf) ~= nout - error('GPLA_MO_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') - end - else - multicf = false; - end - - K = zeros(n,n,nout); - if multicf - for i1=1:nout - K(:,:,i1) = gp_trcov(gp, x, gp.comp_cf{i1}); - end - else - for i1=1:nout - K(:,:,i1) = gp_trcov(gp, x); - end - end - - [e, edata, eprior, f, L, a, E, M, p] = gpla_mo_e(gp_pak(gp), gp, x, y, 'z', z); - - % softmax - f2=reshape(f,n,nout); - - llg = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); - [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); - R = repmat(1./pi2_vec,1,n).*pi2_mat; - RE = zeros(n,n*nout); - for i1=1:nout - RE(:,(1:n)+(i1-1)*n) = R((1:n)+(i1-1)*n,:)'*E(:,:,i1); - end - - inv_iWK=zeros(n,n,nout); - - % Matrices for computing the derivative of determinant term w.r.t. f - A=zeros(nout, nout, n); - Minv=M\(M'\eye(n)); - Minv=(Minv+Minv')./2; - for cc1=1:nout - EMinv=RE(:,(1:n)+(cc1-1)*n)'*Minv; - KEMinv=K(:,:,cc1)*EMinv; - for cc2=1:nout - if cc2>=cc1 - if cc1==cc2 - EMtmp = - EMinv*RE(:,(1:n)+(cc2-1)*n); - EMtmp = EMtmp + E(:,:,cc1); - inv_iWK(:,:,cc1) = EMtmp; - A(cc1,cc1,:) = diag(K(:,:,cc1))-sum((K(:,:,cc1)*EMtmp).*K(:,:,cc1),2); - else - EMtmp = - KEMinv*RE(:,(1:n)+(cc2-1)*n); - A(cc1,cc2,:) = -sum(EMtmp.*K(:,:,cc2),2); - A(cc2,cc1,:) = -sum(EMtmp.*K(:,:,cc2),2); - end - end - end - end - - % Derivative of determinant term w.r.t. f - s2=zeros(n*nout,1); - dw_mat = gp.lik.fh.llg3(gp.lik, y, f, 'latent', z); - for cc3=1:nout - for ii1=1:n - s2(ii1+(cc3-1)*n) = -0.5*trace(A(:,:,ii1)*dw_mat(:,:,cc3,ii1)); - end - end - - % Loop over the covariance functions - for i=1:ncf - DKllg=zeros(size(a)); - EDKllg=zeros(size(a)); - DKffba=zeros(n*nout,1); - - % check in which components the covariance function is present - do = false(nout,1); - if multicf - for z1=1:nout - if any(gp.comp_cf{z1}==i) - do(z1) = true; - end - end - else - do = true(nout,1); - end - - i1=0; - if ~isempty(gprior) - i1 = length(gprior); - end - - % Gradients from covariance functions - gpcf = gp.cf{i}; - DKff = gpcf.fh.cfg(gpcf, x); - gprior_cf = -gpcf.fh.lpg(gpcf); - - for i2 = 1:length(DKff) - i1 = i1+1; - DKffb=DKff{i2}; - - % Derivative of explicit terms - trace_sum_tmp=0; - for z1=1:nout - if do(z1) - DKffba((1:n)+(z1-1)*n)=DKffb*a((1:n)+(z1-1)*n); - trace_sum_tmp = trace_sum_tmp + sum(sum( inv_iWK(:,:,z1) .* DKffb )); - end - end - s1 = 0.5 * a'*DKffba - 0.5.*trace_sum_tmp; - - % Derivative of f w.r.t. theta - for z1=1:nout - if do(z1) - DKllg((1:n)+(z1-1)*n)=DKffb*llg((1:n)+(z1-1)*n); - EDKllg((1:n)+(z1-1)*n)=E(:,:,z1)*DKllg((1:n)+(z1-1)*n); - end - end - s3 = EDKllg - RE'*(M\(M'\(RE*DKllg))); - for z1=1:nout - s3((1:n)+(z1-1)*n)=K(:,:,z1)*s3((1:n)+(z1-1)*n); - end - s3 = DKllg - s3; - - gdata(i1) = -(s1 + s2'*s3); - gprior(i1) = gprior_cf(i2); - - end - - % Set the gradients of hyper-hyperparameter - if length(gprior_cf) > length(DKff) - for i2=length(DKff)+1:length(gprior_cf) - i1 = i1+1; - gdata(i1) = 0; - gprior(i1) = gprior_cf(i2); - end - end - end - -% % ================================================================= -% % Gradient with respect to likelihood function parameters -% if ~isempty(strfind(gp.infer_params, 'likelihood')) ... -% && ~isempty(gp.lik.fh.pak(gp.lik)) -% -% gdata_likelih = 0; -% lik = gp.lik; -% -% g_logPrior = feval(lik.fh.gprior, lik); -% if ~isempty(g_logPrior) -% -% DW_sigma = feval(lik.fh.llg3, lik, y, f, 'latent2+hyper', z); -% DL_sigma = feval(lik.fh.llg, lik, y, f, 'hyper', z); -% b = K * feval(lik.fh.llg2, lik, y, f, 'latent+hyper', z); -% s3 = b - K*(R*b); -% nl= size(DW_sigma,2); -% -% gdata_lik = - DL_sigma - 0.5.*sum(repmat(C2,1,nl).*DW_sigma) - s2'*s3; -% -% % set the gradients into vectors that will be returned -% gdata = [gdata gdata_lik]; -% gprior = [gprior g_logPrior]; -% i1 = length(g_logPrior); -% i2 = length(gdata_lik); -% if i1 > i2 -% gdata = [gdata zeros(1,i1-i2)]; -% end -% end -% end - - g = gdata + gprior; - - % ============================================================ - % FIC - % ============================================================ - case 'FIC' - - % ============================================================ - % PIC - % ============================================================ - case {'PIC' 'PIC_BLOCK'} - - % ============================================================ - % CS+FIC - % ============================================================ - case 'CS+FIC' - - end - - assert(isreal(gdata)) - assert(isreal(gprior)) -end diff --git a/gp/gpla_mo_pred.m b/gp/gpla_mo_pred.m deleted file mode 100644 index 271fec06..00000000 --- a/gp/gpla_mo_pred.m +++ /dev/null @@ -1,205 +0,0 @@ -function [Ef, Varf, lpyt, Ey, Vary] = gpla_mo_pred(gp, x, y, varargin) -%function [Ef, Varf, Ey, Vary, Pyt] = gpla_multinom_pred(gp, x, y, xt, varargin) -%GPLA_MO_PRED Predictions with Gaussian Process Laplace -% approximation with multinom likelihood -% -% Description -% [EFT, VARFT] = GPLA_MO_PRED(GP, X, Y, XT, OPTIONS) takes -% a GP structure GP together with a matrix XT of input vectors, -% matrix X of training inputs and vector Y of training targets, -% and evaluates the predictive distribution at inputs XT. Returns -% a posterior mean EFT and variance VARFT of latent variables. -% -% [EF, VARF, LPYT] = GPLA_MO_PRED(GP, X, Y, XT, 'yt', YT, ...) -% returns also logarithm of the predictive density PYT of the observations YT -% at input locations XT. This can be used for example in the -% cross-validation. Here Y has to be vector. -% -% [EF, VARF, LPYT, EYT, VARYT] = GPLA_MO_PRED(GP, X, Y, XT, 'yt', YT, ...) -% returns also the posterior predictive mean EYT and variance VARYT. -% -% OPTIONS is optional parameter-value pair -% predcf - is index vector telling which covariance functions are -% used for prediction. Default is all (1:gpcfn). See -% additional information below. -% tstind - is a vector/cell array defining, which rows of X belong -% to which training block in *IC type sparse models. Deafult -% is []. In case of PIC, a cell array containing index -% vectors specifying the blocking structure for test data. -% IN FIC and CS+FIC a vector of length n that points out the -% test inputs that are also in the training set (if none, -% set TSTIND = []). -% yt - is optional observed yt in test points -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in -% case of Poisson likelihood we have z_i=E_i, that -% is, expected value for ith case. -% zt - optional observed quantity in triplet (xt_i,yt_i,zt_i) -% Some likelihoods may use this. For example, in -% case of Poisson likelihood we have zt_i=Et_i, that -% is, expected value for ith case. -% -% See also -% GPLA_MO_E, GPLA_MO_G, GP_PRED, DEMO_MULTICLASS -% -% Copyright (c) 2010 Jaakko Riihim�ki - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GPLA_MO_PRED'; - ip.addRequired('gp', @isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || iscell(x) && isvector(x)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) - if numel(varargin)==0 || isnumeric(varargin{1}) - % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); - else - ip.parse(gp, x, y, [], varargin{:}); - end - xt=ip.Results.xt; - yt=ip.Results.yt; - z=ip.Results.z; - zt=ip.Results.zt; - predcf=ip.Results.predcf; - tstind=ip.Results.tstind; - if isempty(xt) - xt=x; - if isempty(tstind) - if iscell(gp) - gptype=gp{1}.type; - else - gptype=gp.type; - end - switch gptype - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:size(x,1); - case 'PIC' - if iscell(gp) - tstind = gp{1}.tr_index; - else - tstind = gp.tr_index; - end - end - end - if isempty(yt) - yt=y; - end - if isempty(zt) - zt=z; - end - end - - Ey=[]; - Vary=[]; - - [tn, nout] = size(y); - - switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' - [e, edata, eprior, f, L, a, E, M, p] = gpla_mo_e(gp_pak(gp), gp, x, y, 'z', z); - - if isfield(gp, 'comp_cf') % own covariance for each ouput component - multicf = true; - if length(gp.comp_cf) ~= nout - error('GPLA_MO_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') - end - if ~isempty(predcf) - if ~iscell(predcf) || length(predcf)~=nout - error(['GPLA_MO_PRED: if own covariance for each output component is used,'... - 'predcf has to be cell array and contain nout (vector) elements. ']) - end - else - predcf = gp.comp_cf; - end - else - multicf = false; - for i1=1:nout - predcf2{i1} = predcf; - end - predcf=predcf2; - end - - ntest=size(xt,1); - K_nf = zeros(ntest,tn,nout); - if multicf - for i1=1:nout - K_nf(:,:,i1) = gp_cov(gp,xt,x,predcf{i1}); - end - else - for i1=1:nout - K_nf(:,:,i1) = gp_cov(gp,xt,x,predcf{i1}); - end - end - - nout=size(y,2); - f2=reshape(f,tn,nout); - - llg_vec = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); - llg = reshape(llg_vec,size(y)); - - %mu_star = K_nf*reshape(a,tn,nout); - a=reshape(a,size(y)); - for i1 = 1:nout - % Ef(:,i1) = K_nf(:,:,i1)*llg(:,i1); - Ef(:,i1) = K_nf(:,:,i1)*a(:,i1); - end - - if nargout > 1 - [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); - Varf=zeros(nout, nout, ntest); - - R=(repmat(1./pi2_vec,1,tn).*pi2_mat); - for i1=1:nout - b=E(:,:,i1)*K_nf(:,:,i1)'; - c_cav = R((1:tn)+(i1-1)*tn,:)*(M\(M'\(R((1:tn)+(i1-1)*tn,:)'*b))); - - for j1=1:nout - c=E(:,:,j1)*c_cav; - Varf(i1,j1,:)=sum(c.*K_nf(:,:,j1)'); - end - - kstarstar = gp_trvar(gp,xt,predcf{i1}); - Varf(i1,i1,:) = squeeze(Varf(i1,i1,:)) + kstarstar - sum(b.*K_nf(:,:,i1)')'; - end - end - % ============================================================ - % FIC - % ============================================================ - case 'FIC' % Predictions with FIC sparse approximation for GP - % ============================================================ - % PIC - % ============================================================ - case {'PIC' 'PIC_BLOCK'} % Predictions with PIC sparse approximation for GP - % ============================================================ - % CS+FIC - % ============================================================ - case 'CS+FIC' % Predictions with CS+FIC sparse approximation for GP - end - - % ============================================================ - % Evaluate also the predictive mean and variance of new observation(s) - % ============================================================ - if nargout > 2 && isempty(yt) - error('yt has to be provided to get lpyt.') - end - if nargout > 3 - [lpyt, Ey, Vary] = gp.lik.fh.predy(gp.lik, Ef, Varf, [], zt); - elseif nargout > 2 - lpyt = gp.lik.fh.predy(gp.lik, Ef, Varf, yt, zt); - end -end \ No newline at end of file diff --git a/gp/gpla_softmax_e.m b/gp/gpla_softmax_e.m deleted file mode 100644 index 5b70cf6e..00000000 --- a/gp/gpla_softmax_e.m +++ /dev/null @@ -1,515 +0,0 @@ -function [e, edata, eprior, f, L, a, E, M, p] = gpla_softmax_e(w, gp, varargin) -%GPLA_SOFTMAX_E Do Laplace approximation and return marginal log posterior -% estimate for softmax likelihood -% -% Description -% E = GPLA_SOFTMAX_E(W, GP, X, Y, OPTIONS) takes a GP data -% structure GP together with a matrix X of input vectors and a -% matrix Y of target vectors, and finds the Laplace -% approximation for the conditional posterior p(Y | X, th), -% where th is the parameters. Returns the energy at th (see -% below). Each row of X corresponds to one input vector and each -% row of Y corresponds to one target vector. -% -% [E, EDATA, EPRIOR] = GPLA_SOFTMAX_E(W, GP, X, Y, OPTIONS) -% returns also the data and prior components of the total -% energy. -% -% The energy is minus log posterior cost function for th: -% E = EDATA + EPRIOR -% = - log p(Y|X, th) - log p(th), -% where th represents the parameters (lengthScale, -% magnSigma2...), X is inputs and Y is observations. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected -% value for ith case. -% -% See also -% GP_SET, GP_E, GPLA_SOFTMAX_G, GPLA_SOFTMAX_PRED - -% Description 2 -% Additional properties meant only for internal use. -% -% GP = GPLA_SOFTMAX_E('init', GP) takes a GP structure GP and -% initializes required fields for the Laplace approximation. -% -% GP = GPLA_SOFTMAX_E('clearcache', GP) takes a GP structure GP and -% cleares the internal cache stored in the nested function workspace -% -% [e, edata, eprior, f, L, a, La2, p] -% = gpla_softmax_e(w, gp, x, y, varargin) -% returns many useful quantities produced by EP algorithm. -% -% The Newton's method is implemented as described in -% Rasmussen and Williams (2006). - -% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki, -% Jarno Vanhatalo, Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GPLA_SOFTMAX_E'; - ip.addRequired('w', @(x) ... - (ischar(x) && strcmp(w, 'init')) || ... - isvector(x) && isreal(x) && all(isfinite(x))); - ip.addRequired('gp',@isstruct); - ip.addOptional('x', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('y', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) - ip.parse(w, gp, varargin{:}); - x=ip.Results.x; - y=ip.Results.y; - z=ip.Results.z; - - if strcmp(w, 'init') - % initialize cache - ch = []; - - % return function handle to the nested function ep_algorithm - % this way each gp has its own peristent memory for EP - gp.fh.e = @laplace_algorithm; - e = gp; - % remove clutter from the nested workspace - clear w gp varargin ip x y z - elseif strcmp(w, 'clearcache') - % clear the cache - gp.fh.e('clearcache'); - else - % call laplace_algorithm using the function handle to the nested function - % this way each gp has its own peristent memory for Laplace - [e, edata, eprior, f, L, a, E, M, p] = gp.fh.e(w, gp, x, y, z); - end - - function [e, edata, eprior, f, L, a, E, M, p] = laplace_algorithm(w, gp, x, y, z) - - if strcmp(w, 'clearcache') - ch=[]; - return - end - % code for the Laplace algorithm - - % check whether saved values can be used - if isempty(z) - datahash=hash_sha512([x y]); - else - datahash=hash_sha512([x y z]); - end - if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash) - % The covariance function parameters or data haven't changed - % so we can return the energy and the site parameters that are saved - e = ch.e; - edata = ch.edata; - eprior = ch.eprior; - f = ch.f; - L = ch.L; - a = ch.a; - E = ch.E; - M = ch.M; - p = ch.p; - else - % The parameters or data have changed since - % the last call for gpla_e. In this case we need to - % re-evaluate the Laplace approximation - gp=gp_unpak(gp, w); - ncf = length(gp.cf); - n = size(x,1); - p = []; - - % Initialize latent values - % zero seems to be a robust choice (Jarno) - f = zeros(size(y(:))); - - % ================================================= - % First Evaluate the data contribution to the error - switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' - K = gp_trcov(gp, x); - - % If K is sparse, permute all the inputs so that evaluations are more efficient - if issparse(K) % Check if compact support covariance is used - p = analyze(K); - y = y(p); - K = K(p,p); - if ~isempty(z) - z = z(p,:); - end - [LD, notpositivedefinite] = ldlchol(K); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - else - % LD = chol(K); - end - - switch gp.latent_opt.optim_method - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by fminunc large scale method - % case 'fminunc_large' - % if ~isfield(gp.latent_opt, 'fminunc_opt') - % opt=optimset('GradObj','on'); - % opt=optimset(opt,'Hessian','on'); - % if issparse(K) - % fhm = @(W, f, varargin) (ldlsolve(LD,f) + repmat(W,1,size(f,2)).*f); % W*f; % - % else - % fhm = @(W, f, varargin) (LD\(LD'\f) + repmat(W,1,size(f,2)).*f); % W*f; % - % end - % opt=optimset(opt,'HessMult', fhm); - % opt=optimset(opt,'TolX', 1e-12); - % opt=optimset(opt,'TolFun', 1e-12); - % opt=optimset(opt,'LargeScale', 'on'); - % opt=optimset(opt,'Display', 'off'); % 'iter' - % else - % opt = gp.latent_opt.fminunc_opt; - % end - % - % if issparse(K) - % fe = @(f, varargin) (0.5*f*(ldlsolve(LD,f')) - feval(gp.lik.fh.ll, gp.lik, y, f', z)); - % fg = @(f, varargin) (ldlsolve(LD,f') - feval(gp.lik.fh.llg, gp.lik, y, f', 'latent', z))'; - % fh = @(f, varargin) (-feval(gp.lik.fh.llg2, gp.lik, y, f', 'latent', z)); %inv(K) + diag(g2(f', gp.lik)) ; % - % else - % fe = @(f, varargin) (0.5*f*(LD\(LD'\f')) - feval(gp.lik.fh.ll, gp.lik, y, f', z)); - % fg = @(f, varargin) (LD\(LD'\f') - feval(gp.lik.fh.llg, gp.lik, y, f', 'latent', z))'; - % fh = @(f, varargin) (-feval(gp.lik.fh.llg2, gp.lik, y, f', 'latent', z)); %inv(K) + diag(g2(f', gp.lik)) ; % - % end - % - % mydeal = @(varargin)varargin{1:nargout}; - % [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); - % f = f'; - % - % if issparse(K) - % a = ldlsolve(LD,f); - % else - % a = LD\(LD'\f); - % end - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by Newton method - case 'newton' - tol = 1e-12; - a = f; - - nout=size(y,2); - f2=reshape(f,n,nout); - - %W = -feval(gp.lik.fh.llg2, gp.lik, y, f2, 'latent', z); - %dlp = feval(gp.lik.fh.llg, gp.lik, y, f2, 'latent', z); - %lp_new = feval(gp.lik.fh.ll, gp.lik, y, f2, z); - - lp_new = gp.lik.fh.ll(gp.lik, y, f2, z); - lp_old = -Inf; - - Kbb=zeros(n*nout,1); - c=zeros(n*nout,1); - ERMMRc=zeros(n*nout,1); - pipif=zeros(n*nout,1); - E=zeros(n,n,nout); - L=zeros(n,n,nout); - - while lp_new - lp_old > tol - lp_old = lp_new; a_old = a; - - % softmax: - expf2 = exp(f2); - pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); - pi_vec=pi2(:); - - E_tmp=zeros(n); - - for i1=1:nout - Dc=sqrt(pi2(:,i1)); - Lc=(Dc*Dc').*K; - Lc(1:n+1:end)=Lc(1:n+1:end)+1; - [Lc, notpositivedefinite]=chol(Lc); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - L(:,:,i1)=Lc; - - Ec=Lc'\diag(Dc); - Ec=Ec'*Ec; - E_tmp=E_tmp+Ec; - E(:,:,i1)=Ec; - end - - [M, notpositivedefinite]=chol(E_tmp); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - - pif=sum(pi2.*f2,2); - - for i1=1:nout - pipif((1:n)+(i1-1)*n)=pi2(:,i1).*pif; - end - - b = pi_vec.*f-pipif+y(:)-pi_vec; - % b = -feval(gp.lik.fh.llg2, gp.lik, y, f2, 'latent', z)*f + ... - % feval(gp.lik.fh.llg, gp.lik, y, f2, 'latent', z); - - for i1=1:nout - Kbb((1:n)+(i1-1)*n)=K*b((1:n)+(i1-1)*n); - c((1:n)+(i1-1)*n)=E(:,:,i1)*Kbb((1:n)+(i1-1)*n); - end - - Rc=sum(reshape(c, n, nout),2); - MMRc=M\(M'\Rc); - for i1=1:nout - ERMMRc((1:n)+(i1-1)*n) = E(:,:,i1)*MMRc; - end - a=b-c+ERMMRc; - - for i1=1:nout - f((1:n)+(i1-1)*n)=K*a((1:n)+(i1-1)*n); - end - f2=reshape(f,n,nout); - - lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); - - i = 0; - while i < 10 && lp_new < lp_old || isnan(sum(f)) - % reduce step size by half - a = (a_old+a)/2; - - for i1=1:nout - f((1:n)+(i1-1)*n)=K*a((1:n)+(i1-1)*n); - end - f2=reshape(f,n,nout); - - lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); - i = i+1; - end - end - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables by stabilized Newton method. - % This is implemented as suggested by Hannes Nickisch (personal communication) - case 'stabilized-newton' - % % Gaussian initialization - % % sigma=gp.lik.sigma; - % % W = ones(n,1)./sigma.^2; - % % sW = sqrt(W); - % % %B = eye(n) + siV*siV'.*K; - % % L=bsxfun(@times,bsxfun(@times,sW,K),sW'); - % % L(1:n+1:end)=L(1:n+1:end)+1; - % % L = chol(L,'lower'); - % % a=sW.*(L'\(L\(sW.*y))); - % % f = K*a; - % - % % initialize to observations - % %f=y; - % - % nu=gp.lik.nu; - % sigma2=gp.lik.sigma2; - % Wmax=(nu+1)/nu/sigma2; - % Wlim=0; - % - % tol = 1e-10; - % W = -feval(gp.lik.fh.llg2, gp.lik, y, f, 'latent'); - % dlp = feval(gp.lik.fh.llg, gp.lik, y, f, 'latent'); - % lp = -(f'*(K\f))/2 +feval(gp.lik.fh.ll, gp.lik, y, f); - % lp_old = -Inf; - % f_old = f+1; - % ge = Inf; %max(abs(a-dlp)); - % - % i1=0; - % % begin Newton's iterations - % while lp - lp_old > tol || max(abs(f-f_old)) > tol - % i1=i1+1; - % - % W = -feval(gp.lik.fh.llg2, gp.lik, y, f, 'latent'); - % dlp = feval(gp.lik.fh.llg, gp.lik, y, f, 'latent'); - % - % W(Wge) ) && WlimWmax*0.5 || i1>5000 - % %fprintf('\n%3d, p(f)=%.12f, max|a-g|=%.12f, %.3f \n',i1,lp,ge,Wlim) - % break - % end - % - % end - % -------------------------------------------------------------------------------- - % find the posterior mode of latent variables with likelihood specific algorithm - % For example, with Student-t likelihood this mean EM-algorithm which is coded in the - % likelih_t file. - case 'lik_specific' - [f, a] = gp.lik.fh.optimizef(gp, y, K); - otherwise - error('gpla_e: Unknown optimization method ! ') - end - - % evaluate the approximate log marginal likelihood - - expf2 = exp(f2); - pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); - pi_vec=pi2(:); - E_tmp=zeros(n); - - for i1=1:nout - Dc=sqrt(pi2(:,i1)); - Lc=(Dc*Dc').*K; - Lc(1:n+1:end)=Lc(1:n+1:end)+1; - [Lc, notpositivedefinite]=chol(Lc); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - L(:,:,i1)=Lc; - - Ec=Lc'\diag(Dc); - Ec=Ec'*Ec; - E_tmp=E_tmp+Ec; - E(:,:,i1)=Ec; - end - [M,notpositivedefinite]=chol(E_tmp); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - - det_diag=0; - det_mat_sum = zeros(n); - for i1 = 1:nout - det_diag=det_diag+sum(log(diag(L(:,:,i1)))); - Kpi=K*diag(pi_vec((1:n)+(i1-1)*n)); - - det_mat_sum=det_mat_sum+E(:,:,i1)*Kpi; - end - - %det_mat_sum=(det_mat_sum+det_mat_sum')./2; - [det_mat, notpositivedefinite]=chol(eye(n)-det_mat_sum); - if notpositivedefinite - [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); - return - end - det_term=sum(log(diag(det_mat)))+det_diag; - - - logZ = a'*f/2 - gp.lik.fh.ll(gp.lik, y, f2, z) + det_term; - edata = logZ; - - % ============================================================ - % FIC - % ============================================================ - case 'FIC' - - % ============================================================ - % PIC - % ============================================================ - case {'PIC' 'PIC_BLOCK'} - - % ============================================================ - % CS+FIC - % ============================================================ - case 'CS+FIC' - - otherwise - error('Unknown type of Gaussian process!') - end - - % ====================================================================== - % Evaluate the prior contribution to the error from covariance functions - % ====================================================================== - eprior = 0; - for i=1:ncf - gpcf = gp.cf{i}; - eprior = eprior - gpcf.fh.lp(gpcf); - end - - % Evaluate the prior contribution to the error from likelihood function - if isfield(gp, 'lik') && isfield(gp.lik, 'p') - lik = gp.lik; - eprior = eprior - lik.fh.lp(lik); - end - - e = edata + eprior; - - % store values to the cache - ch.w = w; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.f = f; - ch.L = L; - ch.a = a; - ch.E = E; - ch.M = M; - ch.p=p; - ch.datahash=datahash; - end - - assert(isreal(edata)) - assert(isreal(eprior)) - - end - -function [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite() - % Instead of stopping to chol error, return NaN - edata=NaN; - e=NaN; - eprior=NaN; - f=NaN; - L=NaN; - a=NaN; - E = NaN; - M = NaN; - p=NaN; - ch.w = w; - ch.e = e; - ch.edata = edata; - ch.eprior = eprior; - ch.f = f; - ch.L = L; - ch.M = M; - ch.E = E; - ch.a = a; - ch.p=p; - ch.datahash=datahash; -end -end diff --git a/gp/gpla_softmax_g.m b/gp/gpla_softmax_g.m deleted file mode 100644 index f8d7a8ea..00000000 --- a/gp/gpla_softmax_g.m +++ /dev/null @@ -1,244 +0,0 @@ -function [g, gdata, gprior] = gpla_softmax_g(w, gp, x, y, varargin) -%GPLA_SOFTMAX_G Evaluate gradient of Laplace approximation's marginal -% log posterior estimate for softmax likelihood (GPLA_SOFTMAX_E) -% -% Description -% G = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) takes a full GP -% hyper-parameter vector W, structure GP a matrix X of -% input vectors and a matrix Y of target vectors, and evaluates -% the gradient G of EP's marginal log posterior estimate . Each -% row of X corresponds to one input vector and each row of Y -% corresponds to one target vector. -% -% [G, GDATA, GPRIOR] = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) also -% returns the data and prior contributions to the gradient. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected -% value for ith case. -% -% See also -% GPLA_SOFTMAX_E, GPLA_E, GPLA_SOFTMAX_PRED - -% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - -ip=inputParser; -ip.FunctionName = 'GPLA_SOFTMAX_G'; -ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); -ip.addRequired('gp',@isstruct); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.parse(w, gp, x, y, varargin{:}); -z=ip.Results.z; - -gp = gp_unpak(gp, w); % unpak the parameters -ncf = length(gp.cf); -n=size(x,1); -nout=size(y,2); - -g = []; -gdata = []; -gprior = []; - -% First Evaluate the data contribution to the error -switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' % A full GP - - K = gp_trcov(gp,x); - - [e, edata, eprior, f, L, a, E, M, p] = gpla_softmax_e(gp_pak(gp), gp, x, y, 'z', z); - - % softmax - f2=reshape(f,n,nout); - expf2 = exp(f2); - pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); - pi_vec=pi2(:); - - DKBy_pi=zeros(n*nout,1); - DKffba=zeros(n*nout,1); - inv_iWK=zeros(n,n,nout); - EDKBy=zeros(n*nout,1); - EME=zeros(n*nout,1); - E_diff=zeros(n*nout,1); - - % Matrices for computing the derivative of determinant term w.r.t. f - A=zeros(nout, nout, n); - Minv=M\(M'\eye(n)); - Minv=(Minv+Minv')./2; - for cc1=1:nout - EMinv=E(:,:,cc1)*Minv; - KEMinv=K*EMinv; - for cc2=1:nout - if cc2>=cc1 - if cc1==cc2 - EMtmp= - EMinv*E(:,:,cc2); - EMtmp=EMtmp + E(:,:,cc1); - inv_iWK(:,:,cc1)=EMtmp; - A(cc1,cc1,:)=diag(K)-sum((K*EMtmp).*K,2); - else - EMtmp= - KEMinv*E(:,:,cc2); - A(cc1,cc2,:)=-sum(EMtmp.*K,2); - A(cc2,cc1,:)=-sum(EMtmp.*K,2); - end - end - end - end - - s2=zeros(n*nout,1); - - for cc3=1:nout - for ii1=1:n - - dw_mat=zeros(nout,nout); - pic=pi_vec(ii1:n:(nout*n)); - - for cc1=1:nout - for cc2=1:nout - - % softmax third derivatives - cc_sum_tmp=0; - if cc1==cc2 && cc1==cc3 && cc2==cc3 - cc_sum_tmp=cc_sum_tmp+pic(cc1); - end - if cc1==cc2 - cc_sum_tmp=cc_sum_tmp-pic(cc1)*pic(cc3); - end - if cc2==cc3 - cc_sum_tmp=cc_sum_tmp-pic(cc1)*pic(cc2); - end - if cc1==cc3 - cc_sum_tmp=cc_sum_tmp-pic(cc1)*pic(cc2); - end - cc_sum_tmp=cc_sum_tmp+2*pic(cc1)*pic(cc2)*pic(cc3); - - dw_mat(cc1,cc2)=cc_sum_tmp; - end - end - % Derivative of determinant term w.r.t. f - s2(ii1+(cc3-1)*n)=-0.5*trace(A(:,:,ii1)*dw_mat); - end - end - - - for i=1:ncf - - i1=0; - if ~isempty(gprior) - i1 = length(gprior); - end - - % Gradients from covariance functions - gpcf = gp.cf{i}; - DKff = gpcf.fh.cfg(gpcf, x); - gprior_cf = -gpcf.fh.lpg(gpcf); - - for i2 = 1:length(DKff) - i1 = i1+1; - DKffb=DKff{i2}; - - trace_sum_tmp=0; - for z1=1:nout - DKffba((1:n)+(z1-1)*n)=DKffb*a((1:n)+(z1-1)*n); - trace_sum_tmp=trace_sum_tmp+sum(sum( inv_iWK(:,:,z1) .* DKffb )); - end - % Derivative of explicit terms - s1 = 0.5 * a'*DKffba - 0.5*trace_sum_tmp; - - y_pi=y(:)-pi_vec; - for z1=1:nout - DKBy_pi((1:n)+(z1-1)*n)=DKffb*y_pi((1:n)+(z1-1)*n); - EDKBy((1:n)+(z1-1)*n)=E(:,:,z1)*DKBy_pi((1:n)+(z1-1)*n); - end - - EDKBys=sum(reshape(EDKBy, n, nout),2); - MMRc=Minv*EDKBys; - - for z1=1:nout - EME((1:n)+(z1-1)*n) = E(:,:,z1)*MMRc; - end - EDK_EME=EDKBy-EME; - - for z1=1:nout - E_diff((1:n)+(z1-1)*n)=K*EDK_EME((1:n)+(z1-1)*n); - end - - % Derivative of f w.r.t. theta - s3=DKBy_pi-E_diff; - - gdata(i1) = -(s1 + s2'*s3); - gprior(i1) = gprior_cf(i2); - - end - - % Set the gradients of hyper-hyperparameter - if length(gprior_cf) > length(DKff) - for i2=length(DKff)+1:length(gprior_cf) - i1 = i1+1; - gdata(i1) = 0; - gprior(i1) = gprior_cf(i2); - end - end - end - -% % ================================================================= -% % Gradient with respect to likelihood function parameters -% if ~isempty(strfind(gp.infer_params, 'likelihood')) ... -% && ~isempty(gp.lik.fh.pak(gp.lik)) -% -% gdata_likelih = 0; -% lik = gp.lik; -% -% g_logPrior = feval(lik.fh.gprior, lik); -% if ~isempty(g_logPrior) -% -% DW_sigma = feval(lik.fh.llg3, lik, y, f, 'latent2+hyper', z); -% DL_sigma = feval(lik.fh.llg, lik, y, f, 'hyper', z); -% b = K * feval(lik.fh.llg2, lik, y, f, 'latent+hyper', z); -% s3 = b - K*(R*b); -% nl= size(DW_sigma,2); -% -% gdata_lik = - DL_sigma - 0.5.*sum(repmat(C2,1,nl).*DW_sigma) - s2'*s3; -% -% % set the gradients into vectors that will be returned -% gdata = [gdata gdata_lik]; -% gprior = [gprior g_logPrior]; -% i1 = length(g_logPrior); -% i2 = length(gdata_lik); -% if i1 > i2 -% gdata = [gdata zeros(1,i1-i2)]; -% end -% end -% end - - g = gdata + gprior; - - % ============================================================ - % FIC - % ============================================================ - case 'FIC' - - % ============================================================ - % PIC - % ============================================================ - case {'PIC' 'PIC_BLOCK'} - - % ============================================================ - % CS+FIC - % ============================================================ - case 'CS+FIC' - - end - - assert(isreal(gdata)) - assert(isreal(gprior)) -end diff --git a/gp/gpla_softmax_pred.m b/gp/gpla_softmax_pred.m deleted file mode 100644 index 448554f5..00000000 --- a/gp/gpla_softmax_pred.m +++ /dev/null @@ -1,176 +0,0 @@ -function [mu_star, Sigm_cc, pi_star, Ey, Vary] = gpla_softmax_pred(gp, x, y, varargin) -%function [Ef, Varf, Ey, Vary, Pyt] = gpla_softmax_pred(gp, x, y, xt, varargin) -%GPLA_SOFTMAX_PRED Predictions with Gaussian Process Laplace -% approximation with softmax likelihood -% -% Description -% [EFT, VARFT] = GPLA_SOFTMAX_PRED(GP, X, Y, XT, OPTIONS) takes -% a GP structure GP together with a matrix XT of input vectors, -% matrix X of training inputs and vector Y of training targets, -% and evaluates the predictive distribution at inputs XT. Returns -% a posterior mean EFT and variance VARFT of latent variables. -% -% [EF, VARF, LPYT] = GPLA_SOFTMAX_PRED(GP, X, Y, XT, 'yt', YT, ...) -% returns also logarithm of the predictive density PYT of the observations YT -% at input locations XT. This can be used for example in the -% cross-validation. Here Y has to be vector. -% -% -% OPTIONS is optional parameter-value pair -% predcf - is index vector telling which covariance functions are -% used for prediction. Default is all (1:gpcfn). See -% additional information below. -% tstind - is a vector/cell array defining, which rows of X belong -% to which training block in *IC type sparse models. Deafult -% is []. In case of PIC, a cell array containing index -% vectors specifying the blocking structure for test data. -% IN FIC and CS+FIC a vector of length n that points out the -% test inputs that are also in the training set (if none, -% set TSTIND = []). -% yt - is optional observed yt in test points -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in -% case of Poisson likelihood we have z_i=E_i, that -% is, expected value for ith case. -% zt - optional observed quantity in triplet (xt_i,yt_i,zt_i) -% Some likelihoods may use this. For example, in -% case of Poisson likelihood we have zt_i=Et_i, that -% is, expected value for ith case. -% -% See also -% GPLA_SOFTMAX_E, GPLA_SOFTMAX_G, GP_PRED, DEMO_MULTICLASS -% -% Copyright (c) 2010 Jaakko Riihim�ki - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GPLA_SOFTMAX_PRED'; - ip.addRequired('gp', @isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) - if numel(varargin)==0 || isnumeric(varargin{1}) - % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); - else - ip.parse(gp, x, y, [], varargin{:}); - end - xt=ip.Results.xt; - yt=ip.Results.yt; - predcf=ip.Results.predcf; - tstind=ip.Results.tstind; - if isempty(xt) - xt=x; - if isempty(tstind) - if iscell(gp) - gptype=gp{1}.type; - else - gptype=gp.type; - end - switch gptype - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:size(x,1); - case 'PIC' - if iscell(gp) - tstind = gp{1}.tr_index; - else - tstind = gp.tr_index; - end - end - end - if isempty(yt) - yt=y; - end - end - - Ey=[]; - Vary=[]; - - [tn, tnin] = size(x); - - switch gp.type - % ============================================================ - % FULL - % ============================================================ - case 'FULL' - [e, edata, eprior, f, L, a, E, M, p] = gpla_softmax_e(gp_pak(gp), gp, x, y, 'z', z); - - ntest=size(xt,1); - K_nf = gp_cov(gp,xt,x,predcf); - K = gp_trcov(gp, x); - - nout=size(y,2); - f2=reshape(f,tn,nout); - - expf2 = exp(f2); - pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); - - mu_star=zeros(ntest,nout); - Sigm_cc=zeros(nout, nout, ntest); - - Minv=M\(M'\eye(tn)); - Minv=(Minv+Minv')./2; - - for i1=1:nout - mu_star(:,i1)=(y(:,i1)-pi2(:,i1))'*K_nf'; - b=E(:,:,i1)*K_nf'; - c_cav=Minv*b; - - for j1=1:nout - c=E(:,:,j1)*c_cav; - Sigm_cc(i1,j1,:)=sum(c.*K_nf'); - end - - kstarstar = gp_trvar(gp,xt,predcf); - Sigm_cc(i1,i1,:) = squeeze(Sigm_cc(i1,i1,:)) + kstarstar - sum(b.*K_nf')'; - end - - S=10000; - pi_star=zeros(ntest,nout); - for i1=1:ntest - Sigm_tmp=(Sigm_cc(:,:,i1)'+Sigm_cc(:,:,i1))./2; - f_star=mvnrnd(mu_star(i1,:), Sigm_tmp, S); - - tmp_star = exp(f_star); - tmp_star = tmp_star./(sum(tmp_star, 2)*ones(1,size(tmp_star,2))); - pi_star(i1,:)=log(mean(tmp_star)); - - end - - % ============================================================ - % FIC - % ============================================================ - case 'FIC' % Predictions with FIC sparse approximation for GP - % ============================================================ - % PIC - % ============================================================ - case {'PIC' 'PIC_BLOCK'} % Predictions with PIC sparse approximation for GP - % ============================================================ - % CS+FIC - % ============================================================ - case 'CS+FIC' % Predictions with CS+FIC sparse approximation for GP - end - - % ============================================================ - % Evaluate also the predictive mean and variance of new observation(s) - % ============================================================ -% if nargout > 2 -% if isempty(yt) -% [Ey, Vary] = feval(gp.lik.fh.predy, gp.lik, Ef, Varf, [], zt); -% else -% [Ey, Vary, Pyt] = feval(gp.lik.fh.predy, gp.lik, Ef, Varf, yt, zt); -% end -% end -end \ No newline at end of file diff --git a/gp/lik_coxph.m b/gp/lik_coxph.m deleted file mode 100644 index 69782ea1..00000000 --- a/gp/lik_coxph.m +++ /dev/null @@ -1,1387 +0,0 @@ -function lik = lik_coxph(varargin) -%LIK_COXPH Create a Cox proportional hazard likelihood structure -% -% Description -% LIK = LIK_COXPH('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a proportional hazard model where a piecewise log-constant -% baseline hazard is assumed. -% -% The likelihood contribution for the ith observation is -% -% l_i = h_i(y_i)^(1-z_i)*[exp(-int_0^y_i*h_i dt)], -% -% where hazard is h_i=h_0(y_i)*exp(f_i). A zero mean Gaussian process -% prior is placed for f = [f_1, f_2,...,f_n] ~ N(0, C). C is the -% covariance matrix, whose elements are given as C_ij = c(x_i, x_j | -% th). The function c(x_i, x_j| th) is covariance function and th its -% parameters, hyperparameters. We place a hyperprior for -% hyperparameters, p(th). -% -% The time axis is partioned into K intervals with equal lengths: -% 0 = s_0 < s_1 < ... < s_K, where s_K > y_i for all i. The baseline -% hazard rate function h_0 is piecewise constant, -% -% h_0(t) = la_k, -% -% when t belongs to the interval (s_{k-1},s_k] and where ft_k=log(la_k). -% The hazard rate function is smoothed by assuming another Gaussian -% process prior ft = [ft_1, ft_2,...,ft_K] ~ N(0, C). -% -% z is a vector of censoring indicators with z = 0 for uncensored event -% and z = 1 for right censored event. -% -% When using the Coxph likelihood you need to give the vector z -% as an extra parameter to each function that requires also y. -% For example, you should call gpla_e as follows: gpla_e(w, gp, -% x, y, 'z', z) -% -% See also -% GP_SET, LIK_*, PRIOR_* -% - -% Copyright (c) 2007-2010 Jarno Vanhatalo & Jouni Hartikainen -% Copyright (c) 2010 Aki Vehtari -% Copyright (c) 2011 Jaakko Riihimäki - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_COXPH'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','S', linspace(0,1.001,50), @(x) isvector(x)); - ip=iparser(ip,'addParamValue','stratificationVariables', [], @(x) isvector(x) && all(rem(x,1)==0)); - ip=iparser(ip,'addParamValue','removeStratificationVariables', [], @(x) ismember(x, {'on' 'off'})); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.type = 'Coxph'; - lik.nondiagW=true; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'Coxph') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - if init || ~ismember('S', ip.UsingDefaults) - s=ip.Results.S; - xtmp=zeros(length(s)-1,1); - for i2=1:(length(s)-1) - xtmp(i2,1)=mean([s(i2) s(i2+1)]); - end - lik.xtime=xtmp; - lik.stime=s; - end - - if ~ismember('stratificationVariables', ip.UsingDefaults) - lik.stratificationVariables = ip.Results.stratificationVariables; - if ~ismember('stratificationVariables', ip.UsingDefaults) - if isequal(ip.Results.removeStratificationVariables, 'on') - lik.removeStratificationVariables=true; - end - end - end - - if init - % Set the function handles to the nested functions - lik.fh.pak = @lik_coxph_pak; - lik.fh.unpak = @lik_coxph_unpak; - lik.fh.ll = @lik_coxph_ll; - lik.fh.llg = @lik_coxph_llg; - lik.fh.llg2 = @lik_coxph_llg2; - lik.fh.llg3 = @lik_coxph_llg3; -% lik.fh.tiltedMoments = @lik_coxph_tiltedMoments; - lik.fh.predy = @lik_coxph_predy; - lik.fh.invlink = @lik_coxph_invlink; - lik.fh.recappend = @lik_coxph_recappend; - lik.fh.predcdf= @lik_coxph_predcdf; - end - - function [w,s] = lik_coxph_pak(lik) - %LIK_COXPH_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_COXPH_PAK(LIK) takes a likelihood structure LIK and - % combines the parameters into a single row vector W. This is - % a mandatory subfunction used for example in energy and - % gradient computations. - % - % w = log(lik.disper) - % - % See also - % LIK_COXPH_UNPAK, GP_PAK - - w=[];s={}; - end - - - function [lik, w] = lik_coxph_unpak(lik, w) - %LIK_COXPH_UNPAK Extract likelihood parameters from the vector. - % - % Description - % [LIK, W] = LIK_COXPH_UNPAK(W, LIK) takes a likelihood - % structure LIK and extracts the parameters from the vector W - % to the LIK structure. This is a mandatory subfunction used - % for example in energy and gradient computations. - % - % Assignment is inverse of - % w = log(lik.disper) - % - % See also - % LIK_COXPH_PAK, GP_UNPAK - - lik=lik; - w=w; - - end - - function ll = lik_coxph_ll(lik, y, f, z) - %LIK_COXPH_LL Log likelihood - % - % Description - % LL = LIK_COXPH_LL(LIK, Y, F, Z) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z, and - % latent values F. Returns the log likelihood, log p(y|f,z). - % This subfunction is needed when using Laplace approximation - % or MCMC for inference with non-Gaussian likelihoods. This - % subfunction is also used in information criteria (DIC, WAIC) - % computations. - % - % See also - % LIK_COXPH_LLG, LIK_COXPH_LLG3, LIK_COXPH_LLG2, GPLA_E - - if isempty(z) - error(['lik_coxph -> lik_coxph_ll: missing z! '... - 'Coxph likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_coxph and gpla_e. ']); - end - - [n,ny]=size(y); - ntime=size(lik.xtime,1); - i3v=ones(size(y,1),1); - if isfield(lik, 'stratificationVariables') - f1=f(1:ntime*lik.n_u); - f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); - for i=1:lik.n_u - i3v(lik.stratind{i})=i; - end - else - f1=f(1:ntime); - f2=f((ntime+1):(ntime+n)); - end - - la1=exp(f1); - eta2=exp(f2); - - nu=1-z; - sd=lik.stime(2)-lik.stime(1); - - if ny==1 - ll=0; - for i1=1:n - i3=i3v(i1); - nft=(i3-1)*ntime; - si=sum(y(i1)>lik.stime); - ll=ll + nu(i1).*(f1(si+nft)+f2(i1)) - (y(i1)-lik.stime(si)).*la1(si+nft).*eta2(i1) ... - - sum(sd.*la1((1+nft):(si-1+nft)).*eta2(i1)); - end - else - - ll=0; - sb=sum(bsxfun(@gt,y(:,1),lik.stime),2); - se=sum(bsxfun(@gt,y(:,2),lik.stime),2); - for i1=1:n - i3=i3v(i1); - nft=(i3-1)*ntime; - if sb(i1)==0 - ll=ll + nu(i1).*(f1(se(i1)+nft)+f2(i1)) - (y(i1,2)-lik.stime(se(i1))).*la1(se(i1)+nft).*eta2(i1) ... - - sum(sd.*la1((1+nft):(se(i1)-1+nft)).*eta2(i1)); - else - - if se(i1)==sb(i1) - ll=ll + nu(i1).*(f1(se(i1)+nft)+f2(i1)) - (y(i1,2)-y(i1,1)).*la1(se(i1)+nft).*eta2(i1); - else - ll=ll + nu(i1).*(f1(se(i1)+nft)+f2(i1)) - (y(i1,2)-lik.stime(se(i1))).*la1(se(i1)+nft).*eta2(i1) ... - - sum(sd.*la1((sb(i1)+1+nft):(se(i1)-1+nft)).*eta2(i1)) - (lik.stime(sb(i1)+1)-y(i1,1)).*la1(sb(i1)+nft).*eta2(i1); - end - end - end - end - end - - function llg = lik_coxph_llg(lik, y, f, param, z) - %LIK_COXPH_LLG Gradient of the log likelihood - % - % Description - % LLG = LIK_COXPH_LLG(LIK, Y, F, PARAM) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z and - % latent values F. Returns the gradient of the log likelihood - % with respect to PARAM. At the moment PARAM can be 'param' or - % 'latent'. This subfunction is needed when using Laplace - % approximation or MCMC for inference with non-Gaussian - % likelihoods. - % - % See also - % LIK_COXPH_LL, LIK_COXPH_LLG2, LIK_COXPH_LLG3, GPLA_E - - if isempty(z) - error(['lik_coxph -> lik_coxph_llg: missing z! '... - 'Coxph likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_coxph and gpla_e. ']); - end - - ntime=size(lik.xtime,1); - - [n,ny]=size(y); - i3v=ones(size(y)); - if isfield(lik, 'stratificationVariables') - f1=f(1:ntime*lik.n_u); - f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); - llg=zeros(ntime*lik.n_u+n,1); - for i=1:lik.n_u - i3v(lik.stratind{i})=i; - end - nf1=lik.n_u*ntime; - else - f1=f(1:ntime); - f2=f((ntime+1):(ntime+n)); - llg=zeros(ntime+n,1); - nf1=ntime; - end - - la1=exp(f1(:)); - eta2=exp(f2(:)); - - nu=1-z; - sd=lik.stime(2)-lik.stime(1); - - switch param - case 'latent' - - if ny==1 - if ~isfield(lik, 'stratificationVariables') - for i1=1:ntime - ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); - end - else - for j=1:lik.n_u - for i1=1:ntime - ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); - end - end - end - for i1=1:n - i3=i3v(i1); - nft=(i3-1)*ntime; - si=sum(y(i1)>lik.stime); - llg(i1+nf1)= nu(i1) - (y(i1)-lik.stime(si)).*la1(si+nft).*eta2(i1) - sum(sd.*la1((1+nft):(si-1+nft)).*eta2(i1)); - end - else - for i1=1:ntime - - % left truncated + follow-up entry: (1) - ind_vkst = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1+1); - % follow-up entry + follow-up exit: (2) - ind_stsp = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1+1); - % follow-up exit: (4) - ind_sp = y(:,1)=lik.stime(i1) & y(:,2) lik_coxph_llg2: missing z! '... - 'Coxph likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_coxph and gpla_e. ']); - end - - [n,ny]=size(y); - ntime=size(lik.xtime,1); - i3v=ones(size(y,1),1); - if isfield(lik, 'stratificationVariables') - f1=f(1:ntime*lik.n_u); - f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); - llg2=zeros(ntime*lik.n_u+n,1); - llg2mat=zeros(ntime*lik.n_u,n); - for i=1:lik.n_u - i3v(lik.stratind{i})=i; - end - nf1=ntime*lik.n_u; - else - f1=f(1:ntime); - f2=f((ntime+1):(ntime+n)); - llg2=zeros(ntime+n,1); - llg2mat=zeros(ntime,n); - nf1=ntime; - end - - la1=exp(f1); - eta2=exp(f2); - - %nu=1-z; - sd=lik.stime(2)-lik.stime(1); - switch param - case 'latent' - - if ny==1 - % 11 - if ~isfield(lik, 'stratificationVariables') - for i1=1:ntime - ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); - %llg2(i1,i1)= sum(-(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind)) - sum(sum(sd.*la1(i1)).*eta2(~ind & y>=lik.stime(i1+1))); - end - else - for j=1:lik.n_u - for i1=1:ntime - ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); - %llg2(i1,i1)= sum(-(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind)) - sum(sum(sd.*la1(i1)).*eta2(~ind & y>=lik.stime(i1+1))); - end - end - end - - % 22 - for i1=1:n - si=sum(y(i1)>lik.stime); - i3=i3v(i1); - llg2(i1+nf1)= -(y(i1)-lik.stime(si)).*la1(si+(i3-1)*ntime).*eta2(i1) - sum(sd.*la1((1+(i3-1)*ntime):(si-1+(i3-1)*ntime)).*eta2(i1)); - %llg2(i1+ntime,i1+ntime)= -(y(i1)-lik.stime(si)).*la1(si).*eta2(i1) - sum(sd.*la1(1:(si-1)).*eta2(i1)); - end - - % derivative wrt f1 and f2: - if ~isfield(lik, 'stratificationVariables') - for i1=1:ntime - ind=y>=lik.stime(i1) & y=lik.stime(i1+1)))) = - sd.*la1(i1).*eta2((~ind & y>=lik.stime(i1+1))); - %llg2(i1,ntime+find(ind))= -(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind); - %llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))) = - sd.*la1(i1).*eta2((~ind & y>=lik.stime(i1+1))); - %llg2(ntime+find(ind),i1)=llg2(i1,ntime+find(ind)); - %llg2(ntime+find((~ind & y>=lik.stime(i1+1))),i1)=llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))); - end - else - for j=1:lik.n_u - ind2=lik.stratind{j}; - for i1=1:ntime - ind=y>=lik.stime(i1) & y=lik.stime(i1+1)))) = - sd.*la1(i1+(j-1)*ntime).*eta2(((~ind & ind2) & y>=lik.stime(i1+1))); - %llg2(i1,ntime+find(ind))= -(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind); - %llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))) = - sd.*la1(i1).*eta2((~ind & y>=lik.stime(i1+1))); - %llg2(ntime+find(ind),i1)=llg2(i1,ntime+find(ind)); - %llg2(ntime+find((~ind & y>=lik.stime(i1+1))),i1)=llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))); - end - end - end - - else - - % 11 - for i1=1:ntime - - % left truncated + follow-up entry: (1) - ind_vkst = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1+1); - % follow-up entry + follow-up exit: (2) - ind_stsp = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1+1); - % follow-up exit: (4) - ind_sp = y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1) & y(:,1)=lik.stime(i1+1); - % follow-up entry + follow-up exit: (2) - ind_stsp = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1+1); - % follow-up exit: (4) - ind_sp = y(:,1)=lik.stime(i1) & y(:,2) lik_coxph_llg3: missing z! '... - 'Coxph likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_coxph and gpla_e. ']); - end - - ntime=size(lik.xtime,1); - - [n,ny]=size(y); - if isfield(lik, 'stratificationVariables') - f1=f(1:ntime*lik.n_u); - f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); - llg3=zeros(ntime*lik.n_u+n,1); - llg3mat=zeros(ntime*lik.n_u,n); - nf1=ntime*lik.n_u; - if j1>nf1 - for j=1:lik.n_u - if lik.stratind{j}(j1-nf1)==1; - i3=j; - break; - end - end - end - else - f1=f(1:ntime); - f2=f((ntime+1):(ntime+n)); - llg3=zeros(ntime+n,1); - llg3mat=zeros(ntime,n); - nf1=ntime; - i3=1; - end - - la1=exp(f1); - eta2=exp(f2); - - %nu=1-z; - sd=lik.stime(2)-lik.stime(1); - - - switch param - case 'latent' - - if ny==1 - - if j1<=nf1 - - indt=rem(j1,ntime); - if indt==0 - indt=ntime; - end - - % 11 - ind=y>=lik.stime(indt) & y=lik.stime(indt)); - - llg3(j1) = sum(-(y(ind & ind2)-lik.stime(indt)).*la1(j1).*eta2(ind & ind2)) - sum(sum(sd.*la1(j1)).*eta2((~ind & ind2) & y>=lik.stime(indt+1))); - - if ~isempty(fi) - valtmp=(-(y(ind & ind2)-lik.stime(indt)).*la1(j1).*eta2(ind & ind2)); - for m2i=1:length(valtmp) - llg3( nf1+fi(m2i)) = valtmp(m2i); - end - end - if ~isempty(fni) - valtmp2=(-sd.*la1(j1).*eta2(((~ind & ind2) & y>=lik.stime(indt+1)))); - for m2i=1:length(valtmp2) - llg3( nf1+fni(m2i)) = valtmp2(m2i); - end - end - - % 12/21 - % derivative wrt f1 and f2: - val1tmp=-(y(ind & ind2)-lik.stime(indt)).*la1(j1).*eta2(ind & ind2); - llg3mat(j1,fi)= val1tmp; - - val2tmp = - sd.*la1(j1).*eta2(((~ind & ind2) & y>=lik.stime(indt+1))); - llg3mat(j1,fni) = val2tmp; - - else - - % 11 - s1=sum(y(j1-nf1)>lik.stime); - llg3((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)) = - sd.*la1((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)).*eta2(j1-nf1); - llg3(s1+(i3-1)*ntime) = -(y(j1-nf1)-lik.stime(s1)).*la1(s1+(i3-1)*ntime).*eta2(j1-nf1); - - % 22 - llg3(j1) = -(y(j1-nf1)-lik.stime(s1)).*la1(s1+(i3-1)*ntime).*eta2(j1-nf1) ... - - sum(sd.*la1((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)).*eta2(j1-nf1)); - - % 12/21 - % derivative wrt f1 and f2: - val3tmp = - sd.*la1((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)).*eta2(j1-nf1); - llg3mat((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime),j1-nf1)= val3tmp; - llg3mat(s1+(i3-1)*ntime,j1-nf1) = -(y(j1-nf1)-lik.stime(s1)).*la1(s1+(i3-1)*ntime).*eta2(j1-nf1); - - end - - else - - if j1<=nf1 - - indt=rem(j1,ntime); - if indt==0 - indt=ntime; - end - if isfield(lik, 'stratificationVariables') - ind2=lik.stratind{(j1-indt)/ntime+1}; - else - ind2=ones(size(y,1),1); - end - - % 11 - % left truncated + follow-up entry: (1) - ind_vkst = y(:,1)>=lik.stime(indt) & y(:,1)=lik.stime(indt+1); - % follow-up entry + follow-up exit: (2) - ind_stsp = y(:,1)>=lik.stime(indt) & y(:,1)=lik.stime(indt) & y(:,2)=lik.stime(indt+1); - % follow-up exit: (4) - ind_sp = y(:,1)=lik.stime(indt) & y(:,2)lik.stime); % begin - se=sum(y(j1-nf1,2)>lik.stime); % end - - nft=(i3-1)*ntime; - % 11 - if sb==0 - llg3((1+nft):(se-1+nft))= -sd.*la1((1+nft):(se-1+nft)).*eta2(j1-nf1); - llg3(se+nft)= -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); - else - if se==sb - llg3(se+nft) = -(y(j1-nf1,2)-y(j1-nf1,1)).*la1(se+nft).*eta2(j1-nf1); - else - llg3(sb+nft) = - (lik.stime(sb+1)-y(j1-nf1,1)).*la1(sb+nft).*eta2(j1-nf1); - llg3((sb+1+nft):(se-1+nft)) = - sd.*la1((sb+1+nft):(se-1+nft)).*eta2(j1-nf1); - llg3(se+nft) = -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); - end - end - - % 12/21 - if sb==0 - llg3mat((1+nft):(se-1+nft),j1-nf1) = -sd.*la1((1+nft):(se-1+nft)).*eta2(j1-nf1); - llg3mat(se+nft,j1-nf1)= -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); - else - if se==sb - llg3mat(se+nft,j1-nf1) = -(y(j1-nf1,2)-y(j1-nf1,1)).*la1(se+nft).*eta2(j1-nf1); - else - llg3mat(sb+nft,j1-nf1) = - (lik.stime(sb+1)-y(j1-nf1,1)).*la1(sb+nft).*eta2(j1-nf1); - llg3mat((sb+1+nft):(se-1+nft),j1-nf1) = - sd.*la1((sb+1+nft):(se-1+nft)).*eta2(j1-nf1); - llg3mat(se+nft,j1-nf1) = -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); - end - end - - % 22 - if sb==0 - llg3(j1)= -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1) - sum(sd.*la1((1+nft):(se-1+nft)).*eta2(j1-nf1)); - else - if se==sb - llg3(j1) = -(y(j1-nf1,2)-y(j1-nf1,1)).*la1(se+nft).*eta2(j1-nf1); - else - llg3(j1) = -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1) - sum(sd.*la1((sb+1+nft):(se-1+nft)).*eta2(j1-nf1)) - (lik.stime(sb+1)-y(j1-nf1,1)).*la1(sb+nft).*eta2(j1-nf1); - end - end - - end - end - end - end - - -% function [logM_0, m_1, sigm2hati1] = lik_coxph_tiltedMoments(lik, y, i1, S2_i, M_i, z) -% %LIK_COXPH_TILTEDMOMENTS Returns the marginal moments for EP algorithm -% % -% % Description -% % [M_0, M_1, M2] = LIK_COXPH_TILTEDMOMENTS(LIK, Y, I, S2, -% % MYY, Z) takes a likelihood structure LIK, incedence counts -% % Y, expected counts Z, index I and cavity variance S2 and -% % mean MYY. Returns the zeroth moment M_0, mean M_1 and -% % variance M_2 of the posterior marginal (see Rasmussen and -% % Williams (2006): Gaussian processes for Machine Learning, -% % page 55). This subfunction is needed when using EP for -% % inference with non-Gaussian likelihoods. -% % -% % See also -% % GPEP_E -% -% [n,ny]=size(y); -% -% % M_i(end); -% % S2_i(end,end); -% fgrid=M_i(end)+sqrt(S2_i(end,end))*[-6 6]; -% fg=linspace(fgrid(1),fgrid(2),15); -% ng=length(fg); -% -% if isfield(gp.lik, 'stratificationVariables') -% ntime=size(lik.xtime,1)*gp.lik.n_u; -% else -% ntime=size(lik.xtime,1); -% end -% -% %f11=f(1:ntime); -% %f2=f((ntime+1):(ntime+n)); -% %la1=exp(f1); -% %eta2=exp(f2); -% -% nu=1-z; -% sd=lik.stime(2)-lik.stime(1); -% if ny==1 -% sb=1; -% se=sum(bsxfun(@gt,y(i1,1),lik.stime),2); -% end -% indf=sb:se; -% sdvec=[ones(se-1,1)*sd; y(i1)-lik.stime(se)]; -% nutmp=zeros(se,1); -% nutmp(se)=nu(i1); -% -% for j1=1:ng -% -% % conditional distribution -% myy=M_i(indf)+S2_i(indf,end)*(1./S2_i(end,end))*(fg(j1)-M_i(end)); -% myy0=myy; -% Sigm=S2_i(indf,indf)-S2_i(indf,end)*(1./S2_i(end,end))*S2_i(end,indf); -% Sigm0=Sigm; -% -% nu_prior=Sigm\myy; -% -% nt=size(myy,1); -% c1=exp(fg(j1)); -% -% % site parameters -% tautilde=zeros(nt,1); -% nutilde=zeros(nt,1); -% ztilde=zeros(nt,1); -% -% max_small_ep_iter=50; -% tol=1e-9; -% small_ep_iter=1; -% -% tautilde0=Inf; nutilde0=Inf; ztilde0=Inf; -% -% logZep_tmp=0; logZep=Inf; -% %while small_ep_iter <= max_small_ep_iter && (sum(abs(tautilde0-tautilde)>tol) || sum(abs(nutilde0-nutilde)>tol) || sum(abs(ztilde0-ztilde)>tol)) -% while small_ep_iter<=max_small_ep_iter && abs(logZep_tmp-logZep)>tol -% logZep_tmp=logZep; -% -% -% %tautilde0=tautilde; nutilde0=nutilde; ztilde0=ztilde; -% -% for k1=1:nt -% -% tau_i=Sigm(k1,k1)^-1-tautilde(k1); -% nu_i = Sigm(k1,k1)^-1*myy(k1)-nutilde(k1); -% myy_i=nu_i/tau_i; -% sigm2_i=tau_i^-1; -% -% % marginal moments -% [logM0(k1), muhati, sigm2hati] = coxph_tiltedMoments(sigm2_i, myy_i, nutmp(k1), sdvec(k1), c1); -% %[M0, muhati, sigm2hati] = coxph_tiltedMoments(lik, y(i1,:), k1, sigm2_i, myy_i, c1, sd_vec(i1), ztmp); -% -% deltatautilde=sigm2hati^-1-tau_i-tautilde(k1); -% tautilde(k1)=tautilde(k1)+deltatautilde; -% nutilde(k1)=sigm2hati^-1*muhati-nu_i; -% -% apu = deltatautilde/(1+deltatautilde*Sigm(k1,k1)); -% Sigm = Sigm - apu*(Sigm(:,k1)*Sigm(:,k1)'); -% -% % The below is how Rasmussen and Williams -% % (2006) do the update. The above version is -% % more robust. -% %apu = deltatautilde^-1+Sigm(k1,k1); -% %apu = (Sigm(:,k1)/apu)*Sigm(:,k1)'; -% %Sigm = Sigm - apu; -% %Sigm=Sigm-(deltatautilde^-1+Sigm(k1,k1))^-1*(Sigm(:,k1)*Sigm(:,k1)'); -% -% %myy=Sigm*nutilde; -% myy=Sigm*(nutilde+nu_prior); -% -% muvec_i(k1,1)=myy_i; -% sigm2vec_i(k1,1)=sigm2_i; -% -% end -% -% -% if tautilde > 0 -% Stilde=tautilde; -% Stildesqroot=diag(sqrt(tautilde)); -% B=eye(nt)+Stildesqroot*Sigm0*Stildesqroot; -% L=chol(B,'lower'); -% -% V=(L\Stildesqroot)*Sigm0; -% Sigm=Sigm0-V'*V; -% %myy=Sigm*nutilde; -% myy=Sigm*(nutilde+nu_prior); -% -% %Ls = chol(Sigm); -% -% % Compute the marginal likelihood -% % Direct formula (3.65): -% % Sigmtilde=diag(1./tautilde); -% % mutilde=inv(Stilde)*nutilde; -% % -% % logZep=-0.5*log(det(Sigmtilde+K))-0.5*mutilde'*inv(K+Sigmtilde)*mutilde+ -% % sum(log(normcdf(y.*muvec_i./sqrt(1+sigm2vec_i))))+ -% % 0.5*sum(log(sigm2vec_i+1./tautilde))+ -% % sum((muvec_i-mutilde).^2./(2*(sigm2vec_i+1./tautilde))) -% -% % 4. term & 1. term -% term41=0.5*sum(log(1+tautilde.*sigm2vec_i))-sum(log(diag(L))); -% -% % 5. term (1/2 element) & 2. term -% T=1./sigm2vec_i; -% Cnutilde = Sigm0*nutilde; -% L2 = V*nutilde; -% term52 = nutilde'*Cnutilde - L2'*L2 - (nutilde'./(T+Stilde)')*nutilde; -% term52 = term52.*0.5; -% -% % 5. term (2/2 element) -% term5=0.5*muvec_i'.*(T./(Stilde+T))'*(Stilde.*muvec_i-2*nutilde); -% -% % 3. term -% term3 = sum(logM0); -% -% V_tmp=(L\Stildesqroot); -% Sigm_inv_tmp=V_tmp'*V_tmp; -% -% term_add1=-0.5*myy0'*Sigm_inv_tmp*myy0; -% term_add2=myy0'*(eye(nt)-Sigm_inv_tmp*Sigm0)*nutilde; -% logZep = -(term41+term52+term5+term3+term_add1+term_add2); -% -% %logZep = -(term41+term52+term5+term3); -% -% small_ep_iter=small_ep_iter+1; -% %iter=iter+1; -% else -% error('tautilde <= 0') -% end -% end -% -% ZZ(j1,1)=exp(-logZep); -% MM(:,j1)=myy; -% SS2(:,:,j1)=Sigm; -% -% end -% -% %m_0=zeros(1,1); -% %m_1=zeros(nt+1,1); -% %sigm2hati1=zeros(nt+1,nt+1); -% % indf -% -% W=normpdf(fg,M_i(end),sqrt(S2_i(end,end)))*(fg(2)-fg(1)); -% -% qw=W.*ZZ'; -% m_0=sum(qw); -% m_1=[sum(bsxfun(@times,qw,MM),2); sum(qw.*fg)]./m_0; -% -% m_211=zeros(nt,nt); -% for k1=1:ng -% m_211=m_211+qw(k1)*(SS2(:,:,k1)+MM(:,k1)*MM(:,k1)'); -% end -% m_212=(qw.*fg)*MM'; -% m_222=(qw.*fg)*fg'; -% -% m_2=[m_211 m_212'; m_212 m_222]./m_0; -% -% sigm2hati1=m_2 - m_1*m_1'; -% logM_0 = log(m_0); -% -% %figure(1),hold on, plot(fg(j1),logZep,'.') -% %figure(2),hold on, plot(fg(j1),exp(-logZep),'.') -% -% end - - function [lpyt,Ey, Vary] = lik_coxph_predy(lik, Ef, Covf, yt, zt) - %LIK_COXPH_PREDY Returns the predictive mean, variance and density of y - % - % Description - % [EY, VARY] = LIK_COXPH_PREDY(LIK, EF, VARF) takes a - % likelihood structure LIK, posterior mean EF and posterior - % Variance VARF of the latent variable and returns the - % posterior predictive mean EY and variance VARY of the - % observations related to the latent variables. This - % subfunction is needed when computing posterior predictive - % distributions for future observations. - % - % [Ey, Vary, PY] = LIK_COXPH_PREDY(LIK, EF, VARF YT, ZT) - % Returns also the predictive density of YT, that is - % p(yt | zt) = \int p(yt | f, zt) p(f|y) df. - % This requires also the incedence counts YT, expected counts ZT. - % This subfunction is needed when computing posterior predictive - % distributions for future observations. - % - % See also - % GPLA_PRED, GPEP_PRED, GPMC_PRED - - if isempty(zt) - error(['lik_coxph -> lik_coxph_predy: missing zt!'... - 'Coxph likelihood needs the expected number of '... - 'occurrences as an extra input zt. See, for '... - 'example, lik_coxph and gpla_e. ']); - end - ntime=size(lik.xtime,1); - - ntest=size(zt,1); - ny=size(yt,2); - - if isfield(lik, 'stratificationVariables') - nf1=lik.n_u*ntime; - i3v=zeros(ntest,1); - for ii=1:length(lik.stratindt) - i3v(lik.stratindt{ii})=ii; - end - else - nf1=ntime; - i3v=ones(ntest,1); - end - - Py = zeros(size(zt)); - %Ey = zeros(size(zt)); - %EVary = zeros(size(zt)); - %VarEy = zeros(size(zt)); - - S=10000; - sd=lik.stime(2)-lik.stime(1); - nu=1-zt; - - [nn1,nn2,c] =size(Covf); - if (c>1) || (nn1==nn2) - mcmc=false; - else - mcmc=true; - end - - for i1=1:ntest - i3=i3v(i1); - if mcmc - Sigm_tmp=([Covf(1:ntime,:); Covf(i1+ntime,:)]); - f_star=bsxfun(@plus,Ef([(1+(i3-1)*ntime):(i3*ntime) i1+nf1])', ... - bsxfun(@times,sqrt(Sigm_tmp'),randn(S,nf1+1))); - else - Sigm_tmp=Covf([(1+(i3-1)*ntime):(i3*ntime) i1+nf1],[(1+(i3-1)*ntime):(i3*ntime) i1+nf1]); - Sigm_tmp=(Sigm_tmp+Sigm_tmp')./2; - f_star=mvnrnd(Ef([(1+(i3-1)*ntime):(i3*ntime) i1+nf1]), Sigm_tmp, S); - end - - f1=f_star(:,1:ntime); - f2=f_star(:,(ntime+1):end); - - la1=exp(f1); - eta2=exp(f2); - - if ny==1 - si=sum(yt(i1)>lik.stime); - Py(i1)=mean(exp(nu(i1).*(f1(:,si)+f2) - (yt(i1)-lik.stime(si)).*la1(:,si).*eta2 ... - - sum(sd.*la1(:,1:(si-1)),2).*eta2)); - else - - sb=sum(bsxfun(@gt,yt(i1,1),lik.stime),2); - se=sum(bsxfun(@gt,yt(i1,2),lik.stime),2); - - if sb==0 - Py(i1) = mean(exp(nu(i1).*(f1(:,se)+f2) - (yt(i1,2)-lik.stime(se)).*la1(:,se).*eta2 - sum(sd.*la1(:,1:(se-1)),2).*eta2)); - else - if se==sb - Py(i1) = mean(exp(nu(i1).*(f1(:,se)+f2) - (yt(i1,2)-yt(i1,1)).*la1(:,se).*eta2)); - else - Py(i1) = mean(exp(nu(i1).*(f1(:,se)+f2) - (yt(i1,2)-lik.stime(se)).*la1(:,se).*eta2 - sum(sd.*la1(:,(sb+1):(se-1)),2).*eta2 - (lik.stime(sb+1)-yt(i1,1)).*la1(:,sb).*eta2)); - end - end - end - end - Ey = []; - Vary = []; - lpyt=log(Py); - - end - -% function [logM_0, m_1, sigm2hati1] = coxph_tiltedMoments(sigm2_i, myy_i, nutmp, sd, c1) -% -% integrand = @(f) exp(-c1.*exp(f).*sd + nutmp*(f+log(c1)) - log(sigm2_i)/2 - log(2*pi)/2 - 0.5*(f-myy_i).^2./sigm2_i); -% RTOL = 1.e-6; -% ATOL = 1.e-10; -% minf=myy_i+sqrt(sigm2_i)*(-6); -% maxf=myy_i+sqrt(sigm2_i)*(6); -% -% [m_0, m_1, m_2] = quad_moments(integrand, minf, maxf, RTOL, ATOL); -% sigm2hati1 = m_2 - m_1.^2; -% -% % If the second central moment is less than cavity variance -% % integrate more precisely. Theoretically for log-concave -% % likelihood should be sigm2hati1 < sigm2_i. -% -% if sigm2hati1 >= sigm2_i -% ATOL = ATOL.^2; -% RTOL = RTOL.^2; -% [m_0, m_1, m_2] = quad_moments(tf, minf, maxf, RTOL, ATOL); -% sigm2hati1 = m_2 - m_1.^2; -% if sigm2hati1 >= sigm2_i -% error('lik_poisson_tilted_moments: sigm2hati1 >= sigm2_i'); -% end -% end -% logM_0 = log(m_0); -% end - - -% function [df,minf,maxf] = init_coxph_norm(yy,myy_i,sigm2_i,avgE,r) -% %INIT_COXPH_NORM -% % -% % Description -% % Return function handle to a function evaluating -% % Negative-Binomial * Gaussian which is used for evaluating -% % (likelihood * cavity) or (likelihood * posterior) Return -% % also useful limits for integration. This is private function -% % for lik_coxph. -% % -% % See also -% % LIK_COXPH_TILTEDMOMENTS, LIK_COXPH_SITEDERIV, -% % LIK_COXPH_PREDY -% -% % avoid repetitive evaluation of constant part -% ldconst = -gammaln(r)-gammaln(yy+1)+gammaln(r+yy)... -% - log(sigm2_i)/2 - log(2*pi)/2; -% % Create function handle for the function to be integrated -% df = @coxph_norm; -% % use log to avoid underflow, and derivates for faster search -% ld = @log_coxph_norm; -% ldg = @log_coxph_norm_g; -% ldg2 = @log_coxph_norm_g2; -% -% % Set the limits for integration -% % Negative-binomial likelihood is log-concave so the coxph_norm -% % function is unimodal, which makes things easier -% if yy==0 -% % with yy==0, the mode of the likelihood is not defined -% % use the mode of the Gaussian (cavity or posterior) as a first guess -% modef = myy_i; -% else -% % use precision weighted mean of the Gaussian approximation -% % of the Negative-Binomial likelihood and Gaussian -% mu=log(yy/avgE); -% s2=(yy+r)./(yy.*r); -% modef = (myy_i/sigm2_i + mu/s2)/(1/sigm2_i + 1/s2); -% end -% % find the mode of the integrand using Newton iterations -% % few iterations is enough, since the first guess in the right direction -% niter=4; % number of Newton iterations -% mindelta=1e-6; % tolerance in stopping Newton iterations -% for ni=1:niter -% g=ldg(modef); -% h=ldg2(modef); -% delta=-g/h; -% modef=modef+delta; -% if abs(delta)(modeld-lddiff) -% minf=minf-step*modes; -% minld=ld(minf); -% iter=iter+1; -% step=step*2; -% if iter>100 -% error(['lik_coxph -> init_coxph_norm: ' ... -% 'integration interval minimun not found ' ... -% 'even after looking hard!']) -% end -% end -% maxld=ld(maxf); -% iter=0; -% step=1; -% while maxld>(modeld-lddiff) -% maxf=maxf+step*modes; -% maxld=ld(maxf); -% iter=iter+1; -% step=step*2; -% if iter>100 -% error(['lik_coxph -> init_coxph_norm: ' ... -% 'integration interval maximun not found ' ... -% 'even after looking hard!']) -% end -% end -% -% % while minld>(modeld-lddiff) -% % minf=minf-modes; -% % minld=ld(minf); -% % iter=iter+1; -% % if iter>100 -% % error(['lik_coxph -> init_coxph_norm: ' ... -% % 'integration interval minimun not found ' ... -% % 'even after looking hard!']) -% % end -% % end -% % maxld=ld(maxf); -% % while maxld>(modeld-lddiff) -% % maxf=maxf+modes; -% % maxld=ld(maxf); -% % iter=iter+1; -% % if iter>100 -% % error(['lik_coxph -> init_coxph_norm: ' ... -% % 'integration interval maximum not found ' ... -% % 'even after looking hard!']) -% % end -% % -% % end -% -% function integrand = coxph_norm(f) -% % Negative-binomial * Gaussian -% mu = avgE.*exp(f); -% integrand = exp(ldconst ... -% +yy.*(log(mu)-log(r+mu))+r.*(log(r)-log(r+mu)) ... -% -0.5*(f-myy_i).^2./sigm2_i); -% end -% -% function log_int = log_coxph_norm(f) -% % log(Negative-binomial * Gaussian) -% % log_coxph_norm is used to avoid underflow when searching -% % integration interval -% mu = avgE.*exp(f); -% log_int = ldconst... -% +yy.*(log(mu)-log(r+mu))+r.*(log(r)-log(r+mu))... -% -0.5*(f-myy_i).^2./sigm2_i; -% end -% -% function g = log_coxph_norm_g(f) -% % d/df log(Negative-binomial * Gaussian) -% % derivative of log_coxph_norm -% mu = avgE.*exp(f); -% g = -(r.*(mu - yy))./(mu.*(mu + r)).*mu ... -% + (myy_i - f)./sigm2_i; -% end -% -% function g2 = log_coxph_norm_g2(f) -% % d^2/df^2 log(Negative-binomial * Gaussian) -% % second derivate of log_coxph_norm -% mu = avgE.*exp(f); -% g2 = -(r*(r + yy))/(mu + r)^2.*mu ... -% -1/sigm2_i; -% end -% -% end - - function p = lik_coxph_invlink(lik, f, z) - %LIK_COXPH_INVLINK Returns values of inverse link function - % - % Description - % P = LIK_COXPH_INVLINK(LIK, F) takes a likelihood structure LIK and - % latent values F and returns the values of inverse link function P. - % This subfunction is needed when using function gp_predprcty. - % - % See also - % LIK_COXPH_LL, LIK_COXPH_PREDY - - p = exp(f); - end - - - - function [cdf,Ey,Vary] = lik_coxph_predcdf(lik,Ef,Covf,yt) - %LIK_LOGLOGISTIC_PREDCDF Returns the predictive cdf evaluated at yt - % - % Description - % CDF = LIK_LOGLOGISTIC_PREDCDF(LIK, EF, VARF, YT) - % Returns the predictive cdf evaluated at YT given likelihood - % structure LIK, posterior mean EF and posterior Variance VARF - % of the latent variable. This subfunction is needed when using - % functions gp_predcdf or gp_kfcv_cdf. - % - % See also - % GP_PREDCDF - - - if isfield(lik, 'stratificationVariables') - ntime=size(lik.xtime,1)*lik.n_u; - else - ntime=size(lik.xtime,1); - end - Ef1 = Ef(1:ntime); Ef(1:ntime) = []; Ef2 = Ef; - nsamps = 10000; - sd=lik.stime(2)-lik.stime(1); - Sigm_tmp=Covf; - [nn1,nn2,cc]=size(Sigm_tmp); - if cc==1 && nn1~=nn2 - f_star=bsxfun(@plus,[Ef1;Ef2]', ... - bsxfun(@times,sqrt(Sigm_tmp'),randn(nsamps,ntime+size(Ef2,1)))); - else - Sigm_tmp=(Sigm_tmp+Sigm_tmp')./2; - % f_star=mvnrnd(Ef1, Sigm_tmp(1:ntime,1:ntime), nsamps); - f_star=mvnrnd([Ef1;Ef2], Sigm_tmp, nsamps); - end - - f1=f_star(:,1:ntime); - f2=f_star(:,(ntime+1):end); - - la1=exp(f1); - eta2=exp(f2); - - mST=zeros(size(eta2,2),1); - if size(yt,2) == 1 - % Integrate from zero to yt - cumsumtmp=cumsum(la1'*sd)'; - % t=binsgeq(gp.lik.xtime,yt(i)); - for i1=1:size(eta2,2) - Stime=exp(-bsxfun(@times,cumsumtmp,eta2(:,i1))); - mStime=mean(Stime); - % for i=1:size(yt,1) - mST(i1)=mStime(binsgeq(lik.xtime,yt(i1))); - %end - end - cdf = 1- mST; - - else - error('Size(yt,2) ~= 1'); - end - - end - - function reclik = lik_coxph_recappend(reclik, ri, lik) - %RECAPPEND Append the parameters to the record - % - % Description - % RECLIK = GPCF_COXPH_RECAPPEND(RECLIK, RI, LIK) takes a - % likelihood record structure RECLIK, record index RI and - % likelihood structure LIK with the current MCMC samples of - % the parameters. Returns RECLIK which contains all the old - % samples and the current samples from LIK. This subfunction - % is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC - - % Initialize record - if nargin == 2 - reclik=ri; - - % Set the function handles - reclik.fh.pak = @lik_coxph_pak; - reclik.fh.unpak = @lik_coxph_unpak; - reclik.fh.lp = @lik_coxph_lp; - reclik.fh.lpg = @lik_coxph_lpg; - reclik.fh.ll = @lik_coxph_ll; - reclik.fh.llg = @lik_coxph_llg; - reclik.fh.llg2 = @lik_coxph_llg2; - reclik.fh.llg3 = @lik_coxph_llg3; -% reclik.fh.tiltedMoments = @lik_coxph_tiltedMoments; - reclik.fh.predy = @lik_coxph_predy; - reclik.fh.invlink = @lik_coxph_invlink; - reclik.fh.recappend = @lik_coxph_recappend; - return - else - - reclik.xtime=lik.xtime; - reclik.stime=lik.stime; - - if isfield(lik, 'stratificationVariables') - reclik.stratificationVariables=lik.stratificationVariables; - if isfield(lik,removeStratificationVariables) - reclik.removeStratificationVariables=lik.removeStratificationVariables; - end - end - end - end - - -end - diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m deleted file mode 100644 index fd1f0b04..00000000 --- a/gp/lik_gaussianbl.m +++ /dev/null @@ -1,369 +0,0 @@ -function lik = lik_gaussianbl(varargin) -%LIK_GAUSSIAN Create a Gaussian likelihood structure -% -% Description -% LIK = LIK_GAUSSIANBL('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a Gaussian likelihood structure in which the named -% parameters have the specified values. Any unspecified -% parameters are set to default values. -% -% LIK = LIK_GAUSSIANBL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood function structure with the named -% parameters altered with the specified values. -% -% Parameters for Gaussian likelihood function [default] -% sigma2 - variance of the independent noise [0.1] for each -% block of inputs. If noiseSigma2 is a vector each -% entry of the vector specifies noise variance for -% a block of inputs defined by the last column of -% the input matrix X. The variances are set for blocks -% according to 'bl_indic' field so that sigma2(i) is a -% noise variance of the inputs whose last column equals -% bl_indic(i). -% bl_indic - block indicator vector [empty matrix]. If -% length(sigma2)>1 bl_indic has to be the same length -% as sigma2. -% sigma2_prior - prior for sigma2 [prior_logunif] -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% See also -% GP_SET, PRIOR_*, LIK_* - -% Internal note: Because Gaussian noise can be combined -% analytically to the covariance matrix, lik_gaussian is internally -% little between lik_* and gpcf_* functions. - -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_GAUSSIANBL'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'addParamValue','bl_indic',0.1, @(x) isvector(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.type = 'GaussianBL'; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'GaussianBL') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('sigma2',ip.UsingDefaults) - lik.sigma2 = ip.Results.sigma2; - lik.bl_indic = ip.Results.bl_indic; - end - - if length(lik.sigma2)> 1 || length(lik.bl_indic) > 1 - if length(lik.sigma2) ~= length(lik.bl_indic) - error('sigma2 and bl_indic has to be same length') - end - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('sigma2_prior',ip.UsingDefaults) - lik.p.sigma2=ip.Results.sigma2_prior; - end - if init - % Set the function handles to the nested functions - lik.fh.pak = @lik_gaussianbl_pak; - lik.fh.unpak = @lik_gaussianbl_unpak; - lik.fh.lp = @lik_gaussianbl_lp; - lik.fh.lpg = @lik_gaussianbl_lpg; - lik.fh.cfg = @lik_gaussianbl_cfg; - lik.fh.trcov = @lik_gaussianbl_trcov; - lik.fh.trvar = @lik_gaussianbl_trvar; - lik.fh.recappend = @lik_gaussianbl_recappend; - end - - function [w s] = lik_gaussianbl_pak(lik) - %LIK_GAUSSIANBL_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_GAUSSIANBL_PAK(LIK) takes a likelihood structure LIK - % and combines the parameters into a single row vector W. - % This is a mandatory subfunction used for example in energy - % and gradient computations. - % - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_UNPAK - - w = []; s = {}; - if ~isempty(lik.p.sigma2) - w = log(lik.sigma2); - if numel(lik.sigma2)>1 - s = [s; sprintf('log(gaussian.sigma2 x %d)',numel(lik.sigma2))]; - else - s = [s; 'log(gaussian.sigma2)']; - end - % Hyperparameters of noiseSigma2 - [wh sh] = lik.p.sigma2.fh.pak(lik.p.sigma2); - w = [w wh]; - s = [s sh]; - end - end - - function [lik, w] = lik_gaussianbl_unpak(lik, w) - %LIK_GAUSSIANBL_UNPAK Extract likelihood parameters from the vector. - % - % Description - % W = LIK_GAUSSIANBL_UNPAK(W, LIK) takes a likelihood structure - % LIK and extracts the parameters from the vector W to the LIK - % structure. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_PAK - - if ~isempty(lik.p.sigma2) - i2=length(lik.sigma2); - lik.sigma2 = exp(w(1:i2)); - w = w(i2+1:end); - - % Hyperparameters of sigma2 - [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); - lik.p.sigma2 = p; - end - end - - function lp = lik_gaussianbl_lp(lik) - %LIK_GAUSSIANBL_LP Evaluate the log prior of likelihood parameters - % - % Description - % LP = LIK_T_LP(LIK) takes a likelihood structure LIK and - % returns log(p(th)), where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_G, GP_E - - lp = 0; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + sum(log(lik.sigma2)); - end - end - - function lpg = lik_gaussianbl_lpg(lik) - %LIK_GAUSSIANBL_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = LIK_GAUSSIANBL_LPG(LIK) takes a Gaussian likelihood - % function structure LIK and returns LPG = d log (p(th))/dth, - % where th is the vector of parameters. This subfunction is - % needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - lpg = []; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - i2=length(lik.sigma2); - - lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); - lpg = lpgs(1:i2).*lik.sigma2 + 1; - if length(lpgs) > 1 - lpg = [lpg lpgs(i2+1:end)]; - end - end - end - - function DKff = lik_gaussianbl_cfg(lik, x, x2) - %LIK_GAUSSIANBL_CFG Evaluate gradient of covariance with respect to - % Gaussian noise - % - % Description - % Gaussian likelihood is a special case since it can be - % analytically combined with covariance functions and thus we - % compute gradient of covariance instead of gradient of likelihood. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X) takes a Gaussian likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of Gaussian noise covariance - % matrix Kff = k(X,X) with respect to th (cell array with - % matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X, X2) takes a Gaussian - % likelihood function structure LIK, a matrix X of input - % vectors and returns DKff, the gradients of Gaussian noise - % covariance matrix Kff = k(X,X) with respect to th (cell - % array with matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - [n, m] =size(x); - - if length(lik.sigma2)==1 - DKff{1} = lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - ind = find(x(:,end)==lik.bl_indic(i1)); - DKff{i1} = sparse(ind,ind,lik.sigma2(i1),n,n); - end - end - - end - - function DKff = lik_gaussianbl_ginput(lik, x, t, g_ind, gdata_ind, gprior_ind, varargin) - %LIK_GAUSSIANBL_GINPUT Evaluate gradient of likelihood function with - % respect to x. - % - % Description - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X, X2) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - end - - function C = lik_gaussianbl_trcov(lik, x) - %LIK_GAUSSIANBL_TRCOV Evaluate training covariance matrix - % corresponding to Gaussian noise - % Description - % C = LIK_GAUSSIANBL_TRCOV(GP, TX) takes in covariance function - % of a Gaussian process GP and matrix TX that contains - % training input vectors. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i and j - % in TX. This subfunction is needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, LIK_GAUSSIANBL_TRVAR, GP_COV, GP_TRCOV - - [n, m] =size(x); - - s2 = zeros(n,1); - if length(lik.sigma2)==1 - s2 = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - s2(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - C = sparse(1:n,1:n,s2,n,n); - - end - - function C = lik_gaussianbl_trvar(lik, x) - %LIK_GAUSSIANBL_TRVAR Evaluate training variance vector - % corresponding to Gaussian noise - % - % Description - % C = LIK_GAUSSIANBL_TRVAR(LIK, TX) takes in covariance function - % of a Gaussian process LIK and matrix TX that contains - % training inputs. Returns variance vector C. Every element i - % of C contains variance of input i in TX. This subfunction is - % needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, GP_COV, GP_TRCOV - - [n, m] =size(x); - - C = zeros(n,1); - if length(lik.sigma2)==1 - C = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - C(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - end - - function reccf = lik_gaussianbl_recappend(reccf, ri, lik) - %RECAPPEND Record append - % - % Description - % RECCF = LIK_GAUSSIANBL_RECAPPEND(RECCF, RI, LIK) takes a - % likelihood function record structure RECCF, record index RI - % and likelihood function structure LIK with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from LIK. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - % Initialize record - if nargin == 2 - reccf.type = 'lik_gaussianbl'; - - % Initialize parameters - reccf.sigma2 = []; - reccf.bl_indic = []; - - % Set the function handles - reccf.fh.pak = @lik_gaussianbl_pak; - reccf.fh.unpak = @lik_gaussianbl_unpak; - reccf.fh.lp = @lik_gaussianbl_lp; - reccf.fh.lpg = @lik_gaussianbl_lpg; - reccf.fh.cfg = @lik_gaussianbl_cfg; - reccf.fh.trcov = @lik_gaussianbl_trcov; - reccf.fh.trvar = @lik_gaussianbl_trvar; - reccf.fh.recappend = @lik_gaussianbl_recappend; - reccf.p=[]; - reccf.p.sigma2=[]; - if ~isempty(ri.p.sigma2) - reccf.p.sigma2 = ri.p.sigma2; - end - return - end - - likp = lik.p; - - % record sigma - if ~isempty(lik.sigma2) - reccf.sigma2(ri,:)=lik.sigma2; - reccf.bl_indic(ri,:)=lik.bl_indic; - if ~isempty(lik.p.sigma2) - reccf.p.sigma2 = likp.sigma2.fh.recappend(reccf.p.sigma2, ri, likp.sigma2); - end - elseif ri==1 - reccf.sigma2=[]; - end - end - -end diff --git a/gp/lik_inputdependentnoise.m b/gp/lik_inputdependentnoise.m deleted file mode 100644 index ba2c6eb2..00000000 --- a/gp/lik_inputdependentnoise.m +++ /dev/null @@ -1,510 +0,0 @@ -function lik = lik_inputdependentnoise(varargin) -%lik_inputdependentnoise Create input-dependent noise likelihood structure -% -% Description -% LIK = LIK_INPUTDEPENDENTNOISE('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a Gaussian likelihood with input dependent noise structure -% in which the named parameters have the specified values. Any unspecified -% parameters are set to default values. -% -% LIK = LIK_INPUTDEPENDENTNOISE(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood function structure with the named -% parameters altered with the specified values. -% -% Parameters for Gaussian likelihood function [default] -% sigma2 - variance [0.1] -% sigma2_prior - prior for sigma2 [prior_logunif] -% n - number of observations per input (See using average -% observations below) -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% The likelihood is defined as follows: -% __ n -% p(y|f1, f2) = || i=1 N(y_i | f1_i, sigma2*exp(f2_i)) -% -% where f1 is the first latent variable defining the mean of the -% gaussian distribution, f2 is the second latent variable defining -% the noise structure and sigma2 is coefficient for noise. -% -% See also -% GP_SET, LIK_*, PRIOR_* -% - -% Copyright (c) 2007-2010 Jarno Vanhatalo & Jouni Hartikainen -% Copyright (c) 2010 Aki Vehtari -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 2 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_INPUTDEPENDENTNOISE'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0); - ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.nondiagW=true; - lik.type = 'Inputdependentnoise'; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'Inputdependentnoise') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('sigma2',ip.UsingDefaults) - lik.sigma2 = ip.Results.sigma2; - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('sigma2_prior',ip.UsingDefaults) - lik.p.sigma2=ip.Results.sigma2_prior; - end - - if init - % Set the function handles to the subfunctions - lik.fh.pak = @lik_inputdependentnoise_pak; - lik.fh.unpak = @lik_inputdependentnoise_unpak; - lik.fh.lp = @lik_inputdependentnoise_lp; - lik.fh.lpg = @lik_inputdependentnoise_lpg; - lik.fh.ll = @lik_inputdependentnoise_ll; - lik.fh.llg = @lik_inputdependentnoise_llg; - lik.fh.llg2 = @lik_inputdependentnoise_llg2; - lik.fh.llg3 = @lik_inputdependentnoise_llg3; - lik.fh.predy = @lik_inputdependentnoise_predy; - lik.fh.invlink = @lik_inputdependentnoise_invlink; - lik.fh.predprcty = @lik_inputdependentnoise_predprcty; - lik.fh.recappend = @lik_inputdependentnoise_recappend; - end - - function [w,s] = lik_inputdependentnoise_pak(lik) - %LIK_INPUTDEPENDENTNOISE_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_INPUTDEPENDENTNOISE_PAK(LIK) takes a likelihood structure LIK and - % combines the parameters into a single row vector W. This is a mandatory - % subfunction used for example in energy and gradient computations. - % - % w = log(lik.sigma2) - % - % See also - % LIK_INPUTDEPENDENTNOISE_UNPAK, GP_PAK - - w = []; s = {}; - if isfield(lik.p, 'sigma2') && ~isempty(lik.p.sigma2) - w = [w log(lik.sigma2)]; - s = [s 'log(gaussian.sigma2)']; - % Hyperparameters of sigma2 - [wh sh] = lik.p.sigma2.fh.pak(lik.p.sigma2); - w = [w wh]; - s = [s sh]; - end - end - - - function [lik, w] = lik_inputdependentnoise_unpak(lik, w) - %LIK_INPUTDEPENDENTNOISE_UNPAK Extract likelihood parameters from the vector. - % - % Description - % [LIK, W] = LIK_INPUTDEPENDENTNOISE_UNPAK(W, LIK) takes a likelihood - % structure LIK and extracts the parameters from the vector W - % to the LIK structure. This is a mandatory subfunction used for - % example in energy and gradient computations. - % - % Assignment is inverse of - % w = log(lik.sigma2) - % - % See also - % LIK_INPUTDEPENDENTNOISE_PAK, GP_UNPAK - - if ~isempty(lik.p.sigma2) - lik.sigma2 = exp(w(1)); - w = w(2:end); - - % Hyperparameters of sigma2 - [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); - lik.p.sigma2 = p; - end - end - - - function lp = lik_inputdependentnoise_lp(lik, varargin) - %LIK_INPUTDEPENDENTNOISE_LP log(prior) of the likelihood parameters - % - % Description - % LP = LIK_INPUTDEPENDENTNOISE_LP(LIK) takes a likelihood structure - % LIK and returns log(p(th)), where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_E - - - % If prior for sigma2 parameter, add its contribution - lp = 0; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + log(lik.sigma2); - end - - end - - - function lpg = lik_inputdependentnoise_lpg(lik) - %LIK_INPUTDEPENDENTNOISE_LPG d log(prior)/dth of the likelihood - % parameters th - % - % Description - % E = LIK_INPUTDEPENDENTNOISE_LPG(LIK) takes a likelihood structure - % LIK and returns d log(p(th))/dth, where th collects the parameters. - % This subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_G - - - lpg = []; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - - lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); - lpg = lpgs(1).*lik.sigma2 + 1; - if length(lpgs) > 1 - lpg = [lpg lpgs(2:end)]; - end - end - end - - function ll = lik_inputdependentnoise_ll(lik, y, ff, z) - %LIK_INPUTDEPENDENTNOISE_LL Log likelihood - % - % Description - % LL = LIK_INPUTDEPENDENTNOISE_LL(LIK, Y, F, Z) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z, and - % latent values F. Returns the log likelihood, log p(y|f,z). - % This subfunction is needed when using Laplace approximation - % or MCMC for inference with non-Gaussian likelihoods. This - % subfunction is also used in information criteria (DIC, WAIC) - % computations. - % - % See also - % LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_E - - - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - expf2=exp(f2); - expf2(isinf(expf2))=realmax; - sigma2 = lik.sigma2; - - ll = sum(-0.5*log(2*pi.*sigma2.*expf2) - 1./(2.*sigma2.*expf2).*(y-f1).^2); - - end - - function llg = lik_inputdependentnoise_llg(lik, y, ff, param, z) - %LIK_INPUTDEPENDENTNOISE_LLG Gradient of the log likelihood - % - % Description - % LLG = LIK_INPUTDEPENDENTNOISE_LLG(LIK, Y, F, PARAM) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z and - % latent values F. Returns the gradient of the log likelihood - % with respect to PARAM. At the moment PARAM can be 'param' or - % 'latent'. This subfunction is needed when using Laplace - % approximation or MCMC for inference with non-Gaussian likelihoods. - % - % See also - % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_LLG2, LIK_INPUTDEPENDENTNOISE_LLG3, GPLA_E - - - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - expf2 = exp(f2); - sigma2 = lik.sigma2; - - switch param - case 'param' - - llg=sum(-0.5./sigma2+(y-f1).^2./(2*expf2.*sigma2^2)); - % correction for the log transformation - llg = llg.*lik.sigma2; - - case 'latent' - - llg1= (y-f1)./(sigma2*expf2); - llg2= -0.5+(y-f1).^2./(2.*expf2.*sigma2); - -% llg1=(-y+f1)/(sqrt(2*pi).*(sigma2.*expf2).^(3/2)).*exp(-1/(2.*sigma2*expf2).*(y-f1).^2); -% llg2=-exp(f2-(y-f1).^2/(2.*expf2.*sigma2)).*sigma2/(2.*sqrt(2.*pi).*(expf2.*sigma2).^(3/2))+exp(-f2-(y-f1).^2/(2*expf2*sigma2)).*(y-f1).^2/(2.*sqrt(2.*pi.*expf2).*sigma2.^(3/2)); - - llg=[llg1; llg2]; - end - end - - function [llg2] = lik_inputdependentnoise_llg2(lik, y, ff, param, z) - %function [pi_vec, pi_mat] = lik_inputdependentnoise_llg2(lik, y, ff, param, z) - %LIK_INPUTDEPENDENTNOISE_LLG2 Second gradients of the log likelihood - % - % Description - % LLG2 = LIK_INPUTDEPENDENTNOISE_LLG2(LIK, Y, F, PARAM) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z, and - % latent values F. Returns the Hessian of the log likelihood - % with respect to PARAM. At the moment PARAM can be only - % 'latent'. LLG2 is a vector with diagonal elements of the - % Hessian matrix (off diagonals are zero). This subfunction - % is needed when using Laplace approximation or EP for - % inference with non-Gaussian likelihoods. - % - % See also - % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, GPLA_E - - - f=ff(:); - - n=length(y); - f1=f(1:n); - f2=f((n+1):2*n); - sigma2 = lik.sigma2; - expf2=exp(f2); - - switch param - case 'param' - - case 'latent' - -% llg2 = [2./(sigma2.*expf2); 3/2.*(y-f1).^2./(sigma2.*expf2)]; -% llg2mat = [diag(1./sqrt(sigma2.*expf2)); diag(-(y-f1)./sqrt(sigma2.*expf2))]; - - llg2_11=-1./(sigma2.*expf2); - llg2_12=-(y-f1)./(sigma2*expf2); - llg2_22=-(y-f1).^2./(2.*sigma2.*expf2); - - llg2 = [llg2_11 llg2_12; llg2_12 llg2_22]; - - case 'latent+param' - - llg2_1=-(y-f1)./(expf2.*sigma2^2); - llg2_2=-(y-f1).^2./(2*sigma2.^2.*expf2); - - llg2=[llg2_1; llg2_2]; - % correction for the log transformation - llg2 = llg2.*lik.sigma2; - - end - end - - function llg3 = lik_inputdependentnoise_llg3(lik, y, ff, param, z) - %LIK_INPUTDEPENDENTNOISE_LLG3 Third gradients of the log likelihood - % - % Description - % LLG3 = LIK_INPUTDEPENDENTNOISE_LLG3(LIK, Y, F, PARAM) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z and - % latent values F and returns the third gradients of the log - % likelihood with respect to PARAM. At the moment PARAM can be - % only 'latent'. LLG3 is a vector with third gradients. This - % subfunction is needed when using Laplace approximation for - % inference with non-Gaussian likelihoods. - % - % See also - % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_E, GPLA_G - - f=ff(:); - - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - expf2=exp(f2); - sigma2 = lik.sigma2; - - switch param - case 'param' - - case 'latent' - nl=2; - llg3=zeros(nl,nl,nl,n); - - % y=0: - % thrid derivative derivative wrt f1 (11) - % llg3(1,1,1,:) = 0 - % thrid derivative derivative wrt f2 (11) - llg3(1,1,2,:) = 1./(expf2.*sigma2); - - % thrid derivative derivative wrt f1 (12/21) - llg3(1,2,1,:) = 1./(expf2.*sigma2); - llg3(2,1,1,:) = llg3(1,2,1,:); - % thrid derivative derivative wrt f2 (12/21) - llg3(1,2,2,:) = (y-f1)./(expf2.*sigma2); - llg3(2,1,2,:) = llg3(1,2,2,:); - - % thrid derivative derivative wrt f1 (22) - llg3(2,2,1,:) = llg3(1,2,2,:); - % thrid derivative derivative wrt f1 (22) - llg3(2,2,2,:) = (y-f1).^2./(2.*expf2.*sigma2); - - case 'latent2+param' - - llg3_11=1./(expf2.*sigma2.^2); - llg3_12=(y-f1)./(expf2.*sigma2.^2); - llg3_22=(y-f1).^2./(2.*expf2.*sigma2.^2); - - - llg3 = [diag(llg3_11) diag(llg3_12); diag(llg3_12) diag(llg3_22)]; - % correction for the log transformation - llg3 = llg3.*lik.sigma2; - - end - end - - - function [lpy, Ey, Vary] = lik_inputdependentnoise_predy(lik, Ef, Varf, yt, z) - %LIK_INPUTDEPENDENTNOISE_PREDY Returns the predictive mean, variance and density of y - % - % Description - % [LPY] = LIK_INPUTDEPENDENTNOISE_PREDY(LIK, EF, VARF, YT) takes a - % likelihood structure LIK, posterior mean EF, posterior - % variance VARF of the latent variable and observations YT and - % returns the logarithm of the predictive density PY of YT, that is - % p(yt | th) = \int p(yt | f, th) p(f|y) df. - % This subfunction is needed when computing posterior predictive - % distributions for future observations. - % - % [LPY, EY, VARY] = LIK_INPUTDEPENDENTNOISE_PREDY(LIK, EF, VARF YT) - % Returns also the posterior predictive mean EY and variance VARY of - % the observations related to the latent variables. This subfunction - % is needed when computing posterior predictive distributions for - % future observations. - % - % See also - % GPLA_PRED, GPEP_PRED, GPMC_PRED - - -% ntest=size(yt,1); - Ef = Ef(:); - ntest = 0.5*size(Ef,1); - Ef1=Ef(1:ntest); Ef2=Ef(ntest+1:end); - if size(Varf,2) == size(Varf,1) - Varf1=diag(Varf(1:ntest,1:ntest));Varf2=diag(Varf(ntest+1:end,ntest+1:end)); - else - if size(Varf,2)==1 - Varf=reshape(Varf,ntest,2); - end - Varf1=Varf(:,1); Varf2=Varf(:,2); - end - sigma2=lik.sigma2; - - lpy = zeros(size(yt)); - - Ey=zeros(size(yt)); - Vary=zeros(size(yt)); - - for i2=1:ntest - m1=Ef1(i2); m2=Ef2(i2); - s1=sqrt(Varf1(i2)); s2=sqrt(Varf2(i2)); - pd=@(f1,f2) norm_pdf(yt(i2), f1, sqrt(sigma2.*exp(f2))).*norm_pdf(f1,Ef1(i2),sqrt(Varf1(i2))).*norm_pdf(f2,Ef2(i2),sqrt(Varf2(i2))); - lpy(i2) = log(dblquad(pd, m1-6.*s1, m1+6.*s1, m2-6.*s2, m2+6.*s2)); - end -% sigma2 = lik.sigma2; -% Ef1=Ef(1:ntest); -% Ef2=Ef((ntest+1):2*ntest); -% Ey = Ef1; -% Vary = Varf + sigma2.*exp(Ef2); - - - end - - function prctys = lik_inputdependentnoise_predprcty(lik, Ef, Varf, zt, prcty) - %LIK_BINOMIAL_PREDPRCTY Returns the percentiles of predictive density of y - % - % Description - % PRCTY = LIK_BINOMIAL_PREDPRCTY(LIK, EF, VARF YT, ZT) - % Returns percentiles of the predictive density PY of YT, that is - % This requires also the succes counts YT, numbers of trials ZT. This - % subfunction is needed when using function gp_predprcty. - % - % See also - % GP_PREDPCTY - - n=size(Ef,1)./2; - prcty = prcty./100; - prcty = norminv(prcty, 0, 1); - prctys = bsxfun(@plus, Ef(1:n), bsxfun(@times, sqrt(Varf(1:n) + lik.sigma2.*exp(Ef(n+1:end))), prcty)); - - end - - function p = lik_inputdependentnoise_invlink(lik, f, z) - %LIK_INPUTDEPENDENTNOISE_INVLINK Returns values of inverse link function - % - % Description - % P = LIK_INPUTDEPENDENTNOISE_INVLINK(LIK, F) takes a likelihood structure LIK and - % latent values F and returns the values of inverse link function P. - % This subfunction is needed when using function gp_predprcty. - % - % See also - % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_PREDY - - p = exp(f); - end - - function reclik = lik_inputdependentnoise_recappend(reclik, ri, lik) - %RECAPPEND Append the parameters to the record - % - % Description - % RECLIK = GPCF_INPUTDEPENDENTNOISE_RECAPPEND(RECLIK, RI, LIK) takes a - % likelihood record structure RECLIK, record index RI and - % likelihood structure LIK with the current MCMC samples of - % the parameters. Returns RECLIK which contains all the old - % samples and the current samples from LIK. This subfunction - % is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC - - % Initialize record - if nargin == 2 - reclik.type = 'Inputdependentnoise'; - reclik.nondiagW=true; - - % Initialize parameter - - % Set the function handles - reclik.fh.pak = @lik_inputdependentnoise_pak; - reclik.fh.unpak = @lik_inputdependentnoise_unpak; - reclik.fh.lp = @lik_inputdependentnoise_lp; - reclik.fh.lpg = @lik_inputdependentnoise_lpg; - reclik.fh.ll = @lik_inputdependentnoise_ll; - reclik.fh.llg = @lik_inputdependentnoise_llg; - reclik.fh.llg2 = @lik_inputdependentnoise_llg2; - reclik.fh.llg3 = @lik_inputdependentnoise_llg3; - reclik.fh.predy = @lik_inputdependentnoise_predy; - reclik.fh.predprcty = @lik_inputdependentnoise_predprcty; - reclik.fh.invlink = @lik_inputdependentnoise_invlink; - reclik.fh.recappend = @lik_inputdependentnoise_recappend; - reclik.p=[]; - if ~isempty(ri.p.sigma2) - reclik.p.sigma2 = ri.p.sigma2; - end - return - end - - reclik.sigma2(ri,:)=lik.sigma2; - if ~isempty(lik.p.sigma2) - reclik.p.sigma2 = feval(lik.p.sigma2.fh.recappend, reclik.p.sigma2, ri, lik.p.sigma2); - end - end -end diff --git a/gp/lik_inputdependentweibull.m b/gp/lik_inputdependentweibull.m deleted file mode 100644 index 49c3d560..00000000 --- a/gp/lik_inputdependentweibull.m +++ /dev/null @@ -1,514 +0,0 @@ -function lik = lik_inputdependentweibull(varargin) -%LIK_INPUTDEPENDENTWEIBULL Create a right censored input dependent Weibull likelihood structure -% -% Description -% LIK = LIK_INPUTDEPENDENTWEIBULL('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a likelihood structure for right censored input dependent -% Weibull survival model in which the named parameters have the -% specified values. Any unspecified parameters are set to default -% values. -% -% LIK = LIK_INPUTDEPENDENTWEIBULL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood structure with the named parameters -% altered with the specified values. -% -% Parameters for Weibull likelihood [default] -% shape - shape parameter r [1] -% shape_prior - prior for shape [prior_logunif] -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% The likelihood is defined as follows: -% __ n -% p(y|f1,f2, z) = || i=1 [ (r*exp(f2_i))^(1-z_i) exp( (1-z_i)*(-f1_i) -% +(1-z_i)*((r*exp(f2_i))-1)*log(y_i) -% -exp(-f1_i)*y_i^(r*exp(f2_i))) ] -% -% where r is the shape parameter of Weibull distribution. -% z is a vector of censoring indicators with z = 0 for uncensored event -% and z = 1 for right censored event. Here the second latent variable f2 -% implies the input dependance to the shape parameter in the original -% Weibull likelihood. -% -% When using the Weibull likelihood you need to give the vector z -% as an extra parameter to each function that requires also y. -% For example, you should call gpla_e as follows: gpla_e(w, gp, -% x, y, 'z', z) -% -% See also -% GP_SET, LIK_*, PRIOR_* -% - -% Copyright (c) 2011 Jaakko Riihimäki -% Copyright (c) 2011 Aki Vehtari -% Copyright (c) 2012 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_INPUTDEPENDENTWEIBULL'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0); - ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.nondiagW=true; - lik.type = 'Inputdependent-Weibull'; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'Weibull') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('shape',ip.UsingDefaults) - lik.shape = ip.Results.shape; - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('shape_prior',ip.UsingDefaults) - lik.p.shape=ip.Results.shape_prior; - end - - if init - % Set the function handles to the subfunctions - lik.fh.pak = @lik_inputdependentweibull_pak; - lik.fh.unpak = @lik_inputdependentweibull_unpak; - lik.fh.lp = @lik_inputdependentweibull_lp; - lik.fh.lpg = @lik_inputdependentweibull_lpg; - lik.fh.ll = @lik_inputdependentweibull_ll; - lik.fh.llg = @lik_inputdependentweibull_llg; - lik.fh.llg2 = @lik_inputdependentweibull_llg2; - lik.fh.llg3 = @lik_inputdependentweibull_llg3; - lik.fh.invlink = @lik_inputdependentweibull_invlink; - lik.fh.predy = @lik_inputdependentweibull_predy; - lik.fh.recappend = @lik_inputdependentweibull_recappend; - end - -end - -function [w,s] = lik_inputdependentweibull_pak(lik) -%LIK_INPUTDEPENDENTWEIBULL_PAK Combine likelihood parameters into one vector. -% -% Description -% W = LIK_INPUTDEPENDENTWEIBULL_PAK(LIK) takes a likelihood structure LIK and -% combines the parameters into a single row vector W. This is a -% mandatory subfunction used for example in energy and gradient -% computations. -% -% w = log(lik.shape) -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_UNPAK, GP_PAK - - w=[];s={}; - if ~isempty(lik.p.shape) - w = log(lik.shape); - s = [s; 'log(weibull.shape)']; - [wh sh] = lik.p.shape.fh.pak(lik.p.shape); - w = [w wh]; - s = [s; sh]; - end -end - - -function [lik, w] = lik_inputdependentweibull_unpak(lik, w) -%LIK_INPUTDEPENDENTWEIBULL_UNPAK Extract likelihood parameters from the vector. -% -% Description -% [LIK, W] = LIK_INPUTDEPENDENTWEIBULL_UNPAK(W, LIK) takes a likelihood -% structure LIK and extracts the parameters from the vector W -% to the LIK structure. This is a mandatory subfunction used -% for example in energy and gradient computations. -% -% Assignment is inverse of -% w = log(lik.shape) -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_PAK, GP_UNPAK - - if ~isempty(lik.p.shape) - lik.shape = exp(w(1)); - w = w(2:end); - [p, w] = lik.p.shape.fh.unpak(lik.p.shape, w); - lik.p.shape = p; - end -end - - -function lp = lik_inputdependentweibull_lp(lik, varargin) -%LIK_INPUTDEPENDENTWEIBULL_LP log(prior) of the likelihood parameters -% -% Description -% LP = LIK_INPUTDEPENDENTWEIBULL_LP(LIK) takes a likelihood structure LIK and -% returns log(p(th)), where th collects the parameters. This -% subfunction is needed when there are likelihood parameters. -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_E - - -% If prior for shape parameter, add its contribution - lp=0; - if ~isempty(lik.p.shape) - lp = lik.p.shape.fh.lp(lik.shape, lik.p.shape) +log(lik.shape); - end - -end - - -function lpg = lik_inputdependentweibull_lpg(lik) -%LIK_INPUTDEPENDENTWEIBULL_LPG d log(prior)/dth of the likelihood -% parameters th -% -% Description -% E = LIK_INPUTDEPENDENTWEIBULL_LPG(LIK) takes a likelihood structure LIK and -% returns d log(p(th))/dth, where th collects the parameters. -% This subfunction is needed when there are likelihood parameters. -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_G - - lpg=[]; - if ~isempty(lik.p.shape) - % Evaluate the gprior with respect to shape - ggs = lik.p.shape.fh.lpg(lik.shape, lik.p.shape); - lpg = ggs(1).*lik.shape + 1; - if length(ggs) > 1 - lpg = [lpg ggs(2:end)]; - end - end -end - -function ll = lik_inputdependentweibull_ll(lik, y, ff, z) -%LIK_INPUTDEPENDENTWEIBULL_LL Log likelihood -% -% Description -% LL = LIK_INPUTDEPENDENTWEIBULL_LL(LIK, Y, F, Z) takes a likelihood -% structure LIK, survival times Y, censoring indicators Z, and -% latent values F. Returns the log likelihood, log p(y|f,z). -% This subfunction is needed when using Laplace approximation -% or MCMC for inference with non-Gaussian likelihoods. This -% subfunction is also used in information criteria (DIC, WAIC) -% computations. -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_E - - if isempty(z) - error(['lik_inputdependentweibull -> lik_inputdependentweibull_ll: missing z! '... - 'Weibull likelihood needs the censoring '... - 'indicators as an extra input z. See, for '... - 'example, lik_inputdependentweibull and gpla_e. ']); - end - - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - expf2=exp(f2); - expf2(isinf(expf2))=realmax; - a = lik.shape; - ll = sum((1-z).*(log(a*expf2) + (a*expf2-1).*log(y)-f1) - exp(-f1).*y.^(a*expf2)); - -end - -function llg = lik_inputdependentweibull_llg(lik, y, ff, param, z) -%LIK_INPUTDEPENDENTWEIBULL_LLG Gradient of the log likelihood -% -% Description -% LLG = LIK_INPUTDEPENDENTWEIBULL_LLG(LIK, Y, F, PARAM) takes a likelihood -% structure LIK, survival times Y, censoring indicators Z and -% latent values F. Returns the gradient of the log likelihood -% with respect to PARAM. At the moment PARAM can be 'param' or -% 'latent'. This subfunction is needed when using Laplace -% approximation or MCMC for inference with non-Gaussian likelihoods. -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_LLG2, LIK_INPUTDEPENDENTWEIBULL_LLG3, GPLA_E - - if isempty(z) - error(['lik_inputdependentweibull -> lik_inputdependentweibull_llg: missing z! '... - 'Weibull likelihood needs the censoring '... - 'indicators as an extra input z. See, for '... - 'example, lik_inputdependentweibull and gpla_e. ']); - end - - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - expf2=exp(f2); - expf2(isinf(expf2))=realmax; - a = lik.shape; - switch param - case 'param' - llg = sum((1-z).*(1./a + expf2.*log(y)) - exp(-f1).*y.^(a.*expf2).*log(y).*expf2); - % correction for the log transformation - llg = llg.*lik.shape; - case 'latent' - llg1 = -(1-z) + exp(-f1).*y.^(a.*expf2); - llg2 = (1-z).*(1 + a.*expf2.*log(y)) - exp(-f1).*y.^(a.*expf2).*log(y).*a.*expf2; - llg = [llg1; llg2]; - end -end - -function llg2 = lik_inputdependentweibull_llg2(lik, y, ff, param, z) -%LIK_INPUTDEPENDENTWEIBULL_LLG2 Second gradients of the log likelihood -% -% Description -% LLG2 = LIK_INPUTDEPENDENTWEIBULL_LLG2(LIK, Y, F, PARAM) takes a likelihood -% structure LIK, survival times Y, censoring indicators Z, and -% latent values F. Returns the hessian of the log likelihood -% with respect to PARAM. At the moment PARAM can be only -% 'latent'. LLG2 is a vector with diagonal elements of the -% Hessian matrix (off diagonals are zero). This subfunction -% is needed when using Laplace approximation or EP for -% inference with non-Gaussian likelihoods. -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, GPLA_E - - if isempty(z) - error(['lik_inputdependentweibull -> lik_inputdependentweibull_llg2: missing z! '... - 'Weibull likelihood needs the censoring '... - 'indicators as an extra input z. See, for '... - 'example, lik_inputdependentweibull and gpla_e. ']); - end - - a = lik.shape; - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - expf2=exp(f2); - expf2(isinf(expf2))=realmax; - switch param - case 'param' - - case 'latent' - t1=exp(-f1).*y.^(a.*expf2); - t2=log(y).*a.*expf2; - t3=t1.*t2; - - llg2_11 = -t1; - llg2_12 = t3; - llg2_22 = (1-z).*t2 - (t2 + 1).*t3; - - llg2 = [llg2_11 llg2_12; llg2_12 llg2_22]; - - case 'latent+param' - t1=expf2.*log(y); - t2=exp(-f1).*y.^(a.*expf2); - t3=t1.*t2; - llg2_1 = t3; - llg2_2 = (1-z).*t1 - (t1.*a + 1).*t3; - llg2 = [llg2_1; llg2_2]; - % correction due to the log transformation - llg2 = llg2.*lik.shape; - end -end - -function llg3 = lik_inputdependentweibull_llg3(lik, y, ff, param, z) -%LIK_INPUTDEPENDENTWEIBULL_LLG3 Third gradients of the log likelihood -% -% Description -% LLG3 = LIK_INPUTDEPENDENTWEIBULL_LLG3(LIK, Y, F, PARAM) takes a likelihood -% structure LIK, survival times Y, censoring indicators Z and -% latent values F and returns the third gradients of the log -% likelihood with respect to PARAM. At the moment PARAM can be -% only 'latent'. LLG3 is a vector with third gradients. This -% subfunction is needed when using Laplace approximation for -% inference with non-Gaussian likelihoods. -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_E, GPLA_G - - if isempty(z) - error(['lik_inputdependentweibull -> lik_inputdependentweibull_llg3: missing z! '... - 'Weibull likelihood needs the censoring '... - 'indicators as an extra input z. See, for '... - 'example, lik_inputdependentweibull and gpla_e. ']); - end - - a = lik.shape; - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - expf2=exp(f2); - expf2(isinf(expf2))=realmax; - switch param - case 'param' - - case 'latent' - t1=a.*expf2.*log(y); - t2=exp(-f1).*y.^(a.*expf2); - t3=t2.*t1; - t4=t3.*t1; - - nl=2; - llg3=zeros(nl,nl,nl,n); - - llg3(1,1,1,:) = t2; - - llg3(2,2,1,:) = t4 + t3; - llg3(2,1,2,:) = llg3(2,2,1,:); - llg3(1,2,2,:) = llg3(2,2,1,:); - - llg3(2,1,1,:) = -t3; - llg3(1,2,1,:) = llg3(2,1,1,:); - llg3(1,1,2,:) = llg3(2,1,1,:); - - llg3(2,2,2,:) = (1-z).*t1 - t4.*t1 - 3.*t4 - t3; - - case 'latent2+param' - t1 = log(y).*expf2; - t2 = exp(-f1).*y.^(a*expf2); - t3 = t2.*t1; - t4 = t3.*t1; - llg3_11 = -t3; - llg3_12 = a.*t4 + t3; - llg3_22 = (1-z).*t1 - a.^2.*t4.*t1 - 3.*a.*t4 - t3; - - llg3 = [diag(llg3_11) diag(llg3_12); diag(llg3_12) diag(llg3_22)]; - % correction due to the log transformation - llg3 = llg3.*lik.shape; - end -end - - -function [lpy, Ey, Vary] = lik_inputdependentweibull_predy(lik, Ef, Varf, yt, zt) -%LIK_INPUTDEPENDENTWEIBULL_PREDY Returns the predictive mean, variance and density of y -% -% Description -% LPY = LIK_INPUTDEPENDENTWEIBULL_PREDY(LIK, EF, VARF YT, ZT) -% Returns logarithm of the predictive density PY of YT, that is -% p(yt | zt) = \int p(yt | f, zt) p(f|y) df. -% This requires also the survival times YT, censoring indicators ZT. -% This subfunction is needed when computing posterior predictive -% distributions for future observations. -% -% [LPY, EY, VARY] = LIK_INPUTDEPENDENTWEIBULL_PREDY(LIK, EF, VARF) takes a -% likelihood structure LIK, posterior mean EF and posterior -% Variance VARF of the latent variable and returns the -% posterior predictive mean EY and variance VARY of the -% observations related to the latent variables. This subfunction -% is needed when computing posterior predictive distributions for -% future observations. -% -% -% See also -% GPLA_PRED, GPEP_PRED, GPMC_PRED - - if isempty(zt) - error(['lik_inputdependentweibull -> lik_inputdependentweibull_predy: missing zt!'... - 'Weibull likelihood needs the censoring '... - 'indicators as an extra input zt. See, for '... - 'example, lik_inputdependentweibull and gpla_e. ']); - end - - yc = 1-zt; - r = lik.shape; - - Ef = Ef(:); - ntest = 0.5*size(Ef,1); - Ef1=Ef(1:ntest); Ef2=Ef(ntest+1:end); - % Varf1=squeeze(Varf(1,1,:)); Varf2=squeeze(Varf(2,2,:)); - if size(Varf,2) == size(Varf,1) - Varf1=diag(Varf(1:ntest,1:ntest));Varf2=diag(Varf(ntest+1:end,ntest+1:end)); - else - Varf1=Varf(:,1); Varf2=Varf(:,2); - end - - Ey=[]; - Vary=[]; - - % Evaluate the posterior predictive densities of the given observations - lpy = zeros(length(yt),1); - - for i2=1:ntest - m1=Ef1(i2); m2=Ef2(i2); - s1=sqrt(Varf1(i2)); s2=sqrt(Varf2(i2)); - % Function handle for Weibull * Gaussian_f1 * Gaussian_f2 - pd=@(f1,f2) exp(yc(i2).*((log(r) + f2) + (r.*exp(f2)-1).*log(yt(i2))-f1) - exp(-f1).*yt(i2).^(r*exp(f2))) ... - .*norm_pdf(f1,Ef1(i2),sqrt(Varf1(i2))).*norm_pdf(f2,Ef2(i2),sqrt(Varf2(i2))); - % Integrate over latent variables - lpy(i2) = log(dblquad(pd, m1-6.*s1, m1+6.*s1, m2-6.*s2, m2+6.*s2)); - end - -end - - -function p = lik_inputdependentweibull_invlink(lik, f, z) -%LIK_INPUTDEPENDENTWEIBULL Returns values of inverse link function -% -% Description -% P = LIK_INPUTDEPENDENTWEIBULL_INVLINK(LIK, F) takes a likelihood structure LIK and -% latent values F and returns the values of inverse link function P. -% This subfunction is needed when using function gp_predprctmu. -% -% See also -% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_PREDY - -p = exp(f); -end - -function reclik = lik_inputdependentweibull_recappend(reclik, ri, lik) -%RECAPPEND Append the parameters to the record -% -% Description -% RECLIK = LIK_INPUTDEPENDENTWEIBULL__RECAPPEND(RECLIK, RI, LIK) takes a -% likelihood record structure RECLIK, record index RI and -% likelihood structure LIK with the current MCMC samples of -% the parameters. Returns RECLIK which contains all the old -% samples and the current samples from LIK. This subfunction -% is needed when using MCMC sampling (gp_mc). -% -% See also -% GP_MC - - if nargin == 2 - % Initialize the record - reclik.type = 'Inputdependent-Weibull'; - reclik.nondiagW=true; - - % Initialize parameter -% reclik.shape = []; - - % Set the function handles - reclik.fh.pak = @lik_inputdependentweibull_pak; - reclik.fh.unpak = @lik_inputdependentweibull_unpak; - reclik.fh.lp = @lik_t_lp; - reclik.fh.lpg = @lik_t_lpg; - reclik.fh.ll = @lik_inputdependentweibull_ll; - reclik.fh.llg = @lik_inputdependentweibull_llg; - reclik.fh.llg2 = @lik_inputdependentweibull_llg2; - reclik.fh.llg3 = @lik_inputdependentweibull_llg3; - reclik.fh.invlink = @lik_inputdependentweibull_invlink; - reclik.fh.predy = @lik_inputdependentweibull_predy; - reclik.fh.recappend = @lik_inputdependentweibull_recappend; - reclik.p=[]; - reclik.p.shape=[]; - if ~isempty(ri.p.shape) - reclik.p.shape = ri.p.shape; - end - else - % Append to the record - reclik.shape(ri,:)=lik.shape; - if ~isempty(lik.p.shape) - reclik.p.shape = lik.p.shape.fh.recappend(reclik.p.shape, ri, lik.p.shape); - end - end -end - diff --git a/gp/lik_zinegbin.m b/gp/lik_zinegbin.m deleted file mode 100644 index 4c012d4e..00000000 --- a/gp/lik_zinegbin.m +++ /dev/null @@ -1,627 +0,0 @@ -function lik = lik_zinegbin(varargin) -%LIK_ZINEGBIN Create a zero-inflated Negative-binomial likelihood structure -% -% Description -% LIK = LIK_ZINEGBIN('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a zero-inflated Negative-binomial likelihood structure in -% which the named parameters have the specified values. Any unspecified -% parameters are set to default values. -% -% LIK = LIK_ZINEGBIN(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood structure with the named parameters -% altered with the specified values. -% -% Parameters for a zero-inflated Negative-binomial likelihood [default] -% disper - dispersion parameter r [10] -% disper_prior - prior for disper [prior_logunif] -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% The likelihood is defined as follows: -% -% p + (1-p)*NegBin(y|y=0), when y=0 -% (1-p)*NegBin(y|y>0), when y>0, -% -% where the probability p is given by a binary classifier with Logit -% likelihood and NegBin is the Negative-binomial distribution -% parametrized for the i'th observation as -% -% NegBin(y_i) = [ (r/(r+mu_i))^r * gamma(r+y_i) -% / ( gamma(r)*gamma(y_i+1) ) -% * (mu/(r+mu_i))^y_i ] -% -% where mu_i = z_i*exp(f_i) and r is the dispersion parameter. -% z is a vector of expected mean and f the latent value vector -% whose components are transformed to relative risk -% exp(f_i). -% -% The latent value vector f=[f1^T f2^T]^T has length 2*N, where N is the -% number of observations. The latents f1 are associated with the -% classification process and the latents f2 with Negative-binomial count -% process. -% -% When using the Zinegbin likelihood you need to give the vector z -% as an extra parameter to each function that requires also y. -% For example, you should call gpla_nd_e as follows: gpla_nd_e(w, gp, -% x, y, 'z', z) -% -% See also -% GP_SET, LIK_*, PRIOR_* -% - -% Copyright (c) 2007-2010 Jarno Vanhatalo & Jouni Hartikainen -% Copyright (c) 2010 Aki Vehtari -% Copyright (c) 2011 Jaakko Riihimäki - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_ZINEGBIN'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0); - ip=iparser(ip,'addParamValue','disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.type = 'Zinegbin'; - lik.nondiagW=true; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'Zinegbin') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('disper',ip.UsingDefaults) - lik.disper = ip.Results.disper; - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('disper_prior',ip.UsingDefaults) - lik.p.disper=ip.Results.disper_prior; - end - - if init - % Set the function handles to the nested functions - lik.fh.pak = @lik_zinegbin_pak; - lik.fh.unpak = @lik_zinegbin_unpak; - lik.fh.lp = @lik_zinegbin_lp; - lik.fh.lpg = @lik_zinegbin_lpg; - lik.fh.ll = @lik_zinegbin_ll; - lik.fh.llg = @lik_zinegbin_llg; - lik.fh.llg2 = @lik_zinegbin_llg2; - lik.fh.llg3 = @lik_zinegbin_llg3; - lik.fh.predy = @lik_zinegbin_predy; - lik.fh.invlink = @lik_zinegbin_invlink; - lik.fh.recappend = @lik_zinegbin_recappend; - end - - function [w,s] = lik_zinegbin_pak(lik) - %LIK_ZINEGBIN_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_ZINEGBIN_PAK(LIK) takes a likelihood structure LIK and - % combines the parameters into a single row vector W. This is a - % mandatory subfunction used for example in energy and gradient - % computations. - % - % w = log(lik.disper) - % - % See also - % LIK_ZINEGBIN_UNPAK, GP_PAK - - w=[];s={}; - if ~isempty(lik.p.disper) - w = log(lik.disper); - s = [s; 'log(zinegbin.disper)']; - [wh sh] = feval(lik.p.disper.fh.pak, lik.p.disper); - w = [w wh]; - s = [s; sh]; - end - end - - - function [lik, w] = lik_zinegbin_unpak(lik, w) - %LIK_ZINEGBIN_UNPAK Extract likelihood parameters from the vector. - % - % Description - % [LIK, W] = LIK_ZINEGBIN_UNPAK(W, LIK) takes a likelihood - % structure LIK and extracts the parameters from the vector W - % to the LIK structure. This is a mandatory subfunction used for - % example in energy and gradient computations. - % - % Assignment is inverse of - % w = log(lik.disper) - % - % See also - % LIK_ZINEGBIN_PAK, GP_UNPAK - - if ~isempty(lik.p.disper) - lik.disper = exp(w(1)); - w = w(2:end); - [p, w] = feval(lik.p.disper.fh.unpak, lik.p.disper, w); - lik.p.disper = p; - end - end - - - function lp = lik_zinegbin_lp(lik, varargin) - %LIK_ZINEGBIN_LP log(prior) of the likelihood parameters - % - % Description - % LP = LIK_ZINEGBIN_LP(LIK) takes a likelihood structure LIK and - % returns log(p(th)), where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, LIK_ZINEGBIN_LLG2, GPLA_E - - - % If prior for dispersion parameter, add its contribution - lp=0; - if ~isempty(lik.p.disper) - lp = feval(lik.p.disper.fh.lp, lik.disper, lik.p.disper) +log(lik.disper); - end - - end - - - function lpg = lik_zinegbin_lpg(lik) - %LIK_ZINEGBIN_LPG d log(prior)/dth of the likelihood - % parameters th - % - % Description - % E = LIK_ZINEGBIN_LPG(LIK) takes a likelihood structure LIK and - % returns d log(p(th))/dth, where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, LIK_ZINEGBIN_LLG2, GPLA_G - - lpg=[]; - if ~isempty(lik.p.disper) - % Evaluate the gprior with respect to disper - ggs = feval(lik.p.disper.fh.lpg, lik.disper, lik.p.disper); - lpg = ggs(1).*lik.disper + 1; - if length(ggs) > 1 - lpg = [lpg ggs(2:end)]; - end - end - end - - function ll = lik_zinegbin_ll(lik, y, ff, z) - %LIK_ZINEGBIN_LL Log likelihood - % - % Description - % LL = LIK_ZINEGBIN_LL(LIK, Y, F, Z) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z, and - % latent values F. Returns the log likelihood, log p(y|f,z). - % This subfunction is needed when using Laplace approximation - % or MCMC for inference with non-Gaussian likelihoods. This - % subfunction is also used in information criteria (DIC, WAIC) - % computations. - % - % See also - % LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, LIK_ZINEGBIN_LLG2, GPLA_E - - if isempty(z) - error(['lik_zinegbin -> lik_zinegbin_ll: missing z! '... - 'Zinegbin likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_zinegbin and gpla_e. ']); - end - - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - y0ind=y==0; - yind=y>0; - - r = lik.disper; - m = exp(f2).*z; - expf1=exp(f1); - - % for y = 0 - lly0 = sum(-log(1+expf1(y0ind)) + log( expf1(y0ind) + (r./(r+m(y0ind))).^r )); - % for y > 0 - lly = sum(-log(1+expf1(yind)) + r.*(log(r) - log(r+m(yind))) + gammaln(r+y(yind)) - gammaln(r) - gammaln(y(yind)+1) + y(yind).*(log(m(yind)) - log(r+m(yind)))); - - ll=lly0+lly; - end - - function llg = lik_zinegbin_llg(lik, y, ff, param, z) - %LIK_ZINEGBIN_LLG Gradient of the log likelihood - % - % Description - % LLG = LIK_ZINEGBIN_LLG(LIK, Y, F, PARAM) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z and - % latent values F. Returns the gradient of the log likelihood - % with respect to PARAM. At the moment PARAM can be 'param' or - % 'latent'. This subfunction is needed when using Laplace - % approximation or MCMC for inference with non-Gaussian likelihoods. - % - % See also - % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_LLG2, LIK_ZINEGBIN_LLG3, GPLA_E - - if isempty(z) - error(['lik_zinegbin -> lik_zinegbin_llg: missing z! '... - 'Zinegbin likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_zinegbin and gpla_e. ']); - end - - f=ff(:); - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - y0ind=y==0; - yind=y>0; - - r = lik.disper; - m = exp(f2).*z; - expf1=exp(f1); - - switch param - case 'param' - - % syms e r m - % simplify(diff(log( e + (r./(r+m)).^r ),r)) - - m0=m(y0ind); - llg1=sum( ((r./(m0 + r)).^r.*(m0 + m0.*log(r./(m0 + r)) + r.*log(r./(m0 + r))))./((expf1(y0ind) + (r./(m0 + r)).^r).*(m0 + r)) ); - - % Derivative using the psi function - llg2 = sum(1 + log(r./(r+m(yind))) - (r+y(yind))./(r+m(yind)) + psi(r + y(yind)) - psi(r)); - - llg = llg1 + llg2; - % correction for the log transformation - llg = llg.*lik.disper; - case 'latent' - - llg1=zeros(n,1); - llg2=zeros(n,1); - - llg1(y0ind)=-expf1(y0ind)./(1+expf1(y0ind)) + expf1(y0ind)./(expf1(y0ind)+ (r./(r+m(y0ind))).^r); - llg2(y0ind)=-1./( expf1(y0ind) + (r./(r+m(y0ind))).^r ) .* (r./(r+m(y0ind))).^(r-1) .* (m(y0ind).*r.^2./((r+m(y0ind)).^2)); - - llg1(yind)=-expf1(yind)./(1+expf1(yind)); - llg2(yind)=y(yind) - (r+y(yind)).*m(yind)./(r+m(yind)); - - llg=[llg1; llg2]; - end - end - - function llg2 = lik_zinegbin_llg2(lik, y, ff, param, z) - %LIK_ZINEGBIN_LLG2 Second gradients of the log likelihood - % - % Description - % LLG2 = LIK_ZINEGBIN_LLG2(LIK, Y, F, PARAM) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z, and - % latent values F. Returns the Hessian of the log likelihood - % with respect to PARAM. At the moment PARAM can be only - % 'latent'. Second gradients form a matrix of size 2N x 2N as - % [diag(LLG2_11) diag(LLG2_12); diag(LLG2_12) diag(LLG2_22)], - % but the function returns only vectors of diagonal elements as - % LLG2 = [LLG2_11 LLG2_12; LLG2_12 LLG2_22] (2Nx2 matrix) since off - % diagonals of the blocks are zero. This subfunction is needed when - % using Laplace approximation or EP for inference with non-Gaussian - % likelihoods. - % - % See also - % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, GPLA_E - - if isempty(z) - error(['lik_zinegbin -> lik_zinegbin_llg2: missing z! '... - 'Zinegbin likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_zinegbin and gpla_e. ']); - end - - f=ff(:); - - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - y0ind=y==0; - yind=y>0; - - r = lik.disper; - m = exp(f2).*z; - expf1=exp(f1); - - switch param - case 'param' - - case 'latent' - - llg2_11=zeros(n,1); - llg2_12=zeros(n,1); - llg2_22=zeros(n,1); - - rmuy0=(r./(r+m(y0ind))).^r; - llg2_11(y0ind)=-expf1(y0ind)./(1+expf1(y0ind)).^2 + expf1(y0ind).*rmuy0./(expf1(y0ind)+rmuy0).^2; - llg2_12(y0ind)=(r./(r+m(y0ind))).^(r-1).*(m(y0ind).*r.^2./(r+m(y0ind)).^2).*expf1(y0ind)./(expf1(y0ind)+rmuy0).^2; - llg2_22(y0ind)=-1./(expf1(y0ind)+rmuy0).*( (r./(r+m(y0ind))).^(2*r-2).*(m(y0ind).*r.^2./(r+m(y0ind)).^2).^2./(expf1(y0ind)+rmuy0) + (r-1).*(r./(r+m(y0ind))).^(r-2).*(-m(y0ind).^2.*r.^3./(r+m(y0ind)).^4) + (r./(r+m(y0ind))).^(r-1).*(m(y0ind).*r.^2.*(r+m(y0ind))-2*m(y0ind).^2.*r.^2)./(r+m(y0ind)).^3); - - llg2_11(yind)=-expf1(yind)./(1+expf1(yind)).^2; - llg2_22(yind)=-m(yind).*(r.^2 + y(yind).*r)./(r+m(yind)).^2; - -% llg2 = [diag(llg2_11) diag(llg2_12); diag(llg2_12) diag(llg2_22)]; - llg2 = [llg2_11 llg2_12; llg2_12 llg2_22]; - -% R=[]; -% nl=2; -% pi_mat=zeros(nl*n, n); -% llg2_12sq=sqrt(llg2_12); -% for i1=1:nl -% pi_mat((1+(i1-1)*n):(nl*n+1):end)=llg2_12sq; -% end -% pi_vec=repmat(llg2_12,nl,1)-[llg2_11; llg2_22]; - %D=diag(pi_vec); - %llg2=-D+pi_mat*pi_mat'; - %imagesc((-diag(pi_vec)+pi_mat*pi_mat') - llg2),colorbar - - case 'latent+param' - - %syms e r m - %simplify(diff(e./(e + (r./(r+m)).^r),r)) - %simplify(diff(-1/( e + (r/(r+m))^r ) * (r/(r+m))^(r-1) * (m*r^2/((r+m)^2)),r)) - - llg2_1=zeros(n,1); - llg2_2=zeros(n,1); - - m0=m(y0ind); - e0=expf1(y0ind); - llg2_1(y0ind)=-(e0.*(r./(m0 + r)).^r.*(m0 + m0.*log(r./(m0 + r)) + r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^2.*(m0 + r)); - llg2_2(y0ind)=-(m0.*(r./(m0 + r)).^r.*(m0.*(r./(m0 + r)).^r + e0.*m0 + e0.*r.^2.*log(r./(m0 + r)) + e0.*m0.*r + e0.*m0.*r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^2.*(m0 + r).^2); - - llg2_2(yind)= (y(yind).*m(yind) - m(yind).^2)./(r+m(yind)).^2; - - llg2=[llg2_1; llg2_2]; - - % correction due to the log transformation - llg2 = llg2.*lik.disper; - end - end - - function llg3 = lik_zinegbin_llg3(lik, y, ff, param, z) - %LIK_ZINEGBIN_LLG3 Third gradients of the log likelihood - % - % Description - % LLG3 = LIK_ZINEGBIN_LLG3(LIK, Y, F, PARAM) takes a likelihood - % structure LIK, incedence counts Y, expected counts Z and - % latent values F and returns the third gradients of the log - % likelihood with respect to PARAM. At the moment PARAM can be - % only 'latent'. LLG3 is a 2-by-2-by-2-by-N array of with third - % gradients, where LLG3(:,:,1,i) is the third derivative wrt f1 for - % the i'th observation and LLG3(:,:,2,i) is the third derivative wrt - % f2 for the i'th observation. This subfunction is needed when using - % Laplace approximation for inference with non-Gaussian likelihoods. - % - % See also - % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG2, GPLA_E, GPLA_G - - if isempty(z) - error(['lik_zinegbin -> lik_zinegbin_llg3: missing z! '... - 'Zinegbin likelihood needs the expected number of '... - 'occurrences as an extra input z. See, for '... - 'example, lik_zinegbin and gpla_e. ']); - end - - f=ff(:); - - n=size(y,1); - f1=f(1:n); - f2=f((n+1):2*n); - y0ind=y==0; - yind=y>0; - - r = lik.disper; - m = exp(f2).*z; - expf1=exp(f1); - - switch param - case 'param' - - case 'latent' - nl=2; - llg3=zeros(nl,nl,nl,n); - - % 11 - %simplify(diff(-exp(f1)/(1+exp(f1))^2 + exp(f1)*(r/(r+exp(f2)*z))^r/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f1)) - %simplify(diff(-exp(f1)/(1+exp(f1))^2 + exp(f1)*(r/(r+exp(f2)*z))^r/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f2)) - - % 12/21 - % simplify(diff((r/(r+exp(f2)*z))^(r-1)*(exp(f2)*z*r^2/(r+exp(f2)*z)^2)*exp(f1)/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f1)) - % simplify(diff((r/(r+exp(f2)*z))^(r-1)*(exp(f2)*z*r^2/(r+exp(f2)*z)^2)*exp(f1)/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f2)) - - % 22 - % simplify(diff(-1/(exp(f1)+(r/(r+z*exp(f2)))^r)*((r/(r+z*exp(f2)))^(2*r-2)*(z*exp(f2)*r^2/(r+z*exp(f2))^2)^2/(exp(f1)+(r/(r+z*exp(f2)))^r) + (r-1)*(r/(r+z*exp(f2)))^(r-2)*(-z*exp(f2)^2*r^3/(r+z*exp(f2))^4) + (r/(r+z*exp(f2)))^(r-1)*(z*exp(f2)*r^2*(r+z*exp(f2))-2*z*exp(f2)^2*r^2)/(r+z*exp(f2))^3),f1)) - % simplify(diff(-1/(exp(f1)+(r/(r+z*exp(f2)))^r)*((r/(r+z*exp(f2)))^(2*r-2)*(z*exp(f2)*r^2/(r+z*exp(f2))^2)^2/(exp(f1)+(r/(r+z*exp(f2)))^r) + (r-1)*(r/(r+z*exp(f2)))^(r-2)*(-z*exp(f2)^2*r^3/(r+z*exp(f2))^4) + (r/(r+z*exp(f2)))^(r-1)*(z*exp(f2)*r^2*(r+z*exp(f2))-2*z*exp(f2)^2*r^2)/(r+z*exp(f2))^3),f2)) - % with symbolic math toolbox: - % simplify(diff(simplify(diff(simplify(diff((-log(1+exp(f1)) + log( exp(f1) + (r./(r+z*exp(f2))).^r )),f2)),f2)),f1)) - - expf2=exp(f2); - m0=m(y0ind); - z0=z(y0ind); - expf1y0ind=expf1(y0ind); - expf1y0ind2=expf1y0ind.^2; - expf2y0ind=expf2(y0ind); - - % y=0: - % thrid derivative derivative wrt f1 (11) - llg3(1,1,1,y0ind) = (2.*expf1y0ind2)./(expf1y0ind + 1).^3 - expf1y0ind./(expf1y0ind + 1).^2 - (2.*expf1y0ind2.*(r./(r + m0)).^r)./(expf1y0ind + (r./(r + m0)).^r).^3 + (expf1y0ind.*(r./(r + m0)).^r)./(expf1y0ind + (r./(r + m0)).^r).^2; - % thrid derivative derivative wrt f2 (11) - llg3(1,1,2,y0ind)=-(r.*m0.*expf1y0ind.*(expf1y0ind - (r./(r + m0)).^r).*(r./(r + m0)).^r)./((r + m0).*(expf1y0ind + (r./(r + m0)).^r).^3); - - % thrid derivative derivative wrt f1 (12/21) - llg3(1,2,1,y0ind) = -(r.*m0.*expf1y0ind.*(expf1y0ind - (r./(r + m0)).^r).*(r./(r + m0)).^r)./((r + m0).*(expf1y0ind + (r./(r + m0)).^r).^3); - llg3(2,1,1,y0ind) = llg3(1,2,1,y0ind); - % thrid derivative derivative wrt f2 (12/21) - llg3(1,2,2,y0ind) = (r.^2.*m0.*expf1y0ind.*(r./(r + m0)).^r.*(expf1y0ind + (r./(r + m0)).^r + m0.*(r./(r + m0)).^r - expf1y0ind.*m0))./((r + m0).^2.*(expf1y0ind + (r./(r + m0)).^r).^3); - llg3(2,1,2,y0ind) = llg3(1,2,2,y0ind); - - % thrid derivative derivative wrt f1 (22) - llg3(2,2,1,y0ind) = (r.^2.*m0.*expf1y0ind.*(r./(r + m0)).^r.*(expf1y0ind + (r./(r + m0)).^r + m0.*(r./(r + m0)).^r - expf1y0ind.*m0))./((r + m0).^2.*(expf1y0ind + (r./(r + m0)).^r).^3); - % thrid derivative derivative wrt f1 (22) - llg3(2,2,2,y0ind) = ((r.^2.*m0.*expf2y0ind.*(z0.*expf1y0ind2 + z0.*(r./(r + m0)).^(2.*r) + 3.*r.*z0.*expf1y0ind2) - r.^2.*m0.*(r.*(r./(r + m0)).^(2.*r) + r.*expf1y0ind2 + r.*expf1y0ind2.*m0.^2)).*(r./(r + m0)).^r + expf1y0ind.*(r.^2.*m0.*expf2y0ind.*(2.*z0 + 3.*r.*z0) - r.^2.*m0.*(2.*r - r.*m0.^2)).*(r./(r + m0)).^(2.*r))./((r + m0).^3.*(expf1y0ind + (r./(r + m0)).^r).^3); - - % y>0: - llg3(1,1,1,yind) = -expf1(yind).*(1-expf1(yind))./(1+expf1(yind)).^3; - llg3(2,2,2,yind) = - m(yind).*(r.^2 + y(yind).*r)./(r + m(yind)).^2 + 2.*m(yind).^2.*(r.^2 + y(yind).*r)./(r + m(yind)).^3; - - case 'latent2+param' - - % simplify(diff(-e/(1+e)^2 + e*((r/(r+m))^r)/(e+((r/(r+m))^r))^2,r)) - % simplify(diff((r/(r+m))^(r-1)*(m*r^2/(r+m)^2)*e/(e+((r/(r+m))^r))^2,r)) - % simplify(diff(-1./(e+((r./(r+m)).^r)).*( (r./(r+m)).^(2*r-2).*(m.*r.^2./(r+m).^2).^2./(e+((r./(r+m)).^r)) + (r-1).*(r./(r+m)).^(r-2).*(-m.^2.*r.^3./(r+m).^4) + (r./(r+m)).^(r-1).*(m.*r.^2.*(r+m)-2*m.^2.*r.^2)./(r+m).^3),r)) - - llg3_11=zeros(n,1); - llg3_12=zeros(n,1); - llg3_22=zeros(n,1); - - e0=expf1(y0ind); - m0=m(y0ind); - - llg3_11(y0ind)=(e0.*(e0 - (r./(m0 + r)).^r).*(r./(m0 + r)).^r.*(m0 + m0.*log(r./(m0 + r)) + r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^3.*(m0 + r)); - llg3_12(y0ind)=(e0.^2.*m0.*(r./(m0 + r)).^r.*(m0 + r.^2.*log(r./(m0 + r)) + m0.*r + m0.*r.*log(r./(m0 + r))) - e0.*m0.*(r./(m0 + r)).^(2.*r).*(r.^2.*log(r./(m0 + r)) - m0 + m0.*r + m0.*r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^3.*(m0 + r).^2); - llg3_22(y0ind)=-(m0.*r.*(r./(m0 + r)).^(2.*r).*(4.*e0.*m0 - 2.*e0.*m0.^2 + e0.*m0.^2.*r + e0.*r.^2.*log(r./(m0 + r)) + e0.*m0.*r + e0.*m0.*r.*log(r./(m0 + r)) + e0.*m0.*r.^2.*log(r./(m0 + r)) + e0.*m0.^2.*r.*log(r./(m0 + r))) + m0.*r.*(r./(m0 + r)).^r.*(2.*m0.*(r./(m0 + r)).^(2.*r) + 2*e0.^2.*m0 - 2.*e0.^2.*m0.^2 + e0.^2.*m0.*r - e0.^2.*m0.^2.*r + e0.^2.*r.^2.*log(r./(m0 + r)) - e0.^2.*m0.*r.^2.*log(r./(m0 + r)) - e0.^2.*m0.^2.*r.*log(r./(m0 + r)) + e0.^2.*m0.*r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^3.*(m0 + r).^3); - - llg3_22(yind) = m(yind).*(y(yind).*r - 2.*r.*m(yind) - m(yind).*y(yind))./(r+m(yind)).^3; - - llg3 = [diag(llg3_11) diag(llg3_12); diag(llg3_12) diag(llg3_22)]; - - % correction due to the log transformation - llg3 = llg3.*lik.disper; - end - end - - function [lpyt,Ey, Vary] = lik_zinegbin_predy(lik, Ef, Covf, yt, zt) - %LIK_ZINEGBIN_PREDY Returns the predictive mean, variance and density of y - % - % Description - % [EY, VARY] = LIK_ZINEGBIN_PREDY(LIK, EF, VARF) takes a - % likelihood structure LIK, posterior mean EF and posterior - % covariance COVF of the latent variable and returns the - % posterior predictive mean EY and variance VARY of the - % observations related to the latent variables. This - % subfunction is needed when computing posterior predictive - % distributions for future observations. - % - % [Ey, Vary, PY] = LIK_ZINEGBIN_PREDY(LIK, EF, VARF YT, ZT) - % Returns also the predictive density of YT, that is - % p(yt | zt) = \int p(yt | f, zt) p(f|y) df. - % This requires also the incedence counts YT, expected counts ZT. - % This subfunction is needed when computing posterior predictive - % distributions for future observations. - % - % See also - % GPLA_PRED, GPEP_PRED, GPMC_PRED - - if isempty(zt) - error(['lik_zinegbin -> lik_zinegbin_predy: missing zt!'... - 'Zinegbin likelihood needs the expected number of '... - 'occurrences as an extra input zt. See, for '... - 'example, lik_zinegbin and gpla_e. ']); - end - - ntest=size(zt,1); - %avgE = zt; - r = lik.disper; - - Py = zeros(size(zt)); - - S=10000; - for i1=1:ntest - Sigm_tmp=Covf(i1:ntest:(2*ntest),i1:ntest:(2*ntest)); - Sigm_tmp=(Sigm_tmp+Sigm_tmp')./2; - f_star=mvnrnd(Ef(i1:ntest:(2*ntest)), Sigm_tmp, S); - - m = exp(f_star(:,2)).*zt(i1); - expf1=exp(f_star(:,1)); - - if yt(i1)==0 - Py(i1)=mean(exp(-log(1+expf1) + log( expf1 + (r./(r+m)).^r ))); - else - Py(i1)=mean(exp(-log(1+expf1) + r.*(log(r) - log(r+m)) + gammaln(r+yt(i1)) - gammaln(r) - gammaln(yt(i1)+1) + yt(i1).*(log(m) - log(r+m)))); - end - end - Ey = []; - Vary = []; - lpyt=log(Py); - end - - function p = lik_zinegbin_invlink(lik, f, z) - %LIK_ZINEGBIN_INVLINK Returns values of inverse link function - % - % Description - % P = LIK_ZINEGBIN_INVLINK(LIK, F) takes a likelihood structure LIK and - % latent values F and returns the values of inverse link function P. - % This subfunction is needed when using function gp_predprctmu. - % - % See also - % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_PREDY - - p = exp(f); - end - - function reclik = lik_zinegbin_recappend(reclik, ri, lik) - %RECAPPEND Append the parameters to the record - % - % Description - % RECLIK = GPCF_ZINEGBIN_RECAPPEND(RECLIK, RI, LIK) takes a - % likelihood record structure RECLIK, record index RI and - % likelihood structure LIK with the current MCMC samples of - % the parameters. Returns RECLIK which contains all the old - % samples and the current samples from LIK. This subfunction - % is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC - - % Initialize record - if nargin == 2 - reclik.type = 'Zinegbin'; - reclik.nondiagW=true; - - % Initialize parameter - reclik.disper = []; - - % Set the function handles - reclik.fh.pak = @lik_zinegbin_pak; - reclik.fh.unpak = @lik_zinegbin_unpak; - reclik.fh.lp = @lik_zinegbin_lp; - reclik.fh.lpg = @lik_zinegbin_lpg; - reclik.fh.ll = @lik_zinegbin_ll; - reclik.fh.llg = @lik_zinegbin_llg; - reclik.fh.llg2 = @lik_zinegbin_llg2; - reclik.fh.llg3 = @lik_zinegbin_llg3; - reclik.fh.predy = @lik_zinegbin_predy; - reclik.fh.invlink = @lik_zinegbin_invlink; - reclik.fh.recappend = @lik_zinegbin_recappend; - reclik.p=[]; - reclik.p.disper=[]; - if ~isempty(ri.p.disper) - reclik.p.disper = ri.p.disper; - end - return - end - - reclik.disper(ri,:)=lik.disper; - if ~isempty(lik.p.disper) - reclik.p.disper = feval(lik.p.disper.fh.recappend, reclik.p.disper, ri, lik.p.disper); - end - end -end - - diff --git a/gp/metric_ibs_gxe.m b/gp/metric_ibs_gxe.m deleted file mode 100644 index 176b5659..00000000 --- a/gp/metric_ibs_gxe.m +++ /dev/null @@ -1,757 +0,0 @@ -function metric = metric_ibs_gxe(do, varargin) -%METRIC_IBS_GXE An Euclidean distance metric for Gaussian process models. -% -% Description -% METRIC = METRIC_ibs_gxe('INIT', NIN) Constructs an data -% structure for using both a distancematrix and a euclidean -% metric used in covariance function of a GP model. -% -% Uses metric_distancematrix and metric_euclidean to combine -% genetic (ibs distance matrix) and environmental factors. These -% have to be set up beforehand and added to this using 'init' or -% 'set'. The input to the GP should a matrix of size n x n+d -% where n is the number of individuals and d the number of -% additional covariates. -% -% pak = function handle to pack function -% (@metric_ibs_gxe_pak) -% unpak = function handle to unpack function -% (@metric_ibs_gxe_unpak) -% e = function handle to energy function -% (@metric_ibs_gxe_e) -% ghyper = function handle to gradient of energy with respect to hyperparameters -% (@metric_ibs_gxe_ghyper) -% ginput = function handle to gradient of function with respect to inducing inputs -% (@metric_ibs_gxe_ginput) -% distance = function handle to distance function of the metric. -% (@metric_ibs_gxe_distance) -% fh_recappend = function handle to append the record function -% (metric_ibs_gxe_recappend) -% -% METRIC = METRIC_ibs_gxe('SET', METRIC, 'FIELD1', VALUE1, 'FIELD2', VALUE2, ...) -% Set the values of fields FIELD1... to the values VALUE1... in METRIC. -% - -% Copyright (c) 2009-2010 Heikki Peura - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - if nargin < 2 - error('Not enough arguments') - end - - if strcmp(do, 'init') - metric.type = 'metric_ibs_gxe'; - n=min(size(varargin{1})); - metric.nin = varargin{1}; - n1=varargin{1}-n; - %metric.components = varargin{2}; - %metric.params=varargin{3}; - %metric.x1x2matrix=[]; - - %metric.x1matrix=[]; - %metric.x2matrix=[]; -% metric.x1=[]; -% metric.x2=[]; - %metric.n1=[]; - %metric.n2=[]; - %metric.gdist=[]; - %metric.params = repmat(1,1,length(metric.components)); - %metric.Kfu=[]; - %metric.Kffstar=[]; - - %metric.metric_gene=metric_distancematrix('init', n,[],'lengthScales',[2]); - %metric.metric_gene.p.params = gamma_p({3 1}); - %metric.metric_env=metric_euclidean('init', n1, varargin{2},'lengthScales',varargin{4}); - %metric.metric_env.p.params = gamma_p({3 1}); - - - % Initialize prior structure - %metric.p=[]; - %metric.p.params=[]; - - % Set the function handles to the nested functions - metric.pak = @metric_ibs_gxe_pak; - metric.unpak = @metric_ibs_gxe_unpak; - metric.e = @metric_ibs_gxe_e; - metric.ghyper = @metric_ibs_gxe_ghyper; - metric.ginput = @metric_ibs_gxe_ginput; - metric.distance = @metric_ibs_gxe_distance; - metric.recappend = @metric_ibs_gxe_recappend; - %metric.matrix = @metric_ibs_gxe_matrix; - %metric.initmatrices = @metric_ibs_gxe_initmatrices; - - if length(varargin) > 2 - if mod(nargin,2) ~=0 - error('Wrong number of arguments') - end - % Loop through all the parameter values that are changed - for i=2:2:length(varargin)-1 - switch varargin{i} -% case 'params' -% if size(varargin{i+1}) ~= size(metric.params) -% error('Incorrect number of parameters given.'); -% end -% metric.params = varargin{i+1}; - case 'metric_gene' - metric.metric_gene = varargin{i+1}; - case 'metric_env' - metric.metric_env = varargin{i+1}; -% case 'x1' -% metric.x1=varargin{i+1}; -% metric.n1=size(varargin{i+1},1); -% [metric.x1matrix,metric.gdist]=metric.matrix(varargin{i+1}); -% case 'x2' -% metric.x2=varargin{i+1}; -% metric.n2=size(varargin{i+1},1); -% metric.x2matrix=metric.matrix(varargin{i+1}); -% case 'x1matrix' -% metric.x1matrix=varargin{i+1}; -% metric.x1matrix=reshape(metric.x1matrix,[1,size(metric.x1matrix)]); -% metric.n1=size(varargin{i+1},1); -% for i=1:length(metric.components) -% metric.gdist{1}=-metric.x1matrix; -% end -% case 'x2matrix' -% metric.x2matrix=varargin{i+1}; -% metric.x2matrix=reshape(metric.x2matrix,[1,size(metric.x2matrix)]); -% metric.n2=size(varargin{i+1},1); -% case 'x1x2matrix' -% metric.x1x2matrix=varargin{i+1}; -% metric.x1x2matrix=reshape(metric.x1x2matrix,[1,size(metric.x1x2matrix)]); - otherwise - error('Wrong parameter name!') - end - end -% if isempty(metric.x1x2matrix) -% metric.x1x2matrix = metric.matrix(metric.x1,metric,x2); -% end - - - - end - end - - % Set the parameter values of covariance function - if strcmp(do, 'set') - if mod(nargin,2) ~=0 - error('Wrong number of arguments') - end - metric = varargin{1}; - % Loop through all the parameter values that are changed - for i=2:2:length(varargin)-1 - switch varargin{i} -% case 'params' -% if size(varargin{i+1}) ~= size(metric.params) -% error('Incorrect number of parameters given.'); -% end -% metric.params = varargin{i+1}; - case 'metric_gene' - metric.metric_gene = varargin{i+1}; - case 'metric_env' - metric.metric_env = varargin{i+1}; -% case 'x1' -% metric.x1=varargin{i+1}; -% metric.n1=size(varargin{i+1},1); -% [metric.x1matrix,metric.gdist]=metric.matrix(varargin{i+1}); -% case 'x2' -% metric.x2=varargin{i+1}; -% metric.n2=size(varargin{i+1},1); -% metric.x2matrix=metric.matrix(varargin{i+1}); -% case 'x1matrix' -% metric.x1matrix=varargin{i+1}; -% metric.x1matrix=reshape(metric.x1matrix,[1,size(metric.x1matrix)]); -% metric.n1=size(varargin{i+1},1); -% for i=1:length(metric.components) -% metric.gdist{1}=-metric.x1matrix; -% end -% case 'x2matrix' -% metric.x2matrix=varargin{i+1}; -% metric.x2matrix=reshape(metric.x2matrix,[1,size(metric.x2matrix)]); -% metric.n2=size(varargin{i+1},1); -% case 'x1x2matrix' -% metric.x1x2matrix=varargin{i+1}; -% metric.x1x2matrix=reshape(metric.x1x2matrix,[1,size(metric.x1x2matrix)]); - otherwise - error('Wrong parameter name!') - end - end -% if isempty(metric.x1x2matrix) -% metric.x1x2matrix = metric.matrix(metric.x1,metric,x2); -% end - end - end - - - function w = metric_ibs_gxe_pak(metric) - %METRIC_ibs_PAK Combine the metric parameters into one vector. - % - % Description - % W = METRIC_ibs_PAK(METRIC, W) takes a metric structure METRIC and - % combines the parameters into a single row vector W. - % - % See also - % METRIC_ibs_UNPAK - -% i1=0;i2=1; -% if ~isempty(w) -% i1 = length(w); -% end -% i1=i1+1; - w=[metric.metric_gene.pak(metric.metric_gene) metric.metric_env.pak(metric.metric_env)]; -% i1=i1+1; - % w=; - end - - function [metric, w] = metric_ibs_gxe_unpak(metric, w) - %METRIC_ibs_UNPAK Separate metric parameter vector into components. - % - % Description - % [METRIC, W] = METRIC_ibs_UNPAK(METRIC, W) takes a metric structure - % METRIC parameter vector W, and returns a metric structure identical to the - % input, except that the parameters has been set to the values in W. Deletes the values - % set to METRIC from W and returns the modified W. - % - % See also - % METRIC_ibs_PAK - % -% i1=1+length(metric.metric_env.components); -% w1=w(2:2+length(metric.metric_env.components)); -% w=w(i1+1:end); - - [metric_gene, w] = metric.metric_gene.unpak(metric.metric_gene, w); - metric.metric_gene=metric_gene; - [metric_env, w] = metric.metric_env.unpak(metric.metric_env, w); - metric.metric_env=metric_env; - end - - function eprior = metric_ibs_gxe_e(metric, x, t) - %METRIC_ibs_E Evaluate the energy of prior of metric parameters - % - % Description - % E = METRIC_ibs_E(METRIC, X, T) takes a metric structure - % METRIC together with a matrix X of input vectors and a matrix T of target - % vectors and evaluates log p(th) x J, where th is a vector of metric parameters - % and J is the Jakobian of transformation exp(w) = th. (Note that the parameters - % are log transformed, when packed.) - % - % See also - % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs_G, GP_E - % - %[n, m] = size(x); - - % Evaluate the prior contribution to the error. The parameters that - % are sampled are from space W = log(w) where w is all the "real" samples. - % On the other hand errors are evaluated in the W-space so we need take - % into account also the Jakobian of transformation W -> w = exp(W). - % See Gelman et al., 2004, Bayesian data Analysis, second edition, p24. - eprior = 0; - eprior=eprior+metric.metric_gene.e(metric.metric_gene,x,t); - eprior=eprior+metric.metric_env.e(metric.metric_env,x,t); - -% mp=metric.p; -% -% if isfield(mp.params, 'p') && ~isempty(mp.params.p) -% eprior=eprior... -% +feval(mp.params.p.s.fe, ... -% gpp.params.a.s, mp.params.p.s.a)... -% -log(mp.params.a.s); -% if any(strcmp(fieldnames(mp.params.p),'nu')) -% eprior=eprior... -% +feval(mp.p.params.nu.fe, ... -% mp.params.a.nu, mp.params.p.nu.a)... -% -log(mp.params.a.nu); -% end -% end -% eprior=eprior... -% +feval(mp.params.fe, ... -% metric.params, mp.params.a)... -% -sum(log(metric.params)); - - end - - function [gdist, gprior_dist] = metric_ibs_gxe_ghyper(metric, x1, x2, mask) - %METRIC_ibs_GHYPER Evaluate the gradient of the metric function and hyperprior - % w.r.t to it's hyperparameters. - % - % Description - % [GDIST, GPRIOR_DIST] = METRIC_ibs_GHYPER(METRIC, X) takes a - % metric structure METRIC together with a matrix X of input vectors and - % return the gradient matrices GDIST and GPRIOR_DIST for each hyperparameter. - % - % [GDIST, GPRIOR_DIST] = METRIC_ibs_GHYPER(METRIC, X, X2) forms the gradient - % matrices between two input vectors X and X2. - % - % [GDIST, GPRIOR_DIST] = METRIC_ibs_GHYPER(METRIC, X, X2, MASK) forms - % the gradients for masked covariances matrices used in sparse approximations. - % - % See also - % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs, GP_E - % - - %mp=metric.p; - %components = metric.components; - - %n = size(x1,1); - m = length(metric.metric_env.lengthScales); - i1=0;i2=1; - %[n1,m1]=size(x1); - -% if nargin < 3 -% n2=n1; -% end - - - gdist=cell(1,m+1); - n1=min(size(x1)); - n=n1; - g1=x1(:,1); - e1=x1(:,2:end); - - %n1=size(x1matrix,1); - dist=0; -% s = 1./metric.params; - if (nargin == 3) - g2=x2(:,1); - e2=x2(:,2:end); - n2=min(size(x2)); - - [gdist_gene,gprior_dist_gene]=metric.metric_gene.ghyper(metric.metric_gene,g1,g2); - - [gdist_env,gprior_dist_env]=metric.metric_env.ghyper(metric.metric_env,e1,e2); - else - [gdist_gene,gprior_dist_gene]=metric.metric_gene.ghyper(metric.metric_gene,g1); - - [gdist_env,gprior_dist_env]=metric.metric_env.ghyper(metric.metric_env,e1); - - end - gdist{1}=gdist_gene{1}; - for i=2:m+1 - - gdist{i}=gdist_env{i-1}; - end - - - gprior_dist=[gprior_dist_gene gprior_dist_env]; -% s = 1./metric.params; -% % if nargin < 3 -% % dist=s.*metric.x1x2matrix; -% % elseif n1==size(metric.x1,1) -% % dist=s.*metric.x1matrix; -% % -% % else -% % dist=s.*metric.x2matrix; -% % end -% -% for i=1:m -% gdist{i}=-s.*sqrt(squeeze(x1matrix)); -% end -% -% % ii1=0; -% % -% dist = 0; -% distc = cell(1,m); -% % Compute the distances for each component set -% for i=1:m -% s = 1./metric.params(i).^2; -% distc{i} = 0; -% for j = 1:length(components{i}) -% distc{i} = distc{i} + bsxfun(@minus,x(:,components{i}(j)),x2(:,components{i}(j))').^2; -% end -% distc{i} = distc{i}.*s; -% % Accumulate to the total distance -% dist = dist + distc{i}; -% end -% dist = sqrt(dist); -% % Loop through component sets -% for i=1:m -% D = -distc{i}; -% D(dist~=0) = D(dist~=0)./dist(dist~=0); -% ii1 = ii1+1; -% gdist{ii1} = D; -% end - -% $$$ elseif nargin == 3 -% $$$ if size(x,2) ~= size(x2,2) -% $$$ error('metric_ibs -> _ghyper: The number of columns in x and x2 has to be the same. ') -% $$$ end -% elseif nargin == 4 -% gdist = cell(1,length(metric.params)+1); -% end -% -% % Evaluate the prior contribution of gradient with respect to lengthScale -% for i2=1:m -% i1=i1+1; -% gprior_dist(i1)=feval(mp.params.fg, ... -% metric.params(i2), ... -% mp.params.a, 'x').*metric.params(i2) - 1; -% end -% -% % Evaluate the prior contribution of gradient with respect to lengthScale.p.s (and lengthScale.p.nu) -% if isfield(mp.params, 'p') && ~isempty(mp.params.p) -% i1=i1+1; -% gprior_dist(i1)=... -% feval(mp.params.p.s.fg, ... -% mp.params.a.s,... -% mp.params.p.s.a, 'x').*mp.params.a.s - 1 ... -% +feval(mp.params.fg, ... -% metric.params, ... -% mp.params.a, 's').*mp.params.a.s; -% if any(strcmp(fieldnames(mp.params.p),'nu')) -% i1=i1+1; -% gprior_dist(i1)=... -% feval(mp.params.p.nu.fg, ... -% mp.params.a.nu,... -% mp.params.p.nu.a, 'x').*mp.params.a.nu -1 ... -% +feval(mp.params.fg, ... -% metric.params, ... -% mp.params.a, 'nu').*mp.params.a.nu; -% end -% end - end - -% function [matrix,gdist] = metric_ibs_gxe_matrix(metric, x1, x2) -% %METRIC_ibs_matrix Compute the ibs distence between -% % one or two matrices. -% % -% % Description -% % [DIST] = METRIC_ibs_matrix(METRIC, X) takes a metric data -% % structure METRIC together with a matrix X of input vectors and -% % calculates the ibs distance matrix DIST. -% % -% % [DIST] = METRIC_ibs_matrix(METRIC, X1, X2) takes a metric data -% % structure METRIC together with a matrices X1 and X2 of input vectors and -% % calculates the ibs distance matrix DIST. -% % -% % See also -% % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs, GP_E -% % -% if nargin == 2 || isempty(x2) -% x2=x1; -% end -% -% [n1,m1]=size(x1); -% [n2,m2]=size(x2); -% -% if m1~=m2 -% error('the number of columns of X1 and X2 has to be same') -% end -% mp=metric.p; -% components = metric.components; -% m = length(components); -% dist = 0; -% i1=0; -% if nargout>1 -% -% ii1 = 0; -% distc = cell(1,m); -% % Compute the distances for each component set -% for i=1:m -% %s = 1./metric.params(i).^2; -% distc{i} = 0; -% for j = 1:length(components{i}) -% distc{i} = distc{i} + bsxfun(@minus,x1(:,components{i}(j)),x2(:,components{i}(j))').^2; -% end -% distc{i} = distc{i}; -% % Accumulate to the total distance -% dist = dist + distc{i}; -% end -% dist = sqrt(dist); -% % Loop through component sets -% for i=1:m -% D = -distc{i}; -% D(dist~=0) = D(dist~=0)./dist(dist~=0); -% ii1 = ii1+1; -% gdist{ii1} = D; -% end -% -% else -% -% for i=1:m -% for j = 1:length(components{i}) -% dist = dist + bsxfun(@minus,x1(:,components{i}(j)),x2(:,components{i}(j))').^2; -% end -% end -% dist=sqrt(dist); -% -% end -% -% matrix = dist; -% -% -% -% -% -% end - - - function [dist] = metric_ibs_gxe_distance(metric, x1, x2) - %METRIC_ibs_DISTANCE Compute the ibs distence between - % one or two matrices. - % - % Description - % [DIST] = METRIC_ibs_DISTANCE(METRIC, X) takes a metric data - % structure METRIC together with a matrix X of input vectors and - % calculates the ibs distance matrix DIST. - % - % [DIST] = METRIC_ibs_DISTANCE(METRIC, X1, X2) takes a metric data - % structure METRIC together with a matrices X1 and X2 of input vectors and - % calculates the ibs distance matrix DIST. - % - % See also - % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs, GP_E - % - - -% if nargin == 2 || isempty(n2) -% n2=n1; -% end - -% [n1,m1]=size(x1); -% [n2,m2]=size(x2); - -% if m1~=m2 -% error('the number of columns of X1 and X2 has to be same') -% end - - - - [n1,m1]=size(x1); - - if nargin == 3 - [n2,m2]=size(x2); - g1=x1(:,1); - g2=x2(:,1); - dist_g=metric.metric_gene.distance(metric.metric_gene,g1,g2); - e1=x1(:,2:end); - e2=x2(:,2:end); - dist_e=metric.metric_env.distance(metric.metric_env,e1,e2); - else - g1=x1(:,1); - dist_g=metric.metric_gene.distance(metric.metric_gene,g1); - e1=x1(:,2:end); - dist_e=metric.metric_env.distance(metric.metric_env,e1); - end - - dist=dist_e+dist_g; - -% n1min=min(size(x1matrix)); -% n1max=max(size(x1matrix)); -% %n1=size(x1matrix,1); -% dist=0; -% % s = 1./metric.params; -% jep=0; -% if (nargin == 3) -% n2min=min(size(x2matrix)); -% n2max=max(size(x2matrix)); -% if n1max<=n2max -% -% gene_matrix1=x1matrix(1:n1min,1:n1min); -% env_matrix1=x1matrix(1:end,n1min+1:end); -% if size(x2matrix,1)==n2max -% gene_matrix2=x2matrix(1:n2min,1:n1min); -% env_matrix2=x2matrix(n2min+1:end,1:end)'; -% else -% gene_matrix2=x2matrix(1:n1min,1:n2min)'; -% env_matrix2=x2matrix(1:end,n2min+1:end); -% end -% else -% jep=1; -% gene_matrix1=x2matrix(1:n2min,1:n2min); -% env_matrix1=x2matrix(1:end,n2min+1:end); -% if size(x1matrix,1)==n1max -% gene_matrix2=x1matrix(1:n1min,1:n2min); -% env_matrix2=x1matrix(n1min+1:end,1:end)'; -% else -% gene_matrix2=x1matrix(1:n2min,1:n1min)'; -% env_matrix2=x1matrix(1:end,n1min+1:end); -% end -% end -% dist_gene=metric.metric_gene.distance(metric.metric_gene,gene_matrix1,gene_matrix2); -% dist_env=metric.metric_env.distance(metric.metric_env,env_matrix1,env_matrix2); -% else -% gene_matrix1=x1matrix(1:n1min,1:n1min); -% env_matrix1=x1matrix(1:end,n1min+1:end); -% dist_gene=metric.metric_gene.distance(metric.metric_gene,gene_matrix1); -% dist_env=metric.metric_env.distance(metric.metric_env,env_matrix1); -% end -% -% dist=dist_gene+dist_env; -% -% if jep==1 -% dist=dist'; -% end - -% % gene effect: square root of manhattan metric -% if (nargin == 3) -% n2=min(size(x2matrix)); -% gene_matrix2=x2matrix(1:n,1:n); -% env_matrix2=x2matrix(n+1:end,n+1:end); -% %n1=size(genematrix1,1); -% %n2=size(genematrix2,1); -% if size(metric.Kfu)==[n1,n2] -% dist=s(1).*sqrt(squeeze(Kfu)); -% elseif size(metric.Kfu)==[n2,n1] -% dist=s(1).*sqrt(squeeze(Kfu))'; -% elseif size(metric.Kffstar)==[n1,n2] -% dist=s(1).*sqrt(squeeze(Kffstar)); -% elseif size(metric.Kffstar)==[n2,n1] -% dist=s(1).*sqrt(squeeze(Kffstar))'; -% else -% dist=s(1).*sqrt(squeeze(genematrix1)); -% end -% % elseif n1~=metric.n1 -% % dist=s.*squeeze(metric.x2matrix); -% else -% dist=s(1).*sqrt(squeeze(genematrix1)); -% env_matrix2=env_matrix1; -% end -% -% % environmental effect: euclidean metric -% components = metric.components; -% m = length(components); -% dist_e = 0; -% -% if isempty(metric.dmatrix) -% for i=1:m -% s = 1./metric.params(i+1).^2; -% for j = 1:length(components{i}) -% dist_e = dist_e + s.*bsxfun(@minus,env_matrix1(:,components{i}(j)),env_matrix2(:,components{i}(j))').^2; -% end -% end -% dist_e = sqrt(dist_e); -% end -% -% dist=dist+dist_e; -% -% %dist=metric.dmatrix; - - - end - - function [ginput, gprior_input] = metric_ibs_gxe_ginput(metric, x1, x2) - %METRIC_ibs_GINPUT Compute the gradient of the ibs distance - % function with respect to input. - %[n, m] =size(x); - ii1 = 0; - components = metric.components; -% -% if nargin == 2 || isempty(x2) -% x2=x1; -% end -% -% [n1,m1]=size(x1); -% [n2,m2]=size(x2); -% -% if m1~=m2 -% error('the number of columns of X1 and X2 has to be same') -% end - -% if n1~=n2 -% dist=metric.x1x2matrix; -% elseif n1==length(metric.x1) -% dist=metric.x1matrix; -% else -% dist=metric.x2matrix; -% end - dist=metric.distance(metric,x1,x2); - -% s = 1./metric.params.^2; -% dist = 0; -% for i=1:length(components) -% for j = 1:length(components{i}) -% dist = dist + s(i).*bsxfun(@minus,x1(:,components{i}(j)),x2(:,components{i}(j))').^2; -% end -% end -% dist = sqrt(dist); - - - [ginput_g, gprior_input_g] = metric.metric_gene.ginput(metric.metric_gene, x1, x2); - [ginput_e, gprior_input_e] = metric.metric_env.ginput(metric.metric_env, x1, x2); - - for i=1:length(ginput_g) - ginput{i}=ginput_g{i}+ginput_e{i}; - gprior_input=[]; %KORJAA - end - -% -% for i=1:m1 -% for j = 1:n1 -% DK = zeros(n1,n2); -% for k = 1:length(components) -% if ismember(i,components{k}) -% DK(j,:) = DK(j,:)+s(k).*bsxfun(@minus,x1(j,i),x2(:,i)'); -% end -% end -% if nargin == 2 -% DK = DK + DK'; -% end -% DK(dist~=0) = DK(dist~=0)./dist(dist~=0); -% -% ii1 = ii1 + 1; -% ginput{ii1} = DK; -% gprior_input(ii1) = 0; -% end -% end - %size(ginput) - %ginput - - end - - - function recmetric = metric_ibs_gxe_recappend(recmetric, ri, metric) - % RECAPPEND - Record append - % Description - % RECMETRIC = METRIC_ibs_RECAPPEND(RECMETRIC, RI, METRIC) takes old covariance - % function record RECMETRIC, record index RI and covariance function structure. - % Appends the parameters of METRIC to the RECMETRIC in the ri'th place. - % - % RECAPPEND returns a structure RECMETRIC containing following record fields: - % lengthHyper - % lengthHyperNu - % lengthScale - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - % Initialize record - if nargin == 2 - recmetric.type = 'metric_ibs_gxe'; - recmetric.nin = ri; - metric.components = recmetric.components; - - % Initialize parameters - recmetric.params = []; - - % Set the function handles - recmetric.pak = @metric_ibs_gxe_pak; - recmetric.unpak = @metric_ibs_gxe_unpak; - recmetric.e = @metric_ibs_gxe_e; - recmetric.ghyper = @metric_ibs_gxe_ghyper; - recmetric.ginput = @metric_ibs_gxe_ginput; - recmetric.distance = @metric_ibs_gxe_distance; - recmetric.recappend = @metric_ibs_gxe_recappend; - recmetric.matrix = @metric_ibs_gxe_matrix; - return - end - mp = metric.p; - - % record parameters - if ~isempty(metric.params) - if ~isempty(mp.params) - recmetric.lengthHyper(ri,:)=mp.params.a.s; - if isfield(mp.params,'p') - if isfield(mp.params.p,'nu') - recmetric.lengthHyperNu(ri,:)=mp.params.a.nu; - end - end - elseif ri==1 - recmetric.lengthHyper=[]; - end - recmetric.params(ri,:)=metric.params; - elseif ri==1 - recmetric.params=[]; - end - end diff --git a/octave_compat/mnpdf.m b/octave_compat/mnpdf.m deleted file mode 100644 index fb1920e4..00000000 --- a/octave_compat/mnpdf.m +++ /dev/null @@ -1,134 +0,0 @@ -## Copyright (C) 2012 Arno Onken -## -## This program is free software: you can redistribute it and/or modify -## it under the terms of the GNU General Public License as published by -## the Free Software Foundation, either version 3 of the License, or -## (at your option) any later version. -## -## This program is distributed in the hope that it will be useful, -## but WITHOUT ANY WARRANTY; without even the implied warranty of -## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -## GNU General Public License for more details. -## -## You should have received a copy of the GNU General Public License -## along with this program. If not, see . - -## -*- texinfo -*- -## @deftypefn {Function File} {@var{y} =} mnpdf (@var{x}, @var{p}) -## Compute the probability density function of the multinomial distribution. -## -## @subheading Arguments -## -## @itemize @bullet -## @item -## @var{x} is vector with a single sample of a multinomial distribution with -## parameter @var{p} or a matrix of random samples from multinomial -## distributions. In the latter case, each row of @var{x} is a sample from a -## multinomial distribution with the corresponding row of @var{p} being its -## parameter. -## -## @item -## @var{p} is a vector with the probabilities of the categories or a matrix -## with each row containing the probabilities of a multinomial sample. -## @end itemize -## -## @subheading Return values -## -## @itemize @bullet -## @item -## @var{y} is a vector of probabilites of the random samples @var{x} from the -## multinomial distribution with corresponding parameter @var{p}. The parameter -## @var{n} of the multinomial distribution is the sum of the elements of each -## row of @var{x}. The length of @var{y} is the number of columns of @var{x}. -## If a row of @var{p} does not sum to @code{1}, then the corresponding element -## of @var{y} will be @code{NaN}. -## @end itemize -## -## @subheading Examples -## -## @example -## @group -## x = [1, 4, 2]; -## p = [0.2, 0.5, 0.3]; -## y = mnpdf (x, p); -## @end group -## -## @group -## x = [1, 4, 2; 1, 0, 9]; -## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; -## y = mnpdf (x, p); -## @end group -## @end example -## -## @subheading References -## -## @enumerate -## @item -## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics -## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001. -## -## @item -## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical -## Distributions}. pages 134-136, Wiley, New York, third edition, 2000. -## @end enumerate -## @end deftypefn - -## Author: Arno Onken -## Description: PDF of the multinomial distribution - -function y = mnpdf (x, p) - - # Check arguments - if (nargin != 2) - print_usage (); - endif - - if (! ismatrix (x) || any (x(:) < 0 | round (x(:) != x(:)))) - error ("mnpdf: x must be a matrix of non-negative integer values"); - endif - if (! ismatrix (p) || any (p(:) < 0)) - error ("mnpdf: p must be a non-empty matrix with rows of probabilities"); - endif - - # Adjust input sizes - if (! isvector (x) || ! isvector (p)) - if (isvector (x)) - x = x(:)'; - endif - if (isvector (p)) - p = p(:)'; - endif - if (size (x, 1) == 1 && size (p, 1) > 1) - x = repmat (x, size (p, 1), 1); - elseif (size (x, 1) > 1 && size (p, 1) == 1) - p = repmat (p, size (x, 1), 1); - endif - endif - # Continue argument check - if (any (size (x) != size (p))) - error ("mnpdf: x and p must have compatible sizes"); - endif - - # Count total number of elements of each multinomial sample - n = sum (x, 2); - # Compute probability density function of the multinomial distribution - t = x .* log (p); - t(x == 0) = 0; - y = exp (gammaln (n+1) - sum (gammaln (x+1), 2) + sum (t, 2)); - # Set invalid rows to NaN - k = (abs (sum (p, 2) - 1) > 1e-6); - y(k) = NaN; - -endfunction - -%!test -%! x = [1, 4, 2]; -%! p = [0.2, 0.5, 0.3]; -%! y = mnpdf (x, p); -%! assert (y, 0.11812, 0.001); - -%!test -%! x = [1, 4, 2; 1, 0, 9]; -%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; -%! y = mnpdf (x, p); -%! assert (y, [0.11812; 0.13422], 0.001); diff --git a/octave_compat/mnrnd.m b/octave_compat/mnrnd.m deleted file mode 100644 index 1332faff..00000000 --- a/octave_compat/mnrnd.m +++ /dev/null @@ -1,184 +0,0 @@ -## Copyright (C) 2012 Arno Onken -## -## This program is free software: you can redistribute it and/or modify -## it under the terms of the GNU General Public License as published by -## the Free Software Foundation, either version 3 of the License, or -## (at your option) any later version. -## -## This program is distributed in the hope that it will be useful, -## but WITHOUT ANY WARRANTY; without even the implied warranty of -## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -## GNU General Public License for more details. -## -## You should have received a copy of the GNU General Public License -## along with this program. If not, see . - -## -*- texinfo -*- -## @deftypefn {Function File} {@var{x} =} mnrnd (@var{n}, @var{p}) -## @deftypefnx {Function File} {@var{x} =} mnrnd (@var{n}, @var{p}, @var{s}) -## Generate random samples from the multinomial distribution. -## -## @subheading Arguments -## -## @itemize @bullet -## @item -## @var{n} is the first parameter of the multinomial distribution. @var{n} can -## be scalar or a vector containing the number of trials of each multinomial -## sample. The elements of @var{n} must be non-negative integers. -## -## @item -## @var{p} is the second parameter of the multinomial distribution. @var{p} can -## be a vector with the probabilities of the categories or a matrix with each -## row containing the probabilities of a multinomial sample. If @var{p} has -## more than one row and @var{n} is non-scalar, then the number of rows of -## @var{p} must match the number of elements of @var{n}. -## -## @item -## @var{s} is the number of multinomial samples to be generated. @var{s} must -## be a non-negative integer. If @var{s} is specified, then @var{n} must be -## scalar and @var{p} must be a vector. -## @end itemize -## -## @subheading Return values -## -## @itemize @bullet -## @item -## @var{x} is a matrix of random samples from the multinomial distribution with -## corresponding parameters @var{n} and @var{p}. Each row corresponds to one -## multinomial sample. The number of columns, therefore, corresponds to the -## number of columns of @var{p}. If @var{s} is not specified, then the number -## of rows of @var{x} is the maximum of the number of elements of @var{n} and -## the number of rows of @var{p}. If a row of @var{p} does not sum to @code{1}, -## then the corresponding row of @var{x} will contain only @code{NaN} values. -## @end itemize -## -## @subheading Examples -## -## @example -## @group -## n = 10; -## p = [0.2, 0.5, 0.3]; -## x = mnrnd (n, p); -## @end group -## -## @group -## n = 10 * ones (3, 1); -## p = [0.2, 0.5, 0.3]; -## x = mnrnd (n, p); -## @end group -## -## @group -## n = (1:2)'; -## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; -## x = mnrnd (n, p); -## @end group -## @end example -## -## @subheading References -## -## @enumerate -## @item -## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics -## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001. -## -## @item -## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical -## Distributions}. pages 134-136, Wiley, New York, third edition, 2000. -## @end enumerate -## @end deftypefn - -## Author: Arno Onken -## Description: Random samples from the multinomial distribution - -function x = mnrnd (n, p, s) - - # Check arguments - if (nargin == 3) - if (! isscalar (n) || n < 0 || round (n) != n) - error ("mnrnd: n must be a non-negative integer"); - endif - if (! isvector (p) || any (p < 0 | p > 1)) - error ("mnrnd: p must be a vector of probabilities"); - endif - if (! isscalar (s) || s < 0 || round (s) != s) - error ("mnrnd: s must be a non-negative integer"); - endif - elseif (nargin == 2) - if (isvector (p) && size (p, 1) > 1) - p = p'; - endif - if (! isvector (n) || any (n < 0 | round (n) != n) || size (n, 2) > 1) - error ("mnrnd: n must be a non-negative integer column vector"); - endif - if (! ismatrix (p) || isempty (p) || any (p < 0 | p > 1)) - error ("mnrnd: p must be a non-empty matrix with rows of probabilities"); - endif - if (! isscalar (n) && size (p, 1) > 1 && length (n) != size (p, 1)) - error ("mnrnd: the length of n must match the number of rows of p"); - endif - else - print_usage (); - endif - - # Adjust input sizes - if (nargin == 3) - n = n * ones (s, 1); - p = repmat (p(:)', s, 1); - elseif (nargin == 2) - if (isscalar (n) && size (p, 1) > 1) - n = n * ones (size (p, 1), 1); - elseif (size (p, 1) == 1) - p = repmat (p, length (n), 1); - endif - endif - sz = size (p); - - # Upper bounds of categories - ub = cumsum (p, 2); - # Make sure that the greatest upper bound is 1 - gub = ub(:, end); - ub(:, end) = 1; - # Lower bounds of categories - lb = [zeros(sz(1), 1) ub(:, 1:(end-1))]; - - # Draw multinomial samples - x = zeros (sz); - for i = 1:sz(1) - # Draw uniform random numbers - r = repmat (rand (n(i), 1), 1, sz(2)); - # Compare the random numbers of r to the cumulated probabilities of p and - # count the number of samples for each category - x(i, :) = sum (r <= repmat (ub(i, :), n(i), 1) & r > repmat (lb(i, :), n(i), 1), 1); - endfor - # Set invalid rows to NaN - k = (abs (gub - 1) > 1e-6); - x(k, :) = NaN; - -endfunction - -%!test -%! n = 10; -%! p = [0.2, 0.5, 0.3]; -%! x = mnrnd (n, p); -%! assert (size (x), size (p)); -%! assert (all (x >= 0)); -%! assert (all (round (x) == x)); -%! assert (sum (x) == n); - -%!test -%! n = 10 * ones (3, 1); -%! p = [0.2, 0.5, 0.3]; -%! x = mnrnd (n, p); -%! assert (size (x), [length(n), length(p)]); -%! assert (all (x >= 0)); -%! assert (all (round (x) == x)); -%! assert (all (sum (x, 2) == n)); - -%!test -%! n = (1:2)'; -%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; -%! x = mnrnd (n, p); -%! assert (size (x), size (p)); -%! assert (all (x >= 0)); -%! assert (all (round (x) == x)); -%! assert (all (sum (x, 2) == n)); From f70ace4d4f4d09ce97f21eb2934efca9e90f2a69 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 11:43:47 +0300 Subject: [PATCH 15/64] bugfix in gp_dic --- gp/gp_dic.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gp/gp_dic.m b/gp/gp_dic.m index d77d36eb..200fe538 100644 --- a/gp/gp_dic.m +++ b/gp/gp_dic.m @@ -354,7 +354,7 @@ w(i,:) = gp_pak(Gp); if isfield(gp{1}.lik.fh,'trcov') % Gaussian - [Ef(:,i), Varf(:,i), lpy, tmp, Vary(:,i)] = fh_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); + [Ef(:,i), Varf(:,i), lpy, tmp, VarY(:,i)] = fh_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); sigma2(:,i) = VarY(:,i) - Varf(:,i); else % non-Gaussian (no need for sigma2) From 3113265100830769c8fe3d6127bc504d9a74b7de Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 11:45:40 +0300 Subject: [PATCH 16/64] bugfix in gp_dic --- gp/gp_dic.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gp/gp_dic.m b/gp/gp_dic.m index d77d36eb..200fe538 100644 --- a/gp/gp_dic.m +++ b/gp/gp_dic.m @@ -354,7 +354,7 @@ w(i,:) = gp_pak(Gp); if isfield(gp{1}.lik.fh,'trcov') % Gaussian - [Ef(:,i), Varf(:,i), lpy, tmp, Vary(:,i)] = fh_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); + [Ef(:,i), Varf(:,i), lpy, tmp, VarY(:,i)] = fh_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); sigma2(:,i) = VarY(:,i) - Varf(:,i); else % non-Gaussian (no need for sigma2) From 842bde7613b507971c6d80a64e84cafb211b71a9 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 11:51:21 +0300 Subject: [PATCH 17/64] correct path to data in demo_multiclass_nested_ep --- gp/demo_multiclass_nested_ep.m | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gp/demo_multiclass_nested_ep.m b/gp/demo_multiclass_nested_ep.m index b6df35c3..a8afe067 100644 --- a/gp/demo_multiclass_nested_ep.m +++ b/gp/demo_multiclass_nested_ep.m @@ -28,7 +28,9 @@ % Copyright (c) 2011-2013 Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari %- load the data -x=load('cdata.txt'); +S = which('demo_multiclass_nested_ep'); +L = strrep(S,'demo_multiclass_nested_ep.m','demodata/cdata.txt'); +x=load(L); y=zeros(size(x,1),3); y(x(:,5)==0,1) = 1; y(x(:,5)==1,2) = 1; From 4b31498b5d22452e2cefdf37ab6527f7ae9deb58 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 11:52:36 +0300 Subject: [PATCH 18/64] correct path to data in demo_multiclass_nested_ep --- gp/demo_multiclass_nested_ep.m | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gp/demo_multiclass_nested_ep.m b/gp/demo_multiclass_nested_ep.m index b6df35c3..a8afe067 100644 --- a/gp/demo_multiclass_nested_ep.m +++ b/gp/demo_multiclass_nested_ep.m @@ -28,7 +28,9 @@ % Copyright (c) 2011-2013 Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari %- load the data -x=load('cdata.txt'); +S = which('demo_multiclass_nested_ep'); +L = strrep(S,'demo_multiclass_nested_ep.m','demodata/cdata.txt'); +x=load(L); y=zeros(size(x,1),3); y(x(:,5)==0,1) = 1; y(x(:,5)==1,2) = 1; From 10b36654184776c2aabb30f0f549874613feb5b3 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 12:12:37 +0300 Subject: [PATCH 19/64] updated changelog --- ChangeLog.txt | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/ChangeLog.txt b/ChangeLog.txt index 6c15b749..4661fcd6 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -1,3 +1,17 @@ +2013-04-24 Version 4.1 + +New features: + - Multinomial probit classification with nested-EP. Jaakko Riihimäki, + Pasi Jylänki and Aki Vehtari (2013). Nested Expectation Propagation + for Gaussian Process Classification with a Multinomial Probit + Likelihood. Journal of Machine Learning Research 14:75-109, 2013. + - Marginal posterior corrections for latent values. Cseke & Heskes + (2011). Approximate Marginals in Latent Gaussian Models. Journal of + Machine Learning Research 12 (2011), 417-454 + - Laplace: cm2 and fact + - EP: fact + + 2013-03-12 Version 4.0 New features: From f9f100169c6a3f021418c532812ff3fdf0feb27e Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 12:23:35 +0300 Subject: [PATCH 20/64] removed some unnecessary files from release --- gp/test_nested.m | 18 - gp/test_package.m | 1649 --------------------------------------------- 2 files changed, 1667 deletions(-) delete mode 100755 gp/test_nested.m delete mode 100644 gp/test_package.m diff --git a/gp/test_nested.m b/gp/test_nested.m deleted file mode 100755 index 37b2505e..00000000 --- a/gp/test_nested.m +++ /dev/null @@ -1,18 +0,0 @@ -function f = test_nested(F) - - x = []; - y = []; - f = @inner; - function out = inner(in) - ind = find(in == x); - if isempty(ind) - out = sin(in); - x(end+1) = in; - y(end+1) = out; - fprintf('evaluate') - else - out = y(ind); - fprintf('memorize') - end - end -end diff --git a/gp/test_package.m b/gp/test_package.m deleted file mode 100644 index 2b24a2c5..00000000 --- a/gp/test_package.m +++ /dev/null @@ -1,1649 +0,0 @@ -% TODO! -% - tulosten oikeellisuuden tarkistus (nyt tarkistetaan vain, ett� funktioissa ei bugeja) -% - metriikat -% - IA:n testaus regressiossa ja muilla likelihoodeilla - -% Initialize the statistics parameters: -warnings = ''; -numwarn = 0; - -%% Regression models - -S = which('test_package'); -L = strrep(S,'test_package.m','demodata/dat.1'); -data=load(L); -data = data(1:2:end,:); -x = [data(:,1) data(:,2)]; -y = data(:,3); -[n, nin] = size(x); - -%---------------------------- -% check covariance functions -%---------------------------- - -fprintf(' \n ================================= \n \n Checking the covariance functions \n \n ================================= \n ') - -covfunc = {'gpcf_sexp' 'gpcf_exp' 'gpcf_matern32' 'gpcf_matern52' ... - 'gpcf_ppcs0' 'gpcf_ppcs1' 'gpcf_ppcs2' 'gpcf_ppcs3' 'gpcf_neuralnetwork'... - 'gpcf_prod'}; - -gpcf2 = gpcf_noise('init', 'noiseSigma2', 0.2^2); -pl = prior_logunif('init'); -gpcf2 = gpcf_noise('set', gpcf2, 'noiseSigma2_prior', pl); - -for i = 1:length(covfunc) - - switch covfunc{i} - case 'gpcf_neuralnetwork' - gpcf1 = feval(covfunc{i}, 'init'); - - % Set prior - pl = prior_logunif('init'); - gpcf1 = gpcf_neuralnetwork('set', gpcf1, 'weightSigma2_prior', pl); - gpcf1 = gpcf_neuralnetwork('set', gpcf1, 'biasSigma2_prior', pl); - - case 'gpcf_prod' - gpcf3 = gpcf_ppcs2('init','nin', nin, 'lengthScale', 1+0.01*randn(1,nin), 'magnSigma2', 0.5); - gpcf4 = gpcf_exp('init', 'lengthScale', 1+0.01*randn(1,nin), 'magnSigma2', 0.2^2); - - % Set prior - pl = prior_logunif('init'); - gpcf3 = gpcf_ppcs2('set', gpcf3, 'lengthScale_prior', pl); - gpcf3 = gpcf_ppcs2('set', gpcf3, 'magnSigma2_prior', pl); - gpcf4 = gpcf_exp('set', gpcf4, 'lengthScale_prior', pl); - gpcf4 = gpcf_exp('set', gpcf4, 'magnSigma2_prior', pl); - - gpcf1 = gpcf_prod('init', 'functions', {gpcf3, gpcf4}); - otherwise - if ~isempty(strfind(covfunc{i}, 'ppcs')) - gpcf1 = feval(covfunc{i}, 'init', 'nin', nin, 'lengthScale', [1 1], 'magnSigma2', 0.2^2); - else - gpcf1 = feval(covfunc{i}, 'init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); - end - - % Set prior - pl = prior_logunif('init'); - gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', pl); - gpcf1 = gpcf_sexp('set', gpcf1, 'magnSigma2_prior', pl); - end - - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.001); - - w=gp_pak(gp); % pack the hyperparameters into one vector - fe=@gp_e; % create a function handle to negative log posterior - fg=@gp_g; % create a function handle to gradient of negative log posterior - - % set the options for scg2 - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - - % do the optimization - w=scg2(fe, w, opt, fg, gp, x, y); - - % Set the optimized hyperparameter values back to the gp structure - gp=gp_unpak(gp,w); - - % --- MCMC approach --- - clear('opt') - opt.nsamples= 30; - opt.repeat=2; - opt.hmc_opt = hmc2_opt; - opt.hmc_opt.steps=2; - opt.hmc_opt.stepadj=0.07; - opt.hmc_opt.persistence=0; - opt.hmc_opt.decay=0.6; - opt.hmc_opt.nsamples=1; - hmc2('state', sum(100*clock)); - - % Do the sampling (this takes approximately 5-10 minutes) - [rfull,g] = gp_mc(gp, x, y, opt); - - % After sampling we delete the burn-in and thin the sample chain - rfull = thin(rfull, 10, 2); - - if sqrt(mean((y - gp_pred(gp, x, y, x)).^2)) > 0.186*2 - warnings = sprintf([warnings '\n * Check the optimization of hyper-parameters of ' covfunc{i} ' with GP regression']); - numwarn = numwarn + 1; - end - - if sqrt(mean((y - mean(mc_pred(rfull, x, y, x),2) ).^2)) > 0.186*2 - warnings = sprintf([warnings '\n * Check the MCMC sampling of hyper-parameters of ' covfunc{i} ' with GP regression']); - numwarn = numwarn + 1; - end - - delta = gradcheck(w, @gp_e, @gp_g, gp, x, y); - - - switch covfunc{i} - case 'gpcf_neuralnetwork' - gpcf1 = gpcf_neuralnetwork('set', gpcf1, 'weightSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf1 = gpcf_neuralnetwork('set', gpcf1, 'weightSigma2_prior', pl, 'biasSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf1 = gpcf_neuralnetwork('set', gpcf1, 'weightSigma2_prior', [], 'biasSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - case 'gpcf_dotproduct' - gpcf1 = gpcf_dotproduct('set', gpcf1, 'constSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf1 = gpcf_dotproduct('set', gpcf1, 'constSigma2_prior', pl, 'coeffSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf1 = gpcf_dotproduct('set', gpcf1, 'constSigma2_prior', [], 'coeffSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y, 'hyper')]; - case 'gpcf_prod' - gpcf4 = gpcf_exp('set', gpcf1, 'lengthScale_prior', []); - gpcf1 = gpcf_prod('init', 'functions', {gpcf3, gpcf4}); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf4 = gpcf_exp('set', gpcf1, 'lengthScale_prior', pl, 'magnSigma2_prior', []); - gpcf1 = gpcf_prod('init', 'functions', {gpcf3, gpcf4}); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf4 = gpcf_exp('set', gpcf1, 'lengthScale_prior', [], 'magnSigma2_prior', []); - gpcf1 = gpcf_prod('init', 'functions', {gpcf3, gpcf4}); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - otherwise - gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', pl, 'magnSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - - gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', [], 'magnSigma2_prior', []); - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); - w=gp_pak(gp); - delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y)]; - end - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters of ' covfunc{i}]); - warning([' Check the gradients of ' covfunc{i}]); - numwarn = numwarn + 1; - end - -end - -gpcf1 = gpcf_sexp('init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); -gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); -gpcf2 = gpcf_noise('set', gpcf2, 'noiseSigma2_prior', []); -gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01); -w=gp_pak(gp); -delta = gradcheck(w, @gp_e, @gp_g, gp, x, y); - - -%% check sparse approximations - -fprintf(' \n ================================= \n \n Checking the sparse approximations \n \n ================================= \n ') - -sparse = {'FIC' 'PIC' 'CS+FIC'}; - -gpcf1 = gpcf_sexp('init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); -gpcf2 = gpcf_noise('init', 'noiseSigma2', 0.2^2); -gpcf3 = gpcf_ppcs2('init', 'nin', nin, 'lengthScale', [1 1], 'magnSigma2', 0.2^2); - -% Set prior -pl = prior_logunif('init'); -gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); -gpcf2 = gpcf_noise('set', gpcf2, 'noiseSigma2_prior', pl); -gpcf3 = gpcf_ppcs2('set', gpcf3, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); - -% Initialize the inducing inputs in a regular grid over the input space -[u1,u2]=meshgrid(linspace(-1.8,1.8,7),linspace(-1.8,1.8,7)); -X_u = [u1(:) u2(:)]; - -% set the data points into clusters -[p1,p2]=meshgrid(-1.8:0.1:1.8,-1.8:0.1:1.8); -p=[p1(:) p2(:)]; -b1 = [-1.7 -0.8 0.1 1 1.9]; -mask = zeros(size(x,1),size(x,1)); -trindex={}; -for i1=1:4 - for i2=1:4 - ind = 1:size(x,1); - ind = ind(: , b1(i1)<=x(ind',1) & x(ind',1) < b1(i1+1)); - ind = ind(: , b1(i2)<=x(ind',2) & x(ind',2) < b1(i2+1)); - trindex{4*(i1-1)+i2} = ind'; - end -end - -for i = 1:length(sparse) - - switch sparse{i} - case 'FIC' - gp = gp_init('init', 'FIC', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01, 'X_u', X_u, 'Xu_prior', prior_unif('init')); - tstindex = 1:n; - case 'PIC' - gp = gp_init('init', 'PIC', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01, 'X_u', X_u); - gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}, 'Xu_prior', prior_unif('init')); - tstindex = trindex; - case 'CS+FIC' - gp = gp_init('init', 'CS+FIC', 'gaussian', {gpcf1, gpcf3}, {gpcf2}, 'jitterSigma2', 0.01, 'X_u', X_u, 'Xu_prior', prior_unif('init')) - tstindex = 1:n; - end - - param = 'covariance+inducing'; - w=gp_pak(gp, param); % pack the hyperparameters into one vector - fe=@gp_e; % create a function handle to negative log posterior - fg=@gp_g; % create a function handle to gradient of negative log posterior - - % set the options for scg2 - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - - % do the optimization - w=scg2(fe, w, opt, fg, gp, x, y);%, param); - - % Set the optimized hyperparameter values back to the gp structure - gp=gp_unpak(gp,w, param); - - delta = gradcheck(w, @gp_e, @gp_g, gp, x, y);%, param); - - % check that gradients are OK - if delta>0.01 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters with ' sparse{i} ' sparse approximation']); - numwarn = numwarn + 1; - end - - % --- MCMC approach --- - clear('opt') - opt.nsamples= 30; - opt.repeat=2; - opt.hmc_opt = hmc2_opt; - opt.hmc_opt.steps=2; - opt.hmc_opt.stepadj=0.03; - opt.hmc_opt.persistence=0; - opt.hmc_opt.decay=0.6; - opt.hmc_opt.nsamples=1; - hmc2('state', sum(100*clock)); - - % Do the sampling (this takes approximately 5-10 minutes) - [rfull,g] = gp_mc(gp, x, y,opt); - - % After sampling we delete the burn-in and thin the sample chain - switch sparse{i} - case 'PIC' - rfull = rmfield(rfull, 'tr_index'); - rfull = thin(rfull, 10, 2); - rfull.tr_index = trindex; - otherwise - rfull = thin(rfull, 10, 2); - end - - if sqrt(mean((y - gp_pred(gp, x, y, x, [], tstindex)).^2)) > 0.186*2 - warnings = sprintf([warnings '\n * Check the optimization of hyper-parameters of ' covfunc{i} ' with GP regression']); - numwarn = numwarn + 1; - end - - if sqrt(mean((y - mean(mc_pred(rfull, x, y, x, [], tstindex),2) ).^2)) > 0.186*2 - warnings = sprintf([warnings '\n * Check the MCMC sampling of hyper-parameters of ' covfunc{i} ' with GP regression']); - numwarn = numwarn + 1; - end - -end - -%---------------------------- -% Check the additive model and -% gp_pred and mc_pred -%---------------------------- - -fprintf(' \n ================================= \n \n Checking the additive model and gp_pred \n \n =================================\n ') - -gpcf1 = gpcf_sexp('init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); -gpcf2 = gpcf_noise('init', 'noiseSigma2', 0.2^2); -gpcf3 = gpcf_ppcs2('init', 'nin', nin, 'lengthScale', [1 1], 'magnSigma2', 0.2^2); - -% Set prior -pl = prior_logunif('init'); -gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); -gpcf2 = gpcf_noise('set', gpcf2, 'noiseSigma2_prior', pl); -gpcf3 = gpcf_ppcs2('set', gpcf3, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); - -% Initialize the inducing inputs in a regular grid over the input space -[u1,u2]=meshgrid(linspace(-1.8,1.8,7),linspace(-1.8,1.8,7)); -X_u = [u1(:) u2(:)]; - -% set the data points into clusters -[p1,p2]=meshgrid(-1.8:0.1:1.8,-1.8:0.1:1.8); -p=[p1(:) p2(:)]; -b1 = [-1.8 -0.8 0.1 1 1.9]; -mask = zeros(size(x,1),size(x,1)); -trindex={}; testindex={}; -for i1=1:4 - for i2=1:4 - ind = 1:size(x,1); - ind = ind(: , b1(i1)<=x(ind',1) & x(ind',1) < b1(i1+1)); - ind = ind(: , b1(i2)<=x(ind',2) & x(ind',2) < b1(i2+1)); - trindex{4*(i1-1)+i2} = ind'; - ind2 = 1:size(p,1); - ind2 = ind2(: , b1(i1)<=p(ind2',1) & p(ind2',1) < b1(i1+1)); - ind2 = ind2(: , b1(i2)<=p(ind2',2) & p(ind2',2) < b1(i2+1)); - testindex{4*(i1-1)+i2} = ind2'; - end -end -p2 = [x', p(1:10,:)']'; -models = {'FULL' 'FIC' 'PIC' 'CS+FIC'}; - -for i=1:length(models) - switch models{i} - case 'FULL' - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1, gpcf3}, {gpcf2}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'FIC' - gp = gp_init('init', 'FIC', 'gaussian', {gpcf1, gpcf3}, {gpcf2}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - case 'PIC' - gp = gp_init('init', 'PIC', 'gaussian', {gpcf1, gpcf3}, {gpcf2}, 'jitterSigma2', 0.01, 'X_u', X_u); - gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}); - tstindex = testindex; - tstindex2 = trindex; - tstindex2{1} = [tstindex2{1} ; [n+1:length(p2)]']; - case 'CS+FIC' - gp = gp_init('init', 'CS+FIC', 'gaussian', {gpcf1, gpcf3}, {gpcf2}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - end - - w=gp_pak(gp, 'covariance'); % pack the hyperparameters into one vector - fe=@gp_e; % create a function handle to negative log posterior - fg=@gp_g; % create a function handle to gradient of negative log posterior - - % set the options for scg2 - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - - % do the optimization - w=scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - - % --- Check predictions --- - [Efa, Varfa] = gp_pred(gp, x, y, p, [], tstindex); - [Ef1, Varf1] = gp_pred(gp, x, y, p, [1], tstindex); - [Ef2, Varf2] = gp_pred(gp, x, y, p, [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-12 - warnings = sprintf([warnings '\n * Check gp_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check gp_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = gp_pred(gp, x, y, p2, [], tstindex2); - [Ef1, Varf1] = gp_pred(gp, x, y, p2, [1], tstindex2); - [Ef2, Varf2] = gp_pred(gp, x, y, p2, [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-12 - warnings = sprintf([warnings '\n * Check gp_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check gp_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = gp_pred(gp, x, y, p2, [], tstindex2); - - if max( abs(Efaa - Ey) ) > 1e-12 - warnings = sprintf([warnings '\n * Check gp_pred for ' models{i} ' model. The predictive mean for f and y do not match.']); - numwarn = numwarn + 1; - end - - if max( abs(Varfaa + gp.noise{1}.noiseSigma2 - Vary) ) > 1e-12 - warnings = sprintf([warnings '\n * Check gp_pred for ' models{i} ' model. The predictive variance for f and y do not match.']); - numwarn = numwarn + 1; - end - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = gp_pred(gp, x, y, x, [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = gp_pred(gp, x, y, x, [], tstindex2, y); - end - - % --- MCMC approach --- - opt=gp_mcopt; - opt.nsamples= 30; - opt.repeat=2; - opt.hmc_opt = hmc2_opt; - opt.hmc_opt.steps=2; - opt.hmc_opt.stepadj=0.07; - opt.hmc_opt.persistence=0; - opt.hmc_opt.decay=0.6; - opt.hmc_opt.nsamples=1; - hmc2('state', sum(100*clock)); - - % Do the sampling (this takes approximately 5-10 minutes) - [rfull,g,rstate1] = gp_mc(opt, gp, x, y); - - % --- Check predictions --- - [Efa, Varfa] = mc_pred(rfull, x, y, p, [], tstindex); - [Ef1, Varf1] = mc_pred(rfull, x, y, p, [1], tstindex); - [Ef2, Varf2] = mc_pred(rfull, x, y, p, [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-12 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = mc_pred(rfull, x, y, p2, [], tstindex2); - [Ef1, Varf1] = mc_pred(rfull, x, y, p2, [1], tstindex2); - [Ef2, Varf2] = mc_pred(rfull, x, y, p2, [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-12 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = mc_pred(rfull, x, y, p2, [], tstindex2); - - if max( abs(Efaa - Ey) ) > 1e-12 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictive mean for f and y do not match.']); - numwarn = numwarn + 1; - end - - if max( abs(Varfaa + repmat(rfull.noise{1}.noiseSigma2',length(p2),1) - Vary) ) > 1e-12 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictive variance for f and y do not match.']); - numwarn = numwarn + 1; - end - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], tstindex2, y); - end -end - - -%% check metrics - - -S = which('test_package'); -L = strrep(S,'test_package.m','demodata/dat.1'); -data=load(L); -data = data(1:2:end,:); -x = [data(:,1) data(:,2)]; -y = data(:,3); -[n, nin] = size(x); - -fprintf(' \n ================================= \n \n Checking the metrics \n \n ================================= \n ') - -% priors -pm = prior_logunif('init'); -pl = prior_logunif('init'); - -% First input -gpcf1 = gpcf_sexp('init', 'magnSigma2', 0.2, 'magnSigma2_prior', pm); -metric1 = metric_euclidean('init', {[1]},'lengthScales',[0.8], 'lengthScales_prior', pl); -% Lastly, plug the metric to the covariance function structure. -gpcf1 = gpcf_sexp('set', gpcf1, 'metric', metric1); - -% Do the same for the second input -gpcf2 = gpcf_sexp('init', 'magnSigma2', 0.2); -metric2 = metric_euclidean('init', {[2]},'lengthScales',[1.2], 'lengthScales_prior', pl); -gpcf2 = gpcf_sexp('set', gpcf2, 'metric', metric2); - -% We also need the noise component -gpcfn = gpcf_noise('init', 'noiseSigma2', 0.2); - -% ... Finally create the GP data structure -gp = gp_init('init', 'FULL', 'gaussian', {gpcf1,gpcf2}, {gpcfn}, 'jitterSigma2', 0.01) - -param = 'covariance'; -gradcheck(gp_pak(gp,param), @gp_e, @gp_g, gp, x, y, param); - -w=gp_pak(gp, 'covariance'); % pack the hyperparameters into one vector -fe=@gp_e; % create a function handle to negative log posterior -fg=@gp_g; % create a function handle to gradient of negative log posterior - -% set the options for scg2 -opt = scg2_opt; -opt.tolfun = 1e-3; -opt.tolx = 1e-3; -opt.display = 1; - -% do the optimization -w=scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - -% Set the optimized hyperparameter values back to the gp structure -gp=gp_unpak(gp,w, 'covariance'); - -% --- MCMC approach --- -opt=gp_mcopt; -opt.nsamples= 30; -opt.repeat=2; -opt.hmc_opt = hmc2_opt; -opt.hmc_opt.steps=2; -opt.hmc_opt.stepadj=0.07; -opt.hmc_opt.persistence=0; -opt.hmc_opt.decay=0.6; -opt.hmc_opt.nsamples=1; -hmc2('state', sum(100*clock)); - -[rfull,g,rstate1] = gp_mc(opt, gp, x, y); - -% After sampling we delete the burn-in and thin the sample chain -rfull = thin(rfull, 10, 2); - -if sqrt(mean((y - gp_pred(gp, x, y, x)).^2)) > 0.186*2 - warnings = sprintf([warnings '\n * Check the optimization of hyper-parameters of metric_euclidean with GP regression']); - numwarn = numwarn + 1; -end - -if sqrt(mean((y - mean(mc_pred(rfull, x, y, x),2) ).^2)) > 0.186*2 - warnings = sprintf([warnings '\n * Check the MCMC sampling of hyper-parameters of metric_euclidean with GP regression']); - numwarn = numwarn + 1; -end - -delta = gradcheck(w, @gp_e, @gp_g, gp, x, y, 'covariance'); - -metric1 = metric_euclidean('init', {[1]},'lengthScales',[0.8], 'lengthScales_prior', pl); -% Lastly, plug the metric to the covariance function structure. -gpcf1 = gpcf_sexp('set', gpcf1, 'metric', metric1); -gp = gp_init('init', 'FULL', 'gaussian', {gpcf1, gpcf2}, {gpcfn}, 'jitterSigma2', 0.01); -w=gp_pak(gp, 'covariance'); -delta = [delta ; gradcheck(w, @gp_e, @gp_g, gp, x, y, 'covariance')]; - -% check that gradients are OK -if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters of metric_euclidean']); - numwarn = numwarn + 1; -end - - - - -% =========================== -% check priors -% =========================== - -S = which('test_package'); -L = strrep(S,'test_package.m','demodata/dat.1'); -data=load(L); -data = data(1:2:end,:); -x = [data(:,1) data(:,2)]; -y = data(:,3); -[n, nin] = size(x); - -fprintf(' \n ================================= \n \n Checking the prior structures \n \n ================================= \n ') - -priorfunc = {'prior_t' 'prior_unif' 'prior_logunif', 'prior_gamma', 'prior_laplace', ... - 'prior_sinvchi2', 'prior_normal', 'prior_invgam'}; - -gpcf2 = gpcf_noise('init', 'noiseSigma2', 0.2^2); -ps = prior_logunif('init'); -gpcf2 = gpcf_noise('set', gpcf2, 'noiseSigma2_prior', ps); - -for i = 1:length(priorfunc) - - gpcf1 = gpcf_sexp('init', 'lengthScale', [1.3 1.4], 'magnSigma2', 0.2^2); - - % Set prior - pl = feval(priorfunc{i}, 'init'); - gpcf1 = gpcf_sexp('set', gpcf1, 'lengthScale_prior', pl); - gpcf1 = gpcf_sexp('set', gpcf1, 'magnSigma2_prior', ps); - - gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.01) - - w=gp_pak(gp, 'covariance'); % pack the hyperparameters into one vector - fe=@gp_e; % create a function handle to negative log posterior - fg=@gp_g; % create a function handle to gradient of negative log posterior - - % set the options for scg2 - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - - % do the optimization - w=scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - - % Set the optimized hyperparameter values back to the gp structure - gp=gp_unpak(gp,w, 'covariance'); - - delta = gradcheck(w, @gp_e, @gp_g, gp, x, y, 'covariance'); - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters of ' covfunc{i}]); - warning([' Check the gradients of ' covfunc{i}]); - numwarn = numwarn + 1; - end - - % --- MCMC approach --- - opt=gp_mcopt; - opt.nsamples= 20; - opt.repeat=2; - opt.hmc_opt = hmc2_opt; - opt.hmc_opt.steps=2; - opt.hmc_opt.stepadj=0.07; - opt.hmc_opt.persistence=0; - opt.hmc_opt.decay=0.6; - opt.hmc_opt.nsamples=1; - hmc2('state', sum(100*clock)); - - % Do the sampling - [rfull,g,rstate1] = gp_mc(opt, gp, x, y); - - % After sampling we delete the burn-in and thin the sample chain - rfull = thin(rfull, 5, 2); -end - - -%% Other models than Gaussian regression - - -% =========================== -% Probit model -% =========================== - -fprintf(' \n ================================= \n \n Check the probit model \n \n =================================\n') - -S = which('demo_classific1'); -L = strrep(S,'demo_classific1.m','demodata/synth.tr'); -x=load(L); -y=x(:,end); -y = 2.*y-1; -x(:,end)=[]; -[n, nin] = size(x); - -% Set prior -pl = prior_logunif('init'); - -gpcf1 = gpcf_sexp('init', 'lengthScale', [0.5 0.7], 'magnSigma2', 0.2^2, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); -gpcf2 = gpcf_ppcs1('init', nin, 'lengthScale', [1.1 1.3], 'magnSigma2', 0.2^2, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); -gpcf3 = gpcf_ppcs3('init', nin, 'lengthScale', [1.1 1.3], 'magnSigma2', 0.5^2, 'lengthScale_prior', pl, 'magnSigma2_prior', pl); - -% Initialize the inducing inputs in a regular grid over the input space -[u1,u2]=meshgrid(linspace(-1.25, 0.9,6),linspace(-0.2, 1.1,6)); -X_u=[u1(:) u2(:)]; -X_u = X_u([3 4 7:18 20:24 26:30 33:36],:); - -% set the data points into clusters -[p1,p2]=meshgrid(-1.25:0.1:0.9, -0.2:0.1:1.1); -p=[p1(:) p2(:)]; -b1 = linspace(-1.25, 0.9, 5); -b2 = linspace(-0.2, 1.1, 5); -trindex={}; testindex={}; -for i1=1:4 - for i2=1:4 - ind = 1:size(x,1); - ind = ind(: , b1(i1)<=x(ind',1) & x(ind',1) < b1(i1+1)); - ind = ind(: , b2(i2)<=x(ind',2) & x(ind',2) < b2(i2+1)); - trindex{4*(i1-1)+i2} = ind'; - ind2 = 1:size(p,1); - ind2 = ind2(: , b1(i1)<=p(ind2',1) & p(ind2',1) < b1(i1+1)); - ind2 = ind2(: , b2(i2)<=p(ind2',2) & p(ind2',2) < b2(i2+1)); - testindex{4*(i1-1)+i2} = ind2'; - end -end -trindex = {trindex{[1:3 5:16]}}; -p2 = [x', p(1:10,:)']'; -models = {'FULL' 'CS-FULL' 'FIC' 'PIC' 'CS+FIC'}; - -likelih = likelih_probit('init', y); - -for i=1:length(models) - switch models{i} - case 'FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'CS-FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf2, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'FIC' - gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - case 'PIC' - gp = gp_init('init', 'PIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}); - tstindex = testindex; - tstindex2 = trindex; - tstindex2{1} = [tstindex2{1} ; [n+1:length(p2)]']; - case 'CS+FIC' - gp = gp_init('init', 'CS+FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - end - - gp = gp_init('set', gp, 'latent_method', {'Laplace', x, y, 'covariance'}); - - fe=@gpla_e; - fg=@gpla_g; - n=length(y); - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - opt.maxiter = 30; - - % do scaled conjugate gradient optimization - w = gp_pak(gp, 'covariance'); - w = scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - gp=gp_unpak(gp,w, 'covariance'); - - delta = gradcheck(w, @gpla_e, @gpla_g, gp, x, y, 'covariance'); - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters for Laplace approximation for probit model and ' models{i} ' GP']); - numwarn = numwarn + 1; - end - - % --- Check predictions --- - [Efa, Varfa] = la_pred(gp, x, y, p, 'covariance', [], tstindex); - [Ef1, Varf1] = la_pred(gp, x, y, p, 'covariance', [1], tstindex); - [Ef2, Varf2] = la_pred(gp, x, y, p, 'covariance', [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = la_pred(gp, x, y, p2, 'covariance', [], tstindex2); - [Ef1, Varf1] = la_pred(gp, x, y, p2, 'covariance', [1], tstindex2); - [Ef2, Varf2] = la_pred(gp, x, y, p2, 'covariance', [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = la_pred(gp, x, y, p2, 'covariance', [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = la_pred(gp, x, y, x, 'covariance', [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = la_pred(gp, x, y, x, 'covariance', [], tstindex2, y); - end - - - % ------------------- - % --- EP approach --- - % ------------------- - switch models{i} - case 'FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'CS-FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf2, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'FIC' - gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.1, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - case 'PIC' - gp = gp_init('init', 'PIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.1, 'X_u', X_u); - gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}); - tstindex = testindex; - tstindex2 = trindex; - tstindex2{1} = [tstindex2{1} ; [n+1:length(p2)]']; - case 'CS+FIC' - gp = gp_init('init', 'CS+FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.5, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - end - - gp = gp_init('set', gp, 'latent_method', {'EP', x, y, 'covariance'}); - - fe=@gpep_e; - fg=@gpep_g; - n=length(y); - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - opt.maxiter = 30; - - % do scaled conjugate gradient optimization - w = gp_pak(gp, 'covariance'); - w=scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - gp=gp_unpak(gp,w, 'covariance'); - - delta = gradcheck(w, @gpep_e, @gpep_g, gp, x, y, 'covariance'); - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters for Laplace approximation for probit model and ' models{i} ' GP']); - warning([' Check the gradients of ' covfunc{i}]); - numwarn = numwarn + 1; - end - - % --- Check predictions --- - [Efa, Varfa] = ep_pred(gp, x, y, p, 'covariance', [], tstindex); - [Ef1, Varf1] = ep_pred(gp, x, y, p, 'covariance', [1], tstindex); - [Ef2, Varf2] = ep_pred(gp, x, y, p, 'covariance', [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = ep_pred(gp, x, y, p2, 'covariance', [], tstindex2); - [Ef1, Varf1] = ep_pred(gp, x, y, p2, 'covariance', [1], tstindex2); - [Ef2, Varf2] = ep_pred(gp, x, y, p2, 'covariance', [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = ep_pred(gp, x, y, p2, 'covariance', [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = ep_pred(gp, x, y, x, 'covariance', [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = ep_pred(gp, x, y, x, 'covariance', [], tstindex2, y); - end - - % --- MCMC approach --- - opt=gp_mcopt; - opt.nsamples= 50; - opt.repeat=2; - opt.hmc_opt = hmc2_opt; - opt.hmc_opt.steps=2; - opt.hmc_opt.stepadj=0.02; - opt.hmc_opt.persistence=0; - opt.hmc_opt.decay=0.6; - opt.hmc_opt.nsamples=1; - opt.latent_opt.display=0; - opt.latent_opt.repeat = 20; - opt.latent_opt.sample_latent_scale = 0.5; - hmc2('state', sum(100*clock)); - -% $$$ gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.0001) - gp=gp_unpak(gp,w, 'covariance'); - gp = gp_init('set', gp, 'latent_method', {'MCMC', zeros(size(y))', @scaled_mh}); - - % Do the sampling (this takes approximately 5-10 minutes) - [rfull,g,rstate1] = gp_mc(opt, gp, x, y); - - % --- Check predictions --- - [Efa, Varfa] = mc_pred(rfull, x, y, p, [], tstindex); - [Ef1, Varf1] = mc_pred(rfull, x, y, p, [1], tstindex); - [Ef2, Varf2] = mc_pred(rfull, x, y, p, [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(mean(Efa) - (mean(Ef1)+mean(Ef2))) ) > 1e-11 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = mc_pred(rfull, x, y, p2, [], tstindex2); - [Ef1, Varf1] = mc_pred(rfull, x, y, p2, [1], tstindex2); - [Ef2, Varf2] = mc_pred(rfull, x, y, p2, [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(mean(Efa) - (mean(Ef1)+mean(Ef2))) ) > 1e-11 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = mc_pred(rfull, x, y, p2, [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], tstindex2, y); - end -end - - -% =========================== -% Logit model -% =========================== - -likelih = likelih_logit('init', y); - -for i=1:length(models) - switch models{i} - case 'FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'CS-FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf2, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'FIC' - gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - case 'PIC' - gp = gp_init('init', 'PIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}); - tstindex = testindex; - tstindex2 = trindex; - tstindex2{1} = [tstindex2{1} ; [n+1:length(p2)]']; - case 'CS+FIC' - gp = gp_init('init', 'CS+FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - end - - gp = gp_init('set', gp, 'latent_method', {'Laplace', x, y, 'covariance'}); - - fe=@gpla_e; - fg=@gpla_g; - n=length(y); - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - opt.maxiter = 30; - - % do scaled conjugate gradient optimization - w = gp_pak(gp, 'covariance'); - w = scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - gp=gp_unpak(gp,w, 'covariance'); - - delta = gradcheck(w, @gpla_e, @gpla_g, gp, x, y, 'covariance'); - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters for Laplace approximation for logit model and ' models{i} ' GP']); - numwarn = numwarn + 1; - end - - % --- Check predictions --- - [Efa, Varfa] = la_pred(gp, x, y, p, 'covariance', [], tstindex); - [Ef1, Varf1] = la_pred(gp, x, y, p, 'covariance', [1], tstindex); - [Ef2, Varf2] = la_pred(gp, x, y, p, 'covariance', [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = la_pred(gp, x, y, p2, 'covariance', [], tstindex2); - [Ef1, Varf1] = la_pred(gp, x, y, p2, 'covariance', [1], tstindex2); - [Ef2, Varf2] = la_pred(gp, x, y, p2, 'covariance', [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = la_pred(gp, x, y, p2, 'covariance', [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = la_pred(gp, x, y, x, 'covariance', [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = la_pred(gp, x, y, x, 'covariance', [], tstindex2, y); - end - - - % ------------------- - % --- EP approach --- - % ------------------- - switch models{i} - case 'FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'CS-FULL' - gp = gp_init('init', 'FULL', likelih, {gpcf2, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'FIC' - gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.1, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - case 'PIC' - gp = gp_init('init', 'PIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.1, 'X_u', X_u); - gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}); - tstindex = testindex; - tstindex2 = trindex; - tstindex2{1} = [tstindex2{1} ; [n+1:length(p2)]']; - case 'CS+FIC' - gp = gp_init('init', 'CS+FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.5, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - end - - gp = gp_init('set', gp, 'latent_method', {'EP', x, y, 'covariance'}); - - fe=@gpep_e; - fg=@gpep_g; - n=length(y); - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - opt.maxiter = 30; - - % do scaled conjugate gradient optimization - w = gp_pak(gp, 'covariance'); - w=scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - gp=gp_unpak(gp,w, 'covariance'); - - delta = gradcheck(w, @gpep_e, @gpep_g, gp, x, y, 'covariance'); - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters for Laplace approximation for logit model and ' models{i} ' GP']); - warning([' Check the gradients of ' covfunc{i}]); - numwarn = numwarn + 1; - end - - % --- Check predictions --- - [Efa, Varfa] = ep_pred(gp, x, y, p, 'covariance', [], tstindex); - [Ef1, Varf1] = ep_pred(gp, x, y, p, 'covariance', [1], tstindex); - [Ef2, Varf2] = ep_pred(gp, x, y, p, 'covariance', [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = ep_pred(gp, x, y, p2, 'covariance', [], tstindex2); - [Ef1, Varf1] = ep_pred(gp, x, y, p2, 'covariance', [1], tstindex2); - [Ef2, Varf2] = ep_pred(gp, x, y, p2, 'covariance', [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with logit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = ep_pred(gp, x, y, p2, 'covariance', [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = ep_pred(gp, x, y, x, 'covariance', [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = ep_pred(gp, x, y, x, 'covariance', [], tstindex2, y); - end - - % --- MCMC approach --- - opt=gp_mcopt; - opt.nsamples= 50; - opt.repeat=2; - opt.hmc_opt = hmc2_opt; - opt.hmc_opt.steps=2; - opt.hmc_opt.stepadj=0.02; - opt.hmc_opt.persistence=0; - opt.hmc_opt.decay=0.6; - opt.hmc_opt.nsamples=1; - opt.latent_opt.display=0; - opt.latent_opt.repeat = 20; - opt.latent_opt.sample_latent_scale = 0.5; - hmc2('state', sum(100*clock)); - -% $$$ gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.0001) - gp=gp_unpak(gp,w, 'covariance'); - gp = gp_init('set', gp, 'latent_method', {'MCMC', zeros(size(y))', @scaled_mh}); - - % Do the sampling (this takes approximately 5-10 minutes) - [rfull,g,rstate1] = gp_mc(opt, gp, x, y); - - % --- Check predictions --- - [Efa, Varfa] = mc_pred(rfull, x, y, p, [], tstindex); - [Ef1, Varf1] = mc_pred(rfull, x, y, p, [1], tstindex); - [Ef2, Varf2] = mc_pred(rfull, x, y, p, [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(mean(Efa) - (mean(Ef1)+mean(Ef2))) ) > 1e-11 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = mc_pred(rfull, x, y, p2, [], tstindex2); - [Ef1, Varf1] = mc_pred(rfull, x, y, p2, [1], tstindex2); - [Ef2, Varf2] = mc_pred(rfull, x, y, p2, [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(mean(Efa) - (mean(Ef1)+mean(Ef2))) ) > 1e-11 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = mc_pred(rfull, x, y, p2, [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], tstindex2, y); - end -end - - -% =========================== -% Poisson model -% =========================== - -fprintf(' \n ================================= \n \n Check the poisson model \n \n =================================\n') - - -S = which('demo_spatial1'); -L = strrep(S,'demo_spatial1.m','demodata/spatial.mat'); -load(L) - -x = xx; -[n,nin] = size(x); - -% Create the covariance function -gpcf1 = gpcf_sexp('init', 'lengthScale', [0.5 0.7], 'magnSigma2', 0.2^2, 'lengthScale_prior', pl, 'magnSigma2_prior', pm); -gpcf2 = gpcf_ppcs1('init', nin, 'lengthScale', [1.1 1.3], 'magnSigma2', 0.2^2, 'lengthScale_prior', pl, 'magnSigma2_prior', pm); -gpcf3 = gpcf_ppcs3('init', nin, 'lengthScale', [1.1 1.3], 'magnSigma2', 0.5^2, 'lengthScale_prior', pl, 'magnSigma2_prior', pm); - -p = x+0.5; -dims = [1 60 1 35]; -[trindex, Xu, tstindex] = set_PIC(xx, dims, 5, 'corners+1xside', 0, p); -p2 = [x', p(1:10,:)']'; -models = {'FULL' 'CS-FULL' 'FIC' 'PIC' 'CS+FIC'}; - -for i=1:length(models) - switch models{i} - case 'FULL' - % reduce the data in order to make the demo faster - ind = find(xx(:,2)<25); - x = xx(ind,:); - y = yy(ind,:); - yee = ye(ind,:); - [n,nin] = size(xx); - - % Create the likelihood structure - likelih = likelih_poisson('init', y, yee); - - gp = gp_init('init', 'FULL', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'CS-FULL' - x = xx; - y = yy; - yee = ye; - [n,nin] = size(xx); - - % Create the likelihood structure - likelih = likelih_poisson('init', y, yee); - - gp = gp_init('init', 'FULL', likelih, {gpcf2, gpcf3}, {}, 'jitterSigma2', 0.01) - tstindex = []; - tstindex2 = 1:n; - case 'FIC' - x = xx; - y = yy; - yee = ye; - [n,nin] = size(xx); - - % Create the likelihood structure - likelih = likelih_poisson('init', y, yee); - - gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - case 'PIC' - x = xx; - y = yy; - yee = ye; - [n,nin] = size(xx); - - % Create the likelihood structure - likelih = likelih_poisson('init', y, yee); - - gp = gp_init('init', 'PIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}); - tstindex = testindex; - tstindex2 = trindex; - tstindex2{1} = [tstindex2{1} ; [n+1:length(p2)]']; - case 'CS+FIC' - x = xx; - y = yy; - yee = ye; - [n,nin] = size(xx); - - % Create the likelihood structure - likelih = likelih_poisson('init', y, yee); - - gp = gp_init('init', 'CS+FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.01, 'X_u', X_u); - tstindex = []; - tstindex2 = 1:n; - end - - gp = gp_init('set', gp, 'latent_method', {'Laplace', x, y, 'covariance'}); - - fe=@gpla_e; - fg=@gpla_g; - n=length(y); - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - opt.maxiter = 30; - - % do scaled conjugate gradient optimization - w = gp_pak(gp, 'covariance'); - w = scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - gp=gp_unpak(gp,w, 'covariance'); - - delta = gradcheck(w, @gpla_e, @gpla_g, gp, x, y, 'covariance'); - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters for Laplace approximation for poisson model and ' models{i} ' GP']); - numwarn = numwarn + 1; - end - - % --- Check predictions --- - [Efa, Varfa] = la_pred(gp, x, y, p, 'covariance', [], tstindex); - [Ef1, Varf1] = la_pred(gp, x, y, p, 'covariance', [1], tstindex); - [Ef2, Varf2] = la_pred(gp, x, y, p, 'covariance', [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with poisson model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with poisson model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = la_pred(gp, x, y, p2, 'covariance', [], tstindex2); - [Ef1, Varf1] = la_pred(gp, x, y, p2, 'covariance', [1], tstindex2); - [Ef2, Varf2] = la_pred(gp, x, y, p2, 'covariance', [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with poisson model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check la_pred for ' models{i} ' GP with poisson model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = la_pred(gp, x, y, p2, 'covariance', [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = la_pred(gp, x, y, x, 'covariance', [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = la_pred(gp, x, y, x, 'covariance', [], tstindex2, y); - end - - - % ------------------- - % --- EP approach --- - % ------------------- -% $$$ switch models{i} -% $$$ case 'FULL' -% $$$ gp = gp_init('init', 'FULL', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.0001) -% $$$ tstindex = []; -% $$$ tstindex2 = 1:n; -% $$$ case 'CS-FULL' -% $$$ gp = gp_init('init', 'FULL', likelih, {gpcf2, gpcf3}, {}, 'jitterSigma2', 0.0001) -% $$$ tstindex = []; -% $$$ tstindex2 = 1:n; -% $$$ case 'FIC' -% $$$ gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.1, 'X_u', X_u); -% $$$ tstindex = []; -% $$$ tstindex2 = 1:n; -% $$$ case 'PIC' -% $$$ gp = gp_init('init', 'PIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.1, 'X_u', X_u); -% $$$ gp = gp_init('set', gp, 'blocks', {'manual', x, trindex}); -% $$$ tstindex = testindex; -% $$$ tstindex2 = trindex; -% $$$ tstindex2{1} = [tstindex2{1} ; [n+1:length(p2)]']; -% $$$ case 'CS+FIC' -% $$$ gp = gp_init('init', 'CS+FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.5, 'X_u', X_u); -% $$$ tstindex = []; -% $$$ tstindex2 = 1:n; -% $$$ end - - gp = gp_init('set', gp, 'latent_method', {'EP', x, y, 'covariance'}); - - fe=@gpep_e; - fg=@gpep_g; - n=length(y); - opt = scg2_opt; - opt.tolfun = 1e-3; - opt.tolx = 1e-3; - opt.display = 1; - opt.maxiter = 30; - - % do scaled conjugate gradient optimization - w = gp_pak(gp, 'covariance'); - w=scg2(fe, w, opt, fg, gp, x, y, 'covariance'); - gp=gp_unpak(gp,w, 'covariance'); - - delta = gradcheck(w, @gpep_e, @gpep_g, gp, x, y, 'covariance'); - - % check that gradients are OK - if delta>0.0001 - warnings = sprintf([warnings '\n * Check the gradients of hyper-parameters for Laplace approximation for probit model and ' models{i} ' GP']); - warning([' Check the gradients of ' covfunc{i}]); - numwarn = numwarn + 1; - end - - % --- Check predictions --- - [Efa, Varfa] = ep_pred(gp, x, y, p, 'covariance', [], tstindex); - [Ef1, Varf1] = ep_pred(gp, x, y, p, 'covariance', [1], tstindex); - [Ef2, Varf2] = ep_pred(gp, x, y, p, 'covariance', [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = ep_pred(gp, x, y, p2, 'covariance', [], tstindex2); - [Ef1, Varf1] = ep_pred(gp, x, y, p2, 'covariance', [1], tstindex2); - [Ef2, Varf2] = ep_pred(gp, x, y, p2, 'covariance', [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 1e-11 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check ep_pred for ' models{i} ' GP with probit model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = ep_pred(gp, x, y, p2, 'covariance', [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = ep_pred(gp, x, y, x, 'covariance', [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = ep_pred(gp, x, y, x, 'covariance', [], tstindex2, y); - end - - % --- MCMC approach --- - opt=gp_mcopt; - opt.nsamples= 50; - opt.repeat=2; - opt.hmc_opt = hmc2_opt; - opt.hmc_opt.steps=2; - opt.hmc_opt.stepadj=0.02; - opt.hmc_opt.persistence=0; - opt.hmc_opt.decay=0.6; - opt.hmc_opt.nsamples=1; - - opt.latent_opt.nsamples=1; - opt.latent_opt.nomit=0; - opt.latent_opt.persistence=0; - opt.latent_opt.repeat=1; - opt.latent_opt.steps=7; - opt.latent_opt.window=1; - opt.latent_opt.stepadj=0.15; - - opt.latent_opt.display=0; - hmc2('state', sum(100*clock)); - -% $$$ gp = gp_init('init', 'FIC', likelih, {gpcf1, gpcf3}, {}, 'jitterSigma2', 0.0001) - gp=gp_unpak(gp,w, 'covariance'); - gp = gp_init('set', gp, 'latent_method', {'MCMC', zeros(size(y))', @scaled_hmc}); - - % Do the sampling (this takes approximately 5-10 minutes) - [rfull,g,rstate1] = gp_mc(opt, gp, x, y); - - % --- Check predictions --- - [Efa, Varfa] = mc_pred(rfull, x, y, p, [], tstindex); - [Ef1, Varf1] = mc_pred(rfull, x, y, p, [1], tstindex); - [Ef2, Varf2] = mc_pred(rfull, x, y, p, [2], tstindex); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(mean(Efa) - (mean(Ef1)+mean(Ef2))) ) > 1e-11 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efa, Varfa] = mc_pred(rfull, x, y, p2, [], tstindex2); - [Ef1, Varf1] = mc_pred(rfull, x, y, p2, [1], tstindex2); - [Ef2, Varf2] = mc_pred(rfull, x, y, p2, [2], tstindex2); - - switch models{i} - case {'FULL' 'CS+FIC'} - if max( abs(mean(Efa) - (mean(Ef1)+mean(Ef2))) ) > 1e-11 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - % In FIC and PIC the additive phenomenon is only approximate - case {'FIC' 'PIC'} - if max( abs(Efa - (Ef1+Ef2)) ) > 0.1 - warnings = sprintf([warnings '\n * Check mc_pred for ' models{i} ' model. The predictions with only one covariance function do not match the full prediction.']); - numwarn = numwarn + 1; - end - end - - [Efaa, Varfaa, Ey, Vary] = mc_pred(rfull, x, y, p2, [], tstindex2); - - switch models{i} - case {'PIC'} - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], trindex, y); - otherwise - [Ef, Varf, Ey, Vary, py] = mc_pred(rfull, x, y, x, [], tstindex2, y); - end -end - - -warnings - - -% =========================== -% Neg-Bin model -% =========================== - - - - - -% =========================== -% Student-t regression -% =========================== - - - -%---------------------------- -% check priors -%---------------------------- - -%---------------------------- -% check sparse approximations -%---------------------------- From 5509c117db5da197bd1279cdcd9e088a6b694569 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 12:41:19 +0300 Subject: [PATCH 21/64] updated changelog --- ChangeLog.txt | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/ChangeLog.txt b/ChangeLog.txt index 4661fcd6..e372581e 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -10,7 +10,25 @@ New features: Machine Learning Research 12 (2011), 417-454 - Laplace: cm2 and fact - EP: fact - + +Improvements + - lgpdens ignores now NaNs instead of giving error + - gp_cpred has a new option 'target' accpeting values 'f' or 'mu' + - unified gp_waic and gp_dic + - by default return mlpd + - option 'form' accetps now values 'mean' 'all' 'sum' and 'dic' + - improved survival demo demo_survival_aft (accalerated failure time) + - renamed and improved from demo_survival_weibull + - rearranged some files to more logical directories + - bug fixes + +New files + - gp_predcm: marginal posterior corrections for latent values. + - demo_improvedmarginals: demonstration of marginal posterior corrections + - demo_improvedmarginals2: demonstration of marginal posterior corrections + - lik_multinomprobit: multinomial probit likelihood + - demo_multiclass_nested_ep: demonstration of nested EP with multinomprobit + 2013-03-12 Version 4.0 From e496a2451f9f0c051cb05b0fa1e1ff1ac5b99782 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 12:44:23 +0300 Subject: [PATCH 22/64] updated version in readme --- README.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.txt b/README.txt index ec412d7e..97d126d8 100644 --- a/README.txt +++ b/README.txt @@ -1,7 +1,7 @@ Last modified: 2013-03-20 16:01:42 EET ----------------------------------------------------------------- -GPstuff: Gaussian process models for Bayesian analysis 4.0 +GPstuff: Gaussian process models for Bayesian analysis 4.1 Maintainers: Aki Vehtari Jarno Vanhatalo From fc648f04399faf74c9519fa5483b54be41364a3c Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 24 Apr 2013 13:28:11 +0300 Subject: [PATCH 23/64] updated marginal posterior corrections in doc --- doc/GPstuff-nodoi.bib | 11 +++++++++++ doc/GPstuffManual.pdf | Bin 1296923 -> 1297182 bytes doc/GPstuffManual.tex | 9 +++++++++ 3 files changed, 20 insertions(+) diff --git a/doc/GPstuff-nodoi.bib b/doc/GPstuff-nodoi.bib index 55fba5e6..828c26b7 100644 --- a/doc/GPstuff-nodoi.bib +++ b/doc/GPstuff-nodoi.bib @@ -1053,6 +1053,17 @@ @article{Cseke+Heskes:2010 pages = {121-128} } +@article{Cseke+Heskes:2011, + author = {Botond Cseke and Tom Heskes}, + title = {Approximate Marginals in Latent Gaussian Models}, + journal = {Journal of Machine Learning Research}, + volume = {12}, + month = {2}, + year = {2011}, + issn = {1532-4435}, + pages = {417--454}, +} + @article{Tipping+Lawrence:2005, author = "Michael E. 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z*}NRKFykj#nZPGlRYp*L5xl5Dq0d@S1_ut}leA6P!l{~#mgn(`6Ebt@#kWo5b=OuF zMznzG9z0R1bCj9&F?$1w#K0!Jy&9$xcdt|a&oHBEK*ssFMmh&1K9Nk@sP(1aj$x1@ Pvh%PaQc+1NN+JFqDH=Cb diff --git a/doc/GPstuffManual.tex b/doc/GPstuffManual.tex index 3983e1c0..e749da0f 100644 --- a/doc/GPstuffManual.tex +++ b/doc/GPstuffManual.tex @@ -1328,6 +1328,15 @@ \subsubsection{Inference with MCMC} Laplace and EP approximations work, in our experience, practically as well as MCMC. +\subsubsection{Marginal posterior corrections} + +\pkg{GPstuff} also provides methods for marginal posterior corrections using either Laplace or EP as a latent method \citep{Cseke+Heskes:2011}. Provided correction methods are either \texttt{cm2} or \texttt{fact} for Laplace and \texttt{fact} for EP. + +Marginal posterior corrections can be computed with function \texttt{gp\_predcm} which returns the corrected posterior distributed for the given indices of input/outputs: +\begin{verbatim} + [pc, fvec, p] = gp_predcm(gp,x,y,'ind', 1, 'fcorr', 'fact'); +\end{verbatim} +The returned value \texttt{pc} corresponds to corrected marginal posterior of for the first input, \texttt{fvec} corresponds to vector of grid points where \texttt{pc} is evaluated and \texttt{p} is the original Gaussian posterior distribution evaluated at \texttt{fvec}. Marginal posterior corrections can also be used for predictions in \texttt{gp\_pred} and/or sampling of latent values in \texttt{gp\_rnd} (see \texttt{demo\_improvedmarginals1} and \texttt{demo\_improvedmarginals2}). \section{Other single latent models} % In this section, we discuss other example applications where From b1c2809d0aae30abd98355dc97745dbf23d71158 Mon Sep 17 00:00:00 2001 From: Aki Vehtari Date: Thu, 25 Apr 2013 12:44:47 +0300 Subject: [PATCH 24/64] Revert "release modifications". Reverted removal of files not for release. This reverts commit ceb6b91021b9a4b64b1895f8a754ac455fd0ab92. --- gp/TODO.txt | 127 +++ gp/demo_SSGPregression.m | 386 +++++++++ gp/demo_censored_t.m | 222 +++++ gp/demo_inputdependentnoise.m | 277 ++++++ gp/demo_inputdependentweibull.m | 114 +++ gp/demo_survival_coxph.m | 175 ++++ gp/demo_zinegbin.m | 93 +++ gp/gp_cvlcriterion.m | 60 ++ gp/gp_lcriterion.m | 68 ++ gp/gp_refpred.m | 468 +++++++++++ gp/gpcf_SSsexp.m | 582 +++++++++++++ gp/gpcf_intcov.m | 754 +++++++++++++++++ gp/gpla_mo_e.m | 369 ++++++++ gp/gpla_mo_g.m | 245 ++++++ gp/gpla_mo_pred.m | 205 +++++ gp/gpla_softmax_e.m | 515 ++++++++++++ gp/gpla_softmax_g.m | 244 ++++++ gp/gpla_softmax_pred.m | 176 ++++ gp/lik_coxph.m | 1387 +++++++++++++++++++++++++++++++ gp/lik_gaussianbl.m | 369 ++++++++ gp/lik_inputdependentnoise.m | 510 ++++++++++++ gp/lik_inputdependentweibull.m | 514 ++++++++++++ gp/lik_zinegbin.m | 627 ++++++++++++++ gp/metric_ibs_gxe.m | 757 +++++++++++++++++ octave_compat/mnpdf.m | 134 +++ octave_compat/mnrnd.m | 184 ++++ 26 files changed, 9562 insertions(+) create mode 100755 gp/TODO.txt create mode 100644 gp/demo_SSGPregression.m create mode 100644 gp/demo_censored_t.m create mode 100644 gp/demo_inputdependentnoise.m create mode 100644 gp/demo_inputdependentweibull.m create mode 100644 gp/demo_survival_coxph.m create mode 100644 gp/demo_zinegbin.m create mode 100644 gp/gp_cvlcriterion.m create mode 100644 gp/gp_lcriterion.m create mode 100644 gp/gp_refpred.m create mode 100644 gp/gpcf_SSsexp.m create mode 100644 gp/gpcf_intcov.m create mode 100644 gp/gpla_mo_e.m create mode 100644 gp/gpla_mo_g.m create mode 100644 gp/gpla_mo_pred.m create mode 100644 gp/gpla_softmax_e.m create mode 100644 gp/gpla_softmax_g.m create mode 100644 gp/gpla_softmax_pred.m create mode 100644 gp/lik_coxph.m create mode 100644 gp/lik_gaussianbl.m create mode 100644 gp/lik_inputdependentnoise.m create mode 100644 gp/lik_inputdependentweibull.m create mode 100644 gp/lik_zinegbin.m create mode 100644 gp/metric_ibs_gxe.m create mode 100644 octave_compat/mnpdf.m create mode 100644 octave_compat/mnrnd.m diff --git a/gp/TODO.txt b/gp/TODO.txt new file mode 100755 index 00000000..493f8fce --- /dev/null +++ b/gp/TODO.txt @@ -0,0 +1,127 @@ +% Author: Jarno Vanhatalo +% Last modified: 2009-12-21 09:09:21 EET + +Modifications: +- jitterSigmas is initialized to zero (used to be 1e-4) +- gpla_e uses Newton method as a default optimization for latent + values (used to be fminunc large scale) + +- Luokittelu: logit - korjaa {0,1}->{-1,1} helppiin + demoon sama + + +RM-HMC toteutus ? +------------------ + + +sinvchi2_* +--------- +- gradientti pieless� + + + + + +=============================================== +LIS�INFOA +=============================================== + + +------ +Vaihtoehtoisten metriikoiden toteutus +------ +Date: Tue, 25 Nov 2008 13:53:31 +0200 (EET) +From: Jarno Vanhatalo +To: Aki Vehtari +Cc: Jaakko Riihim�ki Subject: Re: mallinnuksen vaiheet alustavasti + +On Fri, 21 Nov 2008, Aki Vehtari wrote: + +> +> Kategorisen muuttujan voisi muuten koodata my�s yhdell� sy�tteell� +> niin, ett� et�isyys r +> r(x_i,x_j)=0, jos x_i=x_j +> r(x_i,x_j)=r_c, muuten +> miss� r_c valitaan siten, ett� length-scale:n tulkinta a priori +> vastaava kuin binaarisilla muuttujilla. N�in esim. +> sairaanhoitopiiriefektill� olisi vain yksi relevanssia kuvaava +> hyperparametri ja ko. efekti saadaan mukavasti mukaan tavanomaisiin +> kovarianssifunktioihin. Lis��p� todo-listalle tuollainen. +> Muistakseeni Neal oli toteuttanut t�m�n fbm:ss�, mutta ei demonnut +> sit�, joten sen hy�ty ei aikoinaan auennut. +> > Jarno voisi kommentoida, mit� vaikutusta sill� on, ett� osalle +> kovariaateista et�isyys lasketaan muuten kuin euklidisesti? + +Mit�h�n yll� oleva nyt tarkoitti? Koitanpa vastata kuitenkin kysymykseen. + T�ll� hetkell� kovarianssifunktiot on aika selke�sti kovakoodattu +euklidiselle et�isyydelle. Kaikki funktiot perustuu siihe, ett� on kaksi +vaihtoehtoa: 1) kaikilla sy�tteill� sama length-scale 2) jokaisella +sy�tteell� oma length-scale. Varsin paljon ty�t� tulee aiheuttamaan jo +se, ett� GP-paketti muutettaisiin toimimaan siten, ett� voidaan m��ritell� +osalle sy�tteit� samat ja osalle omat length-scalet. + +Jos halutaan, ett� k�ytt�j� voi m��ritt�� et�isyysfunktion vapaasti, +t�ytyy kaikki kovarianssifunktiot koodata osittain uudestaan. K�yt�nn�ss� +vapaavalintaisen et�isyyden voisi toteuttaa siten, ett� kovarianssifunktio +struktuurille annettaisiin kentt� (esim.) 'metrics'. T�ll�in gpcf_*_trcov, +*_cov, _ghyper jne. funktioihin voisi lis�t� tarkistuksen, onko +struktuurissa kyseist� kentt��. Jos kentt�� ei ole, lasketaan kaikki kuten +t�ll�kin hetkell�. Jos kentt� on, lasketaan kaikki muuten samoin kuin nyt, +mutta et�isyys lasketaan uudella tavalla. + +Esimerkiksi: + +----------- clip ----------------- +function C = gpcf_sexp_trcov(gpcf, x) +% GP_SEXP_TRCOV Evaluate training covariance matrix of inputs. +% +% +% Description +% C = GP_SEXP_TRCOV(GP, TX) takes in covariance +% function of a Gaussian +% process GP and matrix TX that contains training input vectors. +% Returns covariance matrix C. Every element ij of C contains +% covariance between inputs i and j in TX +% +% +% See also +% GPCF_SEXP_COV, GPCF_SEXP_TRVAR, GP_COV, GP_TRCOV + +if ~isfield(gpcf, 'metrics') + % Lasketaan vanhalla tavalla + + [n, m] =size(x); + + s = 1./(gpcf.lengthScale); + s2 = s.^2; + if size(s)==1 + s2 = repmat(s2,1,m); + end + ma = gpcf.magnSigma2; + + C = zeros(n,n); + for ii1=1:n-1 + d = zeros(n-ii1,1); + col_ind = ii1+1:n; + for ii2=1:m + d = d+s2(ii2).*(x(col_ind,ii2)-x(ii1,ii2)).^2; + end + C(col_ind,ii1) = d; + end + C = C+C'; + C = ma.*exp(-C); + +else + % Lasketaan uudella tavalla + feval(gpcf.metrics, gpcf, x) +end +----------- clip ----------------- + +Yll� laitoin gpcf.metrics:n funktio handleksi, koska t�ll�in samaa +metriikkaa voisi k�ytt�� k�tev�sti muissakin kovarianssifunktioissa. + +Koodien pit�isi olla sen verran modulaarisia jo nyt, ett� yll� olevan +tarkistuksen lis��minen taitaa onnistua kohtuu helposti. Lis��n +modifioinnin TODO listaan. + +-Jarno diff --git a/gp/demo_SSGPregression.m b/gp/demo_SSGPregression.m new file mode 100644 index 00000000..d8af1e4c --- /dev/null +++ b/gp/demo_SSGPregression.m @@ -0,0 +1,386 @@ +%DEMO_REGRESSION3 Regression problem demonstration for 2-input +% function with Gaussian process +% +% Description +% The regression problem consist of a data with two input variables +% and one output variable with Gaussian noise. The model constructed +% is following: +% +% The observations y are assumed to satisfy +% +% y = f + e, where e ~ N(0, s^2) +% +% where f is an underlying function, which we are interested in. +% We place a zero mean Gaussian process prior for f, which implies that +% at the observed input locations latent values have prior +% +% f ~ N(0, K), +% +% where K is the covariance matrix, whose elements are given as +% K_ij = k(x_i, x_j | th). The function k(x_i, x_j | th) is covariance +% function and th its parameters, hyperparameters. +% +% Since both likelihood and prior are Gaussian, we obtain a Gaussian +% marginal likelihood +% +% p(y|th) = N(0, K + I*s^2). +% +% By placing a hyperprior for hyperparameters, p(th), we can find the +% maximum a posterior (MAP) estimate for them by maximizing +% +% argmax log p(y|th) + log p(th). +% th +% +% If we want to find an approximation for the posterior of the hyperparameters, +% we can sample them using Markov chain Monte Carlo (MCMC) methods. +% +% After finding MAP estimate or posterior samples of hyperparameters, we can +% use them to make predictions for f: +% +% p(f | y, th) = N(m, S), +% m = +% S = +% +% For more detailed discussion of Gaussian process regression see for example +% Vanhatalo and Vehtari (2008) or +% +% +% See also DEMO_REGRESSION2 + +% Copyright (c) 2008 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 2 or later); please refer to the file +% License.txt, included with the software, for details. + + +% This file is organised in three parts: +% 1) Comparison of SSsexp and sexp covariance functions +% 2) Data analysis using sexp full GP +% 3) Data analysis using SSGP + +%======================================================== +% PART 1 Comparison of SSsexp and sexp covariance functions +%======================================================== + + +xx = [-10:0.05:10]'; +yy = sin(xx/3)*5 + 0.3*randn(size(xx)); +[n,nin] = size(xx); + +% --------------------------- +% --- Construct the model --- +% +% First create squared exponential covariance function with ARD and +% Gaussian noise data structures... +gpcf1 = gpcf_sexp('init', 'lengthScale', 2, 'magnSigma2', 1); +gpcf2 = gpcf_noise('init', 'noiseSigmas2', 0.2^2); + +% ... Then set the prior for the parameters of covariance functions... +gpcf2.p.noiseSigmas2 = sinvchi2_p({0.05^2 0.5}); +gpcf1.p.lengthScale = gamma_p({3 7}); +gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); + +% ... Finally create the GP data structure +gp1 = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.001.^2) + +% Create the squared exponential sparse spectral covariance function +gpcf3 = gpcf_SSsexp('init', 'lengthScale', 2, 'magnSigma2', 1); +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 10); + +% ... Then set the prior for the parameters of covariance functions... +gpcf3.p.lengthScale = gamma_p({3 7}); +gpcf3.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); + +% ... Finally create the GP data structure +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) + +% compare the covariance functions +[K1, C1] = gp_trcov(gp1, xx); +[Phi, S] = gp_trcov(gp2, xx); + +subplot(2,2,1) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('10 spectral points') + +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 50); +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) +[Phi, S] = gp_trcov(gp2, xx); +subplot(2,2,2) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('50 spectral points') + +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 100); +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) +[Phi, S] = gp_trcov(gp2, xx); +subplot(2,2,3) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('100 spectral points') + +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 200); +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) +[Phi, S] = gp_trcov(gp2, xx); +subplot(2,2,4) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('200 spectral points') + +% ================================================ +% PART 2 Data analysis using FULL GP +% ================================================ + +% Load the data +S = which('demo_regression1'); +L = strrep(S,'demo_regression1.m','demos/dat.1'); +data=load(L); +x = [data(:,1) data(:,2)]; +y = data(:,3); +[n, nin] = size(x); + +% Now 'x' consist of the inputs and 'y' of the output. +% 'n' and 'nin' are the number of data points and the +% dimensionality of 'x' (the number of inputs). + + +% --------------------------- +% --- Construct the model --- +% +% First create squared exponential covariance function with ARD and +% Gaussian noise data structures... +gpcf1 = gpcf_sexp('init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); +gpcf2 = gpcf_noise('init', 'noiseSigmas2', 0.2^2); + +% ... Then set the prior for the parameters of covariance functions... +gpcf2.p.noiseSigmas2 = sinvchi2_p({0.05^2 0.5}); +gpcf1.p.lengthScale = gamma_p({3 7}); +gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); + +% ... Finally create the GP data structure +gp1 = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.001.^2) + +% ----------------------------- +% --- Conduct the inference --- +% +% We will make the inference first by finding a maximum a posterior estimate +% for the hyperparameters via gradient based optimization. After this we will +% perform an extensive Markov chain Monte Carlo sampling for the hyperparameters. +% + +% --- MAP estimate using scaled conjugate gradient algorithm --- +% (see scg for more details) + +w1=gp_pak(gp1, 'hyper'); % pack the hyperparameters into one vector +fe=@gp_e; % create a function handle to negative log posterior +fg=@gp_g; % create a function handle to gradient of negative log posterior + +% set the options +opt(1) = 1; +opt(2) = 1e-2; +opt(3) = 3e-1; +opt(9) = 0; +opt(10) = 0; +opt(11) = 0; +opt(14) = 0; + +% do the optimization +[w1, opt, flog]=scg(fe, w1, opt, fg, gp1, x, y, 'hyper'); + +% Set the optimized hyperparameter values back to the gp structure +gp1=gp_unpak(gp1,w1, 'hyper'); + +% NOTICE here that when the hyperparameters are packed into vector with 'gp_pak' +% they are also transformed through logarithm. The reason for this is that they +% are easier to sample with MCMC after log transformation. + +% for the last make prections of the underlying function on a dense grid +% and plot it. Below Ef_full is the predictive mean and Varf_full the predictive +% variance. +[p1,p2]=meshgrid(-1.8:0.05:1.8,-1.8:0.05:1.8); +p=[p1(:) p2(:)]; +[Ef_full, Varf_full] = gp_pred(gp1, x, y, p); + +% Plot the prediction and data +figure(2) +mesh(p1, p2, reshape(Ef_full,73,73)); +hold on +plot3(x(:,1), x(:,2), y, '*') +axis on; +title('The predicted underlying function and the data points (MAP solution)'); + + + +% ================================================ +% PART 3 Data analysis using SSGP +% ================================================ + +% Create the squared exponential sparse spectral covariance function +gpcf3 = gpcf_SSsexp('init', 'lengthScale', [1 1], 'magnSigma2', 0.2^2); +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 200); + +% ... Then set the prior for the parameters of covariance functions... +gpcf3.p.lengthScale = gamma_p({3 7}); +gpcf3.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); + +% ... Finally create the GP data structure +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) + + +% --- MAP estimate using scaled conjugate gradient algorithm --- +% (see scg for more details) + +w2=gp_pak(gp2, 'hyper'); % pack the hyperparameters into one vector +[w2, opt, flog]=scg(fe, w2, opt, fg, gp2, x, y, 'hyper'); + +% Set the optimized hyperparameter values back to the gp structure +gp2=gp_unpak(gp2, w2, 'hyper'); + +% NOTICE here that when the hyperparameters are packed into vector with 'gp_pak' +% they are also transformed through logarithm. The reason for this is that they +% are easier to sample with MCMC after log transformation. + +% for the last make prections of the underlying function on a dense grid +% and plot it. Below Ef_full is the predictive mean and Varf_full the predictive +% variance. +[p1,p2]=meshgrid(-1.8:0.05:1.8,-1.8:0.05:1.8); +p=[p1(:) p2(:)]; +[Ef2, Varf2] = gp_pred(gp2, x, y, p); + +% Plot the prediction and data +figure(1) +mesh(p1, p2, reshape(Ef2,73,73)); +hold on +plot3(x(:,1), x(:,2), y, '*') +axis on; +title('The predicted underlying function and the data points (MAP solution)'); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +[x1, x2] = meshgrid([-5:0.5:5]', [-5:0.5:5]'); +xx = [x1(:) x2(:)]; +[n,nin] = size(xx); + +% --------------------------- +% --- Construct the model --- +% +% First create squared exponential covariance function with ARD and +% Gaussian noise data structures... +gpcf1 = gpcf_sexp('init', 'lengthScale', 4, 'magnSigma2', 1); +gpcf2 = gpcf_noise('init', 'noiseSigmas2', 0.2^2); + +% ... Then set the prior for the parameters of covariance functions... +gpcf2.p.noiseSigmas2 = sinvchi2_p({0.05^2 0.5}); +gpcf1.p.lengthScale = gamma_p({3 7}); +gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); + +% ... Finally create the GP data structure +gp1 = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 0.001.^2) + +% Create the squared exponential sparse spectral covariance function +gpcf3 = gpcf_SSsexp('init', 'lengthScale', 4, 'magnSigma2', 1); +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 10); + +% ... Then set the prior for the parameters of covariance functions... +gpcf3.p.lengthScale = gamma_p({3 7}); +gpcf3.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); + +% ... Finally create the GP data structure +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) + +% compare the covariance functions +[K1, C1] = gp_trcov(gp1, xx); +[Phi, S] = gp_trcov(gp2, xx); + +subplot(2,2,1) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('10 spectral points') + +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 50); +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) +[Phi, S] = gp_trcov(gp2, xx); +subplot(2,2,2) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('50 spectral points') + +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 100); +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) +[Phi, S] = gp_trcov(gp2, xx); +subplot(2,2,3) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('100 spectral points') + +gpcf3 = gpcf_SSsexp('set', gpcf3, 'nfreq', 200); +gp2 = gp_init('init', 'SSGP', 'gaussian', {gpcf3}, {gpcf2}) +[Phi, S] = gp_trcov(gp2, xx); +subplot(2,2,4) +plot(xx,K1(201,:)) +hold on +K2 = Phi*Phi'; +plot(xx,K2(201,:), 'r') +title('200 spectral points') + diff --git a/gp/demo_censored_t.m b/gp/demo_censored_t.m new file mode 100644 index 00000000..457acff1 --- /dev/null +++ b/gp/demo_censored_t.m @@ -0,0 +1,222 @@ +%% generate data +clear; +datai=1; + +% true function f(x) +xx = linspace(-7,5,500)'; +yy = 0.1+0.1*xx+0.2*sin(2.7*xx)+1./(1+xx.^2); + +nu=3; +sigma=0.05; +Hmax=sqrt(3*nu)*sigma; +Hzero=sqrt(nu)*sigma; +a=0.3; +ylim=[-0.4 0.9]; + +x=[linspace(-5.4,-3-a,15) linspace(-3+a,-1-a,15) linspace(-1+a,4,30)]; +xo=[-4 -3 -2 -1 2 3]; +ii=length(xo)+[1:length(x)]; +io=1:length(xo); % outlier indices +x=[xo x]'; % outlier x:s +y = 0.1 + 0.1*x + 0.2*sin(2.7*x) + 1 ./ (1+x.^2); +y(io)=y(io)+Hmax*[-3 -2 1 -1 -1 1]'; % outlier y:s +y(ii)=y(ii)+sigma*randn(size(y(ii))); + +yt=y; +y(yylim(2))=ylim(2); + +figure(1); clf +plot(xx,yy,'k',x,y,'b.',x(io),y(io),'ro') +hold on +plot(repmat(x(io)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') +plot(repmat(x(io)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') +hold off +%save(sprintf('data%d.mat',datai),'x','y','yt','xx','yy','io','ylim') + +%% load data & create gp +%clear +%datai=1; +%load(sprintf('data%d.mat',datai)); +nu=6; +sigma=0.1; +J=0.02; +x=[x randn(size(x))]; +xx=[xx randn(size(xx))]; +[n, nin] = size(x); +ylim=[-0.4 0.9]; + +% gpcf1 = gpcf_dotproduct('init', 'constSigma2',10,'coeffSigma2',ones(1,nin)); +% gpcf1.p.constSigma2 = logunif_p; +% gpcf1.p.coeffSigma2 = logunif_p; + +gpcf1 = gpcf_sexp('init', 'lengthScale',ones(1,nin),'magnSigma2',1); +gpcf1.p.lengthScale = logunif_p; +gpcf1.p.magnSigma2 = logunif_p; + + +% gpcf1 = gpcf_neuralnetwork('init',nin,'biasSigma2',0.1,'weightSigma2',ones(1,nin)); +% gpcf1.p.weightSigma2 = logunif_p; +% gpcf1.p.biasSigma2 = logunif_p; + +% Create the likelihood structure +%likelih = likelih_t('init', nu, sigma); +likelih = likelih_cen_t('init', 'nu', nu, 'sigma', sigma, 'ylim', ylim); +likelih.p.nu = logunif_p; +likelih.p.sigma = logunif_p; +%likelih.fix_nu=1; + +% Laplace approximation Student-t likelihood +param = 'hyper+likelih'; +gp_la = gp_init('init', 'FULL', likelih, {gpcf1}, {}, 'jitterSigma2', J.^2); +gp_la = gp_init('set', gp_la, 'latent_method', {'Laplace', x, y, param}); +gp_la.laplace_opt.optim_method='likelih_specific'; +%gp_la.laplace_opt.optim_method='fminunc_large'; +[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); + +% gradient checking +w = gp_pak(gp_la,param); +w = w+ 0.1*randn(size(w)); +gradcheck(w, @gpla_e, @gpla_g, gp_la, x, y, param) + +opt=optimset('GradObj','on'); +opt=optimset(opt,'TolX', 1e-3); +opt=optimset(opt,'LargeScale', 'off'); +opt=optimset(opt,'Display', 'iter'); +opt=optimset(opt,'Derivativecheck', 'on'); % 'iter' + +w0 = gp_pak(gp_la, param); +mydeal = @(varargin)varargin{1:nargout}; +w = fminunc(@(ww) mydeal(gpla_e(ww, gp_la, x, y, param), gpla_g(ww, gp_la, x, y, param)), w0, opt); +gp_la = gp_unpak(gp_la,w,param); + +fprintf('\nnu=%.3f, sigma=%.3f \nhyper=%s\n',gp_la.likelih.nu,... + gp_la.likelih.sigma,sprintf(' %.2f,',exp(gp_pak(gp_la,'hyper'))) ) + +figure(2) +[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); +W=-gp_la.likelih.fh.llg2(gp_la.likelih,y,f,'latent'); +[foo,ii]=sort(W,'ascend'); +ii=ii(1:5); +plot(xx(:,1),yy,'k',x(:,1),f,'b.',x(:,1),y,'go',x(ii,1),y(ii),'r.') + +[Ef_la, Varf_la] = la_pred(gp_la, x, y, xx, param); +stdf_la = sqrt(Varf_la); + +% plot the predictions and data +nu=gp_la.likelih.nu; +sigma=gp_la.likelih.sigma; +Hmax=sqrt(3*nu)*sigma; +Hzero=sqrt(nu)*sigma; + +figure(1) +h1=plot(xx(:,1),yy,'k',xx(:,1),Ef_la,'b',xx(:,1),Ef_la-2*stdf_la, 'b--',xx(:,1), Ef_la+2*stdf_la, 'b--'); +hold on +h1=[h1(1:2); plot(x(:,1),y,'k.')]; +plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') +plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') +hold off +legend(h1,'True','Laplace','Data') + +% ====================== +% Full MCMC solution +% ====================== +[n, nin] = size(x); +gpcf1 = gpcf_sexp('init', 'lengthScale', repmat(1,1,nin), 'magnSigma2', 0.2^2); +gpcf2 = gpcf_noiset('init', 'ndata', n, 'noiseSigmas2', repmat(1^2,n,1)); % Here set own Sigma2 for every data point + +% Un-fix nu +%gpcf2 = gpcf_noiset('set', gpcf2, 'fix_nu', 0); +gpcf2 = gpcf_noiset('set', gpcf2, 'censored', {[-0.4 0.9], y}); + +% Set the prior for the parameters of covariance functions +gpcf1.p.lengthScale = gamma_p({3 7 3 7}); +gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); + +gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 1e-4.^2) +w = gp_pak(gp, 'hyper') +gp2 = gp_unpak(gp,w, 'hyper') + +opt=gp_mcopt; +opt.repeat=10; +opt.nsamples=10; +opt.hmc_opt.steps=10; +opt.hmc_opt.stepadj=0.1; +opt.hmc_opt.nsamples=1; +hmc2('state', sum(100*clock)); + +opt.gibbs_opt = sls1mm_opt; +opt.gibbs_opt.maxiter = 50; +opt.gibbs_opt.mmlimits = [0 40]; +opt.gibbs_opt.method = 'minmax'; + +% Sample +[r,g,rstate1]=gp_mc(opt, gp, x, y); + +opt.hmc_opt.stepadj=0.08; +opt.nsamples=300; +opt.hmc_opt.steps=10; +opt.hmc_opt.persistence=1; +opt.hmc_opt.decay=0.6; + +[r,g,rstate2]=gp_mc(opt, g, x, y, [], [], r); +rr = r; + +% thin the record +rr = thin(r,100,2); + +figure +hist(rr.noise{1}.nu,20) +title('Mixture model, \nu') +figure +hist(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha,20) +title('Mixture model, \sigma') +figure +hist(rr.cf{1}.lengthScale(:,1),20) +title('Mixture model, length-scale') +figure +hist(rr.cf{1}.magnSigma2,20) +title('Mixture model, magnSigma2') + + +% $$$ >> mean(rr.noise{1}.nu) +% $$$ ans = +% $$$ 1.5096 +% $$$ >> mean(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha) +% $$$ ans = +% $$$ 0.0683 +% $$$ >> mean(rr.cf{1}.lengthScale) +% $$$ ans = +% $$$ 1.0197 +% $$$ >> mean(rr.cf{1}.magnSigma2) +% $$$ ans = +% $$$ 1.2903 + +% make predictions for test set +ypred = repmat(y,1,size(rr.edata,1)); +ypred(gp.noise{1}.imis,:) = rr.noise{1}.cy'; +[Efs, Varfs] = gp_preds(rr,x,ypred,xx); + +Ef = mean(squeeze(Efs),2); +std_f = sqrt(var(Efs,[],2)); + +% Plot the network outputs as '.', and underlying mean with '--' +figure +h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b'); +%h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b',xx(:,1),Ef-2*std_f, 'b--',xx(:,1), Ef+2*std_f, 'b--'); +hold on +h1=[h1(1:2); plot(x(:,1),y,'k.')]; +plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') +plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') +hold off +legend(h1,'True','Laplace','Data') + + +figure +for i=1:12 + subplot(6,2,i) + hist(rr.noise{1}.cy(:,i)) + hold on + plot(yt(gp.noise{1}.imis(i)), 0, 'rx', 'MarkerSize', 20, 'lineWidth', 5) +end + diff --git a/gp/demo_inputdependentnoise.m b/gp/demo_inputdependentnoise.m new file mode 100644 index 00000000..ff1a4b93 --- /dev/null +++ b/gp/demo_inputdependentnoise.m @@ -0,0 +1,277 @@ +%DEMO_INPUTDEPENDENTNOISE Demonstration of input dependent-noise +% model using Gaussian process prior +% +% Description +% Uses toy data sets to demonstrate how inferring +% heteroscedastic noise with input dependent noise model +% differs from standard noise models (Gaussian, Student-t). +% + +% Copyright (c) Ville Tolvanen 2011-2012 +% +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + +%================================= +% 1D Demonstration +%================================= + +% Initialize random stream +prevstream=setrandstream(0); + +% Create toy data +% x = 100*rand([40 1]); +n = 500; +x=linspace(-100,200,n)'; +f1 = [5.*sin(-3+0.2.*x(1:ceil(0.23*n))); 20*sin(0.1*x(ceil(0.23*n)+1:ceil(0.85*n))); 5.*sin(2.8+0.2.*x(ceil(0.85*n)+1:end))]; +f2 = 100*norm_pdf(x,110,20) + 100*norm_pdf(x,-10,20); +sigma2 = 0.5; + +x=x-mean(x); x=x./std(x); +f1 = f1-mean(f1); f1=f1./std(f1); + +y = f1 + sqrt((sigma2.*exp(f2))).*randn(size(x)); +yt = f1(1:5:end); +xt = x(1:5:end); +nt = size(xt,1); +x=x(:); y=y(:); xt=xt(:); + +% Create the covariance functions +pl = prior_t('s2',10); +pm = prior_t('s2',10); +gpcf1 = gpcf_sexp('lengthScale', 0.5, 'magnSigma2', 0.5, ... + 'lengthScale_prior', pl, 'magnSigma2_prior', pm); +gpcf2 = gpcf_exp('lengthScale', 1, 'magnSigma2', 0.1, ... + 'lengthScale_prior', pl, 'magnSigma2_prior', pm); + +% Create the likelihood structure. Don't set prior for sigma2 if covariance +% function magnitude for noise process has a prior. +lik = lik_inputdependentnoise('sigma2', 0.1, 'sigma2_prior', prior_fixed()); + +% NOTE! if multiple covariance functions per latent is used, define +% gp.comp_cf as follows: +% gp = gp_set(..., 'comp_cf' {[1 2] [5 6]}; +gp = gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'jitterSigma2', 1e-9, 'comp_cf', {[1] [2]}); + +% Set the approximate inference method to Laplace +gp = gp_set(gp, 'latent_method', 'Laplace'); +% For more complex problems, maxiter in latent_opt should be increased. +% gp.latent_opt.maxiter=1e6; + +% Set the options for the optimization +opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off'); +% Optimize with the scaled conjugate gradient method +gp=gp_optim(gp,x,y,'opt',opt); + +% make prediction to the data points +[Ef, Varf,lpyt] = gp_pred(gp, x, y, xt, 'yt', yt); +Ef11=Ef(1:nt);Ef12=Ef(nt+1:end); +Varf11=diag(Varf(1:nt,1:nt)); +%prctmus = gp_predprctmu(gp, x, y, xt); +prctmus=[Ef11-1.645*sqrt(Varf11) Ef11 Ef11+1.645*sqrt(Varf11)]; +fprintf('mlpd inputdependentnoise: %.2f\n', mean(lpyt)); + +% Gaussian for comparison +opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off'); +lik2 = lik_gaussian(); +gp2 = gp_set('lik', lik2, 'cf', gpcf1, 'jitterSigma2', 1e-9); +gp2 = gp_optim(gp2,x,y,'opt',opt); +[Ef2, Varf2, lpyt2] = gp_pred(gp2, x, y, xt,'yt',yt); +prctmus2 = gp_predprctmu(gp2, x, y, xt); +fprintf('mlpd gaussian: %.2f\n', mean(lpyt2)); + +% Student-t for comparison +lik=lik_t(); +gp3=gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-9); +opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off'); +gp3 = gp_set(gp3, 'latent_method', 'Laplace'); +gp3=gp_optim(gp3,x,y,'opt',opt); +[Ef3, Varf3,lpyt3] = gp_pred(gp3, x, y, xt, 'yt', yt); +prctmus3 = gp_predprctmu(gp3, x, y, xt); +fprintf('mlpd student-t: %.2f\n', mean(lpyt3)); + +figure; +% plot mean and 5% and 95% quantiles +subplot(3,1,1) +plot(xt,Ef11,'b',xt,prctmus(:,1),'r',xt,prctmus(:,3),'r', x, f1, 'k') +ylim([-3 3]), title('Input dependent noise model'); +legend('Mean','5%','95%','True',2) + +% Compare to Gaussian with homoscedastic scale +subplot(3,1,2), +plot(xt, Ef2,'b',xt,prctmus2(:,1),'r',xt,prctmus2(:,3),'r', x, f1, 'k') +ylim([-3 3]), title('Gaussian noise model') + +% Compare to Student-t with homoscedastic scale +subplot(3,1,3) +plot(xt, Ef3,'b',xt,prctmus3(:,1),'r',xt,prctmus3(:,3),'r', x, f1, 'k') +ylim([-3 3]), title('Student-t noise model') + +figure +s2=gp.lik.sigma2; +plot(xt, s2.*exp(Ef12), '-b',x, sigma2.*exp(f2), '-k', xt, s2.*exp(Ef12 + 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r', xt,s2.*exp(Ef12 - 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r') +legend('Predicted noise variance', 'Real noise variance','95% CI',2); + +%==================================== +% 2D Demonstration +%==================================== +setrandstream(0); + +% Create data from two 2 dimensional gaussians +nt=10; +n=700; +x=[-3+6*rand(0.25*n,1) -3+6*rand(0.25*n,1);-1.5+3*rand(0.75*n,1) -1.5+3*rand(0.75*n,1)]; +mu=[0 0]; +S=[0.2 0;0 0.2]; +sigma2=0.1; +[x1,x2]=meshgrid(linspace(-1,2,nt), linspace(-1,2,nt)); +xt=[x1(:) x2(:)]; +f1t=10*mnorm_pdf(xt,mu,S) + 20*mnorm_pdf(xt,mu+[1.5 1.5], [0.5 0;0 0.5]); +f2t=20*mnorm_pdf(xt,mu, [0.9 0;0 0.9]); +yt=f1t; +f1 = 10*mnorm_pdf(x,mu, S) + 20*mnorm_pdf(x,mu+[1.5 1.5], [0.5 0;0 0.5]); +f2 = 20*mnorm_pdf(x,mu, [0.9 0;0 0.9]); + +y=f1+randn(size(x,1),1).*sqrt(sigma2.*exp(f2)); + +pl = prior_logunif(); +pm = prior_logunif(); +gpcf1 = gpcf_sexp('lengthScale', [1 1.01], 'magnSigma2', 1, ... + 'lengthScale_prior', pl, 'magnSigma2_prior', pm); +gpcf2 = gpcf_sexp('lengthScale', [1 1.01], 'magnSigma2', 0.1, ... + 'lengthScale_prior', pl, 'magnSigma2_prior', pm); + +lik=lik_inputdependentnoise('sigma2', 0.1, 'sigma2_prior', prior_fixed()); + +gp=gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'jitterSigma2', 1e-6, 'comp_cf', {[1] [2]}); +gp = gp_set(gp, 'latent_method', 'Laplace'); +opt=optimset('TolFun',1e-4,'TolX',1e-4,'Display','iter','MaxIter',100,'Derivativecheck','off'); +gp=gp_optim(gp,x,y,'opt',opt); +% Increase maxiter for predictions in case of slow convergence +gp.latent_opt.maxiter=1e6; +[Ef,Varf,lpyt]=gp_pred(gp,x,y,xt, 'yt',yt); +fprintf('mlpd inputdependentnoise: %.2f\n', mean(lpyt)); + +% Gaussian for comparison +lik2 = lik_gaussian('sigma2', sigma2); +gp2 = gp_set('lik', lik2, 'cf', gpcf1, 'jitterSigma2', 1e-6); +gp2 = gp_optim(gp2,x,y,'opt',opt); +[Ef2,Varf2,lpyt2]=gp_pred(gp2,x,y,xt,'yt',yt); +fprintf('mlpd gaussian: %.2f\n', mean(lpyt2)); + +% Student-t for comparison +lik3=lik_t('sigma2', sigma2); +gp3=gp_set('lik', lik3, 'cf', gpcf1, 'jitterSigma2', 1e-6, 'latent_method', 'Laplace'); +gp3=gp_optim(gp3,x,y,'opt',opt); +[Ef3,Varf3,lpyt3]=gp_pred(gp3,x,y,xt,'yt',yt); +fprintf('mlpd student-t: %.2f\n', mean(lpyt3)); + +s2=gp.lik.sigma2; +figure +subplot(3,1,1),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef(1:size(xt,1)), '*') +title('Input dependent noise model'); +colormap hsv, alpha(.4) +subplot(3,1,2),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef2(1:size(xt,1)), '*'); +colormap hsv, alpha(.4) +title('Gaussian noise model'); +subplot(3,1,3),mesh(x1,x2,reshape(f1t,size(x1))),hold on, plot3(xt(:,1),xt(:,2), Ef3(1:size(xt,1)), '*'); +colormap hsv, alpha(.4) +title('Student-t noise model'); + +figure +mesh(x1,x2,sigma2.*exp(reshape(f2t,size(x1)))),hold on, plot3(xt(:,1),xt(:,2), s2.*exp(Ef(size(xt,1)+1:end)), '*'); +title('Real noise versus predicted noise'); +colormap hsv, alpha(.4) + +%============================================ +% Demonstration with homoscedastic noise +%============================================ +setrandstream(0); + +% Create data +n =200; +nt = 200; +x = linspace(-100,200, n)'; +xt = linspace(-100,200, n)'; +f1 = [5.*sin(-3+0.2.*x(1:ceil(0.23*n))); 20*sin(0.1*x(ceil(0.23*n)+1:ceil(0.85*n))); 5.*sin(2.8+0.2.*x(ceil(0.85*n)+1:end))]; +sigma2 = 1; + +x=x-mean(x); x=x./std(x); +xt=xt-mean(xt); xt=xt./std(xt); +f1 = f1-mean(f1); f1=f1./std(f1); +f2 = zeros(size(f1)); + +y = f1 + sqrt(sigma2).*randn(size(x));yt=f1; +x=x(:); y=y(:); xt=xt(:); + +% Create the covariance functions +pl = prior_t('s2',10); +pm = prior_t('s2',10); +gpcf1 = gpcf_sexp('lengthScale', 0.5, 'magnSigma2', 0.5, ... + 'lengthScale_prior', pl, 'magnSigma2_prior', pm); +gpcf2 = gpcf_sexp('lengthScale', 1, 'magnSigma2',0.1, ... + 'lengthScale_prior', pl, 'magnSigma2_prior', pm); + +% Create the the model +lik = lik_inputdependentnoise('sigma2', 0.1, 'sigma2_prior', prior_fixed()); +gp = gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'jitterSigma2', 1e-9, 'comp_cf', {[1] [2]}); + +% Set the approximate inference method to Laplace +gp = gp_set(gp, 'latent_method', 'Laplace'); +opt=optimset('TolFun',1e-4,'TolX',1e-4); + +% if flat priors are used, there might be need to increase +% gp.latent_opt.maxiter for laplace algorithm to converge properly + +% gp.latent_opt.maxiter=1e6; + +gp=gp_optim(gp,x,y,'opt',opt); + +[Ef, Varf,lpyt] = gp_pred(gp, x, y, xt,'yt',yt); +Ef11=Ef(1:nt);Ef12=Ef(nt+1:end); +Varf11=diag(Varf(1:nt,1:nt)); +%prctmus = gp_predprctmu(gp, x, y, xt); +prctmus=[Ef11-1.645*sqrt(Varf11) Ef11 Ef11+1.645*sqrt(Varf11)]; +fprintf('mlpd inputdependentnoise: %.2f\n', mean(lpyt)); + +% Gaussian for comparison +lik2 = lik_gaussian(); +gp2 = gp_set('lik', lik2, 'cf', gpcf1, 'jitterSigma2', 1e-6); +gp2 = gp_optim(gp2,x,y,'opt',opt); +[Ef2, Varf2, lpyt2] = gp_pred(gp2, x, y, xt,'yt',yt); +prctmus2 = gp_predprctmu(gp2, x, y, xt); +fprintf('mlpd gaussian: %.2f\n', mean(lpyt2)); + +% Student-t for comparison +lik=lik_t(); +gp3=gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-6); +gp3 = gp_set(gp3, 'latent_method', 'Laplace'); +gp3=gp_optim(gp3,x,y,'opt',opt); +[Ef3, Varf3,lpyt3] = gp_pred(gp3, x, y, xt,'yt',yt); +prctmus3 = gp_predprctmu(gp3, x, y, xt); +fprintf('mlpd student-t: %.2f\n', mean(lpyt3)); + +figure; +% plot mean and 5% and 95% quantiles +subplot(3,1,1) +plot(xt,Ef11,'b',xt,prctmus(:,1),'r',xt,prctmus(:,3),'r', x, f1, 'k') +ylim([-3 3]), title('Input dependent noise model'); +legend('Mean','5%','95%','True',2) + +% Compare to Gaussian with homoscedastic scale +subplot(3,1,2), +plot(xt, Ef2,'b',xt,prctmus2(:,1),'r',xt,prctmus2(:,3),'r', x, f1, 'k') +ylim([-3 3]), title('Gaussian noise model') + +% Compare to Student-t with homoscedastic scale +subplot(3,1,3) +plot(xt, Ef3,'b',xt,prctmus3(:,1),'r',xt,prctmus3(:,3),'r', x, f1, 'k') +ylim([-3 3]), title('Student-t noise model') + +figure +plot(xt, s2.*exp(Ef12), '-b',x, sigma2.*exp(f2), '-k', xt, s2.*exp(Ef12 + 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r', xt,s2.*exp(Ef12 - 1.96.*sqrt(diag(Varf(nt+1:end, nt+1:end)))), '-r') +ylim([0 2.5]) +legend('Predicted noise variance', 'Real noise variance','95% CI',2); +setrandstream(prevstream); diff --git a/gp/demo_inputdependentweibull.m b/gp/demo_inputdependentweibull.m new file mode 100644 index 00000000..10c5f0b2 --- /dev/null +++ b/gp/demo_inputdependentweibull.m @@ -0,0 +1,114 @@ +S = which('demo_survival_weibull'); +L = strrep(S,'demo_survival_weibull.m','demodata/leukemia.txt'); +leukemiadata=load(L); + +% leukemiadata consists of: +% 'time', 'cens', 'xcoord', 'ycoord', 'age', 'sex', 'wbc', 'tpi', 'district' + +% survival times +y=leukemiadata(:,1); +% scale survival times +y=y/max(y); + +ye=1-leukemiadata(:,2); % event indicator, ye = 0 for uncensored event + % ye = 1 for right censored event + +% choose (for example) 'age', 'sex', 'wbc', and 'tpi' covariates +x0=leukemiadata(:,5:8); +x=x0; +% normalize continuous covariates +x(:,[1 3:4])=bsxfun(@rdivide,bsxfun(@minus,x0(:,[1 3:4]),mean(x0(:,[1 3:4]),1)),std(x0(:,[1 3:4]),1)); + +[n, nin]=size(x); + +% Create the covariance functions +% pl = prior_gaussian('s2',2); +% pm = prior_gaussian('s2',2); +% gpcf1 = gpcf_neuralnetwork('weightSigma2',1, 'biasSigma2', 0.05, 'weightSigma2_prior', pl, 'biasSigma2_prior', pm); +% gpcf2 = gpcf_neuralnetwork('weightSigma2',1, 'biasSigma2', 0.05, 'weightSigma2_prior', pl, 'biasSigma2_prior', pm); +gpcf1 = gpcf_sexp('lengthScale', 1, 'magnSigma2', 1, 'lengthScale_prior', prior_logunif(), 'magnSigma2_prior', prior_logunif()); +gpcf2 = gpcf_sexp('lengthScale', 1, 'magnSigma2', 1, 'lengthScale_prior', prior_logunif(), 'magnSigma2_prior', prior_logunif()); + +% Create the likelihood structure +lik = lik_inputdependentweibull('shape', 0.1, 'shape_prior', prior_logunif()); + +% Create the GP structure +gp = gp_set('lik', lik, 'cf', {gpcf1 gpcf2}, 'comp_cf', {[1] [2]}, 'jitterSigma2', 1e-6); + +% Set the approximate inference method to Laplace +gp = gp_set(gp, 'latent_method', 'Laplace'); + +% Set the options for the optimization +opt=optimset('TolFun',1e-4,'TolX',1e-4,'Derivativecheck','off', 'Display','iter'); +% Optimize with the scaled conjugate gradient method +gp=gp_optim(gp,x,y,'z',ye,'opt',opt); + +xt1=zeros(200,nin); xt1(:,2)=1; +xt2=zeros(200,nin); xt2(:,2)=-1; + +xt1(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200); +xt2(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200); +xt01(:,1)=linspace(min(x0(:,1)), max(x0(:,1)), 200); +xt02(:,1)=linspace(min(x0(:,1)), max(x0(:,1)), 200); + +% Do predictions +[Ef1, Varf1] = gp_pred(gp, x, y, xt1, 'z', ye); +[Ef2, Varf2] = gp_pred(gp, x, y, xt2, 'z', ye); + +% Create normal weibull model +lik = lik_weibull(); +gp2 = gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-6); +gp2 = gp_set(gp2, 'latent_method', 'Laplace'); +gp2=gp_optim(gp2,x,y,'z',ye,'opt',opt); + +[Ef1_2, Varf1_2] = gp_pred(gp2, x, y, xt1, 'z', ye); +[Ef2_2, Varf2_2] = gp_pred(gp2, x, y, xt2, 'z', ye); + +lik = lik_loggaussian(); +gp3 = gp_set('lik', lik, 'cf', gpcf1, 'jitterSigma2', 1e-6); +gp3 = gp_set(gp3, 'latent_method', 'Laplace'); +gp3=gp_optim(gp3,x,y,'z',ye,'opt',opt); + +[Ef1_3, Varf1_3] = gp_pred(gp3, x, y, xt1, 'z', ye); +[Ef2_3, Varf2_3] = gp_pred(gp3, x, y, xt2, 'z', ye); + + +% Plot results +col1=ones(1,3)*0.7; +col2=ones(1,3)*0.3; +figure, hold on, set(gcf, 'color', 'w'), +plot(xt01(:,1), Ef1(1:nt), 'color', col1, 'linewidth', 3) +plot(xt01(:,1), Ef1(1:nt)+1.96*sqrt(diag(Varf1(1:nt,1:nt))), '--', 'color', col1, 'linewidth', 2) +plot(xt01(:,1), Ef1(1:nt)-1.96*sqrt(diag(Varf1(1:nt,1:nt))), '--', 'color', col1, 'linewidth', 2) + +plot(xt02(:,1), Ef2(1:nt), 'color', col2, 'linewidth', 3) +plot(xt02(:,1), Ef2(1:nt)+1.96*sqrt(diag(Varf2(1:nt,1:nt))), '--', 'color', col2, 'linewidth', 2) +plot(xt02(:,1), Ef2(1:nt)-1.96*sqrt(diag(Varf2(1:nt,1:nt))), '--', 'color', col2, 'linewidth', 2) +xlabel('age') +title('effect of age for both sexes, inputdependent-weibull') + +col1=ones(1,3)*0.7; +col2=ones(1,3)*0.3; +figure, hold on, set(gcf, 'color', 'w'), +plot(xt01(:,1), Ef1_2, 'color', col1, 'linewidth', 3) +plot(xt01(:,1), Ef1_2+1.96*sqrt(Varf1_2), '--', 'color', col1, 'linewidth', 2) +plot(xt01(:,1), Ef1_2-1.96*sqrt(Varf1_2), '--', 'color', col1, 'linewidth', 2) + +plot(xt02(:,1), Ef2_2, 'color', col2, 'linewidth', 3) +plot(xt02(:,1), Ef2_2+1.96*sqrt(Varf2_2), '--', 'color', col2, 'linewidth', 2) +plot(xt02(:,1), Ef2_2-1.96*sqrt(Varf2_2), '--', 'color', col2, 'linewidth', 2) +xlabel('age') +title('effect of age for both sexes, weibull') + +col1=ones(1,3)*0.7; +col2=ones(1,3)*0.3; +figure, hold on, set(gcf, 'color', 'w'), +plot(xt01(:,1), Ef1_3, 'color', col1, 'linewidth', 3) +plot(xt01(:,1), Ef1_3+1.96*sqrt(Varf1_3), '--', 'color', col1, 'linewidth', 2) +plot(xt01(:,1), Ef1_3-1.96*sqrt(Varf1_3), '--', 'color', col1, 'linewidth', 2) + +plot(xt02(:,1), Ef2_3, 'color', col2, 'linewidth', 3) +plot(xt02(:,1), Ef2_3+1.96*sqrt(Varf2_3), '--', 'color', col2, 'linewidth', 2) +plot(xt02(:,1), Ef2_3-1.96*sqrt(Varf2_3), '--', 'color', col2, 'linewidth', 2) +xlabel('age') +title('effect of age for both sexes, loggaussian') diff --git a/gp/demo_survival_coxph.m b/gp/demo_survival_coxph.m new file mode 100644 index 00000000..88764288 --- /dev/null +++ b/gp/demo_survival_coxph.m @@ -0,0 +1,175 @@ +%DEMO_SURVIVAL_COXPH Survival model using Cox proportional hazard model +% +% Description +% Survival model using Cox proportional model with a piecewise +% log-constant baseline hazard. The hazard rate is +% +% h(t) = h_0(t)*exp(f), +% +% where the baseline hazard is assumed to piecewise log-constant. +% +% The inference is conducted via Laplace, where we find +% Gaussian approximation for p(f| th, data), where th is the +% maximum a posterior (MAP) estimate for the parameters. +% +% The censoring indicator ye is +% +% ye = 0 for uncensored event +% ye = 1 for right censored event. +% +% If survival times y for n observation are given as nx2 matrix with +% entry times into follow-up in the first column and exit times from +% follow-up in the second column, left truncated right censored +% modelling is possible, for instance, in cases where age is wanted to +% be set as a baseline hazard. +% +% Example data set is leukemia survival data in Northwest England +% presented in (Henderson, R., Shimakura, S., and Gorst, D. (2002). +% Modeling spatial variation in leukemia survival data. Journal of the +% American Statistical Association, 97:965–972). Data set was downloaded +% from http://www.math.ntnu.no/%7Ehrue/r-inla.org/examples/leukemia/leuk.dat +% +% Note that the log-logistic model in DEMO_SURVIVAL_AFT works +% better for this data. +% +% See also DEMO_SURVIVAL_AFT +% + +% Copyright (c) 2011 Jaakko Riihimäki +% Copyright (c) 2013 Aki Vehtari + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +% First load data +S = which('demo_survival_coxph'); +L = strrep(S,'demo_survival_coxph.m','demodata/leukemia.txt'); +leukemiadata=load(L); + +% leukemiadata consists of: +% 'time', 'cens', 'xcoord', 'ycoord', 'age', 'sex', 'wbc', 'tpi', 'district' + +% survival times +y0=leukemiadata(:,1); +% scale survival times +y=y0/500; + +ye=1-leukemiadata(:,2); % event indicator, ye = 0 for uncensored event + % ye = 1 for right censored event + +% for simplicity choose 'age', 'sex', 'wbc', and 'tpi' covariates +x0=leukemiadata(:,5:8); +x=x0; +[n, m]=size(x); +% transform white blood cell count (wbc), which highly skewed +% distribution with zeros for measurements below measurement accuracy +x(:,3)=log10(x(:,3)+0.3); +% normalize continuous covariates +[x(:,[1 3:4]),xmean(:,[1 3:4]),xstd(:,[1 3:4])]=normdata(x(:,[1 3:4])); +% binary sex covariate is not transformed +xmean(2)=0;xstd(2)=1; + +% number of time intervals +ntime=50; +% create finite partition of time axis +S=linspace(0,max(y)+0.001,ntime+1); + +% Create the covariance functions +plh = prior_invt('s2',1, 'nu', 4); +pl = prior_t('s2',1, 'nu', 4); +pm = prior_t('s2',1, 'nu', 4); + +% covariance for hazard function +cfhc = gpcf_constant('constSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); +cfhl = gpcf_linear('coeffSigma2',1,'coeffSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); +cfhs = gpcf_sexp('lengthScale', 1, 'magnSigma2', 1.1, 'lengthScale_prior', plh, 'magnSigma2_prior', pm); +% covariance for proportional part +cfc = gpcf_constant('constSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); +cfl = gpcf_linear('coeffSigma2',1,'coeffSigma2_prior',prior_sinvchi2('s2',1^2,'nu',1)); +cfs = gpcf_sexp('lengthScale', ones(1,m),'lengthScale_prior',pl,'magnSigma2_prior',pm); + +% Create the likelihood structure +lik = lik_coxph('S', S); + +% NOTE! if multiple covariance functions per latent is used, define +% gp.comp_cf as follows: +% gp = gp_set(..., 'comp_cf' {[1 2] [5 6]}; +% where [1 2] are for hazard function, and [5 6] for proportional part +gp = gp_set('lik', lik, 'cf', {cfhc cfhl cfhs cfl cfs}, 'jitterSigma2', 1e-6, 'comp_cf', {[1 2 3] [4 5]}); + +% Set the approximate inference method to Laplace +gp = gp_set(gp, 'latent_method', 'Laplace'); + +opt=optimset('TolFun',1e-2,'TolX',1e-2,'Display','iter'); +gp=gp_optim(gp,x,y,'z',ye,'opt',opt); + +figure +set(gcf,'units','centimeters'); +set(gcf,'pos',[29 6 24 6]) +subplot('position',[0.07 0.21 0.20 0.77]); +i1=2;i2=1; +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); +xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); +xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); +h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); +set(h1(1),'LineWidth',2) +hold on +h2=plot(xtc{2},Ef{2},'k-',xtc{2},Ef{2}-1.64*sqrt(Varf{2}),'k--',xtc{2},Ef{2}+1.64*sqrt(Varf{2}),'k-'); +set(h2(1),'LineWidth',2) +hold off +ylim([-4 2]) +xlabel('Age (years)') +ylabel('Log-hazard') +[hl,hlo]=legend([h1(1), h2(1)],{'Female','Male'},'location','northwest');set(hl,'box','off') + +subplot('position',[0.31 0.21 0.20 0.77]); +i1=2;i2=3; +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); +xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); +xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); +h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); +set(h1(1),'LineWidth',2) +hold on +h2=plot(xtc{2},Ef{2},'k-',xtc{2},Ef{2}-1.64*sqrt(Varf{2}),'k-',xtc{2},Ef{2}+1.64*sqrt(Varf{2}),'k-'); +set(h2(1),'LineWidth',2) +hold off +%ylim([-5 0]) +ylim([-4 2]) +xlim([-1 3]) +xlabel('WBC (log_{10}(50\times10^9/L))') +%ylabel('Expected lifetime (days)') +[hl,hlo]=legend([h1(1), h2(1)],{'Female','Male'},'location','northwest');set(hl,'box','off') + +subplot('position',[0.55 0.21 0.20 0.77]); +i1=2;i2=4; +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); +xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); +xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); +h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); +set(h1(1),'LineWidth',2) +hold on +h2=plot(xtc{2},Ef{2},'k-',xtc{2},Ef{2}-1.64*sqrt(Varf{2}),'k-',xtc{2},Ef{2}+1.64*sqrt(Varf{2}),'k-'); +set(h2(1),'LineWidth',2) +hold off +%ylim([-5 0]) +ylim([-4 2]) +xlim([-7 10]) +xlabel('Townsend deprivation index') +%ylabel('Expected lifetime (days)') +[hl,hlo]=legend([h1(1), h2(1)],{'Female','Male'},'location','northwest');set(hl,'box','off') + +subplot('position',[0.79 0.21 0.20 0.77]); +i2=0;cla +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, i2, 'z', ye); +%gp_cpred(gp, x, y, x, i2, 'z', ye); +xtc=xtc*500/365; +h1=plot(xtc,Ef,'k-',xtc,Ef-1.64*sqrt(Varf),'k-',xtc,Ef+1.64*sqrt(Varf),'k-'); +set(h1(1),'LineWidth',2) +ylim([-5 3]) +%xlim([-1 3]) +xlabel('Time (years)') +%ylabel('Expected lifetime (days)') +hl=legend(h1(1),'Baseline','location','northwest');set(hl,'box','off') +ylim([-4 2]) + diff --git a/gp/demo_zinegbin.m b/gp/demo_zinegbin.m new file mode 100644 index 00000000..e243b3e6 --- /dev/null +++ b/gp/demo_zinegbin.m @@ -0,0 +1,93 @@ +%DEMO_ZINEGBIN Demonstration of zero-inflated Negative-binomial model +% using Gaussian process prior +% +% Description +% Zero-inflated Negative-binomial model provides a way of modelling +% count data with excess of zeros. The latent values for N training +% points are f=(f1_1,f2_1,...,fN_1,f1_2,f2_2,...,fN_2)^T, where latents +% f_1 are associated with classification process and latents f_2 with +% count process. Both processes are given a zero mean Gaussian process +% prior +% +% f ~ N(0, K), +% +% where K is a block diagonal covariance matrix with blocks K_1, K_2 +% whose elements are given by K_ij = k(x_i, x_j | th). The function +% k(x_i, x_j | th) is covariance function and th its parameters. +% +% In this demo we approximate the posterior distribution with Laplace +% approximation. +% +% See also DEMO_SPATIAL2, DEMO_CLASSIFIC1 + +% Copyright (c) 2008-2010 Jarno Vanhatalo +% Copyright (c) 2010 Aki Vehtari +% Copyright (c) 2011 Jaakko Riihimäki + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +% load the data +S = which('demo_spatial1'); +data = load(strrep(S,'demo_spatial1.m','demodata/spatial1.txt')); + +x = data(:,1:2); +ye = data(:,3); +y = data(:,4); + +x0=x; +x=bsxfun(@rdivide,bsxfun(@minus,x0,mean(x0)),std(x0)); + +% Create the covariance functions +pl = prior_t('s2',10); +pm = prior_t('s2',10); +% pm = prior_sqrtunif(); + +pl = prior_t('s2',10); +pm = prior_sqrtunif(); +cf = gpcf_matern32('lengthScale', 5, 'magnSigma2', 0.05, ... + 'lengthScale_prior', pl, 'magnSigma2_prior', pm); + +% Create the likelihood structure +lik = lik_zinegbin('disper_prior', prior_fixed()); + +% NOTE! if multiple covariance functions per latent is used, define +% gp.comp_cf as follows: +% gp = gp_set(..., 'comp_cf' {[1 2] [5 6]}; +gp = gp_set('lik', lik, 'cf', {cf cf}, 'jitterSigma2', 1e-6, 'comp_cf', {[1] [2]}); + +% Set the approximate inference method to Laplace +gp = gp_set(gp, 'latent_method', 'Laplace'); + +% Set the options for the optimization +opt=optimset('TolFun',1e-2,'TolX',1e-2,'Display','iter'); +% Optimize with the BFGS quasi-Newton method +gp=gp_optim(gp,x,y,'z',ye,'opt',opt, 'optimf', @fminlbfgs); + +% make prediction to the data points +[Ef, Varf] = gp_pred(gp, x, y, x, 'z', ye); + +% Define help parameters for plotting +xii=sub2ind([60 35],x0(:,2),x0(:,1)); +[X1,X2]=meshgrid(1:35,1:60); + +% Plot the figures +figure +G=repmat(NaN,size(X1)); +G(xii)=Ef(1:size(x,1)); +pcolor(X1,X2,G),shading flat +colorbar +axis equal +axis([0 35 0 60]) +title('Posterior mean of latent (classification process)') + +% Plot the figures +figure +G=repmat(NaN,size(X1)); +G(xii)=(Ef((size(x,1)+1):end)); +pcolor(X1,X2,G),shading flat +colorbar +axis equal +axis([0 35 0 60]) +title('Posterior mean of latent (count process)') diff --git a/gp/gp_cvlcriterion.m b/gp/gp_cvlcriterion.m new file mode 100644 index 00000000..8c46087d --- /dev/null +++ b/gp/gp_cvlcriterion.m @@ -0,0 +1,60 @@ +function PE2 = gp_cvlcriterion(gp, x, y, varargin) +%GP_CVLCRITERION cross-validation version of L-criterion +% +% Description +% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns cross-validation +% version of L-criterion PE2 given a Gaussian process model GP, +% training inputs X and training outputs Y. +% +% OPTIONS is optional parameter-value pair +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in case of +% Poisson likelihood we have z_i=E_i, that is, expected value +% for ith case. +% +% References +% Marriott, J. M., Spencer, N. M. and Pettitt, A. N. (2001). A +% Bayesian Approach to Selecting Covariates for +% Prediction. Scandinavian Journal of Statistics 28 87–97. +% +% Vehtari & Ojanen (2011). Bayesian preditive methods for model +% assesment and selection. In Statistics Surveys, 6:142-228. +% +% +% See also +% GP_LCRITERION +% + +% Copyright (c) 2011 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'GP_CVLCRITERION'; + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); + % pass these forward + options=struct(); + z = ip.Results.z; + if ~isempty(ip.Results.z) + options.zt=ip.Results.z; + options.z=ip.Results.z; + end + [tn, nin] = size(x); + if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) + % Gaussian likelihood + [tmp,tmp,tmp,Ey,Vary] = gp_loopred(gp, x, y); + PE2 = mean((Ey-y).^2 + Vary); + + else + % Non-Gaussian likelihood + error('cvlcriterion not sensible for non-gaussian likelihoods'); + end + +end + diff --git a/gp/gp_lcriterion.m b/gp/gp_lcriterion.m new file mode 100644 index 00000000..97642777 --- /dev/null +++ b/gp/gp_lcriterion.m @@ -0,0 +1,68 @@ +function L2 = gp_lcriterion(gp, x, y, varargin) +%GP_LCRITERION L-criterion for model selection. +% +% Description +% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns L-criterion L2 +% given a Gaussian process model GP, training inputs X and training +% outputs Y. +% +% OPTIONS is optional parameter-value pair +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in case of +% Poisson likelihood we have z_i=E_i, that is, expected value +% for ith case. +% +% References +% Gelfand, A. E. and Ghosh, S. K. (1998). Model Choice: A Minimum +% Posterior Predictive Loss Approach. Biometrika 85 1–11. +% +% Ibrahim, J. G., Chen, M.-H. and Sinha, D. (2001). Criterion-based +% methods for Bayesian model assessment. Statistica Sinica 11 +% 419–443. +% +% Vehtari & Ojanen (2011). Bayesian preditive methods for model +% assesment and selection. In Statistics Surveys, 6:142-228. +% +% +% See also +% GP_CVLCRITERION +% + +% Copyright (c) 2011 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + + ip=inputParser; + ip.FunctionName = 'GP_LCRITERION'; + ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); + ip.parse(gp, x, y, varargin{:}); + % pass these forward + options=struct(); + z = ip.Results.z; + if ~isempty(ip.Results.z) + options.zt=ip.Results.z; + options.z=ip.Results.z; + end + [tn, nin] = size(x); + if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) + % Gaussian likelihood + [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y); + L2 = sum((y-Ey).^2 + Vary); + + else + % Non-Gaussian likelihood + warning('L-criterion not sensible for non-gaussian likelihoods'); + [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y, options); + L2 = sum((y-Ey).^2 + Vary); + + end + +end + diff --git a/gp/gp_refpred.m b/gp/gp_refpred.m new file mode 100644 index 00000000..dbf0583b --- /dev/null +++ b/gp/gp_refpred.m @@ -0,0 +1,468 @@ +function u_g = gp_refpred(gp1, gp2, x, y, varargin) +% GP_REFPRED Reference predictive approximation to the expected utility of +% single predictions. +% +% Description +% u = GP_REFPRED(GP1, GP2, X, Y, OPTIONS) evaluates reference +% predictive approximation between models GP1 and GP2. Here GP1 is the +% reference model and GP2 is the candidate model. +% +% OPTIONS is optional parameter-value pair +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in case of +% Poisson likelihood we have z_i=E_i, that is, expected value +% for ith case. +% method - method for inference, 'posterior' (default) uses posterior +% predictive density, 'loo' uses leave-one-out predictive +% density (approximative), 'kfcv' uses loo cross-validation +% posterior predictive density, 'joint' uses joint +% posterior predictive density for latent values +% (non-Gaussian likelihood) or observations (Gaussian +% likelihood) +% x2,y2,z2 - Optional values for candidate model gp2. +% If only subset of these is specified, remaining variables +% are set from x,y,z. +% +% See also +% GP_LOOPRED, GP_KFCV +% +% References +% Vehtari & Ojanen (2011). Bayesian preditive methods for model +% assesment and selection. In preparation. +% + +% Copyright (c) 2011-2012 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'GP_REFPRED'; + ip=iparser(ip,'addRequired','gp1',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','gp2',@(x) isstruct(x) || iscell(x)); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','x2', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','y2', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z2', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})); + ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); + ip=iparser(ip,'parse',gp1, gp2, x, y, varargin{:}); + % pass these forward + options=struct(); + x2 = ip.Results.x2; + y2 = ip.Results.y2; + z2 = ip.Results.z2; + z = ip.Results.z; + method = ip.Results.method; + form = ip.Results.form; + if ~isempty(ip.Results.z) + options.zt=ip.Results.z; + options.z=ip.Results.z; + end + if ~isempty(ip.Results.z2) + options2.zt=ip.Results.z2; + options2.z=ip.Results.z2; + else + options2 = options; + z2 = z; + end + [tn, nin] = size(x); + u_g = zeros(size(y)); + opt = optimset('TolX', 1e-4, 'TolFun', 1e-4); + if isempty(x2) + x2 = x; + end + if isempty(y2) + y2 = y; + end + + if isstruct(gp1) + % Single gp or MCMC + + switch gp1.type + case {'FULL' 'VAR' 'DTC' 'SOR'} + tstind = []; + case {'FIC' 'CS+FIC'} + tstind = 1:tn; + case 'PIC' + tstind = gp1.tr_index; + end + if ~isfield(gp1,'etr') + model1 = 1; + if isfield(gp1.lik.fh, 'trcov') + switch method + case 'joint' + + otherwise + fh1 = @(f,Ey,Vary) norm_pdf(f,Ey,sqrt(Vary)); + end + else + fh1 = @(gp,Ef,Varf,f,z) exp(predvec(gp,Ef,(Varf),f,z)); + end + + switch method + case 'posterior' + if ~isequal(gp1.lik.type, 'Coxph') + [Ef1, Varf1, tmp, Ey1, Vary1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); + else + [Ef1, Varf1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); + end + case 'loo' + if ~isfield(gp1.lik.fh, 'trcov') && ~isfield(gp1.lik, 'type_nd') + gp1 = gp_set(gp1, 'latent_method', 'EP'); + end + [Ef1, Varf1, tmp, Ey1, Vary1] = gp_loopred(gp1,x,y, 'z', z); + case 'kfcv' + [tmp, preds] = gp_kfcv(gp1, x, y, 'tstindex', tstind, 'opt', opt, 'display', 'iter', 'k', tn, options); + [Ef1, Varf1, Ey1, Vary1] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); + case 'joint' + [Ef1, Covf1] = gp_jpred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); + end + + else + model1 = 2; + if isfield(gp1.lik.fh, 'trcov') + fh1 = @(f,Ey,Vary) mean(multi_npdf(f,Ey,(Vary)),1); + else + fh1 = @(gp,Ef,Varf,f,z) mean(exp(predvec(gp,Ef,(Varf),f,z)),1); + end + nsamples = length(gp1.edata); + if strcmp(gp1.type, 'PIC') + tr_index = gp1.tr_index; + gp1 = rmfield(gp1, 'tr_index'); + else + tr_index = []; + end + + for j = 1:nsamples + Gp = take_nth(gp1,j); + if strcmp(gp1.type, 'FIC') | strcmp(gp1.type, 'PIC') || strcmp(gp1.type, 'CS+FIC') || strcmp(gp1.type, 'VAR') || strcmp(gp1.type, 'DTC') || strcmp(gp1.type, 'SOR') + Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); + end + Gp.tr_index = tr_index; + gp_array1{j} = Gp; + switch method + case 'posterior' + [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gpmc_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); + case 'loo' + [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_loopred(Gp, x, y, 'z', z); + case 'kfcv' + [tmp, pred] = gp_kfcv(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); + [Ef1(:,j), Varf1(:,j), Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); + end + end + if isequal(method, 'joint') + [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); + end + gp1 = gp_array1; + end + else + % GP IA + switch gp1{1}.type + case {'FULL' 'VAR' 'DTC' 'SOR'} + tstind = []; + case {'FIC' 'CS+FIC'} + tstind = 1:tn; + case 'PIC' + tstind = gp1{1}.tr_index; + end + model1 = 3; + nsamples = length(gp1); + for j = 1:nsamples + Gp = gp1{j}; + weight1(j) = Gp.ia_weight; + w(j,:) = gp_pak(Gp); + switch method + case 'posterior' + [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); + case 'loo' + [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, 'z', z); + case 'kfcv' + [tmp, preds] = gp_pred(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); + [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); + end + end + if isequal(method, 'joint') + [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); + end + if isfield(gp1{1}.lik.fh, 'trcov') + fh1 = @(f,Ey,Vary) sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight1'),1); + else + fh1 = @(gp,Ef,Varf,f,z) (sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight1'),1)); + end + + end + + if isstruct(gp2) + % Single gp or MCMC + switch gp2.type + case {'FULL' 'VAR' 'DTC' 'SOR'} + tstind = []; + case {'FIC' 'CS+FIC'} + tstind = 1:tn; + case 'PIC' + tstind = gp2.tr_index; + end + if ~isfield(gp2,'etr') + model2 = 1; + if isfield(gp2.lik.fh, 'trcov') + fh2 = @(f,Ey,Vary) norm_lpdf(f,Ey,sqrt(Vary)); + else + fh2 = @(gp,Ef,Varf,f,z) predvec(gp,Ef,(Varf),f,z); + end + switch method + case 'posterior' + if ~isequal(gp2.lik.type, 'Coxph') + [Ef2, Varf2, tmp, Ey2, Vary2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); + else + [Ef2, Varf2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); + end + case 'loo' + if ~isfield(gp2.lik.fh, 'trcov') && ~isfield(gp2.lik, 'type_nd') + gp1 = gp_set(gp2, 'latent_method', 'EP'); + end + [Ef2, Varf2, tmp, Ey2, Vary2] = gp_loopred(gp2,x2,y2, 'z', z2); + case 'kfcv' + [tmp, preds] = gp_kfcv(gp2, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); + [Ef2, Varf2, Ey2, Vary2] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); + case 'joint' + [Ef2, Covf2] = gp_jpred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); + end + else + model2 = 2; + if isfield(gp2.lik.fh, 'trcov') + fh2 = @(f,Ey,Vary) log(mean(multi_npdf(f,Ey,(Vary)),1)); + else + fh2 = @(gp,Ef,Varf,f,z) log(mean(exp(predvec(gp,Ef,(Varf),f,z)),1)); + end + nsamples = length(gp2.edata); + if strcmp(gp2.type, 'PIC') + tr_index = gp2.tr_index; + gp2 = rmfield(gp2, 'tr_index'); + else + tr_index = []; + end + + for j = 1:nsamples + Gp = take_nth(gp2,j); + if strcmp(gp2.type, 'FIC') | strcmp(gp2.type, 'PIC') || strcmp(gp2.type, 'CS+FIC') || strcmp(gp2.type, 'VAR') || strcmp(gp2.type, 'DTC') || strcmp(gp2.type, 'SOR') + Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); + end + Gp.tr_index = tr_index; + gp_array2{j} = Gp; + switch method + case 'posterior' + [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gpmc_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); + case 'loo' + [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); + case 'kfcv' + [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_kfcv(Gp, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); + end + + end + if isequal(method, 'joint') + [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); + end + gp2 = gp_array2; + end + else + % GP IA + switch gp2{1}.type + case {'FULL' 'VAR' 'DTC' 'SOR'} + tstind = []; + case {'FIC' 'CS+FIC'} + tstind = 1:tn; + case 'PIC' + tstind = gp2{1}.tr_index; + end + model2 = 3; + nsamples = length(gp2); + for j = 1:nsamples + Gp = gp2{j}; + weight2(j) = Gp.ia_weight; + w(j,:) = gp_pak(Gp); + switch method + case 'posterior' + [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); + case 'loo' + [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); + case 'kfcv' + [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'off', options2); + end + end + if isequal(method, 'joint') + [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); + end + if isfield(gp2{1}.lik.fh, 'trcov') + fh2 = @(f,Ey,Vary) log(sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight2'),1)); + else + fh2 = @(gp,Ef,Varf,f,z) log(sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight2'),1)); + end + + end + + if ((isstruct(gp1) && isfield(gp1.lik.fh, 'trcov')) || (iscell(gp1) && isfield(gp1{1}.lik.fh,'trcov'))) + % Gaussian likelihood + + switch method + case 'joint' + u_g = -0.5.*((Ey1 - Ey2)'*(Covy2\(Ey1-Ey2)) + sum(sum(inv(Covy2).*Covy1)) ... + + tn*log(2*pi) + 2*sum(log(diag(chol(Covy2))))); + otherwise + for i=1:tn + m1 = Ey1(i,:); m2=Ey1(i,:).^2 + Vary1(i,:); + u_g(i) = mean(-1./(2.*Vary2(i,:))*m2 + Ey2(i,:)./Vary2(i,:)*m1 - Ey2(i,:).^2./(2.*Vary2(i,:)) - 0.5*log(2*pi*Vary2(i,:))); + end + end + + else + % Non-Gaussian likelihood + + switch method + case 'joint' + % Joint refpred of latent values + u_g = -0.5.*((Ef1 - Ef2)'*(Covf2\(Ef1-Ef2)) + sum(sum(inv(Covf2).*Covf1)) ... + + tn*log(2*pi) + 2*sum(log(diag(chol(Covf2))))); + + otherwise + if ismember(gp1.lik.type, {'Binomial', 'Poisson', 'Probit', 'Logit', 'Negbin', 'Negbinztr'}) + % Discrete likelihoods + for i=1:tn + if ~isempty(z) + z1 = z(i); + z12 = z2(i); + else + z1 = []; + z12 = []; + end + if model1~=3 + [tmp, tmp, int] = int_limits(gp1, Ef1(i,:), z1); + else + [minf maxf] = int_limits(gp1,Ef1(i,:),z1); + minf = sum(minf.*weight1); + maxf = sum(maxf.*weight1); + int = minf:maxf; + end + u_g(i) = sum(fh1(gp1,Ef1(i,:),Varf1(i,:),int,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),int,z12)); + end + else + % Continuous likelihoods + for i=1:tn + if ~isempty(z) + z1 = z(i); + z12 = z2(i); + else + z1 = []; + z12 = []; + end + if model1~=3 + if ismember(gp1.lik.type, {'Student-t', 'Weibull', 'Coxph'}) + [minf, maxf] = int_limits(gp1, Ef1(i), z1); + else + minf = mean(Ey1(i) - 12.*sqrt(Vary1(i)),2); + minf(minf<0)=0; + maxf = mean(Ey1(i) + 12.*sqrt(Vary1(i)),2); + end + else + minf = sum(bsxfun(@times, weight1, Ey1(i,:)-12.*sqrt(Vary1(i,:))),2); + maxf = sum(bsxfun(@times, weight1, Ey1(i,:)+12.*sqrt(Vary1(i,:))),2); + end + if ~isequal(gp1.lik.type, 'Coxph') + u_g(i) = quadgk(@(f) fh1(gp1,Ef1(i,:),Varf1(i,:),f,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),f,z12), minf, maxf, 'absTol', 1e-3); + else + ntime1=size(gp1.lik.xtime,1); + ntime2=size(gp2.lik.xtime,1); + u_g(i) = quadgk(@(f) fh1(gp1,Ef1([1:ntime1 i],:),Varf1([1:ntime1 i+ntime1],[1:ntime1 i+ntime1]),f,z1).*fh2(gp2,Ef2([1:ntime2 i],:),Varf2([1:ntime2 i+ntime2],[1:ntime2 i+ntime2]),f,z12), minf, maxf, 'absTol', 1e-3); + end + end + end + end + end + if isequal(form, 'mean') + u_g = mean(u_g); + end +end + +function predvec = predvec(gp, Ef, Varf, f, z) + % Compute vector of lpyts from lik.fh.predy when numel(Ef)=numel(Varf)=1 + % and numel(f) != 1. + if isstruct(gp) + if ~isfield(gp, 'etr') + % single gp + predvec=zeros(size(f)); + for i1=1:numel(f) + predvec(i1)=gp.lik.fh.predy(gp.lik,Ef,Varf,f(i1),z); + end + end + else + % ia & mc + predvec=zeros(length(gp), length(f)); + for i=1:numel(f) + for j=1:numel(gp) + predvec(j,i) = gp{j}.lik.fh.predy(gp{j}.lik, Ef(j), Varf(j), f(i), z); + end + end + end +end + +function mpdf = multi_npdf(f, mean, sigma2) +% for every element in f, compute means calculated with +% norm_pdf(f(i), mean, sqrt(sigma2)). If mean and sigma2 +% are vectors, returns length(mean) x length(f) matrix. + + mpdf = zeros(length(mean), length(f)); + for i=1:length(f) + mpdf(:,i) = norm_pdf(f(i), mean, sqrt(sigma2)); + end +end + +function [minf, maxf, interval] = int_limits(gp, Ef, z) +% Return integration limits for quadgk and interval for discrete integration. + if isstruct(gp) + gplik = gp.lik; + else + gplik = gp{1}.lik; + end + switch gplik.type + + case 'Binomial' + p = exp(Ef)./(1+exp(Ef)); + minf = binoinv(0.0001, z, p); + maxf = binoinv(0.9999, z, p); + interval = minf:maxf; + case 'Poisson' + lambda = z.*exp(Ef); + minf = poissinv(0.0001, lambda); + maxf = poissinv(0.9999, lambda); + interval=minf:maxf; + case {'Probit' 'Logit'} + minf = -1*ones(size(Ef)); + maxf = ones(size(Ef)); + interval = [-1 1]; + case {'Negbin' 'Negbinztr'} + r = gplik.disper; + p = z.*exp(Ef); + minf = nbininv(0.0001, r, p); + maxf = nbininv(0.9999, r, p); + interval = minf:maxf; + case 'Student-t' + [n, n2] = size(Ef); + nu = gp.lik.nu; + minf = repmat(tinv(0.01, nu), n, n2); + maxf = repmat(tinv(0.99, nu), n, n2); + interval = []; + case 'Weibull' + % Probably not very sensible... + minf = 1e-5; + maxf = 1e5; + interval = maxf; + case 'Coxph' + minf = 0; + maxf = 1; + interval = maxf; + end + +end diff --git a/gp/gpcf_SSsexp.m b/gp/gpcf_SSsexp.m new file mode 100644 index 00000000..cdc475fe --- /dev/null +++ b/gp/gpcf_SSsexp.m @@ -0,0 +1,582 @@ +function gpcf = gpcf_SSsexp(varargin) +%GPCF_SEXP Create a squared exponential covariance function for Gaussian Process +% +% Description +% +% GPCF = GPCF_SEXP('INIT','nin', NIN) Create and initialize +% squared exponential covariance function fo Gaussian process +% +% The fields and (default values) in GPCF_SEXP are: +% type = 'gpcf_sexp' +% nin = number of inputs (NIN) +% magnSigma2 = general magnitude (squared) for exponential part (sampled with HMC) +% (0.1) +% lengthScale = length scale for each input. This can be either (sampled with HMC) +% scalar (corresponding isotropic) or vector (corresponding ARD). +% (repmat(10, 1, NIN)) +% freq = +% nfreq = +% +% GPCF = GPCF_SEXP(GPCF, 'FIELD1', VALUE1, 'FIELD2', VALUE2, ...) +% Set the values of fields FIELD1... to the values VALUE1... in GPCF. +% +% See also +% +% +% + +% Copyright (c) 2008 Jarno Vanhatalo +% Copyright (c) 2010 Aki Vehtari + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +% allow use with or without init and set options +if nargin<1 + do='init'; +elseif ischar(varargin{1}) + switch varargin{1} + case 'init' + do='init';varargin(1)=[]; + case 'set' + do='set';varargin(1)=[]; + otherwise + do='init'; + end +elseif isstruct(varargin{1}) + do='set'; +else + error('Unknown first argument'); +end + +ip=inputParser; +ip.FunctionName = 'GPCF_SSSEXP'; +ip=iparser(ip,'addOptional','gpcf', [], @isstruct); +ip=iparser(ip,'addParamValue','nin',[], @(x) isscalar(x) && x>0 && mod(x,1)==0); +ip=iparser(ip,'addParamValue','magnSigma2',[], @(x) isscalar(x) && x>0); +ip=iparser(ip,'addParamValue','lengthScale',[], @(x) isvector(x) && all(x>0)); +ip=iparser(ip,'addParamValue','nfreq',[], @isscalar); +ip=iparser(ip,'addParamValue','frequency',[], @isvector); +ip=iparser(ip,'parse',varargin{:}); +gpcf=ip.Results.gpcf; +magnSigma2=ip.Results.magnSigma2; +lengthScale=ip.Results.lengthScale; +nfreq=ip.Results.nfreq; +nin=ip.Results.nin; +frequency=ip.Results.frequency; + +switch do + case 'init' + gpcf.type = 'gpcf_SSsexp'; + gpcf.nin = nin; + + % Initialize parameters + if isempty(lengthScale) + gpcf.lengthScale = 10; + else + gpcf.lengthScale = lengthScale; + end + if isempty(magnSigma2) + gpcf.magnSigma2 = 0.1; + else + gpcf.magnSigma2=magnSigma2; + end + if isempty(frequency) + gpcf.frequency = sqrt(2).*erfinv(2.*hammersley(nin,100) - 1); + else + if size(frequency) ~= gpcf.nin + error('The size of the frequency matrix has to be m x nin!') + else + gpcf.frequency = frequency; + gpcf.nfreq = size(gpcf.frequency,1); + end + end + if isempty(nfreq) + gpcf.nfreq = size(gpcf.frequency,2); + else + gpcf.nfreq = nfreq; + gpcf.frequency = sqrt(2).*erfinv(2.*hammersley(gpcf.nin,gpcf.nfreq) - 1); + end + + % Initialize prior structure + gpcf.p=[]; + gpcf.p.lengthScale=[]; + gpcf.p.magnSigma2=[]; + + % Set the function handles to the subfunctions + gpcf.fh.pak = @gpcf_SSsexp_pak; + gpcf.fh.unpak = @gpcf_SSsexp_unpak; + gpcf.fh.lp = @gpcf_SSsexp_e; + gpcf.fh.ghyper = @gpcf_SSsexp_ghyper; + gpcf.fh.gind = @gpcf_SSsexp_gind; + gpcf.fh.cov = @gpcf_SSsexp_cov; + gpcf.fh.covvec = @gpcf_SSsexp_covvec; + gpcf.fh.trcov = @gpcf_SSsexp_trcov; + gpcf.fh.trvar = @gpcf_SSsexp_trvar; + gpcf.fh.recappend = @gpcf_SSsexp_recappend; + + case 'set' + % Set the parameter values of covariance function + % go through all the parameter values that are changed + if ~isempty(lengthScale) + gpcf.lengthScale = lengthScale; + end + if ~isempty(magnSigma2) + gpcf.magnSigma2=magnSigma2; + end + if ~isempty(frequency) + if size(frequency) ~= gpcf.nin + error('The size of the frequency matrix has to be m x nin!') + else + gpcf.frequency = frequency; + gpcf.nfreq = size(gpcf.frequency,1); + end + end + if ~isempty(nfreq) + gpcf.nfreq =nfreq; + gpcf.frequency = sqrt(2).*erfinv(2.*hammersley(gpcf.nin,gpcf.nfreq) - 1); + end +end +end + + +function w = gpcf_SSsexp_pak(gpcf, w, param) +%GPcf_SEXP_PAK Combine GP covariance function hyper-parameters into one vector. +% +% Description +% W = GP_SEXP_PAK(GPCF, W) takes a Gaussian Process structure GP and +% combines the hyper-parameters into a single row vector W. This is +% a mandatory subfunction used for example in energy and gradient +% computations. +% +% The ordering of the parameters in HP is defined by +% hp = [hyper-params of gp.cf{1}, hyper-params of gp.cf{2}, ...]; +% +% See also +% GPCF_SEXP_UNPAK +% + +% Copyright (c) 2008 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +if nargin == 2 + param = 'hyper'; +end + +switch param + case 'hyper' + gpp=gpcf.p; + + i1=0;i2=1; + if ~isempty(w) + i1 = length(w); + end + i1 = i1+1; + w(i1) = gpcf.magnSigma2; + i2=i1+length(gpcf.lengthScale); + i1=i1+1; + w(i1:i2)=gpcf.lengthScale; + i1=i2+1; + if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) + i1=i1+1; + w(i1)=gpp.lengthScale.a.s; + if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) + i1=i1+1; + w(i1)=gpp.lengthScale.a.nu; + end + end + case 'spectral' + i1=0; + if ~isempty(w) + i1 = length(w); + end + i2=i1+length(gpcf.frequency(:)); + w(i1:i2) = gp.frequency(:)'; + i1=i2; i2=i1+length(gpcf.phase(:)); + w(i1:i2) = gpcf.phase(:) + i1=i2+1; +end +end + + + + +function [gpcf, w] = gpcf_SSsexp_unpak(gpcf, w, param) +%GPCF_SEXP_UNPAK Separate GP covariance function hyper-parameter vector into components. +% +% Description +% GP = GPCF_SEXP_UNPAK(GP, W) takes an Gaussian Process structure GP +% and a hyper-parameter vector W, and returns a Gaussian Process data +% structure identical to the input model, except that the covariance +% hyper-parameters has been set to the of W. This is a mandatory +% subfunction used for example in energy and gradient computations. +% +% See also +% GP_PAK +% + +% Copyright (c) 2008 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +if nargin == 2 + param = 'hyper'; +end + +switch param + case 'hyper' + gpp=gpcf.p; + i1=0;i2=1; + i1=i1+1; + gpcf.magnSigma2=w(i1); + i2=i1+length(gpcf.lengthScale); + i1=i1+1; + gpcf.lengthScale=w(i1:i2); + i1=i2; + if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) + i1=i1+1; + gpcf.p.lengthScale.a.s=w(i1); + if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) + i1=i1+1; + gpcf.p.lengthScale.a.nu=w(i1); + end + end + w = w(i1+1:end); + case 'spectral' + i1=1;i2=length(gpcf.frequency(:)); + gpcf.frequency = reshape(w(i1:i2), size(gpcf.frequency)); + i1 = i2; i2 = i1 + length(gpcf.frequency(:)); + i1 = i1+1; + gpcf.phase = reshape(w(i1:i2), size(gpcf.phase)); + w = w(i1+1:end); +end +end + +function eprior =gpcf_SSsexp_e(gpcf, x, t) +%GPCF_SEXP_LP Evaluate prior contribution of error of covariance function SE. +% +% Description +% E = GPCF_SEXP_LP(W, GP, X, T) takes a gp structure GPCF together +% with a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the error function E. Each row of X corresponds +% to one input vector and each row of T corresponds to one +% target vector. +% +% See also +% GP2, GP2PAK, GP2UNPAK, GP2FWD, GP2R_G +% + +% Copyright (c) 1998-2006 Aki Vehtari + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. +[n, m] =size(x); + +% Evaluate the prior contribution to the error. The parameters that +% are sampled are from space W = log(w) where w is all the "real" samples. +% On the other hand errors are evaluated in the W-space so we need take +% into account also the Jacobian of transformation W -> w = exp(W). +% See Gelman et.all., 2004, Bayesian data Analysis, second edition, p24. +eprior = 0; +gpp=gpcf.p; + +eprior=eprior... + +feval(gpp.magnSigma2.fe, ... + gpcf.magnSigma2, gpp.magnSigma2.a)... + -log(gpcf.magnSigma2); +if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) + eprior=eprior... + +feval(gpp.lengthScale.p.s.fe, ... + gpp.lengthScale.a.s, gpp.lengthScale.p.s.a)... + -log(gpp.lengthScale.a.s); + if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) + eprior=eprior... + +feval(gpp.p.lengthScale.nu.fe, ... + gpp.lengthScale.a.nu, gpp.lengthScale.p.nu.a)... + -log(gpp.lengthScale.a.nu); + end +end +eprior=eprior... + +feval(gpp.lengthScale.fe, ... + gpcf.lengthScale, gpp.lengthScale.a)... + -sum(log(gpcf.lengthScale)); +e_x=x; +e_t=t; +e_ls=gpcf.lengthScale; +e_ms=gpcf.magnSigma2; +e_e = eprior; +end + +function [DKff, gprior] = gpcf_SSsexp_ghyper(gpcf, x, x2) +%GPCF_SEXP_GHYPER Evaluate gradient of error for SE covariance function +% with respect to the hyperparameters. +% +% Descriptioni +% G = GPCF_SEXP_GHYPER(W, GPCF, X, T, G, GDATA, GPRIOR, VARARGIN) takes a gp +% hyper-parameter vector W, structure GPCF a matrix X of input vectors a +% matrix T of target vectors, inverse covariance function , +% and evaluates the error gradient G. Each row of X corresponds to one +% input vector and each row of T corresponds to one target vector. +% +% [G, GDATA, GPRIOR] = GPCF_SEXP_GHYPER(GP, P, T) also returns separately the +% data and prior contributions to the gradient. +% +% See also +% + +% Copyright (c) 2008 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +gpp=gpcf.p; +[n, m] =size(x); + +i1=0;i2=1; + +Cdm = gpcf_SSsexp_trcov(gpcf, x)./2; + +ii1=1; +DKff{ii1} = Cdm; + +% loop over all the lengthScales +if length(gpcf.lengthScale) == 1 + % In the case of isotropic SSSEXP + l = gpcf.lengthScale; + ma2 = gpcf.magnSigma2; + l = repmat(l,1,m); + + s = gpcf.frequency./(repmat(l(:),1,gpcf.nfreq).*sqrt(2).*pi); + C1 = x*s.*sin(2*pi*x*s); + C2 = -x*s.*cos(2*pi*x*s); + D = [C1 ; C2]; + D = 2.*pi.*reshape(D,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); + + ii1 = ii1+1; + DKff{ii1} = D; +else + % In the case ARD is used + l = gpcf.lengthScale; + ma2 = gpcf.magnSigma2; + if size(l)==1 + l = repmat(l,1,m); + end + + s = gpcf.frequency./(repmat(l(:),1,gpcf.nfreq).*sqrt(2).*pi); + C1 = 2.*pi.*sin(2*pi*x*s); + C2 = -2.*pi.*cos(2*pi*x*s); + C = [C1 ; C2]; + C = reshape(C,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); + + for i=1:m + D1 = x(:,i)*s(i,:).*C1; + D2 = x(:,i)*s(i,:).*C2; + D = [D1 ; D2]; + D = reshape(D,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); + + ii1 = ii1+1; + DKff{ii1} = D; + end +end + +% Evaluate the gprior with respect to magnSigma2 +i1 = i1+1; +gprior(i1)=feval(gpp.magnSigma2.fg, ... + gpcf.magnSigma2, ... + gpp.magnSigma2.a, 'x').*gpcf.magnSigma2 - 1; +% Evaluate the prior contribution of gradient with respect to lengthScale.p.s (and lengthScale.p.nu) +if isfield(gpp.lengthScale, 'p') && ~isempty(gpp.lengthScale.p) + i1=i1+1; + gprior(i1)=... + feval(gpp.lengthScale.p.s.fg, ... + gpp.lengthScale.a.s,... + gpp.lengthScale.p.s.a, 'x').*gpp.lengthScale.a.s - 1 ... + +feval(gpp.lengthScale.fg, ... + gpcf.lengthScale, ... + gpp.lengthScale.a, 's').*gpp.lengthScale.a.s; + if any(strcmp(fieldnames(gpp.lengthScale.p),'nu')) + i1=i1+1; + gprior(i1)=... + feval(gpp.lengthScale.p.nu.fg, ... + gpp.lengthScale.a.nu,... + gpp.lengthScale.p.nu.a, 'x').*gpp.lengthScale.a.nu -1 ... + +feval(gpp.lengthScale.fg, ... + gpcf.lengthScale, ... + gpp.lengthScale.a, 'nu').*gpp.lengthScale.a.nu; + end +end +% Evaluate the data contribution of gradient with respect to lengthScale +if length(gpcf.lengthScale)>1 + for i2=1:gpcf.nin + i1=i1+1; + gprior(i1)=feval(gpp.lengthScale.fg, ... + gpcf.lengthScale(i2), ... + gpp.lengthScale.a, 'x').*gpcf.lengthScale(i2) - 1; + end +else + i1=i1+1; + gprior(i1)=feval(gpp.lengthScale.fg, ... + gpcf.lengthScale, ... + gpp.lengthScale.a, 'x').*gpcf.lengthScale -1; + +end + +end + + +function [g_ind, gdata_ind, gprior_ind] = gpcf_SSsexp_gind(gpcf, x, t, g_ind, gdata_ind, gprior_ind, varargin) +%GPCF_SEXP_GIND Evaluate gradient of error for SE covariance function +% with respect to inducing inputs. +% +% Description +% [DKuu_u, DKuf_u] = GPCF_SEXP_GIND(W, GPCF, X, T) +% +% See also +% + +% Copyright (c) 2006 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +gdata_ind = gdata_ind + gradient; +g_ind = gdata_ind; +end + + +function C = gpcf_SSsexp_trcov(gpcf, x) +% GP_SSEXP_TRCOV Evaluate training covariance matrix of inputs. +% +% Description +% C = GP_SSEXP_TRCOV(GP, TX) takes in covariance function of a Gaussian +% process GP and matrix TX that contains training input vectors to +% GP. Returns covariance matrix C. Every element ij of C contains +% covariance between inputs i and j in TX +% +% For covariance function definition see ... + +% Copyright (c) 2008 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +[n, m] =size(x); + +l = gpcf.lengthScale; +ma2 = gpcf.magnSigma2; +if size(l)==1 + l = repmat(l,1,m); +end + +s = gpcf.frequency./(repmat(l(:),1,gpcf.nfreq).*sqrt(2).*pi); +C1 = cos(2*pi*x*s); +C2 = sin(2*pi*x*s); +C = [C1 ; C2]; +C = reshape(C,n,2*gpcf.nfreq).*sqrt(ma2/gpcf.nfreq); + +% $$$ [n, m] =size(x); +% $$$ +% $$$ l = gpcf.lengthScale; +% $$$ ma = gpcf.magnSigma2; +% $$$ if size(l)==1 +% $$$ l = repmat(l,1,m); +% $$$ end +% $$$ s = gpcf.frequency./repmat(l,gpcf.nfreq,1); +% $$$ phi = gpcf.phase; +% $$$ +% $$$ C = cos(2*pi*x*s + phi); +end + +function C = gpcf_SSsexp_trvar(gpcf, x) +% GP_SSEXP_TRVAR Evaluate training variance vector of inputs. +% +% Description +% C = GP_SSEXP_TRVAR(GP, TX) takes in covariance function of a Gaussian +% process GP and matrix TX that contains training input vectors to +% GP. Returns variance vector C. Every element i of C contains +% variance of input i in TX +% +% For covariance function definition see manual or +% Neal R. M. Regression and Classification Using Gaussian +% Process Priors, Bayesian Statistics 6. + +% Copyright (c) 2008 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +% $$$ [n, m] =size(x); +% $$$ +% $$$ C = ones(n,1)*gpcf.magnSigma2*2/gpcf.nfreq; +% $$$ C(C in 1d speed up could be obtained using quadrature +% -> in 2d and higher dimensions speed up is perhaps possible with +% extensions of quadrature +% -> Quasi-Monte Carlo has been tried and helps only little +% * Stochastic integration is highly variable +% -> gradients can not be evaluated accurately enough for which reason +% the inference has to be conducted with MCMC (HMC can not be used) +% +% See also +% GP_SET, GPCF_*, PRIOR_*, METRIC_* + +% Copyright (c) 2007-2011 Jarno Vanhatalo +% Copyright (c) 2010 Aki Vehtari + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + if nargin>0 && ischar(varargin{1}) && ismember(varargin{1},{'init' 'set'}) + % remove init and set + varargin(1)=[]; + end + + ip=inputParser; + ip.FunctionName = 'GPCF_INTCOV'; + ip=iparser(ip,'addOptional','gpcf', [], @isstruct); + ip=iparser(ip,'addParamValue','intArea',[], @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','cf',[], @iscell); + ip=iparser(ip,'addParamValue','NintPoints',200, @(x) isscalar(x) && x>0); + ip=iparser(ip,'parse',varargin{:}); + gpcf=ip.Results.gpcf; + + if isempty(gpcf) + % Check that SuiteSparse is available + init=true; + gpcf.intArea=ip.Results.intArea; + if isempty(gpcf.intArea) + error('intArea has to be given for intcov: gpcf_intcov(''intArea'',INTAREA,...)') + end + gpcf.type = 'gpcf_intcov'; + else + if ~isfield(gpcf,'type') && ~isequal(gpcf.type,'gpcf_intcov') + error('First argument does not seem to be a valid covariance function structure') + end + init=false; + end + + if init || ~ismember('cf',ip.UsingDefaults) + % Initialize parameters + gpcf.cf = {}; + cfs=ip.Results.cf; + if ~isempty(cfs) + for i = 1:length(cfs) + gpcf.cf{i} = cfs{i}; + end + else + error('At least one covariance function has to be given in cf'); + end + end + + if init + % Set the function handles to the nested functions + gpcf.fh.pak = @gpcf_intcov_pak; + gpcf.fh.unpak = @gpcf_intcov_unpak; + gpcf.fh.lp = @gpcf_intcov_lp; + gpcf.fh.lpg = @gpcf_intcov_lpg; + gpcf.fh.cfg = @gpcf_intcov_cfg; + gpcf.fh.ginput = @gpcf_intcov_ginput; + gpcf.fh.cov = @gpcf_intcov_cov; + gpcf.fh.trcov = @gpcf_intcov_trcov; + gpcf.fh.trvar = @gpcf_intcov_trvar; + gpcf.fh.recappend = @gpcf_intcov_recappend; + + % help parameters for storing intermediate results + w0 = []; + w1 = []; + datahash0=0; + datahash1=0; + Ctrcov = []; + Ccov = []; + end + + % Initialize parameters + if init || ~ismember('intArea',ip.UsingDefaults) + gpcf.intArea=ip.Results.intArea; + end + + % Initialize parameters + if init || ~ismember('NintPoints',ip.UsingDefaults) + gpcf.NintPoints=ip.Results.NintPoints; + end + + function [w,s] = gpcf_intcov_pak(gpcf) + %GPCF_INTCOV_PAK Combine GP covariance function parameters into + % one vector + % + % Description + % W = GPCF_INTCOV_PAK(GPCF) takes a covariance function + % structure GPCF and combines the covariance function + % parameters and their hyperparameters into a single row + % vector W. This is a mandatory subfunction used for example + % in energy and gradient computations. + % + % See also + % GPCF_INTCOV_UNPAK + + ncf = length(gpcf.cf); + w = []; s = {}; + + for i=1:ncf + cf = gpcf.cf{i}; + [wi si] = cf.fh.pak(cf); + w = [w wi]; + s = [s; si]; + end + + end + + function [gpcf, w] = gpcf_intcov_unpak(gpcf, w) + %GPCF_INTCOV_UNPAK Sets the covariance function parameters into + % the structure + % + % Description + % [GPCF, W] = GPCF_INTCOV_UNPAK(GPCF, W) takes a covariance + % function structure GPCF and a hyper-parameter vector W, + % and returns a covariance function structure identical + % to the input, except that the covariance hyper-parameters + % have been set to the values in W. Deletes the values set to + % GPCF from W and returns the modified W. This is a mandatory + % subfunction used for example in energy and gradient computations. + % + % Assignment is inverse of + % w = [ log(gpcf.magnSigma2) + % (hyperparameters of gpcf.magnSigma2) + % log(gpcf.lengthScale(:)) + % (hyperparameters of gpcf.lengthScale)]' + % + % See also + % GPCF_INTCOV_PAK + + ncf = length(gpcf.cf); + + for i=1:ncf + cf = gpcf.cf{i}; + [cf, w] = cf.fh.unpak(cf, w); + gpcf.cf{i} = cf; + end + + end + + function lp = gpcf_intcov_lp(gpcf) + %GPCF_INTCOV_LP Evaluate the log prior of covariance function parameters + % + % Description + % LP = GPCF_INTCOV_LP(GPCF, X, T) takes a covariance function + % structure GPCF and returns log(p(th)), where th collects the + % parameters. This is a mandatory subfunction used for example + % in energy computations. + % + % See also + % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LPG, GP_E + + lp = 0; + ncf = length(gpcf.cf); + for i=1:ncf + cf = gpcf.cf{i}; + lp = lp + cf.fh.lp(cf); + end + end + + function lpg = gpcf_intcov_lpg(gpcf) + %GPCF_INTCOV_LPG Evaluate gradient of the log prior with respect + % to the parameters. + % + % Description + % LPG = GPCF_INTCOV_LPG(GPCF) takes a covariance function + % structure GPCF and returns LPG = d log (p(th))/dth, where th + % is the vector of parameters. This is a mandatory subfunction + % used for example in gradient computations. + % + % See also + % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G + + lpg = []; + ncf = length(gpcf.cf); + + % Evaluate the gradients + for i=1:ncf + cf = gpcf.cf{i}; + lpg=[lpg cf.fh.lpg(cf)]; + end + end + + function DKff = gpcf_intcov_cfg(gpcf, x, x2, mask) + %GPCF_INTCOV_CFG Evaluate gradient of covariance function + % with respect to the parameters + % + % Description + % DKff = GPCF_INTCOV_CFG(GPCF, X) takes a covariance function + % structure GPCF, a matrix X of input vectors and returns + % DKff, the gradients of covariance matrix Kff = k(X,X) with + % respect to th (cell array with matrix elements). This is a + % mandatory subfunction used for example in gradient computations. + % + % DKff = GPCF_INTCOV_CFG(GPCF, X, X2) takes a covariance + % function structure GPCF, a matrix X of input vectors and + % returns DKff, the gradients of covariance matrix Kff = + % k(X,X2) with respect to th (cell array with matrix + % elements). This subfunction is needed when using sparse + % approximations (e.g. FIC). + % + % DKff = GPCF_INTCOV_CFG(GPCF, X, [], MASK) takes a covariance + % function structure GPCF, a matrix X of input vectors and + % returns DKff, the diagonal of gradients of covariance matrix + % Kff = k(X,X2) with respect to th (cell array with matrix + % elements). This subfunction is needed when using sparse + % approximations (e.g. FIC). + % + % See also + % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G + + [n, m] =size(x); + + DKff = {}; + % Evaluate: DKff{1} = d Kff / d magnSigma2 + % DKff{2} = d Kff / d lengthScale + % NOTE! Here we have already taken into account that the parameters are transformed + % through log() and thus dK/dlog(p) = p * dK/dp + + % evaluate the gradient for training covariance + if nargin == 2 + + [n1,m1]=size(x); + + intInd1 = find(x(:,end)==1); + pointInd1 = find(x(:,end)==0); + ncf = length(gpcf.cf); + numPoints = gpcf.NintPoints; + intArea = repmat(gpcf.intArea,numPoints,1); + + % point-point covariance + for i1=1:ncf + cf = gpcf.cf{i1}; + temp = cf.fh.cfg(cf, x(pointInd1,1:end-1)); + for j1 = 1:length(temp) + [I,J,R] = find(temp{j1}); + DKff{end+1} = sparse(pointInd1(I),pointInd1(J),R,n1,n1); + end + end + + % point-area covariance + temp2={}; + for j1=1:length(intInd1) + intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + ii1=1; + for i1=1:ncf + cf = gpcf.cf{i1}; + temp = cf.fh.cfg(cf, x(pointInd1,1:end-1),intpoints); + for k1 = 1:length(temp) + temp2{ii1}(:,j1) = mean(temp{k1},2); + ii1=ii1+1; + end + end + end + for i1=1:length(temp2) + [I,J,R] = find(temp2{i1}); + temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); + DKff{i1} = DKff{i1} + temp + temp'; + end + + % area-area covariance + temp2={}; + for j1=1:length(intInd1) + intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + for k1 = 1:length(intInd1) + intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + ii1=1; + for i1=1:ncf + cf = gpcf.cf{i1}; + temp = cf.fh.cfg(cf, intpoints1, intpoints2); + for l1 = 1:length(temp) + temp2{ii1}(j1,k1) = mean(mean(temp{l1})); + ii1=ii1+1; + end + end + end + end + for i1=1:length(temp2) + [I,J,R] = find(temp2{i1}); + temp = sparse(intInd1(I),intInd1(J),R,n1,n1); + DKff{i1} = DKff{i1} + temp; % + temp' + end + + % Evaluate the gradient of non-symmetric covariance (e.g. K_fu) + elseif nargin == 3 + if size(x,2) ~= size(x2,2) + error('gpcf_intcov -> _ghyper: The number of columns in x and x2 has to be the same. ') + end + + % Evaluate: DKff{1} = d mask(Kff,I) / d magnSigma2 + % DKff{2...} = d mask(Kff,I) / d lengthScale + elseif nargin == 4 + + end + + % check if CS covariances are used. If not change C into full matrix + % for speed up + sp = false; + for i1=1:ncf + if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 + sp=true; + end + end + if ~sp + for i1=1:length(DKff) + DKff{i1}=full(DKff{i1}); + end + end + + end + + function DKff = gpcf_intcov_ginput(gpcf, x, x2) + %GPCF_INTCOV_GINPUT Evaluate gradient of covariance function with + % respect to x + % + % Description + % DKff = GPCF_INTCOV_GINPUT(GPCF, X) takes a covariance + % function structure GPCF, a matrix X of input vectors and + % returns DKff, the gradients of covariance matrix Kff = + % k(X,X) with respect to X (cell array with matrix elements). + % This subfunction is needed when computing gradients with + % respect to inducing inputs in sparse approximations. + % + % DKff = GPCF_INTCOV_GINPUT(GPCF, X, X2) takes a covariance + % function structure GPCF, a matrix X of input vectors and + % returns DKff, the gradients of covariance matrix Kff = + % k(X,X2) with respect to X (cell array with matrix elements). + % This subfunction is needed when computing gradients with + % respect to inducing inputs in sparse approximations. + % + % See also + % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G + + end + + + function C = gpcf_intcov_cov(gpcf, x1, x2, varargin) + %GP_INTCOV_COV Evaluate covariance matrix between two input vectors + % + % Description + % C = GP_INTCOV_COV(GP, TX, X) takes in covariance function of + % a Gaussian process GP and two matrixes TX and X that contain + % input vectors to GP. Returns covariance matrix C. Every + % element ij of C contains covariance between inputs i in TX + % and j in X. This is a mandatory subfunction used for example in + % prediction and energy computations. + % + % See also + % GPCF_INTCOV_TRCOV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV + + if isempty(x2) + x2=x1; + end + [n1,m1]=size(x1); + [n2,m2]=size(x2); + + if m1~=m2 + error('the number of columns of X1 and X2 has to be same') + end + + ncf = length(gpcf.cf); + ww = []; + for i1=1:ncf + ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; + end + datahash=hash_sha512([x1, x2]); + fromMem = false; + if ~isempty(w1) + if all(size(ww)==size(w1)) && all(abs(ww-w1)<1e-8) && isequal(datahash,datahash1) + fromMem = true; + end + end + + if fromMem + C = Ccov; + else + % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) + intInd1 = find(x1(:,end)==1); + intInd2 = find(x2(:,end)==1); + pointInd1 = find(x1(:,end)==0); + pointInd2 = find(x2(:,end)==0); + numPoints = gpcf.NintPoints; + intArea = repmat(gpcf.intArea,numPoints,1); + dimInt = numel(gpcf.intArea); + C = sparse(n1,n2); + + % point-point covariance + if any(x1(:,end)==0) && any(x2(:,end)==0) + temp=sparse(0); + for i1=1:ncf + cf = gpcf.cf{i1}; + temp = temp + cf.fh.cov(cf, x1(pointInd1,1:end-1),x2(pointInd2,1:end-1)); + end + [I,J,R] = find(temp); + C = sparse(pointInd1(I),pointInd2(J),R,n1,n2); + end + + % point-area covariance + if any(x1(:,end)==0) && any(x2(:,end)==1) + temp=sparse(length(pointInd1),length(intInd2)); + for j1=1:length(intInd2) + %N=600; + %[tmp1, tmp2] = meshgrid( linspace(x2(intInd2(j1),1),x2(intInd2(j1),1)+intArea(1,1),N) , linspace(x2(intInd2(j1),2),x2(intInd2(j1),2)+intArea(1,2),N)); + %intpoints = [tmp1(:),tmp2(:)]; + intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,m2-1); + %intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*hammersley(m2-1,numPoints)'; + for i1=1:ncf + cf = gpcf.cf{i1}; + temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x1(pointInd1,1:end-1),intpoints),2); + end + end + [I,J,R] = find(temp); + C = C + sparse(pointInd1(I),intInd2(J),R,n1,n2); + end + + % area-point covariance + if any(x1(:,end)==1) && any(x2(:,end)==0) + temp=sparse(length(pointInd2),length(intInd1)); + for j1=1:length(intInd1) + intpoints = repmat(x1(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + for i1=1:ncf + cf = gpcf.cf{i1}; + temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x2(pointInd2,1:dimInt),intpoints),2); + end + end + [I,J,R] = find(temp'); + C = C + sparse(intInd1(I),pointInd2(J),R,n1,n2); + end + + % area-area covariance + if any(x1(:,end)==1) && any(x2(:,end)==1) + temp=sparse(length(intInd1),length(intInd2)); + for j1=1:length(intInd1) + intpoints1 = repmat(x1(intInd1(j1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); + for k1 = 1:length(intInd2) + intpoints2 = repmat(x2(intInd2(k1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); + for i1=1:ncf + cf = gpcf.cf{i1}; + temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); + end + end + end + [I,J,R] = find(temp); + C = C + sparse(intInd1(I),intInd2(J),R,n1,n2); + end + + % check if CS covariances are used. If not change C into full matrix + % for speed up + sp = false; + for i1=1:ncf + if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 + sp=true; + end + end + if ~sp + C=full(C); + end + + % store in the memory + Ccov=C; + datahash1=datahash; + w1=ww; + end + + end + + function C = gpcf_intcov_trcov(gpcf, x) + %GP_INTCOV_TRCOV Evaluate training covariance matrix of inputs + % + % Description + % C = GP_INTCOV_TRCOV(GP, TX) takes in covariance function of a + % Gaussian process GP and matrix TX that contains training + % input vectors. Returns covariance matrix C. Every element ij + % of C contains covariance between inputs i and j in TX. This is + % a mandatory subfunction used for example in prediction and + % energy computations. + % + % See also + % GPCF_INTCOV_COV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV + + ncf = length(gpcf.cf); + ww=[]; + for i1=1:ncf + ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; + end + datahash=hash_sha512(x); + fromMem = false; + if ~isempty(w0) + if all(size(ww)==size(w0)) && all(abs(ww-w0)<1e-8) && isequal(datahash,datahash0) + fromMem = true; + end + end + + if fromMem + C = Ctrcov; + else + [n1,m1]=size(x); + + intInd1 = find(x(:,end)==1); + pointInd1 = find(x(:,end)==0); + numPoints = gpcf.NintPoints; + intArea = repmat(gpcf.intArea,numPoints,1); + dimInt = numel(gpcf.intArea); + %intMethod = gpcf.intMethod; + + % point-point covariance + temp=sparse(0); + for i1=1:ncf + cf = gpcf.cf{i1}; + temp = temp + cf.fh.trcov(cf, x(pointInd1,1:end-1)); + end + [I,J,R] = find(temp); + C = sparse(pointInd1(I),pointInd1(J),R,n1,n1); + + % point-area covariance + temp=sparse(length(pointInd1),length(intInd1)); + randpoints = intArea.*hammersley(dimInt,numPoints)'; + for j1=1:length(intInd1) + intpoints = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints; + %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + for i1=1:ncf + cf = gpcf.cf{i1}; + temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x(pointInd1,1:dimInt),intpoints),2); + end + end + [I,J,R] = find(temp); + temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); + C = C + temp + temp'; + + % % area-area covariance + % temp=sparse(length(intInd1),length(intInd1)); + % for j1=1:length(intInd1) + % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) + % intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + % for k1 = 1:length(intInd1) + % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) + % intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + % for i1=1:ncf + % cf = gpcf.cf{i1}; + % temp(j1,k1) = temp(j1,k1) + mean(mean(feval(cf.fh.cov, cf, intpoints1, intpoints2))); + % end + % end + % end + % [I,J,R] = find(temp); + % C = C + sparse(intInd1(I),intInd1(J),R,n1,n1); + + % area-area covariance + temp=sparse(length(intInd1),length(intInd1)); + temp2=zeros(n1,1); + randpoints = [intArea intArea].*hammersley((dimInt)*2,numPoints)'; + for j1=1:length(intInd1) + intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); + %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + for k1 = j1+1:length(intInd1) + intpoints2 = repmat(x(intInd1(k1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); + %intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + for i1=1:ncf + cf = gpcf.cf{i1}; + temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); + end + end + end + % The covariance matrix seems to get non positive definite. Try to fix + % it by evaluating all the diagonal elements with same random numbers. + %randpoints1 = intArea.*rand(numPoints,dimInt); + %randpoints2 = intArea.*rand(numPoints,dimInt); + %randpoints = intArea.*hammersley(dimInt,numPoints)'; + for j1=1:length(intInd1) + intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); + intpoints2 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); + %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints1; + %intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; + %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; + for i1=1:ncf + cf = gpcf.cf{i1}; + temp2(j1) = temp2(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); + %temp2(j1) = temp2(j1) + mean(mean(feval(cf.fh.trcov, cf, intpoints))); + end + end + [I,J,R] = find(temp); + temp = sparse(intInd1(I),intInd1(J),R,n1,n1); + C = C + temp + temp' + sparse(1:n1,1:n1,temp2); + + C = (C+C')/2; + + % check if CS covariances are used. If not change C into full matrix + % for speed up + sp = false; + for i1=1:ncf + if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 + sp=true; + end + end + if ~sp + C=full(C); + end + + % store in the memory + Ctrcov=C; + datahash0=datahash; + w0=ww; + end + + end + + function C = gpcf_intcov_trvar(gpcf, x) + %GP_INTCOV_TRVAR Evaluate training variance vector + % + % Description + % C = GP_INTCOV_TRVAR(GPCF, TX) takes in covariance function of + % a Gaussian process GPCF and matrix TX that contains training + % inputs. Returns variance vector C. Every element i of C + % contains variance of input i in TX. This is a mandatory + % subfunction used for example in prediction and energy + % computations. + % + % See also + % GPCF_INTCOV_COV, GP_COV, GP_TRCOV + + + [n1,m1]=size(x); + + intInd1 = find(x(:,end)==1); + pointInd1 = find(x(:,end)==0); + ncf = length(gpcf.cf); + numPoints = gpcf.NintPoints; + intArea = repmat(gpcf.intArea,numPoints,1); + + C = zeros(n1,1); + + % point-point covariance + temp = 0; + for i1=1:ncf + cf = gpcf.cf{i1}; + temp = temp + cf.fh.trvar(cf, x(pointInd1,1:end-1)); + end + C(pointInd1) = temp; + + % area-area covariance + temp=zeros(size(intInd1)); + for j1=1:length(intInd1) + intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); + for i1=1:ncf + cf = gpcf.cf{i1}; + temp(j1) = temp(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); + end + end + C(intInd1) = temp; + end + + function reccf = gpcf_intcov_recappend(reccf, ri, gpcf) + %RECAPPEND Record append + % + % Description + % RECCF = GPCF_INTCOV_RECAPPEND(RECCF, RI, GPCF) takes a + % covariance function record structure RECCF, record index RI + % and covariance function structure GPCF with the current MCMC + % samples of the parameters. Returns RECCF which contains all + % the old samples and the current samples from GPCF. This + % subfunction is needed when using MCMC sampling (gp_mc). + % + % See also + % GP_MC and GP_MC -> RECAPPEND + + if nargin == 2 + % Initialize record + reccf.type = 'gpcf_intcov'; + reccf.NintPoints = ri.NintPoints; + reccf.intArea = ri.intArea; + + % Initialize parameters + ncf = length(ri.cf); + for i=1:ncf + cf = ri.cf{i}; + reccf.cf{i} = cf.fh.recappend([], ri.cf{i}); + end + + % Set the function handles + reccf.fh.pak = @gpcf_intcov_pak; + reccf.fh.unpak = @gpcf_intcov_unpak; + reccf.fh.e = @gpcf_intcov_lp; + reccf.fh.lpg = @gpcf_intcov_lpg; + reccf.fh.cfg = @gpcf_intcov_cfg; + reccf.fh.cov = @gpcf_intcov_cov; + reccf.fh.trcov = @gpcf_intcov_trcov; + reccf.fh.trvar = @gpcf_intcov_trvar; + reccf.fh.recappend = @gpcf_intcov_recappend; + else + % Append to the record + + %loop over all of the covariance functions + ncf = length(gpcf.cf); + reccf.NintPoints(ri,:) = gpcf.NintPoints; + reccf.intArea(ri,:) = gpcf.intArea; + for i=1:ncf + cf = gpcf.cf{i}; + reccf.cf{i} = cf.fh.recappend(reccf.cf{i}, ri, cf); + end + end + end +end + diff --git a/gp/gpla_mo_e.m b/gp/gpla_mo_e.m new file mode 100644 index 00000000..bf211579 --- /dev/null +++ b/gp/gpla_mo_e.m @@ -0,0 +1,369 @@ +function [e, edata, eprior, f, L, a, E, M, p] = gpla_mo_e(w, gp, varargin) +%GPLA_MO_E Do Laplace approximation and return marginal log posterior +% estimate for multioutput likelihood +% +% Description +% E = GPLA_MO_E(W, GP, X, Y, OPTIONS) takes a GP data +% structure GP together with a matrix X of input vectors and a +% matrix Y of target vectors, and finds the Laplace +% approximation for the conditional posterior p(Y | X, th), +% where th is the parameters. Returns the energy at th (see +% below). Each row of X corresponds to one input vector and each +% row of Y corresponds to one target vector. +% +% [E, EDATA, EPRIOR] = GPLA_MO_E(W, GP, X, Y, OPTIONS) +% returns also the data and prior components of the total +% energy. +% +% The energy is minus log posterior cost function for th: +% E = EDATA + EPRIOR +% = - log p(Y|X, th) - log p(th), +% where th represents the parameters (lengthScale, +% magnSigma2...), X is inputs and Y is observations. +% +% OPTIONS is optional parameter-value pair +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in case of +% Poisson likelihood we have z_i=E_i, that is, expected +% value for ith case. +% +% See also +% GP_SET, GP_E, GPLA_SOFTMAX_G, GPLA_SOFTMAX_PRED + +% Description 2 +% Additional properties meant only for internal use. +% +% GP = GPLA_MO_E('init', GP) takes a GP structure GP and +% initializes required fields for the Laplace approximation. +% +% GP = GPLA_MO_E('clearcache', GP) takes a GP structure GP and +% cleares the internal cache stored in the nested function workspace +% +% [e, edata, eprior, f, L, a, La2, p] +% = gpla_mo_e(w, gp, x, y, varargin) +% returns many useful quantities produced by EP algorithm. +% +% The Newton's method is implemented as described in +% Rasmussen and Williams (2006). + +% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki, +% Jarno Vanhatalo, Aki Vehtari + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'GPLA_MO_E'; + ip=iparser(ip,'addRequired','w', @(x) ... + (ischar(x) && strcmp(w, 'init')) || ... + isvector(x) && isreal(x) && all(isfinite(x))); + ip=iparser(ip,'addRequired','gp',@isstruct); + ip=iparser(ip,'addOptional','x', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','y', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'parse',w, gp, varargin{:}); + x=ip.Results.x; + y=ip.Results.y; + z=ip.Results.z; + + if strcmp(w, 'init') + % initialize cache + ch = []; + + % return function handle to the nested function ep_algorithm + % this way each gp has its own peristent memory for EP + gp.fh.e = @laplace_algorithm; + e = gp; + % remove clutter from the nested workspace + clear w gp varargin ip x y z + elseif strcmp(w, 'clearcache') + % clear the cache + gp.fh.e('clearcache'); + else + % call laplace_algorithm using the function handle to the nested function + % this way each gp has its own peristent memory for Laplace + [e, edata, eprior, f, L, a, E, M, p] = gp.fh.e(w, gp, x, y, z); + end + + function [e, edata, eprior, f, L, a, E, M, p] = laplace_algorithm(w, gp, x, y, z) + + if strcmp(w, 'clearcache') + ch=[]; + return + end + % code for the Laplace algorithm + + % check whether saved values can be used + if isempty(z) + datahash=hash_sha512([x y]); + else + datahash=hash_sha512([x y z]); + end + if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash) + % The covariance function parameters or data haven't changed + % so we can return the energy and the site parameters that are saved + e = ch.e; + edata = ch.edata; + eprior = ch.eprior; + f = ch.f; + L = ch.L; + E = ch.E; + M = ch.M; + a = ch.a; + p = ch.p; + else + % The parameters or data have changed since + % the last call for gpla_e. In this case we need to + % re-evaluate the Laplace approximation + if size(x,1) ~= size(y,1) + error('GPLA_MO_E: x and y must contain equal amount of rows!') + end + [n,nout] = size(y); + p = []; + + if isfield(gp, 'comp_cf') % own covariance for each ouput component + multicf = true; + if length(gp.comp_cf) ~= nout + error('GPLA_MO_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') + end + else + multicf = false; + end + + gp=gp_unpak(gp, w); + ncf = length(gp.cf); + + % Initialize latent values + % zero seems to be a robust choice (Jarno) + f = zeros(size(y(:))); + + % ================================================= + % First Evaluate the data contribution to the error + switch gp.type + % ============================================================ + % FULL + % ============================================================ + case 'FULL' + K = zeros(n,n,nout); + if multicf + for i1=1:nout + K(:,:,i1) = gp_trcov(gp, x, gp.comp_cf{i1}); + end + else + for i1=1:nout + K(:,:,i1) = gp_trcov(gp, x); + end + end + + switch gp.latent_opt.optim_method + case 'newton' + tol = 1e-12; + a = f; + + f2=reshape(f,n,nout); + + lp_new = gp.lik.fh.ll(gp.lik, y, f2, z); + lp_old = -Inf; + + c=zeros(n*nout,1); + ERMMRc=zeros(n*nout,1); + pipif=zeros(n*nout,1); + E=zeros(n,n,nout); + L=zeros(n,n,nout); + RER = zeros(n,n,nout); + + while lp_new - lp_old > tol + lp_old = lp_new; a_old = a; + + llg = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); + [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); + pi2 = reshape(pi2_vec,size(y)); + + R = repmat(1./pi2_vec,1,n).*pi2_mat; + for i1=1:nout + Dc=sqrt(pi2(:,i1)); + Lc=(Dc*Dc').*K(:,:,i1); + Lc(1:n+1:end)=Lc(1:n+1:end)+1; + [Lc,notpositivedefinite]=chol(Lc); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + L(:,:,i1)=Lc; + + Ec=Lc'\diag(Dc); + Ec=Ec'*Ec; + E(:,:,i1)=Ec; + RER(:,:,i1) = R((1:n)+(i1-1)*n,:)'*Ec*R((1:n)+(i1-1)*n,:); + end + [M, notpositivedefinite]=chol(sum(RER,3)); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + + b = pi2_vec.*f - pi2_mat*(pi2_mat'*f) + llg; + for i1=1:nout + c((1:n)+(i1-1)*n)=E(:,:,i1)*(K(:,:,i1)*b((1:n)+(i1-1)*n)); + end + + RMMRc=R*(M\(M'\(R'*c))); + for i1=1:nout + ERMMRc((1:n)+(i1-1)*n) = E(:,:,i1)*RMMRc((1:n)+(i1-1)*n,:); + end + a=b-c+ERMMRc; + + for i1=1:nout + f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); + end + f2=reshape(f,n,nout); + + lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); + + i = 0; + while i < 10 && lp_new < lp_old || isnan(sum(f)) + % reduce step size by half + a = (a_old+a)/2; + + for i1=1:nout + f((1:n)+(i1-1)*n)=K(:,:,i1)*a((1:n)+(i1-1)*n); + end + f2=reshape(f,n,nout); + + lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); + i = i+1; + end + end + otherwise + error('gpla_e: Unknown optimization method ! ') + end + + [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); + pi2 = reshape(pi2_vec,size(y)); + + zc=0; + Detn=0; + R = repmat(1./pi2_vec,1,n).*pi2_mat; + for i1=1:nout + Dc=sqrt( pi2(:,i1) ); + Lc=(Dc*Dc').*K(:,:,i1); + Lc(1:n+1:end)=Lc(1:n+1:end)+1; + [Lc, notpositivedefinite]=chol(Lc); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + L(:,:,i1)=Lc; + + pi2i = pi2_mat((1:n)+(i1-1)*n,:); + pipi = pi2i'/diag(Dc); + Detn = Detn + pipi*(Lc\(Lc'\diag(Dc)))*K(:,:,i1)*pi2i; + zc = zc + sum(log(diag(Lc))); + + Ec=Lc'\diag(Dc); + Ec=Ec'*Ec; + E(:,:,i1)=Ec; + RER(:,:,i1) = R((1:n)+(i1-1)*n,:)'*Ec*R((1:n)+(i1-1)*n,:); + end + [M, notpositivedefinite]=chol(sum(RER,3)); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + + zc = zc + sum(log(diag(chol( eye(size(K(:,:,i1))) - Detn)))); + + logZ = a'*f/2 - gp.lik.fh.ll(gp.lik, y, f2, z) + zc; + edata = logZ; + + % ============================================================ + % FIC + % ============================================================ + case 'FIC' + + % ============================================================ + % PIC + % ============================================================ + case {'PIC' 'PIC_BLOCK'} + + % ============================================================ + % CS+FIC + % ============================================================ + case 'CS+FIC' + + otherwise + error('Unknown type of Gaussian process!') + end + + % ====================================================================== + % Evaluate the prior contribution to the error from covariance functions + % ====================================================================== + eprior = 0; + if ~isempty(strfind(gp.infer_params, 'covariance')) + for i=1:ncf + gpcf = gp.cf{i}; + eprior = eprior - gpcf.fh.lp(gpcf); + end + end + + % ============================================================ + % Evaluate the prior contribution to the error from Gaussian likelihood + % ============================================================ + % Evaluate the prior contribution to the error from likelihood function + if isfield(gp, 'lik') && isfield(gp.lik, 'p') + lik = gp.lik; + eprior = eprior - lik.fh.lp(lik); + end + + e = edata + eprior; + + % store values to the cache + ch.w = w; + ch.e = e; + ch.edata = edata; + ch.eprior = eprior; + ch.f = f; + ch.L = L; + ch.M = M; + ch.E = E; +% ch.W = W; + ch.n = size(x,1); + %La20 = La2; + ch.a = a; + ch.p=p; + ch.datahash=datahash; + end + + assert(isreal(edata)) + assert(isreal(eprior)) + end + +function [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite() + % Instead of stopping to chol error, return NaN + edata=NaN; + e=NaN; + eprior=NaN; + f=NaN; + L=NaN; + a=NaN; +% La2=NaN; + E = NaN; + M = NaN; + p=NaN; + ch.w = w; + ch.e = e; + ch.edata = edata; + ch.eprior = eprior; + ch.f = f; + ch.L = L; + ch.M = M; + ch.E = E; + ch.n = size(x,1); + ch.La2 = La2; + ch.a = a; + ch.p=p; + ch.datahash=datahash; +end +end + diff --git a/gp/gpla_mo_g.m b/gp/gpla_mo_g.m new file mode 100644 index 00000000..30080447 --- /dev/null +++ b/gp/gpla_mo_g.m @@ -0,0 +1,245 @@ +function [g, gdata, gprior] = gpla_mo_g(w, gp, x, y, varargin) +%GPLA_SOFTMAX_G Evaluate gradient of Laplace approximation's marginal +% log posterior estimate for softmax likelihood (GPLA_SOFTMAX_E) +% +% Description +% G = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) takes a full GP +% hyper-parameter vector W, structure GP a matrix X of +% input vectors and a matrix Y of target vectors, and evaluates +% the gradient G of EP's marginal log posterior estimate . Each +% row of X corresponds to one input vector and each row of Y +% corresponds to one target vector. +% +% [G, GDATA, GPRIOR] = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) also +% returns the data and prior contributions to the gradient. +% +% OPTIONS is optional parameter-value pair +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in case of +% Poisson likelihood we have z_i=E_i, that is, expected +% value for ith case. +% +% See also +% GPLA_SOFTMAX_E, GPLA_E, GPLA_SOFTMAX_PRED + +% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +ip=inputParser; +ip.FunctionName = 'GPLA_MO_G'; +ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); +ip.addRequired('gp',@isstruct); +ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) +ip.parse(w, gp, x, y, varargin{:}); +z=ip.Results.z; + +gp = gp_unpak(gp, w); % unpak the parameters +ncf = length(gp.cf); +n=size(x,1); +nout=size(y,2); + +g = []; +gdata = []; +gprior = []; + +% First Evaluate the data contribution to the error +switch gp.type + % ============================================================ + % FULL + % ============================================================ + case 'FULL' % A full GP + + if isfield(gp, 'comp_cf') % own covariance for each ouput component + multicf = true; + if length(gp.comp_cf) ~= nout + error('GPLA_MO_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') + end + else + multicf = false; + end + + K = zeros(n,n,nout); + if multicf + for i1=1:nout + K(:,:,i1) = gp_trcov(gp, x, gp.comp_cf{i1}); + end + else + for i1=1:nout + K(:,:,i1) = gp_trcov(gp, x); + end + end + + [e, edata, eprior, f, L, a, E, M, p] = gpla_mo_e(gp_pak(gp), gp, x, y, 'z', z); + + % softmax + f2=reshape(f,n,nout); + + llg = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); + [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); + R = repmat(1./pi2_vec,1,n).*pi2_mat; + RE = zeros(n,n*nout); + for i1=1:nout + RE(:,(1:n)+(i1-1)*n) = R((1:n)+(i1-1)*n,:)'*E(:,:,i1); + end + + inv_iWK=zeros(n,n,nout); + + % Matrices for computing the derivative of determinant term w.r.t. f + A=zeros(nout, nout, n); + Minv=M\(M'\eye(n)); + Minv=(Minv+Minv')./2; + for cc1=1:nout + EMinv=RE(:,(1:n)+(cc1-1)*n)'*Minv; + KEMinv=K(:,:,cc1)*EMinv; + for cc2=1:nout + if cc2>=cc1 + if cc1==cc2 + EMtmp = - EMinv*RE(:,(1:n)+(cc2-1)*n); + EMtmp = EMtmp + E(:,:,cc1); + inv_iWK(:,:,cc1) = EMtmp; + A(cc1,cc1,:) = diag(K(:,:,cc1))-sum((K(:,:,cc1)*EMtmp).*K(:,:,cc1),2); + else + EMtmp = - KEMinv*RE(:,(1:n)+(cc2-1)*n); + A(cc1,cc2,:) = -sum(EMtmp.*K(:,:,cc2),2); + A(cc2,cc1,:) = -sum(EMtmp.*K(:,:,cc2),2); + end + end + end + end + + % Derivative of determinant term w.r.t. f + s2=zeros(n*nout,1); + dw_mat = gp.lik.fh.llg3(gp.lik, y, f, 'latent', z); + for cc3=1:nout + for ii1=1:n + s2(ii1+(cc3-1)*n) = -0.5*trace(A(:,:,ii1)*dw_mat(:,:,cc3,ii1)); + end + end + + % Loop over the covariance functions + for i=1:ncf + DKllg=zeros(size(a)); + EDKllg=zeros(size(a)); + DKffba=zeros(n*nout,1); + + % check in which components the covariance function is present + do = false(nout,1); + if multicf + for z1=1:nout + if any(gp.comp_cf{z1}==i) + do(z1) = true; + end + end + else + do = true(nout,1); + end + + i1=0; + if ~isempty(gprior) + i1 = length(gprior); + end + + % Gradients from covariance functions + gpcf = gp.cf{i}; + DKff = gpcf.fh.cfg(gpcf, x); + gprior_cf = -gpcf.fh.lpg(gpcf); + + for i2 = 1:length(DKff) + i1 = i1+1; + DKffb=DKff{i2}; + + % Derivative of explicit terms + trace_sum_tmp=0; + for z1=1:nout + if do(z1) + DKffba((1:n)+(z1-1)*n)=DKffb*a((1:n)+(z1-1)*n); + trace_sum_tmp = trace_sum_tmp + sum(sum( inv_iWK(:,:,z1) .* DKffb )); + end + end + s1 = 0.5 * a'*DKffba - 0.5.*trace_sum_tmp; + + % Derivative of f w.r.t. theta + for z1=1:nout + if do(z1) + DKllg((1:n)+(z1-1)*n)=DKffb*llg((1:n)+(z1-1)*n); + EDKllg((1:n)+(z1-1)*n)=E(:,:,z1)*DKllg((1:n)+(z1-1)*n); + end + end + s3 = EDKllg - RE'*(M\(M'\(RE*DKllg))); + for z1=1:nout + s3((1:n)+(z1-1)*n)=K(:,:,z1)*s3((1:n)+(z1-1)*n); + end + s3 = DKllg - s3; + + gdata(i1) = -(s1 + s2'*s3); + gprior(i1) = gprior_cf(i2); + + end + + % Set the gradients of hyper-hyperparameter + if length(gprior_cf) > length(DKff) + for i2=length(DKff)+1:length(gprior_cf) + i1 = i1+1; + gdata(i1) = 0; + gprior(i1) = gprior_cf(i2); + end + end + end + +% % ================================================================= +% % Gradient with respect to likelihood function parameters +% if ~isempty(strfind(gp.infer_params, 'likelihood')) ... +% && ~isempty(gp.lik.fh.pak(gp.lik)) +% +% gdata_likelih = 0; +% lik = gp.lik; +% +% g_logPrior = feval(lik.fh.gprior, lik); +% if ~isempty(g_logPrior) +% +% DW_sigma = feval(lik.fh.llg3, lik, y, f, 'latent2+hyper', z); +% DL_sigma = feval(lik.fh.llg, lik, y, f, 'hyper', z); +% b = K * feval(lik.fh.llg2, lik, y, f, 'latent+hyper', z); +% s3 = b - K*(R*b); +% nl= size(DW_sigma,2); +% +% gdata_lik = - DL_sigma - 0.5.*sum(repmat(C2,1,nl).*DW_sigma) - s2'*s3; +% +% % set the gradients into vectors that will be returned +% gdata = [gdata gdata_lik]; +% gprior = [gprior g_logPrior]; +% i1 = length(g_logPrior); +% i2 = length(gdata_lik); +% if i1 > i2 +% gdata = [gdata zeros(1,i1-i2)]; +% end +% end +% end + + g = gdata + gprior; + + % ============================================================ + % FIC + % ============================================================ + case 'FIC' + + % ============================================================ + % PIC + % ============================================================ + case {'PIC' 'PIC_BLOCK'} + + % ============================================================ + % CS+FIC + % ============================================================ + case 'CS+FIC' + + end + + assert(isreal(gdata)) + assert(isreal(gprior)) +end diff --git a/gp/gpla_mo_pred.m b/gp/gpla_mo_pred.m new file mode 100644 index 00000000..271fec06 --- /dev/null +++ b/gp/gpla_mo_pred.m @@ -0,0 +1,205 @@ +function [Ef, Varf, lpyt, Ey, Vary] = gpla_mo_pred(gp, x, y, varargin) +%function [Ef, Varf, Ey, Vary, Pyt] = gpla_multinom_pred(gp, x, y, xt, varargin) +%GPLA_MO_PRED Predictions with Gaussian Process Laplace +% approximation with multinom likelihood +% +% Description +% [EFT, VARFT] = GPLA_MO_PRED(GP, X, Y, XT, OPTIONS) takes +% a GP structure GP together with a matrix XT of input vectors, +% matrix X of training inputs and vector Y of training targets, +% and evaluates the predictive distribution at inputs XT. Returns +% a posterior mean EFT and variance VARFT of latent variables. +% +% [EF, VARF, LPYT] = GPLA_MO_PRED(GP, X, Y, XT, 'yt', YT, ...) +% returns also logarithm of the predictive density PYT of the observations YT +% at input locations XT. This can be used for example in the +% cross-validation. Here Y has to be vector. +% +% [EF, VARF, LPYT, EYT, VARYT] = GPLA_MO_PRED(GP, X, Y, XT, 'yt', YT, ...) +% returns also the posterior predictive mean EYT and variance VARYT. +% +% OPTIONS is optional parameter-value pair +% predcf - is index vector telling which covariance functions are +% used for prediction. Default is all (1:gpcfn). See +% additional information below. +% tstind - is a vector/cell array defining, which rows of X belong +% to which training block in *IC type sparse models. Deafult +% is []. In case of PIC, a cell array containing index +% vectors specifying the blocking structure for test data. +% IN FIC and CS+FIC a vector of length n that points out the +% test inputs that are also in the training set (if none, +% set TSTIND = []). +% yt - is optional observed yt in test points +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in +% case of Poisson likelihood we have z_i=E_i, that +% is, expected value for ith case. +% zt - optional observed quantity in triplet (xt_i,yt_i,zt_i) +% Some likelihoods may use this. For example, in +% case of Poisson likelihood we have zt_i=Et_i, that +% is, expected value for ith case. +% +% See also +% GPLA_MO_E, GPLA_MO_G, GP_PRED, DEMO_MULTICLASS +% +% Copyright (c) 2010 Jaakko Riihim�ki + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'GPLA_MO_PRED'; + ip.addRequired('gp', @isstruct); + ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) + ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip.addParamValue('predcf', [], @(x) isempty(x) || iscell(x) && isvector(x)) + ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + if numel(varargin)==0 || isnumeric(varargin{1}) + % inputParser should handle this, but it doesn't + ip.parse(gp, x, y, varargin{:}); + else + ip.parse(gp, x, y, [], varargin{:}); + end + xt=ip.Results.xt; + yt=ip.Results.yt; + z=ip.Results.z; + zt=ip.Results.zt; + predcf=ip.Results.predcf; + tstind=ip.Results.tstind; + if isempty(xt) + xt=x; + if isempty(tstind) + if iscell(gp) + gptype=gp{1}.type; + else + gptype=gp.type; + end + switch gptype + case {'FULL' 'VAR' 'DTC' 'SOR'} + tstind = []; + case {'FIC' 'CS+FIC'} + tstind = 1:size(x,1); + case 'PIC' + if iscell(gp) + tstind = gp{1}.tr_index; + else + tstind = gp.tr_index; + end + end + end + if isempty(yt) + yt=y; + end + if isempty(zt) + zt=z; + end + end + + Ey=[]; + Vary=[]; + + [tn, nout] = size(y); + + switch gp.type + % ============================================================ + % FULL + % ============================================================ + case 'FULL' + [e, edata, eprior, f, L, a, E, M, p] = gpla_mo_e(gp_pak(gp), gp, x, y, 'z', z); + + if isfield(gp, 'comp_cf') % own covariance for each ouput component + multicf = true; + if length(gp.comp_cf) ~= nout + error('GPLA_MO_E: the number of component vectors in gp.comp_cf must be the same as number of outputs.') + end + if ~isempty(predcf) + if ~iscell(predcf) || length(predcf)~=nout + error(['GPLA_MO_PRED: if own covariance for each output component is used,'... + 'predcf has to be cell array and contain nout (vector) elements. ']) + end + else + predcf = gp.comp_cf; + end + else + multicf = false; + for i1=1:nout + predcf2{i1} = predcf; + end + predcf=predcf2; + end + + ntest=size(xt,1); + K_nf = zeros(ntest,tn,nout); + if multicf + for i1=1:nout + K_nf(:,:,i1) = gp_cov(gp,xt,x,predcf{i1}); + end + else + for i1=1:nout + K_nf(:,:,i1) = gp_cov(gp,xt,x,predcf{i1}); + end + end + + nout=size(y,2); + f2=reshape(f,tn,nout); + + llg_vec = gp.lik.fh.llg(gp.lik, y, f2, 'latent', z); + llg = reshape(llg_vec,size(y)); + + %mu_star = K_nf*reshape(a,tn,nout); + a=reshape(a,size(y)); + for i1 = 1:nout + % Ef(:,i1) = K_nf(:,:,i1)*llg(:,i1); + Ef(:,i1) = K_nf(:,:,i1)*a(:,i1); + end + + if nargout > 1 + [pi2_vec, pi2_mat] = gp.lik.fh.llg2(gp.lik, y, f2, 'latent', z); + Varf=zeros(nout, nout, ntest); + + R=(repmat(1./pi2_vec,1,tn).*pi2_mat); + for i1=1:nout + b=E(:,:,i1)*K_nf(:,:,i1)'; + c_cav = R((1:tn)+(i1-1)*tn,:)*(M\(M'\(R((1:tn)+(i1-1)*tn,:)'*b))); + + for j1=1:nout + c=E(:,:,j1)*c_cav; + Varf(i1,j1,:)=sum(c.*K_nf(:,:,j1)'); + end + + kstarstar = gp_trvar(gp,xt,predcf{i1}); + Varf(i1,i1,:) = squeeze(Varf(i1,i1,:)) + kstarstar - sum(b.*K_nf(:,:,i1)')'; + end + end + % ============================================================ + % FIC + % ============================================================ + case 'FIC' % Predictions with FIC sparse approximation for GP + % ============================================================ + % PIC + % ============================================================ + case {'PIC' 'PIC_BLOCK'} % Predictions with PIC sparse approximation for GP + % ============================================================ + % CS+FIC + % ============================================================ + case 'CS+FIC' % Predictions with CS+FIC sparse approximation for GP + end + + % ============================================================ + % Evaluate also the predictive mean and variance of new observation(s) + % ============================================================ + if nargout > 2 && isempty(yt) + error('yt has to be provided to get lpyt.') + end + if nargout > 3 + [lpyt, Ey, Vary] = gp.lik.fh.predy(gp.lik, Ef, Varf, [], zt); + elseif nargout > 2 + lpyt = gp.lik.fh.predy(gp.lik, Ef, Varf, yt, zt); + end +end \ No newline at end of file diff --git a/gp/gpla_softmax_e.m b/gp/gpla_softmax_e.m new file mode 100644 index 00000000..5b70cf6e --- /dev/null +++ b/gp/gpla_softmax_e.m @@ -0,0 +1,515 @@ +function [e, edata, eprior, f, L, a, E, M, p] = gpla_softmax_e(w, gp, varargin) +%GPLA_SOFTMAX_E Do Laplace approximation and return marginal log posterior +% estimate for softmax likelihood +% +% Description +% E = GPLA_SOFTMAX_E(W, GP, X, Y, OPTIONS) takes a GP data +% structure GP together with a matrix X of input vectors and a +% matrix Y of target vectors, and finds the Laplace +% approximation for the conditional posterior p(Y | X, th), +% where th is the parameters. Returns the energy at th (see +% below). Each row of X corresponds to one input vector and each +% row of Y corresponds to one target vector. +% +% [E, EDATA, EPRIOR] = GPLA_SOFTMAX_E(W, GP, X, Y, OPTIONS) +% returns also the data and prior components of the total +% energy. +% +% The energy is minus log posterior cost function for th: +% E = EDATA + EPRIOR +% = - log p(Y|X, th) - log p(th), +% where th represents the parameters (lengthScale, +% magnSigma2...), X is inputs and Y is observations. +% +% OPTIONS is optional parameter-value pair +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in case of +% Poisson likelihood we have z_i=E_i, that is, expected +% value for ith case. +% +% See also +% GP_SET, GP_E, GPLA_SOFTMAX_G, GPLA_SOFTMAX_PRED + +% Description 2 +% Additional properties meant only for internal use. +% +% GP = GPLA_SOFTMAX_E('init', GP) takes a GP structure GP and +% initializes required fields for the Laplace approximation. +% +% GP = GPLA_SOFTMAX_E('clearcache', GP) takes a GP structure GP and +% cleares the internal cache stored in the nested function workspace +% +% [e, edata, eprior, f, L, a, La2, p] +% = gpla_softmax_e(w, gp, x, y, varargin) +% returns many useful quantities produced by EP algorithm. +% +% The Newton's method is implemented as described in +% Rasmussen and Williams (2006). + +% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki, +% Jarno Vanhatalo, Aki Vehtari + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'GPLA_SOFTMAX_E'; + ip.addRequired('w', @(x) ... + (ischar(x) && strcmp(w, 'init')) || ... + isvector(x) && isreal(x) && all(isfinite(x))); + ip.addRequired('gp',@isstruct); + ip.addOptional('x', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))) + ip.addOptional('y', @(x) ~isempty(x) && isnumeric(x) && isreal(x) && all(isfinite(x(:)))) + ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) + ip.parse(w, gp, varargin{:}); + x=ip.Results.x; + y=ip.Results.y; + z=ip.Results.z; + + if strcmp(w, 'init') + % initialize cache + ch = []; + + % return function handle to the nested function ep_algorithm + % this way each gp has its own peristent memory for EP + gp.fh.e = @laplace_algorithm; + e = gp; + % remove clutter from the nested workspace + clear w gp varargin ip x y z + elseif strcmp(w, 'clearcache') + % clear the cache + gp.fh.e('clearcache'); + else + % call laplace_algorithm using the function handle to the nested function + % this way each gp has its own peristent memory for Laplace + [e, edata, eprior, f, L, a, E, M, p] = gp.fh.e(w, gp, x, y, z); + end + + function [e, edata, eprior, f, L, a, E, M, p] = laplace_algorithm(w, gp, x, y, z) + + if strcmp(w, 'clearcache') + ch=[]; + return + end + % code for the Laplace algorithm + + % check whether saved values can be used + if isempty(z) + datahash=hash_sha512([x y]); + else + datahash=hash_sha512([x y z]); + end + if ~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && isequal(datahash,ch.datahash) + % The covariance function parameters or data haven't changed + % so we can return the energy and the site parameters that are saved + e = ch.e; + edata = ch.edata; + eprior = ch.eprior; + f = ch.f; + L = ch.L; + a = ch.a; + E = ch.E; + M = ch.M; + p = ch.p; + else + % The parameters or data have changed since + % the last call for gpla_e. In this case we need to + % re-evaluate the Laplace approximation + gp=gp_unpak(gp, w); + ncf = length(gp.cf); + n = size(x,1); + p = []; + + % Initialize latent values + % zero seems to be a robust choice (Jarno) + f = zeros(size(y(:))); + + % ================================================= + % First Evaluate the data contribution to the error + switch gp.type + % ============================================================ + % FULL + % ============================================================ + case 'FULL' + K = gp_trcov(gp, x); + + % If K is sparse, permute all the inputs so that evaluations are more efficient + if issparse(K) % Check if compact support covariance is used + p = analyze(K); + y = y(p); + K = K(p,p); + if ~isempty(z) + z = z(p,:); + end + [LD, notpositivedefinite] = ldlchol(K); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + else + % LD = chol(K); + end + + switch gp.latent_opt.optim_method + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by fminunc large scale method + % case 'fminunc_large' + % if ~isfield(gp.latent_opt, 'fminunc_opt') + % opt=optimset('GradObj','on'); + % opt=optimset(opt,'Hessian','on'); + % if issparse(K) + % fhm = @(W, f, varargin) (ldlsolve(LD,f) + repmat(W,1,size(f,2)).*f); % W*f; % + % else + % fhm = @(W, f, varargin) (LD\(LD'\f) + repmat(W,1,size(f,2)).*f); % W*f; % + % end + % opt=optimset(opt,'HessMult', fhm); + % opt=optimset(opt,'TolX', 1e-12); + % opt=optimset(opt,'TolFun', 1e-12); + % opt=optimset(opt,'LargeScale', 'on'); + % opt=optimset(opt,'Display', 'off'); % 'iter' + % else + % opt = gp.latent_opt.fminunc_opt; + % end + % + % if issparse(K) + % fe = @(f, varargin) (0.5*f*(ldlsolve(LD,f')) - feval(gp.lik.fh.ll, gp.lik, y, f', z)); + % fg = @(f, varargin) (ldlsolve(LD,f') - feval(gp.lik.fh.llg, gp.lik, y, f', 'latent', z))'; + % fh = @(f, varargin) (-feval(gp.lik.fh.llg2, gp.lik, y, f', 'latent', z)); %inv(K) + diag(g2(f', gp.lik)) ; % + % else + % fe = @(f, varargin) (0.5*f*(LD\(LD'\f')) - feval(gp.lik.fh.ll, gp.lik, y, f', z)); + % fg = @(f, varargin) (LD\(LD'\f') - feval(gp.lik.fh.llg, gp.lik, y, f', 'latent', z))'; + % fh = @(f, varargin) (-feval(gp.lik.fh.llg2, gp.lik, y, f', 'latent', z)); %inv(K) + diag(g2(f', gp.lik)) ; % + % end + % + % mydeal = @(varargin)varargin{1:nargout}; + % [f,fval,exitflag,output] = fminunc(@(ww) mydeal(fe(ww), fg(ww), fh(ww)), f', opt); + % f = f'; + % + % if issparse(K) + % a = ldlsolve(LD,f); + % else + % a = LD\(LD'\f); + % end + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by Newton method + case 'newton' + tol = 1e-12; + a = f; + + nout=size(y,2); + f2=reshape(f,n,nout); + + %W = -feval(gp.lik.fh.llg2, gp.lik, y, f2, 'latent', z); + %dlp = feval(gp.lik.fh.llg, gp.lik, y, f2, 'latent', z); + %lp_new = feval(gp.lik.fh.ll, gp.lik, y, f2, z); + + lp_new = gp.lik.fh.ll(gp.lik, y, f2, z); + lp_old = -Inf; + + Kbb=zeros(n*nout,1); + c=zeros(n*nout,1); + ERMMRc=zeros(n*nout,1); + pipif=zeros(n*nout,1); + E=zeros(n,n,nout); + L=zeros(n,n,nout); + + while lp_new - lp_old > tol + lp_old = lp_new; a_old = a; + + % softmax: + expf2 = exp(f2); + pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); + pi_vec=pi2(:); + + E_tmp=zeros(n); + + for i1=1:nout + Dc=sqrt(pi2(:,i1)); + Lc=(Dc*Dc').*K; + Lc(1:n+1:end)=Lc(1:n+1:end)+1; + [Lc, notpositivedefinite]=chol(Lc); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + L(:,:,i1)=Lc; + + Ec=Lc'\diag(Dc); + Ec=Ec'*Ec; + E_tmp=E_tmp+Ec; + E(:,:,i1)=Ec; + end + + [M, notpositivedefinite]=chol(E_tmp); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + + pif=sum(pi2.*f2,2); + + for i1=1:nout + pipif((1:n)+(i1-1)*n)=pi2(:,i1).*pif; + end + + b = pi_vec.*f-pipif+y(:)-pi_vec; + % b = -feval(gp.lik.fh.llg2, gp.lik, y, f2, 'latent', z)*f + ... + % feval(gp.lik.fh.llg, gp.lik, y, f2, 'latent', z); + + for i1=1:nout + Kbb((1:n)+(i1-1)*n)=K*b((1:n)+(i1-1)*n); + c((1:n)+(i1-1)*n)=E(:,:,i1)*Kbb((1:n)+(i1-1)*n); + end + + Rc=sum(reshape(c, n, nout),2); + MMRc=M\(M'\Rc); + for i1=1:nout + ERMMRc((1:n)+(i1-1)*n) = E(:,:,i1)*MMRc; + end + a=b-c+ERMMRc; + + for i1=1:nout + f((1:n)+(i1-1)*n)=K*a((1:n)+(i1-1)*n); + end + f2=reshape(f,n,nout); + + lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); + + i = 0; + while i < 10 && lp_new < lp_old || isnan(sum(f)) + % reduce step size by half + a = (a_old+a)/2; + + for i1=1:nout + f((1:n)+(i1-1)*n)=K*a((1:n)+(i1-1)*n); + end + f2=reshape(f,n,nout); + + lp_new = -a'*f/2 + gp.lik.fh.ll(gp.lik, y, f2, z); + i = i+1; + end + end + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables by stabilized Newton method. + % This is implemented as suggested by Hannes Nickisch (personal communication) + case 'stabilized-newton' + % % Gaussian initialization + % % sigma=gp.lik.sigma; + % % W = ones(n,1)./sigma.^2; + % % sW = sqrt(W); + % % %B = eye(n) + siV*siV'.*K; + % % L=bsxfun(@times,bsxfun(@times,sW,K),sW'); + % % L(1:n+1:end)=L(1:n+1:end)+1; + % % L = chol(L,'lower'); + % % a=sW.*(L'\(L\(sW.*y))); + % % f = K*a; + % + % % initialize to observations + % %f=y; + % + % nu=gp.lik.nu; + % sigma2=gp.lik.sigma2; + % Wmax=(nu+1)/nu/sigma2; + % Wlim=0; + % + % tol = 1e-10; + % W = -feval(gp.lik.fh.llg2, gp.lik, y, f, 'latent'); + % dlp = feval(gp.lik.fh.llg, gp.lik, y, f, 'latent'); + % lp = -(f'*(K\f))/2 +feval(gp.lik.fh.ll, gp.lik, y, f); + % lp_old = -Inf; + % f_old = f+1; + % ge = Inf; %max(abs(a-dlp)); + % + % i1=0; + % % begin Newton's iterations + % while lp - lp_old > tol || max(abs(f-f_old)) > tol + % i1=i1+1; + % + % W = -feval(gp.lik.fh.llg2, gp.lik, y, f, 'latent'); + % dlp = feval(gp.lik.fh.llg, gp.lik, y, f, 'latent'); + % + % W(Wge) ) && WlimWmax*0.5 || i1>5000 + % %fprintf('\n%3d, p(f)=%.12f, max|a-g|=%.12f, %.3f \n',i1,lp,ge,Wlim) + % break + % end + % + % end + % -------------------------------------------------------------------------------- + % find the posterior mode of latent variables with likelihood specific algorithm + % For example, with Student-t likelihood this mean EM-algorithm which is coded in the + % likelih_t file. + case 'lik_specific' + [f, a] = gp.lik.fh.optimizef(gp, y, K); + otherwise + error('gpla_e: Unknown optimization method ! ') + end + + % evaluate the approximate log marginal likelihood + + expf2 = exp(f2); + pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); + pi_vec=pi2(:); + E_tmp=zeros(n); + + for i1=1:nout + Dc=sqrt(pi2(:,i1)); + Lc=(Dc*Dc').*K; + Lc(1:n+1:end)=Lc(1:n+1:end)+1; + [Lc, notpositivedefinite]=chol(Lc); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + L(:,:,i1)=Lc; + + Ec=Lc'\diag(Dc); + Ec=Ec'*Ec; + E_tmp=E_tmp+Ec; + E(:,:,i1)=Ec; + end + [M,notpositivedefinite]=chol(E_tmp); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + + det_diag=0; + det_mat_sum = zeros(n); + for i1 = 1:nout + det_diag=det_diag+sum(log(diag(L(:,:,i1)))); + Kpi=K*diag(pi_vec((1:n)+(i1-1)*n)); + + det_mat_sum=det_mat_sum+E(:,:,i1)*Kpi; + end + + %det_mat_sum=(det_mat_sum+det_mat_sum')./2; + [det_mat, notpositivedefinite]=chol(eye(n)-det_mat_sum); + if notpositivedefinite + [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite(); + return + end + det_term=sum(log(diag(det_mat)))+det_diag; + + + logZ = a'*f/2 - gp.lik.fh.ll(gp.lik, y, f2, z) + det_term; + edata = logZ; + + % ============================================================ + % FIC + % ============================================================ + case 'FIC' + + % ============================================================ + % PIC + % ============================================================ + case {'PIC' 'PIC_BLOCK'} + + % ============================================================ + % CS+FIC + % ============================================================ + case 'CS+FIC' + + otherwise + error('Unknown type of Gaussian process!') + end + + % ====================================================================== + % Evaluate the prior contribution to the error from covariance functions + % ====================================================================== + eprior = 0; + for i=1:ncf + gpcf = gp.cf{i}; + eprior = eprior - gpcf.fh.lp(gpcf); + end + + % Evaluate the prior contribution to the error from likelihood function + if isfield(gp, 'lik') && isfield(gp.lik, 'p') + lik = gp.lik; + eprior = eprior - lik.fh.lp(lik); + end + + e = edata + eprior; + + % store values to the cache + ch.w = w; + ch.e = e; + ch.edata = edata; + ch.eprior = eprior; + ch.f = f; + ch.L = L; + ch.a = a; + ch.E = E; + ch.M = M; + ch.p=p; + ch.datahash=datahash; + end + + assert(isreal(edata)) + assert(isreal(eprior)) + + end + +function [edata,e,eprior,f,L,a,E,M,p,ch] = set_output_for_notpositivedefinite() + % Instead of stopping to chol error, return NaN + edata=NaN; + e=NaN; + eprior=NaN; + f=NaN; + L=NaN; + a=NaN; + E = NaN; + M = NaN; + p=NaN; + ch.w = w; + ch.e = e; + ch.edata = edata; + ch.eprior = eprior; + ch.f = f; + ch.L = L; + ch.M = M; + ch.E = E; + ch.a = a; + ch.p=p; + ch.datahash=datahash; +end +end diff --git a/gp/gpla_softmax_g.m b/gp/gpla_softmax_g.m new file mode 100644 index 00000000..f8d7a8ea --- /dev/null +++ b/gp/gpla_softmax_g.m @@ -0,0 +1,244 @@ +function [g, gdata, gprior] = gpla_softmax_g(w, gp, x, y, varargin) +%GPLA_SOFTMAX_G Evaluate gradient of Laplace approximation's marginal +% log posterior estimate for softmax likelihood (GPLA_SOFTMAX_E) +% +% Description +% G = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) takes a full GP +% hyper-parameter vector W, structure GP a matrix X of +% input vectors and a matrix Y of target vectors, and evaluates +% the gradient G of EP's marginal log posterior estimate . Each +% row of X corresponds to one input vector and each row of Y +% corresponds to one target vector. +% +% [G, GDATA, GPRIOR] = GPLA_SOFTMAX_G(W, GP, X, Y, OPTIONS) also +% returns the data and prior contributions to the gradient. +% +% OPTIONS is optional parameter-value pair +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in case of +% Poisson likelihood we have z_i=E_i, that is, expected +% value for ith case. +% +% See also +% GPLA_SOFTMAX_E, GPLA_E, GPLA_SOFTMAX_PRED + +% Copyright (c) 2010 Jaakko Riihim�ki, Pasi Jyl�nki + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +ip=inputParser; +ip.FunctionName = 'GPLA_SOFTMAX_G'; +ip.addRequired('w', @(x) isvector(x) && isreal(x) && all(isfinite(x))); +ip.addRequired('gp',@isstruct); +ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) +ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) +ip.parse(w, gp, x, y, varargin{:}); +z=ip.Results.z; + +gp = gp_unpak(gp, w); % unpak the parameters +ncf = length(gp.cf); +n=size(x,1); +nout=size(y,2); + +g = []; +gdata = []; +gprior = []; + +% First Evaluate the data contribution to the error +switch gp.type + % ============================================================ + % FULL + % ============================================================ + case 'FULL' % A full GP + + K = gp_trcov(gp,x); + + [e, edata, eprior, f, L, a, E, M, p] = gpla_softmax_e(gp_pak(gp), gp, x, y, 'z', z); + + % softmax + f2=reshape(f,n,nout); + expf2 = exp(f2); + pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); + pi_vec=pi2(:); + + DKBy_pi=zeros(n*nout,1); + DKffba=zeros(n*nout,1); + inv_iWK=zeros(n,n,nout); + EDKBy=zeros(n*nout,1); + EME=zeros(n*nout,1); + E_diff=zeros(n*nout,1); + + % Matrices for computing the derivative of determinant term w.r.t. f + A=zeros(nout, nout, n); + Minv=M\(M'\eye(n)); + Minv=(Minv+Minv')./2; + for cc1=1:nout + EMinv=E(:,:,cc1)*Minv; + KEMinv=K*EMinv; + for cc2=1:nout + if cc2>=cc1 + if cc1==cc2 + EMtmp= - EMinv*E(:,:,cc2); + EMtmp=EMtmp + E(:,:,cc1); + inv_iWK(:,:,cc1)=EMtmp; + A(cc1,cc1,:)=diag(K)-sum((K*EMtmp).*K,2); + else + EMtmp= - KEMinv*E(:,:,cc2); + A(cc1,cc2,:)=-sum(EMtmp.*K,2); + A(cc2,cc1,:)=-sum(EMtmp.*K,2); + end + end + end + end + + s2=zeros(n*nout,1); + + for cc3=1:nout + for ii1=1:n + + dw_mat=zeros(nout,nout); + pic=pi_vec(ii1:n:(nout*n)); + + for cc1=1:nout + for cc2=1:nout + + % softmax third derivatives + cc_sum_tmp=0; + if cc1==cc2 && cc1==cc3 && cc2==cc3 + cc_sum_tmp=cc_sum_tmp+pic(cc1); + end + if cc1==cc2 + cc_sum_tmp=cc_sum_tmp-pic(cc1)*pic(cc3); + end + if cc2==cc3 + cc_sum_tmp=cc_sum_tmp-pic(cc1)*pic(cc2); + end + if cc1==cc3 + cc_sum_tmp=cc_sum_tmp-pic(cc1)*pic(cc2); + end + cc_sum_tmp=cc_sum_tmp+2*pic(cc1)*pic(cc2)*pic(cc3); + + dw_mat(cc1,cc2)=cc_sum_tmp; + end + end + % Derivative of determinant term w.r.t. f + s2(ii1+(cc3-1)*n)=-0.5*trace(A(:,:,ii1)*dw_mat); + end + end + + + for i=1:ncf + + i1=0; + if ~isempty(gprior) + i1 = length(gprior); + end + + % Gradients from covariance functions + gpcf = gp.cf{i}; + DKff = gpcf.fh.cfg(gpcf, x); + gprior_cf = -gpcf.fh.lpg(gpcf); + + for i2 = 1:length(DKff) + i1 = i1+1; + DKffb=DKff{i2}; + + trace_sum_tmp=0; + for z1=1:nout + DKffba((1:n)+(z1-1)*n)=DKffb*a((1:n)+(z1-1)*n); + trace_sum_tmp=trace_sum_tmp+sum(sum( inv_iWK(:,:,z1) .* DKffb )); + end + % Derivative of explicit terms + s1 = 0.5 * a'*DKffba - 0.5*trace_sum_tmp; + + y_pi=y(:)-pi_vec; + for z1=1:nout + DKBy_pi((1:n)+(z1-1)*n)=DKffb*y_pi((1:n)+(z1-1)*n); + EDKBy((1:n)+(z1-1)*n)=E(:,:,z1)*DKBy_pi((1:n)+(z1-1)*n); + end + + EDKBys=sum(reshape(EDKBy, n, nout),2); + MMRc=Minv*EDKBys; + + for z1=1:nout + EME((1:n)+(z1-1)*n) = E(:,:,z1)*MMRc; + end + EDK_EME=EDKBy-EME; + + for z1=1:nout + E_diff((1:n)+(z1-1)*n)=K*EDK_EME((1:n)+(z1-1)*n); + end + + % Derivative of f w.r.t. theta + s3=DKBy_pi-E_diff; + + gdata(i1) = -(s1 + s2'*s3); + gprior(i1) = gprior_cf(i2); + + end + + % Set the gradients of hyper-hyperparameter + if length(gprior_cf) > length(DKff) + for i2=length(DKff)+1:length(gprior_cf) + i1 = i1+1; + gdata(i1) = 0; + gprior(i1) = gprior_cf(i2); + end + end + end + +% % ================================================================= +% % Gradient with respect to likelihood function parameters +% if ~isempty(strfind(gp.infer_params, 'likelihood')) ... +% && ~isempty(gp.lik.fh.pak(gp.lik)) +% +% gdata_likelih = 0; +% lik = gp.lik; +% +% g_logPrior = feval(lik.fh.gprior, lik); +% if ~isempty(g_logPrior) +% +% DW_sigma = feval(lik.fh.llg3, lik, y, f, 'latent2+hyper', z); +% DL_sigma = feval(lik.fh.llg, lik, y, f, 'hyper', z); +% b = K * feval(lik.fh.llg2, lik, y, f, 'latent+hyper', z); +% s3 = b - K*(R*b); +% nl= size(DW_sigma,2); +% +% gdata_lik = - DL_sigma - 0.5.*sum(repmat(C2,1,nl).*DW_sigma) - s2'*s3; +% +% % set the gradients into vectors that will be returned +% gdata = [gdata gdata_lik]; +% gprior = [gprior g_logPrior]; +% i1 = length(g_logPrior); +% i2 = length(gdata_lik); +% if i1 > i2 +% gdata = [gdata zeros(1,i1-i2)]; +% end +% end +% end + + g = gdata + gprior; + + % ============================================================ + % FIC + % ============================================================ + case 'FIC' + + % ============================================================ + % PIC + % ============================================================ + case {'PIC' 'PIC_BLOCK'} + + % ============================================================ + % CS+FIC + % ============================================================ + case 'CS+FIC' + + end + + assert(isreal(gdata)) + assert(isreal(gprior)) +end diff --git a/gp/gpla_softmax_pred.m b/gp/gpla_softmax_pred.m new file mode 100644 index 00000000..448554f5 --- /dev/null +++ b/gp/gpla_softmax_pred.m @@ -0,0 +1,176 @@ +function [mu_star, Sigm_cc, pi_star, Ey, Vary] = gpla_softmax_pred(gp, x, y, varargin) +%function [Ef, Varf, Ey, Vary, Pyt] = gpla_softmax_pred(gp, x, y, xt, varargin) +%GPLA_SOFTMAX_PRED Predictions with Gaussian Process Laplace +% approximation with softmax likelihood +% +% Description +% [EFT, VARFT] = GPLA_SOFTMAX_PRED(GP, X, Y, XT, OPTIONS) takes +% a GP structure GP together with a matrix XT of input vectors, +% matrix X of training inputs and vector Y of training targets, +% and evaluates the predictive distribution at inputs XT. Returns +% a posterior mean EFT and variance VARFT of latent variables. +% +% [EF, VARF, LPYT] = GPLA_SOFTMAX_PRED(GP, X, Y, XT, 'yt', YT, ...) +% returns also logarithm of the predictive density PYT of the observations YT +% at input locations XT. This can be used for example in the +% cross-validation. Here Y has to be vector. +% +% +% OPTIONS is optional parameter-value pair +% predcf - is index vector telling which covariance functions are +% used for prediction. Default is all (1:gpcfn). See +% additional information below. +% tstind - is a vector/cell array defining, which rows of X belong +% to which training block in *IC type sparse models. Deafult +% is []. In case of PIC, a cell array containing index +% vectors specifying the blocking structure for test data. +% IN FIC and CS+FIC a vector of length n that points out the +% test inputs that are also in the training set (if none, +% set TSTIND = []). +% yt - is optional observed yt in test points +% z - optional observed quantity in triplet (x_i,y_i,z_i) +% Some likelihoods may use this. For example, in +% case of Poisson likelihood we have z_i=E_i, that +% is, expected value for ith case. +% zt - optional observed quantity in triplet (xt_i,yt_i,zt_i) +% Some likelihoods may use this. For example, in +% case of Poisson likelihood we have zt_i=Et_i, that +% is, expected value for ith case. +% +% See also +% GPLA_SOFTMAX_E, GPLA_SOFTMAX_G, GP_PRED, DEMO_MULTICLASS +% +% Copyright (c) 2010 Jaakko Riihim�ki + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'GPLA_SOFTMAX_PRED'; + ip.addRequired('gp', @isstruct); + ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) + ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) + ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) + ip.addParamValue('predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)) + ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + if numel(varargin)==0 || isnumeric(varargin{1}) + % inputParser should handle this, but it doesn't + ip.parse(gp, x, y, varargin{:}); + else + ip.parse(gp, x, y, [], varargin{:}); + end + xt=ip.Results.xt; + yt=ip.Results.yt; + predcf=ip.Results.predcf; + tstind=ip.Results.tstind; + if isempty(xt) + xt=x; + if isempty(tstind) + if iscell(gp) + gptype=gp{1}.type; + else + gptype=gp.type; + end + switch gptype + case {'FULL' 'VAR' 'DTC' 'SOR'} + tstind = []; + case {'FIC' 'CS+FIC'} + tstind = 1:size(x,1); + case 'PIC' + if iscell(gp) + tstind = gp{1}.tr_index; + else + tstind = gp.tr_index; + end + end + end + if isempty(yt) + yt=y; + end + end + + Ey=[]; + Vary=[]; + + [tn, tnin] = size(x); + + switch gp.type + % ============================================================ + % FULL + % ============================================================ + case 'FULL' + [e, edata, eprior, f, L, a, E, M, p] = gpla_softmax_e(gp_pak(gp), gp, x, y, 'z', z); + + ntest=size(xt,1); + K_nf = gp_cov(gp,xt,x,predcf); + K = gp_trcov(gp, x); + + nout=size(y,2); + f2=reshape(f,tn,nout); + + expf2 = exp(f2); + pi2 = expf2./(sum(expf2, 2)*ones(1,nout)); + + mu_star=zeros(ntest,nout); + Sigm_cc=zeros(nout, nout, ntest); + + Minv=M\(M'\eye(tn)); + Minv=(Minv+Minv')./2; + + for i1=1:nout + mu_star(:,i1)=(y(:,i1)-pi2(:,i1))'*K_nf'; + b=E(:,:,i1)*K_nf'; + c_cav=Minv*b; + + for j1=1:nout + c=E(:,:,j1)*c_cav; + Sigm_cc(i1,j1,:)=sum(c.*K_nf'); + end + + kstarstar = gp_trvar(gp,xt,predcf); + Sigm_cc(i1,i1,:) = squeeze(Sigm_cc(i1,i1,:)) + kstarstar - sum(b.*K_nf')'; + end + + S=10000; + pi_star=zeros(ntest,nout); + for i1=1:ntest + Sigm_tmp=(Sigm_cc(:,:,i1)'+Sigm_cc(:,:,i1))./2; + f_star=mvnrnd(mu_star(i1,:), Sigm_tmp, S); + + tmp_star = exp(f_star); + tmp_star = tmp_star./(sum(tmp_star, 2)*ones(1,size(tmp_star,2))); + pi_star(i1,:)=log(mean(tmp_star)); + + end + + % ============================================================ + % FIC + % ============================================================ + case 'FIC' % Predictions with FIC sparse approximation for GP + % ============================================================ + % PIC + % ============================================================ + case {'PIC' 'PIC_BLOCK'} % Predictions with PIC sparse approximation for GP + % ============================================================ + % CS+FIC + % ============================================================ + case 'CS+FIC' % Predictions with CS+FIC sparse approximation for GP + end + + % ============================================================ + % Evaluate also the predictive mean and variance of new observation(s) + % ============================================================ +% if nargout > 2 +% if isempty(yt) +% [Ey, Vary] = feval(gp.lik.fh.predy, gp.lik, Ef, Varf, [], zt); +% else +% [Ey, Vary, Pyt] = feval(gp.lik.fh.predy, gp.lik, Ef, Varf, yt, zt); +% end +% end +end \ No newline at end of file diff --git a/gp/lik_coxph.m b/gp/lik_coxph.m new file mode 100644 index 00000000..69782ea1 --- /dev/null +++ b/gp/lik_coxph.m @@ -0,0 +1,1387 @@ +function lik = lik_coxph(varargin) +%LIK_COXPH Create a Cox proportional hazard likelihood structure +% +% Description +% LIK = LIK_COXPH('PARAM1',VALUE1,'PARAM2,VALUE2,...) +% creates a proportional hazard model where a piecewise log-constant +% baseline hazard is assumed. +% +% The likelihood contribution for the ith observation is +% +% l_i = h_i(y_i)^(1-z_i)*[exp(-int_0^y_i*h_i dt)], +% +% where hazard is h_i=h_0(y_i)*exp(f_i). A zero mean Gaussian process +% prior is placed for f = [f_1, f_2,...,f_n] ~ N(0, C). C is the +% covariance matrix, whose elements are given as C_ij = c(x_i, x_j | +% th). The function c(x_i, x_j| th) is covariance function and th its +% parameters, hyperparameters. We place a hyperprior for +% hyperparameters, p(th). +% +% The time axis is partioned into K intervals with equal lengths: +% 0 = s_0 < s_1 < ... < s_K, where s_K > y_i for all i. The baseline +% hazard rate function h_0 is piecewise constant, +% +% h_0(t) = la_k, +% +% when t belongs to the interval (s_{k-1},s_k] and where ft_k=log(la_k). +% The hazard rate function is smoothed by assuming another Gaussian +% process prior ft = [ft_1, ft_2,...,ft_K] ~ N(0, C). +% +% z is a vector of censoring indicators with z = 0 for uncensored event +% and z = 1 for right censored event. +% +% When using the Coxph likelihood you need to give the vector z +% as an extra parameter to each function that requires also y. +% For example, you should call gpla_e as follows: gpla_e(w, gp, +% x, y, 'z', z) +% +% See also +% GP_SET, LIK_*, PRIOR_* +% + +% Copyright (c) 2007-2010 Jarno Vanhatalo & Jouni Hartikainen +% Copyright (c) 2010 Aki Vehtari +% Copyright (c) 2011 Jaakko Riihimäki + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'LIK_COXPH'; + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','S', linspace(0,1.001,50), @(x) isvector(x)); + ip=iparser(ip,'addParamValue','stratificationVariables', [], @(x) isvector(x) && all(rem(x,1)==0)); + ip=iparser(ip,'addParamValue','removeStratificationVariables', [], @(x) ismember(x, {'on' 'off'})); + ip=iparser(ip,'parse',varargin{:}); + lik=ip.Results.lik; + + if isempty(lik) + init=true; + lik.type = 'Coxph'; + lik.nondiagW=true; + else + if ~isfield(lik,'type') || ~isequal(lik.type,'Coxph') + error('First argument does not seem to be a valid likelihood function structure') + end + init=false; + end + + if init || ~ismember('S', ip.UsingDefaults) + s=ip.Results.S; + xtmp=zeros(length(s)-1,1); + for i2=1:(length(s)-1) + xtmp(i2,1)=mean([s(i2) s(i2+1)]); + end + lik.xtime=xtmp; + lik.stime=s; + end + + if ~ismember('stratificationVariables', ip.UsingDefaults) + lik.stratificationVariables = ip.Results.stratificationVariables; + if ~ismember('stratificationVariables', ip.UsingDefaults) + if isequal(ip.Results.removeStratificationVariables, 'on') + lik.removeStratificationVariables=true; + end + end + end + + if init + % Set the function handles to the nested functions + lik.fh.pak = @lik_coxph_pak; + lik.fh.unpak = @lik_coxph_unpak; + lik.fh.ll = @lik_coxph_ll; + lik.fh.llg = @lik_coxph_llg; + lik.fh.llg2 = @lik_coxph_llg2; + lik.fh.llg3 = @lik_coxph_llg3; +% lik.fh.tiltedMoments = @lik_coxph_tiltedMoments; + lik.fh.predy = @lik_coxph_predy; + lik.fh.invlink = @lik_coxph_invlink; + lik.fh.recappend = @lik_coxph_recappend; + lik.fh.predcdf= @lik_coxph_predcdf; + end + + function [w,s] = lik_coxph_pak(lik) + %LIK_COXPH_PAK Combine likelihood parameters into one vector. + % + % Description + % W = LIK_COXPH_PAK(LIK) takes a likelihood structure LIK and + % combines the parameters into a single row vector W. This is + % a mandatory subfunction used for example in energy and + % gradient computations. + % + % w = log(lik.disper) + % + % See also + % LIK_COXPH_UNPAK, GP_PAK + + w=[];s={}; + end + + + function [lik, w] = lik_coxph_unpak(lik, w) + %LIK_COXPH_UNPAK Extract likelihood parameters from the vector. + % + % Description + % [LIK, W] = LIK_COXPH_UNPAK(W, LIK) takes a likelihood + % structure LIK and extracts the parameters from the vector W + % to the LIK structure. This is a mandatory subfunction used + % for example in energy and gradient computations. + % + % Assignment is inverse of + % w = log(lik.disper) + % + % See also + % LIK_COXPH_PAK, GP_UNPAK + + lik=lik; + w=w; + + end + + function ll = lik_coxph_ll(lik, y, f, z) + %LIK_COXPH_LL Log likelihood + % + % Description + % LL = LIK_COXPH_LL(LIK, Y, F, Z) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z, and + % latent values F. Returns the log likelihood, log p(y|f,z). + % This subfunction is needed when using Laplace approximation + % or MCMC for inference with non-Gaussian likelihoods. This + % subfunction is also used in information criteria (DIC, WAIC) + % computations. + % + % See also + % LIK_COXPH_LLG, LIK_COXPH_LLG3, LIK_COXPH_LLG2, GPLA_E + + if isempty(z) + error(['lik_coxph -> lik_coxph_ll: missing z! '... + 'Coxph likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_coxph and gpla_e. ']); + end + + [n,ny]=size(y); + ntime=size(lik.xtime,1); + i3v=ones(size(y,1),1); + if isfield(lik, 'stratificationVariables') + f1=f(1:ntime*lik.n_u); + f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); + for i=1:lik.n_u + i3v(lik.stratind{i})=i; + end + else + f1=f(1:ntime); + f2=f((ntime+1):(ntime+n)); + end + + la1=exp(f1); + eta2=exp(f2); + + nu=1-z; + sd=lik.stime(2)-lik.stime(1); + + if ny==1 + ll=0; + for i1=1:n + i3=i3v(i1); + nft=(i3-1)*ntime; + si=sum(y(i1)>lik.stime); + ll=ll + nu(i1).*(f1(si+nft)+f2(i1)) - (y(i1)-lik.stime(si)).*la1(si+nft).*eta2(i1) ... + - sum(sd.*la1((1+nft):(si-1+nft)).*eta2(i1)); + end + else + + ll=0; + sb=sum(bsxfun(@gt,y(:,1),lik.stime),2); + se=sum(bsxfun(@gt,y(:,2),lik.stime),2); + for i1=1:n + i3=i3v(i1); + nft=(i3-1)*ntime; + if sb(i1)==0 + ll=ll + nu(i1).*(f1(se(i1)+nft)+f2(i1)) - (y(i1,2)-lik.stime(se(i1))).*la1(se(i1)+nft).*eta2(i1) ... + - sum(sd.*la1((1+nft):(se(i1)-1+nft)).*eta2(i1)); + else + + if se(i1)==sb(i1) + ll=ll + nu(i1).*(f1(se(i1)+nft)+f2(i1)) - (y(i1,2)-y(i1,1)).*la1(se(i1)+nft).*eta2(i1); + else + ll=ll + nu(i1).*(f1(se(i1)+nft)+f2(i1)) - (y(i1,2)-lik.stime(se(i1))).*la1(se(i1)+nft).*eta2(i1) ... + - sum(sd.*la1((sb(i1)+1+nft):(se(i1)-1+nft)).*eta2(i1)) - (lik.stime(sb(i1)+1)-y(i1,1)).*la1(sb(i1)+nft).*eta2(i1); + end + end + end + end + end + + function llg = lik_coxph_llg(lik, y, f, param, z) + %LIK_COXPH_LLG Gradient of the log likelihood + % + % Description + % LLG = LIK_COXPH_LLG(LIK, Y, F, PARAM) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z and + % latent values F. Returns the gradient of the log likelihood + % with respect to PARAM. At the moment PARAM can be 'param' or + % 'latent'. This subfunction is needed when using Laplace + % approximation or MCMC for inference with non-Gaussian + % likelihoods. + % + % See also + % LIK_COXPH_LL, LIK_COXPH_LLG2, LIK_COXPH_LLG3, GPLA_E + + if isempty(z) + error(['lik_coxph -> lik_coxph_llg: missing z! '... + 'Coxph likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_coxph and gpla_e. ']); + end + + ntime=size(lik.xtime,1); + + [n,ny]=size(y); + i3v=ones(size(y)); + if isfield(lik, 'stratificationVariables') + f1=f(1:ntime*lik.n_u); + f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); + llg=zeros(ntime*lik.n_u+n,1); + for i=1:lik.n_u + i3v(lik.stratind{i})=i; + end + nf1=lik.n_u*ntime; + else + f1=f(1:ntime); + f2=f((ntime+1):(ntime+n)); + llg=zeros(ntime+n,1); + nf1=ntime; + end + + la1=exp(f1(:)); + eta2=exp(f2(:)); + + nu=1-z; + sd=lik.stime(2)-lik.stime(1); + + switch param + case 'latent' + + if ny==1 + if ~isfield(lik, 'stratificationVariables') + for i1=1:ntime + ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); + end + else + for j=1:lik.n_u + for i1=1:ntime + ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); + end + end + end + for i1=1:n + i3=i3v(i1); + nft=(i3-1)*ntime; + si=sum(y(i1)>lik.stime); + llg(i1+nf1)= nu(i1) - (y(i1)-lik.stime(si)).*la1(si+nft).*eta2(i1) - sum(sd.*la1((1+nft):(si-1+nft)).*eta2(i1)); + end + else + for i1=1:ntime + + % left truncated + follow-up entry: (1) + ind_vkst = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1+1); + % follow-up entry + follow-up exit: (2) + ind_stsp = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1+1); + % follow-up exit: (4) + ind_sp = y(:,1)=lik.stime(i1) & y(:,2) lik_coxph_llg2: missing z! '... + 'Coxph likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_coxph and gpla_e. ']); + end + + [n,ny]=size(y); + ntime=size(lik.xtime,1); + i3v=ones(size(y,1),1); + if isfield(lik, 'stratificationVariables') + f1=f(1:ntime*lik.n_u); + f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); + llg2=zeros(ntime*lik.n_u+n,1); + llg2mat=zeros(ntime*lik.n_u,n); + for i=1:lik.n_u + i3v(lik.stratind{i})=i; + end + nf1=ntime*lik.n_u; + else + f1=f(1:ntime); + f2=f((ntime+1):(ntime+n)); + llg2=zeros(ntime+n,1); + llg2mat=zeros(ntime,n); + nf1=ntime; + end + + la1=exp(f1); + eta2=exp(f2); + + %nu=1-z; + sd=lik.stime(2)-lik.stime(1); + switch param + case 'latent' + + if ny==1 + % 11 + if ~isfield(lik, 'stratificationVariables') + for i1=1:ntime + ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); + %llg2(i1,i1)= sum(-(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind)) - sum(sum(sd.*la1(i1)).*eta2(~ind & y>=lik.stime(i1+1))); + end + else + for j=1:lik.n_u + for i1=1:ntime + ind=y>=lik.stime(i1) & y=lik.stime(i1+1))); + %llg2(i1,i1)= sum(-(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind)) - sum(sum(sd.*la1(i1)).*eta2(~ind & y>=lik.stime(i1+1))); + end + end + end + + % 22 + for i1=1:n + si=sum(y(i1)>lik.stime); + i3=i3v(i1); + llg2(i1+nf1)= -(y(i1)-lik.stime(si)).*la1(si+(i3-1)*ntime).*eta2(i1) - sum(sd.*la1((1+(i3-1)*ntime):(si-1+(i3-1)*ntime)).*eta2(i1)); + %llg2(i1+ntime,i1+ntime)= -(y(i1)-lik.stime(si)).*la1(si).*eta2(i1) - sum(sd.*la1(1:(si-1)).*eta2(i1)); + end + + % derivative wrt f1 and f2: + if ~isfield(lik, 'stratificationVariables') + for i1=1:ntime + ind=y>=lik.stime(i1) & y=lik.stime(i1+1)))) = - sd.*la1(i1).*eta2((~ind & y>=lik.stime(i1+1))); + %llg2(i1,ntime+find(ind))= -(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind); + %llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))) = - sd.*la1(i1).*eta2((~ind & y>=lik.stime(i1+1))); + %llg2(ntime+find(ind),i1)=llg2(i1,ntime+find(ind)); + %llg2(ntime+find((~ind & y>=lik.stime(i1+1))),i1)=llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))); + end + else + for j=1:lik.n_u + ind2=lik.stratind{j}; + for i1=1:ntime + ind=y>=lik.stime(i1) & y=lik.stime(i1+1)))) = - sd.*la1(i1+(j-1)*ntime).*eta2(((~ind & ind2) & y>=lik.stime(i1+1))); + %llg2(i1,ntime+find(ind))= -(y(ind)-lik.stime(i1)).*la1(i1).*eta2(ind); + %llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))) = - sd.*la1(i1).*eta2((~ind & y>=lik.stime(i1+1))); + %llg2(ntime+find(ind),i1)=llg2(i1,ntime+find(ind)); + %llg2(ntime+find((~ind & y>=lik.stime(i1+1))),i1)=llg2(i1,ntime+find((~ind & y>=lik.stime(i1+1)))); + end + end + end + + else + + % 11 + for i1=1:ntime + + % left truncated + follow-up entry: (1) + ind_vkst = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1+1); + % follow-up entry + follow-up exit: (2) + ind_stsp = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1+1); + % follow-up exit: (4) + ind_sp = y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1) & y(:,1)=lik.stime(i1+1); + % follow-up entry + follow-up exit: (2) + ind_stsp = y(:,1)>=lik.stime(i1) & y(:,1)=lik.stime(i1) & y(:,2)=lik.stime(i1+1); + % follow-up exit: (4) + ind_sp = y(:,1)=lik.stime(i1) & y(:,2) lik_coxph_llg3: missing z! '... + 'Coxph likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_coxph and gpla_e. ']); + end + + ntime=size(lik.xtime,1); + + [n,ny]=size(y); + if isfield(lik, 'stratificationVariables') + f1=f(1:ntime*lik.n_u); + f2=f((ntime*lik.n_u+1):(ntime*lik.n_u+n)); + llg3=zeros(ntime*lik.n_u+n,1); + llg3mat=zeros(ntime*lik.n_u,n); + nf1=ntime*lik.n_u; + if j1>nf1 + for j=1:lik.n_u + if lik.stratind{j}(j1-nf1)==1; + i3=j; + break; + end + end + end + else + f1=f(1:ntime); + f2=f((ntime+1):(ntime+n)); + llg3=zeros(ntime+n,1); + llg3mat=zeros(ntime,n); + nf1=ntime; + i3=1; + end + + la1=exp(f1); + eta2=exp(f2); + + %nu=1-z; + sd=lik.stime(2)-lik.stime(1); + + + switch param + case 'latent' + + if ny==1 + + if j1<=nf1 + + indt=rem(j1,ntime); + if indt==0 + indt=ntime; + end + + % 11 + ind=y>=lik.stime(indt) & y=lik.stime(indt)); + + llg3(j1) = sum(-(y(ind & ind2)-lik.stime(indt)).*la1(j1).*eta2(ind & ind2)) - sum(sum(sd.*la1(j1)).*eta2((~ind & ind2) & y>=lik.stime(indt+1))); + + if ~isempty(fi) + valtmp=(-(y(ind & ind2)-lik.stime(indt)).*la1(j1).*eta2(ind & ind2)); + for m2i=1:length(valtmp) + llg3( nf1+fi(m2i)) = valtmp(m2i); + end + end + if ~isempty(fni) + valtmp2=(-sd.*la1(j1).*eta2(((~ind & ind2) & y>=lik.stime(indt+1)))); + for m2i=1:length(valtmp2) + llg3( nf1+fni(m2i)) = valtmp2(m2i); + end + end + + % 12/21 + % derivative wrt f1 and f2: + val1tmp=-(y(ind & ind2)-lik.stime(indt)).*la1(j1).*eta2(ind & ind2); + llg3mat(j1,fi)= val1tmp; + + val2tmp = - sd.*la1(j1).*eta2(((~ind & ind2) & y>=lik.stime(indt+1))); + llg3mat(j1,fni) = val2tmp; + + else + + % 11 + s1=sum(y(j1-nf1)>lik.stime); + llg3((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)) = - sd.*la1((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)).*eta2(j1-nf1); + llg3(s1+(i3-1)*ntime) = -(y(j1-nf1)-lik.stime(s1)).*la1(s1+(i3-1)*ntime).*eta2(j1-nf1); + + % 22 + llg3(j1) = -(y(j1-nf1)-lik.stime(s1)).*la1(s1+(i3-1)*ntime).*eta2(j1-nf1) ... + - sum(sd.*la1((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)).*eta2(j1-nf1)); + + % 12/21 + % derivative wrt f1 and f2: + val3tmp = - sd.*la1((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime)).*eta2(j1-nf1); + llg3mat((1+(i3-1)*ntime):(s1-1+(i3-1)*ntime),j1-nf1)= val3tmp; + llg3mat(s1+(i3-1)*ntime,j1-nf1) = -(y(j1-nf1)-lik.stime(s1)).*la1(s1+(i3-1)*ntime).*eta2(j1-nf1); + + end + + else + + if j1<=nf1 + + indt=rem(j1,ntime); + if indt==0 + indt=ntime; + end + if isfield(lik, 'stratificationVariables') + ind2=lik.stratind{(j1-indt)/ntime+1}; + else + ind2=ones(size(y,1),1); + end + + % 11 + % left truncated + follow-up entry: (1) + ind_vkst = y(:,1)>=lik.stime(indt) & y(:,1)=lik.stime(indt+1); + % follow-up entry + follow-up exit: (2) + ind_stsp = y(:,1)>=lik.stime(indt) & y(:,1)=lik.stime(indt) & y(:,2)=lik.stime(indt+1); + % follow-up exit: (4) + ind_sp = y(:,1)=lik.stime(indt) & y(:,2)lik.stime); % begin + se=sum(y(j1-nf1,2)>lik.stime); % end + + nft=(i3-1)*ntime; + % 11 + if sb==0 + llg3((1+nft):(se-1+nft))= -sd.*la1((1+nft):(se-1+nft)).*eta2(j1-nf1); + llg3(se+nft)= -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); + else + if se==sb + llg3(se+nft) = -(y(j1-nf1,2)-y(j1-nf1,1)).*la1(se+nft).*eta2(j1-nf1); + else + llg3(sb+nft) = - (lik.stime(sb+1)-y(j1-nf1,1)).*la1(sb+nft).*eta2(j1-nf1); + llg3((sb+1+nft):(se-1+nft)) = - sd.*la1((sb+1+nft):(se-1+nft)).*eta2(j1-nf1); + llg3(se+nft) = -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); + end + end + + % 12/21 + if sb==0 + llg3mat((1+nft):(se-1+nft),j1-nf1) = -sd.*la1((1+nft):(se-1+nft)).*eta2(j1-nf1); + llg3mat(se+nft,j1-nf1)= -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); + else + if se==sb + llg3mat(se+nft,j1-nf1) = -(y(j1-nf1,2)-y(j1-nf1,1)).*la1(se+nft).*eta2(j1-nf1); + else + llg3mat(sb+nft,j1-nf1) = - (lik.stime(sb+1)-y(j1-nf1,1)).*la1(sb+nft).*eta2(j1-nf1); + llg3mat((sb+1+nft):(se-1+nft),j1-nf1) = - sd.*la1((sb+1+nft):(se-1+nft)).*eta2(j1-nf1); + llg3mat(se+nft,j1-nf1) = -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1); + end + end + + % 22 + if sb==0 + llg3(j1)= -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1) - sum(sd.*la1((1+nft):(se-1+nft)).*eta2(j1-nf1)); + else + if se==sb + llg3(j1) = -(y(j1-nf1,2)-y(j1-nf1,1)).*la1(se+nft).*eta2(j1-nf1); + else + llg3(j1) = -(y(j1-nf1,2)-lik.stime(se)).*la1(se+nft).*eta2(j1-nf1) - sum(sd.*la1((sb+1+nft):(se-1+nft)).*eta2(j1-nf1)) - (lik.stime(sb+1)-y(j1-nf1,1)).*la1(sb+nft).*eta2(j1-nf1); + end + end + + end + end + end + end + + +% function [logM_0, m_1, sigm2hati1] = lik_coxph_tiltedMoments(lik, y, i1, S2_i, M_i, z) +% %LIK_COXPH_TILTEDMOMENTS Returns the marginal moments for EP algorithm +% % +% % Description +% % [M_0, M_1, M2] = LIK_COXPH_TILTEDMOMENTS(LIK, Y, I, S2, +% % MYY, Z) takes a likelihood structure LIK, incedence counts +% % Y, expected counts Z, index I and cavity variance S2 and +% % mean MYY. Returns the zeroth moment M_0, mean M_1 and +% % variance M_2 of the posterior marginal (see Rasmussen and +% % Williams (2006): Gaussian processes for Machine Learning, +% % page 55). This subfunction is needed when using EP for +% % inference with non-Gaussian likelihoods. +% % +% % See also +% % GPEP_E +% +% [n,ny]=size(y); +% +% % M_i(end); +% % S2_i(end,end); +% fgrid=M_i(end)+sqrt(S2_i(end,end))*[-6 6]; +% fg=linspace(fgrid(1),fgrid(2),15); +% ng=length(fg); +% +% if isfield(gp.lik, 'stratificationVariables') +% ntime=size(lik.xtime,1)*gp.lik.n_u; +% else +% ntime=size(lik.xtime,1); +% end +% +% %f11=f(1:ntime); +% %f2=f((ntime+1):(ntime+n)); +% %la1=exp(f1); +% %eta2=exp(f2); +% +% nu=1-z; +% sd=lik.stime(2)-lik.stime(1); +% if ny==1 +% sb=1; +% se=sum(bsxfun(@gt,y(i1,1),lik.stime),2); +% end +% indf=sb:se; +% sdvec=[ones(se-1,1)*sd; y(i1)-lik.stime(se)]; +% nutmp=zeros(se,1); +% nutmp(se)=nu(i1); +% +% for j1=1:ng +% +% % conditional distribution +% myy=M_i(indf)+S2_i(indf,end)*(1./S2_i(end,end))*(fg(j1)-M_i(end)); +% myy0=myy; +% Sigm=S2_i(indf,indf)-S2_i(indf,end)*(1./S2_i(end,end))*S2_i(end,indf); +% Sigm0=Sigm; +% +% nu_prior=Sigm\myy; +% +% nt=size(myy,1); +% c1=exp(fg(j1)); +% +% % site parameters +% tautilde=zeros(nt,1); +% nutilde=zeros(nt,1); +% ztilde=zeros(nt,1); +% +% max_small_ep_iter=50; +% tol=1e-9; +% small_ep_iter=1; +% +% tautilde0=Inf; nutilde0=Inf; ztilde0=Inf; +% +% logZep_tmp=0; logZep=Inf; +% %while small_ep_iter <= max_small_ep_iter && (sum(abs(tautilde0-tautilde)>tol) || sum(abs(nutilde0-nutilde)>tol) || sum(abs(ztilde0-ztilde)>tol)) +% while small_ep_iter<=max_small_ep_iter && abs(logZep_tmp-logZep)>tol +% logZep_tmp=logZep; +% +% +% %tautilde0=tautilde; nutilde0=nutilde; ztilde0=ztilde; +% +% for k1=1:nt +% +% tau_i=Sigm(k1,k1)^-1-tautilde(k1); +% nu_i = Sigm(k1,k1)^-1*myy(k1)-nutilde(k1); +% myy_i=nu_i/tau_i; +% sigm2_i=tau_i^-1; +% +% % marginal moments +% [logM0(k1), muhati, sigm2hati] = coxph_tiltedMoments(sigm2_i, myy_i, nutmp(k1), sdvec(k1), c1); +% %[M0, muhati, sigm2hati] = coxph_tiltedMoments(lik, y(i1,:), k1, sigm2_i, myy_i, c1, sd_vec(i1), ztmp); +% +% deltatautilde=sigm2hati^-1-tau_i-tautilde(k1); +% tautilde(k1)=tautilde(k1)+deltatautilde; +% nutilde(k1)=sigm2hati^-1*muhati-nu_i; +% +% apu = deltatautilde/(1+deltatautilde*Sigm(k1,k1)); +% Sigm = Sigm - apu*(Sigm(:,k1)*Sigm(:,k1)'); +% +% % The below is how Rasmussen and Williams +% % (2006) do the update. The above version is +% % more robust. +% %apu = deltatautilde^-1+Sigm(k1,k1); +% %apu = (Sigm(:,k1)/apu)*Sigm(:,k1)'; +% %Sigm = Sigm - apu; +% %Sigm=Sigm-(deltatautilde^-1+Sigm(k1,k1))^-1*(Sigm(:,k1)*Sigm(:,k1)'); +% +% %myy=Sigm*nutilde; +% myy=Sigm*(nutilde+nu_prior); +% +% muvec_i(k1,1)=myy_i; +% sigm2vec_i(k1,1)=sigm2_i; +% +% end +% +% +% if tautilde > 0 +% Stilde=tautilde; +% Stildesqroot=diag(sqrt(tautilde)); +% B=eye(nt)+Stildesqroot*Sigm0*Stildesqroot; +% L=chol(B,'lower'); +% +% V=(L\Stildesqroot)*Sigm0; +% Sigm=Sigm0-V'*V; +% %myy=Sigm*nutilde; +% myy=Sigm*(nutilde+nu_prior); +% +% %Ls = chol(Sigm); +% +% % Compute the marginal likelihood +% % Direct formula (3.65): +% % Sigmtilde=diag(1./tautilde); +% % mutilde=inv(Stilde)*nutilde; +% % +% % logZep=-0.5*log(det(Sigmtilde+K))-0.5*mutilde'*inv(K+Sigmtilde)*mutilde+ +% % sum(log(normcdf(y.*muvec_i./sqrt(1+sigm2vec_i))))+ +% % 0.5*sum(log(sigm2vec_i+1./tautilde))+ +% % sum((muvec_i-mutilde).^2./(2*(sigm2vec_i+1./tautilde))) +% +% % 4. term & 1. term +% term41=0.5*sum(log(1+tautilde.*sigm2vec_i))-sum(log(diag(L))); +% +% % 5. term (1/2 element) & 2. term +% T=1./sigm2vec_i; +% Cnutilde = Sigm0*nutilde; +% L2 = V*nutilde; +% term52 = nutilde'*Cnutilde - L2'*L2 - (nutilde'./(T+Stilde)')*nutilde; +% term52 = term52.*0.5; +% +% % 5. term (2/2 element) +% term5=0.5*muvec_i'.*(T./(Stilde+T))'*(Stilde.*muvec_i-2*nutilde); +% +% % 3. term +% term3 = sum(logM0); +% +% V_tmp=(L\Stildesqroot); +% Sigm_inv_tmp=V_tmp'*V_tmp; +% +% term_add1=-0.5*myy0'*Sigm_inv_tmp*myy0; +% term_add2=myy0'*(eye(nt)-Sigm_inv_tmp*Sigm0)*nutilde; +% logZep = -(term41+term52+term5+term3+term_add1+term_add2); +% +% %logZep = -(term41+term52+term5+term3); +% +% small_ep_iter=small_ep_iter+1; +% %iter=iter+1; +% else +% error('tautilde <= 0') +% end +% end +% +% ZZ(j1,1)=exp(-logZep); +% MM(:,j1)=myy; +% SS2(:,:,j1)=Sigm; +% +% end +% +% %m_0=zeros(1,1); +% %m_1=zeros(nt+1,1); +% %sigm2hati1=zeros(nt+1,nt+1); +% % indf +% +% W=normpdf(fg,M_i(end),sqrt(S2_i(end,end)))*(fg(2)-fg(1)); +% +% qw=W.*ZZ'; +% m_0=sum(qw); +% m_1=[sum(bsxfun(@times,qw,MM),2); sum(qw.*fg)]./m_0; +% +% m_211=zeros(nt,nt); +% for k1=1:ng +% m_211=m_211+qw(k1)*(SS2(:,:,k1)+MM(:,k1)*MM(:,k1)'); +% end +% m_212=(qw.*fg)*MM'; +% m_222=(qw.*fg)*fg'; +% +% m_2=[m_211 m_212'; m_212 m_222]./m_0; +% +% sigm2hati1=m_2 - m_1*m_1'; +% logM_0 = log(m_0); +% +% %figure(1),hold on, plot(fg(j1),logZep,'.') +% %figure(2),hold on, plot(fg(j1),exp(-logZep),'.') +% +% end + + function [lpyt,Ey, Vary] = lik_coxph_predy(lik, Ef, Covf, yt, zt) + %LIK_COXPH_PREDY Returns the predictive mean, variance and density of y + % + % Description + % [EY, VARY] = LIK_COXPH_PREDY(LIK, EF, VARF) takes a + % likelihood structure LIK, posterior mean EF and posterior + % Variance VARF of the latent variable and returns the + % posterior predictive mean EY and variance VARY of the + % observations related to the latent variables. This + % subfunction is needed when computing posterior predictive + % distributions for future observations. + % + % [Ey, Vary, PY] = LIK_COXPH_PREDY(LIK, EF, VARF YT, ZT) + % Returns also the predictive density of YT, that is + % p(yt | zt) = \int p(yt | f, zt) p(f|y) df. + % This requires also the incedence counts YT, expected counts ZT. + % This subfunction is needed when computing posterior predictive + % distributions for future observations. + % + % See also + % GPLA_PRED, GPEP_PRED, GPMC_PRED + + if isempty(zt) + error(['lik_coxph -> lik_coxph_predy: missing zt!'... + 'Coxph likelihood needs the expected number of '... + 'occurrences as an extra input zt. See, for '... + 'example, lik_coxph and gpla_e. ']); + end + ntime=size(lik.xtime,1); + + ntest=size(zt,1); + ny=size(yt,2); + + if isfield(lik, 'stratificationVariables') + nf1=lik.n_u*ntime; + i3v=zeros(ntest,1); + for ii=1:length(lik.stratindt) + i3v(lik.stratindt{ii})=ii; + end + else + nf1=ntime; + i3v=ones(ntest,1); + end + + Py = zeros(size(zt)); + %Ey = zeros(size(zt)); + %EVary = zeros(size(zt)); + %VarEy = zeros(size(zt)); + + S=10000; + sd=lik.stime(2)-lik.stime(1); + nu=1-zt; + + [nn1,nn2,c] =size(Covf); + if (c>1) || (nn1==nn2) + mcmc=false; + else + mcmc=true; + end + + for i1=1:ntest + i3=i3v(i1); + if mcmc + Sigm_tmp=([Covf(1:ntime,:); Covf(i1+ntime,:)]); + f_star=bsxfun(@plus,Ef([(1+(i3-1)*ntime):(i3*ntime) i1+nf1])', ... + bsxfun(@times,sqrt(Sigm_tmp'),randn(S,nf1+1))); + else + Sigm_tmp=Covf([(1+(i3-1)*ntime):(i3*ntime) i1+nf1],[(1+(i3-1)*ntime):(i3*ntime) i1+nf1]); + Sigm_tmp=(Sigm_tmp+Sigm_tmp')./2; + f_star=mvnrnd(Ef([(1+(i3-1)*ntime):(i3*ntime) i1+nf1]), Sigm_tmp, S); + end + + f1=f_star(:,1:ntime); + f2=f_star(:,(ntime+1):end); + + la1=exp(f1); + eta2=exp(f2); + + if ny==1 + si=sum(yt(i1)>lik.stime); + Py(i1)=mean(exp(nu(i1).*(f1(:,si)+f2) - (yt(i1)-lik.stime(si)).*la1(:,si).*eta2 ... + - sum(sd.*la1(:,1:(si-1)),2).*eta2)); + else + + sb=sum(bsxfun(@gt,yt(i1,1),lik.stime),2); + se=sum(bsxfun(@gt,yt(i1,2),lik.stime),2); + + if sb==0 + Py(i1) = mean(exp(nu(i1).*(f1(:,se)+f2) - (yt(i1,2)-lik.stime(se)).*la1(:,se).*eta2 - sum(sd.*la1(:,1:(se-1)),2).*eta2)); + else + if se==sb + Py(i1) = mean(exp(nu(i1).*(f1(:,se)+f2) - (yt(i1,2)-yt(i1,1)).*la1(:,se).*eta2)); + else + Py(i1) = mean(exp(nu(i1).*(f1(:,se)+f2) - (yt(i1,2)-lik.stime(se)).*la1(:,se).*eta2 - sum(sd.*la1(:,(sb+1):(se-1)),2).*eta2 - (lik.stime(sb+1)-yt(i1,1)).*la1(:,sb).*eta2)); + end + end + end + end + Ey = []; + Vary = []; + lpyt=log(Py); + + end + +% function [logM_0, m_1, sigm2hati1] = coxph_tiltedMoments(sigm2_i, myy_i, nutmp, sd, c1) +% +% integrand = @(f) exp(-c1.*exp(f).*sd + nutmp*(f+log(c1)) - log(sigm2_i)/2 - log(2*pi)/2 - 0.5*(f-myy_i).^2./sigm2_i); +% RTOL = 1.e-6; +% ATOL = 1.e-10; +% minf=myy_i+sqrt(sigm2_i)*(-6); +% maxf=myy_i+sqrt(sigm2_i)*(6); +% +% [m_0, m_1, m_2] = quad_moments(integrand, minf, maxf, RTOL, ATOL); +% sigm2hati1 = m_2 - m_1.^2; +% +% % If the second central moment is less than cavity variance +% % integrate more precisely. Theoretically for log-concave +% % likelihood should be sigm2hati1 < sigm2_i. +% +% if sigm2hati1 >= sigm2_i +% ATOL = ATOL.^2; +% RTOL = RTOL.^2; +% [m_0, m_1, m_2] = quad_moments(tf, minf, maxf, RTOL, ATOL); +% sigm2hati1 = m_2 - m_1.^2; +% if sigm2hati1 >= sigm2_i +% error('lik_poisson_tilted_moments: sigm2hati1 >= sigm2_i'); +% end +% end +% logM_0 = log(m_0); +% end + + +% function [df,minf,maxf] = init_coxph_norm(yy,myy_i,sigm2_i,avgE,r) +% %INIT_COXPH_NORM +% % +% % Description +% % Return function handle to a function evaluating +% % Negative-Binomial * Gaussian which is used for evaluating +% % (likelihood * cavity) or (likelihood * posterior) Return +% % also useful limits for integration. This is private function +% % for lik_coxph. +% % +% % See also +% % LIK_COXPH_TILTEDMOMENTS, LIK_COXPH_SITEDERIV, +% % LIK_COXPH_PREDY +% +% % avoid repetitive evaluation of constant part +% ldconst = -gammaln(r)-gammaln(yy+1)+gammaln(r+yy)... +% - log(sigm2_i)/2 - log(2*pi)/2; +% % Create function handle for the function to be integrated +% df = @coxph_norm; +% % use log to avoid underflow, and derivates for faster search +% ld = @log_coxph_norm; +% ldg = @log_coxph_norm_g; +% ldg2 = @log_coxph_norm_g2; +% +% % Set the limits for integration +% % Negative-binomial likelihood is log-concave so the coxph_norm +% % function is unimodal, which makes things easier +% if yy==0 +% % with yy==0, the mode of the likelihood is not defined +% % use the mode of the Gaussian (cavity or posterior) as a first guess +% modef = myy_i; +% else +% % use precision weighted mean of the Gaussian approximation +% % of the Negative-Binomial likelihood and Gaussian +% mu=log(yy/avgE); +% s2=(yy+r)./(yy.*r); +% modef = (myy_i/sigm2_i + mu/s2)/(1/sigm2_i + 1/s2); +% end +% % find the mode of the integrand using Newton iterations +% % few iterations is enough, since the first guess in the right direction +% niter=4; % number of Newton iterations +% mindelta=1e-6; % tolerance in stopping Newton iterations +% for ni=1:niter +% g=ldg(modef); +% h=ldg2(modef); +% delta=-g/h; +% modef=modef+delta; +% if abs(delta)(modeld-lddiff) +% minf=minf-step*modes; +% minld=ld(minf); +% iter=iter+1; +% step=step*2; +% if iter>100 +% error(['lik_coxph -> init_coxph_norm: ' ... +% 'integration interval minimun not found ' ... +% 'even after looking hard!']) +% end +% end +% maxld=ld(maxf); +% iter=0; +% step=1; +% while maxld>(modeld-lddiff) +% maxf=maxf+step*modes; +% maxld=ld(maxf); +% iter=iter+1; +% step=step*2; +% if iter>100 +% error(['lik_coxph -> init_coxph_norm: ' ... +% 'integration interval maximun not found ' ... +% 'even after looking hard!']) +% end +% end +% +% % while minld>(modeld-lddiff) +% % minf=minf-modes; +% % minld=ld(minf); +% % iter=iter+1; +% % if iter>100 +% % error(['lik_coxph -> init_coxph_norm: ' ... +% % 'integration interval minimun not found ' ... +% % 'even after looking hard!']) +% % end +% % end +% % maxld=ld(maxf); +% % while maxld>(modeld-lddiff) +% % maxf=maxf+modes; +% % maxld=ld(maxf); +% % iter=iter+1; +% % if iter>100 +% % error(['lik_coxph -> init_coxph_norm: ' ... +% % 'integration interval maximum not found ' ... +% % 'even after looking hard!']) +% % end +% % +% % end +% +% function integrand = coxph_norm(f) +% % Negative-binomial * Gaussian +% mu = avgE.*exp(f); +% integrand = exp(ldconst ... +% +yy.*(log(mu)-log(r+mu))+r.*(log(r)-log(r+mu)) ... +% -0.5*(f-myy_i).^2./sigm2_i); +% end +% +% function log_int = log_coxph_norm(f) +% % log(Negative-binomial * Gaussian) +% % log_coxph_norm is used to avoid underflow when searching +% % integration interval +% mu = avgE.*exp(f); +% log_int = ldconst... +% +yy.*(log(mu)-log(r+mu))+r.*(log(r)-log(r+mu))... +% -0.5*(f-myy_i).^2./sigm2_i; +% end +% +% function g = log_coxph_norm_g(f) +% % d/df log(Negative-binomial * Gaussian) +% % derivative of log_coxph_norm +% mu = avgE.*exp(f); +% g = -(r.*(mu - yy))./(mu.*(mu + r)).*mu ... +% + (myy_i - f)./sigm2_i; +% end +% +% function g2 = log_coxph_norm_g2(f) +% % d^2/df^2 log(Negative-binomial * Gaussian) +% % second derivate of log_coxph_norm +% mu = avgE.*exp(f); +% g2 = -(r*(r + yy))/(mu + r)^2.*mu ... +% -1/sigm2_i; +% end +% +% end + + function p = lik_coxph_invlink(lik, f, z) + %LIK_COXPH_INVLINK Returns values of inverse link function + % + % Description + % P = LIK_COXPH_INVLINK(LIK, F) takes a likelihood structure LIK and + % latent values F and returns the values of inverse link function P. + % This subfunction is needed when using function gp_predprcty. + % + % See also + % LIK_COXPH_LL, LIK_COXPH_PREDY + + p = exp(f); + end + + + + function [cdf,Ey,Vary] = lik_coxph_predcdf(lik,Ef,Covf,yt) + %LIK_LOGLOGISTIC_PREDCDF Returns the predictive cdf evaluated at yt + % + % Description + % CDF = LIK_LOGLOGISTIC_PREDCDF(LIK, EF, VARF, YT) + % Returns the predictive cdf evaluated at YT given likelihood + % structure LIK, posterior mean EF and posterior Variance VARF + % of the latent variable. This subfunction is needed when using + % functions gp_predcdf or gp_kfcv_cdf. + % + % See also + % GP_PREDCDF + + + if isfield(lik, 'stratificationVariables') + ntime=size(lik.xtime,1)*lik.n_u; + else + ntime=size(lik.xtime,1); + end + Ef1 = Ef(1:ntime); Ef(1:ntime) = []; Ef2 = Ef; + nsamps = 10000; + sd=lik.stime(2)-lik.stime(1); + Sigm_tmp=Covf; + [nn1,nn2,cc]=size(Sigm_tmp); + if cc==1 && nn1~=nn2 + f_star=bsxfun(@plus,[Ef1;Ef2]', ... + bsxfun(@times,sqrt(Sigm_tmp'),randn(nsamps,ntime+size(Ef2,1)))); + else + Sigm_tmp=(Sigm_tmp+Sigm_tmp')./2; + % f_star=mvnrnd(Ef1, Sigm_tmp(1:ntime,1:ntime), nsamps); + f_star=mvnrnd([Ef1;Ef2], Sigm_tmp, nsamps); + end + + f1=f_star(:,1:ntime); + f2=f_star(:,(ntime+1):end); + + la1=exp(f1); + eta2=exp(f2); + + mST=zeros(size(eta2,2),1); + if size(yt,2) == 1 + % Integrate from zero to yt + cumsumtmp=cumsum(la1'*sd)'; + % t=binsgeq(gp.lik.xtime,yt(i)); + for i1=1:size(eta2,2) + Stime=exp(-bsxfun(@times,cumsumtmp,eta2(:,i1))); + mStime=mean(Stime); + % for i=1:size(yt,1) + mST(i1)=mStime(binsgeq(lik.xtime,yt(i1))); + %end + end + cdf = 1- mST; + + else + error('Size(yt,2) ~= 1'); + end + + end + + function reclik = lik_coxph_recappend(reclik, ri, lik) + %RECAPPEND Append the parameters to the record + % + % Description + % RECLIK = GPCF_COXPH_RECAPPEND(RECLIK, RI, LIK) takes a + % likelihood record structure RECLIK, record index RI and + % likelihood structure LIK with the current MCMC samples of + % the parameters. Returns RECLIK which contains all the old + % samples and the current samples from LIK. This subfunction + % is needed when using MCMC sampling (gp_mc). + % + % See also + % GP_MC + + % Initialize record + if nargin == 2 + reclik=ri; + + % Set the function handles + reclik.fh.pak = @lik_coxph_pak; + reclik.fh.unpak = @lik_coxph_unpak; + reclik.fh.lp = @lik_coxph_lp; + reclik.fh.lpg = @lik_coxph_lpg; + reclik.fh.ll = @lik_coxph_ll; + reclik.fh.llg = @lik_coxph_llg; + reclik.fh.llg2 = @lik_coxph_llg2; + reclik.fh.llg3 = @lik_coxph_llg3; +% reclik.fh.tiltedMoments = @lik_coxph_tiltedMoments; + reclik.fh.predy = @lik_coxph_predy; + reclik.fh.invlink = @lik_coxph_invlink; + reclik.fh.recappend = @lik_coxph_recappend; + return + else + + reclik.xtime=lik.xtime; + reclik.stime=lik.stime; + + if isfield(lik, 'stratificationVariables') + reclik.stratificationVariables=lik.stratificationVariables; + if isfield(lik,removeStratificationVariables) + reclik.removeStratificationVariables=lik.removeStratificationVariables; + end + end + end + end + + +end + diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m new file mode 100644 index 00000000..fd1f0b04 --- /dev/null +++ b/gp/lik_gaussianbl.m @@ -0,0 +1,369 @@ +function lik = lik_gaussianbl(varargin) +%LIK_GAUSSIAN Create a Gaussian likelihood structure +% +% Description +% LIK = LIK_GAUSSIANBL('PARAM1',VALUE1,'PARAM2,VALUE2,...) +% creates a Gaussian likelihood structure in which the named +% parameters have the specified values. Any unspecified +% parameters are set to default values. +% +% LIK = LIK_GAUSSIANBL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) +% modify a likelihood function structure with the named +% parameters altered with the specified values. +% +% Parameters for Gaussian likelihood function [default] +% sigma2 - variance of the independent noise [0.1] for each +% block of inputs. If noiseSigma2 is a vector each +% entry of the vector specifies noise variance for +% a block of inputs defined by the last column of +% the input matrix X. The variances are set for blocks +% according to 'bl_indic' field so that sigma2(i) is a +% noise variance of the inputs whose last column equals +% bl_indic(i). +% bl_indic - block indicator vector [empty matrix]. If +% length(sigma2)>1 bl_indic has to be the same length +% as sigma2. +% sigma2_prior - prior for sigma2 [prior_logunif] +% +% Note! If the prior is 'prior_fixed' then the parameter in +% question is considered fixed and it is not handled in +% optimization, grid integration, MCMC etc. +% +% See also +% GP_SET, PRIOR_*, LIK_* + +% Internal note: Because Gaussian noise can be combined +% analytically to the covariance matrix, lik_gaussian is internally +% little between lik_* and gpcf_* functions. + +% Copyright (c) 2007-2011 Jarno Vanhatalo +% Copyright (c) 2010 Aki Vehtari + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'LIK_GAUSSIANBL'; + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isvector(x) && all(x>0)); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','bl_indic',0.1, @(x) isvector(x)); + ip=iparser(ip,'parse',varargin{:}); + lik=ip.Results.lik; + + if isempty(lik) + init=true; + lik.type = 'GaussianBL'; + else + if ~isfield(lik,'type') || ~isequal(lik.type,'GaussianBL') + error('First argument does not seem to be a valid likelihood function structure') + end + init=false; + end + + % Initialize parameters + if init || ~ismember('sigma2',ip.UsingDefaults) + lik.sigma2 = ip.Results.sigma2; + lik.bl_indic = ip.Results.bl_indic; + end + + if length(lik.sigma2)> 1 || length(lik.bl_indic) > 1 + if length(lik.sigma2) ~= length(lik.bl_indic) + error('sigma2 and bl_indic has to be same length') + end + end + % Initialize prior structure + if init + lik.p=[]; + end + if init || ~ismember('sigma2_prior',ip.UsingDefaults) + lik.p.sigma2=ip.Results.sigma2_prior; + end + if init + % Set the function handles to the nested functions + lik.fh.pak = @lik_gaussianbl_pak; + lik.fh.unpak = @lik_gaussianbl_unpak; + lik.fh.lp = @lik_gaussianbl_lp; + lik.fh.lpg = @lik_gaussianbl_lpg; + lik.fh.cfg = @lik_gaussianbl_cfg; + lik.fh.trcov = @lik_gaussianbl_trcov; + lik.fh.trvar = @lik_gaussianbl_trvar; + lik.fh.recappend = @lik_gaussianbl_recappend; + end + + function [w s] = lik_gaussianbl_pak(lik) + %LIK_GAUSSIANBL_PAK Combine likelihood parameters into one vector. + % + % Description + % W = LIK_GAUSSIANBL_PAK(LIK) takes a likelihood structure LIK + % and combines the parameters into a single row vector W. + % This is a mandatory subfunction used for example in energy + % and gradient computations. + % + % w = [ log(lik.sigma2) + % (hyperparameters of lik.magnSigma2)]' + % + % See also + % LIK_GAUSSIANBL_UNPAK + + w = []; s = {}; + if ~isempty(lik.p.sigma2) + w = log(lik.sigma2); + if numel(lik.sigma2)>1 + s = [s; sprintf('log(gaussian.sigma2 x %d)',numel(lik.sigma2))]; + else + s = [s; 'log(gaussian.sigma2)']; + end + % Hyperparameters of noiseSigma2 + [wh sh] = lik.p.sigma2.fh.pak(lik.p.sigma2); + w = [w wh]; + s = [s sh]; + end + end + + function [lik, w] = lik_gaussianbl_unpak(lik, w) + %LIK_GAUSSIANBL_UNPAK Extract likelihood parameters from the vector. + % + % Description + % W = LIK_GAUSSIANBL_UNPAK(W, LIK) takes a likelihood structure + % LIK and extracts the parameters from the vector W to the LIK + % structure. This is a mandatory subfunction used for example + % in energy and gradient computations. + % + % Assignment is inverse of + % w = [ log(lik.sigma2) + % (hyperparameters of lik.magnSigma2)]' + % + % See also + % LIK_GAUSSIANBL_PAK + + if ~isempty(lik.p.sigma2) + i2=length(lik.sigma2); + lik.sigma2 = exp(w(1:i2)); + w = w(i2+1:end); + + % Hyperparameters of sigma2 + [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); + lik.p.sigma2 = p; + end + end + + function lp = lik_gaussianbl_lp(lik) + %LIK_GAUSSIANBL_LP Evaluate the log prior of likelihood parameters + % + % Description + % LP = LIK_T_LP(LIK) takes a likelihood structure LIK and + % returns log(p(th)), where th collects the parameters. This + % subfunction is needed when there are likelihood parameters. + % + % See also + % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_G, GP_E + + lp = 0; + + if ~isempty(lik.p.sigma2) + likp=lik.p; + lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + sum(log(lik.sigma2)); + end + end + + function lpg = lik_gaussianbl_lpg(lik) + %LIK_GAUSSIANBL_LPG Evaluate gradient of the log prior with respect + % to the parameters. + % + % Description + % LPG = LIK_GAUSSIANBL_LPG(LIK) takes a Gaussian likelihood + % function structure LIK and returns LPG = d log (p(th))/dth, + % where th is the vector of parameters. This subfunction is + % needed when there are likelihood parameters. + % + % See also + % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G + + lpg = []; + + if ~isempty(lik.p.sigma2) + likp=lik.p; + i2=length(lik.sigma2); + + lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); + lpg = lpgs(1:i2).*lik.sigma2 + 1; + if length(lpgs) > 1 + lpg = [lpg lpgs(i2+1:end)]; + end + end + end + + function DKff = lik_gaussianbl_cfg(lik, x, x2) + %LIK_GAUSSIANBL_CFG Evaluate gradient of covariance with respect to + % Gaussian noise + % + % Description + % Gaussian likelihood is a special case since it can be + % analytically combined with covariance functions and thus we + % compute gradient of covariance instead of gradient of likelihood. + % + % DKff = LIK_GAUSSIANBL_CFG(LIK, X) takes a Gaussian likelihood + % function structure LIK, a matrix X of input vectors and + % returns DKff, the gradients of Gaussian noise covariance + % matrix Kff = k(X,X) with respect to th (cell array with + % matrix elements). This subfunction is needed only in + % Gaussian likelihoods. + % + % DKff = LIK_GAUSSIANBL_CFG(LIK, X, X2) takes a Gaussian + % likelihood function structure LIK, a matrix X of input + % vectors and returns DKff, the gradients of Gaussian noise + % covariance matrix Kff = k(X,X) with respect to th (cell + % array with matrix elements). This subfunction is needed only in + % Gaussian likelihoods. + % + % See also + % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G + + [n, m] =size(x); + + if length(lik.sigma2)==1 + DKff{1} = lik.sigma2; + else + for i1 = 1:length(lik.bl_indic) + ind = find(x(:,end)==lik.bl_indic(i1)); + DKff{i1} = sparse(ind,ind,lik.sigma2(i1),n,n); + end + end + + end + + function DKff = lik_gaussianbl_ginput(lik, x, t, g_ind, gdata_ind, gprior_ind, varargin) + %LIK_GAUSSIANBL_GINPUT Evaluate gradient of likelihood function with + % respect to x. + % + % Description + % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X) takes a likelihood + % function structure LIK, a matrix X of input vectors and + % returns DKff, the gradients of likelihood matrix Kff = + % k(X,X) with respect to X (cell array with matrix elements). + % This subfunction is needed when computing gradients with + % respect to inducing inputs in sparse approximations. + % + % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X, X2) takes a likelihood + % function structure LIK, a matrix X of input vectors and + % returns DKff, the gradients of likelihood matrix Kff = + % k(X,X2) with respect to X (cell array with matrix elements). + % This subfunction is needed when computing gradients with + % respect to inducing inputs in sparse approximations. + % + % See also + % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G + + end + + function C = lik_gaussianbl_trcov(lik, x) + %LIK_GAUSSIANBL_TRCOV Evaluate training covariance matrix + % corresponding to Gaussian noise + % Description + % C = LIK_GAUSSIANBL_TRCOV(GP, TX) takes in covariance function + % of a Gaussian process GP and matrix TX that contains + % training input vectors. Returns covariance matrix C. Every + % element ij of C contains covariance between inputs i and j + % in TX. This subfunction is needed only in Gaussian likelihoods. + % + % See also + % LIK_GAUSSIANBL_COV, LIK_GAUSSIANBL_TRVAR, GP_COV, GP_TRCOV + + [n, m] =size(x); + + s2 = zeros(n,1); + if length(lik.sigma2)==1 + s2 = ones(n,1).*lik.sigma2; + else + for i1 = 1:length(lik.bl_indic) + s2(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); + end + end + + C = sparse(1:n,1:n,s2,n,n); + + end + + function C = lik_gaussianbl_trvar(lik, x) + %LIK_GAUSSIANBL_TRVAR Evaluate training variance vector + % corresponding to Gaussian noise + % + % Description + % C = LIK_GAUSSIANBL_TRVAR(LIK, TX) takes in covariance function + % of a Gaussian process LIK and matrix TX that contains + % training inputs. Returns variance vector C. Every element i + % of C contains variance of input i in TX. This subfunction is + % needed only in Gaussian likelihoods. + % + % See also + % LIK_GAUSSIANBL_COV, GP_COV, GP_TRCOV + + [n, m] =size(x); + + C = zeros(n,1); + if length(lik.sigma2)==1 + C = ones(n,1).*lik.sigma2; + else + for i1 = 1:length(lik.bl_indic) + C(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); + end + end + + end + + function reccf = lik_gaussianbl_recappend(reccf, ri, lik) + %RECAPPEND Record append + % + % Description + % RECCF = LIK_GAUSSIANBL_RECAPPEND(RECCF, RI, LIK) takes a + % likelihood function record structure RECCF, record index RI + % and likelihood function structure LIK with the current MCMC + % samples of the parameters. Returns RECCF which contains all + % the old samples and the current samples from LIK. This + % subfunction is needed when using MCMC sampling (gp_mc). + % + % See also + % GP_MC and GP_MC -> RECAPPEND + + % Initialize record + if nargin == 2 + reccf.type = 'lik_gaussianbl'; + + % Initialize parameters + reccf.sigma2 = []; + reccf.bl_indic = []; + + % Set the function handles + reccf.fh.pak = @lik_gaussianbl_pak; + reccf.fh.unpak = @lik_gaussianbl_unpak; + reccf.fh.lp = @lik_gaussianbl_lp; + reccf.fh.lpg = @lik_gaussianbl_lpg; + reccf.fh.cfg = @lik_gaussianbl_cfg; + reccf.fh.trcov = @lik_gaussianbl_trcov; + reccf.fh.trvar = @lik_gaussianbl_trvar; + reccf.fh.recappend = @lik_gaussianbl_recappend; + reccf.p=[]; + reccf.p.sigma2=[]; + if ~isempty(ri.p.sigma2) + reccf.p.sigma2 = ri.p.sigma2; + end + return + end + + likp = lik.p; + + % record sigma + if ~isempty(lik.sigma2) + reccf.sigma2(ri,:)=lik.sigma2; + reccf.bl_indic(ri,:)=lik.bl_indic; + if ~isempty(lik.p.sigma2) + reccf.p.sigma2 = likp.sigma2.fh.recappend(reccf.p.sigma2, ri, likp.sigma2); + end + elseif ri==1 + reccf.sigma2=[]; + end + end + +end diff --git a/gp/lik_inputdependentnoise.m b/gp/lik_inputdependentnoise.m new file mode 100644 index 00000000..ba2c6eb2 --- /dev/null +++ b/gp/lik_inputdependentnoise.m @@ -0,0 +1,510 @@ +function lik = lik_inputdependentnoise(varargin) +%lik_inputdependentnoise Create input-dependent noise likelihood structure +% +% Description +% LIK = LIK_INPUTDEPENDENTNOISE('PARAM1',VALUE1,'PARAM2,VALUE2,...) +% creates a Gaussian likelihood with input dependent noise structure +% in which the named parameters have the specified values. Any unspecified +% parameters are set to default values. +% +% LIK = LIK_INPUTDEPENDENTNOISE(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) +% modify a likelihood function structure with the named +% parameters altered with the specified values. +% +% Parameters for Gaussian likelihood function [default] +% sigma2 - variance [0.1] +% sigma2_prior - prior for sigma2 [prior_logunif] +% n - number of observations per input (See using average +% observations below) +% +% Note! If the prior is 'prior_fixed' then the parameter in +% question is considered fixed and it is not handled in +% optimization, grid integration, MCMC etc. +% +% The likelihood is defined as follows: +% __ n +% p(y|f1, f2) = || i=1 N(y_i | f1_i, sigma2*exp(f2_i)) +% +% where f1 is the first latent variable defining the mean of the +% gaussian distribution, f2 is the second latent variable defining +% the noise structure and sigma2 is coefficient for noise. +% +% See also +% GP_SET, LIK_*, PRIOR_* +% + +% Copyright (c) 2007-2010 Jarno Vanhatalo & Jouni Hartikainen +% Copyright (c) 2010 Aki Vehtari +% Copyright (c) 2011 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 2 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'LIK_INPUTDEPENDENTNOISE'; + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); + lik=ip.Results.lik; + + if isempty(lik) + init=true; + lik.nondiagW=true; + lik.type = 'Inputdependentnoise'; + else + if ~isfield(lik,'type') || ~isequal(lik.type,'Inputdependentnoise') + error('First argument does not seem to be a valid likelihood function structure') + end + init=false; + end + + % Initialize parameters + if init || ~ismember('sigma2',ip.UsingDefaults) + lik.sigma2 = ip.Results.sigma2; + end + % Initialize prior structure + if init + lik.p=[]; + end + if init || ~ismember('sigma2_prior',ip.UsingDefaults) + lik.p.sigma2=ip.Results.sigma2_prior; + end + + if init + % Set the function handles to the subfunctions + lik.fh.pak = @lik_inputdependentnoise_pak; + lik.fh.unpak = @lik_inputdependentnoise_unpak; + lik.fh.lp = @lik_inputdependentnoise_lp; + lik.fh.lpg = @lik_inputdependentnoise_lpg; + lik.fh.ll = @lik_inputdependentnoise_ll; + lik.fh.llg = @lik_inputdependentnoise_llg; + lik.fh.llg2 = @lik_inputdependentnoise_llg2; + lik.fh.llg3 = @lik_inputdependentnoise_llg3; + lik.fh.predy = @lik_inputdependentnoise_predy; + lik.fh.invlink = @lik_inputdependentnoise_invlink; + lik.fh.predprcty = @lik_inputdependentnoise_predprcty; + lik.fh.recappend = @lik_inputdependentnoise_recappend; + end + + function [w,s] = lik_inputdependentnoise_pak(lik) + %LIK_INPUTDEPENDENTNOISE_PAK Combine likelihood parameters into one vector. + % + % Description + % W = LIK_INPUTDEPENDENTNOISE_PAK(LIK) takes a likelihood structure LIK and + % combines the parameters into a single row vector W. This is a mandatory + % subfunction used for example in energy and gradient computations. + % + % w = log(lik.sigma2) + % + % See also + % LIK_INPUTDEPENDENTNOISE_UNPAK, GP_PAK + + w = []; s = {}; + if isfield(lik.p, 'sigma2') && ~isempty(lik.p.sigma2) + w = [w log(lik.sigma2)]; + s = [s 'log(gaussian.sigma2)']; + % Hyperparameters of sigma2 + [wh sh] = lik.p.sigma2.fh.pak(lik.p.sigma2); + w = [w wh]; + s = [s sh]; + end + end + + + function [lik, w] = lik_inputdependentnoise_unpak(lik, w) + %LIK_INPUTDEPENDENTNOISE_UNPAK Extract likelihood parameters from the vector. + % + % Description + % [LIK, W] = LIK_INPUTDEPENDENTNOISE_UNPAK(W, LIK) takes a likelihood + % structure LIK and extracts the parameters from the vector W + % to the LIK structure. This is a mandatory subfunction used for + % example in energy and gradient computations. + % + % Assignment is inverse of + % w = log(lik.sigma2) + % + % See also + % LIK_INPUTDEPENDENTNOISE_PAK, GP_UNPAK + + if ~isempty(lik.p.sigma2) + lik.sigma2 = exp(w(1)); + w = w(2:end); + + % Hyperparameters of sigma2 + [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); + lik.p.sigma2 = p; + end + end + + + function lp = lik_inputdependentnoise_lp(lik, varargin) + %LIK_INPUTDEPENDENTNOISE_LP log(prior) of the likelihood parameters + % + % Description + % LP = LIK_INPUTDEPENDENTNOISE_LP(LIK) takes a likelihood structure + % LIK and returns log(p(th)), where th collects the parameters. This + % subfunction is needed when there are likelihood parameters. + % + % See also + % LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_E + + + % If prior for sigma2 parameter, add its contribution + lp = 0; + + if ~isempty(lik.p.sigma2) + likp=lik.p; + lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + log(lik.sigma2); + end + + end + + + function lpg = lik_inputdependentnoise_lpg(lik) + %LIK_INPUTDEPENDENTNOISE_LPG d log(prior)/dth of the likelihood + % parameters th + % + % Description + % E = LIK_INPUTDEPENDENTNOISE_LPG(LIK) takes a likelihood structure + % LIK and returns d log(p(th))/dth, where th collects the parameters. + % This subfunction is needed when there are likelihood parameters. + % + % See also + % LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_G + + + lpg = []; + + if ~isempty(lik.p.sigma2) + likp=lik.p; + + lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); + lpg = lpgs(1).*lik.sigma2 + 1; + if length(lpgs) > 1 + lpg = [lpg lpgs(2:end)]; + end + end + end + + function ll = lik_inputdependentnoise_ll(lik, y, ff, z) + %LIK_INPUTDEPENDENTNOISE_LL Log likelihood + % + % Description + % LL = LIK_INPUTDEPENDENTNOISE_LL(LIK, Y, F, Z) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z, and + % latent values F. Returns the log likelihood, log p(y|f,z). + % This subfunction is needed when using Laplace approximation + % or MCMC for inference with non-Gaussian likelihoods. This + % subfunction is also used in information criteria (DIC, WAIC) + % computations. + % + % See also + % LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_E + + + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + expf2=exp(f2); + expf2(isinf(expf2))=realmax; + sigma2 = lik.sigma2; + + ll = sum(-0.5*log(2*pi.*sigma2.*expf2) - 1./(2.*sigma2.*expf2).*(y-f1).^2); + + end + + function llg = lik_inputdependentnoise_llg(lik, y, ff, param, z) + %LIK_INPUTDEPENDENTNOISE_LLG Gradient of the log likelihood + % + % Description + % LLG = LIK_INPUTDEPENDENTNOISE_LLG(LIK, Y, F, PARAM) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z and + % latent values F. Returns the gradient of the log likelihood + % with respect to PARAM. At the moment PARAM can be 'param' or + % 'latent'. This subfunction is needed when using Laplace + % approximation or MCMC for inference with non-Gaussian likelihoods. + % + % See also + % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_LLG2, LIK_INPUTDEPENDENTNOISE_LLG3, GPLA_E + + + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + expf2 = exp(f2); + sigma2 = lik.sigma2; + + switch param + case 'param' + + llg=sum(-0.5./sigma2+(y-f1).^2./(2*expf2.*sigma2^2)); + % correction for the log transformation + llg = llg.*lik.sigma2; + + case 'latent' + + llg1= (y-f1)./(sigma2*expf2); + llg2= -0.5+(y-f1).^2./(2.*expf2.*sigma2); + +% llg1=(-y+f1)/(sqrt(2*pi).*(sigma2.*expf2).^(3/2)).*exp(-1/(2.*sigma2*expf2).*(y-f1).^2); +% llg2=-exp(f2-(y-f1).^2/(2.*expf2.*sigma2)).*sigma2/(2.*sqrt(2.*pi).*(expf2.*sigma2).^(3/2))+exp(-f2-(y-f1).^2/(2*expf2*sigma2)).*(y-f1).^2/(2.*sqrt(2.*pi.*expf2).*sigma2.^(3/2)); + + llg=[llg1; llg2]; + end + end + + function [llg2] = lik_inputdependentnoise_llg2(lik, y, ff, param, z) + %function [pi_vec, pi_mat] = lik_inputdependentnoise_llg2(lik, y, ff, param, z) + %LIK_INPUTDEPENDENTNOISE_LLG2 Second gradients of the log likelihood + % + % Description + % LLG2 = LIK_INPUTDEPENDENTNOISE_LLG2(LIK, Y, F, PARAM) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z, and + % latent values F. Returns the Hessian of the log likelihood + % with respect to PARAM. At the moment PARAM can be only + % 'latent'. LLG2 is a vector with diagonal elements of the + % Hessian matrix (off diagonals are zero). This subfunction + % is needed when using Laplace approximation or EP for + % inference with non-Gaussian likelihoods. + % + % See also + % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG3, GPLA_E + + + f=ff(:); + + n=length(y); + f1=f(1:n); + f2=f((n+1):2*n); + sigma2 = lik.sigma2; + expf2=exp(f2); + + switch param + case 'param' + + case 'latent' + +% llg2 = [2./(sigma2.*expf2); 3/2.*(y-f1).^2./(sigma2.*expf2)]; +% llg2mat = [diag(1./sqrt(sigma2.*expf2)); diag(-(y-f1)./sqrt(sigma2.*expf2))]; + + llg2_11=-1./(sigma2.*expf2); + llg2_12=-(y-f1)./(sigma2*expf2); + llg2_22=-(y-f1).^2./(2.*sigma2.*expf2); + + llg2 = [llg2_11 llg2_12; llg2_12 llg2_22]; + + case 'latent+param' + + llg2_1=-(y-f1)./(expf2.*sigma2^2); + llg2_2=-(y-f1).^2./(2*sigma2.^2.*expf2); + + llg2=[llg2_1; llg2_2]; + % correction for the log transformation + llg2 = llg2.*lik.sigma2; + + end + end + + function llg3 = lik_inputdependentnoise_llg3(lik, y, ff, param, z) + %LIK_INPUTDEPENDENTNOISE_LLG3 Third gradients of the log likelihood + % + % Description + % LLG3 = LIK_INPUTDEPENDENTNOISE_LLG3(LIK, Y, F, PARAM) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z and + % latent values F and returns the third gradients of the log + % likelihood with respect to PARAM. At the moment PARAM can be + % only 'latent'. LLG3 is a vector with third gradients. This + % subfunction is needed when using Laplace approximation for + % inference with non-Gaussian likelihoods. + % + % See also + % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_LLG, LIK_INPUTDEPENDENTNOISE_LLG2, GPLA_E, GPLA_G + + f=ff(:); + + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + expf2=exp(f2); + sigma2 = lik.sigma2; + + switch param + case 'param' + + case 'latent' + nl=2; + llg3=zeros(nl,nl,nl,n); + + % y=0: + % thrid derivative derivative wrt f1 (11) + % llg3(1,1,1,:) = 0 + % thrid derivative derivative wrt f2 (11) + llg3(1,1,2,:) = 1./(expf2.*sigma2); + + % thrid derivative derivative wrt f1 (12/21) + llg3(1,2,1,:) = 1./(expf2.*sigma2); + llg3(2,1,1,:) = llg3(1,2,1,:); + % thrid derivative derivative wrt f2 (12/21) + llg3(1,2,2,:) = (y-f1)./(expf2.*sigma2); + llg3(2,1,2,:) = llg3(1,2,2,:); + + % thrid derivative derivative wrt f1 (22) + llg3(2,2,1,:) = llg3(1,2,2,:); + % thrid derivative derivative wrt f1 (22) + llg3(2,2,2,:) = (y-f1).^2./(2.*expf2.*sigma2); + + case 'latent2+param' + + llg3_11=1./(expf2.*sigma2.^2); + llg3_12=(y-f1)./(expf2.*sigma2.^2); + llg3_22=(y-f1).^2./(2.*expf2.*sigma2.^2); + + + llg3 = [diag(llg3_11) diag(llg3_12); diag(llg3_12) diag(llg3_22)]; + % correction for the log transformation + llg3 = llg3.*lik.sigma2; + + end + end + + + function [lpy, Ey, Vary] = lik_inputdependentnoise_predy(lik, Ef, Varf, yt, z) + %LIK_INPUTDEPENDENTNOISE_PREDY Returns the predictive mean, variance and density of y + % + % Description + % [LPY] = LIK_INPUTDEPENDENTNOISE_PREDY(LIK, EF, VARF, YT) takes a + % likelihood structure LIK, posterior mean EF, posterior + % variance VARF of the latent variable and observations YT and + % returns the logarithm of the predictive density PY of YT, that is + % p(yt | th) = \int p(yt | f, th) p(f|y) df. + % This subfunction is needed when computing posterior predictive + % distributions for future observations. + % + % [LPY, EY, VARY] = LIK_INPUTDEPENDENTNOISE_PREDY(LIK, EF, VARF YT) + % Returns also the posterior predictive mean EY and variance VARY of + % the observations related to the latent variables. This subfunction + % is needed when computing posterior predictive distributions for + % future observations. + % + % See also + % GPLA_PRED, GPEP_PRED, GPMC_PRED + + +% ntest=size(yt,1); + Ef = Ef(:); + ntest = 0.5*size(Ef,1); + Ef1=Ef(1:ntest); Ef2=Ef(ntest+1:end); + if size(Varf,2) == size(Varf,1) + Varf1=diag(Varf(1:ntest,1:ntest));Varf2=diag(Varf(ntest+1:end,ntest+1:end)); + else + if size(Varf,2)==1 + Varf=reshape(Varf,ntest,2); + end + Varf1=Varf(:,1); Varf2=Varf(:,2); + end + sigma2=lik.sigma2; + + lpy = zeros(size(yt)); + + Ey=zeros(size(yt)); + Vary=zeros(size(yt)); + + for i2=1:ntest + m1=Ef1(i2); m2=Ef2(i2); + s1=sqrt(Varf1(i2)); s2=sqrt(Varf2(i2)); + pd=@(f1,f2) norm_pdf(yt(i2), f1, sqrt(sigma2.*exp(f2))).*norm_pdf(f1,Ef1(i2),sqrt(Varf1(i2))).*norm_pdf(f2,Ef2(i2),sqrt(Varf2(i2))); + lpy(i2) = log(dblquad(pd, m1-6.*s1, m1+6.*s1, m2-6.*s2, m2+6.*s2)); + end +% sigma2 = lik.sigma2; +% Ef1=Ef(1:ntest); +% Ef2=Ef((ntest+1):2*ntest); +% Ey = Ef1; +% Vary = Varf + sigma2.*exp(Ef2); + + + end + + function prctys = lik_inputdependentnoise_predprcty(lik, Ef, Varf, zt, prcty) + %LIK_BINOMIAL_PREDPRCTY Returns the percentiles of predictive density of y + % + % Description + % PRCTY = LIK_BINOMIAL_PREDPRCTY(LIK, EF, VARF YT, ZT) + % Returns percentiles of the predictive density PY of YT, that is + % This requires also the succes counts YT, numbers of trials ZT. This + % subfunction is needed when using function gp_predprcty. + % + % See also + % GP_PREDPCTY + + n=size(Ef,1)./2; + prcty = prcty./100; + prcty = norminv(prcty, 0, 1); + prctys = bsxfun(@plus, Ef(1:n), bsxfun(@times, sqrt(Varf(1:n) + lik.sigma2.*exp(Ef(n+1:end))), prcty)); + + end + + function p = lik_inputdependentnoise_invlink(lik, f, z) + %LIK_INPUTDEPENDENTNOISE_INVLINK Returns values of inverse link function + % + % Description + % P = LIK_INPUTDEPENDENTNOISE_INVLINK(LIK, F) takes a likelihood structure LIK and + % latent values F and returns the values of inverse link function P. + % This subfunction is needed when using function gp_predprcty. + % + % See also + % LIK_INPUTDEPENDENTNOISE_LL, LIK_INPUTDEPENDENTNOISE_PREDY + + p = exp(f); + end + + function reclik = lik_inputdependentnoise_recappend(reclik, ri, lik) + %RECAPPEND Append the parameters to the record + % + % Description + % RECLIK = GPCF_INPUTDEPENDENTNOISE_RECAPPEND(RECLIK, RI, LIK) takes a + % likelihood record structure RECLIK, record index RI and + % likelihood structure LIK with the current MCMC samples of + % the parameters. Returns RECLIK which contains all the old + % samples and the current samples from LIK. This subfunction + % is needed when using MCMC sampling (gp_mc). + % + % See also + % GP_MC + + % Initialize record + if nargin == 2 + reclik.type = 'Inputdependentnoise'; + reclik.nondiagW=true; + + % Initialize parameter + + % Set the function handles + reclik.fh.pak = @lik_inputdependentnoise_pak; + reclik.fh.unpak = @lik_inputdependentnoise_unpak; + reclik.fh.lp = @lik_inputdependentnoise_lp; + reclik.fh.lpg = @lik_inputdependentnoise_lpg; + reclik.fh.ll = @lik_inputdependentnoise_ll; + reclik.fh.llg = @lik_inputdependentnoise_llg; + reclik.fh.llg2 = @lik_inputdependentnoise_llg2; + reclik.fh.llg3 = @lik_inputdependentnoise_llg3; + reclik.fh.predy = @lik_inputdependentnoise_predy; + reclik.fh.predprcty = @lik_inputdependentnoise_predprcty; + reclik.fh.invlink = @lik_inputdependentnoise_invlink; + reclik.fh.recappend = @lik_inputdependentnoise_recappend; + reclik.p=[]; + if ~isempty(ri.p.sigma2) + reclik.p.sigma2 = ri.p.sigma2; + end + return + end + + reclik.sigma2(ri,:)=lik.sigma2; + if ~isempty(lik.p.sigma2) + reclik.p.sigma2 = feval(lik.p.sigma2.fh.recappend, reclik.p.sigma2, ri, lik.p.sigma2); + end + end +end diff --git a/gp/lik_inputdependentweibull.m b/gp/lik_inputdependentweibull.m new file mode 100644 index 00000000..49c3d560 --- /dev/null +++ b/gp/lik_inputdependentweibull.m @@ -0,0 +1,514 @@ +function lik = lik_inputdependentweibull(varargin) +%LIK_INPUTDEPENDENTWEIBULL Create a right censored input dependent Weibull likelihood structure +% +% Description +% LIK = LIK_INPUTDEPENDENTWEIBULL('PARAM1',VALUE1,'PARAM2,VALUE2,...) +% creates a likelihood structure for right censored input dependent +% Weibull survival model in which the named parameters have the +% specified values. Any unspecified parameters are set to default +% values. +% +% LIK = LIK_INPUTDEPENDENTWEIBULL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) +% modify a likelihood structure with the named parameters +% altered with the specified values. +% +% Parameters for Weibull likelihood [default] +% shape - shape parameter r [1] +% shape_prior - prior for shape [prior_logunif] +% +% Note! If the prior is 'prior_fixed' then the parameter in +% question is considered fixed and it is not handled in +% optimization, grid integration, MCMC etc. +% +% The likelihood is defined as follows: +% __ n +% p(y|f1,f2, z) = || i=1 [ (r*exp(f2_i))^(1-z_i) exp( (1-z_i)*(-f1_i) +% +(1-z_i)*((r*exp(f2_i))-1)*log(y_i) +% -exp(-f1_i)*y_i^(r*exp(f2_i))) ] +% +% where r is the shape parameter of Weibull distribution. +% z is a vector of censoring indicators with z = 0 for uncensored event +% and z = 1 for right censored event. Here the second latent variable f2 +% implies the input dependance to the shape parameter in the original +% Weibull likelihood. +% +% When using the Weibull likelihood you need to give the vector z +% as an extra parameter to each function that requires also y. +% For example, you should call gpla_e as follows: gpla_e(w, gp, +% x, y, 'z', z) +% +% See also +% GP_SET, LIK_*, PRIOR_* +% + +% Copyright (c) 2011 Jaakko Riihimäki +% Copyright (c) 2011 Aki Vehtari +% Copyright (c) 2012 Ville Tolvanen + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'LIK_INPUTDEPENDENTWEIBULL'; + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','shape',1, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','shape_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); + lik=ip.Results.lik; + + if isempty(lik) + init=true; + lik.nondiagW=true; + lik.type = 'Inputdependent-Weibull'; + else + if ~isfield(lik,'type') || ~isequal(lik.type,'Weibull') + error('First argument does not seem to be a valid likelihood function structure') + end + init=false; + end + + % Initialize parameters + if init || ~ismember('shape',ip.UsingDefaults) + lik.shape = ip.Results.shape; + end + % Initialize prior structure + if init + lik.p=[]; + end + if init || ~ismember('shape_prior',ip.UsingDefaults) + lik.p.shape=ip.Results.shape_prior; + end + + if init + % Set the function handles to the subfunctions + lik.fh.pak = @lik_inputdependentweibull_pak; + lik.fh.unpak = @lik_inputdependentweibull_unpak; + lik.fh.lp = @lik_inputdependentweibull_lp; + lik.fh.lpg = @lik_inputdependentweibull_lpg; + lik.fh.ll = @lik_inputdependentweibull_ll; + lik.fh.llg = @lik_inputdependentweibull_llg; + lik.fh.llg2 = @lik_inputdependentweibull_llg2; + lik.fh.llg3 = @lik_inputdependentweibull_llg3; + lik.fh.invlink = @lik_inputdependentweibull_invlink; + lik.fh.predy = @lik_inputdependentweibull_predy; + lik.fh.recappend = @lik_inputdependentweibull_recappend; + end + +end + +function [w,s] = lik_inputdependentweibull_pak(lik) +%LIK_INPUTDEPENDENTWEIBULL_PAK Combine likelihood parameters into one vector. +% +% Description +% W = LIK_INPUTDEPENDENTWEIBULL_PAK(LIK) takes a likelihood structure LIK and +% combines the parameters into a single row vector W. This is a +% mandatory subfunction used for example in energy and gradient +% computations. +% +% w = log(lik.shape) +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_UNPAK, GP_PAK + + w=[];s={}; + if ~isempty(lik.p.shape) + w = log(lik.shape); + s = [s; 'log(weibull.shape)']; + [wh sh] = lik.p.shape.fh.pak(lik.p.shape); + w = [w wh]; + s = [s; sh]; + end +end + + +function [lik, w] = lik_inputdependentweibull_unpak(lik, w) +%LIK_INPUTDEPENDENTWEIBULL_UNPAK Extract likelihood parameters from the vector. +% +% Description +% [LIK, W] = LIK_INPUTDEPENDENTWEIBULL_UNPAK(W, LIK) takes a likelihood +% structure LIK and extracts the parameters from the vector W +% to the LIK structure. This is a mandatory subfunction used +% for example in energy and gradient computations. +% +% Assignment is inverse of +% w = log(lik.shape) +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_PAK, GP_UNPAK + + if ~isempty(lik.p.shape) + lik.shape = exp(w(1)); + w = w(2:end); + [p, w] = lik.p.shape.fh.unpak(lik.p.shape, w); + lik.p.shape = p; + end +end + + +function lp = lik_inputdependentweibull_lp(lik, varargin) +%LIK_INPUTDEPENDENTWEIBULL_LP log(prior) of the likelihood parameters +% +% Description +% LP = LIK_INPUTDEPENDENTWEIBULL_LP(LIK) takes a likelihood structure LIK and +% returns log(p(th)), where th collects the parameters. This +% subfunction is needed when there are likelihood parameters. +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_E + + +% If prior for shape parameter, add its contribution + lp=0; + if ~isempty(lik.p.shape) + lp = lik.p.shape.fh.lp(lik.shape, lik.p.shape) +log(lik.shape); + end + +end + + +function lpg = lik_inputdependentweibull_lpg(lik) +%LIK_INPUTDEPENDENTWEIBULL_LPG d log(prior)/dth of the likelihood +% parameters th +% +% Description +% E = LIK_INPUTDEPENDENTWEIBULL_LPG(LIK) takes a likelihood structure LIK and +% returns d log(p(th))/dth, where th collects the parameters. +% This subfunction is needed when there are likelihood parameters. +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_G + + lpg=[]; + if ~isempty(lik.p.shape) + % Evaluate the gprior with respect to shape + ggs = lik.p.shape.fh.lpg(lik.shape, lik.p.shape); + lpg = ggs(1).*lik.shape + 1; + if length(ggs) > 1 + lpg = [lpg ggs(2:end)]; + end + end +end + +function ll = lik_inputdependentweibull_ll(lik, y, ff, z) +%LIK_INPUTDEPENDENTWEIBULL_LL Log likelihood +% +% Description +% LL = LIK_INPUTDEPENDENTWEIBULL_LL(LIK, Y, F, Z) takes a likelihood +% structure LIK, survival times Y, censoring indicators Z, and +% latent values F. Returns the log likelihood, log p(y|f,z). +% This subfunction is needed when using Laplace approximation +% or MCMC for inference with non-Gaussian likelihoods. This +% subfunction is also used in information criteria (DIC, WAIC) +% computations. +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_E + + if isempty(z) + error(['lik_inputdependentweibull -> lik_inputdependentweibull_ll: missing z! '... + 'Weibull likelihood needs the censoring '... + 'indicators as an extra input z. See, for '... + 'example, lik_inputdependentweibull and gpla_e. ']); + end + + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + expf2=exp(f2); + expf2(isinf(expf2))=realmax; + a = lik.shape; + ll = sum((1-z).*(log(a*expf2) + (a*expf2-1).*log(y)-f1) - exp(-f1).*y.^(a*expf2)); + +end + +function llg = lik_inputdependentweibull_llg(lik, y, ff, param, z) +%LIK_INPUTDEPENDENTWEIBULL_LLG Gradient of the log likelihood +% +% Description +% LLG = LIK_INPUTDEPENDENTWEIBULL_LLG(LIK, Y, F, PARAM) takes a likelihood +% structure LIK, survival times Y, censoring indicators Z and +% latent values F. Returns the gradient of the log likelihood +% with respect to PARAM. At the moment PARAM can be 'param' or +% 'latent'. This subfunction is needed when using Laplace +% approximation or MCMC for inference with non-Gaussian likelihoods. +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_LLG2, LIK_INPUTDEPENDENTWEIBULL_LLG3, GPLA_E + + if isempty(z) + error(['lik_inputdependentweibull -> lik_inputdependentweibull_llg: missing z! '... + 'Weibull likelihood needs the censoring '... + 'indicators as an extra input z. See, for '... + 'example, lik_inputdependentweibull and gpla_e. ']); + end + + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + expf2=exp(f2); + expf2(isinf(expf2))=realmax; + a = lik.shape; + switch param + case 'param' + llg = sum((1-z).*(1./a + expf2.*log(y)) - exp(-f1).*y.^(a.*expf2).*log(y).*expf2); + % correction for the log transformation + llg = llg.*lik.shape; + case 'latent' + llg1 = -(1-z) + exp(-f1).*y.^(a.*expf2); + llg2 = (1-z).*(1 + a.*expf2.*log(y)) - exp(-f1).*y.^(a.*expf2).*log(y).*a.*expf2; + llg = [llg1; llg2]; + end +end + +function llg2 = lik_inputdependentweibull_llg2(lik, y, ff, param, z) +%LIK_INPUTDEPENDENTWEIBULL_LLG2 Second gradients of the log likelihood +% +% Description +% LLG2 = LIK_INPUTDEPENDENTWEIBULL_LLG2(LIK, Y, F, PARAM) takes a likelihood +% structure LIK, survival times Y, censoring indicators Z, and +% latent values F. Returns the hessian of the log likelihood +% with respect to PARAM. At the moment PARAM can be only +% 'latent'. LLG2 is a vector with diagonal elements of the +% Hessian matrix (off diagonals are zero). This subfunction +% is needed when using Laplace approximation or EP for +% inference with non-Gaussian likelihoods. +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG3, GPLA_E + + if isempty(z) + error(['lik_inputdependentweibull -> lik_inputdependentweibull_llg2: missing z! '... + 'Weibull likelihood needs the censoring '... + 'indicators as an extra input z. See, for '... + 'example, lik_inputdependentweibull and gpla_e. ']); + end + + a = lik.shape; + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + expf2=exp(f2); + expf2(isinf(expf2))=realmax; + switch param + case 'param' + + case 'latent' + t1=exp(-f1).*y.^(a.*expf2); + t2=log(y).*a.*expf2; + t3=t1.*t2; + + llg2_11 = -t1; + llg2_12 = t3; + llg2_22 = (1-z).*t2 - (t2 + 1).*t3; + + llg2 = [llg2_11 llg2_12; llg2_12 llg2_22]; + + case 'latent+param' + t1=expf2.*log(y); + t2=exp(-f1).*y.^(a.*expf2); + t3=t1.*t2; + llg2_1 = t3; + llg2_2 = (1-z).*t1 - (t1.*a + 1).*t3; + llg2 = [llg2_1; llg2_2]; + % correction due to the log transformation + llg2 = llg2.*lik.shape; + end +end + +function llg3 = lik_inputdependentweibull_llg3(lik, y, ff, param, z) +%LIK_INPUTDEPENDENTWEIBULL_LLG3 Third gradients of the log likelihood +% +% Description +% LLG3 = LIK_INPUTDEPENDENTWEIBULL_LLG3(LIK, Y, F, PARAM) takes a likelihood +% structure LIK, survival times Y, censoring indicators Z and +% latent values F and returns the third gradients of the log +% likelihood with respect to PARAM. At the moment PARAM can be +% only 'latent'. LLG3 is a vector with third gradients. This +% subfunction is needed when using Laplace approximation for +% inference with non-Gaussian likelihoods. +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_LLG, LIK_INPUTDEPENDENTWEIBULL_LLG2, GPLA_E, GPLA_G + + if isempty(z) + error(['lik_inputdependentweibull -> lik_inputdependentweibull_llg3: missing z! '... + 'Weibull likelihood needs the censoring '... + 'indicators as an extra input z. See, for '... + 'example, lik_inputdependentweibull and gpla_e. ']); + end + + a = lik.shape; + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + expf2=exp(f2); + expf2(isinf(expf2))=realmax; + switch param + case 'param' + + case 'latent' + t1=a.*expf2.*log(y); + t2=exp(-f1).*y.^(a.*expf2); + t3=t2.*t1; + t4=t3.*t1; + + nl=2; + llg3=zeros(nl,nl,nl,n); + + llg3(1,1,1,:) = t2; + + llg3(2,2,1,:) = t4 + t3; + llg3(2,1,2,:) = llg3(2,2,1,:); + llg3(1,2,2,:) = llg3(2,2,1,:); + + llg3(2,1,1,:) = -t3; + llg3(1,2,1,:) = llg3(2,1,1,:); + llg3(1,1,2,:) = llg3(2,1,1,:); + + llg3(2,2,2,:) = (1-z).*t1 - t4.*t1 - 3.*t4 - t3; + + case 'latent2+param' + t1 = log(y).*expf2; + t2 = exp(-f1).*y.^(a*expf2); + t3 = t2.*t1; + t4 = t3.*t1; + llg3_11 = -t3; + llg3_12 = a.*t4 + t3; + llg3_22 = (1-z).*t1 - a.^2.*t4.*t1 - 3.*a.*t4 - t3; + + llg3 = [diag(llg3_11) diag(llg3_12); diag(llg3_12) diag(llg3_22)]; + % correction due to the log transformation + llg3 = llg3.*lik.shape; + end +end + + +function [lpy, Ey, Vary] = lik_inputdependentweibull_predy(lik, Ef, Varf, yt, zt) +%LIK_INPUTDEPENDENTWEIBULL_PREDY Returns the predictive mean, variance and density of y +% +% Description +% LPY = LIK_INPUTDEPENDENTWEIBULL_PREDY(LIK, EF, VARF YT, ZT) +% Returns logarithm of the predictive density PY of YT, that is +% p(yt | zt) = \int p(yt | f, zt) p(f|y) df. +% This requires also the survival times YT, censoring indicators ZT. +% This subfunction is needed when computing posterior predictive +% distributions for future observations. +% +% [LPY, EY, VARY] = LIK_INPUTDEPENDENTWEIBULL_PREDY(LIK, EF, VARF) takes a +% likelihood structure LIK, posterior mean EF and posterior +% Variance VARF of the latent variable and returns the +% posterior predictive mean EY and variance VARY of the +% observations related to the latent variables. This subfunction +% is needed when computing posterior predictive distributions for +% future observations. +% +% +% See also +% GPLA_PRED, GPEP_PRED, GPMC_PRED + + if isempty(zt) + error(['lik_inputdependentweibull -> lik_inputdependentweibull_predy: missing zt!'... + 'Weibull likelihood needs the censoring '... + 'indicators as an extra input zt. See, for '... + 'example, lik_inputdependentweibull and gpla_e. ']); + end + + yc = 1-zt; + r = lik.shape; + + Ef = Ef(:); + ntest = 0.5*size(Ef,1); + Ef1=Ef(1:ntest); Ef2=Ef(ntest+1:end); + % Varf1=squeeze(Varf(1,1,:)); Varf2=squeeze(Varf(2,2,:)); + if size(Varf,2) == size(Varf,1) + Varf1=diag(Varf(1:ntest,1:ntest));Varf2=diag(Varf(ntest+1:end,ntest+1:end)); + else + Varf1=Varf(:,1); Varf2=Varf(:,2); + end + + Ey=[]; + Vary=[]; + + % Evaluate the posterior predictive densities of the given observations + lpy = zeros(length(yt),1); + + for i2=1:ntest + m1=Ef1(i2); m2=Ef2(i2); + s1=sqrt(Varf1(i2)); s2=sqrt(Varf2(i2)); + % Function handle for Weibull * Gaussian_f1 * Gaussian_f2 + pd=@(f1,f2) exp(yc(i2).*((log(r) + f2) + (r.*exp(f2)-1).*log(yt(i2))-f1) - exp(-f1).*yt(i2).^(r*exp(f2))) ... + .*norm_pdf(f1,Ef1(i2),sqrt(Varf1(i2))).*norm_pdf(f2,Ef2(i2),sqrt(Varf2(i2))); + % Integrate over latent variables + lpy(i2) = log(dblquad(pd, m1-6.*s1, m1+6.*s1, m2-6.*s2, m2+6.*s2)); + end + +end + + +function p = lik_inputdependentweibull_invlink(lik, f, z) +%LIK_INPUTDEPENDENTWEIBULL Returns values of inverse link function +% +% Description +% P = LIK_INPUTDEPENDENTWEIBULL_INVLINK(LIK, F) takes a likelihood structure LIK and +% latent values F and returns the values of inverse link function P. +% This subfunction is needed when using function gp_predprctmu. +% +% See also +% LIK_INPUTDEPENDENTWEIBULL_LL, LIK_INPUTDEPENDENTWEIBULL_PREDY + +p = exp(f); +end + +function reclik = lik_inputdependentweibull_recappend(reclik, ri, lik) +%RECAPPEND Append the parameters to the record +% +% Description +% RECLIK = LIK_INPUTDEPENDENTWEIBULL__RECAPPEND(RECLIK, RI, LIK) takes a +% likelihood record structure RECLIK, record index RI and +% likelihood structure LIK with the current MCMC samples of +% the parameters. Returns RECLIK which contains all the old +% samples and the current samples from LIK. This subfunction +% is needed when using MCMC sampling (gp_mc). +% +% See also +% GP_MC + + if nargin == 2 + % Initialize the record + reclik.type = 'Inputdependent-Weibull'; + reclik.nondiagW=true; + + % Initialize parameter +% reclik.shape = []; + + % Set the function handles + reclik.fh.pak = @lik_inputdependentweibull_pak; + reclik.fh.unpak = @lik_inputdependentweibull_unpak; + reclik.fh.lp = @lik_t_lp; + reclik.fh.lpg = @lik_t_lpg; + reclik.fh.ll = @lik_inputdependentweibull_ll; + reclik.fh.llg = @lik_inputdependentweibull_llg; + reclik.fh.llg2 = @lik_inputdependentweibull_llg2; + reclik.fh.llg3 = @lik_inputdependentweibull_llg3; + reclik.fh.invlink = @lik_inputdependentweibull_invlink; + reclik.fh.predy = @lik_inputdependentweibull_predy; + reclik.fh.recappend = @lik_inputdependentweibull_recappend; + reclik.p=[]; + reclik.p.shape=[]; + if ~isempty(ri.p.shape) + reclik.p.shape = ri.p.shape; + end + else + % Append to the record + reclik.shape(ri,:)=lik.shape; + if ~isempty(lik.p.shape) + reclik.p.shape = lik.p.shape.fh.recappend(reclik.p.shape, ri, lik.p.shape); + end + end +end + diff --git a/gp/lik_zinegbin.m b/gp/lik_zinegbin.m new file mode 100644 index 00000000..4c012d4e --- /dev/null +++ b/gp/lik_zinegbin.m @@ -0,0 +1,627 @@ +function lik = lik_zinegbin(varargin) +%LIK_ZINEGBIN Create a zero-inflated Negative-binomial likelihood structure +% +% Description +% LIK = LIK_ZINEGBIN('PARAM1',VALUE1,'PARAM2,VALUE2,...) +% creates a zero-inflated Negative-binomial likelihood structure in +% which the named parameters have the specified values. Any unspecified +% parameters are set to default values. +% +% LIK = LIK_ZINEGBIN(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) +% modify a likelihood structure with the named parameters +% altered with the specified values. +% +% Parameters for a zero-inflated Negative-binomial likelihood [default] +% disper - dispersion parameter r [10] +% disper_prior - prior for disper [prior_logunif] +% +% Note! If the prior is 'prior_fixed' then the parameter in +% question is considered fixed and it is not handled in +% optimization, grid integration, MCMC etc. +% +% The likelihood is defined as follows: +% +% p + (1-p)*NegBin(y|y=0), when y=0 +% (1-p)*NegBin(y|y>0), when y>0, +% +% where the probability p is given by a binary classifier with Logit +% likelihood and NegBin is the Negative-binomial distribution +% parametrized for the i'th observation as +% +% NegBin(y_i) = [ (r/(r+mu_i))^r * gamma(r+y_i) +% / ( gamma(r)*gamma(y_i+1) ) +% * (mu/(r+mu_i))^y_i ] +% +% where mu_i = z_i*exp(f_i) and r is the dispersion parameter. +% z is a vector of expected mean and f the latent value vector +% whose components are transformed to relative risk +% exp(f_i). +% +% The latent value vector f=[f1^T f2^T]^T has length 2*N, where N is the +% number of observations. The latents f1 are associated with the +% classification process and the latents f2 with Negative-binomial count +% process. +% +% When using the Zinegbin likelihood you need to give the vector z +% as an extra parameter to each function that requires also y. +% For example, you should call gpla_nd_e as follows: gpla_nd_e(w, gp, +% x, y, 'z', z) +% +% See also +% GP_SET, LIK_*, PRIOR_* +% + +% Copyright (c) 2007-2010 Jarno Vanhatalo & Jouni Hartikainen +% Copyright (c) 2010 Aki Vehtari +% Copyright (c) 2011 Jaakko Riihimäki + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + ip=inputParser; + ip.FunctionName = 'LIK_ZINEGBIN'; + ip=iparser(ip,'addOptional','lik', [], @isstruct); + ip=iparser(ip,'addParamValue','disper',10, @(x) isscalar(x) && x>0); + ip=iparser(ip,'addParamValue','disper_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'parse',varargin{:}); + lik=ip.Results.lik; + + if isempty(lik) + init=true; + lik.type = 'Zinegbin'; + lik.nondiagW=true; + else + if ~isfield(lik,'type') || ~isequal(lik.type,'Zinegbin') + error('First argument does not seem to be a valid likelihood function structure') + end + init=false; + end + + % Initialize parameters + if init || ~ismember('disper',ip.UsingDefaults) + lik.disper = ip.Results.disper; + end + % Initialize prior structure + if init + lik.p=[]; + end + if init || ~ismember('disper_prior',ip.UsingDefaults) + lik.p.disper=ip.Results.disper_prior; + end + + if init + % Set the function handles to the nested functions + lik.fh.pak = @lik_zinegbin_pak; + lik.fh.unpak = @lik_zinegbin_unpak; + lik.fh.lp = @lik_zinegbin_lp; + lik.fh.lpg = @lik_zinegbin_lpg; + lik.fh.ll = @lik_zinegbin_ll; + lik.fh.llg = @lik_zinegbin_llg; + lik.fh.llg2 = @lik_zinegbin_llg2; + lik.fh.llg3 = @lik_zinegbin_llg3; + lik.fh.predy = @lik_zinegbin_predy; + lik.fh.invlink = @lik_zinegbin_invlink; + lik.fh.recappend = @lik_zinegbin_recappend; + end + + function [w,s] = lik_zinegbin_pak(lik) + %LIK_ZINEGBIN_PAK Combine likelihood parameters into one vector. + % + % Description + % W = LIK_ZINEGBIN_PAK(LIK) takes a likelihood structure LIK and + % combines the parameters into a single row vector W. This is a + % mandatory subfunction used for example in energy and gradient + % computations. + % + % w = log(lik.disper) + % + % See also + % LIK_ZINEGBIN_UNPAK, GP_PAK + + w=[];s={}; + if ~isempty(lik.p.disper) + w = log(lik.disper); + s = [s; 'log(zinegbin.disper)']; + [wh sh] = feval(lik.p.disper.fh.pak, lik.p.disper); + w = [w wh]; + s = [s; sh]; + end + end + + + function [lik, w] = lik_zinegbin_unpak(lik, w) + %LIK_ZINEGBIN_UNPAK Extract likelihood parameters from the vector. + % + % Description + % [LIK, W] = LIK_ZINEGBIN_UNPAK(W, LIK) takes a likelihood + % structure LIK and extracts the parameters from the vector W + % to the LIK structure. This is a mandatory subfunction used for + % example in energy and gradient computations. + % + % Assignment is inverse of + % w = log(lik.disper) + % + % See also + % LIK_ZINEGBIN_PAK, GP_UNPAK + + if ~isempty(lik.p.disper) + lik.disper = exp(w(1)); + w = w(2:end); + [p, w] = feval(lik.p.disper.fh.unpak, lik.p.disper, w); + lik.p.disper = p; + end + end + + + function lp = lik_zinegbin_lp(lik, varargin) + %LIK_ZINEGBIN_LP log(prior) of the likelihood parameters + % + % Description + % LP = LIK_ZINEGBIN_LP(LIK) takes a likelihood structure LIK and + % returns log(p(th)), where th collects the parameters. This + % subfunction is needed when there are likelihood parameters. + % + % See also + % LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, LIK_ZINEGBIN_LLG2, GPLA_E + + + % If prior for dispersion parameter, add its contribution + lp=0; + if ~isempty(lik.p.disper) + lp = feval(lik.p.disper.fh.lp, lik.disper, lik.p.disper) +log(lik.disper); + end + + end + + + function lpg = lik_zinegbin_lpg(lik) + %LIK_ZINEGBIN_LPG d log(prior)/dth of the likelihood + % parameters th + % + % Description + % E = LIK_ZINEGBIN_LPG(LIK) takes a likelihood structure LIK and + % returns d log(p(th))/dth, where th collects the parameters. This + % subfunction is needed when there are likelihood parameters. + % + % See also + % LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, LIK_ZINEGBIN_LLG2, GPLA_G + + lpg=[]; + if ~isempty(lik.p.disper) + % Evaluate the gprior with respect to disper + ggs = feval(lik.p.disper.fh.lpg, lik.disper, lik.p.disper); + lpg = ggs(1).*lik.disper + 1; + if length(ggs) > 1 + lpg = [lpg ggs(2:end)]; + end + end + end + + function ll = lik_zinegbin_ll(lik, y, ff, z) + %LIK_ZINEGBIN_LL Log likelihood + % + % Description + % LL = LIK_ZINEGBIN_LL(LIK, Y, F, Z) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z, and + % latent values F. Returns the log likelihood, log p(y|f,z). + % This subfunction is needed when using Laplace approximation + % or MCMC for inference with non-Gaussian likelihoods. This + % subfunction is also used in information criteria (DIC, WAIC) + % computations. + % + % See also + % LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, LIK_ZINEGBIN_LLG2, GPLA_E + + if isempty(z) + error(['lik_zinegbin -> lik_zinegbin_ll: missing z! '... + 'Zinegbin likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_zinegbin and gpla_e. ']); + end + + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + y0ind=y==0; + yind=y>0; + + r = lik.disper; + m = exp(f2).*z; + expf1=exp(f1); + + % for y = 0 + lly0 = sum(-log(1+expf1(y0ind)) + log( expf1(y0ind) + (r./(r+m(y0ind))).^r )); + % for y > 0 + lly = sum(-log(1+expf1(yind)) + r.*(log(r) - log(r+m(yind))) + gammaln(r+y(yind)) - gammaln(r) - gammaln(y(yind)+1) + y(yind).*(log(m(yind)) - log(r+m(yind)))); + + ll=lly0+lly; + end + + function llg = lik_zinegbin_llg(lik, y, ff, param, z) + %LIK_ZINEGBIN_LLG Gradient of the log likelihood + % + % Description + % LLG = LIK_ZINEGBIN_LLG(LIK, Y, F, PARAM) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z and + % latent values F. Returns the gradient of the log likelihood + % with respect to PARAM. At the moment PARAM can be 'param' or + % 'latent'. This subfunction is needed when using Laplace + % approximation or MCMC for inference with non-Gaussian likelihoods. + % + % See also + % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_LLG2, LIK_ZINEGBIN_LLG3, GPLA_E + + if isempty(z) + error(['lik_zinegbin -> lik_zinegbin_llg: missing z! '... + 'Zinegbin likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_zinegbin and gpla_e. ']); + end + + f=ff(:); + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + y0ind=y==0; + yind=y>0; + + r = lik.disper; + m = exp(f2).*z; + expf1=exp(f1); + + switch param + case 'param' + + % syms e r m + % simplify(diff(log( e + (r./(r+m)).^r ),r)) + + m0=m(y0ind); + llg1=sum( ((r./(m0 + r)).^r.*(m0 + m0.*log(r./(m0 + r)) + r.*log(r./(m0 + r))))./((expf1(y0ind) + (r./(m0 + r)).^r).*(m0 + r)) ); + + % Derivative using the psi function + llg2 = sum(1 + log(r./(r+m(yind))) - (r+y(yind))./(r+m(yind)) + psi(r + y(yind)) - psi(r)); + + llg = llg1 + llg2; + % correction for the log transformation + llg = llg.*lik.disper; + case 'latent' + + llg1=zeros(n,1); + llg2=zeros(n,1); + + llg1(y0ind)=-expf1(y0ind)./(1+expf1(y0ind)) + expf1(y0ind)./(expf1(y0ind)+ (r./(r+m(y0ind))).^r); + llg2(y0ind)=-1./( expf1(y0ind) + (r./(r+m(y0ind))).^r ) .* (r./(r+m(y0ind))).^(r-1) .* (m(y0ind).*r.^2./((r+m(y0ind)).^2)); + + llg1(yind)=-expf1(yind)./(1+expf1(yind)); + llg2(yind)=y(yind) - (r+y(yind)).*m(yind)./(r+m(yind)); + + llg=[llg1; llg2]; + end + end + + function llg2 = lik_zinegbin_llg2(lik, y, ff, param, z) + %LIK_ZINEGBIN_LLG2 Second gradients of the log likelihood + % + % Description + % LLG2 = LIK_ZINEGBIN_LLG2(LIK, Y, F, PARAM) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z, and + % latent values F. Returns the Hessian of the log likelihood + % with respect to PARAM. At the moment PARAM can be only + % 'latent'. Second gradients form a matrix of size 2N x 2N as + % [diag(LLG2_11) diag(LLG2_12); diag(LLG2_12) diag(LLG2_22)], + % but the function returns only vectors of diagonal elements as + % LLG2 = [LLG2_11 LLG2_12; LLG2_12 LLG2_22] (2Nx2 matrix) since off + % diagonals of the blocks are zero. This subfunction is needed when + % using Laplace approximation or EP for inference with non-Gaussian + % likelihoods. + % + % See also + % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG3, GPLA_E + + if isempty(z) + error(['lik_zinegbin -> lik_zinegbin_llg2: missing z! '... + 'Zinegbin likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_zinegbin and gpla_e. ']); + end + + f=ff(:); + + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + y0ind=y==0; + yind=y>0; + + r = lik.disper; + m = exp(f2).*z; + expf1=exp(f1); + + switch param + case 'param' + + case 'latent' + + llg2_11=zeros(n,1); + llg2_12=zeros(n,1); + llg2_22=zeros(n,1); + + rmuy0=(r./(r+m(y0ind))).^r; + llg2_11(y0ind)=-expf1(y0ind)./(1+expf1(y0ind)).^2 + expf1(y0ind).*rmuy0./(expf1(y0ind)+rmuy0).^2; + llg2_12(y0ind)=(r./(r+m(y0ind))).^(r-1).*(m(y0ind).*r.^2./(r+m(y0ind)).^2).*expf1(y0ind)./(expf1(y0ind)+rmuy0).^2; + llg2_22(y0ind)=-1./(expf1(y0ind)+rmuy0).*( (r./(r+m(y0ind))).^(2*r-2).*(m(y0ind).*r.^2./(r+m(y0ind)).^2).^2./(expf1(y0ind)+rmuy0) + (r-1).*(r./(r+m(y0ind))).^(r-2).*(-m(y0ind).^2.*r.^3./(r+m(y0ind)).^4) + (r./(r+m(y0ind))).^(r-1).*(m(y0ind).*r.^2.*(r+m(y0ind))-2*m(y0ind).^2.*r.^2)./(r+m(y0ind)).^3); + + llg2_11(yind)=-expf1(yind)./(1+expf1(yind)).^2; + llg2_22(yind)=-m(yind).*(r.^2 + y(yind).*r)./(r+m(yind)).^2; + +% llg2 = [diag(llg2_11) diag(llg2_12); diag(llg2_12) diag(llg2_22)]; + llg2 = [llg2_11 llg2_12; llg2_12 llg2_22]; + +% R=[]; +% nl=2; +% pi_mat=zeros(nl*n, n); +% llg2_12sq=sqrt(llg2_12); +% for i1=1:nl +% pi_mat((1+(i1-1)*n):(nl*n+1):end)=llg2_12sq; +% end +% pi_vec=repmat(llg2_12,nl,1)-[llg2_11; llg2_22]; + %D=diag(pi_vec); + %llg2=-D+pi_mat*pi_mat'; + %imagesc((-diag(pi_vec)+pi_mat*pi_mat') - llg2),colorbar + + case 'latent+param' + + %syms e r m + %simplify(diff(e./(e + (r./(r+m)).^r),r)) + %simplify(diff(-1/( e + (r/(r+m))^r ) * (r/(r+m))^(r-1) * (m*r^2/((r+m)^2)),r)) + + llg2_1=zeros(n,1); + llg2_2=zeros(n,1); + + m0=m(y0ind); + e0=expf1(y0ind); + llg2_1(y0ind)=-(e0.*(r./(m0 + r)).^r.*(m0 + m0.*log(r./(m0 + r)) + r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^2.*(m0 + r)); + llg2_2(y0ind)=-(m0.*(r./(m0 + r)).^r.*(m0.*(r./(m0 + r)).^r + e0.*m0 + e0.*r.^2.*log(r./(m0 + r)) + e0.*m0.*r + e0.*m0.*r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^2.*(m0 + r).^2); + + llg2_2(yind)= (y(yind).*m(yind) - m(yind).^2)./(r+m(yind)).^2; + + llg2=[llg2_1; llg2_2]; + + % correction due to the log transformation + llg2 = llg2.*lik.disper; + end + end + + function llg3 = lik_zinegbin_llg3(lik, y, ff, param, z) + %LIK_ZINEGBIN_LLG3 Third gradients of the log likelihood + % + % Description + % LLG3 = LIK_ZINEGBIN_LLG3(LIK, Y, F, PARAM) takes a likelihood + % structure LIK, incedence counts Y, expected counts Z and + % latent values F and returns the third gradients of the log + % likelihood with respect to PARAM. At the moment PARAM can be + % only 'latent'. LLG3 is a 2-by-2-by-2-by-N array of with third + % gradients, where LLG3(:,:,1,i) is the third derivative wrt f1 for + % the i'th observation and LLG3(:,:,2,i) is the third derivative wrt + % f2 for the i'th observation. This subfunction is needed when using + % Laplace approximation for inference with non-Gaussian likelihoods. + % + % See also + % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_LLG, LIK_ZINEGBIN_LLG2, GPLA_E, GPLA_G + + if isempty(z) + error(['lik_zinegbin -> lik_zinegbin_llg3: missing z! '... + 'Zinegbin likelihood needs the expected number of '... + 'occurrences as an extra input z. See, for '... + 'example, lik_zinegbin and gpla_e. ']); + end + + f=ff(:); + + n=size(y,1); + f1=f(1:n); + f2=f((n+1):2*n); + y0ind=y==0; + yind=y>0; + + r = lik.disper; + m = exp(f2).*z; + expf1=exp(f1); + + switch param + case 'param' + + case 'latent' + nl=2; + llg3=zeros(nl,nl,nl,n); + + % 11 + %simplify(diff(-exp(f1)/(1+exp(f1))^2 + exp(f1)*(r/(r+exp(f2)*z))^r/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f1)) + %simplify(diff(-exp(f1)/(1+exp(f1))^2 + exp(f1)*(r/(r+exp(f2)*z))^r/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f2)) + + % 12/21 + % simplify(diff((r/(r+exp(f2)*z))^(r-1)*(exp(f2)*z*r^2/(r+exp(f2)*z)^2)*exp(f1)/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f1)) + % simplify(diff((r/(r+exp(f2)*z))^(r-1)*(exp(f2)*z*r^2/(r+exp(f2)*z)^2)*exp(f1)/(exp(f1)+(r/(r+exp(f2)*z))^r)^2,f2)) + + % 22 + % simplify(diff(-1/(exp(f1)+(r/(r+z*exp(f2)))^r)*((r/(r+z*exp(f2)))^(2*r-2)*(z*exp(f2)*r^2/(r+z*exp(f2))^2)^2/(exp(f1)+(r/(r+z*exp(f2)))^r) + (r-1)*(r/(r+z*exp(f2)))^(r-2)*(-z*exp(f2)^2*r^3/(r+z*exp(f2))^4) + (r/(r+z*exp(f2)))^(r-1)*(z*exp(f2)*r^2*(r+z*exp(f2))-2*z*exp(f2)^2*r^2)/(r+z*exp(f2))^3),f1)) + % simplify(diff(-1/(exp(f1)+(r/(r+z*exp(f2)))^r)*((r/(r+z*exp(f2)))^(2*r-2)*(z*exp(f2)*r^2/(r+z*exp(f2))^2)^2/(exp(f1)+(r/(r+z*exp(f2)))^r) + (r-1)*(r/(r+z*exp(f2)))^(r-2)*(-z*exp(f2)^2*r^3/(r+z*exp(f2))^4) + (r/(r+z*exp(f2)))^(r-1)*(z*exp(f2)*r^2*(r+z*exp(f2))-2*z*exp(f2)^2*r^2)/(r+z*exp(f2))^3),f2)) + % with symbolic math toolbox: + % simplify(diff(simplify(diff(simplify(diff((-log(1+exp(f1)) + log( exp(f1) + (r./(r+z*exp(f2))).^r )),f2)),f2)),f1)) + + expf2=exp(f2); + m0=m(y0ind); + z0=z(y0ind); + expf1y0ind=expf1(y0ind); + expf1y0ind2=expf1y0ind.^2; + expf2y0ind=expf2(y0ind); + + % y=0: + % thrid derivative derivative wrt f1 (11) + llg3(1,1,1,y0ind) = (2.*expf1y0ind2)./(expf1y0ind + 1).^3 - expf1y0ind./(expf1y0ind + 1).^2 - (2.*expf1y0ind2.*(r./(r + m0)).^r)./(expf1y0ind + (r./(r + m0)).^r).^3 + (expf1y0ind.*(r./(r + m0)).^r)./(expf1y0ind + (r./(r + m0)).^r).^2; + % thrid derivative derivative wrt f2 (11) + llg3(1,1,2,y0ind)=-(r.*m0.*expf1y0ind.*(expf1y0ind - (r./(r + m0)).^r).*(r./(r + m0)).^r)./((r + m0).*(expf1y0ind + (r./(r + m0)).^r).^3); + + % thrid derivative derivative wrt f1 (12/21) + llg3(1,2,1,y0ind) = -(r.*m0.*expf1y0ind.*(expf1y0ind - (r./(r + m0)).^r).*(r./(r + m0)).^r)./((r + m0).*(expf1y0ind + (r./(r + m0)).^r).^3); + llg3(2,1,1,y0ind) = llg3(1,2,1,y0ind); + % thrid derivative derivative wrt f2 (12/21) + llg3(1,2,2,y0ind) = (r.^2.*m0.*expf1y0ind.*(r./(r + m0)).^r.*(expf1y0ind + (r./(r + m0)).^r + m0.*(r./(r + m0)).^r - expf1y0ind.*m0))./((r + m0).^2.*(expf1y0ind + (r./(r + m0)).^r).^3); + llg3(2,1,2,y0ind) = llg3(1,2,2,y0ind); + + % thrid derivative derivative wrt f1 (22) + llg3(2,2,1,y0ind) = (r.^2.*m0.*expf1y0ind.*(r./(r + m0)).^r.*(expf1y0ind + (r./(r + m0)).^r + m0.*(r./(r + m0)).^r - expf1y0ind.*m0))./((r + m0).^2.*(expf1y0ind + (r./(r + m0)).^r).^3); + % thrid derivative derivative wrt f1 (22) + llg3(2,2,2,y0ind) = ((r.^2.*m0.*expf2y0ind.*(z0.*expf1y0ind2 + z0.*(r./(r + m0)).^(2.*r) + 3.*r.*z0.*expf1y0ind2) - r.^2.*m0.*(r.*(r./(r + m0)).^(2.*r) + r.*expf1y0ind2 + r.*expf1y0ind2.*m0.^2)).*(r./(r + m0)).^r + expf1y0ind.*(r.^2.*m0.*expf2y0ind.*(2.*z0 + 3.*r.*z0) - r.^2.*m0.*(2.*r - r.*m0.^2)).*(r./(r + m0)).^(2.*r))./((r + m0).^3.*(expf1y0ind + (r./(r + m0)).^r).^3); + + % y>0: + llg3(1,1,1,yind) = -expf1(yind).*(1-expf1(yind))./(1+expf1(yind)).^3; + llg3(2,2,2,yind) = - m(yind).*(r.^2 + y(yind).*r)./(r + m(yind)).^2 + 2.*m(yind).^2.*(r.^2 + y(yind).*r)./(r + m(yind)).^3; + + case 'latent2+param' + + % simplify(diff(-e/(1+e)^2 + e*((r/(r+m))^r)/(e+((r/(r+m))^r))^2,r)) + % simplify(diff((r/(r+m))^(r-1)*(m*r^2/(r+m)^2)*e/(e+((r/(r+m))^r))^2,r)) + % simplify(diff(-1./(e+((r./(r+m)).^r)).*( (r./(r+m)).^(2*r-2).*(m.*r.^2./(r+m).^2).^2./(e+((r./(r+m)).^r)) + (r-1).*(r./(r+m)).^(r-2).*(-m.^2.*r.^3./(r+m).^4) + (r./(r+m)).^(r-1).*(m.*r.^2.*(r+m)-2*m.^2.*r.^2)./(r+m).^3),r)) + + llg3_11=zeros(n,1); + llg3_12=zeros(n,1); + llg3_22=zeros(n,1); + + e0=expf1(y0ind); + m0=m(y0ind); + + llg3_11(y0ind)=(e0.*(e0 - (r./(m0 + r)).^r).*(r./(m0 + r)).^r.*(m0 + m0.*log(r./(m0 + r)) + r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^3.*(m0 + r)); + llg3_12(y0ind)=(e0.^2.*m0.*(r./(m0 + r)).^r.*(m0 + r.^2.*log(r./(m0 + r)) + m0.*r + m0.*r.*log(r./(m0 + r))) - e0.*m0.*(r./(m0 + r)).^(2.*r).*(r.^2.*log(r./(m0 + r)) - m0 + m0.*r + m0.*r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^3.*(m0 + r).^2); + llg3_22(y0ind)=-(m0.*r.*(r./(m0 + r)).^(2.*r).*(4.*e0.*m0 - 2.*e0.*m0.^2 + e0.*m0.^2.*r + e0.*r.^2.*log(r./(m0 + r)) + e0.*m0.*r + e0.*m0.*r.*log(r./(m0 + r)) + e0.*m0.*r.^2.*log(r./(m0 + r)) + e0.*m0.^2.*r.*log(r./(m0 + r))) + m0.*r.*(r./(m0 + r)).^r.*(2.*m0.*(r./(m0 + r)).^(2.*r) + 2*e0.^2.*m0 - 2.*e0.^2.*m0.^2 + e0.^2.*m0.*r - e0.^2.*m0.^2.*r + e0.^2.*r.^2.*log(r./(m0 + r)) - e0.^2.*m0.*r.^2.*log(r./(m0 + r)) - e0.^2.*m0.^2.*r.*log(r./(m0 + r)) + e0.^2.*m0.*r.*log(r./(m0 + r))))./((e0 + (r./(m0 + r)).^r).^3.*(m0 + r).^3); + + llg3_22(yind) = m(yind).*(y(yind).*r - 2.*r.*m(yind) - m(yind).*y(yind))./(r+m(yind)).^3; + + llg3 = [diag(llg3_11) diag(llg3_12); diag(llg3_12) diag(llg3_22)]; + + % correction due to the log transformation + llg3 = llg3.*lik.disper; + end + end + + function [lpyt,Ey, Vary] = lik_zinegbin_predy(lik, Ef, Covf, yt, zt) + %LIK_ZINEGBIN_PREDY Returns the predictive mean, variance and density of y + % + % Description + % [EY, VARY] = LIK_ZINEGBIN_PREDY(LIK, EF, VARF) takes a + % likelihood structure LIK, posterior mean EF and posterior + % covariance COVF of the latent variable and returns the + % posterior predictive mean EY and variance VARY of the + % observations related to the latent variables. This + % subfunction is needed when computing posterior predictive + % distributions for future observations. + % + % [Ey, Vary, PY] = LIK_ZINEGBIN_PREDY(LIK, EF, VARF YT, ZT) + % Returns also the predictive density of YT, that is + % p(yt | zt) = \int p(yt | f, zt) p(f|y) df. + % This requires also the incedence counts YT, expected counts ZT. + % This subfunction is needed when computing posterior predictive + % distributions for future observations. + % + % See also + % GPLA_PRED, GPEP_PRED, GPMC_PRED + + if isempty(zt) + error(['lik_zinegbin -> lik_zinegbin_predy: missing zt!'... + 'Zinegbin likelihood needs the expected number of '... + 'occurrences as an extra input zt. See, for '... + 'example, lik_zinegbin and gpla_e. ']); + end + + ntest=size(zt,1); + %avgE = zt; + r = lik.disper; + + Py = zeros(size(zt)); + + S=10000; + for i1=1:ntest + Sigm_tmp=Covf(i1:ntest:(2*ntest),i1:ntest:(2*ntest)); + Sigm_tmp=(Sigm_tmp+Sigm_tmp')./2; + f_star=mvnrnd(Ef(i1:ntest:(2*ntest)), Sigm_tmp, S); + + m = exp(f_star(:,2)).*zt(i1); + expf1=exp(f_star(:,1)); + + if yt(i1)==0 + Py(i1)=mean(exp(-log(1+expf1) + log( expf1 + (r./(r+m)).^r ))); + else + Py(i1)=mean(exp(-log(1+expf1) + r.*(log(r) - log(r+m)) + gammaln(r+yt(i1)) - gammaln(r) - gammaln(yt(i1)+1) + yt(i1).*(log(m) - log(r+m)))); + end + end + Ey = []; + Vary = []; + lpyt=log(Py); + end + + function p = lik_zinegbin_invlink(lik, f, z) + %LIK_ZINEGBIN_INVLINK Returns values of inverse link function + % + % Description + % P = LIK_ZINEGBIN_INVLINK(LIK, F) takes a likelihood structure LIK and + % latent values F and returns the values of inverse link function P. + % This subfunction is needed when using function gp_predprctmu. + % + % See also + % LIK_ZINEGBIN_LL, LIK_ZINEGBIN_PREDY + + p = exp(f); + end + + function reclik = lik_zinegbin_recappend(reclik, ri, lik) + %RECAPPEND Append the parameters to the record + % + % Description + % RECLIK = GPCF_ZINEGBIN_RECAPPEND(RECLIK, RI, LIK) takes a + % likelihood record structure RECLIK, record index RI and + % likelihood structure LIK with the current MCMC samples of + % the parameters. Returns RECLIK which contains all the old + % samples and the current samples from LIK. This subfunction + % is needed when using MCMC sampling (gp_mc). + % + % See also + % GP_MC + + % Initialize record + if nargin == 2 + reclik.type = 'Zinegbin'; + reclik.nondiagW=true; + + % Initialize parameter + reclik.disper = []; + + % Set the function handles + reclik.fh.pak = @lik_zinegbin_pak; + reclik.fh.unpak = @lik_zinegbin_unpak; + reclik.fh.lp = @lik_zinegbin_lp; + reclik.fh.lpg = @lik_zinegbin_lpg; + reclik.fh.ll = @lik_zinegbin_ll; + reclik.fh.llg = @lik_zinegbin_llg; + reclik.fh.llg2 = @lik_zinegbin_llg2; + reclik.fh.llg3 = @lik_zinegbin_llg3; + reclik.fh.predy = @lik_zinegbin_predy; + reclik.fh.invlink = @lik_zinegbin_invlink; + reclik.fh.recappend = @lik_zinegbin_recappend; + reclik.p=[]; + reclik.p.disper=[]; + if ~isempty(ri.p.disper) + reclik.p.disper = ri.p.disper; + end + return + end + + reclik.disper(ri,:)=lik.disper; + if ~isempty(lik.p.disper) + reclik.p.disper = feval(lik.p.disper.fh.recappend, reclik.p.disper, ri, lik.p.disper); + end + end +end + + diff --git a/gp/metric_ibs_gxe.m b/gp/metric_ibs_gxe.m new file mode 100644 index 00000000..176b5659 --- /dev/null +++ b/gp/metric_ibs_gxe.m @@ -0,0 +1,757 @@ +function metric = metric_ibs_gxe(do, varargin) +%METRIC_IBS_GXE An Euclidean distance metric for Gaussian process models. +% +% Description +% METRIC = METRIC_ibs_gxe('INIT', NIN) Constructs an data +% structure for using both a distancematrix and a euclidean +% metric used in covariance function of a GP model. +% +% Uses metric_distancematrix and metric_euclidean to combine +% genetic (ibs distance matrix) and environmental factors. These +% have to be set up beforehand and added to this using 'init' or +% 'set'. The input to the GP should a matrix of size n x n+d +% where n is the number of individuals and d the number of +% additional covariates. +% +% pak = function handle to pack function +% (@metric_ibs_gxe_pak) +% unpak = function handle to unpack function +% (@metric_ibs_gxe_unpak) +% e = function handle to energy function +% (@metric_ibs_gxe_e) +% ghyper = function handle to gradient of energy with respect to hyperparameters +% (@metric_ibs_gxe_ghyper) +% ginput = function handle to gradient of function with respect to inducing inputs +% (@metric_ibs_gxe_ginput) +% distance = function handle to distance function of the metric. +% (@metric_ibs_gxe_distance) +% fh_recappend = function handle to append the record function +% (metric_ibs_gxe_recappend) +% +% METRIC = METRIC_ibs_gxe('SET', METRIC, 'FIELD1', VALUE1, 'FIELD2', VALUE2, ...) +% Set the values of fields FIELD1... to the values VALUE1... in METRIC. +% + +% Copyright (c) 2009-2010 Heikki Peura + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + if nargin < 2 + error('Not enough arguments') + end + + if strcmp(do, 'init') + metric.type = 'metric_ibs_gxe'; + n=min(size(varargin{1})); + metric.nin = varargin{1}; + n1=varargin{1}-n; + %metric.components = varargin{2}; + %metric.params=varargin{3}; + %metric.x1x2matrix=[]; + + %metric.x1matrix=[]; + %metric.x2matrix=[]; +% metric.x1=[]; +% metric.x2=[]; + %metric.n1=[]; + %metric.n2=[]; + %metric.gdist=[]; + %metric.params = repmat(1,1,length(metric.components)); + %metric.Kfu=[]; + %metric.Kffstar=[]; + + %metric.metric_gene=metric_distancematrix('init', n,[],'lengthScales',[2]); + %metric.metric_gene.p.params = gamma_p({3 1}); + %metric.metric_env=metric_euclidean('init', n1, varargin{2},'lengthScales',varargin{4}); + %metric.metric_env.p.params = gamma_p({3 1}); + + + % Initialize prior structure + %metric.p=[]; + %metric.p.params=[]; + + % Set the function handles to the nested functions + metric.pak = @metric_ibs_gxe_pak; + metric.unpak = @metric_ibs_gxe_unpak; + metric.e = @metric_ibs_gxe_e; + metric.ghyper = @metric_ibs_gxe_ghyper; + metric.ginput = @metric_ibs_gxe_ginput; + metric.distance = @metric_ibs_gxe_distance; + metric.recappend = @metric_ibs_gxe_recappend; + %metric.matrix = @metric_ibs_gxe_matrix; + %metric.initmatrices = @metric_ibs_gxe_initmatrices; + + if length(varargin) > 2 + if mod(nargin,2) ~=0 + error('Wrong number of arguments') + end + % Loop through all the parameter values that are changed + for i=2:2:length(varargin)-1 + switch varargin{i} +% case 'params' +% if size(varargin{i+1}) ~= size(metric.params) +% error('Incorrect number of parameters given.'); +% end +% metric.params = varargin{i+1}; + case 'metric_gene' + metric.metric_gene = varargin{i+1}; + case 'metric_env' + metric.metric_env = varargin{i+1}; +% case 'x1' +% metric.x1=varargin{i+1}; +% metric.n1=size(varargin{i+1},1); +% [metric.x1matrix,metric.gdist]=metric.matrix(varargin{i+1}); +% case 'x2' +% metric.x2=varargin{i+1}; +% metric.n2=size(varargin{i+1},1); +% metric.x2matrix=metric.matrix(varargin{i+1}); +% case 'x1matrix' +% metric.x1matrix=varargin{i+1}; +% metric.x1matrix=reshape(metric.x1matrix,[1,size(metric.x1matrix)]); +% metric.n1=size(varargin{i+1},1); +% for i=1:length(metric.components) +% metric.gdist{1}=-metric.x1matrix; +% end +% case 'x2matrix' +% metric.x2matrix=varargin{i+1}; +% metric.x2matrix=reshape(metric.x2matrix,[1,size(metric.x2matrix)]); +% metric.n2=size(varargin{i+1},1); +% case 'x1x2matrix' +% metric.x1x2matrix=varargin{i+1}; +% metric.x1x2matrix=reshape(metric.x1x2matrix,[1,size(metric.x1x2matrix)]); + otherwise + error('Wrong parameter name!') + end + end +% if isempty(metric.x1x2matrix) +% metric.x1x2matrix = metric.matrix(metric.x1,metric,x2); +% end + + + + end + end + + % Set the parameter values of covariance function + if strcmp(do, 'set') + if mod(nargin,2) ~=0 + error('Wrong number of arguments') + end + metric = varargin{1}; + % Loop through all the parameter values that are changed + for i=2:2:length(varargin)-1 + switch varargin{i} +% case 'params' +% if size(varargin{i+1}) ~= size(metric.params) +% error('Incorrect number of parameters given.'); +% end +% metric.params = varargin{i+1}; + case 'metric_gene' + metric.metric_gene = varargin{i+1}; + case 'metric_env' + metric.metric_env = varargin{i+1}; +% case 'x1' +% metric.x1=varargin{i+1}; +% metric.n1=size(varargin{i+1},1); +% [metric.x1matrix,metric.gdist]=metric.matrix(varargin{i+1}); +% case 'x2' +% metric.x2=varargin{i+1}; +% metric.n2=size(varargin{i+1},1); +% metric.x2matrix=metric.matrix(varargin{i+1}); +% case 'x1matrix' +% metric.x1matrix=varargin{i+1}; +% metric.x1matrix=reshape(metric.x1matrix,[1,size(metric.x1matrix)]); +% metric.n1=size(varargin{i+1},1); +% for i=1:length(metric.components) +% metric.gdist{1}=-metric.x1matrix; +% end +% case 'x2matrix' +% metric.x2matrix=varargin{i+1}; +% metric.x2matrix=reshape(metric.x2matrix,[1,size(metric.x2matrix)]); +% metric.n2=size(varargin{i+1},1); +% case 'x1x2matrix' +% metric.x1x2matrix=varargin{i+1}; +% metric.x1x2matrix=reshape(metric.x1x2matrix,[1,size(metric.x1x2matrix)]); + otherwise + error('Wrong parameter name!') + end + end +% if isempty(metric.x1x2matrix) +% metric.x1x2matrix = metric.matrix(metric.x1,metric,x2); +% end + end + end + + + function w = metric_ibs_gxe_pak(metric) + %METRIC_ibs_PAK Combine the metric parameters into one vector. + % + % Description + % W = METRIC_ibs_PAK(METRIC, W) takes a metric structure METRIC and + % combines the parameters into a single row vector W. + % + % See also + % METRIC_ibs_UNPAK + +% i1=0;i2=1; +% if ~isempty(w) +% i1 = length(w); +% end +% i1=i1+1; + w=[metric.metric_gene.pak(metric.metric_gene) metric.metric_env.pak(metric.metric_env)]; +% i1=i1+1; + % w=; + end + + function [metric, w] = metric_ibs_gxe_unpak(metric, w) + %METRIC_ibs_UNPAK Separate metric parameter vector into components. + % + % Description + % [METRIC, W] = METRIC_ibs_UNPAK(METRIC, W) takes a metric structure + % METRIC parameter vector W, and returns a metric structure identical to the + % input, except that the parameters has been set to the values in W. Deletes the values + % set to METRIC from W and returns the modified W. + % + % See also + % METRIC_ibs_PAK + % +% i1=1+length(metric.metric_env.components); +% w1=w(2:2+length(metric.metric_env.components)); +% w=w(i1+1:end); + + [metric_gene, w] = metric.metric_gene.unpak(metric.metric_gene, w); + metric.metric_gene=metric_gene; + [metric_env, w] = metric.metric_env.unpak(metric.metric_env, w); + metric.metric_env=metric_env; + end + + function eprior = metric_ibs_gxe_e(metric, x, t) + %METRIC_ibs_E Evaluate the energy of prior of metric parameters + % + % Description + % E = METRIC_ibs_E(METRIC, X, T) takes a metric structure + % METRIC together with a matrix X of input vectors and a matrix T of target + % vectors and evaluates log p(th) x J, where th is a vector of metric parameters + % and J is the Jakobian of transformation exp(w) = th. (Note that the parameters + % are log transformed, when packed.) + % + % See also + % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs_G, GP_E + % + %[n, m] = size(x); + + % Evaluate the prior contribution to the error. The parameters that + % are sampled are from space W = log(w) where w is all the "real" samples. + % On the other hand errors are evaluated in the W-space so we need take + % into account also the Jakobian of transformation W -> w = exp(W). + % See Gelman et al., 2004, Bayesian data Analysis, second edition, p24. + eprior = 0; + eprior=eprior+metric.metric_gene.e(metric.metric_gene,x,t); + eprior=eprior+metric.metric_env.e(metric.metric_env,x,t); + +% mp=metric.p; +% +% if isfield(mp.params, 'p') && ~isempty(mp.params.p) +% eprior=eprior... +% +feval(mp.params.p.s.fe, ... +% gpp.params.a.s, mp.params.p.s.a)... +% -log(mp.params.a.s); +% if any(strcmp(fieldnames(mp.params.p),'nu')) +% eprior=eprior... +% +feval(mp.p.params.nu.fe, ... +% mp.params.a.nu, mp.params.p.nu.a)... +% -log(mp.params.a.nu); +% end +% end +% eprior=eprior... +% +feval(mp.params.fe, ... +% metric.params, mp.params.a)... +% -sum(log(metric.params)); + + end + + function [gdist, gprior_dist] = metric_ibs_gxe_ghyper(metric, x1, x2, mask) + %METRIC_ibs_GHYPER Evaluate the gradient of the metric function and hyperprior + % w.r.t to it's hyperparameters. + % + % Description + % [GDIST, GPRIOR_DIST] = METRIC_ibs_GHYPER(METRIC, X) takes a + % metric structure METRIC together with a matrix X of input vectors and + % return the gradient matrices GDIST and GPRIOR_DIST for each hyperparameter. + % + % [GDIST, GPRIOR_DIST] = METRIC_ibs_GHYPER(METRIC, X, X2) forms the gradient + % matrices between two input vectors X and X2. + % + % [GDIST, GPRIOR_DIST] = METRIC_ibs_GHYPER(METRIC, X, X2, MASK) forms + % the gradients for masked covariances matrices used in sparse approximations. + % + % See also + % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs, GP_E + % + + %mp=metric.p; + %components = metric.components; + + %n = size(x1,1); + m = length(metric.metric_env.lengthScales); + i1=0;i2=1; + %[n1,m1]=size(x1); + +% if nargin < 3 +% n2=n1; +% end + + + gdist=cell(1,m+1); + n1=min(size(x1)); + n=n1; + g1=x1(:,1); + e1=x1(:,2:end); + + %n1=size(x1matrix,1); + dist=0; +% s = 1./metric.params; + if (nargin == 3) + g2=x2(:,1); + e2=x2(:,2:end); + n2=min(size(x2)); + + [gdist_gene,gprior_dist_gene]=metric.metric_gene.ghyper(metric.metric_gene,g1,g2); + + [gdist_env,gprior_dist_env]=metric.metric_env.ghyper(metric.metric_env,e1,e2); + else + [gdist_gene,gprior_dist_gene]=metric.metric_gene.ghyper(metric.metric_gene,g1); + + [gdist_env,gprior_dist_env]=metric.metric_env.ghyper(metric.metric_env,e1); + + end + gdist{1}=gdist_gene{1}; + for i=2:m+1 + + gdist{i}=gdist_env{i-1}; + end + + + gprior_dist=[gprior_dist_gene gprior_dist_env]; +% s = 1./metric.params; +% % if nargin < 3 +% % dist=s.*metric.x1x2matrix; +% % elseif n1==size(metric.x1,1) +% % dist=s.*metric.x1matrix; +% % +% % else +% % dist=s.*metric.x2matrix; +% % end +% +% for i=1:m +% gdist{i}=-s.*sqrt(squeeze(x1matrix)); +% end +% +% % ii1=0; +% % +% dist = 0; +% distc = cell(1,m); +% % Compute the distances for each component set +% for i=1:m +% s = 1./metric.params(i).^2; +% distc{i} = 0; +% for j = 1:length(components{i}) +% distc{i} = distc{i} + bsxfun(@minus,x(:,components{i}(j)),x2(:,components{i}(j))').^2; +% end +% distc{i} = distc{i}.*s; +% % Accumulate to the total distance +% dist = dist + distc{i}; +% end +% dist = sqrt(dist); +% % Loop through component sets +% for i=1:m +% D = -distc{i}; +% D(dist~=0) = D(dist~=0)./dist(dist~=0); +% ii1 = ii1+1; +% gdist{ii1} = D; +% end + +% $$$ elseif nargin == 3 +% $$$ if size(x,2) ~= size(x2,2) +% $$$ error('metric_ibs -> _ghyper: The number of columns in x and x2 has to be the same. ') +% $$$ end +% elseif nargin == 4 +% gdist = cell(1,length(metric.params)+1); +% end +% +% % Evaluate the prior contribution of gradient with respect to lengthScale +% for i2=1:m +% i1=i1+1; +% gprior_dist(i1)=feval(mp.params.fg, ... +% metric.params(i2), ... +% mp.params.a, 'x').*metric.params(i2) - 1; +% end +% +% % Evaluate the prior contribution of gradient with respect to lengthScale.p.s (and lengthScale.p.nu) +% if isfield(mp.params, 'p') && ~isempty(mp.params.p) +% i1=i1+1; +% gprior_dist(i1)=... +% feval(mp.params.p.s.fg, ... +% mp.params.a.s,... +% mp.params.p.s.a, 'x').*mp.params.a.s - 1 ... +% +feval(mp.params.fg, ... +% metric.params, ... +% mp.params.a, 's').*mp.params.a.s; +% if any(strcmp(fieldnames(mp.params.p),'nu')) +% i1=i1+1; +% gprior_dist(i1)=... +% feval(mp.params.p.nu.fg, ... +% mp.params.a.nu,... +% mp.params.p.nu.a, 'x').*mp.params.a.nu -1 ... +% +feval(mp.params.fg, ... +% metric.params, ... +% mp.params.a, 'nu').*mp.params.a.nu; +% end +% end + end + +% function [matrix,gdist] = metric_ibs_gxe_matrix(metric, x1, x2) +% %METRIC_ibs_matrix Compute the ibs distence between +% % one or two matrices. +% % +% % Description +% % [DIST] = METRIC_ibs_matrix(METRIC, X) takes a metric data +% % structure METRIC together with a matrix X of input vectors and +% % calculates the ibs distance matrix DIST. +% % +% % [DIST] = METRIC_ibs_matrix(METRIC, X1, X2) takes a metric data +% % structure METRIC together with a matrices X1 and X2 of input vectors and +% % calculates the ibs distance matrix DIST. +% % +% % See also +% % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs, GP_E +% % +% if nargin == 2 || isempty(x2) +% x2=x1; +% end +% +% [n1,m1]=size(x1); +% [n2,m2]=size(x2); +% +% if m1~=m2 +% error('the number of columns of X1 and X2 has to be same') +% end +% mp=metric.p; +% components = metric.components; +% m = length(components); +% dist = 0; +% i1=0; +% if nargout>1 +% +% ii1 = 0; +% distc = cell(1,m); +% % Compute the distances for each component set +% for i=1:m +% %s = 1./metric.params(i).^2; +% distc{i} = 0; +% for j = 1:length(components{i}) +% distc{i} = distc{i} + bsxfun(@minus,x1(:,components{i}(j)),x2(:,components{i}(j))').^2; +% end +% distc{i} = distc{i}; +% % Accumulate to the total distance +% dist = dist + distc{i}; +% end +% dist = sqrt(dist); +% % Loop through component sets +% for i=1:m +% D = -distc{i}; +% D(dist~=0) = D(dist~=0)./dist(dist~=0); +% ii1 = ii1+1; +% gdist{ii1} = D; +% end +% +% else +% +% for i=1:m +% for j = 1:length(components{i}) +% dist = dist + bsxfun(@minus,x1(:,components{i}(j)),x2(:,components{i}(j))').^2; +% end +% end +% dist=sqrt(dist); +% +% end +% +% matrix = dist; +% +% +% +% +% +% end + + + function [dist] = metric_ibs_gxe_distance(metric, x1, x2) + %METRIC_ibs_DISTANCE Compute the ibs distence between + % one or two matrices. + % + % Description + % [DIST] = METRIC_ibs_DISTANCE(METRIC, X) takes a metric data + % structure METRIC together with a matrix X of input vectors and + % calculates the ibs distance matrix DIST. + % + % [DIST] = METRIC_ibs_DISTANCE(METRIC, X1, X2) takes a metric data + % structure METRIC together with a matrices X1 and X2 of input vectors and + % calculates the ibs distance matrix DIST. + % + % See also + % METRIC_ibs_PAK, METRIC_ibs_UNPAK, METRIC_ibs, GP_E + % + + +% if nargin == 2 || isempty(n2) +% n2=n1; +% end + +% [n1,m1]=size(x1); +% [n2,m2]=size(x2); + +% if m1~=m2 +% error('the number of columns of X1 and X2 has to be same') +% end + + + + [n1,m1]=size(x1); + + if nargin == 3 + [n2,m2]=size(x2); + g1=x1(:,1); + g2=x2(:,1); + dist_g=metric.metric_gene.distance(metric.metric_gene,g1,g2); + e1=x1(:,2:end); + e2=x2(:,2:end); + dist_e=metric.metric_env.distance(metric.metric_env,e1,e2); + else + g1=x1(:,1); + dist_g=metric.metric_gene.distance(metric.metric_gene,g1); + e1=x1(:,2:end); + dist_e=metric.metric_env.distance(metric.metric_env,e1); + end + + dist=dist_e+dist_g; + +% n1min=min(size(x1matrix)); +% n1max=max(size(x1matrix)); +% %n1=size(x1matrix,1); +% dist=0; +% % s = 1./metric.params; +% jep=0; +% if (nargin == 3) +% n2min=min(size(x2matrix)); +% n2max=max(size(x2matrix)); +% if n1max<=n2max +% +% gene_matrix1=x1matrix(1:n1min,1:n1min); +% env_matrix1=x1matrix(1:end,n1min+1:end); +% if size(x2matrix,1)==n2max +% gene_matrix2=x2matrix(1:n2min,1:n1min); +% env_matrix2=x2matrix(n2min+1:end,1:end)'; +% else +% gene_matrix2=x2matrix(1:n1min,1:n2min)'; +% env_matrix2=x2matrix(1:end,n2min+1:end); +% end +% else +% jep=1; +% gene_matrix1=x2matrix(1:n2min,1:n2min); +% env_matrix1=x2matrix(1:end,n2min+1:end); +% if size(x1matrix,1)==n1max +% gene_matrix2=x1matrix(1:n1min,1:n2min); +% env_matrix2=x1matrix(n1min+1:end,1:end)'; +% else +% gene_matrix2=x1matrix(1:n2min,1:n1min)'; +% env_matrix2=x1matrix(1:end,n1min+1:end); +% end +% end +% dist_gene=metric.metric_gene.distance(metric.metric_gene,gene_matrix1,gene_matrix2); +% dist_env=metric.metric_env.distance(metric.metric_env,env_matrix1,env_matrix2); +% else +% gene_matrix1=x1matrix(1:n1min,1:n1min); +% env_matrix1=x1matrix(1:end,n1min+1:end); +% dist_gene=metric.metric_gene.distance(metric.metric_gene,gene_matrix1); +% dist_env=metric.metric_env.distance(metric.metric_env,env_matrix1); +% end +% +% dist=dist_gene+dist_env; +% +% if jep==1 +% dist=dist'; +% end + +% % gene effect: square root of manhattan metric +% if (nargin == 3) +% n2=min(size(x2matrix)); +% gene_matrix2=x2matrix(1:n,1:n); +% env_matrix2=x2matrix(n+1:end,n+1:end); +% %n1=size(genematrix1,1); +% %n2=size(genematrix2,1); +% if size(metric.Kfu)==[n1,n2] +% dist=s(1).*sqrt(squeeze(Kfu)); +% elseif size(metric.Kfu)==[n2,n1] +% dist=s(1).*sqrt(squeeze(Kfu))'; +% elseif size(metric.Kffstar)==[n1,n2] +% dist=s(1).*sqrt(squeeze(Kffstar)); +% elseif size(metric.Kffstar)==[n2,n1] +% dist=s(1).*sqrt(squeeze(Kffstar))'; +% else +% dist=s(1).*sqrt(squeeze(genematrix1)); +% end +% % elseif n1~=metric.n1 +% % dist=s.*squeeze(metric.x2matrix); +% else +% dist=s(1).*sqrt(squeeze(genematrix1)); +% env_matrix2=env_matrix1; +% end +% +% % environmental effect: euclidean metric +% components = metric.components; +% m = length(components); +% dist_e = 0; +% +% if isempty(metric.dmatrix) +% for i=1:m +% s = 1./metric.params(i+1).^2; +% for j = 1:length(components{i}) +% dist_e = dist_e + s.*bsxfun(@minus,env_matrix1(:,components{i}(j)),env_matrix2(:,components{i}(j))').^2; +% end +% end +% dist_e = sqrt(dist_e); +% end +% +% dist=dist+dist_e; +% +% %dist=metric.dmatrix; + + + end + + function [ginput, gprior_input] = metric_ibs_gxe_ginput(metric, x1, x2) + %METRIC_ibs_GINPUT Compute the gradient of the ibs distance + % function with respect to input. + %[n, m] =size(x); + ii1 = 0; + components = metric.components; +% +% if nargin == 2 || isempty(x2) +% x2=x1; +% end +% +% [n1,m1]=size(x1); +% [n2,m2]=size(x2); +% +% if m1~=m2 +% error('the number of columns of X1 and X2 has to be same') +% end + +% if n1~=n2 +% dist=metric.x1x2matrix; +% elseif n1==length(metric.x1) +% dist=metric.x1matrix; +% else +% dist=metric.x2matrix; +% end + dist=metric.distance(metric,x1,x2); + +% s = 1./metric.params.^2; +% dist = 0; +% for i=1:length(components) +% for j = 1:length(components{i}) +% dist = dist + s(i).*bsxfun(@minus,x1(:,components{i}(j)),x2(:,components{i}(j))').^2; +% end +% end +% dist = sqrt(dist); + + + [ginput_g, gprior_input_g] = metric.metric_gene.ginput(metric.metric_gene, x1, x2); + [ginput_e, gprior_input_e] = metric.metric_env.ginput(metric.metric_env, x1, x2); + + for i=1:length(ginput_g) + ginput{i}=ginput_g{i}+ginput_e{i}; + gprior_input=[]; %KORJAA + end + +% +% for i=1:m1 +% for j = 1:n1 +% DK = zeros(n1,n2); +% for k = 1:length(components) +% if ismember(i,components{k}) +% DK(j,:) = DK(j,:)+s(k).*bsxfun(@minus,x1(j,i),x2(:,i)'); +% end +% end +% if nargin == 2 +% DK = DK + DK'; +% end +% DK(dist~=0) = DK(dist~=0)./dist(dist~=0); +% +% ii1 = ii1 + 1; +% ginput{ii1} = DK; +% gprior_input(ii1) = 0; +% end +% end + %size(ginput) + %ginput + + end + + + function recmetric = metric_ibs_gxe_recappend(recmetric, ri, metric) + % RECAPPEND - Record append + % Description + % RECMETRIC = METRIC_ibs_RECAPPEND(RECMETRIC, RI, METRIC) takes old covariance + % function record RECMETRIC, record index RI and covariance function structure. + % Appends the parameters of METRIC to the RECMETRIC in the ri'th place. + % + % RECAPPEND returns a structure RECMETRIC containing following record fields: + % lengthHyper + % lengthHyperNu + % lengthScale + % + % See also + % GP_MC and GP_MC -> RECAPPEND + + % Initialize record + if nargin == 2 + recmetric.type = 'metric_ibs_gxe'; + recmetric.nin = ri; + metric.components = recmetric.components; + + % Initialize parameters + recmetric.params = []; + + % Set the function handles + recmetric.pak = @metric_ibs_gxe_pak; + recmetric.unpak = @metric_ibs_gxe_unpak; + recmetric.e = @metric_ibs_gxe_e; + recmetric.ghyper = @metric_ibs_gxe_ghyper; + recmetric.ginput = @metric_ibs_gxe_ginput; + recmetric.distance = @metric_ibs_gxe_distance; + recmetric.recappend = @metric_ibs_gxe_recappend; + recmetric.matrix = @metric_ibs_gxe_matrix; + return + end + mp = metric.p; + + % record parameters + if ~isempty(metric.params) + if ~isempty(mp.params) + recmetric.lengthHyper(ri,:)=mp.params.a.s; + if isfield(mp.params,'p') + if isfield(mp.params.p,'nu') + recmetric.lengthHyperNu(ri,:)=mp.params.a.nu; + end + end + elseif ri==1 + recmetric.lengthHyper=[]; + end + recmetric.params(ri,:)=metric.params; + elseif ri==1 + recmetric.params=[]; + end + end diff --git a/octave_compat/mnpdf.m b/octave_compat/mnpdf.m new file mode 100644 index 00000000..fb1920e4 --- /dev/null +++ b/octave_compat/mnpdf.m @@ -0,0 +1,134 @@ +## Copyright (C) 2012 Arno Onken +## +## This program is free software: you can redistribute it and/or modify +## it under the terms of the GNU General Public License as published by +## the Free Software Foundation, either version 3 of the License, or +## (at your option) any later version. +## +## This program is distributed in the hope that it will be useful, +## but WITHOUT ANY WARRANTY; without even the implied warranty of +## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +## GNU General Public License for more details. +## +## You should have received a copy of the GNU General Public License +## along with this program. If not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{y} =} mnpdf (@var{x}, @var{p}) +## Compute the probability density function of the multinomial distribution. +## +## @subheading Arguments +## +## @itemize @bullet +## @item +## @var{x} is vector with a single sample of a multinomial distribution with +## parameter @var{p} or a matrix of random samples from multinomial +## distributions. In the latter case, each row of @var{x} is a sample from a +## multinomial distribution with the corresponding row of @var{p} being its +## parameter. +## +## @item +## @var{p} is a vector with the probabilities of the categories or a matrix +## with each row containing the probabilities of a multinomial sample. +## @end itemize +## +## @subheading Return values +## +## @itemize @bullet +## @item +## @var{y} is a vector of probabilites of the random samples @var{x} from the +## multinomial distribution with corresponding parameter @var{p}. The parameter +## @var{n} of the multinomial distribution is the sum of the elements of each +## row of @var{x}. The length of @var{y} is the number of columns of @var{x}. +## If a row of @var{p} does not sum to @code{1}, then the corresponding element +## of @var{y} will be @code{NaN}. +## @end itemize +## +## @subheading Examples +## +## @example +## @group +## x = [1, 4, 2]; +## p = [0.2, 0.5, 0.3]; +## y = mnpdf (x, p); +## @end group +## +## @group +## x = [1, 4, 2; 1, 0, 9]; +## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +## y = mnpdf (x, p); +## @end group +## @end example +## +## @subheading References +## +## @enumerate +## @item +## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics +## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001. +## +## @item +## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical +## Distributions}. pages 134-136, Wiley, New York, third edition, 2000. +## @end enumerate +## @end deftypefn + +## Author: Arno Onken +## Description: PDF of the multinomial distribution + +function y = mnpdf (x, p) + + # Check arguments + if (nargin != 2) + print_usage (); + endif + + if (! ismatrix (x) || any (x(:) < 0 | round (x(:) != x(:)))) + error ("mnpdf: x must be a matrix of non-negative integer values"); + endif + if (! ismatrix (p) || any (p(:) < 0)) + error ("mnpdf: p must be a non-empty matrix with rows of probabilities"); + endif + + # Adjust input sizes + if (! isvector (x) || ! isvector (p)) + if (isvector (x)) + x = x(:)'; + endif + if (isvector (p)) + p = p(:)'; + endif + if (size (x, 1) == 1 && size (p, 1) > 1) + x = repmat (x, size (p, 1), 1); + elseif (size (x, 1) > 1 && size (p, 1) == 1) + p = repmat (p, size (x, 1), 1); + endif + endif + # Continue argument check + if (any (size (x) != size (p))) + error ("mnpdf: x and p must have compatible sizes"); + endif + + # Count total number of elements of each multinomial sample + n = sum (x, 2); + # Compute probability density function of the multinomial distribution + t = x .* log (p); + t(x == 0) = 0; + y = exp (gammaln (n+1) - sum (gammaln (x+1), 2) + sum (t, 2)); + # Set invalid rows to NaN + k = (abs (sum (p, 2) - 1) > 1e-6); + y(k) = NaN; + +endfunction + +%!test +%! x = [1, 4, 2]; +%! p = [0.2, 0.5, 0.3]; +%! y = mnpdf (x, p); +%! assert (y, 0.11812, 0.001); + +%!test +%! x = [1, 4, 2; 1, 0, 9]; +%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +%! y = mnpdf (x, p); +%! assert (y, [0.11812; 0.13422], 0.001); diff --git a/octave_compat/mnrnd.m b/octave_compat/mnrnd.m new file mode 100644 index 00000000..1332faff --- /dev/null +++ b/octave_compat/mnrnd.m @@ -0,0 +1,184 @@ +## Copyright (C) 2012 Arno Onken +## +## This program is free software: you can redistribute it and/or modify +## it under the terms of the GNU General Public License as published by +## the Free Software Foundation, either version 3 of the License, or +## (at your option) any later version. +## +## This program is distributed in the hope that it will be useful, +## but WITHOUT ANY WARRANTY; without even the implied warranty of +## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +## GNU General Public License for more details. +## +## You should have received a copy of the GNU General Public License +## along with this program. If not, see . + +## -*- texinfo -*- +## @deftypefn {Function File} {@var{x} =} mnrnd (@var{n}, @var{p}) +## @deftypefnx {Function File} {@var{x} =} mnrnd (@var{n}, @var{p}, @var{s}) +## Generate random samples from the multinomial distribution. +## +## @subheading Arguments +## +## @itemize @bullet +## @item +## @var{n} is the first parameter of the multinomial distribution. @var{n} can +## be scalar or a vector containing the number of trials of each multinomial +## sample. The elements of @var{n} must be non-negative integers. +## +## @item +## @var{p} is the second parameter of the multinomial distribution. @var{p} can +## be a vector with the probabilities of the categories or a matrix with each +## row containing the probabilities of a multinomial sample. If @var{p} has +## more than one row and @var{n} is non-scalar, then the number of rows of +## @var{p} must match the number of elements of @var{n}. +## +## @item +## @var{s} is the number of multinomial samples to be generated. @var{s} must +## be a non-negative integer. If @var{s} is specified, then @var{n} must be +## scalar and @var{p} must be a vector. +## @end itemize +## +## @subheading Return values +## +## @itemize @bullet +## @item +## @var{x} is a matrix of random samples from the multinomial distribution with +## corresponding parameters @var{n} and @var{p}. Each row corresponds to one +## multinomial sample. The number of columns, therefore, corresponds to the +## number of columns of @var{p}. If @var{s} is not specified, then the number +## of rows of @var{x} is the maximum of the number of elements of @var{n} and +## the number of rows of @var{p}. If a row of @var{p} does not sum to @code{1}, +## then the corresponding row of @var{x} will contain only @code{NaN} values. +## @end itemize +## +## @subheading Examples +## +## @example +## @group +## n = 10; +## p = [0.2, 0.5, 0.3]; +## x = mnrnd (n, p); +## @end group +## +## @group +## n = 10 * ones (3, 1); +## p = [0.2, 0.5, 0.3]; +## x = mnrnd (n, p); +## @end group +## +## @group +## n = (1:2)'; +## p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +## x = mnrnd (n, p); +## @end group +## @end example +## +## @subheading References +## +## @enumerate +## @item +## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics +## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001. +## +## @item +## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical +## Distributions}. pages 134-136, Wiley, New York, third edition, 2000. +## @end enumerate +## @end deftypefn + +## Author: Arno Onken +## Description: Random samples from the multinomial distribution + +function x = mnrnd (n, p, s) + + # Check arguments + if (nargin == 3) + if (! isscalar (n) || n < 0 || round (n) != n) + error ("mnrnd: n must be a non-negative integer"); + endif + if (! isvector (p) || any (p < 0 | p > 1)) + error ("mnrnd: p must be a vector of probabilities"); + endif + if (! isscalar (s) || s < 0 || round (s) != s) + error ("mnrnd: s must be a non-negative integer"); + endif + elseif (nargin == 2) + if (isvector (p) && size (p, 1) > 1) + p = p'; + endif + if (! isvector (n) || any (n < 0 | round (n) != n) || size (n, 2) > 1) + error ("mnrnd: n must be a non-negative integer column vector"); + endif + if (! ismatrix (p) || isempty (p) || any (p < 0 | p > 1)) + error ("mnrnd: p must be a non-empty matrix with rows of probabilities"); + endif + if (! isscalar (n) && size (p, 1) > 1 && length (n) != size (p, 1)) + error ("mnrnd: the length of n must match the number of rows of p"); + endif + else + print_usage (); + endif + + # Adjust input sizes + if (nargin == 3) + n = n * ones (s, 1); + p = repmat (p(:)', s, 1); + elseif (nargin == 2) + if (isscalar (n) && size (p, 1) > 1) + n = n * ones (size (p, 1), 1); + elseif (size (p, 1) == 1) + p = repmat (p, length (n), 1); + endif + endif + sz = size (p); + + # Upper bounds of categories + ub = cumsum (p, 2); + # Make sure that the greatest upper bound is 1 + gub = ub(:, end); + ub(:, end) = 1; + # Lower bounds of categories + lb = [zeros(sz(1), 1) ub(:, 1:(end-1))]; + + # Draw multinomial samples + x = zeros (sz); + for i = 1:sz(1) + # Draw uniform random numbers + r = repmat (rand (n(i), 1), 1, sz(2)); + # Compare the random numbers of r to the cumulated probabilities of p and + # count the number of samples for each category + x(i, :) = sum (r <= repmat (ub(i, :), n(i), 1) & r > repmat (lb(i, :), n(i), 1), 1); + endfor + # Set invalid rows to NaN + k = (abs (gub - 1) > 1e-6); + x(k, :) = NaN; + +endfunction + +%!test +%! n = 10; +%! p = [0.2, 0.5, 0.3]; +%! x = mnrnd (n, p); +%! assert (size (x), size (p)); +%! assert (all (x >= 0)); +%! assert (all (round (x) == x)); +%! assert (sum (x) == n); + +%!test +%! n = 10 * ones (3, 1); +%! p = [0.2, 0.5, 0.3]; +%! x = mnrnd (n, p); +%! assert (size (x), [length(n), length(p)]); +%! assert (all (x >= 0)); +%! assert (all (round (x) == x)); +%! assert (all (sum (x, 2) == n)); + +%!test +%! n = (1:2)'; +%! p = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8]; +%! x = mnrnd (n, p); +%! assert (size (x), size (p)); +%! assert (all (x >= 0)); +%! assert (all (round (x) == x)); +%! assert (all (sum (x, 2) == n)); From f80c3702d556c8cf585eb9c6c2d4b05146153be4 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 30 Jul 2013 15:42:44 +0300 Subject: [PATCH 25/64] demo_survival_weibull -> demo_survival_aft in demo_inputdependentweibull --- gp/demo_inputdependentweibull.m | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gp/demo_inputdependentweibull.m b/gp/demo_inputdependentweibull.m index 10c5f0b2..8f8a4bf4 100644 --- a/gp/demo_inputdependentweibull.m +++ b/gp/demo_inputdependentweibull.m @@ -1,5 +1,5 @@ -S = which('demo_survival_weibull'); -L = strrep(S,'demo_survival_weibull.m','demodata/leukemia.txt'); +S = which('demo_survival_aft'); +L = strrep(S,'demo_survival_aft.m','demodata/leukemia.txt'); leukemiadata=load(L); % leukemiadata consists of: From 8631dbc1ed929be47f680fafb99460f1c55445ea Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 30 Jul 2013 15:44:54 +0300 Subject: [PATCH 26/64] fixed energy computation so that the rest of the multilatent models (inputdependent, zinegbin, cox-ph) work also for octave --- gp/gpla_e.m | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/gp/gpla_e.m b/gp/gpla_e.m index f8fed7f7..c617ccea 100644 --- a/gp/gpla_e.m +++ b/gp/gpla_e.m @@ -1312,8 +1312,12 @@ B=WK; B(1:sum(nl)+1:end) = B(1:sum(nl)+1:end) + 1; - [Ll,Lu]=lu(B); - edata = logZ + 0.5*det(Ll)*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu)))); + [Ll,Lu,p]=lu(B); + if sum(diag(p))==size(p,1) + edata = logZ + 0.5*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu)))); + else + edata = logZ + 0.5*det(p)*prod(sign(diag(Lu))).*sum(log(abs(diag(Lu)))); + end % Return help parameters for gradient and prediction % calculations From ac95886919b405b3c203fdd7ba55dda3fc8813fd Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 30 Jul 2013 15:45:42 +0300 Subject: [PATCH 27/64] added nanmean and nanstd for octave --- octave_compat/nanmean.m | 8 ++++++++ octave_compat/nanstd.m | 10 ++++++++++ 2 files changed, 18 insertions(+) create mode 100644 octave_compat/nanmean.m create mode 100644 octave_compat/nanstd.m diff --git a/octave_compat/nanmean.m b/octave_compat/nanmean.m new file mode 100644 index 00000000..ed679507 --- /dev/null +++ b/octave_compat/nanmean.m @@ -0,0 +1,8 @@ +function m = nanmean(x) + +nnanx=~isnan(x); +x(~nnanx)=0; +m=sum(x)./sum(nnanx); +m(isinf(m))=NaN; +end + diff --git a/octave_compat/nanstd.m b/octave_compat/nanstd.m new file mode 100644 index 00000000..735caedf --- /dev/null +++ b/octave_compat/nanstd.m @@ -0,0 +1,10 @@ +function sd = nanstd(x) + +nanm=nanmean(x); +nnanx=~isnan(x); +x=bsxfun(@minus, x, nanm).^2; +x(~nnanx)=0; +sd=sqrt(sum(x)./(sum(nnanx)-1)); +sd(isinf(sd))=NaN; +end + From 739e39c2452b31cc4458b05f8eb2e82bd200fef5 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 1 Aug 2013 15:39:15 +0300 Subject: [PATCH 28/64] fixed inputparser for ext_auc, hcs, idis and rsqr --- diag/ext_auc.m | 8 ++++---- diag/hcs.m | 12 ++++++------ diag/idis.m | 20 ++++++++++---------- diag/rsqr.m | 16 ++++++++-------- 4 files changed, 28 insertions(+), 28 deletions(-) diff --git a/diag/ext_auc.m b/diag/ext_auc.m index 7a658c99..06379113 100644 --- a/diag/ext_auc.m +++ b/diag/ext_auc.m @@ -21,14 +21,14 @@ % License.txt, included with the software, for details. ip=inputParser; - ip=iparser(ip,'addRequired','P',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','tt', @(x) isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','t', @(x) isreal(x) && all(isfinite(x(:)))) + ip=iparser(ip,'addRequired','P',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','tt', @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','t', @(x) isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'parse',P,tt,t); [n,nin]=size(P); S=1-P; - D=-diff([; ones(n,1) S],1,2); + D=-diff([ones(n,1) S],1,2); sd=tt(2)-tt(1); if isempty(find(tt==t,1)) diff --git a/diag/hcs.m b/diag/hcs.m index 7afe2b0b..dacde91c 100644 --- a/diag/hcs.m +++ b/diag/hcs.m @@ -30,12 +30,12 @@ % License.txt, included with the software, for details. ip=inputParser; -ip=iparser(ip,'addRequired','riskscore',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) -ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) -ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) -ip=iparser(ip,'addParamValue','rsubstream',0,@(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) -ip=iparser(ip,'parse',riskscore,y,z,t,varargin{:}) +ip=iparser(ip,'addRequired','riskscore',@(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)); +ip=iparser(ip,'addParamValue','rsubstream',0,@(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0); +ip=iparser(ip,'parse',riskscore,y,z,t,varargin{:}); rsubstream=ip.Results.rsubstream; diff --git a/diag/idis.m b/diag/idis.m index baf297b9..22bd3f63 100644 --- a/diag/idis.m +++ b/diag/idis.m @@ -41,9 +41,9 @@ % model-based estimator model_based_estimator = true; - ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) + ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0); if nargin > 2 % if more than 2 input arguments, there must be four as only a % single optional parameter is implemented @@ -59,13 +59,13 @@ % Pencina et al. estimator model_based_estimator = false; - ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) - ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip=iparser(ip,'parse',pt, pn, y, z, t, varargin{:}) + ip=iparser(ip,'addRequired','pt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','pn', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)); + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0); + ip=iparser(ip,'parse',pt, pn, y, z, t, varargin{:}); end rsubstream=ip.Results.rsubstream; diff --git a/diag/rsqr.m b/diag/rsqr.m index 2bb6ed3c..cf0ceb58 100644 --- a/diag/rsqr.m +++ b/diag/rsqr.m @@ -39,8 +39,8 @@ % model-based estimator model_based_estimator = true; - ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) + ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0); if nargin > 1 % if more than 1 input arguments, there must be 3 as only a % single optional parameter is implemented @@ -56,12 +56,12 @@ % Pencina et al. estimator model_based_estimator = false; - ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))) - ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)) - ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>0) - ip=iparser(ip,'parse',p, y, z, t, varargin{:}) + ip=iparser(ip,'addRequired','p', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','z', @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','t', @(x) isreal(x) && isscalar(x) && ~isnan(x)); + ip=iparser(ip,'addParamValue','rsubstream', 0, @(x) isreal(x) && isscalar(x) && isfinite(x) && x>=0); + ip=iparser(ip,'parse',p, y, z, t, varargin{:}); end rsubstream=ip.Results.rsubstream; From 7eca4cb90b86f57d9a61cd30c935a15b32c3ecdf Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 1 Aug 2013 15:39:58 +0300 Subject: [PATCH 29/64] speedups of lgpdens now work with octave also --- gp/demo_lgpdens.m | 14 +++++++++----- gp/gpla_e.m | 14 +++++++------- 2 files changed, 16 insertions(+), 12 deletions(-) diff --git a/gp/demo_lgpdens.m b/gp/demo_lgpdens.m index 37ba8ce0..6754e44e 100644 --- a/gp/demo_lgpdens.m +++ b/gp/demo_lgpdens.m @@ -195,8 +195,10 @@ xt=linspace(-6,6,200)'; [p,pq]=lgpdens(x,xt); pla=p; -line(xt,p,'color','r','marker','none','linewidth',2) -line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--') +if ~exist('OCTAVE_VERSION', 'builtin') + line(xt,p,'color','r','marker','none','linewidth',2) + line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--') +end xlim([-7 7]) title('t_4 (Laplace)') % true density @@ -214,11 +216,13 @@ line(xt,p0,'color','k') drawnow -[pks] = ksdensity(x,xt); - disp(['Laplace: ' num2str(sum(p0.*log(pla)))]) disp(['MCMC: ' num2str(sum(p0.*log(pmc)))]) -disp(['ksdensity: ' num2str(sum(p0.*log(pks)))]) + +if ~exist('OCTAVE_VERSION','builtin') + [pks] = ksdensity(x,xt); + disp(['ksdensity: ' num2str(sum(p0.*log(pks)))]) +end % Set back initial random stream setrandstream(prevstream); \ No newline at end of file diff --git a/gp/gpla_e.m b/gp/gpla_e.m index c617ccea..016286aa 100644 --- a/gp/gpla_e.m +++ b/gp/gpla_e.m @@ -698,13 +698,13 @@ if size(x,2)==1 gge=zeros(2*n,1); gge(1:n)=b; - q=ifft(fftKcirc.*fft(gge')); + q=real(ifft(fftKcirc.*fft(gge'))); Kg=q(1:n)'; elseif size(x,2)==2 gge=zeros(2*n2,2*n1); gge(1:n2,1:n1)=reshape(b,n2,n1); - q=ifft2(fftKcirc.*fft2(gge)); + q=real(ifft2(fftKcirc.*fft2(gge))); q=q(1:n2,1:n1); Kg=q(:); else @@ -722,8 +722,8 @@ else [iSg,~]=pcg(@(z) mvm_fft(g2,ny,fftKcirc,[],[],z), v, gp.latent_opt.pcg_tol); end + iSg=real(iSg); a=b-sqrt(ny)*(g2sq.*iSg - g2*(g2'*(iSg./g2sq))); - else if strcmpi(gp.lik.type,'LGPC') R=zeros(n); @@ -789,7 +789,7 @@ if size(x,2)==1 a2=zeros(2*n,1); a2(1:n)=a; - f2=ifft(fftKcirc.*fft(a2')); + f2=real(ifft(fftKcirc.*fft(a2'))); f=f2(1:n)'; if isfield(gp,'meanf') f=f+H'*(B_m*(H*a)); @@ -798,7 +798,7 @@ a2=zeros(2*n2,2*n1); a2(1:n2,1:n1)=reshape(a,n2,n1); - f2=ifft2(fftKcirc.*fft2(a2)); + f2=real(ifft2(fftKcirc.*fft2(a2))); f2=f2(1:n2,1:n1); f=f2(:); if isfield(gp,'meanf') @@ -842,7 +842,7 @@ if size(x,2)==1 a2=zeros(2*n,1); a2(1:n)=a; - f2=ifft(fftKcirc.*fft(a2')); + f2=real(ifft(fftKcirc.*fft(a2'))); f=f2(1:n)'; if isfield(gp,'meanf') @@ -851,7 +851,7 @@ elseif size(x,2)==2 a2=zeros(2*n2,2*n1); a2(1:n2,1:n1)=reshape(a,n2,n1); - f2=ifft2(fftKcirc.*fft2(a2)); + f2=real(ifft2(fftKcirc.*fft2(a2))); f2=f2(1:n2,1:n1); f=f2(:); From 4f92843171f10e3ad714e2edb4ae567730356a06 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 1 Aug 2013 15:40:48 +0300 Subject: [PATCH 30/64] demo_survival_weibull -> demo_survival_aft and some bugfixes for modelcomparison demos --- gp/demo_modelcomparison2.m | 4 ++-- gp/demo_survival_comparison.m | 12 ++++++------ gp/gp_kfcv_cdf.m | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/gp/demo_modelcomparison2.m b/gp/demo_modelcomparison2.m index f59bc69c..d58955a3 100644 --- a/gp/demo_modelcomparison2.m +++ b/gp/demo_modelcomparison2.m @@ -33,8 +33,8 @@ % License.txt, included with the software, for details. % First load data -S = which('demo_survival_weibull'); -L = strrep(S,'demo_survival_weibull.m','demodata/leukemia.txt'); +S = which('demo_survival_aft'); +L = strrep(S,'demo_survival_aft.m','demodata/leukemia.txt'); leukemiadata=load(L); % leukemiadata consists of: diff --git a/gp/demo_survival_comparison.m b/gp/demo_survival_comparison.m index e1cf9cca..38fafa20 100644 --- a/gp/demo_survival_comparison.m +++ b/gp/demo_survival_comparison.m @@ -30,8 +30,8 @@ % License.txt, included with the software, for details. %% First load data -S = which('demo_survival_weibull'); -L = strrep(S,'demo_survival_weibull.m','demodata/leukemia.txt'); +S = which('demo_survival_aft'); +L = strrep(S,'demo_survival_aft.m','demodata/leukemia.txt'); leukemiadata=load(L); % leukemiadata consists of: @@ -176,11 +176,11 @@ [c1,bb1]=hcs(crit1(:,end),y,ye,1,'rsubstream',1); [c2,bb2]=hcs(crit2(:,end),y,ye,1,'rsubstream',1); title('Estimated density of C2-C1') -lgpdens(bb2-bb1) +lgpdens(bb2(:)-bb1(:)) xlabel('Difference in Harrell''s C statistics (C2-C1)'); % We integrate the (C1-C2) estimated density in the (0,inf) interval -zc=lgpdens_cum(bb2-bb1,0,inf); +zc=lgpdens_cum(bb2(:)-bb1(:),0,inf); fprintf('Estimated c statistics for model 1 and 2 respectively: %.3f, %.3f \n', c1, c2); fprintf('cumulative probability in the (0,inf) interval: %.2f \n', zc); @@ -189,9 +189,9 @@ %probability in the (0,inf) interval, al at time 1 [idi,r1,r2,bbid] = idis(crit1(:,end),crit2(:,end),'rsubstream',1); -zidi=lgpdens_cum(bbid,0,inf); +zidi=lgpdens_cum(bbid(:),0,inf); title('IDI estimated density') -lgpdens(bbid) +lgpdens(bbid(:)) fprintf('R^2 statistic for model 1: %.3f \n', r1); fprintf('R^2 statistic for model 2: %.3f \n', r2); diff --git a/gp/gp_kfcv_cdf.m b/gp/gp_kfcv_cdf.m index c8b9fb10..c636cb42 100644 --- a/gp/gp_kfcv_cdf.m +++ b/gp/gp_kfcv_cdf.m @@ -77,7 +77,7 @@ ip=iparser(ip,'addParamValue','trindex', [], @(x) isempty(x) || iscell(x)); ip=iparser(ip,'addParamValue','tstindex', [], @(x) isempty(x) || iscell(x)); ip=iparser(ip,'addParamValue','display', 'on', @(x) islogical(x) || ... - ismember(x,{'iter' 'fold'})); + ismember(x,{'on' 'iter' 'fold'})); ip=iparser(ip,'parse',gp, x, y, varargin{:}); z=ip.Results.z; yt=ip.Results.yt; From 1a944a2b4226ef1112c00906dd029840e0ee4da0 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Fri, 11 Oct 2013 10:23:20 +0300 Subject: [PATCH 31/64] octave compatibility fixed --- gp/demo_inputdependentweibull.m | 1 + gp/demo_lgpdens.m | 6 ++++-- gp/gp_cpred.m | 1 + gp/gp_predcm.m | 7 +++++-- gp/lgpdens.m | 15 ++++++++++++--- gp/lik_negbinztr.m | 26 +++++++++++++------------- 6 files changed, 36 insertions(+), 20 deletions(-) diff --git a/gp/demo_inputdependentweibull.m b/gp/demo_inputdependentweibull.m index 8f8a4bf4..1ddcd2d8 100644 --- a/gp/demo_inputdependentweibull.m +++ b/gp/demo_inputdependentweibull.m @@ -45,6 +45,7 @@ xt1=zeros(200,nin); xt1(:,2)=1; xt2=zeros(200,nin); xt2(:,2)=-1; +nt=200; xt1(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200); xt2(:,1)=linspace(min(x(:,1)), max(x(:,1)), 200); diff --git a/gp/demo_lgpdens.m b/gp/demo_lgpdens.m index 1ef5b4d3..84c5d33a 100644 --- a/gp/demo_lgpdens.m +++ b/gp/demo_lgpdens.m @@ -211,8 +211,10 @@ subplot(2,1,2) [p,pq]=lgpdens(x,xt,'latent_method','MCMC'); pmc=p; -line(xt,p,'color','r','marker','none','linewidth',2) -line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--') +if ~exist('OCTAVE_VERSION','builtin') + line(xt,p,'color','r','marker','none','linewidth',2) + line(xt,pq,'color','r','marker','none','linewidth',1,'linestyle','--') +end xlim([-7 7]) ylim([0 .5]) title('t_4 (MCMC)') diff --git a/gp/gp_cpred.m b/gp/gp_cpred.m index dff8f110..2b22b011 100644 --- a/gp/gp_cpred.m +++ b/gp/gp_cpred.m @@ -35,6 +35,7 @@ ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addRequired','xt', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addRequired','ind', @(x) ~isempty(x) && isvector(x)); +ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addParamValue','var', [], @(x) isreal(x)); ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... diff --git a/gp/gp_predcm.m b/gp/gp_predcm.m index 528866e9..e4f3b081 100644 --- a/gp/gp_predcm.m +++ b/gp/gp_predcm.m @@ -366,13 +366,16 @@ % Check that the correction terms are decreasing in the tails lctmp=lc(:,i1); + lptmp=(lc(:,i1)+lp2(:,i1)); fvectmp=fvec; - while lctmp(end)-lctmp(end-1) > 0 + while lptmp(end)-lptmp(end-1) > 0 lctmp=lctmp(1:end-1); + lptmp=lptmp(1:end-1); fvectmp=fvectmp(1:end-1); end - while lctmp(1)-lctmp(2) > 0 + while lptmp(1)-lptmp(2) > 0 lctmp=lctmp(2:end); + lptmp=lptmp(2:end); fvectmp=fvectmp(2:end); end diff --git a/gp/lgpdens.m b/gp/lgpdens.m index e95325da..a41dea76 100644 --- a/gp/lgpdens.m +++ b/gp/lgpdens.m @@ -79,11 +79,12 @@ ip=iparser(ip,'addParamValue','cond_dens',[], @(x) ismember(x,{'on' 'off'})); ip=iparser(ip,'addParamValue','basis','gaussian', @(x) ismember(x,{'gaussian' 'exp' 'off'})); ip=iparser(ip,'addParamValue','bounded',[0 0], @(x) isnumeric(x) && min(size(x))==1 && max(size(x))==2); - ip=iparser(ip,'parse',x,varargin{:}); % additional undocumented parameters for importance sampling ip=iparser(ip,'addParamValue','n_is',8000, @(x) isnumeric(x) && x>=0); ip=iparser(ip,'addParamValue','n_scale',50, @(x) isnumeric(x) && x>=0); ip=iparser(ip,'addParamValue','contSplit','on', @(x) ismember(x,{'on' 'off'})); + ip=iparser(ip,'parse',x,varargin{:}); + x=ip.Results.x; xt=ip.Results.xt; @@ -306,7 +307,11 @@ % Monte Carlo integration. Econometrica 57:1317-1339. nParam = size(Ef,1); - [V, D] = svd(Covf); + if exist('OCTAVE_VERSION','builtin') + [V,D,tmp]=svd(Covf); + else + [V, D] = svd(Covf); + end eig = diag(real(D)); T = real(V) * sqrt(real(D)); gpis=gp_set(gp,'latent_method','MCMC'); @@ -647,7 +652,11 @@ clear Covft end nParam = size(Ef,1); - [V, D] = svd(Covf); + if exist('OCTAVE_VERSION','builtin') + [V,D,tmp]=svd(Covf); + else + [V, D] = svd(Covf); + end T = real(V) * sqrt(real(D)); eig = diag(real(D)); gpis=gp_set(gp,'latent_method','MCMC'); diff --git a/gp/lik_negbinztr.m b/gp/lik_negbinztr.m index 7c0abe84..63538649 100644 --- a/gp/lik_negbinztr.m +++ b/gp/lik_negbinztr.m @@ -716,7 +716,7 @@ ldconst = eta*(-gammaln(r)-gammaln(yy+1)+gammaln(r+yy))... - log(sigm2_i)/2 - log(2*pi)/2; % Create function handle for the function to be integrated - df = @(f) negbinztr_norm(f, ldconst,avgE,r,yy,myy_i,sigm2_i); + df = @(f) negbinztr_norm(f, ldconst,avgE,r,yy,myy_i,sigm2_i, eta); % use log to avoid underflow, and derivates for faster search ld = @log_negbinztr_norm; ldg = @log_negbinztr_norm_g; @@ -741,8 +741,8 @@ niter=4; % number of Newton iterations mindelta=1e-6; % tolerance in stopping Newton iterations for ni=1:niter - g=ldg(modef,ldconst,avgE,r,yy,myy_i,sigm2_i); - h=ldg2(modef,ldconst,avgE,r,yy,myy_i,sigm2_i); + g=ldg(modef,ldconst,avgE,r,yy,myy_i,sigm2_i,eta); + h=ldg2(modef,ldconst,avgE,r,yy,myy_i,sigm2_i,eta); delta=-g/h; modef=modef+delta; if abs(delta)(modeld-lddiff) minf=minf-step*modes; - minld=ld(minf,ldconst,avgE,r,yy,myy_i,sigm2_i); + minld=ld(minf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta); iter=iter+1; step=step*2; if iter>100 @@ -775,12 +775,12 @@ 'even after looking hard!']) end end - maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i); + maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta); iter=0; step=1; while maxld>(modeld-lddiff) maxf=maxf+step*modes; - maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i); + maxld=ld(maxf,ldconst,avgE,r,yy,myy_i,sigm2_i,eta); iter=iter+1; step=step*2; if iter>100 @@ -791,7 +791,7 @@ end end - function integrand = negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i) + function integrand = negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta) % Negative-binomial * Gaussian mu = avgE.*exp(f); lp0=r.*(log(r) - log(r+mu)); @@ -812,7 +812,7 @@ end end - function log_int = log_negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i) + function log_int = log_negbinztr_norm(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta) % log(Negative-binomial * Gaussian) % log_negbinztr_norm is used to avoid underflow when searching % integration interval @@ -835,7 +835,7 @@ end end - function g = log_negbinztr_norm_g(f,ldconst,avgE,r,yy,myy_i,sigm2_i) + function g = log_negbinztr_norm_g(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta) % d/df log(Negative-binomial * Gaussian) % derivative of log_negbinztr_norm mu = avgE.*exp(f); @@ -851,7 +851,7 @@ end end - function g2 = log_negbinztr_norm_g2(f,ldconst,avgE,r,yy,myy_i,sigm2_i) + function g2 = log_negbinztr_norm_g2(f,ldconst,avgE,r,yy,myy_i,sigm2_i,eta) % d^2/df^2 log(Negative-binomial * Gaussian) % second derivate of log_negbinztr_norm mu = avgE.*exp(f); From 8a70191d4ab7abb31909ac56434cea5b59c0db44 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 26 Nov 2013 12:38:24 +0200 Subject: [PATCH 32/64] updated ChangeLog --- ChangeLog.txt | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/ChangeLog.txt b/ChangeLog.txt index a57dcc08..63118d07 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -1,3 +1,9 @@ +2013-11-26 Version 4.3.1 + +Improvements: + - Updated cpsrf and psrf to follow BDA3: split each chain to two halves and use Geyer's IPSE for n_eff + - Multi-latent models for Octave + 2013-10-14 Version 4.3 Improvements: From a2789451cd8ef0b29e3c2b6f560f39c2633f8a1d Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 28 Nov 2013 13:47:29 +0200 Subject: [PATCH 33/64] release modifications --- gp/demo_censored_t.m | 222 ------- gp/gp_cvlcriterion.m | 60 -- gp/gp_lcriterion.m | 68 --- gp/gp_refpred.m | 468 --------------- gp/gpcf_intcov.m | 754 ------------------------ gp/lik_gaussianbl.m | 369 ------------ octave_compat/fcnchk.m | 3 +- xunit/log/Readme.txt | 4 - xunit/log/create_files.m | 379 ------------ xunit/log/demo_binomial1.txt | 7 - xunit/log/demo_binomial_apc.txt | 61 -- xunit/log/demo_classific.txt | 28 - xunit/log/demo_derivativeobs.txt | 26 - xunit/log/demo_lgcp.txt | 4 - xunit/log/demo_modelassesment1.txt | 567 ------------------ xunit/log/demo_modelassesment2.txt | 479 --------------- xunit/log/demo_multiclass.txt | 661 --------------------- xunit/log/demo_multinom2.txt | 458 -------------- xunit/log/demo_neuralnetcov.txt | 10 - xunit/log/demo_periodic.txt | 47 -- xunit/log/demo_regression1.txt | 60 -- xunit/log/demo_regression_additive1.txt | 61 -- xunit/log/demo_regression_additive2.txt | 20 - xunit/log/demo_regression_hier.txt | 51 -- xunit/log/demo_regression_meanf.txt | 15 - xunit/log/demo_regression_ppcs.txt | 25 - xunit/log/demo_regression_robust.txt | 143 ----- xunit/log/demo_regression_sparse1.txt | 74 --- xunit/log/demo_regression_sparse2.txt | 14 - xunit/log/demo_spatial1.txt | 542 ----------------- xunit/log/demo_spatial2.txt | 28 - xunit/log/demo_survival_coxph.txt | 43 -- xunit/log/demo_survival_weibull.txt | 26 - xunit/log/demo_zinegbin.txt | 30 - 34 files changed, 2 insertions(+), 5805 deletions(-) delete mode 100644 gp/demo_censored_t.m delete mode 100644 gp/gp_cvlcriterion.m delete mode 100644 gp/gp_lcriterion.m delete mode 100644 gp/gp_refpred.m delete mode 100644 gp/gpcf_intcov.m delete mode 100644 gp/lik_gaussianbl.m delete mode 100644 xunit/log/Readme.txt delete mode 100644 xunit/log/create_files.m delete mode 100644 xunit/log/demo_binomial1.txt delete mode 100644 xunit/log/demo_binomial_apc.txt delete mode 100644 xunit/log/demo_classific.txt delete mode 100644 xunit/log/demo_derivativeobs.txt delete mode 100644 xunit/log/demo_lgcp.txt delete mode 100644 xunit/log/demo_modelassesment1.txt delete mode 100644 xunit/log/demo_modelassesment2.txt delete mode 100644 xunit/log/demo_multiclass.txt delete mode 100644 xunit/log/demo_multinom2.txt delete mode 100644 xunit/log/demo_neuralnetcov.txt delete mode 100644 xunit/log/demo_periodic.txt delete mode 100644 xunit/log/demo_regression1.txt delete mode 100644 xunit/log/demo_regression_additive1.txt delete mode 100644 xunit/log/demo_regression_additive2.txt delete mode 100644 xunit/log/demo_regression_hier.txt delete mode 100644 xunit/log/demo_regression_meanf.txt delete mode 100644 xunit/log/demo_regression_ppcs.txt delete mode 100644 xunit/log/demo_regression_robust.txt delete mode 100644 xunit/log/demo_regression_sparse1.txt delete mode 100644 xunit/log/demo_regression_sparse2.txt delete mode 100644 xunit/log/demo_spatial1.txt delete mode 100644 xunit/log/demo_spatial2.txt delete mode 100644 xunit/log/demo_survival_coxph.txt delete mode 100644 xunit/log/demo_survival_weibull.txt delete mode 100644 xunit/log/demo_zinegbin.txt diff --git a/gp/demo_censored_t.m b/gp/demo_censored_t.m deleted file mode 100644 index 457acff1..00000000 --- a/gp/demo_censored_t.m +++ /dev/null @@ -1,222 +0,0 @@ -%% generate data -clear; -datai=1; - -% true function f(x) -xx = linspace(-7,5,500)'; -yy = 0.1+0.1*xx+0.2*sin(2.7*xx)+1./(1+xx.^2); - -nu=3; -sigma=0.05; -Hmax=sqrt(3*nu)*sigma; -Hzero=sqrt(nu)*sigma; -a=0.3; -ylim=[-0.4 0.9]; - -x=[linspace(-5.4,-3-a,15) linspace(-3+a,-1-a,15) linspace(-1+a,4,30)]; -xo=[-4 -3 -2 -1 2 3]; -ii=length(xo)+[1:length(x)]; -io=1:length(xo); % outlier indices -x=[xo x]'; % outlier x:s -y = 0.1 + 0.1*x + 0.2*sin(2.7*x) + 1 ./ (1+x.^2); -y(io)=y(io)+Hmax*[-3 -2 1 -1 -1 1]'; % outlier y:s -y(ii)=y(ii)+sigma*randn(size(y(ii))); - -yt=y; -y(yylim(2))=ylim(2); - -figure(1); clf -plot(xx,yy,'k',x,y,'b.',x(io),y(io),'ro') -hold on -plot(repmat(x(io)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -%save(sprintf('data%d.mat',datai),'x','y','yt','xx','yy','io','ylim') - -%% load data & create gp -%clear -%datai=1; -%load(sprintf('data%d.mat',datai)); -nu=6; -sigma=0.1; -J=0.02; -x=[x randn(size(x))]; -xx=[xx randn(size(xx))]; -[n, nin] = size(x); -ylim=[-0.4 0.9]; - -% gpcf1 = gpcf_dotproduct('init', 'constSigma2',10,'coeffSigma2',ones(1,nin)); -% gpcf1.p.constSigma2 = logunif_p; -% gpcf1.p.coeffSigma2 = logunif_p; - -gpcf1 = gpcf_sexp('init', 'lengthScale',ones(1,nin),'magnSigma2',1); -gpcf1.p.lengthScale = logunif_p; -gpcf1.p.magnSigma2 = logunif_p; - - -% gpcf1 = gpcf_neuralnetwork('init',nin,'biasSigma2',0.1,'weightSigma2',ones(1,nin)); -% gpcf1.p.weightSigma2 = logunif_p; -% gpcf1.p.biasSigma2 = logunif_p; - -% Create the likelihood structure -%likelih = likelih_t('init', nu, sigma); -likelih = likelih_cen_t('init', 'nu', nu, 'sigma', sigma, 'ylim', ylim); -likelih.p.nu = logunif_p; -likelih.p.sigma = logunif_p; -%likelih.fix_nu=1; - -% Laplace approximation Student-t likelihood -param = 'hyper+likelih'; -gp_la = gp_init('init', 'FULL', likelih, {gpcf1}, {}, 'jitterSigma2', J.^2); -gp_la = gp_init('set', gp_la, 'latent_method', {'Laplace', x, y, param}); -gp_la.laplace_opt.optim_method='likelih_specific'; -%gp_la.laplace_opt.optim_method='fminunc_large'; -[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); - -% gradient checking -w = gp_pak(gp_la,param); -w = w+ 0.1*randn(size(w)); -gradcheck(w, @gpla_e, @gpla_g, gp_la, x, y, param) - -opt=optimset('GradObj','on'); -opt=optimset(opt,'TolX', 1e-3); -opt=optimset(opt,'LargeScale', 'off'); -opt=optimset(opt,'Display', 'iter'); -opt=optimset(opt,'Derivativecheck', 'on'); % 'iter' - -w0 = gp_pak(gp_la, param); -mydeal = @(varargin)varargin{1:nargout}; -w = fminunc(@(ww) mydeal(gpla_e(ww, gp_la, x, y, param), gpla_g(ww, gp_la, x, y, param)), w0, opt); -gp_la = gp_unpak(gp_la,w,param); - -fprintf('\nnu=%.3f, sigma=%.3f \nhyper=%s\n',gp_la.likelih.nu,... - gp_la.likelih.sigma,sprintf(' %.2f,',exp(gp_pak(gp_la,'hyper'))) ) - -figure(2) -[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); -W=-gp_la.likelih.fh.llg2(gp_la.likelih,y,f,'latent'); -[foo,ii]=sort(W,'ascend'); -ii=ii(1:5); -plot(xx(:,1),yy,'k',x(:,1),f,'b.',x(:,1),y,'go',x(ii,1),y(ii),'r.') - -[Ef_la, Varf_la] = la_pred(gp_la, x, y, xx, param); -stdf_la = sqrt(Varf_la); - -% plot the predictions and data -nu=gp_la.likelih.nu; -sigma=gp_la.likelih.sigma; -Hmax=sqrt(3*nu)*sigma; -Hzero=sqrt(nu)*sigma; - -figure(1) -h1=plot(xx(:,1),yy,'k',xx(:,1),Ef_la,'b',xx(:,1),Ef_la-2*stdf_la, 'b--',xx(:,1), Ef_la+2*stdf_la, 'b--'); -hold on -h1=[h1(1:2); plot(x(:,1),y,'k.')]; -plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -legend(h1,'True','Laplace','Data') - -% ====================== -% Full MCMC solution -% ====================== -[n, nin] = size(x); -gpcf1 = gpcf_sexp('init', 'lengthScale', repmat(1,1,nin), 'magnSigma2', 0.2^2); -gpcf2 = gpcf_noiset('init', 'ndata', n, 'noiseSigmas2', repmat(1^2,n,1)); % Here set own Sigma2 for every data point - -% Un-fix nu -%gpcf2 = gpcf_noiset('set', gpcf2, 'fix_nu', 0); -gpcf2 = gpcf_noiset('set', gpcf2, 'censored', {[-0.4 0.9], y}); - -% Set the prior for the parameters of covariance functions -gpcf1.p.lengthScale = gamma_p({3 7 3 7}); -gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 1e-4.^2) -w = gp_pak(gp, 'hyper') -gp2 = gp_unpak(gp,w, 'hyper') - -opt=gp_mcopt; -opt.repeat=10; -opt.nsamples=10; -opt.hmc_opt.steps=10; -opt.hmc_opt.stepadj=0.1; -opt.hmc_opt.nsamples=1; -hmc2('state', sum(100*clock)); - -opt.gibbs_opt = sls1mm_opt; -opt.gibbs_opt.maxiter = 50; -opt.gibbs_opt.mmlimits = [0 40]; -opt.gibbs_opt.method = 'minmax'; - -% Sample -[r,g,rstate1]=gp_mc(opt, gp, x, y); - -opt.hmc_opt.stepadj=0.08; -opt.nsamples=300; -opt.hmc_opt.steps=10; -opt.hmc_opt.persistence=1; -opt.hmc_opt.decay=0.6; - -[r,g,rstate2]=gp_mc(opt, g, x, y, [], [], r); -rr = r; - -% thin the record -rr = thin(r,100,2); - -figure -hist(rr.noise{1}.nu,20) -title('Mixture model, \nu') -figure -hist(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha,20) -title('Mixture model, \sigma') -figure -hist(rr.cf{1}.lengthScale(:,1),20) -title('Mixture model, length-scale') -figure -hist(rr.cf{1}.magnSigma2,20) -title('Mixture model, magnSigma2') - - -% $$$ >> mean(rr.noise{1}.nu) -% $$$ ans = -% $$$ 1.5096 -% $$$ >> mean(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha) -% $$$ ans = -% $$$ 0.0683 -% $$$ >> mean(rr.cf{1}.lengthScale) -% $$$ ans = -% $$$ 1.0197 -% $$$ >> mean(rr.cf{1}.magnSigma2) -% $$$ ans = -% $$$ 1.2903 - -% make predictions for test set -ypred = repmat(y,1,size(rr.edata,1)); -ypred(gp.noise{1}.imis,:) = rr.noise{1}.cy'; -[Efs, Varfs] = gp_preds(rr,x,ypred,xx); - -Ef = mean(squeeze(Efs),2); -std_f = sqrt(var(Efs,[],2)); - -% Plot the network outputs as '.', and underlying mean with '--' -figure -h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b'); -%h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b',xx(:,1),Ef-2*std_f, 'b--',xx(:,1), Ef+2*std_f, 'b--'); -hold on -h1=[h1(1:2); plot(x(:,1),y,'k.')]; -plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -legend(h1,'True','Laplace','Data') - - -figure -for i=1:12 - subplot(6,2,i) - hist(rr.noise{1}.cy(:,i)) - hold on - plot(yt(gp.noise{1}.imis(i)), 0, 'rx', 'MarkerSize', 20, 'lineWidth', 5) -end - diff --git a/gp/gp_cvlcriterion.m b/gp/gp_cvlcriterion.m deleted file mode 100644 index 8c46087d..00000000 --- a/gp/gp_cvlcriterion.m +++ /dev/null @@ -1,60 +0,0 @@ -function PE2 = gp_cvlcriterion(gp, x, y, varargin) -%GP_CVLCRITERION cross-validation version of L-criterion -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns cross-validation -% version of L-criterion PE2 given a Gaussian process model GP, -% training inputs X and training outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Marriott, J. M., Spencer, N. M. and Pettitt, A. N. (2001). A -% Bayesian Approach to Selecting Covariates for -% Prediction. Scandinavian Journal of Statistics 28 87–97. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_LCRITERION -% - -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_CVLCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_loopred(gp, x, y); - PE2 = mean((Ey-y).^2 + Vary); - - else - % Non-Gaussian likelihood - error('cvlcriterion not sensible for non-gaussian likelihoods'); - end - -end - diff --git a/gp/gp_lcriterion.m b/gp/gp_lcriterion.m deleted file mode 100644 index 97642777..00000000 --- a/gp/gp_lcriterion.m +++ /dev/null @@ -1,68 +0,0 @@ -function L2 = gp_lcriterion(gp, x, y, varargin) -%GP_LCRITERION L-criterion for model selection. -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns L-criterion L2 -% given a Gaussian process model GP, training inputs X and training -% outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Gelfand, A. E. and Ghosh, S. K. (1998). Model Choice: A Minimum -% Posterior Predictive Loss Approach. Biometrika 85 1–11. -% -% Ibrahim, J. G., Chen, M.-H. and Sinha, D. (2001). Criterion-based -% methods for Bayesian model assessment. Statistica Sinica 11 -% 419–443. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_CVLCRITERION -% - -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - - ip=inputParser; - ip.FunctionName = 'GP_LCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - ip.parse(gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y); - L2 = sum((y-Ey).^2 + Vary); - - else - % Non-Gaussian likelihood - warning('L-criterion not sensible for non-gaussian likelihoods'); - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y, options); - L2 = sum((y-Ey).^2 + Vary); - - end - -end - diff --git a/gp/gp_refpred.m b/gp/gp_refpred.m deleted file mode 100644 index dbf0583b..00000000 --- a/gp/gp_refpred.m +++ /dev/null @@ -1,468 +0,0 @@ -function u_g = gp_refpred(gp1, gp2, x, y, varargin) -% GP_REFPRED Reference predictive approximation to the expected utility of -% single predictions. -% -% Description -% u = GP_REFPRED(GP1, GP2, X, Y, OPTIONS) evaluates reference -% predictive approximation between models GP1 and GP2. Here GP1 is the -% reference model and GP2 is the candidate model. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% method - method for inference, 'posterior' (default) uses posterior -% predictive density, 'loo' uses leave-one-out predictive -% density (approximative), 'kfcv' uses loo cross-validation -% posterior predictive density, 'joint' uses joint -% posterior predictive density for latent values -% (non-Gaussian likelihood) or observations (Gaussian -% likelihood) -% x2,y2,z2 - Optional values for candidate model gp2. -% If only subset of these is specified, remaining variables -% are set from x,y,z. -% -% See also -% GP_LOOPRED, GP_KFCV -% -% References -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In preparation. -% - -% Copyright (c) 2011-2012 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_REFPRED'; - ip=iparser(ip,'addRequired','gp1',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','gp2',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','x2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','y2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})); - ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); - ip=iparser(ip,'parse',gp1, gp2, x, y, varargin{:}); - % pass these forward - options=struct(); - x2 = ip.Results.x2; - y2 = ip.Results.y2; - z2 = ip.Results.z2; - z = ip.Results.z; - method = ip.Results.method; - form = ip.Results.form; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - if ~isempty(ip.Results.z2) - options2.zt=ip.Results.z2; - options2.z=ip.Results.z2; - else - options2 = options; - z2 = z; - end - [tn, nin] = size(x); - u_g = zeros(size(y)); - opt = optimset('TolX', 1e-4, 'TolFun', 1e-4); - if isempty(x2) - x2 = x; - end - if isempty(y2) - y2 = y; - end - - if isstruct(gp1) - % Single gp or MCMC - - switch gp1.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1.tr_index; - end - if ~isfield(gp1,'etr') - model1 = 1; - if isfield(gp1.lik.fh, 'trcov') - switch method - case 'joint' - - otherwise - fh1 = @(f,Ey,Vary) norm_pdf(f,Ey,sqrt(Vary)); - end - else - fh1 = @(gp,Ef,Varf,f,z) exp(predvec(gp,Ef,(Varf),f,z)); - end - - switch method - case 'posterior' - if ~isequal(gp1.lik.type, 'Coxph') - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - else - [Ef1, Varf1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - case 'loo' - if ~isfield(gp1.lik.fh, 'trcov') && ~isfield(gp1.lik, 'type_nd') - gp1 = gp_set(gp1, 'latent_method', 'EP'); - end - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_loopred(gp1,x,y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp1, x, y, 'tstindex', tstind, 'opt', opt, 'display', 'iter', 'k', tn, options); - [Ef1, Varf1, Ey1, Vary1] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef1, Covf1] = gp_jpred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - - else - model1 = 2; - if isfield(gp1.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) mean(multi_npdf(f,Ey,(Vary)),1); - else - fh1 = @(gp,Ef,Varf,f,z) mean(exp(predvec(gp,Ef,(Varf),f,z)),1); - end - nsamples = length(gp1.edata); - if strcmp(gp1.type, 'PIC') - tr_index = gp1.tr_index; - gp1 = rmfield(gp1, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp1,j); - if strcmp(gp1.type, 'FIC') | strcmp(gp1.type, 'PIC') || strcmp(gp1.type, 'CS+FIC') || strcmp(gp1.type, 'VAR') || strcmp(gp1.type, 'DTC') || strcmp(gp1.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array1{j} = Gp; - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gpmc_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_loopred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, pred] = gp_kfcv(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - gp1 = gp_array1; - end - else - % GP IA - switch gp1{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1{1}.tr_index; - end - model1 = 3; - nsamples = length(gp1); - for j = 1:nsamples - Gp = gp1{j}; - weight1(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_pred(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - if isfield(gp1{1}.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight1'),1); - else - fh1 = @(gp,Ef,Varf,f,z) (sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight1'),1)); - end - - end - - if isstruct(gp2) - % Single gp or MCMC - switch gp2.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2.tr_index; - end - if ~isfield(gp2,'etr') - model2 = 1; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) norm_lpdf(f,Ey,sqrt(Vary)); - else - fh2 = @(gp,Ef,Varf,f,z) predvec(gp,Ef,(Varf),f,z); - end - switch method - case 'posterior' - if ~isequal(gp2.lik.type, 'Coxph') - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - else - [Ef2, Varf2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - case 'loo' - if ~isfield(gp2.lik.fh, 'trcov') && ~isfield(gp2.lik, 'type_nd') - gp1 = gp_set(gp2, 'latent_method', 'EP'); - end - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_loopred(gp2,x2,y2, 'z', z2); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp2, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - [Ef2, Varf2, Ey2, Vary2] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef2, Covf2] = gp_jpred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - else - model2 = 2; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(mean(multi_npdf(f,Ey,(Vary)),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(mean(exp(predvec(gp,Ef,(Varf),f,z)),1)); - end - nsamples = length(gp2.edata); - if strcmp(gp2.type, 'PIC') - tr_index = gp2.tr_index; - gp2 = rmfield(gp2, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp2,j); - if strcmp(gp2.type, 'FIC') | strcmp(gp2.type, 'PIC') || strcmp(gp2.type, 'CS+FIC') || strcmp(gp2.type, 'VAR') || strcmp(gp2.type, 'DTC') || strcmp(gp2.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array2{j} = Gp; - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gpmc_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_kfcv(Gp, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - end - - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - gp2 = gp_array2; - end - else - % GP IA - switch gp2{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2{1}.tr_index; - end - model2 = 3; - nsamples = length(gp2); - for j = 1:nsamples - Gp = gp2{j}; - weight2(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'off', options2); - end - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - if isfield(gp2{1}.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight2'),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight2'),1)); - end - - end - - if ((isstruct(gp1) && isfield(gp1.lik.fh, 'trcov')) || (iscell(gp1) && isfield(gp1{1}.lik.fh,'trcov'))) - % Gaussian likelihood - - switch method - case 'joint' - u_g = -0.5.*((Ey1 - Ey2)'*(Covy2\(Ey1-Ey2)) + sum(sum(inv(Covy2).*Covy1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covy2))))); - otherwise - for i=1:tn - m1 = Ey1(i,:); m2=Ey1(i,:).^2 + Vary1(i,:); - u_g(i) = mean(-1./(2.*Vary2(i,:))*m2 + Ey2(i,:)./Vary2(i,:)*m1 - Ey2(i,:).^2./(2.*Vary2(i,:)) - 0.5*log(2*pi*Vary2(i,:))); - end - end - - else - % Non-Gaussian likelihood - - switch method - case 'joint' - % Joint refpred of latent values - u_g = -0.5.*((Ef1 - Ef2)'*(Covf2\(Ef1-Ef2)) + sum(sum(inv(Covf2).*Covf1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covf2))))); - - otherwise - if ismember(gp1.lik.type, {'Binomial', 'Poisson', 'Probit', 'Logit', 'Negbin', 'Negbinztr'}) - % Discrete likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - [tmp, tmp, int] = int_limits(gp1, Ef1(i,:), z1); - else - [minf maxf] = int_limits(gp1,Ef1(i,:),z1); - minf = sum(minf.*weight1); - maxf = sum(maxf.*weight1); - int = minf:maxf; - end - u_g(i) = sum(fh1(gp1,Ef1(i,:),Varf1(i,:),int,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),int,z12)); - end - else - % Continuous likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - if ismember(gp1.lik.type, {'Student-t', 'Weibull', 'Coxph'}) - [minf, maxf] = int_limits(gp1, Ef1(i), z1); - else - minf = mean(Ey1(i) - 12.*sqrt(Vary1(i)),2); - minf(minf<0)=0; - maxf = mean(Ey1(i) + 12.*sqrt(Vary1(i)),2); - end - else - minf = sum(bsxfun(@times, weight1, Ey1(i,:)-12.*sqrt(Vary1(i,:))),2); - maxf = sum(bsxfun(@times, weight1, Ey1(i,:)+12.*sqrt(Vary1(i,:))),2); - end - if ~isequal(gp1.lik.type, 'Coxph') - u_g(i) = quadgk(@(f) fh1(gp1,Ef1(i,:),Varf1(i,:),f,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),f,z12), minf, maxf, 'absTol', 1e-3); - else - ntime1=size(gp1.lik.xtime,1); - ntime2=size(gp2.lik.xtime,1); - u_g(i) = quadgk(@(f) fh1(gp1,Ef1([1:ntime1 i],:),Varf1([1:ntime1 i+ntime1],[1:ntime1 i+ntime1]),f,z1).*fh2(gp2,Ef2([1:ntime2 i],:),Varf2([1:ntime2 i+ntime2],[1:ntime2 i+ntime2]),f,z12), minf, maxf, 'absTol', 1e-3); - end - end - end - end - end - if isequal(form, 'mean') - u_g = mean(u_g); - end -end - -function predvec = predvec(gp, Ef, Varf, f, z) - % Compute vector of lpyts from lik.fh.predy when numel(Ef)=numel(Varf)=1 - % and numel(f) != 1. - if isstruct(gp) - if ~isfield(gp, 'etr') - % single gp - predvec=zeros(size(f)); - for i1=1:numel(f) - predvec(i1)=gp.lik.fh.predy(gp.lik,Ef,Varf,f(i1),z); - end - end - else - % ia & mc - predvec=zeros(length(gp), length(f)); - for i=1:numel(f) - for j=1:numel(gp) - predvec(j,i) = gp{j}.lik.fh.predy(gp{j}.lik, Ef(j), Varf(j), f(i), z); - end - end - end -end - -function mpdf = multi_npdf(f, mean, sigma2) -% for every element in f, compute means calculated with -% norm_pdf(f(i), mean, sqrt(sigma2)). If mean and sigma2 -% are vectors, returns length(mean) x length(f) matrix. - - mpdf = zeros(length(mean), length(f)); - for i=1:length(f) - mpdf(:,i) = norm_pdf(f(i), mean, sqrt(sigma2)); - end -end - -function [minf, maxf, interval] = int_limits(gp, Ef, z) -% Return integration limits for quadgk and interval for discrete integration. - if isstruct(gp) - gplik = gp.lik; - else - gplik = gp{1}.lik; - end - switch gplik.type - - case 'Binomial' - p = exp(Ef)./(1+exp(Ef)); - minf = binoinv(0.0001, z, p); - maxf = binoinv(0.9999, z, p); - interval = minf:maxf; - case 'Poisson' - lambda = z.*exp(Ef); - minf = poissinv(0.0001, lambda); - maxf = poissinv(0.9999, lambda); - interval=minf:maxf; - case {'Probit' 'Logit'} - minf = -1*ones(size(Ef)); - maxf = ones(size(Ef)); - interval = [-1 1]; - case {'Negbin' 'Negbinztr'} - r = gplik.disper; - p = z.*exp(Ef); - minf = nbininv(0.0001, r, p); - maxf = nbininv(0.9999, r, p); - interval = minf:maxf; - case 'Student-t' - [n, n2] = size(Ef); - nu = gp.lik.nu; - minf = repmat(tinv(0.01, nu), n, n2); - maxf = repmat(tinv(0.99, nu), n, n2); - interval = []; - case 'Weibull' - % Probably not very sensible... - minf = 1e-5; - maxf = 1e5; - interval = maxf; - case 'Coxph' - minf = 0; - maxf = 1; - interval = maxf; - end - -end diff --git a/gp/gpcf_intcov.m b/gp/gpcf_intcov.m deleted file mode 100644 index 5c11812a..00000000 --- a/gp/gpcf_intcov.m +++ /dev/null @@ -1,754 +0,0 @@ -function gpcf = gpcf_intcov(varargin) -%GPCF_INTCOV Create an integrated covariance function -% -% Description -% GPCF = GPCF_INTCOV('nin',nin,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates an integrated covariance function structure in which the named -% parameters have the specified values. Any unspecified parameters are -% set to default values. Obligatory parameters are 'intArea', which -% defines the integration areas, and 'cf', which defines the covariance -% function(s) to be integrated. -% -% Notes of usage: -% -% The input matrix X can contain point locations and "lower-left" -% corners of integrated areas (areas are always intervals, cells, cubes -% etc.). Last column of the input X has to contain 1:s for integration -% areas and 0 for point location. For example, if x(3,end) = 1, then the -% third row of x tells the lower-left corner of an integrated area. -% -% Field gpcf.intArea tells the lengths of the integration path along -% each axis. -% -% GPCF = GPCF_INTCOV(GPCF,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a covariance function structure with the named -% parameters altered with the specified values. -% -% Parameters for piece wise polynomial (q=2) covariance function [default] -% IntArea - Integration path lengths per input dimension. -% cf - covariance functions to be integrated -% NintPoints - number of samples for areal integration -% approximation -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% NOTES, WARNINGS! -% * This function is still experimental. It should return correct -% answers in 1 and 2 dimensional problems -% * Evaluation of the integrated covariance is currently implemented -% using stochastic integration. This is awfully slow -% -> in 1d speed up could be obtained using quadrature -% -> in 2d and higher dimensions speed up is perhaps possible with -% extensions of quadrature -% -> Quasi-Monte Carlo has been tried and helps only little -% * Stochastic integration is highly variable -% -> gradients can not be evaluated accurately enough for which reason -% the inference has to be conducted with MCMC (HMC can not be used) -% -% See also -% GP_SET, GPCF_*, PRIOR_*, METRIC_* - -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - if nargin>0 && ischar(varargin{1}) && ismember(varargin{1},{'init' 'set'}) - % remove init and set - varargin(1)=[]; - end - - ip=inputParser; - ip.FunctionName = 'GPCF_INTCOV'; - ip=iparser(ip,'addOptional','gpcf', [], @isstruct); - ip=iparser(ip,'addParamValue','intArea',[], @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','cf',[], @iscell); - ip=iparser(ip,'addParamValue','NintPoints',200, @(x) isscalar(x) && x>0); - ip=iparser(ip,'parse',varargin{:}); - gpcf=ip.Results.gpcf; - - if isempty(gpcf) - % Check that SuiteSparse is available - init=true; - gpcf.intArea=ip.Results.intArea; - if isempty(gpcf.intArea) - error('intArea has to be given for intcov: gpcf_intcov(''intArea'',INTAREA,...)') - end - gpcf.type = 'gpcf_intcov'; - else - if ~isfield(gpcf,'type') && ~isequal(gpcf.type,'gpcf_intcov') - error('First argument does not seem to be a valid covariance function structure') - end - init=false; - end - - if init || ~ismember('cf',ip.UsingDefaults) - % Initialize parameters - gpcf.cf = {}; - cfs=ip.Results.cf; - if ~isempty(cfs) - for i = 1:length(cfs) - gpcf.cf{i} = cfs{i}; - end - else - error('At least one covariance function has to be given in cf'); - end - end - - if init - % Set the function handles to the nested functions - gpcf.fh.pak = @gpcf_intcov_pak; - gpcf.fh.unpak = @gpcf_intcov_unpak; - gpcf.fh.lp = @gpcf_intcov_lp; - gpcf.fh.lpg = @gpcf_intcov_lpg; - gpcf.fh.cfg = @gpcf_intcov_cfg; - gpcf.fh.ginput = @gpcf_intcov_ginput; - gpcf.fh.cov = @gpcf_intcov_cov; - gpcf.fh.trcov = @gpcf_intcov_trcov; - gpcf.fh.trvar = @gpcf_intcov_trvar; - gpcf.fh.recappend = @gpcf_intcov_recappend; - - % help parameters for storing intermediate results - w0 = []; - w1 = []; - datahash0=0; - datahash1=0; - Ctrcov = []; - Ccov = []; - end - - % Initialize parameters - if init || ~ismember('intArea',ip.UsingDefaults) - gpcf.intArea=ip.Results.intArea; - end - - % Initialize parameters - if init || ~ismember('NintPoints',ip.UsingDefaults) - gpcf.NintPoints=ip.Results.NintPoints; - end - - function [w,s] = gpcf_intcov_pak(gpcf) - %GPCF_INTCOV_PAK Combine GP covariance function parameters into - % one vector - % - % Description - % W = GPCF_INTCOV_PAK(GPCF) takes a covariance function - % structure GPCF and combines the covariance function - % parameters and their hyperparameters into a single row - % vector W. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % See also - % GPCF_INTCOV_UNPAK - - ncf = length(gpcf.cf); - w = []; s = {}; - - for i=1:ncf - cf = gpcf.cf{i}; - [wi si] = cf.fh.pak(cf); - w = [w wi]; - s = [s; si]; - end - - end - - function [gpcf, w] = gpcf_intcov_unpak(gpcf, w) - %GPCF_INTCOV_UNPAK Sets the covariance function parameters into - % the structure - % - % Description - % [GPCF, W] = GPCF_INTCOV_UNPAK(GPCF, W) takes a covariance - % function structure GPCF and a hyper-parameter vector W, - % and returns a covariance function structure identical - % to the input, except that the covariance hyper-parameters - % have been set to the values in W. Deletes the values set to - % GPCF from W and returns the modified W. This is a mandatory - % subfunction used for example in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(gpcf.magnSigma2) - % (hyperparameters of gpcf.magnSigma2) - % log(gpcf.lengthScale(:)) - % (hyperparameters of gpcf.lengthScale)]' - % - % See also - % GPCF_INTCOV_PAK - - ncf = length(gpcf.cf); - - for i=1:ncf - cf = gpcf.cf{i}; - [cf, w] = cf.fh.unpak(cf, w); - gpcf.cf{i} = cf; - end - - end - - function lp = gpcf_intcov_lp(gpcf) - %GPCF_INTCOV_LP Evaluate the log prior of covariance function parameters - % - % Description - % LP = GPCF_INTCOV_LP(GPCF, X, T) takes a covariance function - % structure GPCF and returns log(p(th)), where th collects the - % parameters. This is a mandatory subfunction used for example - % in energy computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LPG, GP_E - - lp = 0; - ncf = length(gpcf.cf); - for i=1:ncf - cf = gpcf.cf{i}; - lp = lp + cf.fh.lp(cf); - end - end - - function lpg = gpcf_intcov_lpg(gpcf) - %GPCF_INTCOV_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = GPCF_INTCOV_LPG(GPCF) takes a covariance function - % structure GPCF and returns LPG = d log (p(th))/dth, where th - % is the vector of parameters. This is a mandatory subfunction - % used for example in gradient computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - lpg = []; - ncf = length(gpcf.cf); - - % Evaluate the gradients - for i=1:ncf - cf = gpcf.cf{i}; - lpg=[lpg cf.fh.lpg(cf)]; - end - end - - function DKff = gpcf_intcov_cfg(gpcf, x, x2, mask) - %GPCF_INTCOV_CFG Evaluate gradient of covariance function - % with respect to the parameters - % - % Description - % DKff = GPCF_INTCOV_CFG(GPCF, X) takes a covariance function - % structure GPCF, a matrix X of input vectors and returns - % DKff, the gradients of covariance matrix Kff = k(X,X) with - % respect to th (cell array with matrix elements). This is a - % mandatory subfunction used for example in gradient computations. - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, [], MASK) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the diagonal of gradients of covariance matrix - % Kff = k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - [n, m] =size(x); - - DKff = {}; - % Evaluate: DKff{1} = d Kff / d magnSigma2 - % DKff{2} = d Kff / d lengthScale - % NOTE! Here we have already taken into account that the parameters are transformed - % through log() and thus dK/dlog(p) = p * dK/dp - - % evaluate the gradient for training covariance - if nargin == 2 - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - % point-point covariance - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1)); - for j1 = 1:length(temp) - [I,J,R] = find(temp{j1}); - DKff{end+1} = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - end - end - - % point-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1),intpoints); - for k1 = 1:length(temp) - temp2{ii1}(:,j1) = mean(temp{k1},2); - ii1=ii1+1; - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp + temp'; - end - - % area-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, intpoints1, intpoints2); - for l1 = 1:length(temp) - temp2{ii1}(j1,k1) = mean(mean(temp{l1})); - ii1=ii1+1; - end - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp; % + temp' - end - - % Evaluate the gradient of non-symmetric covariance (e.g. K_fu) - elseif nargin == 3 - if size(x,2) ~= size(x2,2) - error('gpcf_intcov -> _ghyper: The number of columns in x and x2 has to be the same. ') - end - - % Evaluate: DKff{1} = d mask(Kff,I) / d magnSigma2 - % DKff{2...} = d mask(Kff,I) / d lengthScale - elseif nargin == 4 - - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - for i1=1:length(DKff) - DKff{i1}=full(DKff{i1}); - end - end - - end - - function DKff = gpcf_intcov_ginput(gpcf, x, x2) - %GPCF_INTCOV_GINPUT Evaluate gradient of covariance function with - % respect to x - % - % Description - % DKff = GPCF_INTCOV_GINPUT(GPCF, X) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = GPCF_INTCOV_GINPUT(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - end - - - function C = gpcf_intcov_cov(gpcf, x1, x2, varargin) - %GP_INTCOV_COV Evaluate covariance matrix between two input vectors - % - % Description - % C = GP_INTCOV_COV(GP, TX, X) takes in covariance function of - % a Gaussian process GP and two matrixes TX and X that contain - % input vectors to GP. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i in TX - % and j in X. This is a mandatory subfunction used for example in - % prediction and energy computations. - % - % See also - % GPCF_INTCOV_TRCOV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - if isempty(x2) - x2=x1; - end - [n1,m1]=size(x1); - [n2,m2]=size(x2); - - if m1~=m2 - error('the number of columns of X1 and X2 has to be same') - end - - ncf = length(gpcf.cf); - ww = []; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512([x1, x2]); - fromMem = false; - if ~isempty(w1) - if all(size(ww)==size(w1)) && all(abs(ww-w1)<1e-8) && isequal(datahash,datahash1) - fromMem = true; - end - end - - if fromMem - C = Ccov; - else - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - intInd1 = find(x1(:,end)==1); - intInd2 = find(x2(:,end)==1); - pointInd1 = find(x1(:,end)==0); - pointInd2 = find(x2(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - C = sparse(n1,n2); - - % point-point covariance - if any(x1(:,end)==0) && any(x2(:,end)==0) - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.cov(cf, x1(pointInd1,1:end-1),x2(pointInd2,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd2(J),R,n1,n2); - end - - % point-area covariance - if any(x1(:,end)==0) && any(x2(:,end)==1) - temp=sparse(length(pointInd1),length(intInd2)); - for j1=1:length(intInd2) - %N=600; - %[tmp1, tmp2] = meshgrid( linspace(x2(intInd2(j1),1),x2(intInd2(j1),1)+intArea(1,1),N) , linspace(x2(intInd2(j1),2),x2(intInd2(j1),2)+intArea(1,2),N)); - %intpoints = [tmp1(:),tmp2(:)]; - intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,m2-1); - %intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*hammersley(m2-1,numPoints)'; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x1(pointInd1,1:end-1),intpoints),2); - end - end - [I,J,R] = find(temp); - C = C + sparse(pointInd1(I),intInd2(J),R,n1,n2); - end - - % area-point covariance - if any(x1(:,end)==1) && any(x2(:,end)==0) - temp=sparse(length(pointInd2),length(intInd1)); - for j1=1:length(intInd1) - intpoints = repmat(x1(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x2(pointInd2,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp'); - C = C + sparse(intInd1(I),pointInd2(J),R,n1,n2); - end - - % area-area covariance - if any(x1(:,end)==1) && any(x2(:,end)==1) - temp=sparse(length(intInd1),length(intInd2)); - for j1=1:length(intInd1) - intpoints1 = repmat(x1(intInd1(j1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd2) - intpoints2 = repmat(x2(intInd2(k1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - [I,J,R] = find(temp); - C = C + sparse(intInd1(I),intInd2(J),R,n1,n2); - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ccov=C; - datahash1=datahash; - w1=ww; - end - - end - - function C = gpcf_intcov_trcov(gpcf, x) - %GP_INTCOV_TRCOV Evaluate training covariance matrix of inputs - % - % Description - % C = GP_INTCOV_TRCOV(GP, TX) takes in covariance function of a - % Gaussian process GP and matrix TX that contains training - % input vectors. Returns covariance matrix C. Every element ij - % of C contains covariance between inputs i and j in TX. This is - % a mandatory subfunction used for example in prediction and - % energy computations. - % - % See also - % GPCF_INTCOV_COV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - ncf = length(gpcf.cf); - ww=[]; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512(x); - fromMem = false; - if ~isempty(w0) - if all(size(ww)==size(w0)) && all(abs(ww-w0)<1e-8) && isequal(datahash,datahash0) - fromMem = true; - end - end - - if fromMem - C = Ctrcov; - else - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - %intMethod = gpcf.intMethod; - - % point-point covariance - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trcov(cf, x(pointInd1,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - - % point-area covariance - temp=sparse(length(pointInd1),length(intInd1)); - randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x(pointInd1,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp'; - - % % area-area covariance - % temp=sparse(length(intInd1),length(intInd1)); - % for j1=1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for k1 = 1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for i1=1:ncf - % cf = gpcf.cf{i1}; - % temp(j1,k1) = temp(j1,k1) + mean(mean(feval(cf.fh.cov, cf, intpoints1, intpoints2))); - % end - % end - % end - % [I,J,R] = find(temp); - % C = C + sparse(intInd1(I),intInd1(J),R,n1,n1); - - % area-area covariance - temp=sparse(length(intInd1),length(intInd1)); - temp2=zeros(n1,1); - randpoints = [intArea intArea].*hammersley((dimInt)*2,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = j1+1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - % The covariance matrix seems to get non positive definite. Try to fix - % it by evaluating all the diagonal elements with same random numbers. - %randpoints1 = intArea.*rand(numPoints,dimInt); - %randpoints2 = intArea.*rand(numPoints,dimInt); - %randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - intpoints2 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints1; - %intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp2(j1) = temp2(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - %temp2(j1) = temp2(j1) + mean(mean(feval(cf.fh.trcov, cf, intpoints))); - end - end - [I,J,R] = find(temp); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp' + sparse(1:n1,1:n1,temp2); - - C = (C+C')/2; - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ctrcov=C; - datahash0=datahash; - w0=ww; - end - - end - - function C = gpcf_intcov_trvar(gpcf, x) - %GP_INTCOV_TRVAR Evaluate training variance vector - % - % Description - % C = GP_INTCOV_TRVAR(GPCF, TX) takes in covariance function of - % a Gaussian process GPCF and matrix TX that contains training - % inputs. Returns variance vector C. Every element i of C - % contains variance of input i in TX. This is a mandatory - % subfunction used for example in prediction and energy - % computations. - % - % See also - % GPCF_INTCOV_COV, GP_COV, GP_TRCOV - - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - C = zeros(n1,1); - - % point-point covariance - temp = 0; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trvar(cf, x(pointInd1,1:end-1)); - end - C(pointInd1) = temp; - - % area-area covariance - temp=zeros(size(intInd1)); - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1) = temp(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - C(intInd1) = temp; - end - - function reccf = gpcf_intcov_recappend(reccf, ri, gpcf) - %RECAPPEND Record append - % - % Description - % RECCF = GPCF_INTCOV_RECAPPEND(RECCF, RI, GPCF) takes a - % covariance function record structure RECCF, record index RI - % and covariance function structure GPCF with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from GPCF. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - if nargin == 2 - % Initialize record - reccf.type = 'gpcf_intcov'; - reccf.NintPoints = ri.NintPoints; - reccf.intArea = ri.intArea; - - % Initialize parameters - ncf = length(ri.cf); - for i=1:ncf - cf = ri.cf{i}; - reccf.cf{i} = cf.fh.recappend([], ri.cf{i}); - end - - % Set the function handles - reccf.fh.pak = @gpcf_intcov_pak; - reccf.fh.unpak = @gpcf_intcov_unpak; - reccf.fh.e = @gpcf_intcov_lp; - reccf.fh.lpg = @gpcf_intcov_lpg; - reccf.fh.cfg = @gpcf_intcov_cfg; - reccf.fh.cov = @gpcf_intcov_cov; - reccf.fh.trcov = @gpcf_intcov_trcov; - reccf.fh.trvar = @gpcf_intcov_trvar; - reccf.fh.recappend = @gpcf_intcov_recappend; - else - % Append to the record - - %loop over all of the covariance functions - ncf = length(gpcf.cf); - reccf.NintPoints(ri,:) = gpcf.NintPoints; - reccf.intArea(ri,:) = gpcf.intArea; - for i=1:ncf - cf = gpcf.cf{i}; - reccf.cf{i} = cf.fh.recappend(reccf.cf{i}, ri, cf); - end - end - end -end - diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m deleted file mode 100644 index fd1f0b04..00000000 --- a/gp/lik_gaussianbl.m +++ /dev/null @@ -1,369 +0,0 @@ -function lik = lik_gaussianbl(varargin) -%LIK_GAUSSIAN Create a Gaussian likelihood structure -% -% Description -% LIK = LIK_GAUSSIANBL('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a Gaussian likelihood structure in which the named -% parameters have the specified values. Any unspecified -% parameters are set to default values. -% -% LIK = LIK_GAUSSIANBL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood function structure with the named -% parameters altered with the specified values. -% -% Parameters for Gaussian likelihood function [default] -% sigma2 - variance of the independent noise [0.1] for each -% block of inputs. If noiseSigma2 is a vector each -% entry of the vector specifies noise variance for -% a block of inputs defined by the last column of -% the input matrix X. The variances are set for blocks -% according to 'bl_indic' field so that sigma2(i) is a -% noise variance of the inputs whose last column equals -% bl_indic(i). -% bl_indic - block indicator vector [empty matrix]. If -% length(sigma2)>1 bl_indic has to be the same length -% as sigma2. -% sigma2_prior - prior for sigma2 [prior_logunif] -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% See also -% GP_SET, PRIOR_*, LIK_* - -% Internal note: Because Gaussian noise can be combined -% analytically to the covariance matrix, lik_gaussian is internally -% little between lik_* and gpcf_* functions. - -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_GAUSSIANBL'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'addParamValue','bl_indic',0.1, @(x) isvector(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.type = 'GaussianBL'; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'GaussianBL') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('sigma2',ip.UsingDefaults) - lik.sigma2 = ip.Results.sigma2; - lik.bl_indic = ip.Results.bl_indic; - end - - if length(lik.sigma2)> 1 || length(lik.bl_indic) > 1 - if length(lik.sigma2) ~= length(lik.bl_indic) - error('sigma2 and bl_indic has to be same length') - end - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('sigma2_prior',ip.UsingDefaults) - lik.p.sigma2=ip.Results.sigma2_prior; - end - if init - % Set the function handles to the nested functions - lik.fh.pak = @lik_gaussianbl_pak; - lik.fh.unpak = @lik_gaussianbl_unpak; - lik.fh.lp = @lik_gaussianbl_lp; - lik.fh.lpg = @lik_gaussianbl_lpg; - lik.fh.cfg = @lik_gaussianbl_cfg; - lik.fh.trcov = @lik_gaussianbl_trcov; - lik.fh.trvar = @lik_gaussianbl_trvar; - lik.fh.recappend = @lik_gaussianbl_recappend; - end - - function [w s] = lik_gaussianbl_pak(lik) - %LIK_GAUSSIANBL_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_GAUSSIANBL_PAK(LIK) takes a likelihood structure LIK - % and combines the parameters into a single row vector W. - % This is a mandatory subfunction used for example in energy - % and gradient computations. - % - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_UNPAK - - w = []; s = {}; - if ~isempty(lik.p.sigma2) - w = log(lik.sigma2); - if numel(lik.sigma2)>1 - s = [s; sprintf('log(gaussian.sigma2 x %d)',numel(lik.sigma2))]; - else - s = [s; 'log(gaussian.sigma2)']; - end - % Hyperparameters of noiseSigma2 - [wh sh] = lik.p.sigma2.fh.pak(lik.p.sigma2); - w = [w wh]; - s = [s sh]; - end - end - - function [lik, w] = lik_gaussianbl_unpak(lik, w) - %LIK_GAUSSIANBL_UNPAK Extract likelihood parameters from the vector. - % - % Description - % W = LIK_GAUSSIANBL_UNPAK(W, LIK) takes a likelihood structure - % LIK and extracts the parameters from the vector W to the LIK - % structure. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_PAK - - if ~isempty(lik.p.sigma2) - i2=length(lik.sigma2); - lik.sigma2 = exp(w(1:i2)); - w = w(i2+1:end); - - % Hyperparameters of sigma2 - [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); - lik.p.sigma2 = p; - end - end - - function lp = lik_gaussianbl_lp(lik) - %LIK_GAUSSIANBL_LP Evaluate the log prior of likelihood parameters - % - % Description - % LP = LIK_T_LP(LIK) takes a likelihood structure LIK and - % returns log(p(th)), where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_G, GP_E - - lp = 0; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + sum(log(lik.sigma2)); - end - end - - function lpg = lik_gaussianbl_lpg(lik) - %LIK_GAUSSIANBL_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = LIK_GAUSSIANBL_LPG(LIK) takes a Gaussian likelihood - % function structure LIK and returns LPG = d log (p(th))/dth, - % where th is the vector of parameters. This subfunction is - % needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - lpg = []; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - i2=length(lik.sigma2); - - lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); - lpg = lpgs(1:i2).*lik.sigma2 + 1; - if length(lpgs) > 1 - lpg = [lpg lpgs(i2+1:end)]; - end - end - end - - function DKff = lik_gaussianbl_cfg(lik, x, x2) - %LIK_GAUSSIANBL_CFG Evaluate gradient of covariance with respect to - % Gaussian noise - % - % Description - % Gaussian likelihood is a special case since it can be - % analytically combined with covariance functions and thus we - % compute gradient of covariance instead of gradient of likelihood. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X) takes a Gaussian likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of Gaussian noise covariance - % matrix Kff = k(X,X) with respect to th (cell array with - % matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X, X2) takes a Gaussian - % likelihood function structure LIK, a matrix X of input - % vectors and returns DKff, the gradients of Gaussian noise - % covariance matrix Kff = k(X,X) with respect to th (cell - % array with matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - [n, m] =size(x); - - if length(lik.sigma2)==1 - DKff{1} = lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - ind = find(x(:,end)==lik.bl_indic(i1)); - DKff{i1} = sparse(ind,ind,lik.sigma2(i1),n,n); - end - end - - end - - function DKff = lik_gaussianbl_ginput(lik, x, t, g_ind, gdata_ind, gprior_ind, varargin) - %LIK_GAUSSIANBL_GINPUT Evaluate gradient of likelihood function with - % respect to x. - % - % Description - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X, X2) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - end - - function C = lik_gaussianbl_trcov(lik, x) - %LIK_GAUSSIANBL_TRCOV Evaluate training covariance matrix - % corresponding to Gaussian noise - % Description - % C = LIK_GAUSSIANBL_TRCOV(GP, TX) takes in covariance function - % of a Gaussian process GP and matrix TX that contains - % training input vectors. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i and j - % in TX. This subfunction is needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, LIK_GAUSSIANBL_TRVAR, GP_COV, GP_TRCOV - - [n, m] =size(x); - - s2 = zeros(n,1); - if length(lik.sigma2)==1 - s2 = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - s2(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - C = sparse(1:n,1:n,s2,n,n); - - end - - function C = lik_gaussianbl_trvar(lik, x) - %LIK_GAUSSIANBL_TRVAR Evaluate training variance vector - % corresponding to Gaussian noise - % - % Description - % C = LIK_GAUSSIANBL_TRVAR(LIK, TX) takes in covariance function - % of a Gaussian process LIK and matrix TX that contains - % training inputs. Returns variance vector C. Every element i - % of C contains variance of input i in TX. This subfunction is - % needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, GP_COV, GP_TRCOV - - [n, m] =size(x); - - C = zeros(n,1); - if length(lik.sigma2)==1 - C = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - C(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - end - - function reccf = lik_gaussianbl_recappend(reccf, ri, lik) - %RECAPPEND Record append - % - % Description - % RECCF = LIK_GAUSSIANBL_RECAPPEND(RECCF, RI, LIK) takes a - % likelihood function record structure RECCF, record index RI - % and likelihood function structure LIK with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from LIK. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - % Initialize record - if nargin == 2 - reccf.type = 'lik_gaussianbl'; - - % Initialize parameters - reccf.sigma2 = []; - reccf.bl_indic = []; - - % Set the function handles - reccf.fh.pak = @lik_gaussianbl_pak; - reccf.fh.unpak = @lik_gaussianbl_unpak; - reccf.fh.lp = @lik_gaussianbl_lp; - reccf.fh.lpg = @lik_gaussianbl_lpg; - reccf.fh.cfg = @lik_gaussianbl_cfg; - reccf.fh.trcov = @lik_gaussianbl_trcov; - reccf.fh.trvar = @lik_gaussianbl_trvar; - reccf.fh.recappend = @lik_gaussianbl_recappend; - reccf.p=[]; - reccf.p.sigma2=[]; - if ~isempty(ri.p.sigma2) - reccf.p.sigma2 = ri.p.sigma2; - end - return - end - - likp = lik.p; - - % record sigma - if ~isempty(lik.sigma2) - reccf.sigma2(ri,:)=lik.sigma2; - reccf.bl_indic(ri,:)=lik.bl_indic; - if ~isempty(lik.p.sigma2) - reccf.p.sigma2 = likp.sigma2.fh.recappend(reccf.p.sigma2, ri, likp.sigma2); - end - elseif ri==1 - reccf.sigma2=[]; - end - end - -end diff --git a/octave_compat/fcnchk.m b/octave_compat/fcnchk.m index 6aeced1c..ffcb2a08 100644 --- a/octave_compat/fcnchk.m +++ b/octave_compat/fcnchk.m @@ -1,4 +1,5 @@ -function f = fcnchk(x,n ) +function [f,m] = fcnchk(x,n) f=x; +m=''; end diff --git a/xunit/log/Readme.txt b/xunit/log/Readme.txt deleted file mode 100644 index 449cff44..00000000 --- a/xunit/log/Readme.txt +++ /dev/null @@ -1,4 +0,0 @@ -In this folder are logfiles of every demo that xunit tests. These logs are -for debugging purposes. When comparing, remember to initialize random stream. - -Created with GPstuff revision 990. \ No newline at end of file diff --git a/xunit/log/create_files.m b/xunit/log/create_files.m deleted file mode 100644 index 2435866b..00000000 --- a/xunit/log/create_files.m +++ /dev/null @@ -1,379 +0,0 @@ -% Author: Aki Vehtari -% Last modified: 2011-03-10 11:15:15 EET -diary off;clear;close all; -load realValuesBinomial1.mat -var=who(); -setrandstream(0); -diary('demo_binomial1.txt'); -disp('Running: demo_binomial1') -demo_binomial1 -fprintf('\n gp hyperparameters: \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesBinomial1.mat', char(var(1))); -for i=2:length(var) - save('realValuesBinomial1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesBinomial_apc.mat -var=who(); -setrandstream(0); -diary('demo_binomial_apc.txt'); -disp('Running: demo_binomial_apc') -demo_binomial_apc -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesBinomial_apc.mat', char(var(1))); -for i=2:length(var) - save('realValuesBinomial_apc.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesClassific.mat -var=who(); -setrandstream(0); -diary('demo_classific.txt'); -disp('Running: demo_classific') -demo_classific -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesClassific.mat', char(var(1))); -for i=2:length(var) - save('realValuesClassific.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesDerivativeobs.mat -var=who(); -setrandstream(0); -diary('demo_derivativeobs.txt'); -disp('Running: demo_derivativeobs') -demo_derivativeobs -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesDerivativeobs.mat', char(var(1))); -for i=2:length(var) - save('realValuesDerivativeobs.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesLgcp.mat -var=who(); -setrandstream(0); -diary('demo_lgcp.txt'); -disp('Running: demo_lgcp') -demo_lgcp -diary off; -save('realValuesLgcp.mat', char(var(1))); -for i=2:length(var) - save('realValuesLgcp.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesModelAssesment1.mat -var=who(); -setrandstream(0); -diary('demo_modelassesment1.txt'); -disp('Running: demo_modelassesment1') -demo_modelassesment1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesModelAssesment1.mat', char(var(1))); -for i=2:length(var) - save('realValuesModelAssesment1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesModelAssesment2.mat -var=who(); -setrandstream(0); -diary('demo_modelassesment2.txt'); -disp('Running: demo_modelassesment2') -demo_modelassesment2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesModelAssesment2.mat', char(var(1))); -for i=2:length(var) - save('realValuesModelAssesment2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesMulticlass.mat -var=who(); -setrandstream(0); -diary('demo_multiclass.txt'); -disp('Running: demo_multiclass') -demo_multiclass -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesMulticlass.mat', char(var(1))); -for i=2:length(var) - save('realValuesMulticlass.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesNeuralnetcov.mat -var=who(); -setrandstream(0); -diary('demo_neuralnetcov.txt'); -disp('Running: demo_neuralnetcov') -demo_neuralnetcov -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesNeuralnetcov.mat', char(var(1))); -for i=2:length(var) - save('realValuesNeuralnetcov.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesPeriodic.mat -var=who(); -setrandstream(0); -diary('demo_periodic.txt'); -disp('Running: demo_periodic') -demo_periodic -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesPeriodic.mat', char(var(1))); -for i=2:length(var) - save('realValuesPeriodic.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression1.mat -var=who(); -setrandstream(0); -diary('demo_regression1.txt'); -disp('Running: demo_regression1') -demo_regression1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -% drawnow;clear;close all -% disp('Running: demo_regression2') -% demo_regression2 -diary off; -save('realValuesRegression1.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_additive1.mat -var=who(); -setrandstream(0); -diary('demo_regression_additive1.txt'); -disp('Running: demo_regression_additive1') -demo_regression_additive1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_additive1.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_additive1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_additive2.mat -var=who(); -setrandstream(0); -diary('demo_regression_additive2.txt'); -disp('Running: demo_regression_additive2') -demo_regression_additive2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_additive2.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_additive2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_hier.mat -var=who(); -setrandstream(0); -diary('demo_regression_hier.txt'); -disp('Running: demo_regression_hier') -demo_regression_hier -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_hier.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_hier.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_meanf.mat -var=who(); -setrandstream(0); -diary('demo_regression_meanf.txt'); -disp('Running: demo_regression_meanf') -demo_regression_meanf -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_meanf.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_meanf.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_ppcs.mat -var=who(); -setrandstream(0); -diary('demo_regression_ppcs.txt'); -disp('Running: demo_regression_ppcs') -demo_regression_ppcs -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_ppcs.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_ppcs.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_robust.mat -var=who(); -setrandstream(0); -diary('demo_regression_robust.txt'); -disp('Running: demo_regression_robust') -demo_regression_robust -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_robust.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_robust.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_sparse1.mat -var=who(); -setrandstream(0); -diary('demo_regression_sparse1.txt'); -disp('Running: demo_regression_sparse1') -demo_regression_sparse1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_sparse1.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_sparse1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_sparse2.mat -var=who(); -setrandstream(0); -diary('demo_regression_sparse2.txt'); -disp('Running: demo_regression_sparse2') -demo_regression_sparse2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_sparse2.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_sparse2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSpatial1.mat -var=who(); -setrandstream(0); -diary('demo_spatial1.txt'); -disp('Running: demo_spatial1') -demo_spatial1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSpatial1.mat', char(var(1))); -for i=2:length(var) - save('realValuesSpatial1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSpatial2.mat -var=who(); -setrandstream(0); -diary('demo_spatial2.txt'); -disp('Running: demo_spatial2') -demo_spatial2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSpatial2.mat', char(var(1))); -for i=2:length(var) - save('realValuesSpatial2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSurvival_weibull.mat -var=who(); -setrandstream(0); -diary('demo_survival_weibull.txt'); -disp('Running: demo_survival_weibull') -demo_survival_weibull -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSurvival_weibull.mat', char(var(1))); -for i=2:length(var) - save('realValuesSurvival_weibull.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSurvival_coxph.mat -var=who(); -setrandstream(0); -diary('demo_survival_coxph.txt'); -disp('Running: demo_survival_coxph') -demo_survival_coxph -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSurvival_coxph.mat', char(var(1))); -for i=2:length(var) - save('realValuesSurvival_coxph.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesZinegbin.mat -var=who(); -setrandstream(0); -diary('demo_zinegbin.txt'); -disp('Running: demo_zinegbin') -demo_zinegbin -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesZinegbin.mat', char(var(1))); -for i=2:length(var) - save('realValuesZinegbin.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesMultinom.mat -var=who(); -setrandstream(0); -diary('demo_multinom.txt'); -disp('Running: demo_multinom2') -demo_multinom -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesMultinom.mat', char(var(1))); -for i=2:length(var) - save('realValuesMultinom.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - diff --git a/xunit/log/demo_binomial1.txt b/xunit/log/demo_binomial1.txt deleted file mode 100644 index ee5394ac..00000000 --- a/xunit/log/demo_binomial1.txt +++ /dev/null @@ -1,7 +0,0 @@ -Running: demo_binomial1 - TolFun reached. Func-count 37. Final f(x)=169.569. Elapsed time 0.40 - - gp hyperparameters: - - -0.7903 -1.0035 - diff --git a/xunit/log/demo_binomial_apc.txt b/xunit/log/demo_binomial_apc.txt deleted file mode 100644 index 808f0003..00000000 --- a/xunit/log/demo_binomial_apc.txt +++ /dev/null @@ -1,61 +0,0 @@ -Running: demo_binomial_apc - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 1958.28 - 1 2 2 1911.95 0.00221514 - 2 6 6 1877.23 0.001 - 3 9 9 1847 0.01 - 4 12 12 1845.58 0.0163264 - 5 14 14 1787.72 0.5 - 6 18 18 1781.43 0.00238076 - 7 22 22 1771.39 0.0225751 - 8 23 23 1730.37 1 - 9 24 24 1723.09 1 - 10 25 25 1720.62 1 - 11 26 26 1720.31 1 - 12 27 27 1719.5 1 - 13 28 28 1719.15 1 - 14 29 29 1718.86 1 - 15 30 30 1718.68 1 - 16 31 31 1718.47 1 - 17 32 32 1718.41 1 - 18 33 33 1718.28 1 - 19 34 34 1718.15 1 - 20 35 35 1717.93 1 - 21 36 36 1717.68 1 - 22 37 37 1717.1 1 - 23 38 38 1716.73 1 - 24 40 40 1716.48 0.1 - 25 42 42 1716.22 0.1 - 26 44 44 1715.82 0.357035 - 27 45 45 1715.53 1 - 28 46 46 1715.25 1 - 29 47 47 1715 1 - 30 48 48 1714.92 1 - 31 49 49 1714.84 1 - 32 51 51 1714.71 0.22984 - 33 53 53 1714.63 0.1 - 34 54 54 1714.5 1 - 35 59 59 1713.38 0.351144 - 36 63 63 1713.36 0.00471445 - 37 64 64 1713.19 1 - 38 65 65 1712.82 1 - 39 66 66 1712.25 1 - 40 67 67 1711.83 1 - 41 68 68 1711.7 1 - 42 69 69 1711.67 1 - 43 70 70 1711.67 1 - 44 71 71 1711.67 1 - 45 72 72 1711.67 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 46 - Function Count : 73 - Minimum found : 1711.6691 - Intern Time : 0.060406 seconds - Total Time : 14.2611 seconds - - gp hyperparameters (gp_pak(gp)): - - 0.7967 2.4325 -1.1381 1.1745 -3.6054 1.8489 -0.1535 1.9498 1.9306 - diff --git a/xunit/log/demo_classific.txt b/xunit/log/demo_classific.txt deleted file mode 100644 index 936159a9..00000000 --- a/xunit/log/demo_classific.txt +++ /dev/null @@ -1,28 +0,0 @@ -Running: demo_classific -Probit model with Laplace integration over the latent values and -MAP estimate for the parameters - TolFun reached. Func-count 27. Final f(x)=81.5289. Elapsed time 1.17 -Probit model with EP integration over the latent values and -MAP estimate for the parameters - TolFun reached. Func-count 7. Final f(x)=81.748. Elapsed time 6.07 -Probit model with MCMC integration over the latent values and -the parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -1180.356 - 40 -1151.627 - 60 -1127.383 - 80 -1112.349 - 100 -1098.458 - 120 -1105.191 - 140 -1136.302 - 160 -1104.081 - 180 -1063.957 - 200 -1068.583 - 220 -1069.501 -Compare MCMC, Laplace and EP results for two latent variables - - gp hyperparameters (gp_pak(gp)): - - 3.2596 -0.7244 0.0597 - diff --git a/xunit/log/demo_derivativeobs.txt b/xunit/log/demo_derivativeobs.txt deleted file mode 100644 index 8311299f..00000000 --- a/xunit/log/demo_derivativeobs.txt +++ /dev/null @@ -1,26 +0,0 @@ -Running: demo_derivativeobs -GP model without derivative obs -Checking gradient ... - - analytic diffs delta - - 2.6657 2.6657 0.0000 - -3.7894 -3.7894 -0.0000 - 1.6251 1.6251 -0.0000 - - TolX reached. Func-count 19. Final f(x)=2.89367. Elapsed time 0.12 -GP model with derivative obs -Checking gradient ... - - analytic diffs delta - - 4.3476 4.3476 0.0000 - -13.6379 -13.6379 -0.0000 - 4.4016 4.4016 -0.0000 - - TolFun reached. Func-count 26. Final f(x)=-8.68413. Elapsed time 0.20 - - gp hyperparameters (gp_pak(gp)): - - -1.5025 -0.2305 -4.9053 - diff --git a/xunit/log/demo_lgcp.txt b/xunit/log/demo_lgcp.txt deleted file mode 100644 index 29ee1208..00000000 --- a/xunit/log/demo_lgcp.txt +++ /dev/null @@ -1,4 +0,0 @@ -Running: demo_lgcp -Coal disaster data with EP integration over the latent values -Redwood data with Laplace integration over the latent -values and MAP estimate for the parameters diff --git a/xunit/log/demo_modelassesment1.txt b/xunit/log/demo_modelassesment1.txt deleted file mode 100644 index f2cd19af..00000000 --- a/xunit/log/demo_modelassesment1.txt +++ /dev/null @@ -1,567 +0,0 @@ -Running: demo_modelassesment1 -Full GP model with Gaussian noise model -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 16 - Function Count : 24 - Minimum found : 44.1644 - Intern Time : 0.014683 seconds - Total Time : 0.58223 seconds -MAP estimate for the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -MAP estimate for the parameters - LOO-CV -MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.162 sls - 40 44.536 sls - 60 46.492 sls - 80 44.724 sls - 100 46.073 sls - 120 48.356 sls - 140 45.043 sls - 160 44.583 sls - 180 45.860 sls - 200 45.606 sls - 220 45.049 sls -MCMC integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - cycle etr slsrej - 20 47.365 sls - 40 47.871 sls - The CV-fold number: 2/10 - cycle etr slsrej - 20 48.040 sls - 40 47.595 sls - The CV-fold number: 3/10 - cycle etr slsrej - 20 47.035 sls - 40 47.781 sls - The CV-fold number: 4/10 - cycle etr slsrej - 20 46.956 sls - 40 44.509 sls - The CV-fold number: 5/10 - cycle etr slsrej - 20 48.573 sls - 40 46.945 sls - The CV-fold number: 6/10 - cycle etr slsrej - 20 50.441 sls - 40 50.100 sls - The CV-fold number: 7/10 - cycle etr slsrej - 20 47.685 sls - 40 46.701 sls - The CV-fold number: 8/10 - cycle etr slsrej - 20 43.340 sls - 40 41.618 sls - The CV-fold number: 9/10 - cycle etr slsrej - 20 47.305 sls - 40 47.582 sls - The CV-fold number: 10/10 - cycle etr slsrej - 20 47.354 sls - 40 46.455 sls -MCMC integration over the parameters - LOO-CV -Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 335 points - Elapsed time 1.79 seconds - IA-grid: Total elapsed time 2.02 seconds -Grid integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 326 points - Elapsed time 1.51 seconds - IA-grid: Total elapsed time 2.16 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 333 points - Elapsed time 1.58 seconds - IA-grid: Total elapsed time 2.25 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 372 points - Elapsed time 1.76 seconds - IA-grid: Total elapsed time 2.21 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 342 points - Elapsed time 1.60 seconds - IA-grid: Total elapsed time 2.36 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 357 points - Elapsed time 1.67 seconds - IA-grid: Total elapsed time 2.14 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 314 points - Elapsed time 1.47 seconds - IA-grid: Total elapsed time 2.31 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 354 points - Elapsed time 1.60 seconds - IA-grid: Total elapsed time 2.27 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 363 points - Elapsed time 1.66 seconds - IA-grid: Total elapsed time 2.61 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 351 points - Elapsed time 1.65 seconds - IA-grid: Total elapsed time 1.99 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 341 points - Elapsed time 1.58 seconds - IA-grid: Total elapsed time 2.05 seconds -Grid integration over the parameters - LOO-CV -GP with FIC sparse approximation - -gp_fic = - - type: 'FIC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-06 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 19 - Function Count : 30 - Minimum found : 42.5028 - Intern Time : 0.015807 seconds - Total Time : 0.39031 seconds - -n = - - 225 - -MAP estimate for the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -MAP estimate for the parameters - LOO-CV -MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 43.432 sls - 40 47.122 sls - 60 43.719 sls - 80 46.022 sls - 100 43.813 sls - 120 43.664 sls - 140 45.968 sls - 160 43.173 sls - 180 45.430 sls - 200 43.797 sls - 220 45.146 sls -MCMC integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 47.086 sls - 40 46.972 sls - The CV-fold number: 2/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.050 sls - 40 46.532 sls - The CV-fold number: 3/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.107 sls - 40 47.328 sls - The CV-fold number: 4/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 48.334 sls - 40 42.812 sls - The CV-fold number: 5/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.991 sls - 40 45.679 sls - The CV-fold number: 6/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.842 sls - 40 46.800 sls - The CV-fold number: 7/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.764 sls - 40 46.945 sls - The CV-fold number: 8/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 39.821 sls - 40 41.284 sls - The CV-fold number: 9/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 47.632 sls - 40 47.333 sls - The CV-fold number: 10/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.015 sls - 40 44.994 sls -MCMC integration over the parameters - LOO-CV -Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 348 points - Elapsed time 1.48 seconds - IA-grid: Total elapsed time 1.60 seconds -Grid integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 296 points - Elapsed time 1.22 seconds - IA-grid: Total elapsed time 1.83 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 341 points - Elapsed time 1.48 seconds - IA-grid: Total elapsed time 1.88 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 385 points - Elapsed time 1.60 seconds - IA-grid: Total elapsed time 2.07 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 312 points - Elapsed time 1.39 seconds - IA-grid: Total elapsed time 1.98 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 348 points - Elapsed time 1.47 seconds - IA-grid: Total elapsed time 1.78 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 350 points - Elapsed time 1.57 seconds - IA-grid: Total elapsed time 2.05 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 336 points - Elapsed time 1.39 seconds - IA-grid: Total elapsed time 2.42 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 353 points - Elapsed time 1.50 seconds - IA-grid: Total elapsed time 2.04 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 337 points - Elapsed time 1.43 seconds - IA-grid: Total elapsed time 1.78 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 324 points - Elapsed time 1.36 seconds - IA-grid: Total elapsed time 1.80 seconds -Grid integration over the parameters - LOO-CV -GP with PIC sparse approximation -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 14 - Function Count : 22 - Minimum found : 41.3886 - Intern Time : 0.010499 seconds - Total Time : 0.73232 seconds -MAP estimate for the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -MAP estimate for the parameters - LOO-CV -MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 43.063 sls - 40 43.080 sls - 60 42.595 sls - 80 42.580 sls - 100 42.209 sls - 120 42.570 sls - 140 41.681 sls - 160 41.755 sls - 180 42.976 sls - 200 45.913 sls - 220 43.838 sls -MCMC integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 44.577 sls - 40 44.882 sls - The CV-fold number: 2/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.445 sls - 40 47.048 sls - The CV-fold number: 3/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 49.586 sls - 40 45.713 sls - The CV-fold number: 4/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 42.448 sls - 40 44.822 sls - The CV-fold number: 5/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.504 sls - 40 43.310 sls - The CV-fold number: 6/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.215 sls - 40 44.736 sls - The CV-fold number: 7/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.338 sls - 40 45.546 sls - The CV-fold number: 8/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 41.780 sls - 40 41.196 sls - The CV-fold number: 9/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.193 sls - 40 46.335 sls - The CV-fold number: 10/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 43.116 sls - 40 43.045 sls -MCMC integration over the parameters - LOO-CV -Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 338 points - Elapsed time 3.30 seconds - IA-grid: Total elapsed time 3.67 seconds -Grid integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 347 points - Elapsed time 3.35 seconds - IA-grid: Total elapsed time 4.29 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 337 points - Elapsed time 3.29 seconds - IA-grid: Total elapsed time 4.32 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 355 points - Elapsed time 3.42 seconds - IA-grid: Total elapsed time 4.54 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - Elapsed time 1.11 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 348 points - Elapsed time 3.34 seconds - IA-grid: Total elapsed time 4.70 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - Elapsed time 1.09 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 352 points - Elapsed time 3.39 seconds - IA-grid: Total elapsed time 4.71 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 336 points - Elapsed time 3.20 seconds - IA-grid: Total elapsed time 4.09 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - Elapsed time 1.65 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 347 points - Elapsed time 3.32 seconds - IA-grid: Total elapsed time 5.22 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - Elapsed time 1.09 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 342 points - Elapsed time 3.25 seconds - IA-grid: Total elapsed time 4.58 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - Elapsed time 1.30 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 335 points - Elapsed time 3.20 seconds - IA-grid: Total elapsed time 4.74 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 344 points - Elapsed time 3.30 seconds - IA-grid: Total elapsed time 4.41 seconds -Grid integration over the parameters - LOO-CV -Summary of the results - -S = - - full_MAP full_MCMC full_IA FIC_MAP FIC_MCMC FIC_IA PIC_MAP PIC_MCMC PIC_IA - CV-mlpd 0.05 0.06 0.05 0.06 0.06 0.06 0.07 0.08 0.08 - CV-rmse 0.24 0.24 0.24 0.23 0.23 0.23 0.23 0.23 0.23 - LOO-mlpd 0.06 0.05 0.05 0.06 0.05 0.05 0.07 0.06 0.06 - LOO-rmse 0.24 0.24 0.24 0.23 0.23 0.23 0.23 0.23 0.23 - - WAIC_V 0.07 0.06 0.07 0.11 -0.11 -0.11 0.09 0.08 0.08 - WAIC_G 0.10 0.09 0.09 0.17 0.41 0.42 0.12 0.12 0.12 - - DIC_h NaN -0.20 -0.20 NaN -0.19 -0.19 NaN -0.19 -0.18 - DIC_a NaN 0.05 0.07 NaN 0.12 0.12 NaN 0.09 0.09 - DIC_l 0.07 0.00 0.00 0.11 0.00 0.00 0.09 - peff_h NaN 3.47 3.25 NaN 4.20 3.21 NaN 3.64 3.34 - peff_a NaN 43.12 39.79 NaN 81.90 82.93 NaN 46.67 47.16 - peff_l 37.44 0.00 0.00 76.09 0.00 0.00 45.15 - peff_l2 37.61 0.00 0.00 76.28 0.00 0.00 45.22 - - - The notation is as follows: - CV-mlpd = mean log predictive density from the 10-fold CV. - CV-rmse = root mean squared error from the 10-fold LOO-CV. - LOO-mlpd = mean log predictive density from the LOO-CV. - LOO-rmse = root mean squared error from the 10-fold CV. - WAIC_V = WAIC via variance method - WAIC_G = WAIC via Gibbs training utility method - DIC_h = DIC with focus on parameters. - DIC_a = DIC with focus on parameters and latent variables (all). - DIC_l = DIC with focus on latent variables. - peff_h = effective number of parameters (latent variables marginalized). - peff_a = effective number of parameters and latent variables. - peff_l = effective number of latent variables evaluated with gp_peff. - peff_l2 = effective number of latent variables evaluated with gp_dic. - - - - gp hyperparameters (gp_pak(gp)): - - 1.0742 -0.1662 -0.2199 -3.1540 - diff --git a/xunit/log/demo_modelassesment2.txt b/xunit/log/demo_modelassesment2.txt deleted file mode 100644 index a9497f71..00000000 --- a/xunit/log/demo_modelassesment2.txt +++ /dev/null @@ -1,479 +0,0 @@ -Running: demo_modelassesment2 -Data analysis with probit likelihood -Probit with Laplace integration over the latent values -and MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 9 - Function Count : 13 - Minimum found : 78.8894 - Intern Time : 0.007959 seconds - Total Time : 0.57985 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Probit with EP integration over the latent values and MAP -estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 4 - Function Count : 8 - Minimum found : 79.1395 - Intern Time : 0.00512 seconds - Total Time : 10.3899 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Probit with MCMC integration over the latent values and -the parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -575.950 - 40 -502.803 - 60 -629.446 - 80 -581.402 - 100 -598.197 - 120 -591.179 - 140 -572.454 - 160 -536.323 - 180 -568.580 - 200 -577.057 - 220 -580.694 -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -463.609 - 40 -482.611 - The CV-fold number: 2/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -475.936 - 40 -481.111 - The CV-fold number: 3/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -501.412 - 40 -507.953 - The CV-fold number: 4/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -488.295 - 40 -473.341 - The CV-fold number: 5/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -556.474 - 40 -511.740 - The CV-fold number: 6/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -508.823 - 40 -451.618 - The CV-fold number: 7/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -529.089 - 40 -541.232 - The CV-fold number: 8/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -540.700 - 40 -519.126 - The CV-fold number: 9/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -500.448 - 40 -507.837 - The CV-fold number: 10/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -519.303 - 40 -500.739 -[Warning: For 1 data point the effective sample size in IS is less than n/10] -[> In gpmc_loopred at 114 - In gp_loopred at 86 - In demo_modelassesment2 at 137 - In create_files at 97] -Probit with EP integration over the latent values and -grid integration over the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 4 - Function Count : 7 - Minimum found : 79.1395 - Intern Time : 0.004746 seconds - Total Time : 8.9176 seconds - IA-grid: finding the mode - Elapsed time 2.63 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.75 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 34.27 seconds - IA-grid: Total elapsed time 44.66 seconds -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - Elapsed time 16.39 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 4.66 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.15 seconds - IA-grid: Total elapsed time 45.21 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - Elapsed time 19.41 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.44 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.30 seconds - IA-grid: Total elapsed time 49.15 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - Elapsed time 18.38 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 3.68 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 22.99 seconds - IA-grid: Total elapsed time 45.06 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - Elapsed time 18.87 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.60 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.77 seconds - IA-grid: Total elapsed time 48.25 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - Elapsed time 13.70 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.48 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.04 seconds - IA-grid: Total elapsed time 43.23 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - Elapsed time 19.35 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.56 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.71 seconds - IA-grid: Total elapsed time 48.63 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - Elapsed time 18.48 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.53 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.14 seconds - IA-grid: Total elapsed time 47.15 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - Elapsed time 16.33 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 4.63 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.39 seconds - IA-grid: Total elapsed time 44.37 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - Elapsed time 21.39 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.48 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.61 seconds - IA-grid: Total elapsed time 51.49 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - Elapsed time 15.92 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 4.58 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 22.99 seconds - IA-grid: Total elapsed time 43.49 seconds -Data analysis with logit likelihood -Logit with Laplace integration over the latent values and -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 11 - Function Count : 14 - Minimum found : 79.3867 - Intern Time : 0.009223 seconds - Total Time : 0.68307 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Logit with EP integration over the latent values and MAP -estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 4 - Function Count : 9 - Minimum found : 79.5156 - Intern Time : 0.005924 seconds - Total Time : 18.5342 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Logit with MCMC integration over the latent values and -the parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -540.405 - 40 -606.892 - 60 -612.259 - 80 -556.770 - 100 -569.173 - 120 -545.240 - 140 -549.607 - 160 -581.819 - 180 -609.128 - 200 -631.449 -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -511.951 - 40 -540.085 - The CV-fold number: 2/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -514.969 - 40 -532.193 - The CV-fold number: 3/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -466.163 - 40 -508.252 - The CV-fold number: 4/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -539.359 - 40 -466.205 - The CV-fold number: 5/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -516.258 - 40 -542.188 - The CV-fold number: 6/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -508.894 - 40 -441.362 - The CV-fold number: 7/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -503.784 - 40 -526.379 - The CV-fold number: 8/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -564.005 - 40 -535.022 - The CV-fold number: 9/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -557.378 - 40 -505.166 - The CV-fold number: 10/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -569.104 - 40 -397.043 -Logit with EP integration over the latent values and grid -integration over the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 8 - Function Count : 11 - Minimum found : 79.5156 - Intern Time : 0.007063 seconds - Total Time : 21.4417 seconds - IA-grid: finding the mode - Elapsed time 4.03 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 11.88 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 53.17 seconds - IA-grid: Total elapsed time 69.09 seconds -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - Elapsed time 25.47 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.50 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.23 seconds - IA-grid: Total elapsed time 71.20 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - Elapsed time 30.13 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.24 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.20 seconds - IA-grid: Total elapsed time 76.58 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - Elapsed time 29.52 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.33 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 36.49 seconds - IA-grid: Total elapsed time 73.35 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - Elapsed time 30.18 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.77 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 37.62 seconds - IA-grid: Total elapsed time 76.58 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - Elapsed time 30.57 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 9.06 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.56 seconds - IA-grid: Total elapsed time 78.20 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - Elapsed time 30.83 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.83 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 31 points - Elapsed time 41.13 seconds - IA-grid: Total elapsed time 80.79 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - Elapsed time 30.75 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.75 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.27 seconds - IA-grid: Total elapsed time 77.77 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - Elapsed time 25.82 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.10 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.15 seconds - IA-grid: Total elapsed time 71.08 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - Elapsed time 33.55 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.81 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.53 seconds - IA-grid: Total elapsed time 80.89 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - Elapsed time 25.29 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.10 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 36.07 seconds - IA-grid: Total elapsed time 68.47 seconds -Summary of the results - -S = - - pr_Laplace pr_EP pr_MCMC pr_IA lo_Laplace lo_EP lo_MCMC lo_IA - CV-mlpd -0.29 -0.28 -0.29 -0.28 -0.29 -0.28 -0.28 -0.28 - LOO-mlpd -0.28 -0.27 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 - - WAIC -0.31 -0.28 -0.29 -0.28 -0.29 -0.28 -0.29 -0.28 - - DIC_h NaN NaN 163.51 163.20 NaN NaN 164.54 163.98 - DIC_a NaN NaN 140.40 138.95 NaN NaN 143.12 140.56 - DIC_l 148.71 138.45 NaN NaN 147.76 139.38 NaN NaN - peff_h NaN NaN 2.71 2.30 NaN NaN 2.52 2.32 - peff_a NaN NaN 9.48 8.72 NaN NaN 8.57 9.13 - peff_l 8.72 9.19 NaN NaN 8.84 9.18 NaN NaN - peff_l2 13.14 8.48 NaN NaN 12.23 8.53 NaN NaN - - The notation is as follows: - pr_* = probit likelihood and inference method - lo_* = logit likelihood and inference method - CV-mlpd = mean log predictive density from the 10-fold CV. - LOO-mlpd = mean log predictive density from the 10-fold CV. - WAIC = Widely applicable information criterion. - DIC_h = DIC with focus on parameters. - DIC_a = DIC with focus on parameters and laten variables (all). - DIC_l = DIC with focus on latent variables. - peff_h = effective number of parameters (latent variables marginalized). - peff_a = effective number of parameters and latent variables. - peff_l = effective number of latent variables evaluated with gp_peff. - peff_l2 = effective number of latent variables evaluated with gp_dic. - - - - gp hyperparameters (gp_pak(gp)): - - 3.8248 -0.8579 -0.1430 - diff --git a/xunit/log/demo_multiclass.txt b/xunit/log/demo_multiclass.txt deleted file mode 100644 index 6fb59c55..00000000 --- a/xunit/log/demo_multiclass.txt +++ /dev/null @@ -1,661 +0,0 @@ -Running: demo_multiclass -Softmax model with Laplace integration over the latent -values and MAP estimate for the parameters - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 277.318 - 1 2 2 192.117 0.0228515 - 2 4 4 187.466 0.174234 - 3 7 7 178.435 0.0383579 - 4 9 9 177.146 0.428584 - 5 11 11 174.94 0.553983 - 6 13 13 174.885 0.207579 - 7 14 14 174.863 1 - 8 15 15 174.862 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 9 - Function Count : 16 - Minimum found : 174.8621 - Intern Time : 0.012538 seconds - Total Time : 26.5103 seconds - -missed = - - 0.1333 - - -ans = - - 3.3233e+05 - - -ans = - - 1.0e+04 * - - -1.1812 5.6124 4.4558 1.5772 1.5647 - -Checking gradient ... - - analytic diffs delta - - 1.0e+05 * - - -0.3752 -0.3752 -0.0000 - 0.5975 0.5976 -0.0000 - 1.2293 1.2294 -0.0000 - 1.5432 1.5432 -0.0000 - 0.6947 0.6947 -0.0000 - - cycle etr hrej lrej lvScale - 2 -3630.139 0.0e+00 6.3e-01 0.044064 - cycle etr hrej lrej lvScale - 3 -4032.239 1.0e+00 8.0e-01 0.058940 - 4 -4053.019 0.0e+00 8.0e-01 0.060154 - 5 -4028.398 0.0e+00 2.0e-01 0.046021 - 6 -3980.359 1.0e+00 8.0e-01 0.072371 - 7 -3940.029 0.0e+00 4.0e-01 0.047938 - 8 -3893.048 0.0e+00 4.0e-01 0.075384 - 9 -3885.172 0.0e+00 6.0e-01 0.057673 - 10 -3841.865 0.0e+00 1.0e+00 0.090694 - 11 -3790.901 0.0e+00 2.0e-01 0.052012 - 12 -3777.442 0.0e+00 8.0e-01 0.081792 - 13 -3773.174 0.0e+00 4.0e-01 0.072275 - 14 -3757.864 0.0e+00 8.0e-01 0.063865 - 15 -3733.210 0.0e+00 4.0e-01 0.056433 - 16 -3727.272 0.0e+00 8.0e-01 0.057596 - 17 -3755.476 0.0e+00 1.0e+00 0.067894 - 18 -3749.937 0.0e+00 6.0e-01 0.051943 - 19 -3744.558 0.0e+00 1.0e+00 0.094344 - 20 -3726.777 0.0e+00 4.0e-01 0.054106 - 21 -3721.818 0.0e+00 6.0e-01 0.073665 - 22 -3704.419 0.0e+00 6.0e-01 0.075183 - 23 -3708.827 0.0e+00 4.0e-01 0.049800 - 24 -3711.581 0.0e+00 8.0e-01 0.090452 - 25 -3696.164 0.0e+00 6.0e-01 0.079927 - 26 -3698.931 1.0e+00 4.0e-01 0.061149 - 27 -3685.537 0.0e+00 6.0e-01 0.096159 - 28 -3695.580 0.0e+00 1.0e+00 0.073567 - 29 -3698.739 0.0e+00 6.0e-01 0.065007 - 30 -3666.937 0.0e+00 6.0e-01 0.088508 - 31 -3670.672 0.0e+00 8.0e-01 0.067713 - 32 -3667.702 0.0e+00 6.0e-01 0.069109 - 33 -3674.018 0.0e+00 8.0e-01 0.061067 - 34 -3658.782 0.0e+00 6.0e-01 0.083144 - 35 -3676.072 0.0e+00 8.0e-01 0.063610 - 36 -3674.349 0.0e+00 1.0e+00 0.064920 - 37 -3680.623 0.0e+00 8.0e-01 0.076528 - 38 -3675.575 0.0e+00 4.0e-01 0.078105 - 39 -3677.905 0.0e+00 8.0e-01 0.069017 - 40 -3676.888 0.0e+00 8.0e-01 0.108532 - 41 -3673.702 0.0e+00 1.0e+00 0.083033 - 42 -3653.888 0.0e+00 8.0e-01 0.073371 - 43 -3691.960 0.0e+00 1.0e+00 0.086490 - 44 -3685.423 0.0e+00 8.0e-01 0.088272 - 45 -3669.311 0.0e+00 8.0e-01 0.090091 - 46 -3681.940 0.0e+00 8.0e-01 0.068925 - 47 -3716.324 0.0e+00 6.0e-01 0.081248 - 48 -3717.851 0.0e+00 8.0e-01 0.071794 - 49 -3721.918 0.0e+00 8.0e-01 0.054927 - 50 -3707.091 0.0e+00 4.0e-01 0.074783 - 51 -3706.217 0.0e+00 8.0e-01 0.101818 - 52 -3731.470 1.0e+00 8.0e-01 0.077897 - 53 -3716.291 1.0e+00 6.0e-01 0.091825 - 54 -3726.246 0.0e+00 4.0e-01 0.093717 - 55 -3718.661 0.0e+00 6.0e-01 0.046533 - 56 -3731.286 0.0e+00 6.0e-01 0.063356 - 57 -3742.154 0.0e+00 8.0e-01 0.074684 - 58 -3728.339 0.0e+00 8.0e-01 0.088037 - 59 -3724.324 1.0e+00 4.0e-01 0.067353 - 60 -3717.302 0.0e+00 8.0e-01 0.059516 - 61 -3708.863 0.0e+00 6.0e-01 0.060742 - 62 -3725.193 0.0e+00 6.0e-01 0.071603 - 63 -3723.366 0.0e+00 6.0e-01 0.073078 - 64 -3707.145 0.0e+00 1.0e+00 0.114919 - 65 -3717.792 0.0e+00 4.0e-01 0.076121 - 66 -3709.505 0.0e+00 2.0e-01 0.067264 - 67 -3703.030 0.0e+00 8.0e-01 0.079290 - 68 -3708.942 0.0e+00 6.0e-01 0.052521 - 69 -3698.483 1.0e+00 2.0e-01 0.061911 - 70 -3699.155 0.0e+00 8.0e-01 0.063187 - 71 -3705.444 0.0e+00 1.0e+00 0.086030 - 72 -3701.841 0.0e+00 6.0e-01 0.056985 - 73 -3722.685 0.0e+00 8.0e-01 0.043597 - 74 -3713.631 0.0e+00 6.0e-01 0.044495 - 75 -3701.339 0.0e+00 4.0e-01 0.052451 - 76 -3713.346 0.0e+00 6.0e-01 0.061829 - 77 -3716.753 0.0e+00 1.0e+00 0.072884 - 78 -3701.588 0.0e+00 1.0e+00 0.099232 - 79 -3726.711 1.0e+00 8.0e-01 0.075918 - 80 -3725.340 0.0e+00 6.0e-01 0.067084 - 81 -3720.839 0.0e+00 8.0e-01 0.091336 - 82 -3704.350 0.0e+00 8.0e-01 0.052381 - 83 -3679.164 0.0e+00 4.0e-01 0.053460 - 84 -3707.980 0.0e+00 6.0e-01 0.072787 - 85 -3692.998 1.0e+00 8.0e-01 0.085801 - 86 -3688.398 0.0e+00 8.0e-01 0.075817 - 87 -3681.445 0.0e+00 6.0e-01 0.066995 - 88 -3671.677 0.0e+00 8.0e-01 0.068375 - 89 -3661.035 0.0e+00 8.0e-01 0.069784 - 90 -3671.146 0.0e+00 2.0e-01 0.053389 - 91 -3682.621 0.0e+00 8.0e-01 0.083957 - 92 -3683.228 0.0e+00 1.0e+00 0.098968 - 93 -3682.524 0.0e+00 1.0e+00 0.075716 - 94 -3682.648 1.0e+00 2.0e-01 0.066906 - 95 -3663.789 0.0e+00 1.0e+00 0.091093 - 96 -3680.966 0.0e+00 6.0e-01 0.092970 - 97 -3699.825 0.0e+00 8.0e-01 0.071127 - 98 -3697.981 0.0e+00 4.0e-01 0.083845 - 99 -3698.312 0.0e+00 8.0e-01 0.085572 - 100 -3683.373 0.0e+00 1.0e+00 0.065468 - 101 -3711.060 1.0e+00 8.0e-01 0.118908 - 102 -3687.310 0.0e+00 6.0e-01 0.068193 - 103 -3695.434 0.0e+00 6.0e-01 0.060258 - 104 -3701.476 0.0e+00 6.0e-01 0.071032 - 105 -3724.366 0.0e+00 8.0e-01 0.083733 - 106 -3687.923 1.0e+00 8.0e-01 0.085458 - 107 -3674.035 0.0e+00 8.0e-01 0.087219 - 108 -3677.447 0.0e+00 8.0e-01 0.089016 - 109 -3677.125 1.0e+00 6.0e-01 0.078658 - 110 -3704.872 0.0e+00 8.0e-01 0.092722 - 111 -3679.830 0.0e+00 8.0e-01 0.081933 - 112 -3690.769 0.0e+00 6.0e-01 0.062684 - 113 -3698.661 0.0e+00 8.0e-01 0.063975 - 114 -3692.375 0.0e+00 2.0e-01 0.048945 - 115 -3685.783 0.0e+00 4.0e-01 0.049953 - 116 -3709.086 0.0e+00 8.0e-01 0.078554 - 117 -3710.026 0.0e+00 1.0e+00 0.060098 - 118 -3704.776 0.0e+00 8.0e-01 0.109155 - 119 -3684.968 0.0e+00 1.0e+00 0.096454 - 120 -3680.361 0.0e+00 6.0e-01 0.063890 - 121 -3685.339 0.0e+00 1.0e+00 0.075313 - 122 -3677.302 0.0e+00 6.0e-01 0.057619 - 123 -3675.051 0.0e+00 8.0e-01 0.067921 - 124 -3676.066 0.0e+00 8.0e-01 0.060018 - 125 -3697.481 0.0e+00 8.0e-01 0.081715 - 126 -3676.333 0.0e+00 8.0e-01 0.096325 - 127 -3685.888 0.0e+00 6.0e-01 0.063805 - 128 -3675.200 0.0e+00 8.0e-01 0.056380 - 129 -3689.750 0.0e+00 8.0e-01 0.066461 - 130 -3683.769 0.0e+00 8.0e-01 0.044023 - 131 -3685.536 0.0e+00 6.0e-01 0.069228 - 132 -3672.793 0.0e+00 6.0e-01 0.045856 - 133 -3699.680 0.0e+00 6.0e-01 0.072110 - 134 -3698.356 0.0e+00 1.0e+00 0.113397 - 135 -3675.906 0.0e+00 4.0e-01 0.048749 - 136 -3695.722 0.0e+00 6.0e-01 0.066373 - 137 -3685.107 0.0e+00 4.0e-01 0.058650 - 138 -3678.093 0.0e+00 1.0e+00 0.051825 - 139 -3660.909 0.0e+00 6.0e-01 0.070560 - 140 -3670.280 0.0e+00 8.0e-01 0.062350 - 141 -3662.903 0.0e+00 8.0e-01 0.073498 - 142 -3671.009 0.0e+00 8.0e-01 0.048684 - 143 -3678.924 0.0e+00 6.0e-01 0.066284 - 144 -3684.902 0.0e+00 8.0e-01 0.043906 - 145 -3679.772 0.0e+00 2.0e-01 0.044810 - 146 -3697.382 0.0e+00 6.0e-01 0.052822 - 147 -3697.188 0.0e+00 8.0e-01 0.053911 - 148 -3692.277 0.0e+00 1.0e+00 0.073400 - 149 -3678.517 1.0e+00 4.0e-01 0.064859 - 150 -3678.059 0.0e+00 8.0e-01 0.057312 - 151 -3683.841 0.0e+00 6.0e-01 0.058493 - 152 -3690.647 0.0e+00 6.0e-01 0.059699 - 153 -3695.564 0.0e+00 8.0e-01 0.070373 - 154 -3685.961 0.0e+00 6.0e-01 0.062184 - 155 -3686.625 0.0e+00 4.0e-01 0.047574 - 156 -3706.012 0.0e+00 6.0e-01 0.064773 - 157 -3714.564 0.0e+00 4.0e-01 0.049555 - 158 -3712.441 0.0e+00 8.0e-01 0.067470 - 159 -3714.690 0.0e+00 6.0e-01 0.044691 - 160 -3718.592 0.0e+00 8.0e-01 0.045612 - 161 -3713.359 0.0e+00 8.0e-01 0.053767 - 162 -3694.630 0.0e+00 6.0e-01 0.063381 - 163 -3680.489 0.0e+00 4.0e-01 0.056006 - 164 -3680.761 1.0e+00 1.0e+00 0.049489 - 165 -3668.930 0.0e+00 4.0e-01 0.050509 - 166 -3676.813 0.0e+00 8.0e-01 0.038642 - 167 -3690.351 0.0e+00 1.0e+00 0.052612 - 168 -3706.354 0.0e+00 8.0e-01 0.040251 - 169 -3709.019 0.0e+00 6.0e-01 0.073107 - 170 -3715.268 0.0e+00 6.0e-01 0.048425 - 171 -3702.798 0.0e+00 8.0e-01 0.057084 - 172 -3710.075 0.0e+00 8.0e-01 0.043672 - 173 -3706.140 0.0e+00 8.0e-01 0.044572 - 174 -3709.499 0.0e+00 6.0e-01 0.060685 - 175 -3712.606 0.0e+00 2.0e-01 0.040197 - 176 -3721.953 0.0e+00 8.0e-01 0.047384 - 177 -3729.776 0.0e+00 1.0e+00 0.041871 - 178 -3715.200 0.0e+00 6.0e-01 0.042733 - 179 -3709.049 0.0e+00 4.0e-01 0.032694 - 180 -3706.811 0.0e+00 8.0e-01 0.059381 - 181 -3713.991 0.0e+00 8.0e-01 0.052471 - 182 -3708.743 0.0e+00 8.0e-01 0.034756 - 183 -3710.760 0.0e+00 1.0e+00 0.054656 - 184 -3725.810 0.0e+00 4.0e-01 0.041815 - 185 -3706.343 0.0e+00 4.0e-01 0.036949 - 186 -3703.040 0.0e+00 8.0e-01 0.037711 - 187 -3705.821 0.0e+00 8.0e-01 0.033323 - 188 -3718.776 0.0e+00 6.0e-01 0.052402 - 189 -3701.931 0.0e+00 1.0e+00 0.040090 - 190 -3711.377 0.0e+00 8.0e-01 0.063044 - 191 -3713.259 1.0e+00 6.0e-01 0.048232 - 192 -3698.395 0.0e+00 2.0e-01 0.036900 - 193 -3695.592 1.0e+00 8.0e-01 0.037660 - 194 -3699.589 0.0e+00 1.0e+00 0.044394 - 195 -3690.943 0.0e+00 8.0e-01 0.045309 - 196 -3701.044 0.0e+00 6.0e-01 0.053410 - 197 -3685.322 0.0e+00 1.0e+00 0.030630 - 198 -3697.554 0.0e+00 8.0e-01 0.031261 - 199 -3684.124 0.0e+00 1.0e+00 0.075746 - 200 -3687.115 0.0e+00 1.0e+00 0.037610 - 201 -3680.852 1.0e+00 8.0e-01 0.059144 - 202 -3682.976 0.0e+00 8.0e-01 0.039176 - 203 -3685.804 0.0e+00 6.0e-01 0.034618 - 204 -3703.330 1.0e+00 2.0e-01 0.047132 - 205 -3700.588 0.0e+00 0.0e+00 0.036059 - 206 -3718.775 0.0e+00 6.0e-01 0.031863 - 207 -3719.587 1.0e+00 6.0e-01 0.050106 - 208 -3716.997 0.0e+00 6.0e-01 0.038334 - 209 -3720.949 0.0e+00 4.0e-01 0.039124 - 210 -3715.844 0.0e+00 4.0e-01 0.029932 - 211 -3710.903 0.0e+00 8.0e-01 0.054365 - 212 -3703.452 0.0e+00 6.0e-01 0.041593 - 213 -3728.312 0.0e+00 8.0e-01 0.075544 - 214 -3720.173 1.0e+00 6.0e-01 0.037510 - 215 -3715.815 0.0e+00 8.0e-01 0.044217 - 216 -3720.681 0.0e+00 4.0e-01 0.060202 - 217 -3722.977 0.0e+00 6.0e-01 0.046058 - 218 -3723.556 0.0e+00 6.0e-01 0.035237 - 219 -3724.104 0.0e+00 6.0e-01 0.041537 - 220 -3734.039 0.0e+00 4.0e-01 0.036704 - 221 -3731.760 0.0e+00 4.0e-01 0.043266 - 222 -3725.243 0.0e+00 8.0e-01 0.033101 - 223 -3727.375 0.0e+00 1.0e+00 0.039020 - 224 -3740.428 0.0e+00 8.0e-01 0.034479 - 225 -3749.251 0.0e+00 8.0e-01 0.035190 - 226 -3742.266 0.0e+00 6.0e-01 0.035915 - 227 -3743.516 1.0e+00 8.0e-01 0.048899 - 228 -3738.445 0.0e+00 6.0e-01 0.049906 - 229 -3736.723 0.0e+00 1.0e+00 0.067948 - 230 -3739.145 0.0e+00 1.0e+00 0.060041 - 231 -3731.766 0.0e+00 4.0e-01 0.039771 - 232 -3724.404 0.0e+00 8.0e-01 0.030427 - 233 -3715.938 0.0e+00 6.0e-01 0.055264 - 234 -3719.945 0.0e+00 4.0e-01 0.042280 - 235 -3722.815 0.0e+00 1.0e+00 0.057565 - 236 -3734.459 0.0e+00 8.0e-01 0.058751 - 237 -3732.625 0.0e+00 8.0e-01 0.029172 - 238 -3735.004 0.0e+00 2.0e-01 0.034388 - 239 -3748.334 0.0e+00 4.0e-01 0.035096 - 240 -3753.311 0.0e+00 6.0e-01 0.055190 - 241 -3767.828 0.0e+00 8.0e-01 0.048768 - 242 -3737.724 0.0e+00 6.0e-01 0.043094 - 243 -3745.035 0.0e+00 6.0e-01 0.058673 - 244 -3716.257 0.0e+00 6.0e-01 0.051846 - 245 -3710.719 0.0e+00 1.0e+00 0.052914 - 246 -3719.756 0.0e+00 2.0e-01 0.030346 - 247 -3715.939 0.0e+00 6.0e-01 0.055117 - 248 -3725.892 0.0e+00 6.0e-01 0.036509 - 249 -3727.125 0.0e+00 4.0e-01 0.049707 - 250 -3722.521 0.0e+00 1.0e+00 0.090282 - 251 -3723.261 0.0e+00 8.0e-01 0.044828 - 252 -3729.343 0.0e+00 6.0e-01 0.061034 - 253 -3728.795 0.0e+00 6.0e-01 0.046694 - 254 -3705.671 0.0e+00 2.0e-01 0.041261 - 255 -3712.400 0.0e+00 6.0e-01 0.036460 - 256 -3705.006 0.0e+00 8.0e-01 0.057335 - 257 -3700.611 0.0e+00 1.0e+00 0.050664 - 258 -3691.466 0.0e+00 6.0e-01 0.068979 - 259 -3717.621 0.0e+00 1.0e+00 0.070400 - 260 -3711.149 0.0e+00 6.0e-01 0.071851 - 261 -3697.893 0.0e+00 4.0e-01 0.041206 - 262 -3680.579 0.0e+00 1.0e+00 0.048574 - 263 -3684.235 0.0e+00 1.0e+00 0.057259 - 264 -3666.413 0.0e+00 2.0e-01 0.037927 - 265 -3658.248 0.0e+00 1.0e+00 0.044709 - 266 -3683.490 0.0e+00 4.0e-01 0.039506 - 267 -3687.218 0.0e+00 1.0e+00 0.071755 - 268 -3675.972 1.0e+00 8.0e-01 0.054897 - 269 -3699.735 0.0e+00 8.0e-01 0.041999 - 270 -3694.144 0.0e+00 4.0e-01 0.042865 - 271 -3724.429 0.0e+00 8.0e-01 0.050529 - 272 -3735.059 0.0e+00 4.0e-01 0.038657 - 273 -3733.287 0.0e+00 8.0e-01 0.045569 - 274 -3729.118 0.0e+00 1.0e+00 0.053717 - 275 -3733.868 0.0e+00 8.0e-01 0.035581 - 276 -3721.224 0.0e+00 6.0e-01 0.041943 - 277 -3726.049 0.0e+00 6.0e-01 0.076181 - 278 -3738.875 0.0e+00 8.0e-01 0.050461 - 279 -3737.922 0.0e+00 4.0e-01 0.028939 - 280 -3724.463 0.0e+00 6.0e-01 0.060709 - 281 -3724.505 0.0e+00 2.0e-01 0.040213 - 282 -3720.509 0.0e+00 8.0e-01 0.047403 - 283 -3721.817 0.0e+00 4.0e-01 0.031399 - 284 -3731.113 0.0e+00 6.0e-01 0.049377 - 285 -3724.858 0.0e+00 8.0e-01 0.050394 - 286 -3721.349 0.0e+00 8.0e-01 0.051432 - 287 -3708.587 1.0e+00 6.0e-01 0.060628 - 288 -3718.829 0.0e+00 1.0e+00 0.061878 - 289 -3722.181 0.0e+00 6.0e-01 0.040987 - 290 -3722.796 0.0e+00 6.0e-01 0.041831 - 291 -3721.371 0.0e+00 4.0e-01 0.049311 - 292 -3727.242 0.0e+00 1.0e+00 0.043573 - 293 -3716.750 0.0e+00 8.0e-01 0.044471 - 294 -3729.032 0.0e+00 8.0e-01 0.052422 - 295 -3722.139 0.0e+00 8.0e-01 0.046322 - 296 -3716.227 0.0e+00 1.0e+00 0.040932 - 297 -3721.807 0.0e+00 6.0e-01 0.031316 - 298 -3710.509 0.0e+00 6.0e-01 0.042636 - 299 -3720.677 0.0e+00 8.0e-01 0.043515 - 300 -3724.655 0.0e+00 2.0e-01 0.044412 - 301 -3729.844 0.0e+00 4.0e-01 0.052352 - 302 -3714.361 0.0e+00 6.0e-01 0.040053 - 303 -3713.991 0.0e+00 1.0e+00 0.062984 - 304 -3735.443 0.0e+00 8.0e-01 0.048187 - 305 -3745.537 0.0e+00 4.0e-01 0.023926 - 306 -3757.344 0.0e+00 8.0e-01 0.043457 - 307 -3756.918 1.0e+00 6.0e-01 0.038400 - 308 -3754.384 0.0e+00 4.0e-01 0.039192 - 309 -3767.281 0.0e+00 6.0e-01 0.061631 - 310 -3749.366 0.0e+00 8.0e-01 0.035345 - 311 -3742.872 0.0e+00 4.0e-01 0.048122 - 312 -3737.768 0.0e+00 4.0e-01 0.042523 - 313 -3756.021 0.0e+00 8.0e-01 0.050126 - 314 -3762.473 0.0e+00 4.0e-01 0.051159 - 315 -3777.353 0.0e+00 4.0e-01 0.052213 - 316 -3786.970 0.0e+00 6.0e-01 0.061549 - 317 -3771.453 0.0e+00 6.0e-01 0.047088 - 318 -3772.799 0.0e+00 4.0e-01 0.064111 - 319 -3775.510 0.0e+00 1.0e+00 0.065432 - 320 -3773.864 0.0e+00 8.0e-01 0.050059 - 321 -3767.311 1.0e+00 2.0e-01 0.038298 - 322 -3764.422 0.0e+00 6.0e-01 0.052143 - 323 -3775.560 0.0e+00 8.0e-01 0.053218 - 324 -3771.605 0.0e+00 4.0e-01 0.062733 - 325 -3798.178 0.0e+00 4.0e-01 0.047994 - 326 -3781.693 0.0e+00 8.0e-01 0.048983 - 327 -3783.475 0.0e+00 4.0e-01 0.037475 - 328 -3792.127 0.0e+00 1.0e+00 0.068065 - 329 -3777.599 0.0e+00 8.0e-01 0.045086 - 330 -3768.162 0.0e+00 1.0e+00 0.061385 - 331 -3767.561 0.0e+00 1.0e+00 0.054242 - 332 -3752.475 0.0e+00 8.0e-01 0.055360 - 333 -3733.480 0.0e+00 4.0e-01 0.048918 - 334 -3732.343 0.0e+00 8.0e-01 0.032403 - 335 -3717.060 0.0e+00 6.0e-01 0.050955 - 336 -3715.284 0.0e+00 6.0e-01 0.052004 - 337 -3716.678 0.0e+00 2.0e-01 0.039786 - 338 -3706.968 0.0e+00 1.0e+00 0.083464 - 339 -3712.773 0.0e+00 1.0e+00 0.041443 - 340 -3714.739 0.0e+00 8.0e-01 0.075272 - 341 -3736.132 0.0e+00 6.0e-01 0.037375 - 342 -3725.791 0.0e+00 4.0e-01 0.044058 - 343 -3715.450 0.0e+00 2.0e-01 0.033707 - 344 -3712.578 0.0e+00 2.0e-01 0.034401 - 345 -3708.836 0.0e+00 0.0e+00 0.026319 - 346 -3716.540 0.0e+00 2.0e-01 0.026861 - 347 -3723.991 0.0e+00 8.0e-01 0.031664 - 348 -3722.002 1.0e+00 2.0e-01 0.024225 - 349 -3726.193 0.0e+00 6.0e-01 0.038094 - 350 -3730.749 0.0e+00 6.0e-01 0.038879 - 351 -3735.669 0.0e+00 6.0e-01 0.045831 - 352 -3740.050 0.0e+00 4.0e-01 0.035063 - 353 -3735.560 0.0e+00 6.0e-01 0.035786 - 354 -3745.913 1.0e+00 1.0e+00 0.056275 - 355 -3762.028 0.0e+00 2.0e-01 0.037276 - 356 -3757.435 0.0e+00 6.0e-01 0.058618 - 357 -3757.603 0.0e+00 4.0e-01 0.051797 - 358 -3766.324 0.0e+00 4.0e-01 0.052864 - 359 -3770.703 0.0e+00 6.0e-01 0.053953 - 360 -3783.421 0.0e+00 4.0e-01 0.035738 - 361 -3785.726 0.0e+00 8.0e-01 0.036474 - 362 -3769.418 0.0e+00 4.0e-01 0.049660 - 363 -3772.945 1.0e+00 1.0e+00 0.050684 - 364 -3768.027 0.0e+00 8.0e-01 0.038776 - 365 -3765.467 0.0e+00 6.0e-01 0.039575 - 366 -3748.692 0.0e+00 8.0e-01 0.046651 - 367 -3738.499 0.0e+00 8.0e-01 0.035690 - 368 -3742.556 0.0e+00 1.0e+00 0.064824 - 369 -3742.390 0.0e+00 6.0e-01 0.037176 - 370 -3738.341 0.0e+00 6.0e-01 0.037942 - 371 -3742.010 0.0e+00 6.0e-01 0.059666 - 372 -3752.185 0.0e+00 8.0e-01 0.039522 - 373 -3753.579 0.0e+00 6.0e-01 0.040336 - 374 -3758.980 0.0e+00 8.0e-01 0.041167 - 375 -3753.905 0.0e+00 4.0e-01 0.027269 - 376 -3754.015 0.0e+00 1.0e+00 0.049528 - 377 -3754.759 0.0e+00 8.0e-01 0.028404 - 378 -3758.430 0.0e+00 4.0e-01 0.044667 - 379 -3750.959 0.0e+00 2.0e-01 0.039469 - 380 -3739.304 0.0e+00 6.0e-01 0.040283 - 381 -3746.841 0.0e+00 6.0e-01 0.041113 - 382 -3751.657 1.0e+00 4.0e-01 0.031453 - 383 -3755.661 0.0e+00 4.0e-01 0.037077 - 384 -3749.023 1.0e+00 8.0e-01 0.028366 - 385 -3749.210 1.0e+00 6.0e-01 0.033438 - 386 -3763.127 0.0e+00 6.0e-01 0.039417 - 387 -3763.822 0.0e+00 1.0e+00 0.053666 - 388 -3767.692 0.0e+00 4.0e-01 0.047422 - 389 -3762.585 0.0e+00 4.0e-01 0.036280 - 390 -3760.249 1.0e+00 4.0e-01 0.037028 - 391 -3762.815 0.0e+00 8.0e-01 0.037791 - 392 -3763.743 0.0e+00 4.0e-01 0.044548 - 393 -3758.157 0.0e+00 4.0e-01 0.045466 - 394 -3769.838 0.0e+00 6.0e-01 0.053595 - 395 -3772.615 0.0e+00 6.0e-01 0.041003 - 396 -3772.502 0.0e+00 4.0e-01 0.031370 - 397 -3766.345 0.0e+00 1.0e+00 0.056977 - 398 -3778.178 0.0e+00 1.0e+00 0.067164 - 399 -3768.555 0.0e+00 1.0e+00 0.059349 - 400 -3763.353 0.0e+00 2.0e-01 0.034036 - 401 -3772.448 1.0e+00 1.0e+00 0.030076 - 402 -3770.457 0.0e+00 2.0e-01 0.030695 - cycle etr hrej lrej lvScale - 2 -3524.135 0.0e+00 3.7e-02 0.088016 - cycle etr hrej lrej lvScale - 3 -3562.074 1.0e+00 1.0e-01 0.125535 - 4 -3541.193 0.0e+00 0.0e+00 0.150026 - 5 -3564.815 0.0e+00 0.0e+00 0.179295 - 6 -3602.549 0.0e+00 5.0e-02 0.214274 - 7 -3572.052 0.0e+00 5.0e-02 0.179326 - 8 -3569.367 0.0e+00 5.0e-02 0.214311 - 9 -3602.099 1.0e+00 5.0e-02 0.179357 - 10 -3526.159 0.0e+00 0.0e+00 0.214348 - 11 -3536.875 0.0e+00 0.0e+00 0.256165 - 12 -3521.256 0.0e+00 1.0e-01 0.306141 - 13 -3582.144 0.0e+00 1.0e-01 0.365867 - 14 -3576.716 0.0e+00 1.5e-01 0.150155 - 15 -3598.887 1.0e+00 0.0e+00 0.179449 - 16 -3592.807 0.0e+00 0.0e+00 0.214459 - 17 -3551.030 0.0e+00 1.0e-01 0.125687 - 18 -3566.592 0.0e+00 0.0e+00 0.150207 - 19 -3620.999 0.0e+00 1.0e-01 0.179511 - 20 -3555.069 0.0e+00 0.0e+00 0.214533 - 21 -3580.881 0.0e+00 1.0e-01 0.179542 - 22 -3618.200 0.0e+00 5.0e-02 0.214570 - 23 -3612.005 0.0e+00 5.0e-02 0.256431 - 24 -3618.013 0.0e+00 1.5e-01 0.306458 - 25 -3635.865 0.0e+00 5.0e-02 0.366246 - 26 -3650.795 0.0e+00 1.0e-01 0.214644 - 27 -3580.796 0.0e+00 5.0e-02 0.256519 - 28 -3656.894 1.0e+00 1.0e-01 0.214681 - 29 -3624.118 0.0e+00 1.0e-01 0.256563 - 30 -3625.109 0.0e+00 1.0e-01 0.306617 - 31 -3616.719 0.0e+00 1.0e-01 0.179697 - 32 -3619.189 0.0e+00 0.0e+00 0.214755 - 33 -3648.407 0.0e+00 5.0e-02 0.179728 - 34 -3641.885 0.0e+00 0.0e+00 0.214792 - 35 -3657.977 1.0e+00 5.0e-02 0.256696 - 36 -3646.023 0.0e+00 5.0e-02 0.214829 - 37 -3677.506 1.0e+00 5.0e-02 0.179790 - 38 -3647.460 0.0e+00 1.0e-01 0.105369 - 39 -3605.621 0.0e+00 0.0e+00 0.125925 - 40 -3665.499 1.0e+00 1.0e-01 0.105387 - 41 -3591.542 0.0e+00 0.0e+00 0.125947 - 42 -3591.984 0.0e+00 1.0e-01 0.150518 - 43 -3591.479 0.0e+00 5.0e-02 0.125969 - 44 -3604.143 0.0e+00 5.0e-02 0.105423 - 45 -3589.678 0.0e+00 5.0e-02 0.125990 - 46 -3558.215 0.0e+00 1.5e-01 0.105441 - 47 -3554.836 0.0e+00 1.0e-01 0.088244 - 48 -3574.375 1.0e+00 5.0e-02 0.105459 - 49 -3598.370 0.0e+00 1.0e-01 0.126034 - 50 -3518.387 0.0e+00 1.0e-01 0.105478 - 51 -3607.122 0.0e+00 0.0e+00 0.126056 - 52 -3601.566 0.0e+00 0.0e+00 0.150648 - 53 -3553.245 0.0e+00 0.0e+00 0.180038 - 54 -3570.285 0.0e+00 1.5e-01 0.150674 - 55 -3610.691 0.0e+00 0.0e+00 0.180069 - 56 -3527.260 0.0e+00 5.0e-02 0.215200 - 57 -3551.827 0.0e+00 0.0e+00 0.257183 - 58 -3528.128 0.0e+00 5.0e-02 0.215237 - 59 -3530.464 0.0e+00 1.0e-01 0.257228 - 60 -3571.013 0.0e+00 5.0e-02 0.215274 - 61 -3593.270 0.0e+00 5.0e-02 0.180163 - 62 -3552.982 0.0e+00 0.0e+00 0.215311 - 63 -3623.216 1.0e+00 5.0e-02 0.180194 - 64 -3591.110 1.0e+00 0.0e+00 0.215348 - 65 -3585.398 1.0e+00 5.0e-02 0.180225 - 66 -3646.095 0.0e+00 0.0e+00 0.215385 - 67 -3586.716 0.0e+00 5.0e-02 0.180256 - 68 -3572.294 0.0e+00 0.0e+00 0.215422 - 69 -3574.439 0.0e+00 5.0e-02 0.180287 - 70 -3536.728 0.0e+00 0.0e+00 0.215459 - 71 -3540.617 0.0e+00 5.0e-02 0.180318 - 72 -3554.297 0.0e+00 0.0e+00 0.215497 - 73 -3560.663 0.0e+00 1.0e-01 0.126295 - 74 -3553.255 0.0e+00 0.0e+00 0.150934 - 75 -3555.007 0.0e+00 1.0e-01 0.088457 - 76 -3558.546 0.0e+00 5.0e-02 0.074030 - 77 -3563.610 0.0e+00 1.5e-01 0.088472 - 78 -3560.466 0.0e+00 0.0e+00 0.105733 - 79 -3522.967 0.0e+00 5.0e-02 0.126360 - 80 -3545.619 1.0e+00 5.0e-02 0.105751 - 81 -3525.372 0.0e+00 5.0e-02 0.126382 - 82 -3479.605 1.0e+00 5.0e-02 0.105769 - 83 -3470.606 0.0e+00 5.0e-02 0.126404 - 84 -3475.978 0.0e+00 1.0e-01 0.105787 - 85 -3542.650 0.0e+00 0.0e+00 0.126426 - 86 -3512.980 0.0e+00 0.0e+00 0.151090 - 87 -3442.320 0.0e+00 0.0e+00 0.180567 - 88 -3449.878 0.0e+00 0.0e+00 0.215794 - 89 -3437.835 0.0e+00 1.0e-01 0.180598 - 90 -3444.027 0.0e+00 0.0e+00 0.215831 - 91 -3439.391 0.0e+00 1.0e-01 0.180629 - 92 -3456.088 0.0e+00 1.0e-01 0.151168 - 93 -3467.684 0.0e+00 0.0e+00 0.180660 - 94 -3482.265 0.0e+00 5.0e-02 0.215906 - 95 -3483.640 0.0e+00 5.0e-02 0.180691 - 96 -3472.015 0.0e+00 0.0e+00 0.215943 - 97 -3476.392 0.0e+00 5.0e-02 0.180723 - 98 -3533.072 0.0e+00 0.0e+00 0.215980 - 99 -3521.049 0.0e+00 1.0e-01 0.180754 - 100 -3480.643 1.0e+00 0.0e+00 0.216017 - 101 -3517.527 0.0e+00 5.0e-02 0.258161 - 102 -3546.011 0.0e+00 5.0e-02 0.216055 - 103 -3501.579 1.0e+00 5.0e-02 0.180816 - 104 -3522.381 0.0e+00 5.0e-02 0.151325 - 105 -3527.038 0.0e+00 5.0e-02 0.126644 - 106 -3489.119 0.0e+00 5.0e-02 0.105988 - 107 -3479.274 0.0e+00 5.0e-02 0.088701 - 108 -3537.881 0.0e+00 0.0e+00 0.106006 - 109 -3532.658 0.0e+00 0.0e+00 0.126687 - 110 -3502.880 0.0e+00 5.0e-02 0.106025 - 111 -3543.865 0.0e+00 0.0e+00 0.126709 - 112 -3502.826 0.0e+00 0.0e+00 0.151429 - 113 -3494.208 0.0e+00 5.0e-02 0.126731 - 114 -3485.333 0.0e+00 5.0e-02 0.151455 - 115 -3470.294 0.0e+00 0.0e+00 0.181003 - 116 -3503.965 0.0e+00 1.0e-01 0.216316 - 117 -3454.615 0.0e+00 2.0e-01 0.258517 - 118 -3446.564 1.0e+00 5.0e-02 0.216353 - 119 -3402.736 0.0e+00 2.0e-01 0.126797 - 120 -3511.119 0.0e+00 1.0e-01 0.151534 - 121 -3514.592 0.0e+00 5.0e-02 0.126819 - 122 -3464.280 1.0e+00 0.0e+00 0.151560 - 123 -3530.224 0.0e+00 0.0e+00 0.181128 - 124 -3526.283 0.0e+00 0.0e+00 0.216465 - 125 -3513.249 0.0e+00 0.0e+00 0.258696 - 126 -3533.774 0.0e+00 5.0e-02 0.216502 - 127 -3537.291 0.0e+00 5.0e-02 0.181191 - 128 -3512.321 1.0e+00 0.0e+00 0.216540 - 129 -3523.673 0.0e+00 5.0e-02 0.181222 - 130 -3530.500 0.0e+00 0.0e+00 0.216577 - 131 -3556.255 0.0e+00 5.0e-02 0.258829 - 132 -3618.916 0.0e+00 1.0e-01 0.216614 - 133 -3586.459 0.0e+00 0.0e+00 0.258874 - 134 -3564.340 0.0e+00 1.0e-01 0.151717 - 135 -3618.817 1.0e+00 0.0e+00 0.181316 - 136 -3550.663 0.0e+00 0.0e+00 0.216689 - 137 -3592.564 0.0e+00 5.0e-02 0.181347 - 138 -3593.443 0.0e+00 0.0e+00 0.216726 - 139 -3541.975 0.0e+00 1.0e-01 0.259008 - 140 -3514.542 0.0e+00 1.0e-01 0.151795 - 141 -3506.857 0.0e+00 0.0e+00 0.181409 - 142 -3504.011 0.0e+00 0.0e+00 0.216801 - 143 -3508.699 0.0e+00 5.0e-02 0.181441 - 144 -3496.819 0.0e+00 0.0e+00 0.216838 - 145 -3532.806 0.0e+00 1.0e-01 0.181472 - 146 -3531.291 0.0e+00 0.0e+00 0.216876 - 147 -3497.687 0.0e+00 5.0e-02 0.181503 - 148 -3532.855 0.0e+00 0.0e+00 0.216913 - 149 -3486.034 0.0e+00 1.5e-01 0.259231 - 150 -3543.279 0.0e+00 5.0e-02 0.216951 - 151 -3536.476 1.0e+00 5.0e-02 0.181566 - 152 -3528.742 0.0e+00 0.0e+00 0.216988 - 153 -3535.399 1.0e+00 1.0e-01 0.259321 - 154 -3503.728 1.0e+00 2.0e-01 0.309912 - 155 -3535.103 0.0e+00 0.0e+00 0.370374 - 156 -3527.950 0.0e+00 0.0e+00 0.442631 - 157 -3616.567 0.0e+00 1.5e-01 0.181660 - 158 -3551.923 0.0e+00 0.0e+00 0.217100 - 159 -3547.251 0.0e+00 5.0e-02 0.259455 - 160 -3574.566 0.0e+00 1.0e-01 0.217138 - 161 -3595.425 0.0e+00 5.0e-02 0.181722 - 162 -3631.501 0.0e+00 1.0e-01 0.152083 - 163 -3609.873 0.0e+00 0.0e+00 0.181754 - 164 -3554.477 1.0e+00 5.0e-02 0.152110 - 165 -3602.740 0.0e+00 5.0e-02 0.127301 - 166 -3616.468 0.0e+00 0.0e+00 0.152136 - 167 -3623.736 0.0e+00 0.0e+00 0.181817 - 168 -3603.437 0.0e+00 5.0e-02 0.152162 - 169 -3582.453 0.0e+00 5.0e-02 0.127344 - 170 -3612.595 0.0e+00 0.0e+00 0.152188 - 171 -3620.902 0.0e+00 5.0e-02 0.127366 - 172 -3580.758 0.0e+00 1.0e-01 0.106593 - 173 -3670.976 0.0e+00 0.0e+00 0.127388 - 174 -3642.592 0.0e+00 5.0e-02 0.106611 - 175 -3671.133 0.0e+00 0.0e+00 0.127410 - 176 -3683.211 0.0e+00 5.0e-02 0.106630 - 177 -3621.218 0.0e+00 0.0e+00 0.127432 - 178 -3667.380 0.0e+00 0.0e+00 0.152293 - 179 -3640.574 0.0e+00 5.0e-02 0.182005 - 180 -3697.859 0.0e+00 5.0e-02 0.217512 - 181 -3648.317 0.0e+00 1.0e-01 0.182036 - 182 -3663.619 0.0e+00 5.0e-02 0.217550 - 183 -3683.071 0.0e+00 5.0e-02 0.259992 - 184 -3680.697 0.0e+00 5.0e-02 0.217587 - 185 -3665.018 0.0e+00 5.0e-02 0.182099 - 186 -3651.655 0.0e+00 5.0e-02 0.152398 - 187 -3667.394 0.0e+00 1.0e-01 0.089315 - 188 -3651.608 0.0e+00 5.0e-02 0.106740 - 189 -3680.677 0.0e+00 0.0e+00 0.127564 - 190 -3612.208 0.0e+00 1.0e-01 0.106758 - 191 -3649.622 0.0e+00 5.0e-02 0.127586 - 192 -3658.561 0.0e+00 5.0e-02 0.106777 - 193 -3655.282 0.0e+00 5.0e-02 0.089361 - 194 -3635.263 0.0e+00 0.0e+00 0.106795 - 195 -3645.611 0.0e+00 5.0e-02 0.089377 - 196 -3621.950 0.0e+00 5.0e-02 0.074799 - 197 -3615.108 0.0e+00 0.0e+00 0.089392 - 198 -3664.521 0.0e+00 0.0e+00 0.106832 - 199 -3710.118 0.0e+00 0.0e+00 0.127674 - 200 -3612.505 0.0e+00 0.0e+00 0.152583 - 201 -3673.448 0.0e+00 0.0e+00 0.182350 - 202 -3617.285 0.0e+00 0.0e+00 0.217925 - - gp hyperparameters (gp_pak(gp)): - - 2.8831 -0.6152 -0.5214 1.5709 1.6167 - diff --git a/xunit/log/demo_multinom2.txt b/xunit/log/demo_multinom2.txt deleted file mode 100644 index bbe520c6..00000000 --- a/xunit/log/demo_multinom2.txt +++ /dev/null @@ -1,458 +0,0 @@ -Running: demo_multinom2 - Iteration Func-count f(x) Lambda - 0 1 667.05 - 1 3 666.162 10 - 2 5 666.133 5 - 3 7 666.049 2.5 - 4 9 665.97 1.25 - 5 11 665.77 0.625 - 6 13 665.562 0.312 - 7 15 665.52 0.156 - 8 17 665.422 0.0781 - 9 19 665.396 0.0391 - 10 21 665.388 0.0195 - 11 23 665.375 0.00977 - 12 25 665.373 0.00488 - 13 27 665.372 0.00244 - 14 29 665.362 0.00122 - 15 31 665.362 0.00061 - 16 33 665.36 0.000305 - 17 35 665.36 0.000153 - 18 37 665.359 7.63e-05 - 19 39 665.359 3.81e-05 - Iteration Func-count f(x) Lambda - 20 41 665.359 1.91e-05 - TolFun reached. Func-count 41. Final f(x)=665.359. Elapsed time 2.77 - -ans = - - 2.1696e+09 - - -ans = - - 1.0e+09 * - - -0.4921 7.2227 -0.0082 0.0545 -0.0311 0.3121 - -Checking gradient ... - - analytic diffs delta - - 1.0e+09 * - - -0.4551 -0.4551 0.0000 - 5.4149 5.4149 -0.0000 - -0.4460 -0.4460 0.0000 - 5.4797 5.4797 0.0000 - -0.0334 -0.0334 0.0000 - 0.3870 0.3870 0.0000 - - cycle etr hrej lrej lvScale - 2 156.359 0.0e+00 6.5e-01 0.012046 - cycle etr hrej lrej lvScale - 3 157.257 0.0e+00 8.0e-01 0.013950 - 4 161.475 0.0e+00 8.0e-01 0.012327 - 5 159.985 0.0e+00 6.0e-01 0.010893 - 6 169.846 0.0e+00 8.0e-01 0.012840 - 7 167.945 1.0e+00 1.0e+00 0.008505 - 8 164.013 0.0e+00 8.0e-01 0.008681 - 9 153.421 0.0e+00 1.0e+00 0.010233 - 10 157.735 0.0e+00 6.0e-01 0.010444 - 11 161.598 1.0e+00 4.0e-01 0.010659 - 12 161.596 0.0e+00 8.0e-01 0.007060 - 13 160.688 1.0e+00 1.0e+00 0.011102 - 14 156.292 0.0e+00 8.0e-01 0.013088 - 15 158.644 0.0e+00 8.0e-01 0.011565 - 16 156.455 0.0e+00 8.0e-01 0.013632 - 17 153.234 0.0e+00 6.0e-01 0.013913 - 18 160.783 1.0e+00 6.0e-01 0.010644 - 19 164.635 1.0e+00 1.0e+00 0.012548 - 20 163.625 0.0e+00 1.0e+00 0.011088 - 21 167.372 0.0e+00 8.0e-01 0.009798 - 22 167.201 0.0e+00 8.0e-01 0.011549 - 23 165.862 0.0e+00 6.0e-01 0.010205 - 24 159.330 0.0e+00 1.0e+00 0.010416 - 25 163.818 0.0e+00 1.0e+00 0.009204 - 26 158.448 0.0e+00 8.0e-01 0.008133 - 27 157.371 0.0e+00 8.0e-01 0.009587 - 28 159.491 0.0e+00 6.0e-01 0.011301 - 29 159.372 0.0e+00 8.0e-01 0.007486 - 30 160.784 0.0e+00 6.0e-01 0.008824 - 31 159.277 0.0e+00 6.0e-01 0.010402 - 32 164.664 0.0e+00 1.0e+00 0.012262 - 33 168.117 0.0e+00 1.0e+00 0.012514 - 34 169.466 0.0e+00 8.0e-01 0.009574 - 35 171.116 0.0e+00 4.0e-01 0.008460 - 36 161.176 0.0e+00 6.0e-01 0.008634 - 37 166.950 0.0e+00 1.0e+00 0.008812 - 38 165.522 0.0e+00 8.0e-01 0.010388 - 39 163.881 0.0e+00 8.0e-01 0.012245 - 40 165.813 0.0e+00 6.0e-01 0.009368 - 41 172.858 0.0e+00 6.0e-01 0.009561 - 42 165.203 0.0e+00 8.0e-01 0.008449 - 43 170.088 0.0e+00 8.0e-01 0.011503 - 44 164.721 0.0e+00 8.0e-01 0.008801 - 45 159.681 0.0e+00 8.0e-01 0.010374 - 46 165.540 0.0e+00 8.0e-01 0.010588 - 47 169.582 0.0e+00 4.0e-01 0.009356 - 48 170.860 0.0e+00 1.0e+00 0.016993 - 49 177.454 0.0e+00 8.0e-01 0.009745 - 50 171.890 0.0e+00 4.0e-01 0.009946 - 51 165.886 0.0e+00 6.0e-01 0.008789 - 52 173.465 0.0e+00 6.0e-01 0.008970 - 53 170.794 0.0e+00 8.0e-01 0.012213 - 54 170.756 0.0e+00 4.0e-01 0.008090 - 55 168.973 0.0e+00 6.0e-01 0.012721 - 56 166.499 1.0e+00 6.0e-01 0.011241 - 57 166.758 0.0e+00 8.0e-01 0.009933 - 58 174.514 0.0e+00 6.0e-01 0.011709 - 59 171.855 0.0e+00 6.0e-01 0.011950 - 60 173.692 0.0e+00 6.0e-01 0.012196 - 61 168.576 1.0e+00 1.0e+00 0.009331 - 62 166.416 0.0e+00 8.0e-01 0.008245 - 63 159.746 0.0e+00 1.0e+00 0.017297 - 64 161.607 0.0e+00 8.0e-01 0.011457 - 65 163.900 0.0e+00 1.0e+00 0.011693 - 66 158.364 0.0e+00 8.0e-01 0.008946 - 67 162.022 0.0e+00 6.0e-01 0.009130 - 68 169.017 0.0e+00 1.0e+00 0.010763 - 69 178.510 0.0e+00 2.0e-01 0.010985 - 70 175.933 0.0e+00 8.0e-01 0.011211 - 71 169.211 0.0e+00 8.0e-01 0.011442 - 72 176.649 0.0e+00 8.0e-01 0.013488 - 73 167.428 0.0e+00 6.0e-01 0.008934 - 74 169.717 0.0e+00 4.0e-01 0.006835 - 75 177.425 0.0e+00 8.0e-01 0.009306 - 76 174.727 0.0e+00 8.0e-01 0.010970 - 77 178.367 0.0e+00 6.0e-01 0.008393 - 78 173.809 0.0e+00 6.0e-01 0.009893 - 79 175.943 0.0e+00 8.0e-01 0.013470 - 80 181.158 0.0e+00 6.0e-01 0.007725 - 81 178.612 0.0e+00 8.0e-01 0.009106 - 82 173.321 0.0e+00 1.0e+00 0.012398 - 83 176.062 0.0e+00 8.0e-01 0.009485 - 84 167.543 0.0e+00 6.0e-01 0.011181 - 85 175.101 0.0e+00 6.0e-01 0.009880 - 86 172.307 0.0e+00 8.0e-01 0.008730 - 87 169.521 1.0e+00 1.0e+00 0.007715 - 88 168.497 0.0e+00 1.0e+00 0.010504 - 89 166.175 0.0e+00 8.0e-01 0.006024 - 90 173.977 0.0e+00 1.0e+00 0.012637 - 91 176.837 0.0e+00 6.0e-01 0.009668 - 92 181.901 0.0e+00 4.0e-01 0.008543 - 93 172.883 1.0e+00 8.0e-01 0.008719 - 94 170.368 0.0e+00 8.0e-01 0.013711 - 95 174.394 1.0e+00 1.0e+00 0.010490 - 96 177.244 0.0e+00 6.0e-01 0.008025 - 97 178.995 0.0e+00 6.0e-01 0.012620 - 98 178.069 0.0e+00 8.0e-01 0.009655 - 99 180.800 0.0e+00 6.0e-01 0.008531 - 100 174.505 0.0e+00 8.0e-01 0.011616 - 101 175.351 0.0e+00 8.0e-01 0.011855 - 102 179.470 0.0e+00 8.0e-01 0.012099 - 103 177.976 0.0e+00 2.0e-01 0.008014 - 104 182.093 0.0e+00 1.0e+00 0.010912 - 105 172.053 0.0e+00 1.0e+00 0.006258 - 106 170.423 0.0e+00 6.0e-01 0.007377 - 107 170.464 1.0e+00 8.0e-01 0.010043 - 108 183.559 0.0e+00 1.0e+00 0.010250 - 109 178.303 0.0e+00 6.0e-01 0.007842 - 110 176.921 0.0e+00 8.0e-01 0.006000 - 111 174.411 0.0e+00 6.0e-01 0.008169 - 112 177.309 0.0e+00 8.0e-01 0.006249 - 113 167.766 1.0e+00 6.0e-01 0.011351 - 114 173.204 0.0e+00 8.0e-01 0.010030 - 115 179.276 0.0e+00 4.0e-01 0.011823 - 116 169.726 0.0e+00 1.0e+00 0.012067 - 117 169.920 0.0e+00 8.0e-01 0.009232 - 118 172.653 0.0e+00 8.0e-01 0.010883 - 119 175.321 1.0e+00 6.0e-01 0.007208 - 120 175.183 0.0e+00 4.0e-01 0.008497 - 121 180.561 0.0e+00 8.0e-01 0.007509 - 122 181.311 0.0e+00 8.0e-01 0.005745 - 123 175.901 0.0e+00 1.0e+00 0.012051 - 124 185.062 0.0e+00 8.0e-01 0.012299 - 125 183.583 0.0e+00 1.0e+00 0.009410 - 126 173.059 1.0e+00 8.0e-01 0.008315 - 127 171.756 1.0e+00 6.0e-01 0.006361 - 128 175.898 0.0e+00 6.0e-01 0.010003 - 129 177.441 0.0e+00 1.0e+00 0.008839 - 130 182.386 0.0e+00 6.0e-01 0.010420 - 131 183.284 1.0e+00 6.0e-01 0.007972 - 132 182.193 0.0e+00 6.0e-01 0.007044 - 133 178.569 0.0e+00 6.0e-01 0.008304 - 134 180.102 1.0e+00 1.0e+00 0.009788 - 135 184.320 0.0e+00 1.0e+00 0.007489 - 136 193.059 0.0e+00 1.0e+00 0.010196 - 137 195.626 1.0e+00 8.0e-01 0.012019 - 138 190.064 0.0e+00 8.0e-01 0.010620 - 139 189.116 1.0e+00 8.0e-01 0.008125 - 140 185.394 1.0e+00 6.0e-01 0.008293 - 141 196.754 0.0e+00 1.0e+00 0.009775 - 142 186.629 0.0e+00 2.0e-01 0.008638 - 143 179.249 0.0e+00 8.0e-01 0.010182 - 144 177.936 0.0e+00 1.0e+00 0.008997 - 145 175.642 0.0e+00 8.0e-01 0.009183 - 146 182.668 0.0e+00 4.0e-01 0.009372 - 147 174.765 1.0e+00 4.0e-01 0.007170 - 148 177.649 0.0e+00 6.0e-01 0.009762 - 149 176.405 0.0e+00 4.0e-01 0.009963 - 150 179.383 1.0e+00 4.0e-01 0.008804 - 151 184.767 0.0e+00 6.0e-01 0.007780 - 152 182.435 0.0e+00 6.0e-01 0.007940 - 153 191.981 0.0e+00 8.0e-01 0.009360 - 154 189.905 1.0e+00 1.0e+00 0.009552 - 155 190.726 0.0e+00 8.0e-01 0.011260 - 156 195.099 0.0e+00 1.0e+00 0.013274 - 157 198.185 1.0e+00 8.0e-01 0.013547 - 158 191.782 1.0e+00 0.0e+00 0.007769 - 159 187.238 0.0e+00 8.0e-01 0.009158 - 160 190.440 0.0e+00 6.0e-01 0.009347 - 161 188.311 0.0e+00 6.0e-01 0.008259 - 162 190.902 0.0e+00 6.0e-01 0.008430 - 163 185.705 0.0e+00 4.0e-01 0.007449 - 164 181.333 0.0e+00 6.0e-01 0.010142 - 165 180.513 1.0e+00 1.0e+00 0.007759 - 166 184.605 0.0e+00 8.0e-01 0.007919 - 167 181.282 1.0e+00 8.0e-01 0.010782 - 168 183.953 0.0e+00 6.0e-01 0.007142 - 169 187.976 0.0e+00 6.0e-01 0.009723 - 170 191.848 0.0e+00 8.0e-01 0.009924 - 171 193.848 0.0e+00 1.0e+00 0.013511 - 172 192.649 0.0e+00 6.0e-01 0.011939 - 173 193.614 0.0e+00 4.0e-01 0.009134 - 174 192.403 1.0e+00 6.0e-01 0.008071 - 175 186.923 0.0e+00 4.0e-01 0.007132 - 176 192.365 0.0e+00 8.0e-01 0.008407 - 177 197.227 0.0e+00 8.0e-01 0.011447 - 178 203.187 0.0e+00 4.0e-01 0.011682 - 179 197.782 0.0e+00 8.0e-01 0.007738 - 180 187.833 1.0e+00 8.0e-01 0.009122 - 181 193.912 0.0e+00 8.0e-01 0.008060 - 182 188.663 0.0e+00 1.0e+00 0.009502 - 183 188.342 1.0e+00 4.0e-01 0.008396 - 184 188.795 0.0e+00 6.0e-01 0.009897 - 185 194.310 0.0e+00 4.0e-01 0.008746 - 186 192.717 1.0e+00 8.0e-01 0.013753 - 187 191.832 0.0e+00 4.0e-01 0.009110 - 188 197.498 0.0e+00 8.0e-01 0.010738 - 189 201.995 0.0e+00 1.0e+00 0.012658 - 190 197.710 0.0e+00 1.0e+00 0.008385 - 191 205.262 0.0e+00 6.0e-01 0.009884 - 192 199.665 0.0e+00 2.0e-01 0.008734 - 193 208.372 1.0e+00 6.0e-01 0.008914 - 194 209.445 1.0e+00 8.0e-01 0.010508 - 195 208.505 0.0e+00 8.0e-01 0.010724 - 196 203.697 0.0e+00 6.0e-01 0.009476 - 197 204.809 0.0e+00 8.0e-01 0.011171 - 198 204.319 0.0e+00 8.0e-01 0.011401 - 199 208.969 1.0e+00 4.0e-01 0.015522 - 200 200.086 0.0e+00 8.0e-01 0.013716 - 201 204.236 0.0e+00 6.0e-01 0.010494 - 202 208.312 0.0e+00 2.0e-01 0.008028 - 203 205.038 0.0e+00 8.0e-01 0.012625 - 204 198.966 0.0e+00 8.0e-01 0.009659 - 205 199.780 0.0e+00 6.0e-01 0.009858 - 206 205.617 0.0e+00 8.0e-01 0.013421 - 207 204.040 1.0e+00 8.0e-01 0.006664 - 208 207.401 0.0e+00 1.0e+00 0.010480 - 209 203.947 0.0e+00 1.0e+00 0.009260 - 210 210.390 0.0e+00 4.0e-01 0.006134 - 211 203.241 0.0e+00 2.0e-01 0.007231 - 212 202.869 0.0e+00 8.0e-01 0.009845 - 213 197.385 1.0e+00 4.0e-01 0.005646 - 214 200.787 0.0e+00 8.0e-01 0.007687 - 215 200.136 0.0e+00 2.0e-01 0.005881 - 216 201.036 1.0e+00 1.0e+00 0.006932 - 217 201.337 1.0e+00 1.0e+00 0.010901 - 218 201.909 0.0e+00 6.0e-01 0.006252 - 219 202.871 0.0e+00 4.0e-01 0.006381 - 220 205.048 0.0e+00 6.0e-01 0.008687 - 221 203.206 0.0e+00 6.0e-01 0.007677 - 222 203.927 1.0e+00 6.0e-01 0.009049 - 223 206.990 0.0e+00 8.0e-01 0.010667 - 224 208.856 0.0e+00 4.0e-01 0.008161 - 225 210.793 1.0e+00 6.0e-01 0.009620 - 226 205.935 0.0e+00 0.0e+00 0.009818 - 227 206.084 0.0e+00 8.0e-01 0.010021 - 228 203.764 0.0e+00 6.0e-01 0.008855 - 229 216.459 0.0e+00 6.0e-01 0.010438 - 230 207.503 1.0e+00 6.0e-01 0.007986 - 231 202.295 0.0e+00 6.0e-01 0.009413 - 232 203.335 1.0e+00 6.0e-01 0.007202 - 233 209.133 1.0e+00 6.0e-01 0.008489 - 234 213.060 0.0e+00 1.0e+00 0.011558 - 235 214.467 0.0e+00 6.0e-01 0.007656 - 236 205.045 0.0e+00 0.0e+00 0.005071 - 237 204.761 0.0e+00 2.0e-01 0.009211 - 238 207.939 0.0e+00 4.0e-01 0.008139 - 239 201.539 0.0e+00 8.0e-01 0.011082 - 240 204.214 0.0e+00 8.0e-01 0.008478 - 241 203.463 0.0e+00 4.0e-01 0.008653 - 242 203.016 0.0e+00 0.0e+00 0.008831 - 243 209.313 0.0e+00 8.0e-01 0.009013 - 244 209.819 0.0e+00 6.0e-01 0.007964 - 245 204.906 1.0e+00 4.0e-01 0.009388 - 246 209.594 0.0e+00 4.0e-01 0.009582 - 247 210.335 0.0e+00 8.0e-01 0.009779 - 248 213.297 0.0e+00 6.0e-01 0.008641 - 249 216.751 0.0e+00 4.0e-01 0.010186 - 250 211.783 0.0e+00 4.0e-01 0.009001 - 251 215.484 0.0e+00 4.0e-01 0.006886 - 252 219.181 0.0e+00 8.0e-01 0.008118 - 253 217.257 0.0e+00 8.0e-01 0.008285 - 254 216.117 0.0e+00 6.0e-01 0.008455 - 255 216.106 0.0e+00 2.0e-01 0.006469 - 256 219.987 0.0e+00 1.0e+00 0.007626 - 257 222.947 0.0e+00 4.0e-01 0.006738 - 258 221.112 0.0e+00 8.0e-01 0.009174 - 259 223.664 0.0e+00 8.0e-01 0.006077 - 260 223.992 0.0e+00 1.0e+00 0.011037 - 261 228.945 1.0e+00 4.0e-01 0.006330 - 262 226.490 0.0e+00 6.0e-01 0.006460 - 263 228.962 0.0e+00 2.0e-01 0.007615 - 264 223.991 0.0e+00 6.0e-01 0.007772 - 265 218.634 0.0e+00 6.0e-01 0.005946 - 266 220.307 0.0e+00 6.0e-01 0.008096 - 267 219.308 0.0e+00 4.0e-01 0.007154 - 268 221.455 0.0e+00 8.0e-01 0.007301 - 269 226.036 0.0e+00 1.0e+00 0.009941 - 270 221.629 0.0e+00 4.0e-01 0.006585 - 271 220.981 1.0e+00 4.0e-01 0.006720 - 272 220.121 1.0e+00 4.0e-01 0.009150 - 273 226.119 0.0e+00 1.0e+00 0.010786 - 274 218.897 0.0e+00 6.0e-01 0.008252 - 275 220.138 0.0e+00 1.0e+00 0.008422 - 276 214.790 0.0e+00 6.0e-01 0.006443 - 277 215.496 0.0e+00 6.0e-01 0.006576 - 278 218.136 0.0e+00 6.0e-01 0.005811 - 279 220.088 1.0e+00 4.0e-01 0.006850 - 280 218.769 0.0e+00 0.0e+00 0.009326 - 281 220.113 0.0e+00 1.0e+00 0.012697 - 282 215.305 0.0e+00 6.0e-01 0.009714 - 283 219.481 0.0e+00 8.0e-01 0.007432 - 284 226.209 0.0e+00 2.0e-01 0.007585 - 285 228.087 0.0e+00 4.0e-01 0.007741 - 286 235.480 0.0e+00 4.0e-01 0.009125 - 287 227.164 0.0e+00 4.0e-01 0.009313 - 288 224.791 1.0e+00 8.0e-01 0.010979 - 289 215.757 0.0e+00 4.0e-01 0.008399 - 290 214.847 0.0e+00 1.0e+00 0.008572 - 291 220.206 0.0e+00 6.0e-01 0.011671 - 292 211.892 0.0e+00 8.0e-01 0.006693 - 293 217.738 0.0e+00 6.0e-01 0.010526 - 294 212.707 0.0e+00 4.0e-01 0.006972 - 295 206.181 1.0e+00 8.0e-01 0.009493 - 296 208.083 1.0e+00 8.0e-01 0.008388 - 297 213.780 1.0e+00 6.0e-01 0.007412 - 298 222.840 0.0e+00 1.0e+00 0.010092 - 299 214.123 1.0e+00 8.0e-01 0.010300 - 300 217.693 0.0e+00 8.0e-01 0.009101 - 301 219.578 0.0e+00 1.0e+00 0.010728 - 302 215.666 0.0e+00 6.0e-01 0.009480 - 303 218.998 0.0e+00 8.0e-01 0.009675 - 304 220.226 0.0e+00 6.0e-01 0.008550 - 305 222.323 1.0e+00 8.0e-01 0.011640 - 306 221.416 0.0e+00 1.0e+00 0.008905 - 307 218.223 1.0e+00 8.0e-01 0.010498 - 308 224.811 0.0e+00 6.0e-01 0.014293 - 309 221.475 0.0e+00 1.0e+00 0.016848 - 310 220.295 1.0e+00 6.0e-01 0.009662 - 311 220.776 0.0e+00 4.0e-01 0.007392 - 312 211.511 0.0e+00 6.0e-01 0.011625 - 313 215.090 0.0e+00 1.0e+00 0.010272 - 314 220.149 0.0e+00 6.0e-01 0.007859 - 315 218.134 0.0e+00 1.0e+00 0.009264 - 316 219.394 0.0e+00 6.0e-01 0.007087 - 317 216.735 0.0e+00 8.0e-01 0.006263 - 318 222.824 0.0e+00 4.0e-01 0.007382 - 319 224.603 0.0e+00 6.0e-01 0.008702 - 320 226.799 0.0e+00 1.0e+00 0.011849 - 321 230.733 1.0e+00 8.0e-01 0.012093 - 322 226.180 0.0e+00 6.0e-01 0.008010 - 323 224.414 0.0e+00 6.0e-01 0.007078 - 324 220.028 0.0e+00 6.0e-01 0.008343 - 325 230.144 0.0e+00 1.0e+00 0.013121 - 326 235.101 1.0e+00 6.0e-01 0.010038 - 327 237.102 1.0e+00 8.0e-01 0.007680 - 328 236.717 1.0e+00 4.0e-01 0.005087 - 329 228.661 1.0e+00 8.0e-01 0.010671 - 330 232.343 1.0e+00 4.0e-01 0.009430 - 331 236.028 0.0e+00 1.0e+00 0.008332 - 332 236.942 1.0e+00 1.0e+00 0.007363 - 333 236.948 1.0e+00 8.0e-01 0.006506 - 334 231.984 0.0e+00 8.0e-01 0.007669 - 335 230.414 1.0e+00 6.0e-01 0.007827 - 336 228.770 0.0e+00 6.0e-01 0.005988 - 337 232.417 0.0e+00 8.0e-01 0.008153 - 338 223.628 0.0e+00 1.0e+00 0.009611 - 339 227.307 0.0e+00 6.0e-01 0.008493 - 340 231.087 0.0e+00 1.0e+00 0.013355 - 341 228.021 0.0e+00 8.0e-01 0.010218 - 342 231.036 0.0e+00 1.0e+00 0.010428 - 343 242.331 0.0e+00 4.0e-01 0.007978 - 344 237.033 0.0e+00 8.0e-01 0.010862 - 345 239.879 0.0e+00 4.0e-01 0.009598 - 346 239.868 0.0e+00 4.0e-01 0.007343 - 347 234.694 1.0e+00 6.0e-01 0.009998 - 348 237.252 0.0e+00 8.0e-01 0.010204 - 349 243.731 0.0e+00 8.0e-01 0.010414 - 350 249.174 1.0e+00 1.0e+00 0.010629 - 351 243.015 1.0e+00 8.0e-01 0.007040 - 352 246.255 0.0e+00 8.0e-01 0.009585 - 353 244.004 1.0e+00 8.0e-01 0.007333 - 354 233.162 0.0e+00 4.0e-01 0.007485 - 355 236.075 0.0e+00 4.0e-01 0.007639 - 356 239.861 1.0e+00 4.0e-01 0.007796 - 357 244.492 0.0e+00 6.0e-01 0.009190 - 358 239.084 0.0e+00 1.0e+00 0.009379 - 359 241.691 1.0e+00 8.0e-01 0.009573 - 360 245.867 1.0e+00 1.0e+00 0.007324 - 361 237.458 0.0e+00 8.0e-01 0.008633 - 362 252.237 0.0e+00 4.0e-01 0.006605 - 363 245.365 0.0e+00 6.0e-01 0.006741 - 364 244.945 0.0e+00 6.0e-01 0.006880 - 365 238.102 0.0e+00 4.0e-01 0.009367 - 366 243.180 0.0e+00 6.0e-01 0.009560 - 367 251.924 0.0e+00 6.0e-01 0.007314 - 368 253.439 0.0e+00 4.0e-01 0.007465 - 369 253.396 1.0e+00 6.0e-01 0.008799 - 370 257.970 0.0e+00 6.0e-01 0.008981 - 371 261.071 1.0e+00 8.0e-01 0.005949 - 372 256.318 1.0e+00 8.0e-01 0.006071 - 373 255.676 0.0e+00 6.0e-01 0.007157 - 374 249.874 0.0e+00 6.0e-01 0.005475 - 375 250.746 0.0e+00 4.0e-01 0.007455 - 376 246.887 0.0e+00 1.0e+00 0.007608 - 377 243.658 0.0e+00 6.0e-01 0.006723 - 378 248.560 0.0e+00 8.0e-01 0.009153 - 379 247.412 0.0e+00 6.0e-01 0.007003 - 380 253.421 0.0e+00 8.0e-01 0.009534 - 381 249.074 0.0e+00 8.0e-01 0.009731 - 382 260.240 0.0e+00 4.0e-01 0.006446 - 383 254.969 0.0e+00 8.0e-01 0.010136 - 384 247.101 0.0e+00 4.0e-01 0.005033 - 385 246.375 0.0e+00 6.0e-01 0.006852 - 386 243.302 0.0e+00 6.0e-01 0.008078 - 387 249.613 0.0e+00 4.0e-01 0.007138 - 388 249.801 0.0e+00 6.0e-01 0.008414 - 389 248.792 0.0e+00 8.0e-01 0.007435 - 390 247.630 0.0e+00 6.0e-01 0.010123 - 391 257.252 0.0e+00 6.0e-01 0.005805 - 392 248.133 0.0e+00 8.0e-01 0.010544 - 393 254.207 0.0e+00 4.0e-01 0.006984 - 394 259.771 0.0e+00 1.0e+00 0.009509 - 395 257.093 1.0e+00 4.0e-01 0.007275 - 396 258.354 0.0e+00 6.0e-01 0.007425 - 397 257.276 0.0e+00 4.0e-01 0.006561 - 398 252.730 0.0e+00 8.0e-01 0.011917 - 399 259.424 0.0e+00 8.0e-01 0.009117 - 400 258.863 0.0e+00 6.0e-01 0.006975 - 401 257.788 0.0e+00 4.0e-01 0.007119 - 402 266.545 0.0e+00 8.0e-01 0.011194 - - gp hyperparameters (gp_pak(gp)): - - 0.8942 0.7866 0.9250 0.0264 -0.1950 1.0640 - diff --git a/xunit/log/demo_neuralnetcov.txt b/xunit/log/demo_neuralnetcov.txt deleted file mode 100644 index ce03db18..00000000 --- a/xunit/log/demo_neuralnetcov.txt +++ /dev/null @@ -1,10 +0,0 @@ -Running: demo_neuralnetcov - TolFun reached. Func-count 34. Final f(x)=-49.0877. Elapsed time 1.53 - TolFun reached. Func-count 33. Final f(x)=-92.1279. Elapsed time 2.08 - TolFun reached. Func-count 48. Final f(x)=-123.011. Elapsed time 0.34 - TolFun reached. Func-count 25. Final f(x)=-126.821. Elapsed time 0.23 - - gp hyperparameters (gp_pak(gp)): - - -3.6553 -0.9891 -5.8739 - diff --git a/xunit/log/demo_periodic.txt b/xunit/log/demo_periodic.txt deleted file mode 100644 index a62ff2e0..00000000 --- a/xunit/log/demo_periodic.txt +++ /dev/null @@ -1,47 +0,0 @@ -Running: demo_periodic - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 27 - Function Count : 47 - Minimum found : 592.0151 - Intern Time : 0.029977 seconds - Total Time : 7.7959 seconds - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 594.839 - 1 6 6 594.693 3.3339e-07 - 2 12 12 212.954 0.00863052 - 3 16 16 204.308 0.00634928 - 4 18 18 201.028 0.1 - 5 21 21 200.216 0.0234747 - 6 25 25 198.293 0.025 - 7 28 28 184.818 0.25 - 8 30 30 183.63 0.1 - 9 31 31 182.288 1 - 10 33 33 181.886 0.347089 - 11 35 35 181.51 0.338729 - 12 37 37 181.175 0.42325 - 13 39 39 181.069 0.44846 - 14 42 42 181.065 0.0412808 - 15 44 44 181.06 0.194428 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Line search cannot find an acceptable point along the current search direction - Iterations : 16 - Function Count : 62 - Minimum found : 181.0597 - Intern Time : 0.039376 seconds - Total Time : 15.7249 seconds - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 17 - Function Count : 25 - Minimum found : 245.8374 - Intern Time : 0.013333 seconds - Total Time : 4.0708 seconds - - gp hyperparameters (gp_pak(gp)): - - -1.7857 3.9625 -0.4798 -0.1300 4.6376 -2.9785 -0.4421 0.9111 2.4305 - diff --git a/xunit/log/demo_regression1.txt b/xunit/log/demo_regression1.txt deleted file mode 100644 index 59e2af14..00000000 --- a/xunit/log/demo_regression1.txt +++ /dev/null @@ -1,60 +0,0 @@ -Running: demo_regression1 -GP with Gaussian noise model - -gpcf = - - type: 'gpcf_sexp' - lengthScale: [1.1000 1.2000] - magnSigma2: 0.0400 - p: [1x1 struct] - fh: [1x1 struct] - - -K = - - 0.0400 0.0187 0.0019 - 0.0187 0.0400 0.0187 - 0.0019 0.0187 0.0400 - - -C = - - 0.0800 0.0187 0.0019 - 0.0187 0.0800 0.0187 - 0.0019 0.0187 0.0800 - - MAP estimate for the parameters - TolFun reached. Func-count 33. Final f(x)=41.4902. Elapsed time 0.86 - 'log(sexp.magnSigma2)' - 'log(sexp.lengthScale x 2)' - 'log(gaussian.sigma2)' - - 3.5983 0.8838 0.8323 0.0428 - - Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 341 points - Elapsed time 14.22 seconds - IA-grid: Total elapsed time 30.19 seconds - MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 44.827 sls - 40 42.705 sls - 60 42.124 sls - 80 44.214 sls - 100 42.966 sls - 120 43.217 sls - 140 41.865 sls - 160 44.764 sls - 180 42.241 sls - 200 41.686 sls - 220 42.983 sls -Done - - gp hyperparameters (gp_pak(gp)): - - 1.2805 -0.1235 -0.1836 -3.1521 - diff --git a/xunit/log/demo_regression_additive1.txt b/xunit/log/demo_regression_additive1.txt deleted file mode 100644 index efddd92e..00000000 --- a/xunit/log/demo_regression_additive1.txt +++ /dev/null @@ -1,61 +0,0 @@ -Running: demo_regression_additive1 - -gp = - - type: 'FULL' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - fh: [1x1 struct] - - TolX reached. Func-count 56. Final f(x)=600.635. Elapsed time 8.77 - -gp_fic = - - type: 'FIC' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - X_u: [24x1 double] - nind: 24 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 45. Final f(x)=1257.57. Elapsed time 1.90 - -gp_pic = - - type: 'PIC' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-06 - X_u: [24x1 double] - nind: 24 - p: [1x1 struct] - tr_index: [] - fh: [1x1 struct] - - TolX reached. Func-count 45. Final f(x)=636.321. Elapsed time 4.28 - TolFun reached. Func-count 3. Final f(x)=636.321. Elapsed time 0.28 - -gp_csfic = - - type: 'CS+FIC' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - X_u: [24x1 double] - nind: 24 - p: [1x1 struct] - fh: [1x1 struct] - - TolX reached. Func-count 41. Final f(x)=600.526. Elapsed time 3.36 - - gp hyperparameters (gp_pak(gp)): - - 5.7476 4.8844 1.6333 2.0965 -3.5407 - diff --git a/xunit/log/demo_regression_additive2.txt b/xunit/log/demo_regression_additive2.txt deleted file mode 100644 index 01509102..00000000 --- a/xunit/log/demo_regression_additive2.txt +++ /dev/null @@ -1,20 +0,0 @@ -Running: demo_regression_additive2 -Constant + linear covariance function - TolFun reached. Func-count 31. Final f(x)=103.189. Elapsed time 0.84 -Constant + squared exponential covariance function -(w.r.t. the first input dimension) + linear (w.r.t. -the second input dimension) - TolFun reached. Func-count 33. Final f(x)=54.0344. Elapsed time 1.00 -Additive squared exponential covariance function - TolFun reached. Func-count 35. Final f(x)=26.9207. Elapsed time 1.00 -Squared exponential covariance function - TolFun reached. Func-count 29. Final f(x)=30.6121. Elapsed time 0.77 -Additive neural network covariance function - TolFun reached. Func-count 33. Final f(x)=24.4157. Elapsed time 1.63 -Neural network covariance function - TolFun reached. Func-count 54. Final f(x)=24.2467. Elapsed time 2.00 - - gp hyperparameters (gp_pak(gp)): - - -3.1648 1.4213 0.5037 -2.9099 - diff --git a/xunit/log/demo_regression_hier.txt b/xunit/log/demo_regression_hier.txt deleted file mode 100644 index cce94e7b..00000000 --- a/xunit/log/demo_regression_hier.txt +++ /dev/null @@ -1,51 +0,0 @@ -Running: demo_regression_hier -1) Linear model with intercept and slope wrt time - TolFun reached. Func-count 19. Final f(x)=19.4149. Elapsed time 0.19 -2) Linear model with hierarchical intercept - TolFun reached. Func-count 25. Final f(x)=-37.2922. Elapsed time 0.42 -3) Linear model with hierarchical intercept and slope - TolFun reached. Func-count 33. Final f(x)=-46.4251. Elapsed time 0.65 -4) Nonlinear model with hierarchical intercept - TolFun reached. Func-count 40. Final f(x)=-45.8041. Elapsed time 0.70 -5) Non-linear hierarchical model 1 with MAP - TolFun reached. Func-count 88. Final f(x)=-77.8152. Elapsed time 2.33 -6) Non-linear hierarchical model 1 with IA - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 147 points for 9 parameters - IA-CCD: autoscaling in 18 directions - IA-CCD: scaling minmax [0.55 1.61] - IA-CCD: evaluating density at 147 points - Elapsed time 1.62 seconds - IA-CCD: Total elapsed time 2.92 seconds -7) Non-linear hierarchical model 2 with MAP - TolFun reached. Func-count 39. Final f(x)=-80.2512. Elapsed time 0.79 -8) Non-linear hierarchical model 2 with IA - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 45 points for 6 parameters - IA-CCD: autoscaling in 12 directions - IA-CCD: scaling minmax [0.82 1.37] - IA-CCD: evaluating density at 45 points -9) Non-linear hierarchical model 3 with IA - TolFun reached. Func-count 43. Final f(x)=-64.4989. Elapsed time 1.27 - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 79 points for 7 parameters - IA-CCD: autoscaling in 14 directions - IA-CCD: scaling minmax [0.63 1.40] - IA-CCD: evaluating density at 79 points - IA-CCD: Total elapsed time 1.72 seconds -10) Missing data example - TolFun reached. Func-count 49. Final f(x)=-4.10284. Elapsed time 0.87 - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 79 points for 7 parameters - IA-CCD: autoscaling in 14 directions - IA-CCD: scaling minmax [0.50 1.44] - IA-CCD: evaluating density at 79 points - - gp hyperparameters (gp_pak(gp)): - - -0.1987 -3.4072 1.0782 0.4515 -3.4082 -4.7715 -4.8805 - diff --git a/xunit/log/demo_regression_meanf.txt b/xunit/log/demo_regression_meanf.txt deleted file mode 100644 index 98998534..00000000 --- a/xunit/log/demo_regression_meanf.txt +++ /dev/null @@ -1,15 +0,0 @@ -Running: demo_regression_meanf -Checking gradient ... - - analytic diffs delta - - -6.7439 -6.7439 0.0000 - 2.1897 2.1897 -0.0000 - -2.3321 -2.3321 0.0000 - - TolFun reached. Func-count 19. Final f(x)=16.4578. Elapsed time 0.14 - - gp hyperparameters (gp_pak(gp)): - - 1.6749 -0.3565 -1.4619 - diff --git a/xunit/log/demo_regression_ppcs.txt b/xunit/log/demo_regression_ppcs.txt deleted file mode 100644 index f84e05ac..00000000 --- a/xunit/log/demo_regression_ppcs.txt +++ /dev/null @@ -1,25 +0,0 @@ -Running: demo_regression_ppcs - Iteration Func-count f(x) Lambda - 0 1 15150 - 1 3 14366.9 10 - 2 5 14366.9 5 - 3 6 14366.9 20 - 4 7 14366.9 80 - 5 8 13860.1 320 - 6 10 13859.9 320 - 7 12 13449.3 160 - 8 14 13274.3 160 - 9 16 13257.4 80 - 10 18 13254 40 - 11 20 13253.7 20 - 12 22 13253.6 10 - 13 24 13253.4 5 - 14 26 13253.4 2.5 - 15 28 13253.4 1.25 - TolX reached. Func-count 28. Final f(x)=13253.4. Elapsed time 49685.06 -Proportion of non-zeros is 0.0475 - - gp hyperparameters (gp_pak(gp)): - - 2.7497 1.1142 1.4814 1.3157 - diff --git a/xunit/log/demo_regression_robust.txt b/xunit/log/demo_regression_robust.txt deleted file mode 100644 index 0b4c8c4a..00000000 --- a/xunit/log/demo_regression_robust.txt +++ /dev/null @@ -1,143 +0,0 @@ -Running: demo_regression_robust - -gp = - - type: 'FULL' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - fh: [1x1 struct] - -Gaussian noise model and MAP estimate for parameters - TolFun reached. Func-count 23. Final f(x)=27.0141. Elapsed time 0.16 - -S1 = - -length-scale: 1.066, magnSigma2: 5.420 - - -Scale mixture Gaussian (~=Student-t) noise model -using MCMC integration over the latent values and parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 -38.105 sls - 40 -51.018 sls - 60 -54.371 sls - 80 -54.989 sls - 100 -61.803 sls - 120 -65.385 sls - 140 -50.697 sls - 160 -64.071 sls - 180 -47.628 sls - 200 -53.372 sls - 220 -55.422 sls - 240 -55.152 sls - 260 -59.509 sls - 280 -45.947 sls - 300 -53.208 sls - -S2 = - -length-scale: 1.051, magnSigma2: 7.908 - - -Student-t noise model using Laplace integration over the -latent values and MAP estimate for the parameters - TolFun reached. Func-count 27. Final f(x)=-24.079. Elapsed time 1.10 - -S3 = - -length-scale: 1.047, magnSigma2: 3.093 - - -Student-t noise model using EP integration over the -latent values and MAP estimate for parameters - Iteration Func-count f(x) Lambda - 0 1 5.0753 - 1 3 -0.390577 10 - 2 5 -20.2665 5 - 3 7 -21.8841 2.5 - 4 9 -23.5458 1.25 - 5 11 -23.9733 0.625 - 6 13 -24.8947 0.312 - 7 15 -25.1996 0.156 - 8 17 -25.3812 0.0781 - 9 19 -25.4287 0.0391 - 10 21 -25.5222 0.0195 - 11 23 -25.5357 0.00977 - 12 25 -25.5999 0.00488 - 13 27 -25.6188 0.00244 - 14 29 -25.6517 0.00122 - 15 31 -25.6525 0.00061 - TolFun reached. Func-count 31. Final f(x)=-25.6525. Elapsed time 18.06 - -S4 = - -length-scale: 1.040, magnSigma2: 2.919 - - -Student-t noise model with nu= 4 and using MCMC integration -over the latent values and parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -164.266 - 40 -187.416 - 60 -200.662 - 80 -219.012 - 100 -236.264 - 120 -241.545 - 140 -258.494 - 160 -264.050 - 180 -264.817 - 200 -271.898 - 220 -270.786 - 240 -277.278 - 260 -275.685 - 280 -289.677 - 300 -284.182 - 320 -293.980 - 340 -291.834 - 360 -294.377 - 380 -307.009 - 400 -303.441 - -S5 = - -length-scale: 0.897, magnSigma2: 2.133 - - -Student-t noise model with nu=4 using Laplace integration over -the latent values and MAP estimate for the parameters - TolFun reached. Func-count 19. Final f(x)=-14.9305. Elapsed time 0.67 - -S6 = - -length-scale: 1.040, magnSigma2: 3.095 - - -Student-t noise model with nu=4 using EP integration over -the latent values and MAP estimate for parameters - Iteration Func-count f(x) Lambda - 0 1 5.0753 - 1 3 -0.138385 10 - 2 5 -13.6439 5 - 3 7 -14.0719 2.5 - 4 9 -14.1846 1.25 - 5 11 -14.8554 0.625 - 6 13 -15.1515 0.312 - 7 15 -15.1629 0.156 - 8 17 -15.1788 0.0781 - 9 19 -15.1794 0.0391 - TolFun reached. Func-count 19. Final f(x)=-15.1794. Elapsed time 8.06 - -S7 = - -length-scale: 1.033, magnSigma2: 2.966 - - - - gp hyperparameters (gp_pak(gp)): - - 1.0871 0.0321 -4.6357 - diff --git a/xunit/log/demo_regression_sparse1.txt b/xunit/log/demo_regression_sparse1.txt deleted file mode 100644 index 6afc5719..00000000 --- a/xunit/log/demo_regression_sparse1.txt +++ /dev/null @@ -1,74 +0,0 @@ -Running: demo_regression_sparse1 - -gp = - - type: 'FULL' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-08 - fh: [1x1 struct] - - TolFun reached. Func-count 27. Final f(x)=52.5982. Elapsed time 12.12 - -gp_fic = - - type: 'FIC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 53. Final f(x)=46.3892. Elapsed time 0.75 - -gp_pic = - - type: 'PIC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - tr_index: {1x16 cell} - fh: [1x1 struct] - - TolFun reached. Func-count 29. Final f(x)=45.3107. Elapsed time 1.14 - -gp_var = - - type: 'VAR' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood+inducing' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 40. Final f(x)=51.8727. Elapsed time 5.13 - -gp_dtc = - - type: 'DTC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 65. Final f(x)=46.3104. Elapsed time 0.86 - - gp hyperparameters (gp_pak(gp)): - - 0.4291 1.1773 1.0540 -3.2363 - diff --git a/xunit/log/demo_regression_sparse2.txt b/xunit/log/demo_regression_sparse2.txt deleted file mode 100644 index 65b6ac00..00000000 --- a/xunit/log/demo_regression_sparse2.txt +++ /dev/null @@ -1,14 +0,0 @@ -Running: demo_regression_sparse2 -Full GP - TolFun reached. Func-count 35. Final f(x)=51.3654. Elapsed time 0.24 -FIC GP - TolX reached. Func-count 153. Final f(x)=47.1448. Elapsed time 1.79 -VAR GP - TolFun reached. Func-count 70. Final f(x)=65.8332. Elapsed time 0.82 -DTC GP - TolX reached. Func-count 15. Final f(x)=48.4383. Elapsed time 0.17 - - gp hyperparameters (gp_pak(gp)): - - 1.6935 -0.6707 -2.9350 - diff --git a/xunit/log/demo_spatial1.txt b/xunit/log/demo_spatial1.txt deleted file mode 100644 index 7cc27e2f..00000000 --- a/xunit/log/demo_spatial1.txt +++ /dev/null @@ -1,542 +0,0 @@ -Running: demo_spatial1 - TolFun reached. Func-count 35. Final f(x)= 2843. Elapsed time 1.84 - -S1 = - -MAP: length-scale: 14.3, magnSigma: 0.153 - - - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 12 points - IA-grid: Total elapsed time 2.73 seconds - -S2 = - -IA-GRID: 90% CI - length-scale: [8.8,33.1], magnSigma: [0.099,0.325] - - - cycle etr hrej lrej - 2 784.636 0.0e+00 1.1e-01 0.365678 - -h1 = - - 986.2429 - 987.2424 - - mean hmcrej: 0.00 latrej: 0.04 - mean hmcrej: 0.00 latrej: 0.03 - mean hmcrej: 0.04 latrej: 0.02 - mean hmcrej: 0.03 latrej: 0.02 - mean hmcrej: 0.03 latrej: 0.04 - mean hmcrej: 0.03 latrej: 0.06 - mean hmcrej: 0.02 latrej: 0.06 - mean hmcrej: 0.02 latrej: 0.05 - mean hmcrej: 0.02 latrej: 0.05 - mean hmcrej: 0.03 latrej: 0.04 - mean hmcrej: 0.03 latrej: 0.05 - mean hmcrej: 0.03 latrej: 0.05 - mean hmcrej: 0.04 latrej: 0.05 - mean hmcrej: 0.04 latrej: 0.04 - mean hmcrej: 0.05 latrej: 0.04 - mean hmcrej: 0.04 latrej: 0.05 - mean hmcrej: 0.06 latrej: 0.05 - 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mean hmcrej: 0.07 latrej: 0.06 - mean hmcrej: 0.07 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - -S3 = - -MCMC: 90% CI - length-scale: [7.1,30.2], magnSigma: [0.076,0.351] - - -MAP: length-scale: 14.3, magnSigma: 0.153 - -IA-GRID: 90% CI - length-scale: [8.8,33.1], magnSigma: [0.099,0.325] - -MCMC: 90% CI - length-scale: [7.1,30.2], magnSigma: [0.076,0.351] - - - gp hyperparameters (gp_pak(gp)): - - -4.8189 3.4074 - diff --git a/xunit/log/demo_spatial2.txt b/xunit/log/demo_spatial2.txt deleted file mode 100644 index 91c38a5b..00000000 --- a/xunit/log/demo_spatial2.txt +++ /dev/null @@ -1,28 +0,0 @@ -Running: demo_spatial2 -GP with negative-binomial observation model, Laplace -integration over the latent values and MAP estimate -for the parameters - Iteration Func-count f(x) Lambda - 0 1 5285.4 - 1 3 5285.36 10 - 2 4 5240.14 40 - 3 6 5220.82 40 - 4 8 5216.28 20 - 5 10 5215.93 10 - 6 12 5215.82 5 - 7 14 5215.81 2.5 - TolX reached. Func-count 14. Final f(x)=5215.81. Elapsed time 2241.59 -Proportion of non-zeros is 0.0715 -GP with negative-binomial observation model, EP -integration over the latent values and MAP estimate -for the parameters - Iteration Func-count f(x) Lambda - 0 1 5215.8 - 1 3 5215.8 10 - TolX reached. Func-count 3. Final f(x)=5215.8. Elapsed time 1216.06 -Proportion of non-zeros is 0.0715 - - gp hyperparameters (gp_pak(gp)): - - -2.1786 2.2593 2.8956 - diff --git a/xunit/log/demo_survival_coxph.txt b/xunit/log/demo_survival_coxph.txt deleted file mode 100644 index 5d4160e8..00000000 --- a/xunit/log/demo_survival_coxph.txt +++ /dev/null @@ -1,43 +0,0 @@ -Running: demo_survival_coxph - Iteration Func-count f(x) Lambda - 0 1 -1388 - 1 3 -1420.42 10 - 2 5 -1429.98 5 - 3 7 -1432.25 2.5 - 4 9 -1433.34 1.25 - 5 11 -1433.78 1.25 - 6 13 -1433.83 0.625 - 7 15 -1433.85 0.312 - 8 17 -1433.85 0.156 - TolFun reached. Func-count 17. Final f(x)=-1433.85. Elapsed time 55.53 - Iteration Func-count f(x) Lambda - 0 1 -2850.3 - 1 3 -2869.66 10 - 2 5 -3051.69 34.3 - 3 7 -3093.55 17.2 - 4 9 -3123.91 8.58 - 5 11 -3126.45 4.29 - 6 13 -3154.01 17.2 - 7 15 -3156 8.58 - 8 17 -3163.33 4.29 - 9 19 -3165.61 2.14 - 10 21 -3166.29 2.14 - 11 23 -3168.02 1.07 - 12 25 -3168.84 0.536 - 13 27 -3169.92 0.536 - 14 29 -3170.04 0.268 - 15 31 -3170.04 0.381 - 16 32 -3170.4 1.52 - 17 34 -3172.66 6.1 - 18 36 -3174.05 3.05 - 19 38 -3174.07 1.52 - Iteration Func-count f(x) Lambda - 20 40 -3174.1 0.762 - 21 42 -3174.14 0.381 - 22 44 -3174.15 0.191 - TolFun reached. Func-count 44. Final f(x)=-3174.15. Elapsed time 135.67 - - gp hyperparameters (gp_pak(gp)): - - 1.5871 -0.7967 0.8074 0.6256 -3.6855 -5.9779 - diff --git a/xunit/log/demo_survival_weibull.txt b/xunit/log/demo_survival_weibull.txt deleted file mode 100644 index 0e48f18e..00000000 --- a/xunit/log/demo_survival_weibull.txt +++ /dev/null @@ -1,26 +0,0 @@ -Running: demo_survival_weibull - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 -980.812 - 1 2 2 -1305.26 0.000680212 - 2 6 6 -1428.27 0.0625 - 3 8 8 -1447.95 0.5 - 4 10 10 -1458.77 0.359102 - 5 12 12 -1460.06 0.365497 - 6 13 13 -1461.48 1 - 7 14 14 -1462.2 1 - 8 16 16 -1462.43 0.428259 - 9 17 17 -1462.51 1 - 10 18 18 -1462.52 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 11 - Function Count : 19 - Minimum found : -1462.5191 - Intern Time : 0.016215 seconds - Total Time : 22.6051 seconds - - gp hyperparameters (gp_pak(gp)): - - 0.7765 -0.3490 -1.2271 0.1221 -1.5384 -0.5387 - diff --git a/xunit/log/demo_zinegbin.txt b/xunit/log/demo_zinegbin.txt deleted file mode 100644 index 0fe9c8dc..00000000 --- a/xunit/log/demo_zinegbin.txt +++ /dev/null @@ -1,30 +0,0 @@ -Running: demo_zinegbin - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 2964.84 - 1 2 2 2908.76 0.0146046 - 2 4 4 2900.33 0.1 - 3 5 5 2885.72 1 - 4 7 7 2864.1 0.295664 - 5 8 8 2848.64 1 - 6 9 9 2842.71 1 - 7 10 10 2842.05 1 - 8 12 12 2841.84 0.26605 - 9 14 14 2841.81 0.143904 - 10 15 15 2841.77 1 - 11 16 16 2841.71 1 - 12 17 17 2841.7 1 - 13 18 18 2841.69 1 - 14 19 19 2841.69 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 15 - Function Count : 20 - Minimum found : 2841.6901 - Intern Time : 0.018068 seconds - Total Time : 111.7079 seconds - - gp hyperparameters (gp_pak(gp)): - - 4.0478 1.3173 -2.6332 1.4244 - From 4ec3da28b32f16789ec2fa14d53df3c6c0b0a24d Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 6 Mar 2014 14:04:35 +0200 Subject: [PATCH 34/64] Changed nested functions to subfunctions (octave GPstuff works now with octave 3.8.0). Fixed c-source files for octave. --- OCTAVEGPSTUFF_README | 2 +- diag/diag_install.m | 8 +++ dist/dist_install.m | 26 ++++++++ gp/demo_multiclass.m | 6 +- gp/gp_e.m | 2 +- gp/gp_install.m | 2 +- gp/gp_set.m | 2 +- gp/gpep_e.m | 5 +- gp/gpla_e.m | 8 ++- gp/lik_coxph.m | 5 +- gp/lik_gaussianbl.m | 4 +- gp/lik_inputdependentnoise.m | 2 +- gp/lik_lgpc.m | 6 +- gp/lik_loggaussian.m | 4 +- gp/lik_logit.m | 6 +- gp/lik_loglogistic.m | 4 +- gp/lik_multinomprobit.m | 2 +- gp/lik_negbin.m | 7 +- gp/lik_negbinztr.m | 47 ++++++------- gp/lik_poisson.m | 2 +- gp/lik_qgp.m | 7 +- gp/lik_t.m | 104 ++++++++++++++--------------- gp/lik_weibull.m | 6 +- gp/lik_zinegbin.m | 2 +- gp/linuxCsource/dist_euclidean.c | 5 ++ gp/linuxCsource/ldlrowmodify.c | 2 + gp/linuxCsource/trcov.c | 2 + gp/scaled_hmc.m | 4 +- matlab_install.m | 109 ------------------------------- mc/mc_install.m | 16 +++++ 30 files changed, 168 insertions(+), 239 deletions(-) delete mode 100644 matlab_install.m diff --git a/OCTAVEGPSTUFF_README b/OCTAVEGPSTUFF_README index 4c37d5d2..8d7a950e 100644 --- a/OCTAVEGPSTUFF_README +++ b/OCTAVEGPSTUFF_README @@ -1,3 +1,3 @@ This is Octave version of the GPstuff toolbox for matlab. In order to use Octave GPstuff, the following packages are needed: Statistics and gsl. Octave version of GPstuff doesn't have all the functionalities of the matlab-version at the moment, namely -piecewise-polynomial covariance functions and some multilatent models. Octave GPstuff requires Octave version 3.6.3 or later. +piecewise-polynomial covariance functions. Octave GPstuff requires Octave version 3.6.3 or later. diff --git a/diag/diag_install.m b/diag/diag_install.m index 858e0bd5..7f1b191f 100644 --- a/diag/diag_install.m +++ b/diag/diag_install.m @@ -1,7 +1,15 @@ function diag_install if ispc % A windows version of Matlab + if ~exist('OCTAVE_VERSION','builtin') mex -O -output bbprctile winCsource\bbprctile.c + else + mex --output bbprctile.mex winCsource\bbprctile.c + end else + if ~exist('OCTAVE_VERSION','builtin') mex -O -output bbprctile linuxCsource/bbprctile.c + else + mex --output bbprctile.mex linuxCsource/bbprctile.c + end end \ No newline at end of file diff --git a/dist/dist_install.m b/dist/dist_install.m index 6052b4d2..175e9bc4 100644 --- a/dist/dist_install.m +++ b/dist/dist_install.m @@ -1,6 +1,7 @@ function dist_install if ispc % A windows version of Matlab + if ~exist('OCTAVE_VERSION','builtin') mex -O -output cond_invgam_invgam1 winCsource\cond_invgam_invgam1.c winCsource\ars.c winCsource\rand.c mex -O -output digamma1 winCsource\digamma1.c mex -O -output dirrand winCsource\dirrand.c @@ -11,7 +12,20 @@ mex -O -output invgamrand1 winCsource\invgamrand1.c winCsource\rand.c mex -O -output tanh_f winCsource\tanh_f.c mex -O -output trand winCsource\trand.c winCsource\rand.c + else + mex --output cond_invgam_invgam1.mex winCsource\cond_invgam_invgam1.c winCsource\ars.c winCsource\rand.c + mex --output digamma1.mex winCsource\digamma1.c + mex --output dirrand.mex winCsource\dirrand.c + mex --output exprand.mex winCsource\exprand.c + mex --output gamrand.mex winCsource\gamrand.c winCsource\rand.c + mex --output gamrand1.mex winCsource\gamrand1.c winCsource\rand.c + mex --output invgamrand.mex winCsource\invgamrand.c winCsource\rand.c + mex --output invgamrand1.mex winCsource\invgamrand1.c winCsource\rand.c + mex --output tanh_f.mex winCsource\tanh_f.c + mex --output trand.mex winCsource\trand.c winCsource\rand.c + end else + if ~exist('OCTAVE_VERSION','builtin') mex -O -output cond_invgam_invgam1 linuxCsource/cond_invgam_invgam1.c linuxCsource/ars.c linuxCsource/rand.c; mex -O -output digamma1 linuxCsource/digamma1.c; mex -O -output dirrand linuxCsource/dirrand.c; @@ -22,5 +36,17 @@ mex -O -output invgamrand1 linuxCsource/invgamrand1.c linuxCsource/rand.c; mex -O -output tanh_f linuxCsource/tanh_f.c; mex -O -output trand linuxCsource/trand.c linuxCsource/rand.c; + else + mex --output cond_invgam_invgam1.mex linuxCsource/cond_invgam_invgam1.c linuxCsource/ars.c linuxCsource/rand.c; + mex --output digamma1.mex linuxCsource/digamma1.c; + mex --output dirrand.mex linuxCsource/dirrand.c; + mex --output exprand.mex linuxCsource/exprand.c; + mex --output gamrand.mex linuxCsource/gamrand.c linuxCsource/rand.c; + mex --output gamrand1.mex linuxCsource/gamrand1.c linuxCsource/rand.c; + mex --output invgamrand.mex linuxCsource/invgamrand.c linuxCsource/rand.c; + mex --output invgamrand1.mex linuxCsource/invgamrand1.c linuxCsource/rand.c; + mex --output tanh_f.mex linuxCsource/tanh_f.c; + mex --output trand.mex linuxCsource/trand.c linuxCsource/rand.c; + end end \ No newline at end of file diff --git a/gp/demo_multiclass.m b/gp/demo_multiclass.m index 417eec19..a003a289 100644 --- a/gp/demo_multiclass.m +++ b/gp/demo_multiclass.m @@ -111,9 +111,9 @@ plot(x(y(:,2)==1,1),x(y(:,2)==1,2),'x', 'linewidth', 2); plot(x(y(:,3)==1,1),x(y(:,3)==1,2),'kd', 'linewidth', 2); axis([-0.4 1.4 -0.4 1.4]) -contour(xtg1, xtg2, reshape(exp(pg(:,1)),30,30),'r', 'linewidth', 2) -contour(xtg1, xtg2, reshape(exp(pg(:,2)),30,30),'b', 'linewidth', 2) -contour(xtg1, xtg2, reshape(exp(pg(:,3)),30,30),'k', 'linewidth', 2) +contour(xtg1, xtg2, reshape(exp(pg(:,1)),30,30),0.1:0.1:1,'r', 'linewidth', 2) +contour(xtg1, xtg2, reshape(exp(pg(:,2)),30,30),0.1:0.1:1,'b', 'linewidth', 2) +contour(xtg1, xtg2, reshape(exp(pg(:,3)),30,30),0.1:0.1:1,'k', 'linewidth', 2) % MCMC approach diff --git a/gp/gp_e.m b/gp/gp_e.m index 53ff4cd7..4ae037b1 100755 --- a/gp/gp_e.m +++ b/gp/gp_e.m @@ -530,6 +530,7 @@ end e = edata + eprior; +end function [edata, eprior, e] = set_output_for_notpositivedefinite() %instead of stopping to chol error, return NaN @@ -538,5 +539,4 @@ e = NaN; end -end diff --git a/gp/gp_install.m b/gp/gp_install.m index 89fc5b94..596d5d25 100644 --- a/gp/gp_install.m +++ b/gp/gp_install.m @@ -1,6 +1,6 @@ function gp_install(suiteSparse) % Matlab function to compile all the c-files to mex in the GPstuff/gp -% folder. The function is called from GPstuff/matlab_install.m but +% folder. The function is called from GPstuff/gpstuff_install.m but % can be run separately also. % % If you want to use GPstuff without compactly supported (CS) diff --git a/gp/gp_set.m b/gp/gp_set.m index 82c72a87..90e97f61 100644 --- a/gp/gp_set.m +++ b/gp/gp_set.m @@ -349,7 +349,7 @@ error('No latent method needed with a Gaussian likelihood') end if isfield(gp,'latent_method') - gp=rmfield(gp,'latent_method') + gp=rmfield(gp,'latent_method'); end gp.fh.e=@gp_e; gp.fh.g=@gp_g; diff --git a/gp/gpep_e.m b/gp/gpep_e.m index 27190ccd..f4599adf 100644 --- a/gp/gpep_e.m +++ b/gp/gpep_e.m @@ -94,6 +94,7 @@ % call ep_algorithm using the function handle to the nested function % this way each gp has its own peristent memory for EP [e, edata, eprior, param] = gp.fh.ne(w, gp, x, y, z); +end end function [e, edata, eprior, param] = ep_algorithm(w, gp, x, y, z) @@ -2556,7 +2557,8 @@ end end - end + end + function [e, edata, eprior, param] = set_output_for_notpositivedefinite() % Instead of stopping to chol error, return NaN @@ -2580,7 +2582,6 @@ % ch.w = NaN; end -end function [m_q,S_q,lnZ_q,L1,L2]=evaluate_q(nu_q,tau_q,K,display) diff --git a/gp/gpla_e.m b/gp/gpla_e.m index 486884bd..1189d04e 100644 --- a/gp/gpla_e.m +++ b/gp/gpla_e.m @@ -99,6 +99,7 @@ [e, edata, eprior, param] = gp.fh.ne(w, gp, x, y, z); end +end function [e, edata, eprior, param] = laplace_algorithm(w, gp, x, y, z) if 0%~isempty(ch) && all(size(w)==size(ch.w)) && all(abs(w-ch.w)<1e-8) && ... @@ -2070,7 +2071,9 @@ % ============================================================== % Begin of the nested functions % ============================================================== -% +% +end + function [e, g, h] = egh(f, varargin) ikf = iKf(f'); e = 0.5*f*ikf - gp.lik.fh.ll(gp.lik, y, f', z); @@ -2090,7 +2093,7 @@ ikf = ldlsolve(VD,f) - L*(L'*f); end end -end + function [edata,e,eprior,param] = set_output_for_notpositivedefinite() % Instead of stopping to chol error, return NaN edata=NaN; @@ -2111,4 +2114,3 @@ % ch.w = NaN; end -end diff --git a/gp/lik_coxph.m b/gp/lik_coxph.m index 7a932ac0..7b58862f 100644 --- a/gp/lik_coxph.m +++ b/gp/lik_coxph.m @@ -100,7 +100,7 @@ lik.fh.recappend = @lik_coxph_recappend; lik.fh.predcdf= @lik_coxph_predcdf; end - +end function [w,s] = lik_coxph_pak(lik) %LIK_COXPH_PAK Combine likelihood parameters into one vector. % @@ -1384,6 +1384,3 @@ end end - -end - diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m index fd1f0b04..66ffa555 100644 --- a/gp/lik_gaussianbl.m +++ b/gp/lik_gaussianbl.m @@ -91,7 +91,7 @@ lik.fh.trvar = @lik_gaussianbl_trvar; lik.fh.recappend = @lik_gaussianbl_recappend; end - +end function [w s] = lik_gaussianbl_pak(lik) %LIK_GAUSSIANBL_PAK Combine likelihood parameters into one vector. % @@ -366,4 +366,4 @@ end end -end + diff --git a/gp/lik_inputdependentnoise.m b/gp/lik_inputdependentnoise.m index ba2c6eb2..be877d17 100644 --- a/gp/lik_inputdependentnoise.m +++ b/gp/lik_inputdependentnoise.m @@ -87,6 +87,7 @@ lik.fh.predprcty = @lik_inputdependentnoise_predprcty; lik.fh.recappend = @lik_inputdependentnoise_recappend; end +end function [w,s] = lik_inputdependentnoise_pak(lik) %LIK_INPUTDEPENDENTNOISE_PAK Combine likelihood parameters into one vector. @@ -507,4 +508,3 @@ reclik.p.sigma2 = feval(lik.p.sigma2.fh.recappend, reclik.p.sigma2, ri, lik.p.sigma2); end end -end diff --git a/gp/lik_lgpc.m b/gp/lik_lgpc.m index afaf7281..643f3d59 100644 --- a/gp/lik_lgpc.m +++ b/gp/lik_lgpc.m @@ -421,7 +421,7 @@ 'even after looking hard!']) end end - +end function integrand = lgpc_norm(f, ldconst, avgE, yy, myy_i, sigm2_i) % LGPC * Gaussian mu = avgE.*exp(f); @@ -455,9 +455,7 @@ g2 = -mu... -1/sigm2_i; end - -end - + function mu = lik_lgpc_invlink(lik, f, z) %LIK_LGPC_INVLINK Returns values of inverse link function % diff --git a/gp/lik_loggaussian.m b/gp/lik_loggaussian.m index bb532e0f..c4786ce8 100644 --- a/gp/lik_loggaussian.m +++ b/gp/lik_loggaussian.m @@ -500,6 +500,7 @@ [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f, yy, yc, s2).*tf(f)./m_0, minf, maxf); g_i = g_i.*s2; +end function g = deriv(f, yy, yc, s2) if yc==0 @@ -511,7 +512,6 @@ g = -1./(2.*s2) + r.^2./(2.*s2^2); end end -end function [lpy, Ey, Vary] = lik_loggaussian_predy(lik, Ef, Varf, yt, zt) %LIK_LOGGAUSSIAN_PREDY Returns the predictive mean, variance and density of y @@ -674,6 +674,7 @@ end end +end function integrand = loggaussian_norm(f, ldconst, yy, yc, s2, myy_i, sigm2_i) % loggaussian * Gaussian if yc @@ -733,7 +734,6 @@ -1/sigm2_i; end -end function cdf = lik_loggaussian_predcdf(lik, Ef, Varf, yt) %LIK_LOGGAUSSIAN_PREDCDF Returns the predictive cdf evaluated at yt diff --git a/gp/lik_logit.m b/gp/lik_logit.m index ba6ac89c..52fe80be 100755 --- a/gp/lik_logit.m +++ b/gp/lik_logit.m @@ -403,7 +403,7 @@ 'even after looking hard!']) end end - +end function integrand = logit_norm(f, ldconst, yy, myy_i, sigm2_i) % Logit * Gaussian integrand = exp(ldconst ... @@ -434,9 +434,7 @@ g2 = -a*(yy./(a+1)).^2 ... -1/sigm2_i; end - -end - + function p = lik_logit_invlink(lik, f, z) %LIK_LOGIT_INVLINK Returns values of inverse link function % diff --git a/gp/lik_loglogistic.m b/gp/lik_loglogistic.m index a29ca6df..0646b7bd 100644 --- a/gp/lik_loglogistic.m +++ b/gp/lik_loglogistic.m @@ -495,6 +495,7 @@ [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; +end function g = deriv(f, yc, yy, r) m = yy./exp(f); @@ -504,7 +505,6 @@ g = (1/r+log(m)) - 2./(1+m.^r).*m.^r.*log(m); end end -end function [lpy, Ey, Vary] = lik_loglogistic_predy(lik, Ef, Varf, yt, zt) %LIK_LOGLOGISTIC_PREDY Returns the predictive mean, variance and density of y @@ -667,6 +667,7 @@ 'even after looking hard!']) end end +end function integrand = loglogistic_norm(f, ldconst, yc, r, yy, myy_i, sigm2_i) % loglogistic * Gaussian @@ -726,7 +727,6 @@ -1/sigm2_i; end -end function cdf = lik_loglogistic_predcdf(lik, Ef, Varf, yt) %LIK_LOGLOGISTIC_PREDCDF Returns the predictive cdf evaluated at yt diff --git a/gp/lik_multinomprobit.m b/gp/lik_multinomprobit.m index 290294e3..238d45ca 100644 --- a/gp/lik_multinomprobit.m +++ b/gp/lik_multinomprobit.m @@ -50,6 +50,7 @@ lik.fh.recappend = @lik_multinomprobit_recappend; end +end function [w,s] = lik_multinomprobit_pak(lik) %LIK_MULTINOMPROBIT_PAK Combine likelihood parameters into one vector. @@ -416,4 +417,3 @@ end end -end diff --git a/gp/lik_negbin.m b/gp/lik_negbin.m index 47081c6f..2fafd63b 100644 --- a/gp/lik_negbin.m +++ b/gp/lik_negbin.m @@ -447,7 +447,7 @@ [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; - +end function g = deriv(f, r, avgE, yy) mu = avgE.*exp(f); % Derivative using the psi function @@ -459,7 +459,6 @@ % g = g + 1 ./ (i2 + r); % end end -end function [lpy, Ey, Vary] = lik_negbin_predy(lik, Ef, Varf, yt, zt) %LIK_NEGBIN_PREDY Returns the predictive mean, variance and density of y @@ -669,7 +668,7 @@ 'even after looking hard!']) end end - +end function integrand = negbin_norm(f, yy, ldconst, r, avgE, myy_i, sigm2_i) % Negative-binomial * Gaussian mu = avgE.*exp(f); @@ -703,8 +702,6 @@ g2 = -(r*(r + yy))/(mu + r)^2.*mu ... -1/sigm2_i; end - -end function mu = lik_negbin_invlink(lik, f, z) %LIK_NEGBIN_INVLINK Returns values of inverse link function diff --git a/gp/lik_negbinztr.m b/gp/lik_negbinztr.m index 63538649..53398155 100644 --- a/gp/lik_negbinztr.m +++ b/gp/lik_negbinztr.m @@ -531,16 +531,9 @@ % Integrate with quadgk [m_0, fhncnt] = quadgk(tf, minf, maxf); - [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); + [g_i, fhncnt] = quadgk(@(f) td(f,avgE,r,yy).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; - function g = deriv(f) - mu = avgE.*exp(f); - % Derivative using the psi function - g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r); - lp0=r.*(log(r) - log(r+mu)); - g = g -(1./(1 - exp(-lp0)).*(log(r./(mu + r)) - r./(mu + r) + 1)); - end end function [g_i] = lik_negbinztr_siteDeriv2(lik, y, i1, sigm2_i, myy_i, z, eta, lnZhat) @@ -587,14 +580,6 @@ [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; - function g = deriv(f, avgE, r, yy) - mu = avgE.*exp(f); - % Derivative using the psi function - g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r); - lp0=r.*(log(r) - log(r+mu)); - g = g -(1./(1 - exp(-lp0)).*(log(r./(mu + r)) - r./(mu + r) + 1)); -% g = eta.*g; - end end function upfact = lik_negbinztr_upfact(gp, y, mu, ll, z) @@ -926,22 +911,22 @@ prctys(i1,i2)=a; end end - - function expll = llvec(lik,yt,f,z) - % Compute vector of likelihoods of single predictions - n = length(yt); - if n>0 - for i=1:n - expll(i) = exp(lik.fh.ll(lik, yt(i), f, z)); - end - else - expll = 0; +end +function expll = llvec(lik,yt,f,z) + % Compute vector of likelihoods of single predictions + n = length(yt); + if n>0 + for i=1:n + expll(i) = exp(lik.fh.ll(lik, yt(i), f, z)); end + else + expll = 0; end - end + + function mu = lik_negbinztr_invlink(lik, f, z) %LIK_NEGBINZTR_INVLINK Returns values of inverse link function % @@ -1008,3 +993,11 @@ end end end + +function g = deriv(f,avgE,r,yy) + mu = avgE.*exp(f); + % Derivative using the psi function + g = 1 + log(r./(r+mu)) - (r+yy)./(r+mu) + psi(r + yy) - psi(r); + lp0=r.*(log(r) - log(r+mu)); + g = g -(1./(1 - exp(-lp0)).*(log(r./(mu + r)) - r./(mu + r) + 1)); +end diff --git a/gp/lik_poisson.m b/gp/lik_poisson.m index 183812c1..18830be5 100644 --- a/gp/lik_poisson.m +++ b/gp/lik_poisson.m @@ -478,6 +478,7 @@ 'even after looking hard!']) end end +end function integrand = poisson_norm(f,avgE,ldconst,yy,myy_i,sigm2_i) % Poisson * Gaussian @@ -513,7 +514,6 @@ -1/sigm2_i; end -end function mu = lik_poisson_invlink(lik, f, z) %LIK_POISSON_INVLINK Returns values of inverse link function diff --git a/gp/lik_qgp.m b/gp/lik_qgp.m index 6eeb8f1d..5dad5c88 100644 --- a/gp/lik_qgp.m +++ b/gp/lik_qgp.m @@ -402,13 +402,13 @@ [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f, yy, sigma2, tau).*tf(f)./m_0, minf, maxf); g_i = g_i.*sigma2; +end function g = deriv(f, yy, sigma2, tau) g = -1/(2.*sigma2) + (yy-f)./(2.*sigma2^(3/2)).*(tau-(yy<=f)); end -end function [lpy, Ey, Vary] = lik_qgp_predy(lik, Ef, Varf, yt, zt) %LIK_QGP_PREDY Returns the predictive mean, variance and density of y @@ -557,7 +557,7 @@ 'even after looking hard!']) end end - +end function integrand = qgp_norm(f, ldconst, yy, sigma2, tau, myy_i, sigm2_i) % Quantile-GP * Gaussian integrand = exp(ldconst ... @@ -581,9 +581,6 @@ + (myy_i - f)./sigm2_i; end - -end - function mu = lik_qgp_invlink(lik, f, z) %LIK_QGP_INVLINK Returns values of inverse link function % diff --git a/gp/lik_t.m b/gp/lik_t.m index af655f98..c0c739fe 100644 --- a/gp/lik_t.m +++ b/gp/lik_t.m @@ -452,12 +452,6 @@ logM_0(i) = log(m_0); end - function integrand = zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i) - r = yy-f; - term = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; - integrand = exp(term + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2)); - integrand = integrand.*exp(- 0.5 * (f-myy_i).^2./sigm2_i - log(sigm2_i)/2 - log(2*pi)/2); % - end end function [g_i] = lik_t_siteDeriv(lik, y, i1, sigm2_i, myy_i, z) @@ -548,24 +542,6 @@ [g_i(2), fhncnt] = quadgk(@(f) znu(f).*zm(f) , lambdaconf(1), lambdaconf(2)); g_i(2) = g_i(2)/m_0.*nu.*log(nu); end - - function integrand = zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i) - r = yy-f; - term = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; - integrand = exp(term + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2)); - integrand = integrand.*exp(- 0.5 * (f-myy_i).^2./sigm2_i - log(sigm2_i)/2 - log(2*pi)/2); - end - - function g = deriv_nu(f, yy, nu, sigma2, myy_i, sigm2_i) - r = yy-f; - temp = 1 + r.^2./nu./sigma2; - g = psi((nu+1)/2)./2 - psi(nu/2)./2 - 1./(2.*nu) - log(temp)./2 + (nu+1)./(2.*temp).*(r./nu).^2./sigma2; - end - - function g = deriv_sigma2(f, yy, nu, sigma2, myy_i, sigm2_i) - r = yy-f; - g = -1/sigma2/2 + (nu+1)./2.*r.^2./(nu.*sigma2.^2 + r.^2.*sigma2); - end end @@ -859,36 +835,6 @@ end end - - function lpdf = lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta) - % logarithm of the tilted distribution - r = yy-f; - lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; - lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2); - lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2); - end - - function g = deriv_nu(f,yy,nu,sigma2) - % derivative of the log-likelihood wrt nu - r = yy-f; - temp = r.^2 ./(nu*sigma2); - g = psi((nu+1)/2) - psi(nu/2) - 1/nu; - g = g + (1+1/nu).*temp./(1+temp); - - % for small values use a more accurate method for log(1+x) - ii = temp<1e3; - g(ii) = g(ii) - log1p(temp(ii)); - g(~ii) = g(~ii) - log(1+temp(~ii)); - g = g*0.5; - - end - - function g = deriv_sigma2(f,yy,nu,sigma2) - % derivative of the log-likelihood wrt sigma2 - r = yy-f; - temp = r.^2 /sigma2; - g = -1/sigma2/2 + ((1+1/nu)/2) * temp ./ (1 + temp/nu) /sigma2; - end end @@ -1185,3 +1131,53 @@ end end + + +function integrand = zeroth_moment(f, yy, nu, sigma2, myy_i, sigm2_i) +r = yy-f; +term = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; +integrand = exp(term + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2)); +integrand = integrand.*exp(- 0.5 * (f-myy_i).^2./sigm2_i - log(sigm2_i)/2 - log(2*pi)/2); % +end + + +function lpdf = lpt(f,C,yy,nu,sigma2,myy_i,sigm2_i,eta) +% logarithm of the tilted distribution +r = yy-f; +lpdf = gammaln((nu + 1) / 2) - gammaln(nu/2) -log(nu.*pi.*sigma2)/2; +lpdf = lpdf + log(1 + r.^2./nu./sigma2) .* (-(nu+1)/2); +lpdf = lpdf*eta - (0.5/sigm2_i) * (f-myy_i).^2 + (C-log(2*pi*sigm2_i)/2); +end + +function g = deriv_nu(f,yy,nu,sigma2) +% derivative of the log-likelihood wrt nu +r = yy-f; +temp = r.^2 ./(nu*sigma2); +g = psi((nu+1)/2) - psi(nu/2) - 1/nu; +g = g + (1+1/nu).*temp./(1+temp); + +% for small values use a more accurate method for log(1+x) +ii = temp<1e3; +g(ii) = g(ii) - log1p(temp(ii)); +g(~ii) = g(~ii) - log(1+temp(~ii)); +g = g*0.5; + +end + +function g = deriv_sigma2(f,yy,nu,sigma2) +% derivative of the log-likelihood wrt sigma2 +r = yy-f; +temp = r.^2 /sigma2; +g = -1/sigma2/2 + ((1+1/nu)/2) * temp ./ (1 + temp/nu) /sigma2; +end + +function g = deriv_nu2(f, yy, nu, sigma2, myy_i, sigm2_i) +r = yy-f; +temp = 1 + r.^2./nu./sigma2; +g = psi((nu+1)/2)./2 - psi(nu/2)./2 - 1./(2.*nu) - log(temp)./2 + (nu+1)./(2.*temp).*(r./nu).^2./sigma2; +end + +function g = deriv_sigma22(f, yy, nu, sigma2, myy_i, sigm2_i) +r = yy-f; +g = -1/sigma2/2 + (nu+1)./2.*r.^2./(nu.*sigma2.^2 + r.^2.*sigma2); +end diff --git a/gp/lik_weibull.m b/gp/lik_weibull.m index a11dd2b2..bc80b68f 100644 --- a/gp/lik_weibull.m +++ b/gp/lik_weibull.m @@ -448,6 +448,7 @@ [m_0, fhncnt] = quadgk(tf, minf, maxf); [g_i, fhncnt] = quadgk(@(f) td(f).*tf(f)./m_0, minf, maxf); g_i = g_i.*r; +end function g = deriv(f, yc, r, yy) if yc==0 @@ -456,7 +457,6 @@ g = (1./r + log(yy)) - exp(-f).*yy.^r.*log(yy); end end -end function [lpy, Ey, Vary] = lik_weibull_predy(lik, Ef, Varf, yt, zt) %LIK_WEIBULL_PREDY Returns the predictive mean, variance and density of y @@ -639,7 +639,8 @@ 'even after looking hard!']) end end - +end + function integrand = weibull_norm(f, ldconst, yc, yy, r, myy_i, sigm2_i) % Weibull * Gaussian if yc @@ -691,7 +692,6 @@ -1/sigm2_i; end -end function cdf = lik_weibull_predcdf(lik, Ef, Varf, yt) %LIK_WEIBULL_PREDCDF Returns the predictive cdf evaluated at yt diff --git a/gp/lik_zinegbin.m b/gp/lik_zinegbin.m index 4c012d4e..c1cc98a6 100644 --- a/gp/lik_zinegbin.m +++ b/gp/lik_zinegbin.m @@ -104,6 +104,7 @@ lik.fh.invlink = @lik_zinegbin_invlink; lik.fh.recappend = @lik_zinegbin_recappend; end +end function [w,s] = lik_zinegbin_pak(lik) %LIK_ZINEGBIN_PAK Combine likelihood parameters into one vector. @@ -622,6 +623,5 @@ reclik.p.disper = feval(lik.p.disper.fh.recappend, reclik.p.disper, ri, lik.p.disper); end end -end diff --git a/gp/linuxCsource/dist_euclidean.c b/gp/linuxCsource/dist_euclidean.c index 77912945..df3213e7 100644 --- a/gp/linuxCsource/dist_euclidean.c +++ b/gp/linuxCsource/dist_euclidean.c @@ -24,7 +24,12 @@ #include #include #include "mex.h" +#ifndef HAVE_OCTAVE #include "matrix.h" +#endif +#ifndef true +#define true 1 +#endif #define max(a,b) (((a) > (b)) ? (a) : (b)) void mexFunction(const int nlhs, mxArray *plhs[], diff --git a/gp/linuxCsource/ldlrowmodify.c b/gp/linuxCsource/ldlrowmodify.c index e85e0eb8..23bb6274 100644 --- a/gp/linuxCsource/ldlrowmodify.c +++ b/gp/linuxCsource/ldlrowmodify.c @@ -31,7 +31,9 @@ #include #include #include "mex.h" +#ifndef HAVE_OCTAVE #include "matrix.h" +#endif #define max(a,b) (((a) > (b)) ? (a) : (b)) #define min(a,b) (((a) < (b)) ? (a) : (b)) diff --git a/gp/linuxCsource/trcov.c b/gp/linuxCsource/trcov.c index 48037a5f..1f4ca1d0 100644 --- a/gp/linuxCsource/trcov.c +++ b/gp/linuxCsource/trcov.c @@ -24,7 +24,9 @@ #include #include #include "mex.h" +#ifndef HAVE_OCTAVE #include "matrix.h" +#endif #define max(a, b) (((a) > (b)) ? (a) : (b)) #define PI (3.141592653589793) void cumsum2(mwIndex *p, mwIndex *c, mwIndex n); diff --git a/gp/scaled_hmc.m b/gp/scaled_hmc.m index 639348b5..7fde6e81 100644 --- a/gp/scaled_hmc.m +++ b/gp/scaled_hmc.m @@ -185,7 +185,7 @@ diagn.opt = opt; diagn.rej = rej; diagn.lvs = opt.stepadj; - +end function [g, gdata, gprior] = f_g(w, gp, x, y, u, z, output) %F_G Evaluate gradient function for transformed GP latent values % @@ -691,4 +691,4 @@ output.Linv=Linv; end end -end + diff --git a/matlab_install.m b/matlab_install.m deleted file mode 100644 index 30f4ab75..00000000 --- a/matlab_install.m +++ /dev/null @@ -1,109 +0,0 @@ -function matlab_install(SuiteSparse_path) -% Matlab function to compile all the c-files to mex in the GPstuff toolbox. -% -% Some of the sparse GP functionalities in the toolbox require -% SuiteSparse toolbox by Tim Davis: -% http://www.cise.ufl.edu/research/sparse/SuiteSparse/current/SuiteSparse/ -% -% This package includes the SuiteSparse version 3.4. -% -% * To install without SuiteSparse run matlab_install -% * To install with SuiteSparse run matlab_install('SuiteSparseOn') -% -% The function matlab_install compiles the mex-files and prints on -% the screen, which directories should be added to Matlab paths. - -% Copyright (c) 2008-2012 Jarno Vanhatalo - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - - - if nargin < 1 - SuiteSparse_path = []; - fprintf('\n The path to the SuiteSparse package is not provided. \n') - fprintf('\n Installing GPstuff without compactly supported covariance functions. \n') - fprintf(' You are not able to use the following covariance functions: \n') - fprintf(' gpcf_ppcs0 \n gpcf_ppcs1 \n gpcf_ppcs2 \n gpcf_ppcs3 \n\n\n') - elseif strcmp(SuiteSparse_path, 'SuiteSparseOn') - cdir = pwd; - cd SuiteSparse - SuiteSparse_path = path_spaces([pwd '/']); - - - % Compile SuiteSparse - fprintf('Compiling SuiteSparse. This may take a while \n \n') - paths = SuiteSparse_install(false); - - cd(cdir) - fprintf('Compiling GPstuff. This may take a while \n \n') - else - error('Unknown input argument. See help matlab_install for usage.') - end - - % Go to diag/ and compile the mex-functions - fprintf('\n Compiling files in diag.\n \n') - cd('diag') - diag_install - cd('..') - - % Go to dist/ and compile the mex-functions - fprintf('\n Compiling files in dist.\n \n') - cd('dist') - dist_install - cd('..') - - % Go to gp/ and compile the mex-functions - fprintf('\n Compiling files in gp.\n \n') - cd('gp') - gp_install(SuiteSparse_path) - cd('..') - - % Go to mc/ and compile the mex-functions - fprintf('\n Compiling files in mc. \n \n') - cd('mc') - mc_install - cd('..') - - PP = pwd; - S{1} = [PP '/diag']; - S{2} = [PP '/dist']; - S{3} = [PP '/gp']; - S{4} = [PP '/mc']; - S{5} = [PP '/misc']; - S{6} = [PP '/optim']; - S{7} = [PP '/xunit']; - - fprintf ('\n The following paths have been added. You may wish to add them\n') ; - fprintf ('permanently, using the MATLAB pathtool command or copying the below\n') ; - fprintf ('lines to your startup.m file. \n\n'); - for i = 1:length(S) - addpath(S{i}); - fprintf ('addpath %s\n', S{i}) ; - end - - if nargin==1 - fprintf ('\n') - for k = 1:length (paths) - fprintf ('addpath %s\n', paths {k}) ; - end - end -end - -function path = path_spaces(path) -% Build correct path if path includes spaces - -space_ind=strfind(path, ' '); -path=strrep(path, ' ', ''' '); -space_ind=space_ind+length(space_ind); -for i=1:length(space_ind) - - indd=strfind(path(space_ind(i):end), '/'); - path=[path(1:space_ind(i)+indd(1)-2) '''/' path(space_ind(i)+indd(1):end)]; - space_ind=space_ind+1; - -end - -end diff --git a/mc/mc_install.m b/mc/mc_install.m index 64193724..68729a05 100644 --- a/mc/mc_install.m +++ b/mc/mc_install.m @@ -2,15 +2,31 @@ if ispc % A windows version of Matlab + if ~exist('OCTAVE_VERSION','builtin') mex -O -output bbmean winCSource\bbmean.c mex -O -output resampres winCsource\resampres.c winCsource\binsgeq.c mex -O -output resampsim winCsource\resampsim.c winCsource\binsgeq.c mex -O -output resampstr winCsource\resampstr.c winCsource\binsgeq.c mex -O -output resampdet winCsource\resampdet.c winCsource\binsgeq.c + else + mex --output bbmean.mex winCSource\bbmean.c + mex --output resampres.mex winCsource\resampres.c winCsource\binsgeq.c + mex --output resampsim.mex winCsource\resampsim.c winCsource\binsgeq.c + mex --output resampstr.mex winCsource\resampstr.c winCsource\binsgeq.c + mex --output resampdet.mex winCsource\resampdet.c winCsource\binsgeq.c + end else + if ~exist('OCTAVE_VERSION','builtin') mex -O -output bbmean linuxCsource/bbmean.c mex -O -output resampres linuxCsource/resampres.c linuxCsource/binsgeq.c mex -O -output resampsim linuxCsource/resampsim.c linuxCsource/binsgeq.c mex -O -output resampstr linuxCsource/resampstr.c linuxCsource/binsgeq.c mex -O -output resampdet linuxCsource/resampdet.c linuxCsource/binsgeq.c + else + mex --output bbmean linuxCsource/bbmean.c + mex --output resampres.mex linuxCsource/resampres.c linuxCsource/binsgeq.c + mex --output resampsim.mex linuxCsource/resampsim.c linuxCsource/binsgeq.c + mex --output resampstr.mex linuxCsource/resampstr.c linuxCsource/binsgeq.c + mex --output resampdet.mex linuxCsource/resampdet.c linuxCsource/binsgeq.c + end end From 3d500306e31a24f9ee8e2d1d96e58ecaca2569af Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 6 Mar 2014 14:05:39 +0200 Subject: [PATCH 35/64] Changed matlab_install -> gpstuff_install --- gpstuff_install.m | 108 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 gpstuff_install.m diff --git a/gpstuff_install.m b/gpstuff_install.m new file mode 100644 index 00000000..fe0f6759 --- /dev/null +++ b/gpstuff_install.m @@ -0,0 +1,108 @@ +function gpstuff_install(SuiteSparse_path) +% Matlab/Octave function to compile all the c-files to mex in the GPstuff toolbox. +% +% Some of the sparse GP functionalities in the toolbox require +% SuiteSparse toolbox by Tim Davis: +% http://www.cise.ufl.edu/research/sparse/SuiteSparse/current/SuiteSparse/ +% +% This package includes the SuiteSparse version 3.4. Note that Octave +% GPstuff doesn't work with SuiteSparse at the moment. +% +% * To install without SuiteSparse run matlab_install +% * To install with SuiteSparse run matlab_install('SuiteSparseOn') +% +% The function matlab_install compiles the mex-files and prints on +% the screen, which directories should be added to Matlab paths. + +% Copyright (c) 2008-2012 Jarno Vanhatalo + +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + + if nargin < 1 + SuiteSparse_path = []; + fprintf('\n The path to the SuiteSparse package is not provided. \n') + fprintf('\n Installing GPstuff without compactly supported covariance functions. \n') + fprintf(' You are not able to use the following covariance functions: \n') + fprintf(' gpcf_ppcs0 \n gpcf_ppcs1 \n gpcf_ppcs2 \n gpcf_ppcs3 \n\n\n') + elseif strcmp(SuiteSparse_path, 'SuiteSparseOn') + cdir = pwd; + cd SuiteSparse + SuiteSparse_path = path_spaces([pwd '/']); + + + % Compile SuiteSparse + fprintf('Compiling SuiteSparse. This may take a while \n \n') + paths = SuiteSparse_install(false); + + cd(cdir) + fprintf('Compiling GPstuff. This may take a while \n \n') + else + error('Unknown input argument. See help matlab_install for usage.') + end + + % Go to diag/ and compile the mex-functions + fprintf('\n Compiling files in diag.\n \n') + cd('diag') + diag_install + cd('..') + + % Go to dist/ and compile the mex-functions + fprintf('\n Compiling files in dist.\n \n') + cd('dist') + dist_install + cd('..') + + % Go to gp/ and compile the mex-functions + fprintf('\n Compiling files in gp.\n \n') + cd('gp') + gp_install(SuiteSparse_path) + cd('..') + + % Go to mc/ and compile the mex-functions + fprintf('\n Compiling files in mc. \n \n') + cd('mc') + mc_install + cd('..') + + PP = pwd; + S{1} = [PP '/diag']; + S{2} = [PP '/dist']; + S{3} = [PP '/gp']; + S{4} = [PP '/mc']; + S{5} = [PP '/misc']; + S{6} = [PP '/optim']; + S{7} = [PP '/xunit']; + + fprintf ('\n The following paths have been added. You may wish to add them\n') ; + fprintf ('permanently, using the MATLAB pathtool command or copying the below\n') ; + fprintf ('lines to your startup.m file. \n\n'); + for i = 1:length(S) + addpath(S{i}); + fprintf ('addpath %s\n', S{i}) ; + end + + if nargin==1 + fprintf ('\n') + for k = 1:length (paths) + fprintf ('addpath %s\n', paths {k}) ; + end + end +end + +function path = path_spaces(path) +% Build correct path if path includes spaces + +space_ind=strfind(path, ' '); +path=strrep(path, ' ', ''' '); +space_ind=space_ind+length(space_ind); +for i=1:length(space_ind) + + indd=strfind(path(space_ind(i):end), '/'); + path=[path(1:space_ind(i)+indd(1)-2) '''/' path(space_ind(i)+indd(1):end)]; + space_ind=space_ind+1; + +end + +end From 9e2aa211a6411c8e6df6a4a457b990f7178b37a7 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 6 Mar 2014 14:08:23 +0200 Subject: [PATCH 36/64] typo fix in mc_install --- mc/mc_install.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mc/mc_install.m b/mc/mc_install.m index 68729a05..ef0ffd4f 100644 --- a/mc/mc_install.m +++ b/mc/mc_install.m @@ -23,7 +23,7 @@ mex -O -output resampstr linuxCsource/resampstr.c linuxCsource/binsgeq.c mex -O -output resampdet linuxCsource/resampdet.c linuxCsource/binsgeq.c else - mex --output bbmean linuxCsource/bbmean.c + mex --output bbmean.mex linuxCsource/bbmean.c mex --output resampres.mex linuxCsource/resampres.c linuxCsource/binsgeq.c mex --output resampsim.mex linuxCsource/resampsim.c linuxCsource/binsgeq.c mex --output resampstr.mex linuxCsource/resampstr.c linuxCsource/binsgeq.c From 9c286ad71863aff3d3cf04fa57fe8c3009c92e4c Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 6 Mar 2014 14:22:22 +0200 Subject: [PATCH 37/64] Updated changelog --- ChangeLog.txt | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/ChangeLog.txt b/ChangeLog.txt index 63118d07..37e80006 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -1,3 +1,8 @@ +2013-03-06 Version 4.4 +Big fixes + - Changed nested functions to subfunctions for compatibility with Octave 3.8.0 + - Fixed compilation of C source files with Octave. + 2013-11-26 Version 4.3.1 Improvements: From fa7fe6802aa30581e78dc9fd6d186d859c7dd3cb Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 12 Mar 2014 10:13:08 +0200 Subject: [PATCH 38/64] Added tests that work both octave and matlab and removed xunit --- README.txt | 8 +- startup.m | 2 +- test_gpstuff/matlab/realValues_binomial1.mat | Bin 0 -> 9324 bytes test_gpstuff/matlab/realValues_classific.mat | Bin 0 -> 471868 bytes .../matlab/realValues_derivativeobs.mat | Bin 0 -> 3631 bytes test_gpstuff/matlab/realValues_multinom.mat | Bin 0 -> 25204 bytes .../matlab/realValues_neuralnetcov.mat | Bin .../matlab/realValues_periodic.mat | Bin .../matlab/realValues_regression1.mat | Bin 0 -> 62104 bytes .../realValues_regression_additive1.mat | Bin 0 -> 26091 bytes .../matlab/realValues_regression_hier.mat | Bin .../matlab/realValues_regression_sparse1.mat | Bin .../matlab/realValues_survival_aft.mat | Bin test_gpstuff/octave/realValues_binomial1.mat | 1219 +++ test_gpstuff/octave/realValues_classific.mat | 2437 +++++ .../octave/realValues_derivativeobs.mat | 509 + test_gpstuff/octave/realValues_multinom.mat | 735 ++ .../octave/realValues_neuralnetcov.mat | 825 ++ test_gpstuff/octave/realValues_periodic.mat | 2529 +++++ .../octave/realValues_regression1.mat | 8251 +++++++++++++++++ .../realValues_regression_additive1.mat | 3401 +++++++ .../octave/realValues_regression_hier.mat | 37 + .../octave/realValues_regression_sparse1.mat | 6876 ++++++++++++++ .../octave/realValues_survival_aft.mat | 463 + test_gpstuff/run_tests.m | 58 + xunit/Readme.txt | 39 - xunit/log/Readme.txt | 4 - xunit/log/create_files.m | 379 - xunit/log/demo_binomial1.txt | 7 - xunit/log/demo_binomial_apc.txt | 61 - xunit/log/demo_classific.txt | 28 - xunit/log/demo_derivativeobs.txt | 26 - xunit/log/demo_lgcp.txt | 4 - xunit/log/demo_modelassesment1.txt | 567 -- xunit/log/demo_modelassesment2.txt | 479 - xunit/log/demo_multiclass.txt | 661 -- xunit/log/demo_multinom2.txt | 458 - xunit/log/demo_neuralnetcov.txt | 10 - xunit/log/demo_periodic.txt | 47 - xunit/log/demo_regression1.txt | 60 - xunit/log/demo_regression_additive1.txt | 61 - xunit/log/demo_regression_additive2.txt | 20 - xunit/log/demo_regression_hier.txt | 51 - xunit/log/demo_regression_meanf.txt | 15 - xunit/log/demo_regression_ppcs.txt | 25 - xunit/log/demo_regression_robust.txt | 143 - xunit/log/demo_regression_sparse1.txt | 74 - xunit/log/demo_regression_sparse2.txt | 14 - xunit/log/demo_spatial1.txt | 542 -- xunit/log/demo_spatial2.txt | 28 - xunit/log/demo_survival_coxph.txt | 43 - xunit/log/demo_survival_weibull.txt | 26 - xunit/log/demo_zinegbin.txt | 30 - xunit/realValuesBinomial1.mat | Bin 9332 -> 0 bytes xunit/realValuesBinomial_apc.mat | Bin 18953 -> 0 bytes xunit/realValuesClassific.mat | Bin 33958 -> 0 bytes xunit/realValuesDerivativeobs.mat | Bin 3631 -> 0 bytes xunit/realValuesLgcp.mat | Bin 2514 -> 0 bytes xunit/realValuesLoopred.mat | Bin 24268 -> 0 bytes xunit/realValuesModelAssesment1.mat | Bin 1559 -> 0 bytes xunit/realValuesModelAssesment2.mat | Bin 1113 -> 0 bytes xunit/realValuesMulticlass.mat | Bin 13581 -> 0 bytes xunit/realValuesMulticlass_nested_ep.mat | Bin 85103 -> 0 bytes xunit/realValuesMultinom.mat | Bin 25185 -> 0 bytes xunit/realValuesRegression1.mat | Bin 3221 -> 0 bytes xunit/realValuesRegression_additive1.mat | Bin 26117 -> 0 bytes xunit/realValuesRegression_additive2.mat | Bin 12940 -> 0 bytes xunit/realValuesRegression_meanf.mat | Bin 688 -> 0 bytes xunit/realValuesRegression_ppcs.mat | Bin 18063 -> 0 bytes xunit/realValuesRegression_robust.mat | Bin 11408 -> 0 bytes xunit/realValuesRegression_sparse2.mat | Bin 3243 -> 0 bytes xunit/realValuesSpatial1.mat | Bin 1978 -> 0 bytes xunit/realValuesSpatial2.mat | Bin 5145 -> 0 bytes xunit/realValuesSurvival_coxph.mat | Bin 1070 -> 0 bytes xunit/realValuesZinegbin.mat | Bin 1849 -> 0 bytes xunit/test_all.m | 36 - xunit/test_binomial1.m | 46 - xunit/test_binomial_apc.m | 44 - xunit/test_classific.m | 58 - xunit/test_derivativeobs.m | 47 - xunit/test_lgcp.m | 38 - xunit/test_loopred.m | 64 - xunit/test_modelassesment1.m | 117 - xunit/test_modelassesment2.m | 96 - xunit/test_multiclass.m | 40 - xunit/test_multiclass_nested_ep.m | 38 - xunit/test_multinom.m | 38 - xunit/test_neuralnetcov.m | 38 - xunit/test_periodic.m | 49 - xunit/test_regression1.m | 93 - xunit/test_regression_additive1.m | 49 - xunit/test_regression_additive2.m | 35 - xunit/test_regression_hier.m | 35 - xunit/test_regression_meanf.m | 36 - xunit/test_regression_ppcs.m | 44 - xunit/test_regression_robust.m | 44 - xunit/test_regression_sparse1.m | 59 - xunit/test_regression_sparse2.m | 46 - xunit/test_spatial1.m | 47 - xunit/test_spatial2.m | 46 - xunit/test_survival_aft.m | 39 - xunit/test_survival_coxph.m | 37 - xunit/test_zinegbin.m | 38 - 103 files changed, 27345 insertions(+), 5304 deletions(-) create mode 100644 test_gpstuff/matlab/realValues_binomial1.mat create mode 100644 test_gpstuff/matlab/realValues_classific.mat create mode 100644 test_gpstuff/matlab/realValues_derivativeobs.mat create mode 100644 test_gpstuff/matlab/realValues_multinom.mat rename xunit/realValuesNeuralnetcov.mat => test_gpstuff/matlab/realValues_neuralnetcov.mat (100%) rename xunit/realValuesPeriodic.mat => test_gpstuff/matlab/realValues_periodic.mat (100%) create mode 100644 test_gpstuff/matlab/realValues_regression1.mat create mode 100644 test_gpstuff/matlab/realValues_regression_additive1.mat rename xunit/realValuesRegression_hier.mat => test_gpstuff/matlab/realValues_regression_hier.mat (100%) rename xunit/realValuesRegression_sparse1.mat => test_gpstuff/matlab/realValues_regression_sparse1.mat (100%) rename xunit/realValuesSurvival_aft.mat => test_gpstuff/matlab/realValues_survival_aft.mat (100%) create mode 100644 test_gpstuff/octave/realValues_binomial1.mat create mode 100644 test_gpstuff/octave/realValues_classific.mat create mode 100644 test_gpstuff/octave/realValues_derivativeobs.mat create mode 100644 test_gpstuff/octave/realValues_multinom.mat create mode 100644 test_gpstuff/octave/realValues_neuralnetcov.mat create mode 100644 test_gpstuff/octave/realValues_periodic.mat create mode 100644 test_gpstuff/octave/realValues_regression1.mat create mode 100644 test_gpstuff/octave/realValues_regression_additive1.mat create mode 100644 test_gpstuff/octave/realValues_regression_hier.mat create mode 100644 test_gpstuff/octave/realValues_regression_sparse1.mat create mode 100644 test_gpstuff/octave/realValues_survival_aft.mat create mode 100644 test_gpstuff/run_tests.m delete mode 100644 xunit/Readme.txt delete mode 100644 xunit/log/Readme.txt delete mode 100644 xunit/log/create_files.m delete mode 100644 xunit/log/demo_binomial1.txt delete mode 100644 xunit/log/demo_binomial_apc.txt delete mode 100644 xunit/log/demo_classific.txt delete mode 100644 xunit/log/demo_derivativeobs.txt delete mode 100644 xunit/log/demo_lgcp.txt delete mode 100644 xunit/log/demo_modelassesment1.txt delete mode 100644 xunit/log/demo_modelassesment2.txt delete mode 100644 xunit/log/demo_multiclass.txt delete mode 100644 xunit/log/demo_multinom2.txt delete mode 100644 xunit/log/demo_neuralnetcov.txt delete mode 100644 xunit/log/demo_periodic.txt delete mode 100644 xunit/log/demo_regression1.txt delete mode 100644 xunit/log/demo_regression_additive1.txt delete mode 100644 xunit/log/demo_regression_additive2.txt delete mode 100644 xunit/log/demo_regression_hier.txt delete mode 100644 xunit/log/demo_regression_meanf.txt delete mode 100644 xunit/log/demo_regression_ppcs.txt delete mode 100644 xunit/log/demo_regression_robust.txt delete mode 100644 xunit/log/demo_regression_sparse1.txt delete mode 100644 xunit/log/demo_regression_sparse2.txt delete mode 100644 xunit/log/demo_spatial1.txt delete mode 100644 xunit/log/demo_spatial2.txt delete mode 100644 xunit/log/demo_survival_coxph.txt delete mode 100644 xunit/log/demo_survival_weibull.txt delete mode 100644 xunit/log/demo_zinegbin.txt delete mode 100644 xunit/realValuesBinomial1.mat delete mode 100644 xunit/realValuesBinomial_apc.mat delete mode 100644 xunit/realValuesClassific.mat delete mode 100644 xunit/realValuesDerivativeobs.mat delete mode 100644 xunit/realValuesLgcp.mat delete mode 100644 xunit/realValuesLoopred.mat delete mode 100644 xunit/realValuesModelAssesment1.mat delete mode 100644 xunit/realValuesModelAssesment2.mat delete mode 100644 xunit/realValuesMulticlass.mat delete mode 100644 xunit/realValuesMulticlass_nested_ep.mat delete mode 100644 xunit/realValuesMultinom.mat delete mode 100644 xunit/realValuesRegression1.mat delete mode 100644 xunit/realValuesRegression_additive1.mat delete mode 100644 xunit/realValuesRegression_additive2.mat delete mode 100644 xunit/realValuesRegression_meanf.mat delete mode 100644 xunit/realValuesRegression_ppcs.mat delete mode 100644 xunit/realValuesRegression_robust.mat delete mode 100644 xunit/realValuesRegression_sparse2.mat delete mode 100644 xunit/realValuesSpatial1.mat delete mode 100644 xunit/realValuesSpatial2.mat delete mode 100644 xunit/realValuesSurvival_coxph.mat delete mode 100644 xunit/realValuesZinegbin.mat delete mode 100644 xunit/test_all.m delete mode 100644 xunit/test_binomial1.m delete mode 100644 xunit/test_binomial_apc.m delete mode 100644 xunit/test_classific.m delete mode 100644 xunit/test_derivativeobs.m delete mode 100644 xunit/test_lgcp.m delete mode 100644 xunit/test_loopred.m delete mode 100644 xunit/test_modelassesment1.m delete mode 100644 xunit/test_modelassesment2.m delete mode 100644 xunit/test_multiclass.m delete mode 100644 xunit/test_multiclass_nested_ep.m delete mode 100644 xunit/test_multinom.m delete mode 100644 xunit/test_neuralnetcov.m delete mode 100644 xunit/test_periodic.m delete mode 100644 xunit/test_regression1.m delete mode 100644 xunit/test_regression_additive1.m delete mode 100644 xunit/test_regression_additive2.m delete mode 100644 xunit/test_regression_hier.m delete mode 100644 xunit/test_regression_meanf.m delete mode 100644 xunit/test_regression_ppcs.m delete mode 100644 xunit/test_regression_robust.m delete mode 100644 xunit/test_regression_sparse1.m delete mode 100644 xunit/test_regression_sparse2.m delete mode 100644 xunit/test_spatial1.m delete mode 100644 xunit/test_spatial2.m delete mode 100644 xunit/test_survival_aft.m delete mode 100644 xunit/test_survival_coxph.m delete mode 100644 xunit/test_zinegbin.m diff --git a/README.txt b/README.txt index 97d126d8..e8185471 100644 --- a/README.txt +++ b/README.txt @@ -1,7 +1,7 @@ Last modified: 2013-03-20 16:01:42 EET ----------------------------------------------------------------- -GPstuff: Gaussian process models for Bayesian analysis 4.1 +GPstuff: Gaussian process models for Bayesian analysis 4.3.2 Maintainers: Aki Vehtari Jarno Vanhatalo @@ -52,7 +52,7 @@ Table of contents: 1) Basic installation without compactly supported covariance functions - * Install the GPstuff package by running matlab_install in this + * Install the GPstuff package by running gpstuff_install in this folder * With this option you are able to use all the other functions @@ -66,10 +66,10 @@ Table of contents: use these functions (gpcf_ppcs*) you need the sparse GP functionalities in GPstuff which are build over SuiteSparse toolbox. To take full advantage of the CS covariance functions - install GPstuff by running matlab_install('SuiteSparseOn' ) in the + install GPstuff by running gpstuff_install('SuiteSparseOn' ) in the present directory. - The function matlab_install compiles the mex-files and prints on + The function gpstuff_install compiles the mex-files and prints on the screen, which directories should be added to Matlab paths. 3. CONTENTS diff --git a/startup.m b/startup.m index 17d60105..dae8b45c 100644 --- a/startup.m +++ b/startup.m @@ -5,7 +5,7 @@ if exist('OCTAVE_VERSION', 'builtin') subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'xunit', 'octave_compat', 'inputparser'}; else - subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'xunit'}; + subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'xunit', 'inputparser'}; end for sf=subfolders addpath(strrep(S,[F '.m'],sf{:})) diff --git a/test_gpstuff/matlab/realValues_binomial1.mat b/test_gpstuff/matlab/realValues_binomial1.mat new file mode 100644 index 0000000000000000000000000000000000000000..4877391bb3161c8a3840ffbe51cd662f14e68ce5 GIT binary patch literal 9324 zcma)=Ra+Dcpsf`VBqXJ~ySp2td+2WIZV(0;=?2M>?(SwlT0pvU=&m7#+TT9sIrnGX zto0Mt`>IOms7Ogu^09MLs!Hjw**ZAcuu^I`S$NsHdN>PHDyXRGOY!rvQp$MPSa{i3 zQ@XkcQ|kTKsumuUT-=nLLc+ZK!hF1x+?-szl>eW3_y6))07J%4!vC8gt*g49%2v2o>>i{}ge^SArbqT-^% z*G=fA9skpP`?*_2vexg#u-neK<|(rvPn@lUE*BCgh*Yo}rT?$K_zas7hMep6R+IB^ 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b/test_gpstuff/matlab/realValues_regression_hier.mat similarity index 100% rename from xunit/realValuesRegression_hier.mat rename to test_gpstuff/matlab/realValues_regression_hier.mat diff --git a/xunit/realValuesRegression_sparse1.mat b/test_gpstuff/matlab/realValues_regression_sparse1.mat similarity index 100% rename from xunit/realValuesRegression_sparse1.mat rename to test_gpstuff/matlab/realValues_regression_sparse1.mat diff --git a/xunit/realValuesSurvival_aft.mat b/test_gpstuff/matlab/realValues_survival_aft.mat similarity index 100% rename from xunit/realValuesSurvival_aft.mat rename to test_gpstuff/matlab/realValues_survival_aft.mat diff --git a/test_gpstuff/octave/realValues_binomial1.mat b/test_gpstuff/octave/realValues_binomial1.mat new file mode 100644 index 00000000..c9a8dbe3 --- /dev/null +++ b/test_gpstuff/octave/realValues_binomial1.mat @@ -0,0 +1,1219 @@ +# Created by Octave 3.8.0, Fri Mar 07 16:08:41 2014 EET +# name: Eyt_la +# type: matrix +# rows: 400 +# columns: 1 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0.0562201264964801 0.1674238605483383 0.4524293436020446 + 0.05043691293325759 0.1573573828582119 0.449893417482812 + 0.04924451936082075 0.1555107675338821 0.4484097403127163 + + diff --git a/test_gpstuff/run_tests.m b/test_gpstuff/run_tests.m new file mode 100644 index 00000000..e0690b9d --- /dev/null +++ b/test_gpstuff/run_tests.m @@ -0,0 +1,58 @@ +% Run selected demos, compare results from demos to old results and save +% any error messages. + +demos={'demo_binomial1', ... + 'demo_classific', 'demo_derivativeobs', ... + 'demo_multinom', 'demo_neuralnetcov', ... + 'demo_periodic', 'demo_regression1', 'demo_regression_additive1', ... + 'demo_regression_hier', 'demo_regression_sparse1', 'demo_survival_aft'}; + +invalid_demos={}; +iter=0; +failed=0; +for ii=1:length(demos) + iter=iter+1; + setrandstream(0); + if exist('OCTAVE_VERSION','builtin') + values.real=load(['octave/realValues_' strrep(demos{ii}, 'demo_', '')]); + else + values.real=load(['matlab/realValues_' strrep(demos{ii}, 'demo_', '')]); + end + field=fieldnames(values.real); + fprintf('\nRunning demo: %s\n\n', demos{ii}); + try + eval(demos{ii}); + for j=1:length(field) + iter=iter+1; + if mean(mean((abs(getfield(values.real, field{j}) - eval(field{j})))./getfield(values.real, field{j})))>1e-2 + failed=failed+1; + invalid_demos(failed).name=demos{ii}; + invalid_demos(failed).paramName=field{j}; + invalid_demos(failed).paramValue_old=getfield(values.real, field{j}); + invalid_demos(failed).paramValue_new=eval(field{j}); + invalid_demos(failed).error='New values dont match with old saved values with relative tolerance of 1e-2.'; + end + end + catch err + % Error while running a demo + iter=iter+length(field); + failed=failed+1; + invalid_demos(failed).name=demos{ii}; + invalid_demos(failed).paramName=''; + invalid_demos(failed).paramValue_old=[]; + invalid_demos(failed).paramValue_new=[]; + % Save error structure to field error. + invalid_demos(failed).error=err; + for j=1:length(field) + failed=failed+1; + invalid_demos(failed).name=demos{ii}; + invalid_demos(failed).paramName=field{j}; + invalid_demos(failed).paramValue_old=getfield(values.real, field{j}); + invalid_demos(failed).paramValue_new=[]; + invalid_demos(failed).error='Error while runnig the demo.'; + end + end + close all; +end +invalid_demos +fprintf('Failed %d of %d tests\n',failed, iter); \ No newline at end of file diff --git a/xunit/Readme.txt b/xunit/Readme.txt deleted file mode 100644 index 9fbf1a83..00000000 --- a/xunit/Readme.txt +++ /dev/null @@ -1,39 +0,0 @@ -Tests in this directory use MATLAB xUnit Test Framework toolbox. -Test structure in every test is basically the same: Run demo, save values from demo, compare those saved test values to previously saved "correct" values. Users can run these tests to determine if some calculations within demos don't give correct answers. -Every m-file in the directory is written so that it includes(or can include) multiple tests for a given demo. There is one m-file for every demo made while writing this document. -Script test_all compiles all the tests in the directory in one TestSuite object and runs them. After running the tests, logger object gives test results, what went wrong etc. -As previously mentioned, every test in the directory, present at the moment, uses same build for testing. First, to include multiple tests in one m-file, you should start your own test code with these two lines: - -1 function test_suite = testName -2 initTestSuite; - -Every test file and test should include string "test", either in front of the name or after the name, e.g. testName or nameTest. To write the actual tests in the m-file, you use subfunctions, including string "test" again. - -4 function testDemo -5 demo_regression1; -6 save('testValues/testRegression1'); -7 drawnow;clear;close all - -xUnit doesn't recognize tests if the name of the main m-file, or the subfunction, doesn't have string "test" in it. For transparency, every test written at the moment, is named test_demoName. -To compare the test values to real values, most of the tests use two test functions assertElementsAlmostEqual and assertVectorsAlmostEqual, provided by the xUnit Test Framework. -Function assertElementsAlmostEqual(a, b, options) compares elements of a to b, with 'options' providing method and tolerance level, e.g. assertElementsAlmostEqual(1, 2, 'absolute', 0.5) returns error, -while assertElementsAlmostEqual(1, 1.2, 'relative', 0.2) doesn't. By default, the comparison uses relative tolerance test. Function assertVectorsAlmostEqual works the same way. -While writing your own test, it might also be wise to set the random number stream before taking real values and test values so that the test failing isn't because randomness. - -9 function testElements -10 k = 10.1 -11 l = 10 -12 assertElementsAlmostEqual(k, l, 'absolute', 0.5) % Works fine -13 assertElementsAlmostEqual(k, l, 'absolute', 0.001) % Error -14 assertElementsAlmostEqual(k, l, 'relative', 0.01) % Works fine(1% error tolerance level) - - -You can run a specific test with command "runtests testName", and all the tests in the root directory(functions which include string "test") simply by writing "runtests". -As mentioned before, script testAll runs all of the tests also, but with it, you can check results later, simply by examining logger object. - -For more thorough tutorial to MATLAB xUnit Test Framework and for some additional testing functions, visit -http://www.mathworks.com/matlabcentral/fx_files/22846/11/content/matlab_xunit/doc/xunit_product_page.html - -Real values from revision 990. - - diff --git a/xunit/log/Readme.txt b/xunit/log/Readme.txt deleted file mode 100644 index 449cff44..00000000 --- a/xunit/log/Readme.txt +++ /dev/null @@ -1,4 +0,0 @@ -In this folder are logfiles of every demo that xunit tests. These logs are -for debugging purposes. When comparing, remember to initialize random stream. - -Created with GPstuff revision 990. \ No newline at end of file diff --git a/xunit/log/create_files.m b/xunit/log/create_files.m deleted file mode 100644 index 2435866b..00000000 --- a/xunit/log/create_files.m +++ /dev/null @@ -1,379 +0,0 @@ -% Author: Aki Vehtari -% Last modified: 2011-03-10 11:15:15 EET -diary off;clear;close all; -load realValuesBinomial1.mat -var=who(); -setrandstream(0); -diary('demo_binomial1.txt'); -disp('Running: demo_binomial1') -demo_binomial1 -fprintf('\n gp hyperparameters: \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesBinomial1.mat', char(var(1))); -for i=2:length(var) - save('realValuesBinomial1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesBinomial_apc.mat -var=who(); -setrandstream(0); -diary('demo_binomial_apc.txt'); -disp('Running: demo_binomial_apc') -demo_binomial_apc -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesBinomial_apc.mat', char(var(1))); -for i=2:length(var) - save('realValuesBinomial_apc.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesClassific.mat -var=who(); -setrandstream(0); -diary('demo_classific.txt'); -disp('Running: demo_classific') -demo_classific -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesClassific.mat', char(var(1))); -for i=2:length(var) - save('realValuesClassific.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesDerivativeobs.mat -var=who(); -setrandstream(0); -diary('demo_derivativeobs.txt'); -disp('Running: demo_derivativeobs') -demo_derivativeobs -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesDerivativeobs.mat', char(var(1))); -for i=2:length(var) - save('realValuesDerivativeobs.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesLgcp.mat -var=who(); -setrandstream(0); -diary('demo_lgcp.txt'); -disp('Running: demo_lgcp') -demo_lgcp -diary off; -save('realValuesLgcp.mat', char(var(1))); -for i=2:length(var) - save('realValuesLgcp.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesModelAssesment1.mat -var=who(); -setrandstream(0); -diary('demo_modelassesment1.txt'); -disp('Running: demo_modelassesment1') -demo_modelassesment1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesModelAssesment1.mat', char(var(1))); -for i=2:length(var) - save('realValuesModelAssesment1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesModelAssesment2.mat -var=who(); -setrandstream(0); -diary('demo_modelassesment2.txt'); -disp('Running: demo_modelassesment2') -demo_modelassesment2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesModelAssesment2.mat', char(var(1))); -for i=2:length(var) - save('realValuesModelAssesment2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesMulticlass.mat -var=who(); -setrandstream(0); -diary('demo_multiclass.txt'); -disp('Running: demo_multiclass') -demo_multiclass -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesMulticlass.mat', char(var(1))); -for i=2:length(var) - save('realValuesMulticlass.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesNeuralnetcov.mat -var=who(); -setrandstream(0); -diary('demo_neuralnetcov.txt'); -disp('Running: demo_neuralnetcov') -demo_neuralnetcov -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesNeuralnetcov.mat', char(var(1))); -for i=2:length(var) - save('realValuesNeuralnetcov.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesPeriodic.mat -var=who(); -setrandstream(0); -diary('demo_periodic.txt'); -disp('Running: demo_periodic') -demo_periodic -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesPeriodic.mat', char(var(1))); -for i=2:length(var) - save('realValuesPeriodic.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression1.mat -var=who(); -setrandstream(0); -diary('demo_regression1.txt'); -disp('Running: demo_regression1') -demo_regression1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -% drawnow;clear;close all -% disp('Running: demo_regression2') -% demo_regression2 -diary off; -save('realValuesRegression1.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_additive1.mat -var=who(); -setrandstream(0); -diary('demo_regression_additive1.txt'); -disp('Running: demo_regression_additive1') -demo_regression_additive1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_additive1.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_additive1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_additive2.mat -var=who(); -setrandstream(0); -diary('demo_regression_additive2.txt'); -disp('Running: demo_regression_additive2') -demo_regression_additive2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_additive2.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_additive2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_hier.mat -var=who(); -setrandstream(0); -diary('demo_regression_hier.txt'); -disp('Running: demo_regression_hier') -demo_regression_hier -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_hier.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_hier.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_meanf.mat -var=who(); -setrandstream(0); -diary('demo_regression_meanf.txt'); -disp('Running: demo_regression_meanf') -demo_regression_meanf -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_meanf.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_meanf.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_ppcs.mat -var=who(); -setrandstream(0); -diary('demo_regression_ppcs.txt'); -disp('Running: demo_regression_ppcs') -demo_regression_ppcs -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_ppcs.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_ppcs.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_robust.mat -var=who(); -setrandstream(0); -diary('demo_regression_robust.txt'); -disp('Running: demo_regression_robust') -demo_regression_robust -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_robust.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_robust.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_sparse1.mat -var=who(); -setrandstream(0); -diary('demo_regression_sparse1.txt'); -disp('Running: demo_regression_sparse1') -demo_regression_sparse1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_sparse1.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_sparse1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesRegression_sparse2.mat -var=who(); -setrandstream(0); -diary('demo_regression_sparse2.txt'); -disp('Running: demo_regression_sparse2') -demo_regression_sparse2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesRegression_sparse2.mat', char(var(1))); -for i=2:length(var) - save('realValuesRegression_sparse2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSpatial1.mat -var=who(); -setrandstream(0); -diary('demo_spatial1.txt'); -disp('Running: demo_spatial1') -demo_spatial1 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSpatial1.mat', char(var(1))); -for i=2:length(var) - save('realValuesSpatial1.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSpatial2.mat -var=who(); -setrandstream(0); -diary('demo_spatial2.txt'); -disp('Running: demo_spatial2') -demo_spatial2 -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSpatial2.mat', char(var(1))); -for i=2:length(var) - save('realValuesSpatial2.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSurvival_weibull.mat -var=who(); -setrandstream(0); -diary('demo_survival_weibull.txt'); -disp('Running: demo_survival_weibull') -demo_survival_weibull -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSurvival_weibull.mat', char(var(1))); -for i=2:length(var) - save('realValuesSurvival_weibull.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesSurvival_coxph.mat -var=who(); -setrandstream(0); -diary('demo_survival_coxph.txt'); -disp('Running: demo_survival_coxph') -demo_survival_coxph -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesSurvival_coxph.mat', char(var(1))); -for i=2:length(var) - save('realValuesSurvival_coxph.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesZinegbin.mat -var=who(); -setrandstream(0); -diary('demo_zinegbin.txt'); -disp('Running: demo_zinegbin') -demo_zinegbin -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesZinegbin.mat', char(var(1))); -for i=2:length(var) - save('realValuesZinegbin.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - -load realValuesMultinom.mat -var=who(); -setrandstream(0); -diary('demo_multinom.txt'); -disp('Running: demo_multinom2') -demo_multinom -fprintf('\n gp hyperparameters (gp_pak(gp)): \n \n') -disp(gp_pak(gp)) -diary off; -save('realValuesMultinom.mat', char(var(1))); -for i=2:length(var) - save('realValuesMultinom.mat', char(var(i)), '-append'); -end -drawnow;clear;close all; - diff --git a/xunit/log/demo_binomial1.txt b/xunit/log/demo_binomial1.txt deleted file mode 100644 index ee5394ac..00000000 --- a/xunit/log/demo_binomial1.txt +++ /dev/null @@ -1,7 +0,0 @@ -Running: demo_binomial1 - TolFun reached. Func-count 37. Final f(x)=169.569. Elapsed time 0.40 - - gp hyperparameters: - - -0.7903 -1.0035 - diff --git a/xunit/log/demo_binomial_apc.txt b/xunit/log/demo_binomial_apc.txt deleted file mode 100644 index 808f0003..00000000 --- a/xunit/log/demo_binomial_apc.txt +++ /dev/null @@ -1,61 +0,0 @@ -Running: demo_binomial_apc - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 1958.28 - 1 2 2 1911.95 0.00221514 - 2 6 6 1877.23 0.001 - 3 9 9 1847 0.01 - 4 12 12 1845.58 0.0163264 - 5 14 14 1787.72 0.5 - 6 18 18 1781.43 0.00238076 - 7 22 22 1771.39 0.0225751 - 8 23 23 1730.37 1 - 9 24 24 1723.09 1 - 10 25 25 1720.62 1 - 11 26 26 1720.31 1 - 12 27 27 1719.5 1 - 13 28 28 1719.15 1 - 14 29 29 1718.86 1 - 15 30 30 1718.68 1 - 16 31 31 1718.47 1 - 17 32 32 1718.41 1 - 18 33 33 1718.28 1 - 19 34 34 1718.15 1 - 20 35 35 1717.93 1 - 21 36 36 1717.68 1 - 22 37 37 1717.1 1 - 23 38 38 1716.73 1 - 24 40 40 1716.48 0.1 - 25 42 42 1716.22 0.1 - 26 44 44 1715.82 0.357035 - 27 45 45 1715.53 1 - 28 46 46 1715.25 1 - 29 47 47 1715 1 - 30 48 48 1714.92 1 - 31 49 49 1714.84 1 - 32 51 51 1714.71 0.22984 - 33 53 53 1714.63 0.1 - 34 54 54 1714.5 1 - 35 59 59 1713.38 0.351144 - 36 63 63 1713.36 0.00471445 - 37 64 64 1713.19 1 - 38 65 65 1712.82 1 - 39 66 66 1712.25 1 - 40 67 67 1711.83 1 - 41 68 68 1711.7 1 - 42 69 69 1711.67 1 - 43 70 70 1711.67 1 - 44 71 71 1711.67 1 - 45 72 72 1711.67 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 46 - Function Count : 73 - Minimum found : 1711.6691 - Intern Time : 0.060406 seconds - Total Time : 14.2611 seconds - - gp hyperparameters (gp_pak(gp)): - - 0.7967 2.4325 -1.1381 1.1745 -3.6054 1.8489 -0.1535 1.9498 1.9306 - diff --git a/xunit/log/demo_classific.txt b/xunit/log/demo_classific.txt deleted file mode 100644 index 936159a9..00000000 --- a/xunit/log/demo_classific.txt +++ /dev/null @@ -1,28 +0,0 @@ -Running: demo_classific -Probit model with Laplace integration over the latent values and -MAP estimate for the parameters - TolFun reached. Func-count 27. Final f(x)=81.5289. Elapsed time 1.17 -Probit model with EP integration over the latent values and -MAP estimate for the parameters - TolFun reached. Func-count 7. Final f(x)=81.748. Elapsed time 6.07 -Probit model with MCMC integration over the latent values and -the parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -1180.356 - 40 -1151.627 - 60 -1127.383 - 80 -1112.349 - 100 -1098.458 - 120 -1105.191 - 140 -1136.302 - 160 -1104.081 - 180 -1063.957 - 200 -1068.583 - 220 -1069.501 -Compare MCMC, Laplace and EP results for two latent variables - - gp hyperparameters (gp_pak(gp)): - - 3.2596 -0.7244 0.0597 - diff --git a/xunit/log/demo_derivativeobs.txt b/xunit/log/demo_derivativeobs.txt deleted file mode 100644 index 8311299f..00000000 --- a/xunit/log/demo_derivativeobs.txt +++ /dev/null @@ -1,26 +0,0 @@ -Running: demo_derivativeobs -GP model without derivative obs -Checking gradient ... - - analytic diffs delta - - 2.6657 2.6657 0.0000 - -3.7894 -3.7894 -0.0000 - 1.6251 1.6251 -0.0000 - - TolX reached. Func-count 19. Final f(x)=2.89367. Elapsed time 0.12 -GP model with derivative obs -Checking gradient ... - - analytic diffs delta - - 4.3476 4.3476 0.0000 - -13.6379 -13.6379 -0.0000 - 4.4016 4.4016 -0.0000 - - TolFun reached. Func-count 26. Final f(x)=-8.68413. Elapsed time 0.20 - - gp hyperparameters (gp_pak(gp)): - - -1.5025 -0.2305 -4.9053 - diff --git a/xunit/log/demo_lgcp.txt b/xunit/log/demo_lgcp.txt deleted file mode 100644 index 29ee1208..00000000 --- a/xunit/log/demo_lgcp.txt +++ /dev/null @@ -1,4 +0,0 @@ -Running: demo_lgcp -Coal disaster data with EP integration over the latent values -Redwood data with Laplace integration over the latent -values and MAP estimate for the parameters diff --git a/xunit/log/demo_modelassesment1.txt b/xunit/log/demo_modelassesment1.txt deleted file mode 100644 index f2cd19af..00000000 --- a/xunit/log/demo_modelassesment1.txt +++ /dev/null @@ -1,567 +0,0 @@ -Running: demo_modelassesment1 -Full GP model with Gaussian noise model -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 16 - Function Count : 24 - Minimum found : 44.1644 - Intern Time : 0.014683 seconds - Total Time : 0.58223 seconds -MAP estimate for the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -MAP estimate for the parameters - LOO-CV -MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.162 sls - 40 44.536 sls - 60 46.492 sls - 80 44.724 sls - 100 46.073 sls - 120 48.356 sls - 140 45.043 sls - 160 44.583 sls - 180 45.860 sls - 200 45.606 sls - 220 45.049 sls -MCMC integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - cycle etr slsrej - 20 47.365 sls - 40 47.871 sls - The CV-fold number: 2/10 - cycle etr slsrej - 20 48.040 sls - 40 47.595 sls - The CV-fold number: 3/10 - cycle etr slsrej - 20 47.035 sls - 40 47.781 sls - The CV-fold number: 4/10 - cycle etr slsrej - 20 46.956 sls - 40 44.509 sls - The CV-fold number: 5/10 - cycle etr slsrej - 20 48.573 sls - 40 46.945 sls - The CV-fold number: 6/10 - cycle etr slsrej - 20 50.441 sls - 40 50.100 sls - The CV-fold number: 7/10 - cycle etr slsrej - 20 47.685 sls - 40 46.701 sls - The CV-fold number: 8/10 - cycle etr slsrej - 20 43.340 sls - 40 41.618 sls - The CV-fold number: 9/10 - cycle etr slsrej - 20 47.305 sls - 40 47.582 sls - The CV-fold number: 10/10 - cycle etr slsrej - 20 47.354 sls - 40 46.455 sls -MCMC integration over the parameters - LOO-CV -Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 335 points - Elapsed time 1.79 seconds - IA-grid: Total elapsed time 2.02 seconds -Grid integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 326 points - Elapsed time 1.51 seconds - IA-grid: Total elapsed time 2.16 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 333 points - Elapsed time 1.58 seconds - IA-grid: Total elapsed time 2.25 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 372 points - Elapsed time 1.76 seconds - IA-grid: Total elapsed time 2.21 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 342 points - Elapsed time 1.60 seconds - IA-grid: Total elapsed time 2.36 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 357 points - Elapsed time 1.67 seconds - IA-grid: Total elapsed time 2.14 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 314 points - Elapsed time 1.47 seconds - IA-grid: Total elapsed time 2.31 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 354 points - Elapsed time 1.60 seconds - IA-grid: Total elapsed time 2.27 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 363 points - Elapsed time 1.66 seconds - IA-grid: Total elapsed time 2.61 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 351 points - Elapsed time 1.65 seconds - IA-grid: Total elapsed time 1.99 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 341 points - Elapsed time 1.58 seconds - IA-grid: Total elapsed time 2.05 seconds -Grid integration over the parameters - LOO-CV -GP with FIC sparse approximation - -gp_fic = - - type: 'FIC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-06 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 19 - Function Count : 30 - Minimum found : 42.5028 - Intern Time : 0.015807 seconds - Total Time : 0.39031 seconds - -n = - - 225 - -MAP estimate for the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -MAP estimate for the parameters - LOO-CV -MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 43.432 sls - 40 47.122 sls - 60 43.719 sls - 80 46.022 sls - 100 43.813 sls - 120 43.664 sls - 140 45.968 sls - 160 43.173 sls - 180 45.430 sls - 200 43.797 sls - 220 45.146 sls -MCMC integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 47.086 sls - 40 46.972 sls - The CV-fold number: 2/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.050 sls - 40 46.532 sls - The CV-fold number: 3/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.107 sls - 40 47.328 sls - The CV-fold number: 4/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 48.334 sls - 40 42.812 sls - The CV-fold number: 5/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.991 sls - 40 45.679 sls - The CV-fold number: 6/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.842 sls - 40 46.800 sls - The CV-fold number: 7/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.764 sls - 40 46.945 sls - The CV-fold number: 8/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 39.821 sls - 40 41.284 sls - The CV-fold number: 9/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 47.632 sls - 40 47.333 sls - The CV-fold number: 10/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.015 sls - 40 44.994 sls -MCMC integration over the parameters - LOO-CV -Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 348 points - Elapsed time 1.48 seconds - IA-grid: Total elapsed time 1.60 seconds -Grid integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 296 points - Elapsed time 1.22 seconds - IA-grid: Total elapsed time 1.83 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 341 points - Elapsed time 1.48 seconds - IA-grid: Total elapsed time 1.88 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 385 points - Elapsed time 1.60 seconds - IA-grid: Total elapsed time 2.07 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 312 points - Elapsed time 1.39 seconds - IA-grid: Total elapsed time 1.98 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 348 points - Elapsed time 1.47 seconds - IA-grid: Total elapsed time 1.78 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 350 points - Elapsed time 1.57 seconds - IA-grid: Total elapsed time 2.05 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 336 points - Elapsed time 1.39 seconds - IA-grid: Total elapsed time 2.42 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 353 points - Elapsed time 1.50 seconds - IA-grid: Total elapsed time 2.04 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 337 points - Elapsed time 1.43 seconds - IA-grid: Total elapsed time 1.78 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 324 points - Elapsed time 1.36 seconds - IA-grid: Total elapsed time 1.80 seconds -Grid integration over the parameters - LOO-CV -GP with PIC sparse approximation -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 14 - Function Count : 22 - Minimum found : 41.3886 - Intern Time : 0.010499 seconds - Total Time : 0.73232 seconds -MAP estimate for the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -MAP estimate for the parameters - LOO-CV -MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 43.063 sls - 40 43.080 sls - 60 42.595 sls - 80 42.580 sls - 100 42.209 sls - 120 42.570 sls - 140 41.681 sls - 160 41.755 sls - 180 42.976 sls - 200 45.913 sls - 220 43.838 sls -MCMC integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 44.577 sls - 40 44.882 sls - The CV-fold number: 2/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.445 sls - 40 47.048 sls - The CV-fold number: 3/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 49.586 sls - 40 45.713 sls - The CV-fold number: 4/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 42.448 sls - 40 44.822 sls - The CV-fold number: 5/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.504 sls - 40 43.310 sls - The CV-fold number: 6/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.215 sls - 40 44.736 sls - The CV-fold number: 7/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 45.338 sls - 40 45.546 sls - The CV-fold number: 8/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 41.780 sls - 40 41.196 sls - The CV-fold number: 9/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 46.193 sls - 40 46.335 sls - The CV-fold number: 10/10 - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 43.116 sls - 40 43.045 sls -MCMC integration over the parameters - LOO-CV -Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 338 points - Elapsed time 3.30 seconds - IA-grid: Total elapsed time 3.67 seconds -Grid integration over the parameters - k-fold-CV -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 347 points - Elapsed time 3.35 seconds - IA-grid: Total elapsed time 4.29 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 337 points - Elapsed time 3.29 seconds - IA-grid: Total elapsed time 4.32 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 355 points - Elapsed time 3.42 seconds - IA-grid: Total elapsed time 4.54 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - Elapsed time 1.11 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 348 points - Elapsed time 3.34 seconds - IA-grid: Total elapsed time 4.70 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - Elapsed time 1.09 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 352 points - Elapsed time 3.39 seconds - IA-grid: Total elapsed time 4.71 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 336 points - Elapsed time 3.20 seconds - IA-grid: Total elapsed time 4.09 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - Elapsed time 1.65 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 347 points - Elapsed time 3.32 seconds - IA-grid: Total elapsed time 5.22 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - Elapsed time 1.09 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 342 points - Elapsed time 3.25 seconds - IA-grid: Total elapsed time 4.58 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - Elapsed time 1.30 seconds - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 335 points - Elapsed time 3.20 seconds - IA-grid: Total elapsed time 4.74 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 344 points - Elapsed time 3.30 seconds - IA-grid: Total elapsed time 4.41 seconds -Grid integration over the parameters - LOO-CV -Summary of the results - -S = - - full_MAP full_MCMC full_IA FIC_MAP FIC_MCMC FIC_IA PIC_MAP PIC_MCMC PIC_IA - CV-mlpd 0.05 0.06 0.05 0.06 0.06 0.06 0.07 0.08 0.08 - CV-rmse 0.24 0.24 0.24 0.23 0.23 0.23 0.23 0.23 0.23 - LOO-mlpd 0.06 0.05 0.05 0.06 0.05 0.05 0.07 0.06 0.06 - LOO-rmse 0.24 0.24 0.24 0.23 0.23 0.23 0.23 0.23 0.23 - - WAIC_V 0.07 0.06 0.07 0.11 -0.11 -0.11 0.09 0.08 0.08 - WAIC_G 0.10 0.09 0.09 0.17 0.41 0.42 0.12 0.12 0.12 - - DIC_h NaN -0.20 -0.20 NaN -0.19 -0.19 NaN -0.19 -0.18 - DIC_a NaN 0.05 0.07 NaN 0.12 0.12 NaN 0.09 0.09 - DIC_l 0.07 0.00 0.00 0.11 0.00 0.00 0.09 - peff_h NaN 3.47 3.25 NaN 4.20 3.21 NaN 3.64 3.34 - peff_a NaN 43.12 39.79 NaN 81.90 82.93 NaN 46.67 47.16 - peff_l 37.44 0.00 0.00 76.09 0.00 0.00 45.15 - peff_l2 37.61 0.00 0.00 76.28 0.00 0.00 45.22 - - - The notation is as follows: - CV-mlpd = mean log predictive density from the 10-fold CV. - CV-rmse = root mean squared error from the 10-fold LOO-CV. - LOO-mlpd = mean log predictive density from the LOO-CV. - LOO-rmse = root mean squared error from the 10-fold CV. - WAIC_V = WAIC via variance method - WAIC_G = WAIC via Gibbs training utility method - DIC_h = DIC with focus on parameters. - DIC_a = DIC with focus on parameters and latent variables (all). - DIC_l = DIC with focus on latent variables. - peff_h = effective number of parameters (latent variables marginalized). - peff_a = effective number of parameters and latent variables. - peff_l = effective number of latent variables evaluated with gp_peff. - peff_l2 = effective number of latent variables evaluated with gp_dic. - - - - gp hyperparameters (gp_pak(gp)): - - 1.0742 -0.1662 -0.2199 -3.1540 - diff --git a/xunit/log/demo_modelassesment2.txt b/xunit/log/demo_modelassesment2.txt deleted file mode 100644 index a9497f71..00000000 --- a/xunit/log/demo_modelassesment2.txt +++ /dev/null @@ -1,479 +0,0 @@ -Running: demo_modelassesment2 -Data analysis with probit likelihood -Probit with Laplace integration over the latent values -and MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 9 - Function Count : 13 - Minimum found : 78.8894 - Intern Time : 0.007959 seconds - Total Time : 0.57985 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Probit with EP integration over the latent values and MAP -estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 4 - Function Count : 8 - Minimum found : 79.1395 - Intern Time : 0.00512 seconds - Total Time : 10.3899 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Probit with MCMC integration over the latent values and -the parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -575.950 - 40 -502.803 - 60 -629.446 - 80 -581.402 - 100 -598.197 - 120 -591.179 - 140 -572.454 - 160 -536.323 - 180 -568.580 - 200 -577.057 - 220 -580.694 -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -463.609 - 40 -482.611 - The CV-fold number: 2/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -475.936 - 40 -481.111 - The CV-fold number: 3/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -501.412 - 40 -507.953 - The CV-fold number: 4/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -488.295 - 40 -473.341 - The CV-fold number: 5/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -556.474 - 40 -511.740 - The CV-fold number: 6/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -508.823 - 40 -451.618 - The CV-fold number: 7/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -529.089 - 40 -541.232 - The CV-fold number: 8/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -540.700 - 40 -519.126 - The CV-fold number: 9/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -500.448 - 40 -507.837 - The CV-fold number: 10/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -519.303 - 40 -500.739 -[Warning: For 1 data point the effective sample size in IS is less than n/10] -[> In gpmc_loopred at 114 - In gp_loopred at 86 - In demo_modelassesment2 at 137 - In create_files at 97] -Probit with EP integration over the latent values and -grid integration over the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 4 - Function Count : 7 - Minimum found : 79.1395 - Intern Time : 0.004746 seconds - Total Time : 8.9176 seconds - IA-grid: finding the mode - Elapsed time 2.63 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.75 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 34.27 seconds - IA-grid: Total elapsed time 44.66 seconds -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - Elapsed time 16.39 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 4.66 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.15 seconds - IA-grid: Total elapsed time 45.21 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - Elapsed time 19.41 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.44 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.30 seconds - IA-grid: Total elapsed time 49.15 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - Elapsed time 18.38 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 3.68 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 22.99 seconds - IA-grid: Total elapsed time 45.06 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - Elapsed time 18.87 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.60 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.77 seconds - IA-grid: Total elapsed time 48.25 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - Elapsed time 13.70 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.48 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.04 seconds - IA-grid: Total elapsed time 43.23 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - Elapsed time 19.35 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.56 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.71 seconds - IA-grid: Total elapsed time 48.63 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - Elapsed time 18.48 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.53 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.14 seconds - IA-grid: Total elapsed time 47.15 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - Elapsed time 16.33 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 4.63 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 23.39 seconds - IA-grid: Total elapsed time 44.37 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - Elapsed time 21.39 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 5.48 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 24.61 seconds - IA-grid: Total elapsed time 51.49 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - Elapsed time 15.92 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 4.58 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 22.99 seconds - IA-grid: Total elapsed time 43.49 seconds -Data analysis with logit likelihood -Logit with Laplace integration over the latent values and -MAP estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 11 - Function Count : 14 - Minimum found : 79.3867 - Intern Time : 0.009223 seconds - Total Time : 0.68307 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Logit with EP integration over the latent values and MAP -estimate for the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 4 - Function Count : 9 - Minimum found : 79.5156 - Intern Time : 0.005924 seconds - Total Time : 18.5342 seconds -Evaluating the CV utility. The inference method is MAP. - The CV-fold number: 1/10 - The CV-fold number: 2/10 - The CV-fold number: 3/10 - The CV-fold number: 4/10 - The CV-fold number: 5/10 - The CV-fold number: 6/10 - The CV-fold number: 7/10 - The CV-fold number: 8/10 - The CV-fold number: 9/10 - The CV-fold number: 10/10 -Logit with MCMC integration over the latent values and -the parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -540.405 - 40 -606.892 - 60 -612.259 - 80 -556.770 - 100 -569.173 - 120 -545.240 - 140 -549.607 - 160 -581.819 - 180 -609.128 - 200 -631.449 -Evaluating the CV utility. The inference method is MCMC. - The CV-fold number: 1/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -511.951 - 40 -540.085 - The CV-fold number: 2/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -514.969 - 40 -532.193 - The CV-fold number: 3/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -466.163 - 40 -508.252 - The CV-fold number: 4/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -539.359 - 40 -466.205 - The CV-fold number: 5/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -516.258 - 40 -542.188 - The CV-fold number: 6/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -508.894 - 40 -441.362 - The CV-fold number: 7/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -503.784 - 40 -526.379 - The CV-fold number: 8/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -564.005 - 40 -535.022 - The CV-fold number: 9/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -557.378 - 40 -505.166 - The CV-fold number: 10/10 - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -569.104 - 40 -397.043 -Logit with EP integration over the latent values and grid -integration over the parameters - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 8 - Function Count : 11 - Minimum found : 79.5156 - Intern Time : 0.007063 seconds - Total Time : 21.4417 seconds - IA-grid: finding the mode - Elapsed time 4.03 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 11.88 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 53.17 seconds - IA-grid: Total elapsed time 69.09 seconds -Evaluating the CV utility. The inference method is IA. - The CV-fold number: 1/10 - IA-grid: finding the mode - Elapsed time 25.47 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.50 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.23 seconds - IA-grid: Total elapsed time 71.20 seconds - The CV-fold number: 2/10 - IA-grid: finding the mode - Elapsed time 30.13 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.24 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.20 seconds - IA-grid: Total elapsed time 76.58 seconds - The CV-fold number: 3/10 - IA-grid: finding the mode - Elapsed time 29.52 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.33 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 36.49 seconds - IA-grid: Total elapsed time 73.35 seconds - The CV-fold number: 4/10 - IA-grid: finding the mode - Elapsed time 30.18 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.77 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 37.62 seconds - IA-grid: Total elapsed time 76.58 seconds - The CV-fold number: 5/10 - IA-grid: finding the mode - Elapsed time 30.57 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 9.06 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.56 seconds - IA-grid: Total elapsed time 78.20 seconds - The CV-fold number: 6/10 - IA-grid: finding the mode - Elapsed time 30.83 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.83 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 31 points - Elapsed time 41.13 seconds - IA-grid: Total elapsed time 80.79 seconds - The CV-fold number: 7/10 - IA-grid: finding the mode - Elapsed time 30.75 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.75 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.27 seconds - IA-grid: Total elapsed time 77.77 seconds - The CV-fold number: 8/10 - IA-grid: finding the mode - Elapsed time 25.82 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.10 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.15 seconds - IA-grid: Total elapsed time 71.08 seconds - The CV-fold number: 9/10 - IA-grid: finding the mode - Elapsed time 33.55 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 8.81 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 38.53 seconds - IA-grid: Total elapsed time 80.89 seconds - The CV-fold number: 10/10 - IA-grid: finding the mode - Elapsed time 25.29 seconds - IA-grid: computing Hessian using multiplication - Elapsed time 7.10 seconds - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 28 points - Elapsed time 36.07 seconds - IA-grid: Total elapsed time 68.47 seconds -Summary of the results - -S = - - pr_Laplace pr_EP pr_MCMC pr_IA lo_Laplace lo_EP lo_MCMC lo_IA - CV-mlpd -0.29 -0.28 -0.29 -0.28 -0.29 -0.28 -0.28 -0.28 - LOO-mlpd -0.28 -0.27 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 - - WAIC -0.31 -0.28 -0.29 -0.28 -0.29 -0.28 -0.29 -0.28 - - DIC_h NaN NaN 163.51 163.20 NaN NaN 164.54 163.98 - DIC_a NaN NaN 140.40 138.95 NaN NaN 143.12 140.56 - DIC_l 148.71 138.45 NaN NaN 147.76 139.38 NaN NaN - peff_h NaN NaN 2.71 2.30 NaN NaN 2.52 2.32 - peff_a NaN NaN 9.48 8.72 NaN NaN 8.57 9.13 - peff_l 8.72 9.19 NaN NaN 8.84 9.18 NaN NaN - peff_l2 13.14 8.48 NaN NaN 12.23 8.53 NaN NaN - - The notation is as follows: - pr_* = probit likelihood and inference method - lo_* = logit likelihood and inference method - CV-mlpd = mean log predictive density from the 10-fold CV. - LOO-mlpd = mean log predictive density from the 10-fold CV. - WAIC = Widely applicable information criterion. - DIC_h = DIC with focus on parameters. - DIC_a = DIC with focus on parameters and laten variables (all). - DIC_l = DIC with focus on latent variables. - peff_h = effective number of parameters (latent variables marginalized). - peff_a = effective number of parameters and latent variables. - peff_l = effective number of latent variables evaluated with gp_peff. - peff_l2 = effective number of latent variables evaluated with gp_dic. - - - - gp hyperparameters (gp_pak(gp)): - - 3.8248 -0.8579 -0.1430 - diff --git a/xunit/log/demo_multiclass.txt b/xunit/log/demo_multiclass.txt deleted file mode 100644 index 6fb59c55..00000000 --- a/xunit/log/demo_multiclass.txt +++ /dev/null @@ -1,661 +0,0 @@ -Running: demo_multiclass -Softmax model with Laplace integration over the latent -values and MAP estimate for the parameters - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 277.318 - 1 2 2 192.117 0.0228515 - 2 4 4 187.466 0.174234 - 3 7 7 178.435 0.0383579 - 4 9 9 177.146 0.428584 - 5 11 11 174.94 0.553983 - 6 13 13 174.885 0.207579 - 7 14 14 174.863 1 - 8 15 15 174.862 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 9 - Function Count : 16 - Minimum found : 174.8621 - Intern Time : 0.012538 seconds - Total Time : 26.5103 seconds - -missed = - - 0.1333 - - -ans = - - 3.3233e+05 - - -ans = - - 1.0e+04 * - - -1.1812 5.6124 4.4558 1.5772 1.5647 - -Checking gradient ... - - analytic diffs delta - - 1.0e+05 * - - -0.3752 -0.3752 -0.0000 - 0.5975 0.5976 -0.0000 - 1.2293 1.2294 -0.0000 - 1.5432 1.5432 -0.0000 - 0.6947 0.6947 -0.0000 - - cycle etr hrej lrej lvScale - 2 -3630.139 0.0e+00 6.3e-01 0.044064 - cycle etr hrej lrej lvScale - 3 -4032.239 1.0e+00 8.0e-01 0.058940 - 4 -4053.019 0.0e+00 8.0e-01 0.060154 - 5 -4028.398 0.0e+00 2.0e-01 0.046021 - 6 -3980.359 1.0e+00 8.0e-01 0.072371 - 7 -3940.029 0.0e+00 4.0e-01 0.047938 - 8 -3893.048 0.0e+00 4.0e-01 0.075384 - 9 -3885.172 0.0e+00 6.0e-01 0.057673 - 10 -3841.865 0.0e+00 1.0e+00 0.090694 - 11 -3790.901 0.0e+00 2.0e-01 0.052012 - 12 -3777.442 0.0e+00 8.0e-01 0.081792 - 13 -3773.174 0.0e+00 4.0e-01 0.072275 - 14 -3757.864 0.0e+00 8.0e-01 0.063865 - 15 -3733.210 0.0e+00 4.0e-01 0.056433 - 16 -3727.272 0.0e+00 8.0e-01 0.057596 - 17 -3755.476 0.0e+00 1.0e+00 0.067894 - 18 -3749.937 0.0e+00 6.0e-01 0.051943 - 19 -3744.558 0.0e+00 1.0e+00 0.094344 - 20 -3726.777 0.0e+00 4.0e-01 0.054106 - 21 -3721.818 0.0e+00 6.0e-01 0.073665 - 22 -3704.419 0.0e+00 6.0e-01 0.075183 - 23 -3708.827 0.0e+00 4.0e-01 0.049800 - 24 -3711.581 0.0e+00 8.0e-01 0.090452 - 25 -3696.164 0.0e+00 6.0e-01 0.079927 - 26 -3698.931 1.0e+00 4.0e-01 0.061149 - 27 -3685.537 0.0e+00 6.0e-01 0.096159 - 28 -3695.580 0.0e+00 1.0e+00 0.073567 - 29 -3698.739 0.0e+00 6.0e-01 0.065007 - 30 -3666.937 0.0e+00 6.0e-01 0.088508 - 31 -3670.672 0.0e+00 8.0e-01 0.067713 - 32 -3667.702 0.0e+00 6.0e-01 0.069109 - 33 -3674.018 0.0e+00 8.0e-01 0.061067 - 34 -3658.782 0.0e+00 6.0e-01 0.083144 - 35 -3676.072 0.0e+00 8.0e-01 0.063610 - 36 -3674.349 0.0e+00 1.0e+00 0.064920 - 37 -3680.623 0.0e+00 8.0e-01 0.076528 - 38 -3675.575 0.0e+00 4.0e-01 0.078105 - 39 -3677.905 0.0e+00 8.0e-01 0.069017 - 40 -3676.888 0.0e+00 8.0e-01 0.108532 - 41 -3673.702 0.0e+00 1.0e+00 0.083033 - 42 -3653.888 0.0e+00 8.0e-01 0.073371 - 43 -3691.960 0.0e+00 1.0e+00 0.086490 - 44 -3685.423 0.0e+00 8.0e-01 0.088272 - 45 -3669.311 0.0e+00 8.0e-01 0.090091 - 46 -3681.940 0.0e+00 8.0e-01 0.068925 - 47 -3716.324 0.0e+00 6.0e-01 0.081248 - 48 -3717.851 0.0e+00 8.0e-01 0.071794 - 49 -3721.918 0.0e+00 8.0e-01 0.054927 - 50 -3707.091 0.0e+00 4.0e-01 0.074783 - 51 -3706.217 0.0e+00 8.0e-01 0.101818 - 52 -3731.470 1.0e+00 8.0e-01 0.077897 - 53 -3716.291 1.0e+00 6.0e-01 0.091825 - 54 -3726.246 0.0e+00 4.0e-01 0.093717 - 55 -3718.661 0.0e+00 6.0e-01 0.046533 - 56 -3731.286 0.0e+00 6.0e-01 0.063356 - 57 -3742.154 0.0e+00 8.0e-01 0.074684 - 58 -3728.339 0.0e+00 8.0e-01 0.088037 - 59 -3724.324 1.0e+00 4.0e-01 0.067353 - 60 -3717.302 0.0e+00 8.0e-01 0.059516 - 61 -3708.863 0.0e+00 6.0e-01 0.060742 - 62 -3725.193 0.0e+00 6.0e-01 0.071603 - 63 -3723.366 0.0e+00 6.0e-01 0.073078 - 64 -3707.145 0.0e+00 1.0e+00 0.114919 - 65 -3717.792 0.0e+00 4.0e-01 0.076121 - 66 -3709.505 0.0e+00 2.0e-01 0.067264 - 67 -3703.030 0.0e+00 8.0e-01 0.079290 - 68 -3708.942 0.0e+00 6.0e-01 0.052521 - 69 -3698.483 1.0e+00 2.0e-01 0.061911 - 70 -3699.155 0.0e+00 8.0e-01 0.063187 - 71 -3705.444 0.0e+00 1.0e+00 0.086030 - 72 -3701.841 0.0e+00 6.0e-01 0.056985 - 73 -3722.685 0.0e+00 8.0e-01 0.043597 - 74 -3713.631 0.0e+00 6.0e-01 0.044495 - 75 -3701.339 0.0e+00 4.0e-01 0.052451 - 76 -3713.346 0.0e+00 6.0e-01 0.061829 - 77 -3716.753 0.0e+00 1.0e+00 0.072884 - 78 -3701.588 0.0e+00 1.0e+00 0.099232 - 79 -3726.711 1.0e+00 8.0e-01 0.075918 - 80 -3725.340 0.0e+00 6.0e-01 0.067084 - 81 -3720.839 0.0e+00 8.0e-01 0.091336 - 82 -3704.350 0.0e+00 8.0e-01 0.052381 - 83 -3679.164 0.0e+00 4.0e-01 0.053460 - 84 -3707.980 0.0e+00 6.0e-01 0.072787 - 85 -3692.998 1.0e+00 8.0e-01 0.085801 - 86 -3688.398 0.0e+00 8.0e-01 0.075817 - 87 -3681.445 0.0e+00 6.0e-01 0.066995 - 88 -3671.677 0.0e+00 8.0e-01 0.068375 - 89 -3661.035 0.0e+00 8.0e-01 0.069784 - 90 -3671.146 0.0e+00 2.0e-01 0.053389 - 91 -3682.621 0.0e+00 8.0e-01 0.083957 - 92 -3683.228 0.0e+00 1.0e+00 0.098968 - 93 -3682.524 0.0e+00 1.0e+00 0.075716 - 94 -3682.648 1.0e+00 2.0e-01 0.066906 - 95 -3663.789 0.0e+00 1.0e+00 0.091093 - 96 -3680.966 0.0e+00 6.0e-01 0.092970 - 97 -3699.825 0.0e+00 8.0e-01 0.071127 - 98 -3697.981 0.0e+00 4.0e-01 0.083845 - 99 -3698.312 0.0e+00 8.0e-01 0.085572 - 100 -3683.373 0.0e+00 1.0e+00 0.065468 - 101 -3711.060 1.0e+00 8.0e-01 0.118908 - 102 -3687.310 0.0e+00 6.0e-01 0.068193 - 103 -3695.434 0.0e+00 6.0e-01 0.060258 - 104 -3701.476 0.0e+00 6.0e-01 0.071032 - 105 -3724.366 0.0e+00 8.0e-01 0.083733 - 106 -3687.923 1.0e+00 8.0e-01 0.085458 - 107 -3674.035 0.0e+00 8.0e-01 0.087219 - 108 -3677.447 0.0e+00 8.0e-01 0.089016 - 109 -3677.125 1.0e+00 6.0e-01 0.078658 - 110 -3704.872 0.0e+00 8.0e-01 0.092722 - 111 -3679.830 0.0e+00 8.0e-01 0.081933 - 112 -3690.769 0.0e+00 6.0e-01 0.062684 - 113 -3698.661 0.0e+00 8.0e-01 0.063975 - 114 -3692.375 0.0e+00 2.0e-01 0.048945 - 115 -3685.783 0.0e+00 4.0e-01 0.049953 - 116 -3709.086 0.0e+00 8.0e-01 0.078554 - 117 -3710.026 0.0e+00 1.0e+00 0.060098 - 118 -3704.776 0.0e+00 8.0e-01 0.109155 - 119 -3684.968 0.0e+00 1.0e+00 0.096454 - 120 -3680.361 0.0e+00 6.0e-01 0.063890 - 121 -3685.339 0.0e+00 1.0e+00 0.075313 - 122 -3677.302 0.0e+00 6.0e-01 0.057619 - 123 -3675.051 0.0e+00 8.0e-01 0.067921 - 124 -3676.066 0.0e+00 8.0e-01 0.060018 - 125 -3697.481 0.0e+00 8.0e-01 0.081715 - 126 -3676.333 0.0e+00 8.0e-01 0.096325 - 127 -3685.888 0.0e+00 6.0e-01 0.063805 - 128 -3675.200 0.0e+00 8.0e-01 0.056380 - 129 -3689.750 0.0e+00 8.0e-01 0.066461 - 130 -3683.769 0.0e+00 8.0e-01 0.044023 - 131 -3685.536 0.0e+00 6.0e-01 0.069228 - 132 -3672.793 0.0e+00 6.0e-01 0.045856 - 133 -3699.680 0.0e+00 6.0e-01 0.072110 - 134 -3698.356 0.0e+00 1.0e+00 0.113397 - 135 -3675.906 0.0e+00 4.0e-01 0.048749 - 136 -3695.722 0.0e+00 6.0e-01 0.066373 - 137 -3685.107 0.0e+00 4.0e-01 0.058650 - 138 -3678.093 0.0e+00 1.0e+00 0.051825 - 139 -3660.909 0.0e+00 6.0e-01 0.070560 - 140 -3670.280 0.0e+00 8.0e-01 0.062350 - 141 -3662.903 0.0e+00 8.0e-01 0.073498 - 142 -3671.009 0.0e+00 8.0e-01 0.048684 - 143 -3678.924 0.0e+00 6.0e-01 0.066284 - 144 -3684.902 0.0e+00 8.0e-01 0.043906 - 145 -3679.772 0.0e+00 2.0e-01 0.044810 - 146 -3697.382 0.0e+00 6.0e-01 0.052822 - 147 -3697.188 0.0e+00 8.0e-01 0.053911 - 148 -3692.277 0.0e+00 1.0e+00 0.073400 - 149 -3678.517 1.0e+00 4.0e-01 0.064859 - 150 -3678.059 0.0e+00 8.0e-01 0.057312 - 151 -3683.841 0.0e+00 6.0e-01 0.058493 - 152 -3690.647 0.0e+00 6.0e-01 0.059699 - 153 -3695.564 0.0e+00 8.0e-01 0.070373 - 154 -3685.961 0.0e+00 6.0e-01 0.062184 - 155 -3686.625 0.0e+00 4.0e-01 0.047574 - 156 -3706.012 0.0e+00 6.0e-01 0.064773 - 157 -3714.564 0.0e+00 4.0e-01 0.049555 - 158 -3712.441 0.0e+00 8.0e-01 0.067470 - 159 -3714.690 0.0e+00 6.0e-01 0.044691 - 160 -3718.592 0.0e+00 8.0e-01 0.045612 - 161 -3713.359 0.0e+00 8.0e-01 0.053767 - 162 -3694.630 0.0e+00 6.0e-01 0.063381 - 163 -3680.489 0.0e+00 4.0e-01 0.056006 - 164 -3680.761 1.0e+00 1.0e+00 0.049489 - 165 -3668.930 0.0e+00 4.0e-01 0.050509 - 166 -3676.813 0.0e+00 8.0e-01 0.038642 - 167 -3690.351 0.0e+00 1.0e+00 0.052612 - 168 -3706.354 0.0e+00 8.0e-01 0.040251 - 169 -3709.019 0.0e+00 6.0e-01 0.073107 - 170 -3715.268 0.0e+00 6.0e-01 0.048425 - 171 -3702.798 0.0e+00 8.0e-01 0.057084 - 172 -3710.075 0.0e+00 8.0e-01 0.043672 - 173 -3706.140 0.0e+00 8.0e-01 0.044572 - 174 -3709.499 0.0e+00 6.0e-01 0.060685 - 175 -3712.606 0.0e+00 2.0e-01 0.040197 - 176 -3721.953 0.0e+00 8.0e-01 0.047384 - 177 -3729.776 0.0e+00 1.0e+00 0.041871 - 178 -3715.200 0.0e+00 6.0e-01 0.042733 - 179 -3709.049 0.0e+00 4.0e-01 0.032694 - 180 -3706.811 0.0e+00 8.0e-01 0.059381 - 181 -3713.991 0.0e+00 8.0e-01 0.052471 - 182 -3708.743 0.0e+00 8.0e-01 0.034756 - 183 -3710.760 0.0e+00 1.0e+00 0.054656 - 184 -3725.810 0.0e+00 4.0e-01 0.041815 - 185 -3706.343 0.0e+00 4.0e-01 0.036949 - 186 -3703.040 0.0e+00 8.0e-01 0.037711 - 187 -3705.821 0.0e+00 8.0e-01 0.033323 - 188 -3718.776 0.0e+00 6.0e-01 0.052402 - 189 -3701.931 0.0e+00 1.0e+00 0.040090 - 190 -3711.377 0.0e+00 8.0e-01 0.063044 - 191 -3713.259 1.0e+00 6.0e-01 0.048232 - 192 -3698.395 0.0e+00 2.0e-01 0.036900 - 193 -3695.592 1.0e+00 8.0e-01 0.037660 - 194 -3699.589 0.0e+00 1.0e+00 0.044394 - 195 -3690.943 0.0e+00 8.0e-01 0.045309 - 196 -3701.044 0.0e+00 6.0e-01 0.053410 - 197 -3685.322 0.0e+00 1.0e+00 0.030630 - 198 -3697.554 0.0e+00 8.0e-01 0.031261 - 199 -3684.124 0.0e+00 1.0e+00 0.075746 - 200 -3687.115 0.0e+00 1.0e+00 0.037610 - 201 -3680.852 1.0e+00 8.0e-01 0.059144 - 202 -3682.976 0.0e+00 8.0e-01 0.039176 - 203 -3685.804 0.0e+00 6.0e-01 0.034618 - 204 -3703.330 1.0e+00 2.0e-01 0.047132 - 205 -3700.588 0.0e+00 0.0e+00 0.036059 - 206 -3718.775 0.0e+00 6.0e-01 0.031863 - 207 -3719.587 1.0e+00 6.0e-01 0.050106 - 208 -3716.997 0.0e+00 6.0e-01 0.038334 - 209 -3720.949 0.0e+00 4.0e-01 0.039124 - 210 -3715.844 0.0e+00 4.0e-01 0.029932 - 211 -3710.903 0.0e+00 8.0e-01 0.054365 - 212 -3703.452 0.0e+00 6.0e-01 0.041593 - 213 -3728.312 0.0e+00 8.0e-01 0.075544 - 214 -3720.173 1.0e+00 6.0e-01 0.037510 - 215 -3715.815 0.0e+00 8.0e-01 0.044217 - 216 -3720.681 0.0e+00 4.0e-01 0.060202 - 217 -3722.977 0.0e+00 6.0e-01 0.046058 - 218 -3723.556 0.0e+00 6.0e-01 0.035237 - 219 -3724.104 0.0e+00 6.0e-01 0.041537 - 220 -3734.039 0.0e+00 4.0e-01 0.036704 - 221 -3731.760 0.0e+00 4.0e-01 0.043266 - 222 -3725.243 0.0e+00 8.0e-01 0.033101 - 223 -3727.375 0.0e+00 1.0e+00 0.039020 - 224 -3740.428 0.0e+00 8.0e-01 0.034479 - 225 -3749.251 0.0e+00 8.0e-01 0.035190 - 226 -3742.266 0.0e+00 6.0e-01 0.035915 - 227 -3743.516 1.0e+00 8.0e-01 0.048899 - 228 -3738.445 0.0e+00 6.0e-01 0.049906 - 229 -3736.723 0.0e+00 1.0e+00 0.067948 - 230 -3739.145 0.0e+00 1.0e+00 0.060041 - 231 -3731.766 0.0e+00 4.0e-01 0.039771 - 232 -3724.404 0.0e+00 8.0e-01 0.030427 - 233 -3715.938 0.0e+00 6.0e-01 0.055264 - 234 -3719.945 0.0e+00 4.0e-01 0.042280 - 235 -3722.815 0.0e+00 1.0e+00 0.057565 - 236 -3734.459 0.0e+00 8.0e-01 0.058751 - 237 -3732.625 0.0e+00 8.0e-01 0.029172 - 238 -3735.004 0.0e+00 2.0e-01 0.034388 - 239 -3748.334 0.0e+00 4.0e-01 0.035096 - 240 -3753.311 0.0e+00 6.0e-01 0.055190 - 241 -3767.828 0.0e+00 8.0e-01 0.048768 - 242 -3737.724 0.0e+00 6.0e-01 0.043094 - 243 -3745.035 0.0e+00 6.0e-01 0.058673 - 244 -3716.257 0.0e+00 6.0e-01 0.051846 - 245 -3710.719 0.0e+00 1.0e+00 0.052914 - 246 -3719.756 0.0e+00 2.0e-01 0.030346 - 247 -3715.939 0.0e+00 6.0e-01 0.055117 - 248 -3725.892 0.0e+00 6.0e-01 0.036509 - 249 -3727.125 0.0e+00 4.0e-01 0.049707 - 250 -3722.521 0.0e+00 1.0e+00 0.090282 - 251 -3723.261 0.0e+00 8.0e-01 0.044828 - 252 -3729.343 0.0e+00 6.0e-01 0.061034 - 253 -3728.795 0.0e+00 6.0e-01 0.046694 - 254 -3705.671 0.0e+00 2.0e-01 0.041261 - 255 -3712.400 0.0e+00 6.0e-01 0.036460 - 256 -3705.006 0.0e+00 8.0e-01 0.057335 - 257 -3700.611 0.0e+00 1.0e+00 0.050664 - 258 -3691.466 0.0e+00 6.0e-01 0.068979 - 259 -3717.621 0.0e+00 1.0e+00 0.070400 - 260 -3711.149 0.0e+00 6.0e-01 0.071851 - 261 -3697.893 0.0e+00 4.0e-01 0.041206 - 262 -3680.579 0.0e+00 1.0e+00 0.048574 - 263 -3684.235 0.0e+00 1.0e+00 0.057259 - 264 -3666.413 0.0e+00 2.0e-01 0.037927 - 265 -3658.248 0.0e+00 1.0e+00 0.044709 - 266 -3683.490 0.0e+00 4.0e-01 0.039506 - 267 -3687.218 0.0e+00 1.0e+00 0.071755 - 268 -3675.972 1.0e+00 8.0e-01 0.054897 - 269 -3699.735 0.0e+00 8.0e-01 0.041999 - 270 -3694.144 0.0e+00 4.0e-01 0.042865 - 271 -3724.429 0.0e+00 8.0e-01 0.050529 - 272 -3735.059 0.0e+00 4.0e-01 0.038657 - 273 -3733.287 0.0e+00 8.0e-01 0.045569 - 274 -3729.118 0.0e+00 1.0e+00 0.053717 - 275 -3733.868 0.0e+00 8.0e-01 0.035581 - 276 -3721.224 0.0e+00 6.0e-01 0.041943 - 277 -3726.049 0.0e+00 6.0e-01 0.076181 - 278 -3738.875 0.0e+00 8.0e-01 0.050461 - 279 -3737.922 0.0e+00 4.0e-01 0.028939 - 280 -3724.463 0.0e+00 6.0e-01 0.060709 - 281 -3724.505 0.0e+00 2.0e-01 0.040213 - 282 -3720.509 0.0e+00 8.0e-01 0.047403 - 283 -3721.817 0.0e+00 4.0e-01 0.031399 - 284 -3731.113 0.0e+00 6.0e-01 0.049377 - 285 -3724.858 0.0e+00 8.0e-01 0.050394 - 286 -3721.349 0.0e+00 8.0e-01 0.051432 - 287 -3708.587 1.0e+00 6.0e-01 0.060628 - 288 -3718.829 0.0e+00 1.0e+00 0.061878 - 289 -3722.181 0.0e+00 6.0e-01 0.040987 - 290 -3722.796 0.0e+00 6.0e-01 0.041831 - 291 -3721.371 0.0e+00 4.0e-01 0.049311 - 292 -3727.242 0.0e+00 1.0e+00 0.043573 - 293 -3716.750 0.0e+00 8.0e-01 0.044471 - 294 -3729.032 0.0e+00 8.0e-01 0.052422 - 295 -3722.139 0.0e+00 8.0e-01 0.046322 - 296 -3716.227 0.0e+00 1.0e+00 0.040932 - 297 -3721.807 0.0e+00 6.0e-01 0.031316 - 298 -3710.509 0.0e+00 6.0e-01 0.042636 - 299 -3720.677 0.0e+00 8.0e-01 0.043515 - 300 -3724.655 0.0e+00 2.0e-01 0.044412 - 301 -3729.844 0.0e+00 4.0e-01 0.052352 - 302 -3714.361 0.0e+00 6.0e-01 0.040053 - 303 -3713.991 0.0e+00 1.0e+00 0.062984 - 304 -3735.443 0.0e+00 8.0e-01 0.048187 - 305 -3745.537 0.0e+00 4.0e-01 0.023926 - 306 -3757.344 0.0e+00 8.0e-01 0.043457 - 307 -3756.918 1.0e+00 6.0e-01 0.038400 - 308 -3754.384 0.0e+00 4.0e-01 0.039192 - 309 -3767.281 0.0e+00 6.0e-01 0.061631 - 310 -3749.366 0.0e+00 8.0e-01 0.035345 - 311 -3742.872 0.0e+00 4.0e-01 0.048122 - 312 -3737.768 0.0e+00 4.0e-01 0.042523 - 313 -3756.021 0.0e+00 8.0e-01 0.050126 - 314 -3762.473 0.0e+00 4.0e-01 0.051159 - 315 -3777.353 0.0e+00 4.0e-01 0.052213 - 316 -3786.970 0.0e+00 6.0e-01 0.061549 - 317 -3771.453 0.0e+00 6.0e-01 0.047088 - 318 -3772.799 0.0e+00 4.0e-01 0.064111 - 319 -3775.510 0.0e+00 1.0e+00 0.065432 - 320 -3773.864 0.0e+00 8.0e-01 0.050059 - 321 -3767.311 1.0e+00 2.0e-01 0.038298 - 322 -3764.422 0.0e+00 6.0e-01 0.052143 - 323 -3775.560 0.0e+00 8.0e-01 0.053218 - 324 -3771.605 0.0e+00 4.0e-01 0.062733 - 325 -3798.178 0.0e+00 4.0e-01 0.047994 - 326 -3781.693 0.0e+00 8.0e-01 0.048983 - 327 -3783.475 0.0e+00 4.0e-01 0.037475 - 328 -3792.127 0.0e+00 1.0e+00 0.068065 - 329 -3777.599 0.0e+00 8.0e-01 0.045086 - 330 -3768.162 0.0e+00 1.0e+00 0.061385 - 331 -3767.561 0.0e+00 1.0e+00 0.054242 - 332 -3752.475 0.0e+00 8.0e-01 0.055360 - 333 -3733.480 0.0e+00 4.0e-01 0.048918 - 334 -3732.343 0.0e+00 8.0e-01 0.032403 - 335 -3717.060 0.0e+00 6.0e-01 0.050955 - 336 -3715.284 0.0e+00 6.0e-01 0.052004 - 337 -3716.678 0.0e+00 2.0e-01 0.039786 - 338 -3706.968 0.0e+00 1.0e+00 0.083464 - 339 -3712.773 0.0e+00 1.0e+00 0.041443 - 340 -3714.739 0.0e+00 8.0e-01 0.075272 - 341 -3736.132 0.0e+00 6.0e-01 0.037375 - 342 -3725.791 0.0e+00 4.0e-01 0.044058 - 343 -3715.450 0.0e+00 2.0e-01 0.033707 - 344 -3712.578 0.0e+00 2.0e-01 0.034401 - 345 -3708.836 0.0e+00 0.0e+00 0.026319 - 346 -3716.540 0.0e+00 2.0e-01 0.026861 - 347 -3723.991 0.0e+00 8.0e-01 0.031664 - 348 -3722.002 1.0e+00 2.0e-01 0.024225 - 349 -3726.193 0.0e+00 6.0e-01 0.038094 - 350 -3730.749 0.0e+00 6.0e-01 0.038879 - 351 -3735.669 0.0e+00 6.0e-01 0.045831 - 352 -3740.050 0.0e+00 4.0e-01 0.035063 - 353 -3735.560 0.0e+00 6.0e-01 0.035786 - 354 -3745.913 1.0e+00 1.0e+00 0.056275 - 355 -3762.028 0.0e+00 2.0e-01 0.037276 - 356 -3757.435 0.0e+00 6.0e-01 0.058618 - 357 -3757.603 0.0e+00 4.0e-01 0.051797 - 358 -3766.324 0.0e+00 4.0e-01 0.052864 - 359 -3770.703 0.0e+00 6.0e-01 0.053953 - 360 -3783.421 0.0e+00 4.0e-01 0.035738 - 361 -3785.726 0.0e+00 8.0e-01 0.036474 - 362 -3769.418 0.0e+00 4.0e-01 0.049660 - 363 -3772.945 1.0e+00 1.0e+00 0.050684 - 364 -3768.027 0.0e+00 8.0e-01 0.038776 - 365 -3765.467 0.0e+00 6.0e-01 0.039575 - 366 -3748.692 0.0e+00 8.0e-01 0.046651 - 367 -3738.499 0.0e+00 8.0e-01 0.035690 - 368 -3742.556 0.0e+00 1.0e+00 0.064824 - 369 -3742.390 0.0e+00 6.0e-01 0.037176 - 370 -3738.341 0.0e+00 6.0e-01 0.037942 - 371 -3742.010 0.0e+00 6.0e-01 0.059666 - 372 -3752.185 0.0e+00 8.0e-01 0.039522 - 373 -3753.579 0.0e+00 6.0e-01 0.040336 - 374 -3758.980 0.0e+00 8.0e-01 0.041167 - 375 -3753.905 0.0e+00 4.0e-01 0.027269 - 376 -3754.015 0.0e+00 1.0e+00 0.049528 - 377 -3754.759 0.0e+00 8.0e-01 0.028404 - 378 -3758.430 0.0e+00 4.0e-01 0.044667 - 379 -3750.959 0.0e+00 2.0e-01 0.039469 - 380 -3739.304 0.0e+00 6.0e-01 0.040283 - 381 -3746.841 0.0e+00 6.0e-01 0.041113 - 382 -3751.657 1.0e+00 4.0e-01 0.031453 - 383 -3755.661 0.0e+00 4.0e-01 0.037077 - 384 -3749.023 1.0e+00 8.0e-01 0.028366 - 385 -3749.210 1.0e+00 6.0e-01 0.033438 - 386 -3763.127 0.0e+00 6.0e-01 0.039417 - 387 -3763.822 0.0e+00 1.0e+00 0.053666 - 388 -3767.692 0.0e+00 4.0e-01 0.047422 - 389 -3762.585 0.0e+00 4.0e-01 0.036280 - 390 -3760.249 1.0e+00 4.0e-01 0.037028 - 391 -3762.815 0.0e+00 8.0e-01 0.037791 - 392 -3763.743 0.0e+00 4.0e-01 0.044548 - 393 -3758.157 0.0e+00 4.0e-01 0.045466 - 394 -3769.838 0.0e+00 6.0e-01 0.053595 - 395 -3772.615 0.0e+00 6.0e-01 0.041003 - 396 -3772.502 0.0e+00 4.0e-01 0.031370 - 397 -3766.345 0.0e+00 1.0e+00 0.056977 - 398 -3778.178 0.0e+00 1.0e+00 0.067164 - 399 -3768.555 0.0e+00 1.0e+00 0.059349 - 400 -3763.353 0.0e+00 2.0e-01 0.034036 - 401 -3772.448 1.0e+00 1.0e+00 0.030076 - 402 -3770.457 0.0e+00 2.0e-01 0.030695 - cycle etr hrej lrej lvScale - 2 -3524.135 0.0e+00 3.7e-02 0.088016 - cycle etr hrej lrej lvScale - 3 -3562.074 1.0e+00 1.0e-01 0.125535 - 4 -3541.193 0.0e+00 0.0e+00 0.150026 - 5 -3564.815 0.0e+00 0.0e+00 0.179295 - 6 -3602.549 0.0e+00 5.0e-02 0.214274 - 7 -3572.052 0.0e+00 5.0e-02 0.179326 - 8 -3569.367 0.0e+00 5.0e-02 0.214311 - 9 -3602.099 1.0e+00 5.0e-02 0.179357 - 10 -3526.159 0.0e+00 0.0e+00 0.214348 - 11 -3536.875 0.0e+00 0.0e+00 0.256165 - 12 -3521.256 0.0e+00 1.0e-01 0.306141 - 13 -3582.144 0.0e+00 1.0e-01 0.365867 - 14 -3576.716 0.0e+00 1.5e-01 0.150155 - 15 -3598.887 1.0e+00 0.0e+00 0.179449 - 16 -3592.807 0.0e+00 0.0e+00 0.214459 - 17 -3551.030 0.0e+00 1.0e-01 0.125687 - 18 -3566.592 0.0e+00 0.0e+00 0.150207 - 19 -3620.999 0.0e+00 1.0e-01 0.179511 - 20 -3555.069 0.0e+00 0.0e+00 0.214533 - 21 -3580.881 0.0e+00 1.0e-01 0.179542 - 22 -3618.200 0.0e+00 5.0e-02 0.214570 - 23 -3612.005 0.0e+00 5.0e-02 0.256431 - 24 -3618.013 0.0e+00 1.5e-01 0.306458 - 25 -3635.865 0.0e+00 5.0e-02 0.366246 - 26 -3650.795 0.0e+00 1.0e-01 0.214644 - 27 -3580.796 0.0e+00 5.0e-02 0.256519 - 28 -3656.894 1.0e+00 1.0e-01 0.214681 - 29 -3624.118 0.0e+00 1.0e-01 0.256563 - 30 -3625.109 0.0e+00 1.0e-01 0.306617 - 31 -3616.719 0.0e+00 1.0e-01 0.179697 - 32 -3619.189 0.0e+00 0.0e+00 0.214755 - 33 -3648.407 0.0e+00 5.0e-02 0.179728 - 34 -3641.885 0.0e+00 0.0e+00 0.214792 - 35 -3657.977 1.0e+00 5.0e-02 0.256696 - 36 -3646.023 0.0e+00 5.0e-02 0.214829 - 37 -3677.506 1.0e+00 5.0e-02 0.179790 - 38 -3647.460 0.0e+00 1.0e-01 0.105369 - 39 -3605.621 0.0e+00 0.0e+00 0.125925 - 40 -3665.499 1.0e+00 1.0e-01 0.105387 - 41 -3591.542 0.0e+00 0.0e+00 0.125947 - 42 -3591.984 0.0e+00 1.0e-01 0.150518 - 43 -3591.479 0.0e+00 5.0e-02 0.125969 - 44 -3604.143 0.0e+00 5.0e-02 0.105423 - 45 -3589.678 0.0e+00 5.0e-02 0.125990 - 46 -3558.215 0.0e+00 1.5e-01 0.105441 - 47 -3554.836 0.0e+00 1.0e-01 0.088244 - 48 -3574.375 1.0e+00 5.0e-02 0.105459 - 49 -3598.370 0.0e+00 1.0e-01 0.126034 - 50 -3518.387 0.0e+00 1.0e-01 0.105478 - 51 -3607.122 0.0e+00 0.0e+00 0.126056 - 52 -3601.566 0.0e+00 0.0e+00 0.150648 - 53 -3553.245 0.0e+00 0.0e+00 0.180038 - 54 -3570.285 0.0e+00 1.5e-01 0.150674 - 55 -3610.691 0.0e+00 0.0e+00 0.180069 - 56 -3527.260 0.0e+00 5.0e-02 0.215200 - 57 -3551.827 0.0e+00 0.0e+00 0.257183 - 58 -3528.128 0.0e+00 5.0e-02 0.215237 - 59 -3530.464 0.0e+00 1.0e-01 0.257228 - 60 -3571.013 0.0e+00 5.0e-02 0.215274 - 61 -3593.270 0.0e+00 5.0e-02 0.180163 - 62 -3552.982 0.0e+00 0.0e+00 0.215311 - 63 -3623.216 1.0e+00 5.0e-02 0.180194 - 64 -3591.110 1.0e+00 0.0e+00 0.215348 - 65 -3585.398 1.0e+00 5.0e-02 0.180225 - 66 -3646.095 0.0e+00 0.0e+00 0.215385 - 67 -3586.716 0.0e+00 5.0e-02 0.180256 - 68 -3572.294 0.0e+00 0.0e+00 0.215422 - 69 -3574.439 0.0e+00 5.0e-02 0.180287 - 70 -3536.728 0.0e+00 0.0e+00 0.215459 - 71 -3540.617 0.0e+00 5.0e-02 0.180318 - 72 -3554.297 0.0e+00 0.0e+00 0.215497 - 73 -3560.663 0.0e+00 1.0e-01 0.126295 - 74 -3553.255 0.0e+00 0.0e+00 0.150934 - 75 -3555.007 0.0e+00 1.0e-01 0.088457 - 76 -3558.546 0.0e+00 5.0e-02 0.074030 - 77 -3563.610 0.0e+00 1.5e-01 0.088472 - 78 -3560.466 0.0e+00 0.0e+00 0.105733 - 79 -3522.967 0.0e+00 5.0e-02 0.126360 - 80 -3545.619 1.0e+00 5.0e-02 0.105751 - 81 -3525.372 0.0e+00 5.0e-02 0.126382 - 82 -3479.605 1.0e+00 5.0e-02 0.105769 - 83 -3470.606 0.0e+00 5.0e-02 0.126404 - 84 -3475.978 0.0e+00 1.0e-01 0.105787 - 85 -3542.650 0.0e+00 0.0e+00 0.126426 - 86 -3512.980 0.0e+00 0.0e+00 0.151090 - 87 -3442.320 0.0e+00 0.0e+00 0.180567 - 88 -3449.878 0.0e+00 0.0e+00 0.215794 - 89 -3437.835 0.0e+00 1.0e-01 0.180598 - 90 -3444.027 0.0e+00 0.0e+00 0.215831 - 91 -3439.391 0.0e+00 1.0e-01 0.180629 - 92 -3456.088 0.0e+00 1.0e-01 0.151168 - 93 -3467.684 0.0e+00 0.0e+00 0.180660 - 94 -3482.265 0.0e+00 5.0e-02 0.215906 - 95 -3483.640 0.0e+00 5.0e-02 0.180691 - 96 -3472.015 0.0e+00 0.0e+00 0.215943 - 97 -3476.392 0.0e+00 5.0e-02 0.180723 - 98 -3533.072 0.0e+00 0.0e+00 0.215980 - 99 -3521.049 0.0e+00 1.0e-01 0.180754 - 100 -3480.643 1.0e+00 0.0e+00 0.216017 - 101 -3517.527 0.0e+00 5.0e-02 0.258161 - 102 -3546.011 0.0e+00 5.0e-02 0.216055 - 103 -3501.579 1.0e+00 5.0e-02 0.180816 - 104 -3522.381 0.0e+00 5.0e-02 0.151325 - 105 -3527.038 0.0e+00 5.0e-02 0.126644 - 106 -3489.119 0.0e+00 5.0e-02 0.105988 - 107 -3479.274 0.0e+00 5.0e-02 0.088701 - 108 -3537.881 0.0e+00 0.0e+00 0.106006 - 109 -3532.658 0.0e+00 0.0e+00 0.126687 - 110 -3502.880 0.0e+00 5.0e-02 0.106025 - 111 -3543.865 0.0e+00 0.0e+00 0.126709 - 112 -3502.826 0.0e+00 0.0e+00 0.151429 - 113 -3494.208 0.0e+00 5.0e-02 0.126731 - 114 -3485.333 0.0e+00 5.0e-02 0.151455 - 115 -3470.294 0.0e+00 0.0e+00 0.181003 - 116 -3503.965 0.0e+00 1.0e-01 0.216316 - 117 -3454.615 0.0e+00 2.0e-01 0.258517 - 118 -3446.564 1.0e+00 5.0e-02 0.216353 - 119 -3402.736 0.0e+00 2.0e-01 0.126797 - 120 -3511.119 0.0e+00 1.0e-01 0.151534 - 121 -3514.592 0.0e+00 5.0e-02 0.126819 - 122 -3464.280 1.0e+00 0.0e+00 0.151560 - 123 -3530.224 0.0e+00 0.0e+00 0.181128 - 124 -3526.283 0.0e+00 0.0e+00 0.216465 - 125 -3513.249 0.0e+00 0.0e+00 0.258696 - 126 -3533.774 0.0e+00 5.0e-02 0.216502 - 127 -3537.291 0.0e+00 5.0e-02 0.181191 - 128 -3512.321 1.0e+00 0.0e+00 0.216540 - 129 -3523.673 0.0e+00 5.0e-02 0.181222 - 130 -3530.500 0.0e+00 0.0e+00 0.216577 - 131 -3556.255 0.0e+00 5.0e-02 0.258829 - 132 -3618.916 0.0e+00 1.0e-01 0.216614 - 133 -3586.459 0.0e+00 0.0e+00 0.258874 - 134 -3564.340 0.0e+00 1.0e-01 0.151717 - 135 -3618.817 1.0e+00 0.0e+00 0.181316 - 136 -3550.663 0.0e+00 0.0e+00 0.216689 - 137 -3592.564 0.0e+00 5.0e-02 0.181347 - 138 -3593.443 0.0e+00 0.0e+00 0.216726 - 139 -3541.975 0.0e+00 1.0e-01 0.259008 - 140 -3514.542 0.0e+00 1.0e-01 0.151795 - 141 -3506.857 0.0e+00 0.0e+00 0.181409 - 142 -3504.011 0.0e+00 0.0e+00 0.216801 - 143 -3508.699 0.0e+00 5.0e-02 0.181441 - 144 -3496.819 0.0e+00 0.0e+00 0.216838 - 145 -3532.806 0.0e+00 1.0e-01 0.181472 - 146 -3531.291 0.0e+00 0.0e+00 0.216876 - 147 -3497.687 0.0e+00 5.0e-02 0.181503 - 148 -3532.855 0.0e+00 0.0e+00 0.216913 - 149 -3486.034 0.0e+00 1.5e-01 0.259231 - 150 -3543.279 0.0e+00 5.0e-02 0.216951 - 151 -3536.476 1.0e+00 5.0e-02 0.181566 - 152 -3528.742 0.0e+00 0.0e+00 0.216988 - 153 -3535.399 1.0e+00 1.0e-01 0.259321 - 154 -3503.728 1.0e+00 2.0e-01 0.309912 - 155 -3535.103 0.0e+00 0.0e+00 0.370374 - 156 -3527.950 0.0e+00 0.0e+00 0.442631 - 157 -3616.567 0.0e+00 1.5e-01 0.181660 - 158 -3551.923 0.0e+00 0.0e+00 0.217100 - 159 -3547.251 0.0e+00 5.0e-02 0.259455 - 160 -3574.566 0.0e+00 1.0e-01 0.217138 - 161 -3595.425 0.0e+00 5.0e-02 0.181722 - 162 -3631.501 0.0e+00 1.0e-01 0.152083 - 163 -3609.873 0.0e+00 0.0e+00 0.181754 - 164 -3554.477 1.0e+00 5.0e-02 0.152110 - 165 -3602.740 0.0e+00 5.0e-02 0.127301 - 166 -3616.468 0.0e+00 0.0e+00 0.152136 - 167 -3623.736 0.0e+00 0.0e+00 0.181817 - 168 -3603.437 0.0e+00 5.0e-02 0.152162 - 169 -3582.453 0.0e+00 5.0e-02 0.127344 - 170 -3612.595 0.0e+00 0.0e+00 0.152188 - 171 -3620.902 0.0e+00 5.0e-02 0.127366 - 172 -3580.758 0.0e+00 1.0e-01 0.106593 - 173 -3670.976 0.0e+00 0.0e+00 0.127388 - 174 -3642.592 0.0e+00 5.0e-02 0.106611 - 175 -3671.133 0.0e+00 0.0e+00 0.127410 - 176 -3683.211 0.0e+00 5.0e-02 0.106630 - 177 -3621.218 0.0e+00 0.0e+00 0.127432 - 178 -3667.380 0.0e+00 0.0e+00 0.152293 - 179 -3640.574 0.0e+00 5.0e-02 0.182005 - 180 -3697.859 0.0e+00 5.0e-02 0.217512 - 181 -3648.317 0.0e+00 1.0e-01 0.182036 - 182 -3663.619 0.0e+00 5.0e-02 0.217550 - 183 -3683.071 0.0e+00 5.0e-02 0.259992 - 184 -3680.697 0.0e+00 5.0e-02 0.217587 - 185 -3665.018 0.0e+00 5.0e-02 0.182099 - 186 -3651.655 0.0e+00 5.0e-02 0.152398 - 187 -3667.394 0.0e+00 1.0e-01 0.089315 - 188 -3651.608 0.0e+00 5.0e-02 0.106740 - 189 -3680.677 0.0e+00 0.0e+00 0.127564 - 190 -3612.208 0.0e+00 1.0e-01 0.106758 - 191 -3649.622 0.0e+00 5.0e-02 0.127586 - 192 -3658.561 0.0e+00 5.0e-02 0.106777 - 193 -3655.282 0.0e+00 5.0e-02 0.089361 - 194 -3635.263 0.0e+00 0.0e+00 0.106795 - 195 -3645.611 0.0e+00 5.0e-02 0.089377 - 196 -3621.950 0.0e+00 5.0e-02 0.074799 - 197 -3615.108 0.0e+00 0.0e+00 0.089392 - 198 -3664.521 0.0e+00 0.0e+00 0.106832 - 199 -3710.118 0.0e+00 0.0e+00 0.127674 - 200 -3612.505 0.0e+00 0.0e+00 0.152583 - 201 -3673.448 0.0e+00 0.0e+00 0.182350 - 202 -3617.285 0.0e+00 0.0e+00 0.217925 - - gp hyperparameters (gp_pak(gp)): - - 2.8831 -0.6152 -0.5214 1.5709 1.6167 - diff --git a/xunit/log/demo_multinom2.txt b/xunit/log/demo_multinom2.txt deleted file mode 100644 index bbe520c6..00000000 --- a/xunit/log/demo_multinom2.txt +++ /dev/null @@ -1,458 +0,0 @@ -Running: demo_multinom2 - Iteration Func-count f(x) Lambda - 0 1 667.05 - 1 3 666.162 10 - 2 5 666.133 5 - 3 7 666.049 2.5 - 4 9 665.97 1.25 - 5 11 665.77 0.625 - 6 13 665.562 0.312 - 7 15 665.52 0.156 - 8 17 665.422 0.0781 - 9 19 665.396 0.0391 - 10 21 665.388 0.0195 - 11 23 665.375 0.00977 - 12 25 665.373 0.00488 - 13 27 665.372 0.00244 - 14 29 665.362 0.00122 - 15 31 665.362 0.00061 - 16 33 665.36 0.000305 - 17 35 665.36 0.000153 - 18 37 665.359 7.63e-05 - 19 39 665.359 3.81e-05 - Iteration Func-count f(x) Lambda - 20 41 665.359 1.91e-05 - TolFun reached. Func-count 41. Final f(x)=665.359. Elapsed time 2.77 - -ans = - - 2.1696e+09 - - -ans = - - 1.0e+09 * - - -0.4921 7.2227 -0.0082 0.0545 -0.0311 0.3121 - -Checking gradient ... - - analytic diffs delta - - 1.0e+09 * - - -0.4551 -0.4551 0.0000 - 5.4149 5.4149 -0.0000 - -0.4460 -0.4460 0.0000 - 5.4797 5.4797 0.0000 - -0.0334 -0.0334 0.0000 - 0.3870 0.3870 0.0000 - - cycle etr hrej lrej lvScale - 2 156.359 0.0e+00 6.5e-01 0.012046 - cycle etr hrej lrej lvScale - 3 157.257 0.0e+00 8.0e-01 0.013950 - 4 161.475 0.0e+00 8.0e-01 0.012327 - 5 159.985 0.0e+00 6.0e-01 0.010893 - 6 169.846 0.0e+00 8.0e-01 0.012840 - 7 167.945 1.0e+00 1.0e+00 0.008505 - 8 164.013 0.0e+00 8.0e-01 0.008681 - 9 153.421 0.0e+00 1.0e+00 0.010233 - 10 157.735 0.0e+00 6.0e-01 0.010444 - 11 161.598 1.0e+00 4.0e-01 0.010659 - 12 161.596 0.0e+00 8.0e-01 0.007060 - 13 160.688 1.0e+00 1.0e+00 0.011102 - 14 156.292 0.0e+00 8.0e-01 0.013088 - 15 158.644 0.0e+00 8.0e-01 0.011565 - 16 156.455 0.0e+00 8.0e-01 0.013632 - 17 153.234 0.0e+00 6.0e-01 0.013913 - 18 160.783 1.0e+00 6.0e-01 0.010644 - 19 164.635 1.0e+00 1.0e+00 0.012548 - 20 163.625 0.0e+00 1.0e+00 0.011088 - 21 167.372 0.0e+00 8.0e-01 0.009798 - 22 167.201 0.0e+00 8.0e-01 0.011549 - 23 165.862 0.0e+00 6.0e-01 0.010205 - 24 159.330 0.0e+00 1.0e+00 0.010416 - 25 163.818 0.0e+00 1.0e+00 0.009204 - 26 158.448 0.0e+00 8.0e-01 0.008133 - 27 157.371 0.0e+00 8.0e-01 0.009587 - 28 159.491 0.0e+00 6.0e-01 0.011301 - 29 159.372 0.0e+00 8.0e-01 0.007486 - 30 160.784 0.0e+00 6.0e-01 0.008824 - 31 159.277 0.0e+00 6.0e-01 0.010402 - 32 164.664 0.0e+00 1.0e+00 0.012262 - 33 168.117 0.0e+00 1.0e+00 0.012514 - 34 169.466 0.0e+00 8.0e-01 0.009574 - 35 171.116 0.0e+00 4.0e-01 0.008460 - 36 161.176 0.0e+00 6.0e-01 0.008634 - 37 166.950 0.0e+00 1.0e+00 0.008812 - 38 165.522 0.0e+00 8.0e-01 0.010388 - 39 163.881 0.0e+00 8.0e-01 0.012245 - 40 165.813 0.0e+00 6.0e-01 0.009368 - 41 172.858 0.0e+00 6.0e-01 0.009561 - 42 165.203 0.0e+00 8.0e-01 0.008449 - 43 170.088 0.0e+00 8.0e-01 0.011503 - 44 164.721 0.0e+00 8.0e-01 0.008801 - 45 159.681 0.0e+00 8.0e-01 0.010374 - 46 165.540 0.0e+00 8.0e-01 0.010588 - 47 169.582 0.0e+00 4.0e-01 0.009356 - 48 170.860 0.0e+00 1.0e+00 0.016993 - 49 177.454 0.0e+00 8.0e-01 0.009745 - 50 171.890 0.0e+00 4.0e-01 0.009946 - 51 165.886 0.0e+00 6.0e-01 0.008789 - 52 173.465 0.0e+00 6.0e-01 0.008970 - 53 170.794 0.0e+00 8.0e-01 0.012213 - 54 170.756 0.0e+00 4.0e-01 0.008090 - 55 168.973 0.0e+00 6.0e-01 0.012721 - 56 166.499 1.0e+00 6.0e-01 0.011241 - 57 166.758 0.0e+00 8.0e-01 0.009933 - 58 174.514 0.0e+00 6.0e-01 0.011709 - 59 171.855 0.0e+00 6.0e-01 0.011950 - 60 173.692 0.0e+00 6.0e-01 0.012196 - 61 168.576 1.0e+00 1.0e+00 0.009331 - 62 166.416 0.0e+00 8.0e-01 0.008245 - 63 159.746 0.0e+00 1.0e+00 0.017297 - 64 161.607 0.0e+00 8.0e-01 0.011457 - 65 163.900 0.0e+00 1.0e+00 0.011693 - 66 158.364 0.0e+00 8.0e-01 0.008946 - 67 162.022 0.0e+00 6.0e-01 0.009130 - 68 169.017 0.0e+00 1.0e+00 0.010763 - 69 178.510 0.0e+00 2.0e-01 0.010985 - 70 175.933 0.0e+00 8.0e-01 0.011211 - 71 169.211 0.0e+00 8.0e-01 0.011442 - 72 176.649 0.0e+00 8.0e-01 0.013488 - 73 167.428 0.0e+00 6.0e-01 0.008934 - 74 169.717 0.0e+00 4.0e-01 0.006835 - 75 177.425 0.0e+00 8.0e-01 0.009306 - 76 174.727 0.0e+00 8.0e-01 0.010970 - 77 178.367 0.0e+00 6.0e-01 0.008393 - 78 173.809 0.0e+00 6.0e-01 0.009893 - 79 175.943 0.0e+00 8.0e-01 0.013470 - 80 181.158 0.0e+00 6.0e-01 0.007725 - 81 178.612 0.0e+00 8.0e-01 0.009106 - 82 173.321 0.0e+00 1.0e+00 0.012398 - 83 176.062 0.0e+00 8.0e-01 0.009485 - 84 167.543 0.0e+00 6.0e-01 0.011181 - 85 175.101 0.0e+00 6.0e-01 0.009880 - 86 172.307 0.0e+00 8.0e-01 0.008730 - 87 169.521 1.0e+00 1.0e+00 0.007715 - 88 168.497 0.0e+00 1.0e+00 0.010504 - 89 166.175 0.0e+00 8.0e-01 0.006024 - 90 173.977 0.0e+00 1.0e+00 0.012637 - 91 176.837 0.0e+00 6.0e-01 0.009668 - 92 181.901 0.0e+00 4.0e-01 0.008543 - 93 172.883 1.0e+00 8.0e-01 0.008719 - 94 170.368 0.0e+00 8.0e-01 0.013711 - 95 174.394 1.0e+00 1.0e+00 0.010490 - 96 177.244 0.0e+00 6.0e-01 0.008025 - 97 178.995 0.0e+00 6.0e-01 0.012620 - 98 178.069 0.0e+00 8.0e-01 0.009655 - 99 180.800 0.0e+00 6.0e-01 0.008531 - 100 174.505 0.0e+00 8.0e-01 0.011616 - 101 175.351 0.0e+00 8.0e-01 0.011855 - 102 179.470 0.0e+00 8.0e-01 0.012099 - 103 177.976 0.0e+00 2.0e-01 0.008014 - 104 182.093 0.0e+00 1.0e+00 0.010912 - 105 172.053 0.0e+00 1.0e+00 0.006258 - 106 170.423 0.0e+00 6.0e-01 0.007377 - 107 170.464 1.0e+00 8.0e-01 0.010043 - 108 183.559 0.0e+00 1.0e+00 0.010250 - 109 178.303 0.0e+00 6.0e-01 0.007842 - 110 176.921 0.0e+00 8.0e-01 0.006000 - 111 174.411 0.0e+00 6.0e-01 0.008169 - 112 177.309 0.0e+00 8.0e-01 0.006249 - 113 167.766 1.0e+00 6.0e-01 0.011351 - 114 173.204 0.0e+00 8.0e-01 0.010030 - 115 179.276 0.0e+00 4.0e-01 0.011823 - 116 169.726 0.0e+00 1.0e+00 0.012067 - 117 169.920 0.0e+00 8.0e-01 0.009232 - 118 172.653 0.0e+00 8.0e-01 0.010883 - 119 175.321 1.0e+00 6.0e-01 0.007208 - 120 175.183 0.0e+00 4.0e-01 0.008497 - 121 180.561 0.0e+00 8.0e-01 0.007509 - 122 181.311 0.0e+00 8.0e-01 0.005745 - 123 175.901 0.0e+00 1.0e+00 0.012051 - 124 185.062 0.0e+00 8.0e-01 0.012299 - 125 183.583 0.0e+00 1.0e+00 0.009410 - 126 173.059 1.0e+00 8.0e-01 0.008315 - 127 171.756 1.0e+00 6.0e-01 0.006361 - 128 175.898 0.0e+00 6.0e-01 0.010003 - 129 177.441 0.0e+00 1.0e+00 0.008839 - 130 182.386 0.0e+00 6.0e-01 0.010420 - 131 183.284 1.0e+00 6.0e-01 0.007972 - 132 182.193 0.0e+00 6.0e-01 0.007044 - 133 178.569 0.0e+00 6.0e-01 0.008304 - 134 180.102 1.0e+00 1.0e+00 0.009788 - 135 184.320 0.0e+00 1.0e+00 0.007489 - 136 193.059 0.0e+00 1.0e+00 0.010196 - 137 195.626 1.0e+00 8.0e-01 0.012019 - 138 190.064 0.0e+00 8.0e-01 0.010620 - 139 189.116 1.0e+00 8.0e-01 0.008125 - 140 185.394 1.0e+00 6.0e-01 0.008293 - 141 196.754 0.0e+00 1.0e+00 0.009775 - 142 186.629 0.0e+00 2.0e-01 0.008638 - 143 179.249 0.0e+00 8.0e-01 0.010182 - 144 177.936 0.0e+00 1.0e+00 0.008997 - 145 175.642 0.0e+00 8.0e-01 0.009183 - 146 182.668 0.0e+00 4.0e-01 0.009372 - 147 174.765 1.0e+00 4.0e-01 0.007170 - 148 177.649 0.0e+00 6.0e-01 0.009762 - 149 176.405 0.0e+00 4.0e-01 0.009963 - 150 179.383 1.0e+00 4.0e-01 0.008804 - 151 184.767 0.0e+00 6.0e-01 0.007780 - 152 182.435 0.0e+00 6.0e-01 0.007940 - 153 191.981 0.0e+00 8.0e-01 0.009360 - 154 189.905 1.0e+00 1.0e+00 0.009552 - 155 190.726 0.0e+00 8.0e-01 0.011260 - 156 195.099 0.0e+00 1.0e+00 0.013274 - 157 198.185 1.0e+00 8.0e-01 0.013547 - 158 191.782 1.0e+00 0.0e+00 0.007769 - 159 187.238 0.0e+00 8.0e-01 0.009158 - 160 190.440 0.0e+00 6.0e-01 0.009347 - 161 188.311 0.0e+00 6.0e-01 0.008259 - 162 190.902 0.0e+00 6.0e-01 0.008430 - 163 185.705 0.0e+00 4.0e-01 0.007449 - 164 181.333 0.0e+00 6.0e-01 0.010142 - 165 180.513 1.0e+00 1.0e+00 0.007759 - 166 184.605 0.0e+00 8.0e-01 0.007919 - 167 181.282 1.0e+00 8.0e-01 0.010782 - 168 183.953 0.0e+00 6.0e-01 0.007142 - 169 187.976 0.0e+00 6.0e-01 0.009723 - 170 191.848 0.0e+00 8.0e-01 0.009924 - 171 193.848 0.0e+00 1.0e+00 0.013511 - 172 192.649 0.0e+00 6.0e-01 0.011939 - 173 193.614 0.0e+00 4.0e-01 0.009134 - 174 192.403 1.0e+00 6.0e-01 0.008071 - 175 186.923 0.0e+00 4.0e-01 0.007132 - 176 192.365 0.0e+00 8.0e-01 0.008407 - 177 197.227 0.0e+00 8.0e-01 0.011447 - 178 203.187 0.0e+00 4.0e-01 0.011682 - 179 197.782 0.0e+00 8.0e-01 0.007738 - 180 187.833 1.0e+00 8.0e-01 0.009122 - 181 193.912 0.0e+00 8.0e-01 0.008060 - 182 188.663 0.0e+00 1.0e+00 0.009502 - 183 188.342 1.0e+00 4.0e-01 0.008396 - 184 188.795 0.0e+00 6.0e-01 0.009897 - 185 194.310 0.0e+00 4.0e-01 0.008746 - 186 192.717 1.0e+00 8.0e-01 0.013753 - 187 191.832 0.0e+00 4.0e-01 0.009110 - 188 197.498 0.0e+00 8.0e-01 0.010738 - 189 201.995 0.0e+00 1.0e+00 0.012658 - 190 197.710 0.0e+00 1.0e+00 0.008385 - 191 205.262 0.0e+00 6.0e-01 0.009884 - 192 199.665 0.0e+00 2.0e-01 0.008734 - 193 208.372 1.0e+00 6.0e-01 0.008914 - 194 209.445 1.0e+00 8.0e-01 0.010508 - 195 208.505 0.0e+00 8.0e-01 0.010724 - 196 203.697 0.0e+00 6.0e-01 0.009476 - 197 204.809 0.0e+00 8.0e-01 0.011171 - 198 204.319 0.0e+00 8.0e-01 0.011401 - 199 208.969 1.0e+00 4.0e-01 0.015522 - 200 200.086 0.0e+00 8.0e-01 0.013716 - 201 204.236 0.0e+00 6.0e-01 0.010494 - 202 208.312 0.0e+00 2.0e-01 0.008028 - 203 205.038 0.0e+00 8.0e-01 0.012625 - 204 198.966 0.0e+00 8.0e-01 0.009659 - 205 199.780 0.0e+00 6.0e-01 0.009858 - 206 205.617 0.0e+00 8.0e-01 0.013421 - 207 204.040 1.0e+00 8.0e-01 0.006664 - 208 207.401 0.0e+00 1.0e+00 0.010480 - 209 203.947 0.0e+00 1.0e+00 0.009260 - 210 210.390 0.0e+00 4.0e-01 0.006134 - 211 203.241 0.0e+00 2.0e-01 0.007231 - 212 202.869 0.0e+00 8.0e-01 0.009845 - 213 197.385 1.0e+00 4.0e-01 0.005646 - 214 200.787 0.0e+00 8.0e-01 0.007687 - 215 200.136 0.0e+00 2.0e-01 0.005881 - 216 201.036 1.0e+00 1.0e+00 0.006932 - 217 201.337 1.0e+00 1.0e+00 0.010901 - 218 201.909 0.0e+00 6.0e-01 0.006252 - 219 202.871 0.0e+00 4.0e-01 0.006381 - 220 205.048 0.0e+00 6.0e-01 0.008687 - 221 203.206 0.0e+00 6.0e-01 0.007677 - 222 203.927 1.0e+00 6.0e-01 0.009049 - 223 206.990 0.0e+00 8.0e-01 0.010667 - 224 208.856 0.0e+00 4.0e-01 0.008161 - 225 210.793 1.0e+00 6.0e-01 0.009620 - 226 205.935 0.0e+00 0.0e+00 0.009818 - 227 206.084 0.0e+00 8.0e-01 0.010021 - 228 203.764 0.0e+00 6.0e-01 0.008855 - 229 216.459 0.0e+00 6.0e-01 0.010438 - 230 207.503 1.0e+00 6.0e-01 0.007986 - 231 202.295 0.0e+00 6.0e-01 0.009413 - 232 203.335 1.0e+00 6.0e-01 0.007202 - 233 209.133 1.0e+00 6.0e-01 0.008489 - 234 213.060 0.0e+00 1.0e+00 0.011558 - 235 214.467 0.0e+00 6.0e-01 0.007656 - 236 205.045 0.0e+00 0.0e+00 0.005071 - 237 204.761 0.0e+00 2.0e-01 0.009211 - 238 207.939 0.0e+00 4.0e-01 0.008139 - 239 201.539 0.0e+00 8.0e-01 0.011082 - 240 204.214 0.0e+00 8.0e-01 0.008478 - 241 203.463 0.0e+00 4.0e-01 0.008653 - 242 203.016 0.0e+00 0.0e+00 0.008831 - 243 209.313 0.0e+00 8.0e-01 0.009013 - 244 209.819 0.0e+00 6.0e-01 0.007964 - 245 204.906 1.0e+00 4.0e-01 0.009388 - 246 209.594 0.0e+00 4.0e-01 0.009582 - 247 210.335 0.0e+00 8.0e-01 0.009779 - 248 213.297 0.0e+00 6.0e-01 0.008641 - 249 216.751 0.0e+00 4.0e-01 0.010186 - 250 211.783 0.0e+00 4.0e-01 0.009001 - 251 215.484 0.0e+00 4.0e-01 0.006886 - 252 219.181 0.0e+00 8.0e-01 0.008118 - 253 217.257 0.0e+00 8.0e-01 0.008285 - 254 216.117 0.0e+00 6.0e-01 0.008455 - 255 216.106 0.0e+00 2.0e-01 0.006469 - 256 219.987 0.0e+00 1.0e+00 0.007626 - 257 222.947 0.0e+00 4.0e-01 0.006738 - 258 221.112 0.0e+00 8.0e-01 0.009174 - 259 223.664 0.0e+00 8.0e-01 0.006077 - 260 223.992 0.0e+00 1.0e+00 0.011037 - 261 228.945 1.0e+00 4.0e-01 0.006330 - 262 226.490 0.0e+00 6.0e-01 0.006460 - 263 228.962 0.0e+00 2.0e-01 0.007615 - 264 223.991 0.0e+00 6.0e-01 0.007772 - 265 218.634 0.0e+00 6.0e-01 0.005946 - 266 220.307 0.0e+00 6.0e-01 0.008096 - 267 219.308 0.0e+00 4.0e-01 0.007154 - 268 221.455 0.0e+00 8.0e-01 0.007301 - 269 226.036 0.0e+00 1.0e+00 0.009941 - 270 221.629 0.0e+00 4.0e-01 0.006585 - 271 220.981 1.0e+00 4.0e-01 0.006720 - 272 220.121 1.0e+00 4.0e-01 0.009150 - 273 226.119 0.0e+00 1.0e+00 0.010786 - 274 218.897 0.0e+00 6.0e-01 0.008252 - 275 220.138 0.0e+00 1.0e+00 0.008422 - 276 214.790 0.0e+00 6.0e-01 0.006443 - 277 215.496 0.0e+00 6.0e-01 0.006576 - 278 218.136 0.0e+00 6.0e-01 0.005811 - 279 220.088 1.0e+00 4.0e-01 0.006850 - 280 218.769 0.0e+00 0.0e+00 0.009326 - 281 220.113 0.0e+00 1.0e+00 0.012697 - 282 215.305 0.0e+00 6.0e-01 0.009714 - 283 219.481 0.0e+00 8.0e-01 0.007432 - 284 226.209 0.0e+00 2.0e-01 0.007585 - 285 228.087 0.0e+00 4.0e-01 0.007741 - 286 235.480 0.0e+00 4.0e-01 0.009125 - 287 227.164 0.0e+00 4.0e-01 0.009313 - 288 224.791 1.0e+00 8.0e-01 0.010979 - 289 215.757 0.0e+00 4.0e-01 0.008399 - 290 214.847 0.0e+00 1.0e+00 0.008572 - 291 220.206 0.0e+00 6.0e-01 0.011671 - 292 211.892 0.0e+00 8.0e-01 0.006693 - 293 217.738 0.0e+00 6.0e-01 0.010526 - 294 212.707 0.0e+00 4.0e-01 0.006972 - 295 206.181 1.0e+00 8.0e-01 0.009493 - 296 208.083 1.0e+00 8.0e-01 0.008388 - 297 213.780 1.0e+00 6.0e-01 0.007412 - 298 222.840 0.0e+00 1.0e+00 0.010092 - 299 214.123 1.0e+00 8.0e-01 0.010300 - 300 217.693 0.0e+00 8.0e-01 0.009101 - 301 219.578 0.0e+00 1.0e+00 0.010728 - 302 215.666 0.0e+00 6.0e-01 0.009480 - 303 218.998 0.0e+00 8.0e-01 0.009675 - 304 220.226 0.0e+00 6.0e-01 0.008550 - 305 222.323 1.0e+00 8.0e-01 0.011640 - 306 221.416 0.0e+00 1.0e+00 0.008905 - 307 218.223 1.0e+00 8.0e-01 0.010498 - 308 224.811 0.0e+00 6.0e-01 0.014293 - 309 221.475 0.0e+00 1.0e+00 0.016848 - 310 220.295 1.0e+00 6.0e-01 0.009662 - 311 220.776 0.0e+00 4.0e-01 0.007392 - 312 211.511 0.0e+00 6.0e-01 0.011625 - 313 215.090 0.0e+00 1.0e+00 0.010272 - 314 220.149 0.0e+00 6.0e-01 0.007859 - 315 218.134 0.0e+00 1.0e+00 0.009264 - 316 219.394 0.0e+00 6.0e-01 0.007087 - 317 216.735 0.0e+00 8.0e-01 0.006263 - 318 222.824 0.0e+00 4.0e-01 0.007382 - 319 224.603 0.0e+00 6.0e-01 0.008702 - 320 226.799 0.0e+00 1.0e+00 0.011849 - 321 230.733 1.0e+00 8.0e-01 0.012093 - 322 226.180 0.0e+00 6.0e-01 0.008010 - 323 224.414 0.0e+00 6.0e-01 0.007078 - 324 220.028 0.0e+00 6.0e-01 0.008343 - 325 230.144 0.0e+00 1.0e+00 0.013121 - 326 235.101 1.0e+00 6.0e-01 0.010038 - 327 237.102 1.0e+00 8.0e-01 0.007680 - 328 236.717 1.0e+00 4.0e-01 0.005087 - 329 228.661 1.0e+00 8.0e-01 0.010671 - 330 232.343 1.0e+00 4.0e-01 0.009430 - 331 236.028 0.0e+00 1.0e+00 0.008332 - 332 236.942 1.0e+00 1.0e+00 0.007363 - 333 236.948 1.0e+00 8.0e-01 0.006506 - 334 231.984 0.0e+00 8.0e-01 0.007669 - 335 230.414 1.0e+00 6.0e-01 0.007827 - 336 228.770 0.0e+00 6.0e-01 0.005988 - 337 232.417 0.0e+00 8.0e-01 0.008153 - 338 223.628 0.0e+00 1.0e+00 0.009611 - 339 227.307 0.0e+00 6.0e-01 0.008493 - 340 231.087 0.0e+00 1.0e+00 0.013355 - 341 228.021 0.0e+00 8.0e-01 0.010218 - 342 231.036 0.0e+00 1.0e+00 0.010428 - 343 242.331 0.0e+00 4.0e-01 0.007978 - 344 237.033 0.0e+00 8.0e-01 0.010862 - 345 239.879 0.0e+00 4.0e-01 0.009598 - 346 239.868 0.0e+00 4.0e-01 0.007343 - 347 234.694 1.0e+00 6.0e-01 0.009998 - 348 237.252 0.0e+00 8.0e-01 0.010204 - 349 243.731 0.0e+00 8.0e-01 0.010414 - 350 249.174 1.0e+00 1.0e+00 0.010629 - 351 243.015 1.0e+00 8.0e-01 0.007040 - 352 246.255 0.0e+00 8.0e-01 0.009585 - 353 244.004 1.0e+00 8.0e-01 0.007333 - 354 233.162 0.0e+00 4.0e-01 0.007485 - 355 236.075 0.0e+00 4.0e-01 0.007639 - 356 239.861 1.0e+00 4.0e-01 0.007796 - 357 244.492 0.0e+00 6.0e-01 0.009190 - 358 239.084 0.0e+00 1.0e+00 0.009379 - 359 241.691 1.0e+00 8.0e-01 0.009573 - 360 245.867 1.0e+00 1.0e+00 0.007324 - 361 237.458 0.0e+00 8.0e-01 0.008633 - 362 252.237 0.0e+00 4.0e-01 0.006605 - 363 245.365 0.0e+00 6.0e-01 0.006741 - 364 244.945 0.0e+00 6.0e-01 0.006880 - 365 238.102 0.0e+00 4.0e-01 0.009367 - 366 243.180 0.0e+00 6.0e-01 0.009560 - 367 251.924 0.0e+00 6.0e-01 0.007314 - 368 253.439 0.0e+00 4.0e-01 0.007465 - 369 253.396 1.0e+00 6.0e-01 0.008799 - 370 257.970 0.0e+00 6.0e-01 0.008981 - 371 261.071 1.0e+00 8.0e-01 0.005949 - 372 256.318 1.0e+00 8.0e-01 0.006071 - 373 255.676 0.0e+00 6.0e-01 0.007157 - 374 249.874 0.0e+00 6.0e-01 0.005475 - 375 250.746 0.0e+00 4.0e-01 0.007455 - 376 246.887 0.0e+00 1.0e+00 0.007608 - 377 243.658 0.0e+00 6.0e-01 0.006723 - 378 248.560 0.0e+00 8.0e-01 0.009153 - 379 247.412 0.0e+00 6.0e-01 0.007003 - 380 253.421 0.0e+00 8.0e-01 0.009534 - 381 249.074 0.0e+00 8.0e-01 0.009731 - 382 260.240 0.0e+00 4.0e-01 0.006446 - 383 254.969 0.0e+00 8.0e-01 0.010136 - 384 247.101 0.0e+00 4.0e-01 0.005033 - 385 246.375 0.0e+00 6.0e-01 0.006852 - 386 243.302 0.0e+00 6.0e-01 0.008078 - 387 249.613 0.0e+00 4.0e-01 0.007138 - 388 249.801 0.0e+00 6.0e-01 0.008414 - 389 248.792 0.0e+00 8.0e-01 0.007435 - 390 247.630 0.0e+00 6.0e-01 0.010123 - 391 257.252 0.0e+00 6.0e-01 0.005805 - 392 248.133 0.0e+00 8.0e-01 0.010544 - 393 254.207 0.0e+00 4.0e-01 0.006984 - 394 259.771 0.0e+00 1.0e+00 0.009509 - 395 257.093 1.0e+00 4.0e-01 0.007275 - 396 258.354 0.0e+00 6.0e-01 0.007425 - 397 257.276 0.0e+00 4.0e-01 0.006561 - 398 252.730 0.0e+00 8.0e-01 0.011917 - 399 259.424 0.0e+00 8.0e-01 0.009117 - 400 258.863 0.0e+00 6.0e-01 0.006975 - 401 257.788 0.0e+00 4.0e-01 0.007119 - 402 266.545 0.0e+00 8.0e-01 0.011194 - - gp hyperparameters (gp_pak(gp)): - - 0.8942 0.7866 0.9250 0.0264 -0.1950 1.0640 - diff --git a/xunit/log/demo_neuralnetcov.txt b/xunit/log/demo_neuralnetcov.txt deleted file mode 100644 index ce03db18..00000000 --- a/xunit/log/demo_neuralnetcov.txt +++ /dev/null @@ -1,10 +0,0 @@ -Running: demo_neuralnetcov - TolFun reached. Func-count 34. Final f(x)=-49.0877. Elapsed time 1.53 - TolFun reached. Func-count 33. Final f(x)=-92.1279. Elapsed time 2.08 - TolFun reached. Func-count 48. Final f(x)=-123.011. Elapsed time 0.34 - TolFun reached. Func-count 25. Final f(x)=-126.821. Elapsed time 0.23 - - gp hyperparameters (gp_pak(gp)): - - -3.6553 -0.9891 -5.8739 - diff --git a/xunit/log/demo_periodic.txt b/xunit/log/demo_periodic.txt deleted file mode 100644 index a62ff2e0..00000000 --- a/xunit/log/demo_periodic.txt +++ /dev/null @@ -1,47 +0,0 @@ -Running: demo_periodic - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 27 - Function Count : 47 - Minimum found : 592.0151 - Intern Time : 0.029977 seconds - Total Time : 7.7959 seconds - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 594.839 - 1 6 6 594.693 3.3339e-07 - 2 12 12 212.954 0.00863052 - 3 16 16 204.308 0.00634928 - 4 18 18 201.028 0.1 - 5 21 21 200.216 0.0234747 - 6 25 25 198.293 0.025 - 7 28 28 184.818 0.25 - 8 30 30 183.63 0.1 - 9 31 31 182.288 1 - 10 33 33 181.886 0.347089 - 11 35 35 181.51 0.338729 - 12 37 37 181.175 0.42325 - 13 39 39 181.069 0.44846 - 14 42 42 181.065 0.0412808 - 15 44 44 181.06 0.194428 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Line search cannot find an acceptable point along the current search direction - Iterations : 16 - Function Count : 62 - Minimum found : 181.0597 - Intern Time : 0.039376 seconds - Total Time : 15.7249 seconds - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in the objective function value was less than TolFun. - Iterations : 17 - Function Count : 25 - Minimum found : 245.8374 - Intern Time : 0.013333 seconds - Total Time : 4.0708 seconds - - gp hyperparameters (gp_pak(gp)): - - -1.7857 3.9625 -0.4798 -0.1300 4.6376 -2.9785 -0.4421 0.9111 2.4305 - diff --git a/xunit/log/demo_regression1.txt b/xunit/log/demo_regression1.txt deleted file mode 100644 index 59e2af14..00000000 --- a/xunit/log/demo_regression1.txt +++ /dev/null @@ -1,60 +0,0 @@ -Running: demo_regression1 -GP with Gaussian noise model - -gpcf = - - type: 'gpcf_sexp' - lengthScale: [1.1000 1.2000] - magnSigma2: 0.0400 - p: [1x1 struct] - fh: [1x1 struct] - - -K = - - 0.0400 0.0187 0.0019 - 0.0187 0.0400 0.0187 - 0.0019 0.0187 0.0400 - - -C = - - 0.0800 0.0187 0.0019 - 0.0187 0.0800 0.0187 - 0.0019 0.0187 0.0800 - - MAP estimate for the parameters - TolFun reached. Func-count 33. Final f(x)=41.4902. Elapsed time 0.86 - 'log(sexp.magnSigma2)' - 'log(sexp.lengthScale x 2)' - 'log(gaussian.sigma2)' - - 3.5983 0.8838 0.8323 0.0428 - - Grid integration over the parameters - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 341 points - Elapsed time 14.22 seconds - IA-grid: Total elapsed time 30.19 seconds - MCMC integration over the parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 44.827 sls - 40 42.705 sls - 60 42.124 sls - 80 44.214 sls - 100 42.966 sls - 120 43.217 sls - 140 41.865 sls - 160 44.764 sls - 180 42.241 sls - 200 41.686 sls - 220 42.983 sls -Done - - gp hyperparameters (gp_pak(gp)): - - 1.2805 -0.1235 -0.1836 -3.1521 - diff --git a/xunit/log/demo_regression_additive1.txt b/xunit/log/demo_regression_additive1.txt deleted file mode 100644 index efddd92e..00000000 --- a/xunit/log/demo_regression_additive1.txt +++ /dev/null @@ -1,61 +0,0 @@ -Running: demo_regression_additive1 - -gp = - - type: 'FULL' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - fh: [1x1 struct] - - TolX reached. Func-count 56. Final f(x)=600.635. Elapsed time 8.77 - -gp_fic = - - type: 'FIC' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - X_u: [24x1 double] - nind: 24 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 45. Final f(x)=1257.57. Elapsed time 1.90 - -gp_pic = - - type: 'PIC' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-06 - X_u: [24x1 double] - nind: 24 - p: [1x1 struct] - tr_index: [] - fh: [1x1 struct] - - TolX reached. Func-count 45. Final f(x)=636.321. Elapsed time 4.28 - TolFun reached. Func-count 3. Final f(x)=636.321. Elapsed time 0.28 - -gp_csfic = - - type: 'CS+FIC' - lik: [1x1 struct] - cf: {[1x1 struct] [1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - X_u: [24x1 double] - nind: 24 - p: [1x1 struct] - fh: [1x1 struct] - - TolX reached. Func-count 41. Final f(x)=600.526. Elapsed time 3.36 - - gp hyperparameters (gp_pak(gp)): - - 5.7476 4.8844 1.6333 2.0965 -3.5407 - diff --git a/xunit/log/demo_regression_additive2.txt b/xunit/log/demo_regression_additive2.txt deleted file mode 100644 index 01509102..00000000 --- a/xunit/log/demo_regression_additive2.txt +++ /dev/null @@ -1,20 +0,0 @@ -Running: demo_regression_additive2 -Constant + linear covariance function - TolFun reached. Func-count 31. Final f(x)=103.189. Elapsed time 0.84 -Constant + squared exponential covariance function -(w.r.t. the first input dimension) + linear (w.r.t. -the second input dimension) - TolFun reached. Func-count 33. Final f(x)=54.0344. Elapsed time 1.00 -Additive squared exponential covariance function - TolFun reached. Func-count 35. Final f(x)=26.9207. Elapsed time 1.00 -Squared exponential covariance function - TolFun reached. Func-count 29. Final f(x)=30.6121. Elapsed time 0.77 -Additive neural network covariance function - TolFun reached. Func-count 33. Final f(x)=24.4157. Elapsed time 1.63 -Neural network covariance function - TolFun reached. Func-count 54. Final f(x)=24.2467. Elapsed time 2.00 - - gp hyperparameters (gp_pak(gp)): - - -3.1648 1.4213 0.5037 -2.9099 - diff --git a/xunit/log/demo_regression_hier.txt b/xunit/log/demo_regression_hier.txt deleted file mode 100644 index cce94e7b..00000000 --- a/xunit/log/demo_regression_hier.txt +++ /dev/null @@ -1,51 +0,0 @@ -Running: demo_regression_hier -1) Linear model with intercept and slope wrt time - TolFun reached. Func-count 19. Final f(x)=19.4149. Elapsed time 0.19 -2) Linear model with hierarchical intercept - TolFun reached. Func-count 25. Final f(x)=-37.2922. Elapsed time 0.42 -3) Linear model with hierarchical intercept and slope - TolFun reached. Func-count 33. Final f(x)=-46.4251. Elapsed time 0.65 -4) Nonlinear model with hierarchical intercept - TolFun reached. Func-count 40. Final f(x)=-45.8041. Elapsed time 0.70 -5) Non-linear hierarchical model 1 with MAP - TolFun reached. Func-count 88. Final f(x)=-77.8152. Elapsed time 2.33 -6) Non-linear hierarchical model 1 with IA - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 147 points for 9 parameters - IA-CCD: autoscaling in 18 directions - IA-CCD: scaling minmax [0.55 1.61] - IA-CCD: evaluating density at 147 points - Elapsed time 1.62 seconds - IA-CCD: Total elapsed time 2.92 seconds -7) Non-linear hierarchical model 2 with MAP - TolFun reached. Func-count 39. Final f(x)=-80.2512. Elapsed time 0.79 -8) Non-linear hierarchical model 2 with IA - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 45 points for 6 parameters - IA-CCD: autoscaling in 12 directions - IA-CCD: scaling minmax [0.82 1.37] - IA-CCD: evaluating density at 45 points -9) Non-linear hierarchical model 3 with IA - TolFun reached. Func-count 43. Final f(x)=-64.4989. Elapsed time 1.27 - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 79 points for 7 parameters - IA-CCD: autoscaling in 14 directions - IA-CCD: scaling minmax [0.63 1.40] - IA-CCD: evaluating density at 79 points - IA-CCD: Total elapsed time 1.72 seconds -10) Missing data example - TolFun reached. Func-count 49. Final f(x)=-4.10284. Elapsed time 0.87 - IA-CCD: finding the mode - IA-CCD: computing Hessian using multiplication - IA-CCD: 79 points for 7 parameters - IA-CCD: autoscaling in 14 directions - IA-CCD: scaling minmax [0.50 1.44] - IA-CCD: evaluating density at 79 points - - gp hyperparameters (gp_pak(gp)): - - -0.1987 -3.4072 1.0782 0.4515 -3.4082 -4.7715 -4.8805 - diff --git a/xunit/log/demo_regression_meanf.txt b/xunit/log/demo_regression_meanf.txt deleted file mode 100644 index 98998534..00000000 --- a/xunit/log/demo_regression_meanf.txt +++ /dev/null @@ -1,15 +0,0 @@ -Running: demo_regression_meanf -Checking gradient ... - - analytic diffs delta - - -6.7439 -6.7439 0.0000 - 2.1897 2.1897 -0.0000 - -2.3321 -2.3321 0.0000 - - TolFun reached. Func-count 19. Final f(x)=16.4578. Elapsed time 0.14 - - gp hyperparameters (gp_pak(gp)): - - 1.6749 -0.3565 -1.4619 - diff --git a/xunit/log/demo_regression_ppcs.txt b/xunit/log/demo_regression_ppcs.txt deleted file mode 100644 index f84e05ac..00000000 --- a/xunit/log/demo_regression_ppcs.txt +++ /dev/null @@ -1,25 +0,0 @@ -Running: demo_regression_ppcs - Iteration Func-count f(x) Lambda - 0 1 15150 - 1 3 14366.9 10 - 2 5 14366.9 5 - 3 6 14366.9 20 - 4 7 14366.9 80 - 5 8 13860.1 320 - 6 10 13859.9 320 - 7 12 13449.3 160 - 8 14 13274.3 160 - 9 16 13257.4 80 - 10 18 13254 40 - 11 20 13253.7 20 - 12 22 13253.6 10 - 13 24 13253.4 5 - 14 26 13253.4 2.5 - 15 28 13253.4 1.25 - TolX reached. Func-count 28. Final f(x)=13253.4. Elapsed time 49685.06 -Proportion of non-zeros is 0.0475 - - gp hyperparameters (gp_pak(gp)): - - 2.7497 1.1142 1.4814 1.3157 - diff --git a/xunit/log/demo_regression_robust.txt b/xunit/log/demo_regression_robust.txt deleted file mode 100644 index 0b4c8c4a..00000000 --- a/xunit/log/demo_regression_robust.txt +++ /dev/null @@ -1,143 +0,0 @@ -Running: demo_regression_robust - -gp = - - type: 'FULL' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-09 - fh: [1x1 struct] - -Gaussian noise model and MAP estimate for parameters - TolFun reached. Func-count 23. Final f(x)=27.0141. Elapsed time 0.16 - -S1 = - -length-scale: 1.066, magnSigma2: 5.420 - - -Scale mixture Gaussian (~=Student-t) noise model -using MCMC integration over the latent values and parameters - Using SLS sampler for hyperparameters - cycle etr slsrej - 20 -38.105 sls - 40 -51.018 sls - 60 -54.371 sls - 80 -54.989 sls - 100 -61.803 sls - 120 -65.385 sls - 140 -50.697 sls - 160 -64.071 sls - 180 -47.628 sls - 200 -53.372 sls - 220 -55.422 sls - 240 -55.152 sls - 260 -59.509 sls - 280 -45.947 sls - 300 -53.208 sls - -S2 = - -length-scale: 1.051, magnSigma2: 7.908 - - -Student-t noise model using Laplace integration over the -latent values and MAP estimate for the parameters - TolFun reached. Func-count 27. Final f(x)=-24.079. Elapsed time 1.10 - -S3 = - -length-scale: 1.047, magnSigma2: 3.093 - - -Student-t noise model using EP integration over the -latent values and MAP estimate for parameters - Iteration Func-count f(x) Lambda - 0 1 5.0753 - 1 3 -0.390577 10 - 2 5 -20.2665 5 - 3 7 -21.8841 2.5 - 4 9 -23.5458 1.25 - 5 11 -23.9733 0.625 - 6 13 -24.8947 0.312 - 7 15 -25.1996 0.156 - 8 17 -25.3812 0.0781 - 9 19 -25.4287 0.0391 - 10 21 -25.5222 0.0195 - 11 23 -25.5357 0.00977 - 12 25 -25.5999 0.00488 - 13 27 -25.6188 0.00244 - 14 29 -25.6517 0.00122 - 15 31 -25.6525 0.00061 - TolFun reached. Func-count 31. Final f(x)=-25.6525. Elapsed time 18.06 - -S4 = - -length-scale: 1.040, magnSigma2: 2.919 - - -Student-t noise model with nu= 4 and using MCMC integration -over the latent values and parameters - Using SSLS sampler for hyperparameters and ESLS for latent values - cycle etr - 20 -164.266 - 40 -187.416 - 60 -200.662 - 80 -219.012 - 100 -236.264 - 120 -241.545 - 140 -258.494 - 160 -264.050 - 180 -264.817 - 200 -271.898 - 220 -270.786 - 240 -277.278 - 260 -275.685 - 280 -289.677 - 300 -284.182 - 320 -293.980 - 340 -291.834 - 360 -294.377 - 380 -307.009 - 400 -303.441 - -S5 = - -length-scale: 0.897, magnSigma2: 2.133 - - -Student-t noise model with nu=4 using Laplace integration over -the latent values and MAP estimate for the parameters - TolFun reached. Func-count 19. Final f(x)=-14.9305. Elapsed time 0.67 - -S6 = - -length-scale: 1.040, magnSigma2: 3.095 - - -Student-t noise model with nu=4 using EP integration over -the latent values and MAP estimate for parameters - Iteration Func-count f(x) Lambda - 0 1 5.0753 - 1 3 -0.138385 10 - 2 5 -13.6439 5 - 3 7 -14.0719 2.5 - 4 9 -14.1846 1.25 - 5 11 -14.8554 0.625 - 6 13 -15.1515 0.312 - 7 15 -15.1629 0.156 - 8 17 -15.1788 0.0781 - 9 19 -15.1794 0.0391 - TolFun reached. Func-count 19. Final f(x)=-15.1794. Elapsed time 8.06 - -S7 = - -length-scale: 1.033, magnSigma2: 2.966 - - - - gp hyperparameters (gp_pak(gp)): - - 1.0871 0.0321 -4.6357 - diff --git a/xunit/log/demo_regression_sparse1.txt b/xunit/log/demo_regression_sparse1.txt deleted file mode 100644 index 6afc5719..00000000 --- a/xunit/log/demo_regression_sparse1.txt +++ /dev/null @@ -1,74 +0,0 @@ -Running: demo_regression_sparse1 - -gp = - - type: 'FULL' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-08 - fh: [1x1 struct] - - TolFun reached. Func-count 27. Final f(x)=52.5982. Elapsed time 12.12 - -gp_fic = - - type: 'FIC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 53. Final f(x)=46.3892. Elapsed time 0.75 - -gp_pic = - - type: 'PIC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - tr_index: {1x16 cell} - fh: [1x1 struct] - - TolFun reached. Func-count 29. Final f(x)=45.3107. Elapsed time 1.14 - -gp_var = - - type: 'VAR' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood+inducing' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 40. Final f(x)=51.8727. Elapsed time 5.13 - -gp_dtc = - - type: 'DTC' - lik: [1x1 struct] - cf: {[1x1 struct]} - infer_params: 'covariance+likelihood' - jitterSigma2: 1.0000e-04 - X_u: [36x2 double] - nind: 36 - p: [1x1 struct] - fh: [1x1 struct] - - TolFun reached. Func-count 65. Final f(x)=46.3104. Elapsed time 0.86 - - gp hyperparameters (gp_pak(gp)): - - 0.4291 1.1773 1.0540 -3.2363 - diff --git a/xunit/log/demo_regression_sparse2.txt b/xunit/log/demo_regression_sparse2.txt deleted file mode 100644 index 65b6ac00..00000000 --- a/xunit/log/demo_regression_sparse2.txt +++ /dev/null @@ -1,14 +0,0 @@ -Running: demo_regression_sparse2 -Full GP - TolFun reached. Func-count 35. Final f(x)=51.3654. Elapsed time 0.24 -FIC GP - TolX reached. Func-count 153. Final f(x)=47.1448. Elapsed time 1.79 -VAR GP - TolFun reached. Func-count 70. Final f(x)=65.8332. Elapsed time 0.82 -DTC GP - TolX reached. Func-count 15. Final f(x)=48.4383. Elapsed time 0.17 - - gp hyperparameters (gp_pak(gp)): - - 1.6935 -0.6707 -2.9350 - diff --git a/xunit/log/demo_spatial1.txt b/xunit/log/demo_spatial1.txt deleted file mode 100644 index 7cc27e2f..00000000 --- a/xunit/log/demo_spatial1.txt +++ /dev/null @@ -1,542 +0,0 @@ -Running: demo_spatial1 - TolFun reached. Func-count 35. Final f(x)= 2843. Elapsed time 1.84 - -S1 = - -MAP: length-scale: 14.3, magnSigma: 0.153 - - - IA-grid: finding the mode - IA-grid: computing Hessian using multiplication - IA-grid: evaluating density in a grid - IA-grid: evaluated density at 12 points - IA-grid: Total elapsed time 2.73 seconds - -S2 = - -IA-GRID: 90% CI - length-scale: [8.8,33.1], magnSigma: [0.099,0.325] - - - cycle etr hrej lrej - 2 784.636 0.0e+00 1.1e-01 0.365678 - -h1 = - - 986.2429 - 987.2424 - - mean hmcrej: 0.00 latrej: 0.04 - mean hmcrej: 0.00 latrej: 0.03 - mean hmcrej: 0.04 latrej: 0.02 - mean hmcrej: 0.03 latrej: 0.02 - mean hmcrej: 0.03 latrej: 0.04 - mean hmcrej: 0.03 latrej: 0.06 - mean hmcrej: 0.02 latrej: 0.06 - mean hmcrej: 0.02 latrej: 0.05 - mean hmcrej: 0.02 latrej: 0.05 - mean hmcrej: 0.03 latrej: 0.04 - mean hmcrej: 0.03 latrej: 0.05 - mean hmcrej: 0.03 latrej: 0.05 - mean hmcrej: 0.04 latrej: 0.05 - mean hmcrej: 0.04 latrej: 0.04 - mean hmcrej: 0.05 latrej: 0.04 - mean hmcrej: 0.04 latrej: 0.05 - mean hmcrej: 0.06 latrej: 0.05 - 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mean hmcrej: 0.07 latrej: 0.06 - mean hmcrej: 0.07 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - mean hmcrej: 0.06 latrej: 0.06 - -S3 = - -MCMC: 90% CI - length-scale: [7.1,30.2], magnSigma: [0.076,0.351] - - -MAP: length-scale: 14.3, magnSigma: 0.153 - -IA-GRID: 90% CI - length-scale: [8.8,33.1], magnSigma: [0.099,0.325] - -MCMC: 90% CI - length-scale: [7.1,30.2], magnSigma: [0.076,0.351] - - - gp hyperparameters (gp_pak(gp)): - - -4.8189 3.4074 - diff --git a/xunit/log/demo_spatial2.txt b/xunit/log/demo_spatial2.txt deleted file mode 100644 index 91c38a5b..00000000 --- a/xunit/log/demo_spatial2.txt +++ /dev/null @@ -1,28 +0,0 @@ -Running: demo_spatial2 -GP with negative-binomial observation model, Laplace -integration over the latent values and MAP estimate -for the parameters - Iteration Func-count f(x) Lambda - 0 1 5285.4 - 1 3 5285.36 10 - 2 4 5240.14 40 - 3 6 5220.82 40 - 4 8 5216.28 20 - 5 10 5215.93 10 - 6 12 5215.82 5 - 7 14 5215.81 2.5 - TolX reached. Func-count 14. Final f(x)=5215.81. Elapsed time 2241.59 -Proportion of non-zeros is 0.0715 -GP with negative-binomial observation model, EP -integration over the latent values and MAP estimate -for the parameters - Iteration Func-count f(x) Lambda - 0 1 5215.8 - 1 3 5215.8 10 - TolX reached. Func-count 3. Final f(x)=5215.8. Elapsed time 1216.06 -Proportion of non-zeros is 0.0715 - - gp hyperparameters (gp_pak(gp)): - - -2.1786 2.2593 2.8956 - diff --git a/xunit/log/demo_survival_coxph.txt b/xunit/log/demo_survival_coxph.txt deleted file mode 100644 index 5d4160e8..00000000 --- a/xunit/log/demo_survival_coxph.txt +++ /dev/null @@ -1,43 +0,0 @@ -Running: demo_survival_coxph - Iteration Func-count f(x) Lambda - 0 1 -1388 - 1 3 -1420.42 10 - 2 5 -1429.98 5 - 3 7 -1432.25 2.5 - 4 9 -1433.34 1.25 - 5 11 -1433.78 1.25 - 6 13 -1433.83 0.625 - 7 15 -1433.85 0.312 - 8 17 -1433.85 0.156 - TolFun reached. Func-count 17. Final f(x)=-1433.85. Elapsed time 55.53 - Iteration Func-count f(x) Lambda - 0 1 -2850.3 - 1 3 -2869.66 10 - 2 5 -3051.69 34.3 - 3 7 -3093.55 17.2 - 4 9 -3123.91 8.58 - 5 11 -3126.45 4.29 - 6 13 -3154.01 17.2 - 7 15 -3156 8.58 - 8 17 -3163.33 4.29 - 9 19 -3165.61 2.14 - 10 21 -3166.29 2.14 - 11 23 -3168.02 1.07 - 12 25 -3168.84 0.536 - 13 27 -3169.92 0.536 - 14 29 -3170.04 0.268 - 15 31 -3170.04 0.381 - 16 32 -3170.4 1.52 - 17 34 -3172.66 6.1 - 18 36 -3174.05 3.05 - 19 38 -3174.07 1.52 - Iteration Func-count f(x) Lambda - 20 40 -3174.1 0.762 - 21 42 -3174.14 0.381 - 22 44 -3174.15 0.191 - TolFun reached. Func-count 44. Final f(x)=-3174.15. Elapsed time 135.67 - - gp hyperparameters (gp_pak(gp)): - - 1.5871 -0.7967 0.8074 0.6256 -3.6855 -5.9779 - diff --git a/xunit/log/demo_survival_weibull.txt b/xunit/log/demo_survival_weibull.txt deleted file mode 100644 index 0e48f18e..00000000 --- a/xunit/log/demo_survival_weibull.txt +++ /dev/null @@ -1,26 +0,0 @@ -Running: demo_survival_weibull - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 -980.812 - 1 2 2 -1305.26 0.000680212 - 2 6 6 -1428.27 0.0625 - 3 8 8 -1447.95 0.5 - 4 10 10 -1458.77 0.359102 - 5 12 12 -1460.06 0.365497 - 6 13 13 -1461.48 1 - 7 14 14 -1462.2 1 - 8 16 16 -1462.43 0.428259 - 9 17 17 -1462.51 1 - 10 18 18 -1462.52 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 11 - Function Count : 19 - Minimum found : -1462.5191 - Intern Time : 0.016215 seconds - Total Time : 22.6051 seconds - - gp hyperparameters (gp_pak(gp)): - - 0.7765 -0.3490 -1.2271 0.1221 -1.5384 -0.5387 - diff --git a/xunit/log/demo_zinegbin.txt b/xunit/log/demo_zinegbin.txt deleted file mode 100644 index 0fe9c8dc..00000000 --- a/xunit/log/demo_zinegbin.txt +++ /dev/null @@ -1,30 +0,0 @@ -Running: demo_zinegbin - Iteration Func-count Grad-count f(x) Step-size - 0 1 1 2964.84 - 1 2 2 2908.76 0.0146046 - 2 4 4 2900.33 0.1 - 3 5 5 2885.72 1 - 4 7 7 2864.1 0.295664 - 5 8 8 2848.64 1 - 6 9 9 2842.71 1 - 7 10 10 2842.05 1 - 8 12 12 2841.84 0.26605 - 9 14 14 2841.81 0.143904 - 10 15 15 2841.77 1 - 11 16 16 2841.71 1 - 12 17 17 2841.7 1 - 13 18 18 2841.69 1 - 14 19 19 2841.69 1 - Optimizer Results - Algorithm Used: Broyden-Fletcher-Goldfarb-Shanno (BFGS) - Exit message : Change in x was smaller than the specified tolerance TolX. - Iterations : 15 - Function Count : 20 - Minimum found : 2841.6901 - Intern Time : 0.018068 seconds - Total Time : 111.7079 seconds - - gp hyperparameters (gp_pak(gp)): - - 4.0478 1.3173 -2.6332 1.4244 - diff --git a/xunit/realValuesBinomial1.mat b/xunit/realValuesBinomial1.mat deleted file mode 100644 index 7a2f7350113f9de23dd53ee54f38a343d9801345..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 9332 zcma)>RaYAfpk-U26pFiBarfd-+}+*X-Jy^|ibHV?A5z>UIECOY#oaZyOEUMaHS<3E z>HLIq_EDA8QIV9Q=I7v|R+ZFYw{vu{Wuw+`vGlQX_i`1cmRC_Tko?BSMlJ1SYw2Ta zL+$P+OfBc-NUh}SMole9&CM^&%P-8sOU=W@%|reF$%p^PM@3bv|H*K)4<7>Z*!55i zRE^2R2?(ZCnGgOjk(x;R@KZ13?wniZPig3s=a)GzODqeYGph3kly{xvWd&pc4#u9A70*ZlY}Tlf z?Xk--jqPcK2Q98hntDDcM;1rN(wYJdQQlZM40O56^9Gz;8trlFMcY8Js1d8)`XG7? 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zrfZtFsPH$$ae&Yd0000{0{{ScoGpPvAarW27I2&*leQ<`<3cNvS`n$yeeDm+~zn!Ojf_N-o zs}0DtPkt%V+rT`Npc*Z70asa=wnsD#LQ9kS%y|c3iddhknglk~wJNIL4x~Le_b=(0j9#kWyrK5k z_fEj}KR&Mtod&ipwZ&321N^Cgu~o%x&_;}H?pxylZFj}N$7&I>JL^F{;f+e8M0eZD zAaO-44)0iszBz4#%ivm%5E?9ACR}d=u!+O46 zM-WTz#UI=^4=j2=A_cR+-OSG7Ug{~-XeFH*R z&bD2dE)Wjeze@he4d%J3?XisuU>aqM|2#*KT>dnE;jR~w)XwWC5`9qcpIZ1KOoGC= zjEUL13{e|-D=}mRUOGi$r5F6rvZXJiY*LQIF%=k59*9c5C%1H45UQ)zmt`{EXbM*! z)maIeEj3|f$2?$uBDs3gk8&s6C_Pd@_cNMGk6{+r{iRh+Me{qaQZM$? z`wcy^ZaPZ!0ddCXy&h0olUtt+(s}7dQA4d4>V^#34;#fu2Ge)@&oYP)_qQfpks|+H zc2V@L 50 - assertElementsAlmostEqual(values.real.Eft_map(1:50), values.test.Eft_map(1:50), 'relative', 0.1); - assertElementsAlmostEqual(values.test.Varft_map(1:50), values.real.Varft_map(1:50), 'relative', 0.1); - else - assertElementsAlmostEqual(values.test.Eft_map, values.real.Eft_map, 'relative', 0.1); - assertElementsAlmostEqual(values.test.Varft_map, values.real.Varft_map, 'relative', 0.1); - end - - - function testPredictedMeanVarianceMC - values.real = load('realValuesRegression1.mat','Eft_mc','Varft_mc'); - values.test = load(strrep(which('test_regression1.m'), 'test_regression1.m', 'testValues/testRegression1.mat'),'Eft_mc','Varft_mc'); - if length(values.test.Eft_mc) > 50 - assertElementsAlmostEqual(mean(mean(values.test.Eft_mc(1:50))), mean(mean(values.real.Eft_mc(1:50))), 'relative', 0.1); - assertElementsAlmostEqual(mean(values.test.Varft_mc(1:50)), mean(values.real.Varft_mc(1:50)), 'absolute', 0.3); - else - assertElementsAlmostEqual(mean(mean(values.test.Eft_mc)), mean(mean(values.real.Eft_mc)), 'relative', 0.1); - assertElementsAlmostEqual(mean(mean(values.test.Varft_mc)), mean(mean(values.real.Varft_mc)), 'absolute', 0.3); - end - - - function testPredictedMeanVarianceIA - values.real = load('realValuesRegression1.mat','Eft_ia','Varft_ia'); - values.test = load(strrep(which('test_regression1.m'), 'test_regression1.m', 'testValues/testRegression1.mat'),'Eft_ia','Varft_ia'); - if length(values.test.Eft_ia) > 50 - assertElementsAlmostEqual(mean(values.test.Eft_ia(1:50)), mean(values.real.Eft_ia(1:50)), 'relative', 0.1); - assertElementsAlmostEqual(mean(values.test.Varft_ia(1:50)), mean(values.real.Varft_ia(1:50)), 'relative', 0.1); - else - assertElementsAlmostEqual(mean(values.test.Eft_ia), mean(values.real.Eft_ia), 'relative', 0.1); - assertElementsAlmostEqual(mean(values.test.Varft_ia), mean(values.real.Varft_ia), 'relative', 0.1); - end - - - - diff --git a/xunit/test_regression_additive1.m b/xunit/test_regression_additive1.m deleted file mode 100644 index 17bcbc8e..00000000 --- a/xunit/test_regression_additive1.m +++ /dev/null @@ -1,49 +0,0 @@ -function test_suite = test_regression_additive1 - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_ADDITIVE1 - -initTestSuite; - -function testDemo - % Set random number stream so that test failing isn't because randomness. - % Run demo & save test values. - prevstream=setrandstream(0); - - disp('Running: demo_regression_additive1') - demo_regression_additive1 - path = which('test_regression_additive1.m'); - path = strrep(path,'test_regression_additive1.m', 'testValues'); - if ~(exist(path, 'dir') == 7) - mkdir(path) - end - path = strcat(path, '/testRegression_additive1'); - save(path, 'Eft_fic', 'Varft_fic', 'Eft_pic', 'Varft_pic', ... - 'Eft_csfic', 'Varft_csfic'); - - % Set back initial random stream - setrandstream(prevstream); - drawnow;clear;close all - -% Compare test values to real values. - -function testPredictionsFIC - values.real = load('realValuesRegression_additive1.mat', 'Eft_fic', 'Varft_fic'); - values.test = load(strrep(which('test_regression_additive1.m'), 'test_regression_additive1.m', 'testValues/testRegression_additive1.mat'), 'Eft_fic', 'Varft_fic'); - assertElementsAlmostEqual((values.real.Eft_fic), (values.test.Eft_fic), 'absolute', 0.1); - assertElementsAlmostEqual(mean(values.real.Varft_fic), mean(values.test.Varft_fic), 'absolute', 0.1); - -function testPredictionsPIC - values.real = load('realValuesRegression_additive1.mat', 'Eft_pic', 'Varft_pic'); - values.test = load(strrep(which('test_regression_additive1.m'), 'test_regression_additive1.m', 'testValues/testRegression_additive1.mat'), 'Eft_pic', 'Varft_pic'); - assertElementsAlmostEqual((values.real.Eft_pic), (values.test.Eft_pic), 'absolute', 0.1); - assertElementsAlmostEqual((values.real.Varft_pic), (values.test.Varft_pic), 'absolute', 0.1); - - -function testPredictionsSparse - values.real = load('realValuesRegression_additive1.mat', 'Eft_csfic', 'Varft_csfic'); - values.test = load(strrep(which('test_regression_additive1.m'), 'test_regression_additive1.m', 'testValues/testRegression_additive1.mat'), 'Eft_csfic', 'Varft_csfic'); - assertElementsAlmostEqual((values.real.Eft_csfic), (values.test.Eft_csfic), 'absolute', 0.1); - assertElementsAlmostEqual((values.real.Varft_csfic), (values.test.Varft_csfic), 'absolute', 0.1); \ No newline at end of file diff --git a/xunit/test_regression_additive2.m b/xunit/test_regression_additive2.m deleted file mode 100644 index ed3aa2aa..00000000 --- a/xunit/test_regression_additive2.m +++ /dev/null @@ -1,35 +0,0 @@ -function test_suite = test_regression_additive2 - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_ADDITIVE2 - -initTestSuite; - - -function testDemo - % Set random number stream so that test failing isn't because randomness. - % Run demo & save test values. - prevstream=setrandstream(0); - - disp('Running: demo_regression_additive2') - demo_regression_additive2 - path = which('test_regression_additive2.m'); - path = strrep(path,'test_regression_additive2.m', 'testValues'); - if ~(exist(path, 'dir') == 7) - mkdir(path) - end - path = strcat(path, '/testRegression_additive2'); - save(path, 'Eft_map'); - - % Set back initial random stream - setrandstream(prevstream); - drawnow;clear;close all - -% Compare test values to real values. - -function testNeuralNetworkCFPrediction - values.real = load('realValuesRegression_additive2.mat','Eft_map'); - values.test = load(strrep(which('test_regression_additive2.m'), 'test_regression_additive2.m', 'testValues/testRegression_additive2.mat'),'Eft_map'); - assertElementsAlmostEqual(mean(values.real.Eft_map), mean(values.test.Eft_map), 'relative', 0.05); \ No newline at end of file diff --git a/xunit/test_regression_hier.m b/xunit/test_regression_hier.m deleted file mode 100644 index 51cbd7ef..00000000 --- a/xunit/test_regression_hier.m +++ /dev/null @@ -1,35 +0,0 @@ -function test_suite = test_regression_hier - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_HIER - -initTestSuite; - -% Set random number stream so that test failing isn't because randomness. -% Run demo & save test values. - -function testDemo -prevstream=setrandstream(0); - -disp('Running: demo_regression_hier') -demo_regression_hier -path = which('test_regression_hier.m'); -path = strrep(path,'test_regression_hier.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testRegression_hier'); -save(path, 'Eff'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - - -function testPredictionMissingData -values.real = load('realValuesRegression_hier', 'Eff'); -values.test = load(strrep(which('test_regression_hier.m'), 'test_regression_hier.m', 'testValues/testRegression_hier'), 'Eff'); -assertVectorsAlmostEqual(mean(values.real.Eff), mean(values.test.Eff), 'relative', 0.01); - diff --git a/xunit/test_regression_meanf.m b/xunit/test_regression_meanf.m deleted file mode 100644 index 05176216..00000000 --- a/xunit/test_regression_meanf.m +++ /dev/null @@ -1,36 +0,0 @@ -function test_suite = test_regression_meanf - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_MEANF - -initTestSuite; - - - -function testDemo -% Set random number stream so that test failing isn't because randomness. -% Run demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_regression_meanf') -demo_regression_meanf -path = which('test_regression_meanf.m'); -path = strrep(path,'test_regression_meanf.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testRegression_meanf'); -save(path, 'Eft'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - - -function testPredictions -values.real = load('realValuesRegression_meanf.mat', 'Eft'); -values.test = load(strrep(which('test_regression_meanf.m'), 'test_regression_meanf.m', 'testValues/testRegression_meanf.mat'), 'Eft'); -assertElementsAlmostEqual(mean(values.real.Eft), mean(values.test.Eft), 'relative', 0.10); - diff --git a/xunit/test_regression_ppcs.m b/xunit/test_regression_ppcs.m deleted file mode 100644 index fcc9c714..00000000 --- a/xunit/test_regression_ppcs.m +++ /dev/null @@ -1,44 +0,0 @@ -function test_suite = test_regression_ppcs - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_PPCS - -initTestSuite; - - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_regression_ppcs') -demo_regression_ppcs -K = K(1:50, 1:50); -Ef = Ef(1:100); -path = which('test_regression_ppcs.m'); -path = strrep(path,'test_regression_ppcs.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testRegression_ppcs'); -save(path, 'K', 'Ef') - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testCovarianceMatrix -values.real = load('realValuesRegression_ppcs.mat', 'K'); -values.test = load(strrep(which('test_regression_ppcs.m'), 'test_regression_ppcs.m', 'testValues/testRegression_ppcs.mat'), 'K'); -assertElementsAlmostEqual(mean(full(values.real.K)), mean(full(values.test.K)), 'relative', 0.1) - - -function testPrediction -values.real = load('realValuesRegression_ppcs.mat', 'Ef'); -values.test = load(strrep(which('test_regression_ppcs.m'), 'test_regression_ppcs.m', 'testValues/testRegression_ppcs.mat'), 'Ef'); -assertElementsAlmostEqual(mean(values.real.Ef), mean(values.test.Ef), 'relative', 0.1); - diff --git a/xunit/test_regression_robust.m b/xunit/test_regression_robust.m deleted file mode 100644 index f6c0fb76..00000000 --- a/xunit/test_regression_robust.m +++ /dev/null @@ -1,44 +0,0 @@ -function test_suite = test_regression_robust - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_ROBUST - -initTestSuite; - - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_regression_robust') -demo_regression_robust -path = which('test_regression_robust.m'); -path = strrep(path,'test_regression_robust.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testRegression_robust'); -w=gp_pak(rr); -save(path, 'Eft', 'Varft', 'w') - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testPredictionEP -values.real = load('realValuesRegression_robust', 'Eft', 'Varft'); -values.test = load(strrep(which('test_regression_robust.m'), 'test_regression_robust.m', 'testValues/testRegression_robust'), 'Eft', 'Varft'); -assertElementsAlmostEqual(mean(values.real.Eft), mean(values.test.Eft), 'relative', 0.05); -assertElementsAlmostEqual(mean(values.real.Varft), mean(values.test.Varft), 'relative', 0.05); - -function testMCMCSamples -values.real = load('realValuesRegression_robust', 'w'); -values.test = load(strrep(which('test_regression_robust.m'), 'test_regression_robust.m', 'testValues/testRegression_robust'), 'w'); -assertElementsAlmostEqual(mean(values.real.w), mean(values.test.w), 'absolute', 0.5); -assertElementsAlmostEqual(mean(values.real.w), mean(values.test.w), 'absolute', 0.5); - diff --git a/xunit/test_regression_sparse1.m b/xunit/test_regression_sparse1.m deleted file mode 100644 index f3d26957..00000000 --- a/xunit/test_regression_sparse1.m +++ /dev/null @@ -1,59 +0,0 @@ -function test_suite = test_regression_sparse1 - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_SPARSE1 - -initTestSuite; - - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_regression_sparse1') -demo_regression_sparse1 -path = which('test_regression_sparse1'); -path = strrep(path,'test_regression_sparse1.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testRegression_sparse1'); -save(path, 'Eft_fic', 'Eft_pic', 'Eft_var', 'Eft_dtc', 'Eft_cs'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testPredictionsCS -values.real = load('realValuesRegression_sparse1', 'Eft_cs'); -values.test = load(strrep(which('test_regression_sparse1.m'), 'test_regression_sparse1.m', 'testValues/testRegression_sparse1'), 'Eft_cs'); -assertElementsAlmostEqual((values.real.Eft_cs), (values.test.Eft_cs), 'absolute', 0.1); - - -function testPredictionsFIC -values.real = load('realValuesRegression_sparse1', 'Eft_fic'); -values.test = load(strrep(which('test_regression_sparse1.m'), 'test_regression_sparse1.m', 'testValues/testRegression_sparse1'), 'Eft_fic'); -assertElementsAlmostEqual((values.real.Eft_fic), (values.test.Eft_fic), 'absolute', 0.1); - - -function testPredictionsPIC -values.real = load('realValuesRegression_sparse1', 'Eft_pic'); -values.test = load(strrep(which('test_regression_sparse1.m'), 'test_regression_sparse1.m', 'testValues/testRegression_sparse1'), 'Eft_pic'); -assertElementsAlmostEqual((values.real.Eft_pic), (values.test.Eft_pic), 'absolute', 0.1); - - -function testPredictionsVAR -values.real = load('realValuesRegression_sparse1', 'Eft_var'); -values.test = load(strrep(which('test_regression_sparse1.m'), 'test_regression_sparse1.m', 'testValues/testRegression_sparse1'), 'Eft_var'); -assertElementsAlmostEqual((values.real.Eft_var), (values.test.Eft_var), 'absolute', 0.1); - - -function testPredictionsDTC -values.real = load('realValuesRegression_sparse1', 'Eft_dtc'); -values.test = load(strrep(which('test_regression_sparse1.m'), 'test_regression_sparse1.m', 'testValues/testRegression_sparse1'), 'Eft_dtc'); -assertElementsAlmostEqual((values.real.Eft_dtc), (values.test.Eft_dtc), 'absolute', 0.1); diff --git a/xunit/test_regression_sparse2.m b/xunit/test_regression_sparse2.m deleted file mode 100644 index 1dd273ef..00000000 --- a/xunit/test_regression_sparse2.m +++ /dev/null @@ -1,46 +0,0 @@ -function test_suite = test_regression_sparse2 - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_REGRESSION_SPARSE2 - -initTestSuite; - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_regression_sparse2') -demo_regression_sparse2 -Eft_full = Eft_full(1:100); -Eft_var = Eft_var(1:100); -Varft_full = Varft_full(1:100); -Varft_var = Varft_var(1:100); -path = which('test_regression_sparse2.m'); -path = strrep(path,'test_regression_sparse2.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testRegression_sparse2'); -save(path, 'Eft_full', 'Eft_var', 'Varft_full', 'Varft_var'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testPredictionsFull -values.real = load('realValuesRegression_sparse2.mat', 'Eft_full', 'Varft_full'); -values.test = load(strrep(which('test_regression_sparse2.m'), 'test_regression_sparse2.m', 'testValues/testRegression_sparse2.mat'),'Eft_full', 'Varft_full'); -assertElementsAlmostEqual(mean(values.real.Eft_full), mean(values.test.Eft_full), 'relative', 0.1); -assertElementsAlmostEqual(mean(values.real.Varft_full), mean(values.test.Varft_full), 'relative', 0.1); - - -function testPredictionsVar -values.real = load('realValuesRegression_sparse2.mat', 'Eft_var', 'Varft_var'); -values.test = load(strrep(which('test_regression_sparse2.m'), 'test_regression_sparse2.m', 'testValues/testRegression_sparse2.mat'), 'Eft_var', 'Varft_var'); -assertElementsAlmostEqual((values.real.Eft_var), (values.test.Eft_var), 'relative', 0.1); -assertElementsAlmostEqual((values.real.Varft_var), (values.test.Varft_var), 'relative', 0.1); diff --git a/xunit/test_spatial1.m b/xunit/test_spatial1.m deleted file mode 100644 index 129021bc..00000000 --- a/xunit/test_spatial1.m +++ /dev/null @@ -1,47 +0,0 @@ -function test_suite = test_spatial1 - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_SPATIAL1 - -initTestSuite; - - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_spatial1') -demo_spatial1 -Ef = Ef(1:100); -Varf = Varf(1:100); -path = which('test_spatial1.m'); -path = strrep(path,'test_spatial1.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testSpatial1'); -save(path, 'Elth', 'Elth2', 'Ef', 'Varf'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - - -% Compare test values to real values. - -function testEstimatesIA -values.real = load('realValuesSpatial1.mat', 'Elth', 'Elth2'); -values.test = load(strrep(which('test_spatial1.m'), 'test_spatial1.m', 'testValues/testSpatial1.mat'), 'Elth', 'Elth2'); -assertElementsAlmostEqual(values.real.Elth, values.test.Elth, 'relative', 0.1); -assertElementsAlmostEqual(values.real.Elth2, values.test.Elth2, 'relative', 0.1); - - -function testPredictionIA -values.real = load('realValuesSpatial1.mat', 'Ef', 'Varf'); -values.test = load(strrep(which('test_spatial1.m'), 'test_spatial1.m', 'testValues/testSpatial1.mat'), 'Ef', 'Varf'); -assertElementsAlmostEqual(mean(values.real.Ef), mean(values.test.Ef), 'relative', 0.1); -assertElementsAlmostEqual(mean(values.real.Varf), mean(values.test.Varf), 'relative', 0.1); - diff --git a/xunit/test_spatial2.m b/xunit/test_spatial2.m deleted file mode 100644 index 4ec77831..00000000 --- a/xunit/test_spatial2.m +++ /dev/null @@ -1,46 +0,0 @@ -function test_suite = test_spatial2 - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_SPATIAL2 - -initTestSuite; - - - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_spatial2') -demo_spatial2 -Ef = Ef(1:100); -Varf = Varf(1:100); -C = C(1:50, 1:50); -path = which('test_spatial2.m'); -path = strrep(path,'test_spatial2.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testSpatial2'); -save(path, 'Ef', 'Varf', 'C'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testPredictionsEP -values.real = load('realValuesSpatial2.mat', 'Ef', 'Varf'); -values.test = load(strrep(which('test_spatial2.m'), 'test_spatial2.m', 'testValues/testSpatial2.mat'), 'Ef', 'Varf'); -assertElementsAlmostEqual(mean(values.test.Ef), mean(values.real.Ef), 'relative', 0.1); -assertElementsAlmostEqual(mean(values.test.Varf), mean(values.real.Varf), 'relative', 0.1); - - -function testCovarianceMatrix -values.real = load('realValuesSpatial2.mat', 'C'); -values.test = load(strrep(which('test_spatial2.m'), 'test_spatial2.m', 'testValues/testSpatial2.mat'), 'C'); -assertElementsAlmostEqual(mean(values.real.C), mean(values.test.C), 'relative', 0.1); \ No newline at end of file diff --git a/xunit/test_survival_aft.m b/xunit/test_survival_aft.m deleted file mode 100644 index d0c84c5d..00000000 --- a/xunit/test_survival_aft.m +++ /dev/null @@ -1,39 +0,0 @@ -function test_suite = test_survival_aft - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_SURVIVAL_WEIBULL - -% Copyright (c) 2011-2012 Ville Tolvanen - -initTestSuite; - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_survival_aft') -demo_survival_aft; -path = which('test_survival_aft.m'); -path = strrep(path,'test_survival_aft.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testSurvival_aft'); -save(path, 'pmu'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testPredictions -values.real = load('realValuesSurvival_aft', 'pmu'); -values.test = load(strrep(which('test_survival_aft.m'), 'test_survival_aft.m', 'testValues/testSurvival_aft'), 'pmu'); -assertVectorsAlmostEqual(values.real.pmu(:,1), values.test.pmu(:,1), 'relative', 0.05); -assertVectorsAlmostEqual(values.real.pmu(:,2), values.test.pmu(:,2), 'relative', 0.05); -assertVectorsAlmostEqual(values.real.pmu(:,3), values.test.pmu(:,3), 'relative', 0.05); - diff --git a/xunit/test_survival_coxph.m b/xunit/test_survival_coxph.m deleted file mode 100644 index 02df37e0..00000000 --- a/xunit/test_survival_coxph.m +++ /dev/null @@ -1,37 +0,0 @@ -function test_suite = test_survival_coxph - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_SURVIVAL_COXPH - -% Copyright (c) 2011-2012 Ville Tolvanen - -initTestSuite; - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_survival_coxph') -demo_survival_coxph; -path = which('test_survival_coxph.m'); -path = strrep(path,'test_survival_coxph.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testSurvival_coxph'); -save(path, 'Ef','Varf'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testPredictionsCoxph -values.real = load('realValuesSurvival_coxph', 'Ef','Varf'); -values.test = load(strrep(which('test_survival_coxph.m'), 'test_survival_coxph.m', 'testValues/testSurvival_coxph'), 'Ef','Varf'); -assertElementsAlmostEqual(values.real.Ef, values.test.Ef, 'absolute', 0.10); -assertElementsAlmostEqual(values.real.Varf, values.test.Varf, 'absolute', 0.10); diff --git a/xunit/test_zinegbin.m b/xunit/test_zinegbin.m deleted file mode 100644 index 5baa6456..00000000 --- a/xunit/test_zinegbin.m +++ /dev/null @@ -1,38 +0,0 @@ -function test_suite = test_zinegbin - -% Run specific demo and save values for comparison. -% -% See also -% TEST_ALL, DEMO_ZINEGBIN - -% Copyright (c) 2011-2012 Ville Tolvanen - -initTestSuite; - -function testDemo -% Set random number stream so that failing isn't because randomness. Run -% demo & save test values. -prevstream=setrandstream(0); - -disp('Running: demo_zinegbin') -demo_zinegbin; -path = which('test_zinegbin.m'); -path = strrep(path,'test_zinegbin.m', 'testValues'); -if ~(exist(path, 'dir') == 7) - mkdir(path) -end -path = strcat(path, '/testZinegbin'); -Ef=Ef(1:100); Varf=diag(Varf(1:100,1:100)); -save(path, 'Ef', 'Varf'); - -% Set back initial random stream -setrandstream(prevstream); -drawnow;clear;close all - -% Compare test values to real values. - -function testPredictionsZinegbin -values.real = load('realValuesZinegbin', 'Ef', 'Varf'); -values.test = load(strrep(which('test_zinegbin.m'), 'test_zinegbin.m', 'testValues/testZinegbin'), 'Ef', 'Varf'); -assertElementsAlmostEqual(values.real.Ef, values.test.Ef, 'relative', 0.10); -assertElementsAlmostEqual(values.real.Varf, values.test.Varf, 'relative', 0.10); From 68dda8d027af9df6006ea09b94a0c6c3b989c290 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 12 Mar 2014 10:48:46 +0200 Subject: [PATCH 39/64] Removed xunit from readme and added new readme in folder test_gpstuff --- README.txt | 11 ++++------- startup.m | 4 ++-- test_gpstuff/README.txt | 4 ++++ test_gpstuff/run_tests.m | 7 ++++--- 4 files changed, 14 insertions(+), 12 deletions(-) create mode 100644 test_gpstuff/README.txt diff --git a/README.txt b/README.txt index e8185471..95283bca 100644 --- a/README.txt +++ b/README.txt @@ -75,7 +75,7 @@ Table of contents: 3. CONTENTS The GPstuff packge contains the following subdirectories: - diag dist gp mc misc optim xunit SuiteSparse + diag dist gp mc misc optim test_gpstuff SuiteSparse Each folder contains Contents.m, which summarizes the functions in the folder. @@ -85,12 +85,9 @@ Table of contents: 4. TESTING THE INSTALLATION - Installation can be tested by running command test_all, which - runs all demos and compares the computed results to pre-saved - results. Running test_all takes about one hour and it requires - that 'xunit' toolbox is in the Matlab path. xunit package can be - downloaded from - http://www.mathworks.com/matlabcentral/fileexchange/22846-matlab-xunit-test-framework + Installation can be tested by running command run_tests, which + runs specific demos and compares the computed results to pre-saved + results. 5. USER QUIDE (VERY SHORT) diff --git a/startup.m b/startup.m index dae8b45c..a5ef5134 100644 --- a/startup.m +++ b/startup.m @@ -3,9 +3,9 @@ F = mfilename; S = which(F); if exist('OCTAVE_VERSION', 'builtin') - subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'xunit', 'octave_compat', 'inputparser'}; + subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'test_gpstuff' 'octave_compat', 'inputparser'}; else - subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'xunit', 'inputparser'}; + subfolders={'diag' 'dist' 'gp' 'mc' 'misc' 'optim' 'test_gpstuff' 'inputparser'}; end for sf=subfolders addpath(strrep(S,[F '.m'],sf{:})) diff --git a/test_gpstuff/README.txt b/test_gpstuff/README.txt new file mode 100644 index 00000000..c9963ad8 --- /dev/null +++ b/test_gpstuff/README.txt @@ -0,0 +1,4 @@ +This folder has tests for testing the GPstuff package with both octave and +matlab. The tests can be run by running the script run_tests. The script runs +specific demos to see that they run properly. Furthermore, the results from the +demos are compared to old results to check if the values have changed. \ No newline at end of file diff --git a/test_gpstuff/run_tests.m b/test_gpstuff/run_tests.m index e0690b9d..757a9722 100644 --- a/test_gpstuff/run_tests.m +++ b/test_gpstuff/run_tests.m @@ -10,13 +10,14 @@ invalid_demos={}; iter=0; failed=0; +path=strrep(which('run_tests'), 'run_tests.m', ''); for ii=1:length(demos) iter=iter+1; setrandstream(0); if exist('OCTAVE_VERSION','builtin') - values.real=load(['octave/realValues_' strrep(demos{ii}, 'demo_', '')]); + values.real=load([path 'octave/realValues_' strrep(demos{ii}, 'demo_', '')]); else - values.real=load(['matlab/realValues_' strrep(demos{ii}, 'demo_', '')]); + values.real=load([path 'matlab/realValues_' strrep(demos{ii}, 'demo_', '')]); end field=fieldnames(values.real); fprintf('\nRunning demo: %s\n\n', demos{ii}); @@ -55,4 +56,4 @@ close all; end invalid_demos -fprintf('Failed %d of %d tests\n',failed, iter); \ No newline at end of file +fprintf('Failed %d of %d tests. Checko struct invalid_demos for further details.\n',failed, iter); \ No newline at end of file From be31d4a4586c68cb6ce69f9c048efaaa0bab942e Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 2 Apr 2014 16:17:02 +0300 Subject: [PATCH 40/64] Check if RcppOctaev is active in setrandstream --- misc/setrandstream.m | 90 +++++++++++++++++++++++--------------------- 1 file changed, 48 insertions(+), 42 deletions(-) diff --git a/misc/setrandstream.m b/misc/setrandstream.m index ce17b619..9c980851 100644 --- a/misc/setrandstream.m +++ b/misc/setrandstream.m @@ -36,54 +36,60 @@ % License (version 3 or later); please refer to the file % License.txt, included with the software, for details. -if nargin>=1 && ~isnumeric(seed) - % First argument is random stream object - stream=seed; - if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 - prevstream = RandStream.setDefaultStream(stream); +p=which('randn'); +p=strfind(p, 'RcppOctave'); +% Check if RcppOctave is active as the seed initialization does not work +% in RcppOctave +if isempty(p) + if nargin>=1 && ~isnumeric(seed) + % First argument is random stream object + stream=seed; + if str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 + prevstream = RandStream.setDefaultStream(stream); + else + prevstream=rng(stream); + end else - prevstream=rng(stream); - end -else - if nargin<2 - if nargin<1 - % Get current random stream - if exist('OCTAVE_VERSION', 'builtin') - prevstream(1) = randn('seed'); - prevstream(2) = rand('seed'); - elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 - prevstream = RandStream.getDefaultStream(); + if nargin<2 + if nargin<1 + % Get current random stream + if exist('OCTAVE_VERSION', 'builtin') + prevstream(1) = randn('seed'); + prevstream(2) = rand('seed'); + elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 + prevstream = RandStream.getDefaultStream(); + else + prevstream = rng; + end + return else - prevstream = rng; + % If stream is not provided, use Mersenne Twister + stream='mt19937ar'; end - return - else - % If stream is not provided, use Mersenne Twister - stream='mt19937ar'; - end - end - if isempty(seed) - % Default seed - seed=0; - end - if exist('OCTAVE_VERSION', 'builtin') - prevstream(1) = randn('seed'); - prevstream(2) = rand('seed'); - if length(seed)==1 - seed(2)=seed(1); end - randn('seed', seed(1)); - rand('seed', seed(2)); - elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 - if ischar(stream) - stream = RandStream(stream,'Seed',seed); + if isempty(seed) + % Default seed + seed=0; end - prevstream = RandStream.setDefaultStream(stream); - else - if ischar(stream) - prevstream = rng(seed,stream); + if exist('OCTAVE_VERSION', 'builtin') + prevstream(1) = randn('seed'); + prevstream(2) = rand('seed'); + if length(seed)==1 + seed(2)=seed(1); + end + randn('seed', seed(1)); + rand('seed', seed(2)); + elseif str2double(regexprep(version('-release'), '[a-c]', '')) < 2012 + if ischar(stream) + stream = RandStream(stream,'Seed',seed); + end + prevstream = RandStream.setDefaultStream(stream); else - prevstream=rng(stream); + if ischar(stream) + prevstream = rng(seed,stream); + else + prevstream=rng(stream); + end end end end From d060b29ac731607588e291d7476b522aa4209046 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Thu, 3 Apr 2014 09:54:50 +0300 Subject: [PATCH 41/64] Return empty prevstream if RcppOctave is active --- misc/setrandstream.m | 2 ++ 1 file changed, 2 insertions(+) diff --git a/misc/setrandstream.m b/misc/setrandstream.m index 9c980851..344ab019 100644 --- a/misc/setrandstream.m +++ b/misc/setrandstream.m @@ -92,6 +92,8 @@ end end end +else + prevstream=[]; end end From 9e36c9092cd24930bf521fe17d3571cafedd4007 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Fri, 4 Apr 2014 16:19:24 +0300 Subject: [PATCH 42/64] Octave modifications to the new files & few Octave specific fixes --- gp/cf_se_to_ss.m | 8 ++++++-- gp/demo_kalman1.m | 2 +- gp/demo_kalman2.m | 6 +++--- gp/demo_monotonic2.m | 9 ++++++++- gp/demo_survival_aft.m | 4 ++-- gp/gp_cpred.m | 1 + gp/gp_e.m | 2 +- gp/gp_g.m | 2 +- gp/gp_monotonic.m | 42 +++++++++++++++++++++++--------------- gp/gp_plot.m | 46 +++++++++++++++++++++--------------------- gp/gp_predprctmu.m | 4 ++-- gp/gpcf_periodic.m | 13 ++++++------ gp/gpep_predgrad.m | 30 +++++++++++++-------------- 13 files changed, 95 insertions(+), 74 deletions(-) diff --git a/gp/cf_se_to_ss.m b/gp/cf_se_to_ss.m index fb1e00e3..f0518c31 100644 --- a/gp/cf_se_to_ss.m +++ b/gp/cf_se_to_ss.m @@ -141,8 +141,12 @@ % Solve the corresponding Lyapunov problem % F*Pinf + Pinf*F' + L*Qc*L' = 0 - Pinf = lyap2(F,L*Qc*L'); - + if ~exist('OCTAVE_VERSION', 'builtin') + Pinf = lyap(F,L*Qc*L'); + else + F=real(F); + Pinf = lyap(F',L*Qc*L'); + end % The same thing can be solved as a solution to the % algebraic Riccati equation (less stable) %Pinf = are(F',zeros(size(F)),L*Qc*L'); diff --git a/gp/demo_kalman1.m b/gp/demo_kalman1.m index e27049c8..6d2c54d7 100644 --- a/gp/demo_kalman1.m +++ b/gp/demo_kalman1.m @@ -77,7 +77,7 @@ gp = gp_optim(gp, x, y); % Predict values at test inputs xt - [Eft,Varft] = gp_pred(gp, x, y, 'xt', xt); + [Eft,Varft] = gp_pred(gp, x, y, xt); %% Compare against full GP solution (table) diff --git a/gp/demo_kalman2.m b/gp/demo_kalman2.m index 5ed70f93..4d4e2845 100644 --- a/gp/demo_kalman2.m +++ b/gp/demo_kalman2.m @@ -89,7 +89,7 @@ 'lengthScale_prior',pl,'magnSigma2_prior',pl); % Finally create the GP structure - gp = gp_set('lik', lik, 'cf', {gpcf1,gpcf2,gpcf3}); + gp = gp_set('lik', lik, 'cf', {gpcf1,gpcf2,gpcf3}, 'jitterSigma2',1e-6); % Set type to KALMAN gp = gp_set(gp,'type','KALMAN'); @@ -101,13 +101,13 @@ opt=optimset('TolFun',1e-4,'TolX',1e-4,'Display','iter'); % Find hyperparameters by optimization (BFGS) - gp=gp_optim(gp,x,y,'opt',opt,'optimf',@fminlbfgs); + gp=gp_optim(gp,x,y,'opt',opt,'optimf',@fminscg); % Set the test points xt = (x(end):1/12:x(end)+10)'; % Predict values - [Eft,Varft] = gp_pred(gp, x, y,'xt',xt); + [Eft,Varft] = gp_pred(gp, x, y,xt); % Also predict the latent components separately [Eft1, Varft1] = gp_pred(gp, x, y, x, 'predcf', [1 3]); diff --git a/gp/demo_monotonic2.m b/gp/demo_monotonic2.m index f21ba546..bf5ab436 100644 --- a/gp/demo_monotonic2.m +++ b/gp/demo_monotonic2.m @@ -34,7 +34,14 @@ % y is the number of insured who died S = which('demo_monotonic2'); L = strrep(S,'demo_monotonic2.m','demodata/broffit.txt'); -d=readtable(L,'Delimiter',';'); +if ~exist('OCTAVE_VERSION','builtin') + d=readtable(L,'Delimiter',';'); +else + dd=importdata(L); + d.age=dd.data(:,1); + d.N=dd.data(:,2); + d.y=dd.data(:,3); +end x=d.age; y=d.y; z=d.N.*(sum(d.y)./sum(d.N)); diff --git a/gp/demo_survival_aft.m b/gp/demo_survival_aft.m index e449a033..04e47c82 100644 --- a/gp/demo_survival_aft.m +++ b/gp/demo_survival_aft.m @@ -76,8 +76,8 @@ % Create the likelihood structure % log-logistic works best for this data -lik = lik_loglogistic(); -%lik = lik_loggaussian(); +% lik = lik_loglogistic(); +lik = lik_loggaussian(); %lik = lik_weibull(); % Create the GP structure diff --git a/gp/gp_cpred.m b/gp/gp_cpred.m index b958ab83..090d4e9c 100644 --- a/gp/gp_cpred.m +++ b/gp/gp_cpred.m @@ -311,6 +311,7 @@ case 'f' [Ef, Varf] = gp_pred(gp, x, y, xt, options); case 'mu' + options prctmu = gp_predprctmu(gp, x, y, xt, options, 'prct', 50); Ef = prctmu; Varf = []; case 'cdf' diff --git a/gp/gp_e.m b/gp/gp_e.m index 16d4e154..6b4e0deb 100755 --- a/gp/gp_e.m +++ b/gp/gp_e.m @@ -499,7 +499,7 @@ % % [3] Simo Sarkka (2006). Recursive Bayesian inference on stochastic % differential equations. Doctoral dissertation, Helsinki - % University of Technology, Filand. + % University of Technology, Finland. % % Ensure that this is a purely temporal problem diff --git a/gp/gp_g.m b/gp/gp_g.m index dc60b8ec..c4b4536a 100755 --- a/gp/gp_g.m +++ b/gp/gp_g.m @@ -1441,7 +1441,7 @@ P = P - K*S*K'; end - + gdata=real(gdata); % Take all priors into account in the gradient gprior = []; if ~isempty(strfind(gp.infer_params, 'covariance')) diff --git a/gp/gp_monotonic.m b/gp/gp_monotonic.m index 0a366d10..9d161935 100644 --- a/gp/gp_monotonic.m +++ b/gp/gp_monotonic.m @@ -56,16 +56,16 @@ % parse inputs ip=inputParser; ip.FunctionName = 'GP_MONOTONIC'; -ip.addRequired('gp',@isstruct); -ip.addOptional('x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('nv', [], @(x) isreal(x) && isscalar(x)) -ip.addParamValue('optimf', @fminscg, @(x) isa(x,'function_handle')) -ip.addParamValue('opt', [], @isstruct) -ip.addParamValue('optimize', 'off', @(x) ismember(x, {'on', 'off'})); -ip.addParamValue('nvd', [], @(x) isreal(x)); -ip.parse(gp, varargin{:}); +ip=iparser(ip,'addRequired','gp',@isstruct); +ip=iparser(ip,'addOptional','x', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','y', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isnumeric(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','nv', [], @(x) isreal(x) && isscalar(x)); +ip=iparser(ip,'addParamValue','optimf', @fminscg, @(x) isa(x,'function_handle')); +ip=iparser(ip,'addParamValue','opt', [], @isstruct); +ip=iparser(ip,'addParamValue','optimize', 'off', @(x) ismember(x, {'on', 'off'})); +ip=iparser(ip,'addParamValue','nvd', [], @(x) isreal(x)); +ip=iparser(ip,'parse',gp, varargin{:}); x=ip.Results.x; y=ip.Results.y; z=ip.Results.z; @@ -98,10 +98,16 @@ end nvd=length(gp.nvd); if ~isfield(gp, 'xv') - S=warning('off','stats:kmeans:EmptyCluster'); - [tmp,xv]=kmeans(x, nv, 'Start','uniform', ... - 'EmptyAction', 'singleton'); - warning(S); + if ~isempty(which('kmeans')) + S=warning('off','stats:kmeans:EmptyCluster'); + [tmp,xv]=kmeans(x, nv, 'Start','uniform', ... + 'EmptyAction', 'singleton'); + warning(S); + else + % If no kmeans, take random subset of inputs + rri=randperm(size(x,1)); + xv=x(rri(1:nv),:); + end gp.xv=xv; end xv=gp.xv; @@ -112,7 +118,9 @@ gp=gp_set(gp,'latent_method','EP'); gp.latent_opt.init_prev='off'; gp.latent_opt.maxiter=100; -gpep_e('clearcache',gp); +if ~exist('OCTAVE_VERSION','builtin') + gpep_e('clearcache',gp); +end if isequal(optimize, 'on') % Optimize the parameters gp=gp_optim(gp,x,y,'opt',opt, 'z', z, 'optimf', optimf); @@ -141,7 +149,9 @@ fprintf('Added %d virtual observations.\n', length(inds)); xv=[xv;x(inds,:)]; gp.xv=xv; - gpep_e('clearcache',gp); + if ~exist('OCTAVE_VERSION','builtin') + gpep_e('clearcache',gp); + end if isequal(optimize, 'on') gp=gp_optim(gp,x,y,'opt',opt,'z',z, 'optimf', optimf); end diff --git a/gp/gp_plot.m b/gp/gp_plot.m index 854f9668..14eabb34 100644 --- a/gp/gp_plot.m +++ b/gp/gp_plot.m @@ -72,25 +72,25 @@ ip=inputParser; ip.FunctionName = 'GP_PLOT'; -ip.addRequired('gp',@(x) isstruct(x) || iscell(x)); -ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) -ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) -ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>=0)) -ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) -ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'})); -ip.addParamValue('tr', 0.25, @(x) isreal(x) && all(isfinite(x(:)))) -ip.addParamValue('target', 'mu', @(x) ismember(x,{'f','mu'})) -ip.addParamValue('normdata', struct(), @(x) isempty(x) || isstruct(x)) +ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); +ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>=0)); +ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); +ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'})); +ip=iparser(ip,'addParamValue','tr', 0.25, @(x) isreal(x) && all(isfinite(x(:)))); +ip=iparser(ip,'addParamValue','target', 'mu', @(x) ismember(x,{'f','mu'})); +ip=iparser(ip,'addParamValue','normdata', struct(), @(x) isempty(x) || isstruct(x)); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; zt=ip.Results.zt; @@ -126,13 +126,13 @@ nd=ip.Results.normdata; ipnd=inputParser; ipnd.FunctionName = 'normdata'; -ipnd.addParamValue('xmean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); -ipnd.addParamValue('xstd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); -ipnd.addParamValue('xlog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); -ipnd.addParamValue('ymean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); -ipnd.addParamValue('ystd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); -ipnd.addParamValue('ylog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); -ipnd.parse(nd); +ipnd=iparser(ipnd,'addParamValue','xmean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ipnd=iparser(ipnd,'addParamValue','xstd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ipnd=iparser(ipnd,'addParamValue','xlog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ipnd=iparser(ipnd,'addParamValue','ymean',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ipnd=iparser(ipnd,'addParamValue','ystd',1,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ipnd=iparser(ipnd,'addParamValue','ylog',0,@(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); +ipnd=iparser(ipnd,'parse',nd); nd=ipnd.Results; if iscell(gp) diff --git a/gp/gp_predprctmu.m b/gp/gp_predprctmu.m index ee335320..f214476e 100644 --- a/gp/gp_predprctmu.m +++ b/gp/gp_predprctmu.m @@ -47,8 +47,8 @@ ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addParamValue','nsamp', 5000, @(x) isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... diff --git a/gp/gpcf_periodic.m b/gp/gpcf_periodic.m index c59f3db1..92d950ab 100644 --- a/gp/gpcf_periodic.m +++ b/gp/gpcf_periodic.m @@ -46,6 +46,12 @@ % % See also % GP_SET, GPCF_*, PRIOR_* +% +% References: +% Arno Solin and Simo Sarkka (2014). Explicit link between periodic +% covariance functions and state space models. Accepted for +% publication in Proceedings of the Seventeenth International +% Conference on Artifcial Intelligence and Statistics (AISTATS 2014). % Copyright (c) 2009-2010 Heikki Peura % Copyright (c) 2010 Aki Vehtari @@ -1118,13 +1124,6 @@ % df(t)/dt = F f(t) + L w(t), % where w(t) is a white noise process. The observation model now % corresponds to y_k = H f(t_k) + r_k, where r_k ~ N(0,sigma2). -% -% References: -% Arno Solin and Simo Sarkka (2014). Explicit link between periodic -% covariance functions and state space models. Accepted for -% publication in Proceedings of the Seventeenth International -% Conference on Artifcial Intelligence and Statistics (AISTATS 2014). -% % Case squared exponential (i.e. quasi-periodic) if gpcf.decay diff --git a/gp/gpep_predgrad.m b/gp/gpep_predgrad.m index a963849d..ed7742e2 100644 --- a/gp/gpep_predgrad.m +++ b/gp/gpep_predgrad.m @@ -67,24 +67,24 @@ % License.txt, included with the software, for details. ip=inputParser; - ip.FunctionName = 'GPEP_PRED'; - ip.addRequired('gp', @isstruct); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) - ip.addParamValue('fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'})) + ip.FunctionName = 'GPEP_PREDGRAD'; + ip=iparser(ip,'addRequired','gp', @isstruct); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); + ip=iparser(ip,'addParamValue','fcorr', 'off', @(x) ismember(x, {'off', 'fact', 'cm2', 'on'})); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp, x, y, varargin{:}); + ip=iparser(ip,'parse',gp, x, y, varargin{:}); else - ip.parse(gp, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; From c29a4b66aad97a8f99ba8e3991372e2931f4a251 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 9 Apr 2014 10:15:14 +0300 Subject: [PATCH 43/64] Octave modifications --- gp/demo_hierarchial.m | 30 +++++++++++++++++++++++------- gp/gp_set.m | 2 +- gp/gpia_jpreds.m | 26 +++++++++++++------------- gp/lik_lgpc.m | 2 +- 4 files changed, 38 insertions(+), 22 deletions(-) diff --git a/gp/demo_hierarchial.m b/gp/demo_hierarchial.m index 189c54be..1f9cd3f4 100644 --- a/gp/demo_hierarchial.m +++ b/gp/demo_hierarchial.m @@ -112,8 +112,13 @@ end gpcf1 = gpcf_matern32('lengthScale', 1, 'magnSigma2', 0.03, ... 'lengthScale_prior', pl, 'magnSigma2_prior', prior_fixed()); -gpcf2 = gpcf_ppcs3('nin',nin,'lengthScale', 5, 'magnSigma2', 0.05,... - 'lengthScale_prior', pl, 'magnSigma2_prior', prior_fixed()); +if exist('ldlchol') + gpcf2 = gpcf_ppcs3('nin',nin,'lengthScale', 5, 'magnSigma2', 0.05,... + 'lengthScale_prior', pl, 'magnSigma2_prior', prior_fixed()); +else + gpcf2 = gpcf_matern52('lengthScale',5,'magnSigma2', 0.05, ... + 'lengthScale_prior',pl, 'magnSigma2_prior', prior_fixed()); +end lik = lik_negbin(); @@ -125,8 +130,13 @@ gp3 = gp_set('type', 'PIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, 'Xu_prior', pxu, ... 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing',... 'tr_index', trindex); -gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu, ... - 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing'); +if exist('ldlchol') + gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu, ... + 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing'); +else + gp4 = gp_set('type', 'SOR', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu, ... + 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing'); +end gp4.cf{1}.p.lengthScale=prior_fixed(); gp4.cf{1}.p.magnSigma2=prior_fixed(); @@ -152,9 +162,15 @@ gp3 = gp_set('type', 'PIC', 'lik', lik, 'cf', gpcf1, 'X_u', Xu, 'Xu_prior', pxu, ... 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing',... 'tr_index', trindex, 'latent_method', 'EP'); -gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu, ... - 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing', ... - 'latent_method', 'EP'); +if exist('ldlchol') + gp4 = gp_set('type', 'CS+FIC', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu, ... + 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing', ... + 'latent_method', 'EP'); +else + gp4 = gp_set('type', 'SOR', 'lik', lik, 'cf', {gpcf1 gpcf2}, 'X_u', Xu, 'Xu_prior', pxu, ... + 'jitterSigma2', 1e-4, 'infer_params', 'covariance+likelihood+inducing', ... + 'latent_method', 'EP'); +end gp4.cf{1}.p.lengthScale=prior_fixed(); gp4.cf{1}.p.magnSigma2=prior_fixed(); diff --git a/gp/gp_set.m b/gp/gp_set.m index 28c8fd08..55c5793f 100644 --- a/gp/gp_set.m +++ b/gp/gp_set.m @@ -206,7 +206,7 @@ ip=iparser(ip,'addParamValue','latent_method','Laplace', @(x) ischar(x) || iscell(x)); ip=iparser(ip,'addParamValue','latent_opt',struct(), @isstruct); ip=iparser(ip,'addParamValue','X_u',[], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','Xu_prior',prior_unif, @(x) isstruct(x) || isempty(x)); + ip=iparser(ip,'addParamValue','Xu_prior',prior_unif, @(x) iscell(x) || isstruct(x) || isempty(x)); ip=iparser(ip,'addParamValue','tr_index', [], @(x) ~isempty(x) || iscell(x)); ip=iparser(ip,'addParamValue','comp_cf', [], @(x) iscell(x)); ip=iparser(ip,'addParamValue','derivobs','off', @(x) islogical(x) || isscalar(x) || ... diff --git a/gp/gpia_jpreds.m b/gp/gpia_jpreds.m index fbeee5af..df2c73cd 100644 --- a/gp/gpia_jpreds.m +++ b/gp/gpia_jpreds.m @@ -70,22 +70,22 @@ ip=inputParser; ip.FunctionName = 'GPIA_JPREDS'; - ip.addRequired('gp_array', @iscell); - ip.addRequired('x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addRequired('y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))) - ip.addOptional('xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))) - ip.addParamValue('yt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('z', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('zt', [], @(x) isreal(x) && all(isfinite(x(:)))) - ip.addParamValue('predcf', [], @(x) isempty(x) || ... - isvector(x) && isreal(x) && all(isfinite(x)&x>0)) - ip.addParamValue('tstind', [], @(x) isempty(x) || iscell(x) ||... - (isvector(x) && isreal(x) && all(isfinite(x)&x>0))) + ip=iparser(ip,'addRequired','gp_array', @iscell); + ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addOptional','xt', [], @(x) isempty(x) || (isreal(x) && all(isfinite(x(:))))); + ip=iparser(ip,'addParamValue','yt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','zt', [], @(x) isreal(x) && all(isfinite(x(:)))); + ip=iparser(ip,'addParamValue','predcf', [], @(x) isempty(x) || ... + isvector(x) && isreal(x) && all(isfinite(x)&x>0)); + ip=iparser(ip,'addParamValue','tstind', [], @(x) isempty(x) || iscell(x) ||... + (isvector(x) && isreal(x) && all(isfinite(x)&x>0))); if numel(varargin)==0 || isnumeric(varargin{1}) % inputParser should handle this, but it doesn't - ip.parse(gp_array, x, y, varargin{:}); + ip=iparser(ip,'parse',gp_array, x, y, varargin{:}); else - ip.parse(gp_array, x, y, [], varargin{:}); + ip=iparser(ip,'parse',gp_array, x, y, [], varargin{:}); end xt=ip.Results.xt; yt=ip.Results.yt; diff --git a/gp/lik_lgpc.m b/gp/lik_lgpc.m index bdace4d2..afbc31ae 100644 --- a/gp/lik_lgpc.m +++ b/gp/lik_lgpc.m @@ -252,7 +252,7 @@ error('Not implemented') end -function mu = lik_lpgpc_invlink(lik, f, z) +function mu = lik_lgpc_invlink(lik, f, z) %LIK_LPGPC_INVLINK Returns values of inverse link function % % Description From e15f1cbf0b0e2b07d1eef44cdb1196f983801079 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 9 Apr 2014 10:15:37 +0300 Subject: [PATCH 44/64] updated Changelog --- ChangeLog.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ChangeLog.txt b/ChangeLog.txt index 92bdf47d..12d367ea 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -1,3 +1,4 @@ +<<<<<<< .merge_file_a5eegv 2014-4-3 Version 4.4 New features @@ -46,6 +47,8 @@ Big fixes - Changed nested functions to subfunctions for compatibility with Octave 3.8.0 - Fixed compilation of C source files with Octave. +======= +>>>>>>> .merge_file_0AHvYo 2013-11-26 Version 4.3.1 Improvements: From e9754f4b3b39ca02c90fcd42fcf1add9dde97688 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 9 Apr 2014 10:23:14 +0300 Subject: [PATCH 45/64] merge --- gp/demo_censored_t.m | 222 ------------------------------------------- 1 file changed, 222 deletions(-) delete mode 100644 gp/demo_censored_t.m diff --git a/gp/demo_censored_t.m b/gp/demo_censored_t.m deleted file mode 100644 index 457acff1..00000000 --- a/gp/demo_censored_t.m +++ /dev/null @@ -1,222 +0,0 @@ -%% generate data -clear; -datai=1; - -% true function f(x) -xx = linspace(-7,5,500)'; -yy = 0.1+0.1*xx+0.2*sin(2.7*xx)+1./(1+xx.^2); - -nu=3; -sigma=0.05; -Hmax=sqrt(3*nu)*sigma; -Hzero=sqrt(nu)*sigma; -a=0.3; -ylim=[-0.4 0.9]; - -x=[linspace(-5.4,-3-a,15) linspace(-3+a,-1-a,15) linspace(-1+a,4,30)]; -xo=[-4 -3 -2 -1 2 3]; -ii=length(xo)+[1:length(x)]; -io=1:length(xo); % outlier indices -x=[xo x]'; % outlier x:s -y = 0.1 + 0.1*x + 0.2*sin(2.7*x) + 1 ./ (1+x.^2); -y(io)=y(io)+Hmax*[-3 -2 1 -1 -1 1]'; % outlier y:s -y(ii)=y(ii)+sigma*randn(size(y(ii))); - -yt=y; -y(yylim(2))=ylim(2); - -figure(1); clf -plot(xx,yy,'k',x,y,'b.',x(io),y(io),'ro') -hold on -plot(repmat(x(io)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -%save(sprintf('data%d.mat',datai),'x','y','yt','xx','yy','io','ylim') - -%% load data & create gp -%clear -%datai=1; -%load(sprintf('data%d.mat',datai)); -nu=6; -sigma=0.1; -J=0.02; -x=[x randn(size(x))]; -xx=[xx randn(size(xx))]; -[n, nin] = size(x); -ylim=[-0.4 0.9]; - -% gpcf1 = gpcf_dotproduct('init', 'constSigma2',10,'coeffSigma2',ones(1,nin)); -% gpcf1.p.constSigma2 = logunif_p; -% gpcf1.p.coeffSigma2 = logunif_p; - -gpcf1 = gpcf_sexp('init', 'lengthScale',ones(1,nin),'magnSigma2',1); -gpcf1.p.lengthScale = logunif_p; -gpcf1.p.magnSigma2 = logunif_p; - - -% gpcf1 = gpcf_neuralnetwork('init',nin,'biasSigma2',0.1,'weightSigma2',ones(1,nin)); -% gpcf1.p.weightSigma2 = logunif_p; -% gpcf1.p.biasSigma2 = logunif_p; - -% Create the likelihood structure -%likelih = likelih_t('init', nu, sigma); -likelih = likelih_cen_t('init', 'nu', nu, 'sigma', sigma, 'ylim', ylim); -likelih.p.nu = logunif_p; -likelih.p.sigma = logunif_p; -%likelih.fix_nu=1; - -% Laplace approximation Student-t likelihood -param = 'hyper+likelih'; -gp_la = gp_init('init', 'FULL', likelih, {gpcf1}, {}, 'jitterSigma2', J.^2); -gp_la = gp_init('set', gp_la, 'latent_method', {'Laplace', x, y, param}); -gp_la.laplace_opt.optim_method='likelih_specific'; -%gp_la.laplace_opt.optim_method='fminunc_large'; -[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); - -% gradient checking -w = gp_pak(gp_la,param); -w = w+ 0.1*randn(size(w)); -gradcheck(w, @gpla_e, @gpla_g, gp_la, x, y, param) - -opt=optimset('GradObj','on'); -opt=optimset(opt,'TolX', 1e-3); -opt=optimset(opt,'LargeScale', 'off'); -opt=optimset(opt,'Display', 'iter'); -opt=optimset(opt,'Derivativecheck', 'on'); % 'iter' - -w0 = gp_pak(gp_la, param); -mydeal = @(varargin)varargin{1:nargout}; -w = fminunc(@(ww) mydeal(gpla_e(ww, gp_la, x, y, param), gpla_g(ww, gp_la, x, y, param)), w0, opt); -gp_la = gp_unpak(gp_la,w,param); - -fprintf('\nnu=%.3f, sigma=%.3f \nhyper=%s\n',gp_la.likelih.nu,... - gp_la.likelih.sigma,sprintf(' %.2f,',exp(gp_pak(gp_la,'hyper'))) ) - -figure(2) -[e, edata, eprior, f, L, a, La2] = gpla_e(gp_pak(gp_la,param), gp_la, x, y, param); -W=-gp_la.likelih.fh.llg2(gp_la.likelih,y,f,'latent'); -[foo,ii]=sort(W,'ascend'); -ii=ii(1:5); -plot(xx(:,1),yy,'k',x(:,1),f,'b.',x(:,1),y,'go',x(ii,1),y(ii),'r.') - -[Ef_la, Varf_la] = la_pred(gp_la, x, y, xx, param); -stdf_la = sqrt(Varf_la); - -% plot the predictions and data -nu=gp_la.likelih.nu; -sigma=gp_la.likelih.sigma; -Hmax=sqrt(3*nu)*sigma; -Hzero=sqrt(nu)*sigma; - -figure(1) -h1=plot(xx(:,1),yy,'k',xx(:,1),Ef_la,'b',xx(:,1),Ef_la-2*stdf_la, 'b--',xx(:,1), Ef_la+2*stdf_la, 'b--'); -hold on -h1=[h1(1:2); plot(x(:,1),y,'k.')]; -plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -legend(h1,'True','Laplace','Data') - -% ====================== -% Full MCMC solution -% ====================== -[n, nin] = size(x); -gpcf1 = gpcf_sexp('init', 'lengthScale', repmat(1,1,nin), 'magnSigma2', 0.2^2); -gpcf2 = gpcf_noiset('init', 'ndata', n, 'noiseSigmas2', repmat(1^2,n,1)); % Here set own Sigma2 for every data point - -% Un-fix nu -%gpcf2 = gpcf_noiset('set', gpcf2, 'fix_nu', 0); -gpcf2 = gpcf_noiset('set', gpcf2, 'censored', {[-0.4 0.9], y}); - -% Set the prior for the parameters of covariance functions -gpcf1.p.lengthScale = gamma_p({3 7 3 7}); -gpcf1.p.magnSigma2 = sinvchi2_p({0.05^2 0.5}); - -gp = gp_init('init', 'FULL', 'gaussian', {gpcf1}, {gpcf2}, 'jitterSigma2', 1e-4.^2) -w = gp_pak(gp, 'hyper') -gp2 = gp_unpak(gp,w, 'hyper') - -opt=gp_mcopt; -opt.repeat=10; -opt.nsamples=10; -opt.hmc_opt.steps=10; -opt.hmc_opt.stepadj=0.1; -opt.hmc_opt.nsamples=1; -hmc2('state', sum(100*clock)); - -opt.gibbs_opt = sls1mm_opt; -opt.gibbs_opt.maxiter = 50; -opt.gibbs_opt.mmlimits = [0 40]; -opt.gibbs_opt.method = 'minmax'; - -% Sample -[r,g,rstate1]=gp_mc(opt, gp, x, y); - -opt.hmc_opt.stepadj=0.08; -opt.nsamples=300; -opt.hmc_opt.steps=10; -opt.hmc_opt.persistence=1; -opt.hmc_opt.decay=0.6; - -[r,g,rstate2]=gp_mc(opt, g, x, y, [], [], r); -rr = r; - -% thin the record -rr = thin(r,100,2); - -figure -hist(rr.noise{1}.nu,20) -title('Mixture model, \nu') -figure -hist(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha,20) -title('Mixture model, \sigma') -figure -hist(rr.cf{1}.lengthScale(:,1),20) -title('Mixture model, length-scale') -figure -hist(rr.cf{1}.magnSigma2,20) -title('Mixture model, magnSigma2') - - -% $$$ >> mean(rr.noise{1}.nu) -% $$$ ans = -% $$$ 1.5096 -% $$$ >> mean(sqrt(rr.noise{1}.tau2).*rr.noise{1}.alpha) -% $$$ ans = -% $$$ 0.0683 -% $$$ >> mean(rr.cf{1}.lengthScale) -% $$$ ans = -% $$$ 1.0197 -% $$$ >> mean(rr.cf{1}.magnSigma2) -% $$$ ans = -% $$$ 1.2903 - -% make predictions for test set -ypred = repmat(y,1,size(rr.edata,1)); -ypred(gp.noise{1}.imis,:) = rr.noise{1}.cy'; -[Efs, Varfs] = gp_preds(rr,x,ypred,xx); - -Ef = mean(squeeze(Efs),2); -std_f = sqrt(var(Efs,[],2)); - -% Plot the network outputs as '.', and underlying mean with '--' -figure -h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b'); -%h1=plot(xx(:,1),yy,'k',xx(:,1),Ef,'b',xx(:,1),Ef-2*std_f, 'b--',xx(:,1), Ef+2*std_f, 'b--'); -hold on -h1=[h1(1:2); plot(x(:,1),y,'k.')]; -plot(repmat(x(io,1)',2,1),[y(io)'-Hmax; y(io)'+Hmax],'r.-') -plot(repmat(x(io,1)',2,1),[y(io)'-Hzero; y(io)'+Hzero],'g.-') -hold off -legend(h1,'True','Laplace','Data') - - -figure -for i=1:12 - subplot(6,2,i) - hist(rr.noise{1}.cy(:,i)) - hold on - plot(yt(gp.noise{1}.imis(i)), 0, 'rx', 'MarkerSize', 20, 'lineWidth', 5) -end - From 200d37a2458aa1c028e7fac702c2b057f5ab407e Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Wed, 9 Apr 2014 10:58:38 +0300 Subject: [PATCH 46/64] data for demo_minimal --- gp/demodata/enso.txt | 168 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 168 insertions(+) create mode 100644 gp/demodata/enso.txt diff --git a/gp/demodata/enso.txt b/gp/demodata/enso.txt new file mode 100644 index 00000000..1bbc66c8 --- /dev/null +++ b/gp/demodata/enso.txt @@ -0,0 +1,168 @@ + 12.90000 1.000000 + 11.30000 2.000000 + 10.60000 3.000000 + 11.20000 4.000000 + 10.90000 5.000000 + 7.500000 6.000000 + 7.700000 7.000000 + 11.70000 8.000000 + 12.90000 9.000000 + 14.30000 10.000000 + 10.90000 11.00000 + 13.70000 12.00000 + 17.10000 13.00000 + 14.00000 14.00000 + 15.30000 15.00000 + 8.500000 16.00000 + 5.700000 17.00000 + 5.500000 18.00000 + 7.600000 19.00000 + 8.600000 20.00000 + 7.300000 21.00000 + 7.600000 22.00000 + 12.70000 23.00000 + 11.00000 24.00000 + 12.70000 25.00000 + 12.90000 26.00000 + 13.00000 27.00000 + 10.90000 28.00000 + 10.400000 29.00000 + 10.200000 30.00000 + 8.000000 31.00000 + 10.90000 32.00000 + 13.60000 33.00000 + 10.500000 34.00000 + 9.200000 35.00000 + 12.40000 36.00000 + 12.70000 37.00000 + 13.30000 38.00000 + 10.100000 39.00000 + 7.800000 40.00000 + 4.800000 41.00000 + 3.000000 42.00000 + 2.500000 43.00000 + 6.300000 44.00000 + 9.700000 45.00000 + 11.60000 46.00000 + 8.600000 47.00000 + 12.40000 48.00000 + 10.500000 49.00000 + 13.30000 50.00000 + 10.400000 51.00000 + 8.100000 52.00000 + 3.700000 53.00000 + 10.70000 54.00000 + 5.100000 55.00000 + 10.400000 56.00000 + 10.90000 57.00000 + 11.70000 58.00000 + 11.40000 59.00000 + 13.70000 60.00000 + 14.10000 61.00000 + 14.00000 62.00000 + 12.50000 63.00000 + 6.300000 64.00000 + 9.600000 65.00000 + 11.70000 66.00000 + 5.000000 67.00000 + 10.80000 68.00000 + 12.70000 69.00000 + 10.80000 70.00000 + 11.80000 71.00000 + 12.60000 72.00000 + 15.70000 73.00000 + 12.60000 74.00000 + 14.80000 75.00000 + 7.800000 76.00000 + 7.100000 77.00000 + 11.20000 78.00000 + 8.100000 79.00000 + 6.400000 80.00000 + 5.200000 81.00000 + 12.00000 82.00000 + 10.200000 83.00000 + 12.70000 84.00000 + 10.200000 85.00000 + 14.70000 86.00000 + 12.20000 87.00000 + 7.100000 88.00000 + 5.700000 89.00000 + 6.700000 90.00000 + 3.900000 91.00000 + 8.500000 92.00000 + 8.300000 93.00000 + 10.80000 94.00000 + 16.70000 95.00000 + 12.60000 96.00000 + 12.50000 97.00000 + 12.50000 98.00000 + 9.800000 99.00000 + 7.200000 100.00000 + 4.100000 101.00000 + 10.60000 102.00000 + 10.100000 103.00000 + 10.100000 104.00000 + 11.90000 105.00000 + 13.60000 106.0000 + 16.30000 107.0000 + 17.60000 108.0000 + 15.50000 109.0000 + 16.00000 110.0000 + 15.20000 111.0000 + 11.20000 112.0000 + 14.30000 113.0000 + 14.50000 114.0000 + 8.500000 115.0000 + 12.00000 116.0000 + 12.70000 117.0000 + 11.30000 118.0000 + 14.50000 119.0000 + 15.10000 120.0000 + 10.400000 121.0000 + 11.50000 122.0000 + 13.40000 123.0000 + 7.500000 124.0000 + 0.6000000 125.0000 + 0.3000000 126.0000 + 5.500000 127.0000 + 5.000000 128.0000 + 4.600000 129.0000 + 8.200000 130.0000 + 9.900000 131.0000 + 9.200000 132.0000 + 12.50000 133.0000 + 10.90000 134.0000 + 9.900000 135.0000 + 8.900000 136.0000 + 7.600000 137.0000 + 9.500000 138.0000 + 8.400000 139.0000 + 10.70000 140.0000 + 13.60000 141.0000 + 13.70000 142.0000 + 13.70000 143.0000 + 16.50000 144.0000 + 16.80000 145.0000 + 17.10000 146.0000 + 15.40000 147.0000 + 9.500000 148.0000 + 6.100000 149.0000 + 10.100000 150.0000 + 9.300000 151.0000 + 5.300000 152.0000 + 11.20000 153.0000 + 16.60000 154.0000 + 15.60000 155.0000 + 12.00000 156.0000 + 11.50000 157.0000 + 8.600000 158.0000 + 13.80000 159.0000 + 8.700000 160.0000 + 8.600000 161.0000 + 8.600000 162.0000 + 8.700000 163.0000 + 12.80000 164.0000 + 13.20000 165.0000 + 14.00000 166.0000 + 13.40000 167.0000 + 14.80000 168.0000 From 208dc7c18bc7cd2a412cd76d49b6c12a751dffe3 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Fri, 11 Apr 2014 14:30:06 +0300 Subject: [PATCH 47/64] Octave modifications --- gp/demo_modelcomparison2.m | 16 ++++++++-------- gp/demo_multiclass.m | 14 +++++++------- gp/demo_survival_coxph.m | 8 ++++---- gp/gp_monotonic.m | 6 ------ gp/gpep_e.m | 2 +- gp/lik_qgp.m | 2 +- test_gpstuff/run_tests.m | 2 +- 7 files changed, 22 insertions(+), 28 deletions(-) diff --git a/gp/demo_modelcomparison2.m b/gp/demo_modelcomparison2.m index 240009d9..ce5d228f 100644 --- a/gp/demo_modelcomparison2.m +++ b/gp/demo_modelcomparison2.m @@ -263,11 +263,11 @@ [cll,bblg]=hcs(critlg(:,size(tt,2)),y,D(:,size(tt,2)),tt(size(tt,2)),'rsubstream',1); title('Estimated density of CLG-CLL') hold on -lgpdens(bblg-bbll) +lgpdens(bblg'-bbll') hold off % We integrate the (CLG-CLL) estimated density in the (0,inf) interval -zc=lgpdens_cum(bblg-bbll,0,inf); +zc=lgpdens_cum(bblg'-bbll',0,inf); fprintf(['Estimated c statistics for Log Gaussian and Log Logistic respectively: ', num2str(clg) ' ' num2str(cll)]); fprintf(['Cumulative probability in the (0,inf) interval: ', num2str(zc)]); @@ -277,11 +277,11 @@ [ccph,bbcph]=hcs(critcph(:,size(tt,2)),y,D(:,size(tt,2)),tt(size(tt,2)),'rsubstream',1); title('Estimated density of CCW-CCPH') hold on -lgpdens(bbw-bbcph) +lgpdens(bbw'-bbcph') hold off % We integrate the (CW-CCPH) estimated density in the (0,inf) interval -zc=lgpdens_cum(bbw-bbcph,0,inf); +zc=lgpdens_cum(bbw'-bbcph',0,inf); fprintf(['Estimated C-statistics for Weibull and Cox-ph respectively: ', num2str(cw) ' ' num2str(ccph)]); fprintf(['Cumulative probability in the (0,inf) interval: ', num2str(zc)]); @@ -293,7 +293,7 @@ % (0,inf) interval at time 1 [idi1,bbid1,rll,rlg] = idis(critll(:,size(tt,2)),critlg(:,size(tt,2)),'rsubstream',1); -zidi1=lgpdens_cum(bbid1,0,inf); +zidi1=lgpdens_cum(bbid1',0,inf); fprintf(['\n R^2 statistic for log-logistic model:', num2str(rll)]); fprintf(['\n R^2 statistic for log-Gaussian model:', num2str(rlg)]); @@ -307,11 +307,11 @@ title('IDI estimated density between log-logistic and log-Gaussian') hold on -lgpdens(bbid1) +lgpdens(bbid1') hold off [idi2,bbid2,rw,rcph] = idis(critw(:,size(tt,2)),critcph(:,size(tt,2)),'rsubstream',1); -zidi2=lgpdens_cum(bbid2,0,inf); +zidi2=lgpdens_cum(bbid2',0,inf); fprintf(['\n R^2 statistic for Weibull model:', num2str(rw)]); @@ -326,7 +326,7 @@ title('IDI estimated density between Weibull and Cox-ph') hold on -lgpdens(bbid2) +lgpdens(bbid2') hold off %% Ext AUC diff --git a/gp/demo_multiclass.m b/gp/demo_multiclass.m index a003a289..837424d9 100644 --- a/gp/demo_multiclass.m +++ b/gp/demo_multiclass.m @@ -163,9 +163,9 @@ plot(x(y(:,2)==1,1),x(y(:,2)==1,2),'x', 'linewidth', 2); plot(x(y(:,3)==1,1),x(y(:,3)==1,2),'kd', 'linewidth', 2); axis([-0.4 1.4 -0.4 1.4]) -contour(xtg1, xtg2, reshape(pg_mc(:,1),30,30),'r', 'linewidth', 2) -contour(xtg1, xtg2, reshape(pg_mc(:,2),30,30),'b', 'linewidth', 2) -contour(xtg1, xtg2, reshape(pg_mc(:,3),30,30),'k', 'linewidth', 2) +contour(xtg1, xtg2, reshape(pg_mc(:,1),30,30),10,'r', 'linewidth', 2) +contour(xtg1, xtg2, reshape(pg_mc(:,2),30,30),10,'b', 'linewidth', 2) +contour(xtg1, xtg2, reshape(pg_mc(:,3),30,30),10,'k', 'linewidth', 2) @@ -198,7 +198,7 @@ % re-set some of the sampling options hmc_opt.repeat=1; -hmc_opt.steps=4; +hmc_opt.steps=4;i_d hmc_opt.stepadj=0.02; latent_opt.repeat = 20; hmc2('state', sum(100*clock)); @@ -219,8 +219,8 @@ plot(x(y(:,2)==1,1),x(y(:,2)==1,2),'x', 'linewidth', 2); plot(x(y(:,3)==1,1),x(y(:,3)==1,2),'kd', 'linewidth', 2); axis([-0.4 1.4 -0.4 1.4]) -contour(xtg1, xtg2, reshape(pg_mc2(:,1),30,30),'r', 'linewidth', 2) -contour(xtg1, xtg2, reshape(pg_mc2(:,2),30,30),'b', 'linewidth', 2) -contour(xtg1, xtg2, reshape(pg_mc2(:,3),30,30),'k', 'linewidth', 2) +contour(xtg1, xtg2, reshape(pg_mc2(:,1),30,30),10,'r', 'linewidth', 2) +contour(xtg1, xtg2, reshape(pg_mc2(:,2),30,30),10,'b', 'linewidth', 2) +contour(xtg1, xtg2, reshape(pg_mc2(:,3),30,30),10,'k', 'linewidth', 2) diff --git a/gp/demo_survival_coxph.m b/gp/demo_survival_coxph.m index ecc48eb2..edba632e 100644 --- a/gp/demo_survival_coxph.m +++ b/gp/demo_survival_coxph.m @@ -123,7 +123,7 @@ set(gcf,'pos',[29 6 24 6]) subplot('position',[0.07 0.21 0.20 0.77]); i1=2;i2=1; -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye, 'target', 'f'); xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); @@ -139,7 +139,7 @@ subplot('position',[0.31 0.21 0.20 0.77]); i1=2;i2=3; -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye, 'target', 'f'); xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); @@ -157,7 +157,7 @@ subplot('position',[0.55 0.21 0.20 0.77]); i1=2;i2=4; -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye); +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, [i1 i2], 'z', ye, 'target', 'f'); xtc{1}=denormdata(xtc{1},xmean(i2),xstd(i2)); xtc{2}=denormdata(xtc{2},xmean(i2),xstd(i2)); h1=plot(xtc{1},Ef{1},'k--',xtc{1},Ef{1}-1.64*sqrt(Varf{1}),'k--',xtc{1},Ef{1}+1.64*sqrt(Varf{1}),'k--'); @@ -175,7 +175,7 @@ subplot('position',[0.79 0.21 0.20 0.77]); i2=0;cla -[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, i2, 'z', ye); +[Ef,Varf,xtc]=gp_cpred(gp, x, y, x, i2, 'z', ye, 'target', 'f'); %gp_cpred(gp, x, y, x, i2, 'z', ye); xtc=xtc*500/365; h1=plot(xtc,Ef,'k-',xtc,Ef-1.64*sqrt(Varf),'k-',xtc,Ef+1.64*sqrt(Varf),'k-'); diff --git a/gp/gp_monotonic.m b/gp/gp_monotonic.m index 9d161935..e7cf5a5f 100644 --- a/gp/gp_monotonic.m +++ b/gp/gp_monotonic.m @@ -118,9 +118,6 @@ gp=gp_set(gp,'latent_method','EP'); gp.latent_opt.init_prev='off'; gp.latent_opt.maxiter=100; -if ~exist('OCTAVE_VERSION','builtin') - gpep_e('clearcache',gp); -end if isequal(optimize, 'on') % Optimize the parameters gp=gp_optim(gp,x,y,'opt',opt, 'z', z, 'optimf', optimf); @@ -149,9 +146,6 @@ fprintf('Added %d virtual observations.\n', length(inds)); xv=[xv;x(inds,:)]; gp.xv=xv; - if ~exist('OCTAVE_VERSION','builtin') - gpep_e('clearcache',gp); - end if isequal(optimize, 'on') gp=gp_optim(gp,x,y,'opt',opt,'z',z, 'optimf', optimf); end diff --git a/gp/gpep_e.m b/gp/gpep_e.m index cd8f0676..c4aae787 100644 --- a/gp/gpep_e.m +++ b/gp/gpep_e.m @@ -1045,7 +1045,7 @@ if isfield(gp, 'lik_mono') [La2,notpositivedefinite]=chol(Sigm); if notpositivedefinite - [e, edata, eprior, param, ch] = set_output_for_notpositivedefinite(); + [e, edata, eprior, param] = set_output_for_notpositivedefinite(); return end % iter diff --git a/gp/lik_qgp.m b/gp/lik_qgp.m index 90e00f60..5333378d 100644 --- a/gp/lik_qgp.m +++ b/gp/lik_qgp.m @@ -100,7 +100,7 @@ end -function [w s] = lik_qgp_pak(lik) +function [w s h] = lik_qgp_pak(lik) %LIK_QGP_PAK Combine likelihood parameters into one vector. % % Description diff --git a/test_gpstuff/run_tests.m b/test_gpstuff/run_tests.m index 757a9722..1a5d6333 100644 --- a/test_gpstuff/run_tests.m +++ b/test_gpstuff/run_tests.m @@ -56,4 +56,4 @@ close all; end invalid_demos -fprintf('Failed %d of %d tests. Checko struct invalid_demos for further details.\n',failed, iter); \ No newline at end of file +fprintf('Failed %d of %d tests. Check struct invalid_demos for further details.\n',failed, iter); \ No newline at end of file From 16dda3d87aa2ebaf47577141ba7c4e0eabac4a3e Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Fri, 11 Apr 2014 14:35:43 +0300 Subject: [PATCH 48/64] remove xunit --- xunit/realValuesBinomial1.mat | Bin 9310 -> 0 bytes xunit/realValuesHierarchial.mat | Bin 1656 -> 0 bytes xunit/realValuesKalman1.mat | Bin 128453 -> 0 bytes xunit/realValuesKalman2.mat | Bin 2179 -> 0 bytes xunit/test_all.m | 40 ---------- xunit/test_binomial1.m | 50 ------------ xunit/test_binomial_apc.m | 48 ------------ xunit/test_classific.m | 62 --------------- xunit/test_derivativeobs.m | 51 ------------- xunit/test_hierarchial.m | 56 -------------- xunit/test_kalman1.m | 42 ----------- xunit/test_kalman2.m | 41 ---------- xunit/test_lgcp.m | 42 ----------- xunit/test_loopred.m | 68 ----------------- xunit/test_modelassesment1.m | 121 ------------------------------ xunit/test_modelassesment2.m | 100 ------------------------ xunit/test_multiclass_nested_ep.m | 42 ----------- xunit/test_multinom.m | 42 ----------- xunit/test_survival_aft.m | 43 ----------- xunit/test_survival_coxph.m | 41 ---------- xunit/test_zinegbin.m | 42 ----------- 21 files changed, 931 deletions(-) delete mode 100644 xunit/realValuesBinomial1.mat delete mode 100644 xunit/realValuesHierarchial.mat delete mode 100644 xunit/realValuesKalman1.mat delete mode 100644 xunit/realValuesKalman2.mat delete mode 100644 xunit/test_all.m delete mode 100644 xunit/test_binomial1.m delete mode 100644 xunit/test_binomial_apc.m delete mode 100644 xunit/test_classific.m delete mode 100644 xunit/test_derivativeobs.m delete mode 100644 xunit/test_hierarchial.m delete mode 100644 xunit/test_kalman1.m delete mode 100644 xunit/test_kalman2.m delete mode 100644 xunit/test_lgcp.m delete mode 100644 xunit/test_loopred.m delete mode 100644 xunit/test_modelassesment1.m delete mode 100644 xunit/test_modelassesment2.m delete mode 100644 xunit/test_multiclass_nested_ep.m delete mode 100644 xunit/test_multinom.m delete mode 100644 xunit/test_survival_aft.m delete mode 100644 xunit/test_survival_coxph.m delete mode 100644 xunit/test_zinegbin.m diff --git a/xunit/realValuesBinomial1.mat b/xunit/realValuesBinomial1.mat deleted file mode 100644 index 78e9fe3c777d35a00b40ac12a82aa4960de4c8e1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 9310 zcma)>RZ|=cpe2I@cXxM(0D}zf9^BpC-G|^7+#$hTLU4y5!5xCT4>mXh?63bk_#&Pc> zWK>>BVeauG^xbpX#r4Oij~X+x%!^2RQoPHh--B}x*6aM%KmCn;Ds1N1+i3_9kE^iX zbo5Y*)Ouh(n_zy3rt!It%C}anEA>20v^cOc=~*h$-urWRsbt15l&E9whZlmQX~y}s z{9fZhdnERA&Oq%>?hLl3UeJlNR*fqoQ_4#hmw64=jkcKA^Dyf5RL_$4C_9KN8gmFI zGa`GUx3EbDDY~o0=PHVL5-GRHI%6nYjPIkVIE}WjM@0qAelMkw>Aq@32vlzoEZATT zi|%?^R`}+l>q?*(kX**>^kob^C~J3_ls3u&x{U`77iY&DGgc zI;yb9Ns~9?5qVmTkX1mEFEB~d`6rU)v2K#g8uD)SCE$4!4+az$?*wINViM7mAA!@q zD{8$I+B=Gwme3ou)kCsFl-kZH@1+2Sx6i2dRx=q0oAtK~31^ 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For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Marriott, J. M., Spencer, N. M. and Pettitt, A. N. (2001). A -% Bayesian Approach to Selecting Covariates for -% Prediction. Scandinavian Journal of Statistics 28 87–97. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_LCRITERION -% -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_CVLCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_loopred(gp, x, y); - PE2 = mean((Ey-y).^2 + Vary); - - else - % Non-Gaussian likelihood - error('cvlcriterion not sensible for non-gaussian likelihoods'); - end - -end - diff --git a/gp/gp_lcriterion.m b/gp/gp_lcriterion.m deleted file mode 100644 index 20136998..00000000 --- a/gp/gp_lcriterion.m +++ /dev/null @@ -1,68 +0,0 @@ -function L2 = gp_lcriterion(gp, x, y, varargin) -%GP_LCRITERION L-criterion for model selection. -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns L-criterion L2 -% given a Gaussian process model GP, training inputs X and training -% outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Gelfand, A. E. and Ghosh, S. K. (1998). Model Choice: A Minimum -% Posterior Predictive Loss Approach. Biometrika 85 1–11. -% -% Ibrahim, J. G., Chen, M.-H. and Sinha, D. (2001). Criterion-based -% methods for Bayesian model assessment. Statistica Sinica 11 -% 419–443. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_CVLCRITERION -% -% -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - - ip=inputParser; - ip.FunctionName = 'GP_LCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - ip.parse(gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y); - L2 = sum((y-Ey).^2 + Vary); - - else - % Non-Gaussian likelihood - warning('L-criterion not sensible for non-gaussian likelihoods'); - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y, options); - L2 = sum((y-Ey).^2 + Vary); - - end - -end - diff --git a/gp/gp_refpred.m b/gp/gp_refpred.m deleted file mode 100644 index fc73cf73..00000000 --- a/gp/gp_refpred.m +++ /dev/null @@ -1,467 +0,0 @@ -function u_g = gp_refpred(gp1, gp2, x, y, varargin) -% GP_REFPRED Reference predictive approximation to the expected utility of -% single predictions. -% -% Description -% u = GP_REFPRED(GP1, GP2, X, Y, OPTIONS) evaluates reference -% predictive approximation between models GP1 and GP2. Here GP1 is the -% reference model and GP2 is the candidate model. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% method - method for inference, 'posterior' (default) uses posterior -% predictive density, 'loo' uses leave-one-out predictive -% density (approximative), 'kfcv' uses loo cross-validation -% posterior predictive density, 'joint' uses joint -% posterior predictive density for latent values -% (non-Gaussian likelihood) or observations (Gaussian -% likelihood) -% x2,y2,z2 - Optional values for candidate model gp2. -% If only subset of these is specified, remaining variables -% are set from x,y,z. -% -% See also -% GP_LOOPRED, GP_KFCV -% -% References -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In preparation. -% -% Copyright (c) 2011-2012 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_REFPRED'; - ip=iparser(ip,'addRequired','gp1',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','gp2',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','x2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','y2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})); - ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); - ip=iparser(ip,'parse',gp1, gp2, x, y, varargin{:}); - % pass these forward - options=struct(); - x2 = ip.Results.x2; - y2 = ip.Results.y2; - z2 = ip.Results.z2; - z = ip.Results.z; - method = ip.Results.method; - form = ip.Results.form; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - if ~isempty(ip.Results.z2) - options2.zt=ip.Results.z2; - options2.z=ip.Results.z2; - else - options2 = options; - z2 = z; - end - [tn, nin] = size(x); - u_g = zeros(size(y)); - opt = optimset('TolX', 1e-4, 'TolFun', 1e-4); - if isempty(x2) - x2 = x; - end - if isempty(y2) - y2 = y; - end - - if isstruct(gp1) - % Single gp or MCMC - - switch gp1.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1.tr_index; - end - if ~isfield(gp1,'etr') - model1 = 1; - if isfield(gp1.lik.fh, 'trcov') - switch method - case 'joint' - - otherwise - fh1 = @(f,Ey,Vary) norm_pdf(f,Ey,sqrt(Vary)); - end - else - fh1 = @(gp,Ef,Varf,f,z) exp(predvec(gp,Ef,(Varf),f,z)); - end - - switch method - case 'posterior' - if ~isequal(gp1.lik.type, 'Coxph') - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - else - [Ef1, Varf1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - case 'loo' - if ~isfield(gp1.lik.fh, 'trcov') && ~isfield(gp1.lik, 'type_nd') - gp1 = gp_set(gp1, 'latent_method', 'EP'); - end - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_loopred(gp1,x,y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp1, x, y, 'tstindex', tstind, 'opt', opt, 'display', 'iter', 'k', tn, options); - [Ef1, Varf1, Ey1, Vary1] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef1, Covf1] = gp_jpred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - - else - model1 = 2; - if isfield(gp1.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) mean(multi_npdf(f,Ey,(Vary)),1); - else - fh1 = @(gp,Ef,Varf,f,z) mean(exp(predvec(gp,Ef,(Varf),f,z)),1); - end - nsamples = length(gp1.edata); - if strcmp(gp1.type, 'PIC') - tr_index = gp1.tr_index; - gp1 = rmfield(gp1, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp1,j); - if strcmp(gp1.type, 'FIC') | strcmp(gp1.type, 'PIC') || strcmp(gp1.type, 'CS+FIC') || strcmp(gp1.type, 'VAR') || strcmp(gp1.type, 'DTC') || strcmp(gp1.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array1{j} = Gp; - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gpmc_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_loopred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, pred] = gp_kfcv(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - gp1 = gp_array1; - end - else - % GP IA - switch gp1{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1{1}.tr_index; - end - model1 = 3; - nsamples = length(gp1); - for j = 1:nsamples - Gp = gp1{j}; - weight1(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_pred(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - if isfield(gp1{1}.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight1'),1); - else - fh1 = @(gp,Ef,Varf,f,z) (sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight1'),1)); - end - - end - - if isstruct(gp2) - % Single gp or MCMC - switch gp2.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2.tr_index; - end - if ~isfield(gp2,'etr') - model2 = 1; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) norm_lpdf(f,Ey,sqrt(Vary)); - else - fh2 = @(gp,Ef,Varf,f,z) predvec(gp,Ef,(Varf),f,z); - end - switch method - case 'posterior' - if ~isequal(gp2.lik.type, 'Coxph') - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - else - [Ef2, Varf2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - case 'loo' - if ~isfield(gp2.lik.fh, 'trcov') && ~isfield(gp2.lik, 'type_nd') - gp1 = gp_set(gp2, 'latent_method', 'EP'); - end - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_loopred(gp2,x2,y2, 'z', z2); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp2, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - [Ef2, Varf2, Ey2, Vary2] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef2, Covf2] = gp_jpred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - else - model2 = 2; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(mean(multi_npdf(f,Ey,(Vary)),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(mean(exp(predvec(gp,Ef,(Varf),f,z)),1)); - end - nsamples = length(gp2.edata); - if strcmp(gp2.type, 'PIC') - tr_index = gp2.tr_index; - gp2 = rmfield(gp2, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp2,j); - if strcmp(gp2.type, 'FIC') | strcmp(gp2.type, 'PIC') || strcmp(gp2.type, 'CS+FIC') || strcmp(gp2.type, 'VAR') || strcmp(gp2.type, 'DTC') || strcmp(gp2.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array2{j} = Gp; - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gpmc_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_kfcv(Gp, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - end - - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - gp2 = gp_array2; - end - else - % GP IA - switch gp2{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2{1}.tr_index; - end - model2 = 3; - nsamples = length(gp2); - for j = 1:nsamples - Gp = gp2{j}; - weight2(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'off', options2); - end - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - if isfield(gp2{1}.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight2'),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight2'),1)); - end - - end - - if ((isstruct(gp1) && isfield(gp1.lik.fh, 'trcov')) || (iscell(gp1) && isfield(gp1{1}.lik.fh,'trcov'))) - % Gaussian likelihood - - switch method - case 'joint' - u_g = -0.5.*((Ey1 - Ey2)'*(Covy2\(Ey1-Ey2)) + sum(sum(inv(Covy2).*Covy1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covy2))))); - otherwise - for i=1:tn - m1 = Ey1(i,:); m2=Ey1(i,:).^2 + Vary1(i,:); - u_g(i) = mean(-1./(2.*Vary2(i,:))*m2 + Ey2(i,:)./Vary2(i,:)*m1 - Ey2(i,:).^2./(2.*Vary2(i,:)) - 0.5*log(2*pi*Vary2(i,:))); - end - end - - else - % Non-Gaussian likelihood - - switch method - case 'joint' - % Joint refpred of latent values - u_g = -0.5.*((Ef1 - Ef2)'*(Covf2\(Ef1-Ef2)) + sum(sum(inv(Covf2).*Covf1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covf2))))); - - otherwise - if ismember(gp1.lik.type, {'Binomial', 'Poisson', 'Probit', 'Logit', 'Negbin', 'Negbinztr'}) - % Discrete likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - [tmp, tmp, int] = int_limits(gp1, Ef1(i,:), z1); - else - [minf maxf] = int_limits(gp1,Ef1(i,:),z1); - minf = sum(minf.*weight1); - maxf = sum(maxf.*weight1); - int = minf:maxf; - end - u_g(i) = sum(fh1(gp1,Ef1(i,:),Varf1(i,:),int,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),int,z12)); - end - else - % Continuous likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - if ismember(gp1.lik.type, {'Student-t', 'Weibull', 'Coxph'}) - [minf, maxf] = int_limits(gp1, Ef1(i), z1); - else - minf = mean(Ey1(i) - 12.*sqrt(Vary1(i)),2); - minf(minf<0)=0; - maxf = mean(Ey1(i) + 12.*sqrt(Vary1(i)),2); - end - else - minf = sum(bsxfun(@times, weight1, Ey1(i,:)-12.*sqrt(Vary1(i,:))),2); - maxf = sum(bsxfun(@times, weight1, Ey1(i,:)+12.*sqrt(Vary1(i,:))),2); - end - if ~isequal(gp1.lik.type, 'Coxph') - u_g(i) = quadgk(@(f) fh1(gp1,Ef1(i,:),Varf1(i,:),f,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),f,z12), minf, maxf, 'absTol', 1e-3); - else - ntime1=size(gp1.lik.xtime,1); - ntime2=size(gp2.lik.xtime,1); - u_g(i) = quadgk(@(f) fh1(gp1,Ef1([1:ntime1 i],:),Varf1([1:ntime1 i+ntime1],[1:ntime1 i+ntime1]),f,z1).*fh2(gp2,Ef2([1:ntime2 i],:),Varf2([1:ntime2 i+ntime2],[1:ntime2 i+ntime2]),f,z12), minf, maxf, 'absTol', 1e-3); - end - end - end - end - end - if isequal(form, 'mean') - u_g = mean(u_g); - end -end - -function predvec = predvec(gp, Ef, Varf, f, z) - % Compute vector of lpyts from lik.fh.predy when numel(Ef)=numel(Varf)=1 - % and numel(f) != 1. - if isstruct(gp) - if ~isfield(gp, 'etr') - % single gp - predvec=zeros(size(f)); - for i1=1:numel(f) - predvec(i1)=gp.lik.fh.predy(gp.lik,Ef,Varf,f(i1),z); - end - end - else - % ia & mc - predvec=zeros(length(gp), length(f)); - for i=1:numel(f) - for j=1:numel(gp) - predvec(j,i) = gp{j}.lik.fh.predy(gp{j}.lik, Ef(j), Varf(j), f(i), z); - end - end - end -end - -function mpdf = multi_npdf(f, mean, sigma2) -% for every element in f, compute means calculated with -% norm_pdf(f(i), mean, sqrt(sigma2)). If mean and sigma2 -% are vectors, returns length(mean) x length(f) matrix. - - mpdf = zeros(length(mean), length(f)); - for i=1:length(f) - mpdf(:,i) = norm_pdf(f(i), mean, sqrt(sigma2)); - end -end - -function [minf, maxf, interval] = int_limits(gp, Ef, z) -% Return integration limits for quadgk and interval for discrete integration. - if isstruct(gp) - gplik = gp.lik; - else - gplik = gp{1}.lik; - end - switch gplik.type - - case 'Binomial' - p = exp(Ef)./(1+exp(Ef)); - minf = binoinv(0.0001, z, p); - maxf = binoinv(0.9999, z, p); - interval = minf:maxf; - case 'Poisson' - lambda = z.*exp(Ef); - minf = poissinv(0.0001, lambda); - maxf = poissinv(0.9999, lambda); - interval=minf:maxf; - case {'Probit' 'Logit'} - minf = -1*ones(size(Ef)); - maxf = ones(size(Ef)); - interval = [-1 1]; - case {'Negbin' 'Negbinztr'} - r = gplik.disper; - p = z.*exp(Ef); - minf = nbininv(0.0001, r, p); - maxf = nbininv(0.9999, r, p); - interval = minf:maxf; - case 'Student-t' - [n, n2] = size(Ef); - nu = gp.lik.nu; - minf = repmat(tinv(0.01, nu), n, n2); - maxf = repmat(tinv(0.99, nu), n, n2); - interval = []; - case 'Weibull' - % Probably not very sensible... - minf = 1e-5; - maxf = 1e5; - interval = maxf; - case 'Coxph' - minf = 0; - maxf = 1; - interval = maxf; - end - -end diff --git a/gp/gpcf_intcov.m b/gp/gpcf_intcov.m deleted file mode 100644 index 8e73439d..00000000 --- a/gp/gpcf_intcov.m +++ /dev/null @@ -1,755 +0,0 @@ -function gpcf = gpcf_intcov(varargin) -%GPCF_INTCOV Create an integrated covariance function -% -% Description -% GPCF = GPCF_INTCOV('nin',nin,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates an integrated covariance function structure in which the named -% parameters have the specified values. Any unspecified parameters are -% set to default values. Obligatory parameters are 'intArea', which -% defines the integration areas, and 'cf', which defines the covariance -% function(s) to be integrated. -% -% Notes of usage: -% -% The input matrix X can contain point locations and "lower-left" -% corners of integrated areas (areas are always intervals, cells, cubes -% etc.). Last column of the input X has to contain 1:s for integration -% areas and 0 for point location. For example, if x(3,end) = 1, then the -% third row of x tells the lower-left corner of an integrated area. -% -% Field gpcf.intArea tells the lengths of the integration path along -% each axis. -% -% GPCF = GPCF_INTCOV(GPCF,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a covariance function structure with the named -% parameters altered with the specified values. -% -% Parameters for piece wise polynomial (q=2) covariance function [default] -% IntArea - Integration path lengths per input dimension. -% cf - covariance functions to be integrated -% NintPoints - number of samples for areal integration -% approximation -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% NOTES, WARNINGS! -% * This function is still experimental. It should return correct -% answers in 1 and 2 dimensional problems -% * Evaluation of the integrated covariance is currently implemented -% using stochastic integration. This is awfully slow -% -> in 1d speed up could be obtained using quadrature -% -> in 2d and higher dimensions speed up is perhaps possible with -% extensions of quadrature -% -> Quasi-Monte Carlo has been tried and helps only little -% * Stochastic integration is highly variable -% -> gradients can not be evaluated accurately enough for which reason -% the inference has to be conducted with MCMC (HMC can not be used) -% -% See also -% GP_SET, GPCF_*, PRIOR_*, METRIC_* -% -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - if nargin>0 && ischar(varargin{1}) && ismember(varargin{1},{'init' 'set'}) - % remove init and set - varargin(1)=[]; - end - - ip=inputParser; - ip.FunctionName = 'GPCF_INTCOV'; - ip=iparser(ip,'addOptional','gpcf', [], @isstruct); - ip=iparser(ip,'addParamValue','intArea',[], @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','cf',[], @iscell); - ip=iparser(ip,'addParamValue','NintPoints',200, @(x) isscalar(x) && x>0); - ip=iparser(ip,'parse',varargin{:}); - gpcf=ip.Results.gpcf; - - if isempty(gpcf) - % Check that SuiteSparse is available - init=true; - gpcf.intArea=ip.Results.intArea; - if isempty(gpcf.intArea) - error('intArea has to be given for intcov: gpcf_intcov(''intArea'',INTAREA,...)') - end - gpcf.type = 'gpcf_intcov'; - else - if ~isfield(gpcf,'type') && ~isequal(gpcf.type,'gpcf_intcov') - error('First argument does not seem to be a valid covariance function structure') - end - init=false; - end - - if init || ~ismember('cf',ip.UsingDefaults) - % Initialize parameters - gpcf.cf = {}; - cfs=ip.Results.cf; - if ~isempty(cfs) - for i = 1:length(cfs) - gpcf.cf{i} = cfs{i}; - end - else - error('At least one covariance function has to be given in cf'); - end - end - - if init - % Set the function handles to the nested functions - gpcf.fh.pak = @gpcf_intcov_pak; - gpcf.fh.unpak = @gpcf_intcov_unpak; - gpcf.fh.lp = @gpcf_intcov_lp; - gpcf.fh.lpg = @gpcf_intcov_lpg; - gpcf.fh.cfg = @gpcf_intcov_cfg; - gpcf.fh.ginput = @gpcf_intcov_ginput; - gpcf.fh.cov = @gpcf_intcov_cov; - gpcf.fh.trcov = @gpcf_intcov_trcov; - gpcf.fh.trvar = @gpcf_intcov_trvar; - gpcf.fh.recappend = @gpcf_intcov_recappend; - - % help parameters for storing intermediate results - w0 = []; - w1 = []; - datahash0=0; - datahash1=0; - Ctrcov = []; - Ccov = []; - end - - % Initialize parameters - if init || ~ismember('intArea',ip.UsingDefaults) - gpcf.intArea=ip.Results.intArea; - end - - % Initialize parameters - if init || ~ismember('NintPoints',ip.UsingDefaults) - gpcf.NintPoints=ip.Results.NintPoints; - end - - function [w,s,h] = gpcf_intcov_pak(gpcf) - %GPCF_INTCOV_PAK Combine GP covariance function parameters into - % one vector - % - % Description - % W = GPCF_INTCOV_PAK(GPCF) takes a covariance function - % structure GPCF and combines the covariance function - % parameters and their hyperparameters into a single row - % vector W. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % See also - % GPCF_INTCOV_UNPAK - - ncf = length(gpcf.cf); - w = []; s = {}; h=[]; - - for i=1:ncf - cf = gpcf.cf{i}; - [wi, si, hi] = cf.fh.pak(cf); - w = [w wi]; - s = [s; si]; - h = [h 1+hi]; - end - - end - - function [gpcf, w] = gpcf_intcov_unpak(gpcf, w) - %GPCF_INTCOV_UNPAK Sets the covariance function parameters into - % the structure - % - % Description - % [GPCF, W] = GPCF_INTCOV_UNPAK(GPCF, W) takes a covariance - % function structure GPCF and a hyper-parameter vector W, - % and returns a covariance function structure identical - % to the input, except that the covariance hyper-parameters - % have been set to the values in W. Deletes the values set to - % GPCF from W and returns the modified W. This is a mandatory - % subfunction used for example in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(gpcf.magnSigma2) - % (hyperparameters of gpcf.magnSigma2) - % log(gpcf.lengthScale(:)) - % (hyperparameters of gpcf.lengthScale)]' - % - % See also - % GPCF_INTCOV_PAK - - ncf = length(gpcf.cf); - - for i=1:ncf - cf = gpcf.cf{i}; - [cf, w] = cf.fh.unpak(cf, w); - gpcf.cf{i} = cf; - end - - end - - function lp = gpcf_intcov_lp(gpcf) - %GPCF_INTCOV_LP Evaluate the log prior of covariance function parameters - % - % Description - % LP = GPCF_INTCOV_LP(GPCF, X, T) takes a covariance function - % structure GPCF and returns log(p(th)), where th collects the - % parameters. This is a mandatory subfunction used for example - % in energy computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LPG, GP_E - - lp = 0; - ncf = length(gpcf.cf); - for i=1:ncf - cf = gpcf.cf{i}; - lp = lp + cf.fh.lp(cf); - end - end - - function lpg = gpcf_intcov_lpg(gpcf) - %GPCF_INTCOV_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = GPCF_INTCOV_LPG(GPCF) takes a covariance function - % structure GPCF and returns LPG = d log (p(th))/dth, where th - % is the vector of parameters. This is a mandatory subfunction - % used for example in gradient computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - lpg = []; - ncf = length(gpcf.cf); - - % Evaluate the gradients - for i=1:ncf - cf = gpcf.cf{i}; - lpg=[lpg cf.fh.lpg(cf)]; - end - end - - function DKff = gpcf_intcov_cfg(gpcf, x, x2, mask) - %GPCF_INTCOV_CFG Evaluate gradient of covariance function - % with respect to the parameters - % - % Description - % DKff = GPCF_INTCOV_CFG(GPCF, X) takes a covariance function - % structure GPCF, a matrix X of input vectors and returns - % DKff, the gradients of covariance matrix Kff = k(X,X) with - % respect to th (cell array with matrix elements). This is a - % mandatory subfunction used for example in gradient computations. - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, [], MASK) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the diagonal of gradients of covariance matrix - % Kff = k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - [n, m] =size(x); - - DKff = {}; - % Evaluate: DKff{1} = d Kff / d magnSigma2 - % DKff{2} = d Kff / d lengthScale - % NOTE! Here we have already taken into account that the parameters are transformed - % through log() and thus dK/dlog(p) = p * dK/dp - - % evaluate the gradient for training covariance - if nargin == 2 - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - % point-point covariance - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1)); - for j1 = 1:length(temp) - [I,J,R] = find(temp{j1}); - DKff{end+1} = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - end - end - - % point-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1),intpoints); - for k1 = 1:length(temp) - temp2{ii1}(:,j1) = mean(temp{k1},2); - ii1=ii1+1; - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp + temp'; - end - - % area-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, intpoints1, intpoints2); - for l1 = 1:length(temp) - temp2{ii1}(j1,k1) = mean(mean(temp{l1})); - ii1=ii1+1; - end - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp; % + temp' - end - - % Evaluate the gradient of non-symmetric covariance (e.g. K_fu) - elseif nargin == 3 - if size(x,2) ~= size(x2,2) - error('gpcf_intcov -> _ghyper: The number of columns in x and x2 has to be the same. ') - end - - % Evaluate: DKff{1} = d mask(Kff,I) / d magnSigma2 - % DKff{2...} = d mask(Kff,I) / d lengthScale - elseif nargin == 4 - - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - for i1=1:length(DKff) - DKff{i1}=full(DKff{i1}); - end - end - - end - - function DKff = gpcf_intcov_ginput(gpcf, x, x2) - %GPCF_INTCOV_GINPUT Evaluate gradient of covariance function with - % respect to x - % - % Description - % DKff = GPCF_INTCOV_GINPUT(GPCF, X) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = GPCF_INTCOV_GINPUT(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - end - - - function C = gpcf_intcov_cov(gpcf, x1, x2, varargin) - %GP_INTCOV_COV Evaluate covariance matrix between two input vectors - % - % Description - % C = GP_INTCOV_COV(GP, TX, X) takes in covariance function of - % a Gaussian process GP and two matrixes TX and X that contain - % input vectors to GP. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i in TX - % and j in X. This is a mandatory subfunction used for example in - % prediction and energy computations. - % - % See also - % GPCF_INTCOV_TRCOV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - if isempty(x2) - x2=x1; - end - [n1,m1]=size(x1); - [n2,m2]=size(x2); - - if m1~=m2 - error('the number of columns of X1 and X2 has to be same') - end - - ncf = length(gpcf.cf); - ww = []; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512([x1, x2]); - fromMem = false; - if ~isempty(w1) - if all(size(ww)==size(w1)) && all(abs(ww-w1)<1e-8) && isequal(datahash,datahash1) - fromMem = true; - end - end - - if fromMem - C = Ccov; - else - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - intInd1 = find(x1(:,end)==1); - intInd2 = find(x2(:,end)==1); - pointInd1 = find(x1(:,end)==0); - pointInd2 = find(x2(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - C = sparse(n1,n2); - - % point-point covariance - if any(x1(:,end)==0) && any(x2(:,end)==0) - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.cov(cf, x1(pointInd1,1:end-1),x2(pointInd2,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd2(J),R,n1,n2); - end - - % point-area covariance - if any(x1(:,end)==0) && any(x2(:,end)==1) - temp=sparse(length(pointInd1),length(intInd2)); - for j1=1:length(intInd2) - %N=600; - %[tmp1, tmp2] = meshgrid( linspace(x2(intInd2(j1),1),x2(intInd2(j1),1)+intArea(1,1),N) , linspace(x2(intInd2(j1),2),x2(intInd2(j1),2)+intArea(1,2),N)); - %intpoints = [tmp1(:),tmp2(:)]; - intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,m2-1); - %intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*hammersley(m2-1,numPoints)'; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x1(pointInd1,1:end-1),intpoints),2); - end - end - [I,J,R] = find(temp); - C = C + sparse(pointInd1(I),intInd2(J),R,n1,n2); - end - - % area-point covariance - if any(x1(:,end)==1) && any(x2(:,end)==0) - temp=sparse(length(pointInd2),length(intInd1)); - for j1=1:length(intInd1) - intpoints = repmat(x1(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x2(pointInd2,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp'); - C = C + sparse(intInd1(I),pointInd2(J),R,n1,n2); - end - - % area-area covariance - if any(x1(:,end)==1) && any(x2(:,end)==1) - temp=sparse(length(intInd1),length(intInd2)); - for j1=1:length(intInd1) - intpoints1 = repmat(x1(intInd1(j1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd2) - intpoints2 = repmat(x2(intInd2(k1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - [I,J,R] = find(temp); - C = C + sparse(intInd1(I),intInd2(J),R,n1,n2); - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ccov=C; - datahash1=datahash; - w1=ww; - end - - end - - function C = gpcf_intcov_trcov(gpcf, x) - %GP_INTCOV_TRCOV Evaluate training covariance matrix of inputs - % - % Description - % C = GP_INTCOV_TRCOV(GP, TX) takes in covariance function of a - % Gaussian process GP and matrix TX that contains training - % input vectors. Returns covariance matrix C. Every element ij - % of C contains covariance between inputs i and j in TX. This is - % a mandatory subfunction used for example in prediction and - % energy computations. - % - % See also - % GPCF_INTCOV_COV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - ncf = length(gpcf.cf); - ww=[]; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512(x); - fromMem = false; - if ~isempty(w0) - if all(size(ww)==size(w0)) && all(abs(ww-w0)<1e-8) && isequal(datahash,datahash0) - fromMem = true; - end - end - - if fromMem - C = Ctrcov; - else - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - %intMethod = gpcf.intMethod; - - % point-point covariance - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trcov(cf, x(pointInd1,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - - % point-area covariance - temp=sparse(length(pointInd1),length(intInd1)); - randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x(pointInd1,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp'; - - % % area-area covariance - % temp=sparse(length(intInd1),length(intInd1)); - % for j1=1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for k1 = 1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for i1=1:ncf - % cf = gpcf.cf{i1}; - % temp(j1,k1) = temp(j1,k1) + mean(mean(feval(cf.fh.cov, cf, intpoints1, intpoints2))); - % end - % end - % end - % [I,J,R] = find(temp); - % C = C + sparse(intInd1(I),intInd1(J),R,n1,n1); - - % area-area covariance - temp=sparse(length(intInd1),length(intInd1)); - temp2=zeros(n1,1); - randpoints = [intArea intArea].*hammersley((dimInt)*2,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = j1+1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - % The covariance matrix seems to get non positive definite. Try to fix - % it by evaluating all the diagonal elements with same random numbers. - %randpoints1 = intArea.*rand(numPoints,dimInt); - %randpoints2 = intArea.*rand(numPoints,dimInt); - %randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - intpoints2 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints1; - %intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp2(j1) = temp2(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - %temp2(j1) = temp2(j1) + mean(mean(feval(cf.fh.trcov, cf, intpoints))); - end - end - [I,J,R] = find(temp); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp' + sparse(1:n1,1:n1,temp2); - - C = (C+C')/2; - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ctrcov=C; - datahash0=datahash; - w0=ww; - end - - end - - function C = gpcf_intcov_trvar(gpcf, x) - %GP_INTCOV_TRVAR Evaluate training variance vector - % - % Description - % C = GP_INTCOV_TRVAR(GPCF, TX) takes in covariance function of - % a Gaussian process GPCF and matrix TX that contains training - % inputs. Returns variance vector C. Every element i of C - % contains variance of input i in TX. This is a mandatory - % subfunction used for example in prediction and energy - % computations. - % - % See also - % GPCF_INTCOV_COV, GP_COV, GP_TRCOV - - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - C = zeros(n1,1); - - % point-point covariance - temp = 0; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trvar(cf, x(pointInd1,1:end-1)); - end - C(pointInd1) = temp; - - % area-area covariance - temp=zeros(size(intInd1)); - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1) = temp(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - C(intInd1) = temp; - end - - function reccf = gpcf_intcov_recappend(reccf, ri, gpcf) - %RECAPPEND Record append - % - % Description - % RECCF = GPCF_INTCOV_RECAPPEND(RECCF, RI, GPCF) takes a - % covariance function record structure RECCF, record index RI - % and covariance function structure GPCF with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from GPCF. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - if nargin == 2 - % Initialize record - reccf.type = 'gpcf_intcov'; - reccf.NintPoints = ri.NintPoints; - reccf.intArea = ri.intArea; - - % Initialize parameters - ncf = length(ri.cf); - for i=1:ncf - cf = ri.cf{i}; - reccf.cf{i} = cf.fh.recappend([], ri.cf{i}); - end - - % Set the function handles - reccf.fh.pak = @gpcf_intcov_pak; - reccf.fh.unpak = @gpcf_intcov_unpak; - reccf.fh.e = @gpcf_intcov_lp; - reccf.fh.lpg = @gpcf_intcov_lpg; - reccf.fh.cfg = @gpcf_intcov_cfg; - reccf.fh.cov = @gpcf_intcov_cov; - reccf.fh.trcov = @gpcf_intcov_trcov; - reccf.fh.trvar = @gpcf_intcov_trvar; - reccf.fh.recappend = @gpcf_intcov_recappend; - else - % Append to the record - - %loop over all of the covariance functions - ncf = length(gpcf.cf); - reccf.NintPoints(ri,:) = gpcf.NintPoints; - reccf.intArea(ri,:) = gpcf.intArea; - for i=1:ncf - cf = gpcf.cf{i}; - reccf.cf{i} = cf.fh.recappend(reccf.cf{i}, ri, cf); - end - end - end -end - diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m deleted file mode 100644 index aaaf22d3..00000000 --- a/gp/lik_gaussianbl.m +++ /dev/null @@ -1,372 +0,0 @@ -function lik = lik_gaussianbl(varargin) -%LIK_GAUSSIAN Create a Gaussian likelihood structure -% -% Description -% LIK = LIK_GAUSSIANBL('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a Gaussian likelihood structure in which the named -% parameters have the specified values. Any unspecified -% parameters are set to default values. -% -% LIK = LIK_GAUSSIANBL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood function structure with the named -% parameters altered with the specified values. -% -% Parameters for Gaussian likelihood function [default] -% sigma2 - variance of the independent noise [0.1] for each -% block of inputs. If noiseSigma2 is a vector each -% entry of the vector specifies noise variance for -% a block of inputs defined by the last column of -% the input matrix X. The variances are set for blocks -% according to 'bl_indic' field so that sigma2(i) is a -% noise variance of the inputs whose last column equals -% bl_indic(i). -% bl_indic - block indicator vector [empty matrix]. If -% length(sigma2)>1 bl_indic has to be the same length -% as sigma2. -% sigma2_prior - prior for sigma2 [prior_logunif] -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% See also -% GP_SET, PRIOR_*, LIK_* - -% Internal note: Because Gaussian noise can be combined -% analytically to the covariance matrix, lik_gaussian is internally -% little between lik_* and gpcf_* functions. -% -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_GAUSSIANBL'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'addParamValue','bl_indic',0.1, @(x) isvector(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.type = 'GaussianBL'; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'GaussianBL') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('sigma2',ip.UsingDefaults) - lik.sigma2 = ip.Results.sigma2; - lik.bl_indic = ip.Results.bl_indic; - end - - if length(lik.sigma2)> 1 || length(lik.bl_indic) > 1 - if length(lik.sigma2) ~= length(lik.bl_indic) - error('sigma2 and bl_indic has to be same length') - end - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('sigma2_prior',ip.UsingDefaults) - lik.p.sigma2=ip.Results.sigma2_prior; - end - if init - % Set the function handles to the nested functions - lik.fh.pak = @lik_gaussianbl_pak; - lik.fh.unpak = @lik_gaussianbl_unpak; - lik.fh.lp = @lik_gaussianbl_lp; - lik.fh.lpg = @lik_gaussianbl_lpg; - lik.fh.cfg = @lik_gaussianbl_cfg; - lik.fh.trcov = @lik_gaussianbl_trcov; - lik.fh.trvar = @lik_gaussianbl_trvar; - lik.fh.recappend = @lik_gaussianbl_recappend; - end -end - - function [w s,h] = lik_gaussianbl_pak(lik) - %LIK_GAUSSIANBL_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_GAUSSIANBL_PAK(LIK) takes a likelihood structure LIK - % and combines the parameters into a single row vector W. - % This is a mandatory subfunction used for example in energy - % and gradient computations. - % - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_UNPAK - - w = []; s = {}; h=[]; - if ~isempty(lik.p.sigma2) - w = log(lik.sigma2); - if numel(lik.sigma2)>1 - s = [s; sprintf('log(gaussian.sigma2 x %d)',numel(lik.sigma2))]; - else - s = [s; 'log(gaussian.sigma2)']; - end - h = [h zeros(1,numel(lik.sigma))]; - % Hyperparameters of noiseSigma2 - [wh, sh,hh] = lik.p.sigma2.fh.pak(lik.p.sigma2); - w = [w wh]; - s = [s sh]; - h = [h hh]; - end - end - - function [lik, w] = lik_gaussianbl_unpak(lik, w) - %LIK_GAUSSIANBL_UNPAK Extract likelihood parameters from the vector. - % - % Description - % W = LIK_GAUSSIANBL_UNPAK(W, LIK) takes a likelihood structure - % LIK and extracts the parameters from the vector W to the LIK - % structure. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_PAK - - if ~isempty(lik.p.sigma2) - i2=length(lik.sigma2); - lik.sigma2 = exp(w(1:i2)); - w = w(i2+1:end); - - % Hyperparameters of sigma2 - [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); - lik.p.sigma2 = p; - end - end - - function lp = lik_gaussianbl_lp(lik) - %LIK_GAUSSIANBL_LP Evaluate the log prior of likelihood parameters - % - % Description - % LP = LIK_T_LP(LIK) takes a likelihood structure LIK and - % returns log(p(th)), where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_G, GP_E - - lp = 0; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + sum(log(lik.sigma2)); - end - end - - function lpg = lik_gaussianbl_lpg(lik) - %LIK_GAUSSIANBL_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = LIK_GAUSSIANBL_LPG(LIK) takes a Gaussian likelihood - % function structure LIK and returns LPG = d log (p(th))/dth, - % where th is the vector of parameters. This subfunction is - % needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - lpg = []; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - i2=length(lik.sigma2); - - lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); - lpg = lpgs(1:i2).*lik.sigma2 + 1; - if length(lpgs) > 1 - lpg = [lpg lpgs(i2+1:end)]; - end - end - end - - function DKff = lik_gaussianbl_cfg(lik, x, x2) - %LIK_GAUSSIANBL_CFG Evaluate gradient of covariance with respect to - % Gaussian noise - % - % Description - % Gaussian likelihood is a special case since it can be - % analytically combined with covariance functions and thus we - % compute gradient of covariance instead of gradient of likelihood. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X) takes a Gaussian likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of Gaussian noise covariance - % matrix Kff = k(X,X) with respect to th (cell array with - % matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X, X2) takes a Gaussian - % likelihood function structure LIK, a matrix X of input - % vectors and returns DKff, the gradients of Gaussian noise - % covariance matrix Kff = k(X,X) with respect to th (cell - % array with matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - [n, m] =size(x); - - if length(lik.sigma2)==1 - DKff{1} = lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - ind = find(x(:,end)==lik.bl_indic(i1)); - DKff{i1} = sparse(ind,ind,lik.sigma2(i1),n,n); - end - end - - end - - function DKff = lik_gaussianbl_ginput(lik, x, t, g_ind, gdata_ind, gprior_ind, varargin) - %LIK_GAUSSIANBL_GINPUT Evaluate gradient of likelihood function with - % respect to x. - % - % Description - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X, X2) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - end - - function C = lik_gaussianbl_trcov(lik, x) - %LIK_GAUSSIANBL_TRCOV Evaluate training covariance matrix - % corresponding to Gaussian noise - % Description - % C = LIK_GAUSSIANBL_TRCOV(GP, TX) takes in covariance function - % of a Gaussian process GP and matrix TX that contains - % training input vectors. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i and j - % in TX. This subfunction is needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, LIK_GAUSSIANBL_TRVAR, GP_COV, GP_TRCOV - - [n, m] =size(x); - - s2 = zeros(n,1); - if length(lik.sigma2)==1 - s2 = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - s2(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - C = sparse(1:n,1:n,s2,n,n); - - end - - function C = lik_gaussianbl_trvar(lik, x) - %LIK_GAUSSIANBL_TRVAR Evaluate training variance vector - % corresponding to Gaussian noise - % - % Description - % C = LIK_GAUSSIANBL_TRVAR(LIK, TX) takes in covariance function - % of a Gaussian process LIK and matrix TX that contains - % training inputs. Returns variance vector C. Every element i - % of C contains variance of input i in TX. This subfunction is - % needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, GP_COV, GP_TRCOV - - [n, m] =size(x); - - C = zeros(n,1); - if length(lik.sigma2)==1 - C = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - C(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - end - - function reccf = lik_gaussianbl_recappend(reccf, ri, lik) - %RECAPPEND Record append - % - % Description - % RECCF = LIK_GAUSSIANBL_RECAPPEND(RECCF, RI, LIK) takes a - % likelihood function record structure RECCF, record index RI - % and likelihood function structure LIK with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from LIK. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - % Initialize record - if nargin == 2 - reccf.type = 'lik_gaussianbl'; - - % Initialize parameters - reccf.sigma2 = []; - reccf.bl_indic = []; - - % Set the function handles - reccf.fh.pak = @lik_gaussianbl_pak; - reccf.fh.unpak = @lik_gaussianbl_unpak; - reccf.fh.lp = @lik_gaussianbl_lp; - reccf.fh.lpg = @lik_gaussianbl_lpg; - reccf.fh.cfg = @lik_gaussianbl_cfg; - reccf.fh.trcov = @lik_gaussianbl_trcov; - reccf.fh.trvar = @lik_gaussianbl_trvar; - reccf.fh.recappend = @lik_gaussianbl_recappend; - reccf.p=[]; - reccf.p.sigma2=[]; - if ~isempty(ri.p.sigma2) - reccf.p.sigma2 = ri.p.sigma2; - end - return - end - - likp = lik.p; - - % record sigma - if ~isempty(lik.sigma2) - reccf.sigma2(ri,:)=lik.sigma2; - reccf.bl_indic(ri,:)=lik.bl_indic; - if ~isempty(lik.p.sigma2) - reccf.p.sigma2 = likp.sigma2.fh.recappend(reccf.p.sigma2, ri, likp.sigma2); - end - elseif ri==1 - reccf.sigma2=[]; - end - end - - From c6855d08be4cc56683fa8517c269a4a5a96455c0 Mon Sep 17 00:00:00 2001 From: Ville Tolvanen Date: Tue, 15 Apr 2014 11:48:24 +0300 Subject: [PATCH 50/64] Delete unnecessary files for release --- gp/gp_cvlcriterion.m | 59 ---- gp/gp_lcriterion.m | 68 ---- gp/gp_refpred.m | 467 -------------------------- gp/gpcf_intcov.m | 755 ------------------------------------------- gp/lik_gaussianbl.m | 372 --------------------- 5 files changed, 1721 deletions(-) delete mode 100644 gp/gp_cvlcriterion.m delete mode 100644 gp/gp_lcriterion.m delete mode 100644 gp/gp_refpred.m delete mode 100644 gp/gpcf_intcov.m delete mode 100644 gp/lik_gaussianbl.m diff --git a/gp/gp_cvlcriterion.m b/gp/gp_cvlcriterion.m deleted file mode 100644 index c304c911..00000000 --- a/gp/gp_cvlcriterion.m +++ /dev/null @@ -1,59 +0,0 @@ -function PE2 = gp_cvlcriterion(gp, x, y, varargin) -%GP_CVLCRITERION cross-validation version of L-criterion -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns cross-validation -% version of L-criterion PE2 given a Gaussian process model GP, -% training inputs X and training outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Marriott, J. M., Spencer, N. M. and Pettitt, A. N. (2001). A -% Bayesian Approach to Selecting Covariates for -% Prediction. Scandinavian Journal of Statistics 28 87–97. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_LCRITERION -% -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_CVLCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_loopred(gp, x, y); - PE2 = mean((Ey-y).^2 + Vary); - - else - % Non-Gaussian likelihood - error('cvlcriterion not sensible for non-gaussian likelihoods'); - end - -end - diff --git a/gp/gp_lcriterion.m b/gp/gp_lcriterion.m deleted file mode 100644 index 20136998..00000000 --- a/gp/gp_lcriterion.m +++ /dev/null @@ -1,68 +0,0 @@ -function L2 = gp_lcriterion(gp, x, y, varargin) -%GP_LCRITERION L-criterion for model selection. -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns L-criterion L2 -% given a Gaussian process model GP, training inputs X and training -% outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Gelfand, A. E. and Ghosh, S. K. (1998). Model Choice: A Minimum -% Posterior Predictive Loss Approach. Biometrika 85 1–11. -% -% Ibrahim, J. G., Chen, M.-H. and Sinha, D. (2001). Criterion-based -% methods for Bayesian model assessment. Statistica Sinica 11 -% 419–443. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_CVLCRITERION -% -% -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - - ip=inputParser; - ip.FunctionName = 'GP_LCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - ip.parse(gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y); - L2 = sum((y-Ey).^2 + Vary); - - else - % Non-Gaussian likelihood - warning('L-criterion not sensible for non-gaussian likelihoods'); - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y, options); - L2 = sum((y-Ey).^2 + Vary); - - end - -end - diff --git a/gp/gp_refpred.m b/gp/gp_refpred.m deleted file mode 100644 index fc73cf73..00000000 --- a/gp/gp_refpred.m +++ /dev/null @@ -1,467 +0,0 @@ -function u_g = gp_refpred(gp1, gp2, x, y, varargin) -% GP_REFPRED Reference predictive approximation to the expected utility of -% single predictions. -% -% Description -% u = GP_REFPRED(GP1, GP2, X, Y, OPTIONS) evaluates reference -% predictive approximation between models GP1 and GP2. Here GP1 is the -% reference model and GP2 is the candidate model. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% method - method for inference, 'posterior' (default) uses posterior -% predictive density, 'loo' uses leave-one-out predictive -% density (approximative), 'kfcv' uses loo cross-validation -% posterior predictive density, 'joint' uses joint -% posterior predictive density for latent values -% (non-Gaussian likelihood) or observations (Gaussian -% likelihood) -% x2,y2,z2 - Optional values for candidate model gp2. -% If only subset of these is specified, remaining variables -% are set from x,y,z. -% -% See also -% GP_LOOPRED, GP_KFCV -% -% References -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In preparation. -% -% Copyright (c) 2011-2012 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_REFPRED'; - ip=iparser(ip,'addRequired','gp1',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','gp2',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','x2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','y2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})); - ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); - ip=iparser(ip,'parse',gp1, gp2, x, y, varargin{:}); - % pass these forward - options=struct(); - x2 = ip.Results.x2; - y2 = ip.Results.y2; - z2 = ip.Results.z2; - z = ip.Results.z; - method = ip.Results.method; - form = ip.Results.form; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - if ~isempty(ip.Results.z2) - options2.zt=ip.Results.z2; - options2.z=ip.Results.z2; - else - options2 = options; - z2 = z; - end - [tn, nin] = size(x); - u_g = zeros(size(y)); - opt = optimset('TolX', 1e-4, 'TolFun', 1e-4); - if isempty(x2) - x2 = x; - end - if isempty(y2) - y2 = y; - end - - if isstruct(gp1) - % Single gp or MCMC - - switch gp1.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1.tr_index; - end - if ~isfield(gp1,'etr') - model1 = 1; - if isfield(gp1.lik.fh, 'trcov') - switch method - case 'joint' - - otherwise - fh1 = @(f,Ey,Vary) norm_pdf(f,Ey,sqrt(Vary)); - end - else - fh1 = @(gp,Ef,Varf,f,z) exp(predvec(gp,Ef,(Varf),f,z)); - end - - switch method - case 'posterior' - if ~isequal(gp1.lik.type, 'Coxph') - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - else - [Ef1, Varf1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - case 'loo' - if ~isfield(gp1.lik.fh, 'trcov') && ~isfield(gp1.lik, 'type_nd') - gp1 = gp_set(gp1, 'latent_method', 'EP'); - end - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_loopred(gp1,x,y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp1, x, y, 'tstindex', tstind, 'opt', opt, 'display', 'iter', 'k', tn, options); - [Ef1, Varf1, Ey1, Vary1] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef1, Covf1] = gp_jpred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - - else - model1 = 2; - if isfield(gp1.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) mean(multi_npdf(f,Ey,(Vary)),1); - else - fh1 = @(gp,Ef,Varf,f,z) mean(exp(predvec(gp,Ef,(Varf),f,z)),1); - end - nsamples = length(gp1.edata); - if strcmp(gp1.type, 'PIC') - tr_index = gp1.tr_index; - gp1 = rmfield(gp1, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp1,j); - if strcmp(gp1.type, 'FIC') | strcmp(gp1.type, 'PIC') || strcmp(gp1.type, 'CS+FIC') || strcmp(gp1.type, 'VAR') || strcmp(gp1.type, 'DTC') || strcmp(gp1.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array1{j} = Gp; - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gpmc_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_loopred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, pred] = gp_kfcv(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - gp1 = gp_array1; - end - else - % GP IA - switch gp1{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1{1}.tr_index; - end - model1 = 3; - nsamples = length(gp1); - for j = 1:nsamples - Gp = gp1{j}; - weight1(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_pred(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - if isfield(gp1{1}.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight1'),1); - else - fh1 = @(gp,Ef,Varf,f,z) (sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight1'),1)); - end - - end - - if isstruct(gp2) - % Single gp or MCMC - switch gp2.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2.tr_index; - end - if ~isfield(gp2,'etr') - model2 = 1; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) norm_lpdf(f,Ey,sqrt(Vary)); - else - fh2 = @(gp,Ef,Varf,f,z) predvec(gp,Ef,(Varf),f,z); - end - switch method - case 'posterior' - if ~isequal(gp2.lik.type, 'Coxph') - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - else - [Ef2, Varf2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - case 'loo' - if ~isfield(gp2.lik.fh, 'trcov') && ~isfield(gp2.lik, 'type_nd') - gp1 = gp_set(gp2, 'latent_method', 'EP'); - end - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_loopred(gp2,x2,y2, 'z', z2); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp2, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - [Ef2, Varf2, Ey2, Vary2] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef2, Covf2] = gp_jpred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - else - model2 = 2; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(mean(multi_npdf(f,Ey,(Vary)),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(mean(exp(predvec(gp,Ef,(Varf),f,z)),1)); - end - nsamples = length(gp2.edata); - if strcmp(gp2.type, 'PIC') - tr_index = gp2.tr_index; - gp2 = rmfield(gp2, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp2,j); - if strcmp(gp2.type, 'FIC') | strcmp(gp2.type, 'PIC') || strcmp(gp2.type, 'CS+FIC') || strcmp(gp2.type, 'VAR') || strcmp(gp2.type, 'DTC') || strcmp(gp2.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array2{j} = Gp; - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gpmc_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_kfcv(Gp, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - end - - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - gp2 = gp_array2; - end - else - % GP IA - switch gp2{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2{1}.tr_index; - end - model2 = 3; - nsamples = length(gp2); - for j = 1:nsamples - Gp = gp2{j}; - weight2(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'off', options2); - end - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - if isfield(gp2{1}.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight2'),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight2'),1)); - end - - end - - if ((isstruct(gp1) && isfield(gp1.lik.fh, 'trcov')) || (iscell(gp1) && isfield(gp1{1}.lik.fh,'trcov'))) - % Gaussian likelihood - - switch method - case 'joint' - u_g = -0.5.*((Ey1 - Ey2)'*(Covy2\(Ey1-Ey2)) + sum(sum(inv(Covy2).*Covy1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covy2))))); - otherwise - for i=1:tn - m1 = Ey1(i,:); m2=Ey1(i,:).^2 + Vary1(i,:); - u_g(i) = mean(-1./(2.*Vary2(i,:))*m2 + Ey2(i,:)./Vary2(i,:)*m1 - Ey2(i,:).^2./(2.*Vary2(i,:)) - 0.5*log(2*pi*Vary2(i,:))); - end - end - - else - % Non-Gaussian likelihood - - switch method - case 'joint' - % Joint refpred of latent values - u_g = -0.5.*((Ef1 - Ef2)'*(Covf2\(Ef1-Ef2)) + sum(sum(inv(Covf2).*Covf1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covf2))))); - - otherwise - if ismember(gp1.lik.type, {'Binomial', 'Poisson', 'Probit', 'Logit', 'Negbin', 'Negbinztr'}) - % Discrete likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - [tmp, tmp, int] = int_limits(gp1, Ef1(i,:), z1); - else - [minf maxf] = int_limits(gp1,Ef1(i,:),z1); - minf = sum(minf.*weight1); - maxf = sum(maxf.*weight1); - int = minf:maxf; - end - u_g(i) = sum(fh1(gp1,Ef1(i,:),Varf1(i,:),int,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),int,z12)); - end - else - % Continuous likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - if ismember(gp1.lik.type, {'Student-t', 'Weibull', 'Coxph'}) - [minf, maxf] = int_limits(gp1, Ef1(i), z1); - else - minf = mean(Ey1(i) - 12.*sqrt(Vary1(i)),2); - minf(minf<0)=0; - maxf = mean(Ey1(i) + 12.*sqrt(Vary1(i)),2); - end - else - minf = sum(bsxfun(@times, weight1, Ey1(i,:)-12.*sqrt(Vary1(i,:))),2); - maxf = sum(bsxfun(@times, weight1, Ey1(i,:)+12.*sqrt(Vary1(i,:))),2); - end - if ~isequal(gp1.lik.type, 'Coxph') - u_g(i) = quadgk(@(f) fh1(gp1,Ef1(i,:),Varf1(i,:),f,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),f,z12), minf, maxf, 'absTol', 1e-3); - else - ntime1=size(gp1.lik.xtime,1); - ntime2=size(gp2.lik.xtime,1); - u_g(i) = quadgk(@(f) fh1(gp1,Ef1([1:ntime1 i],:),Varf1([1:ntime1 i+ntime1],[1:ntime1 i+ntime1]),f,z1).*fh2(gp2,Ef2([1:ntime2 i],:),Varf2([1:ntime2 i+ntime2],[1:ntime2 i+ntime2]),f,z12), minf, maxf, 'absTol', 1e-3); - end - end - end - end - end - if isequal(form, 'mean') - u_g = mean(u_g); - end -end - -function predvec = predvec(gp, Ef, Varf, f, z) - % Compute vector of lpyts from lik.fh.predy when numel(Ef)=numel(Varf)=1 - % and numel(f) != 1. - if isstruct(gp) - if ~isfield(gp, 'etr') - % single gp - predvec=zeros(size(f)); - for i1=1:numel(f) - predvec(i1)=gp.lik.fh.predy(gp.lik,Ef,Varf,f(i1),z); - end - end - else - % ia & mc - predvec=zeros(length(gp), length(f)); - for i=1:numel(f) - for j=1:numel(gp) - predvec(j,i) = gp{j}.lik.fh.predy(gp{j}.lik, Ef(j), Varf(j), f(i), z); - end - end - end -end - -function mpdf = multi_npdf(f, mean, sigma2) -% for every element in f, compute means calculated with -% norm_pdf(f(i), mean, sqrt(sigma2)). If mean and sigma2 -% are vectors, returns length(mean) x length(f) matrix. - - mpdf = zeros(length(mean), length(f)); - for i=1:length(f) - mpdf(:,i) = norm_pdf(f(i), mean, sqrt(sigma2)); - end -end - -function [minf, maxf, interval] = int_limits(gp, Ef, z) -% Return integration limits for quadgk and interval for discrete integration. - if isstruct(gp) - gplik = gp.lik; - else - gplik = gp{1}.lik; - end - switch gplik.type - - case 'Binomial' - p = exp(Ef)./(1+exp(Ef)); - minf = binoinv(0.0001, z, p); - maxf = binoinv(0.9999, z, p); - interval = minf:maxf; - case 'Poisson' - lambda = z.*exp(Ef); - minf = poissinv(0.0001, lambda); - maxf = poissinv(0.9999, lambda); - interval=minf:maxf; - case {'Probit' 'Logit'} - minf = -1*ones(size(Ef)); - maxf = ones(size(Ef)); - interval = [-1 1]; - case {'Negbin' 'Negbinztr'} - r = gplik.disper; - p = z.*exp(Ef); - minf = nbininv(0.0001, r, p); - maxf = nbininv(0.9999, r, p); - interval = minf:maxf; - case 'Student-t' - [n, n2] = size(Ef); - nu = gp.lik.nu; - minf = repmat(tinv(0.01, nu), n, n2); - maxf = repmat(tinv(0.99, nu), n, n2); - interval = []; - case 'Weibull' - % Probably not very sensible... - minf = 1e-5; - maxf = 1e5; - interval = maxf; - case 'Coxph' - minf = 0; - maxf = 1; - interval = maxf; - end - -end diff --git a/gp/gpcf_intcov.m b/gp/gpcf_intcov.m deleted file mode 100644 index 8e73439d..00000000 --- a/gp/gpcf_intcov.m +++ /dev/null @@ -1,755 +0,0 @@ -function gpcf = gpcf_intcov(varargin) -%GPCF_INTCOV Create an integrated covariance function -% -% Description -% GPCF = GPCF_INTCOV('nin',nin,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates an integrated covariance function structure in which the named -% parameters have the specified values. Any unspecified parameters are -% set to default values. Obligatory parameters are 'intArea', which -% defines the integration areas, and 'cf', which defines the covariance -% function(s) to be integrated. -% -% Notes of usage: -% -% The input matrix X can contain point locations and "lower-left" -% corners of integrated areas (areas are always intervals, cells, cubes -% etc.). Last column of the input X has to contain 1:s for integration -% areas and 0 for point location. For example, if x(3,end) = 1, then the -% third row of x tells the lower-left corner of an integrated area. -% -% Field gpcf.intArea tells the lengths of the integration path along -% each axis. -% -% GPCF = GPCF_INTCOV(GPCF,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a covariance function structure with the named -% parameters altered with the specified values. -% -% Parameters for piece wise polynomial (q=2) covariance function [default] -% IntArea - Integration path lengths per input dimension. -% cf - covariance functions to be integrated -% NintPoints - number of samples for areal integration -% approximation -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% NOTES, WARNINGS! -% * This function is still experimental. It should return correct -% answers in 1 and 2 dimensional problems -% * Evaluation of the integrated covariance is currently implemented -% using stochastic integration. This is awfully slow -% -> in 1d speed up could be obtained using quadrature -% -> in 2d and higher dimensions speed up is perhaps possible with -% extensions of quadrature -% -> Quasi-Monte Carlo has been tried and helps only little -% * Stochastic integration is highly variable -% -> gradients can not be evaluated accurately enough for which reason -% the inference has to be conducted with MCMC (HMC can not be used) -% -% See also -% GP_SET, GPCF_*, PRIOR_*, METRIC_* -% -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - if nargin>0 && ischar(varargin{1}) && ismember(varargin{1},{'init' 'set'}) - % remove init and set - varargin(1)=[]; - end - - ip=inputParser; - ip.FunctionName = 'GPCF_INTCOV'; - ip=iparser(ip,'addOptional','gpcf', [], @isstruct); - ip=iparser(ip,'addParamValue','intArea',[], @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','cf',[], @iscell); - ip=iparser(ip,'addParamValue','NintPoints',200, @(x) isscalar(x) && x>0); - ip=iparser(ip,'parse',varargin{:}); - gpcf=ip.Results.gpcf; - - if isempty(gpcf) - % Check that SuiteSparse is available - init=true; - gpcf.intArea=ip.Results.intArea; - if isempty(gpcf.intArea) - error('intArea has to be given for intcov: gpcf_intcov(''intArea'',INTAREA,...)') - end - gpcf.type = 'gpcf_intcov'; - else - if ~isfield(gpcf,'type') && ~isequal(gpcf.type,'gpcf_intcov') - error('First argument does not seem to be a valid covariance function structure') - end - init=false; - end - - if init || ~ismember('cf',ip.UsingDefaults) - % Initialize parameters - gpcf.cf = {}; - cfs=ip.Results.cf; - if ~isempty(cfs) - for i = 1:length(cfs) - gpcf.cf{i} = cfs{i}; - end - else - error('At least one covariance function has to be given in cf'); - end - end - - if init - % Set the function handles to the nested functions - gpcf.fh.pak = @gpcf_intcov_pak; - gpcf.fh.unpak = @gpcf_intcov_unpak; - gpcf.fh.lp = @gpcf_intcov_lp; - gpcf.fh.lpg = @gpcf_intcov_lpg; - gpcf.fh.cfg = @gpcf_intcov_cfg; - gpcf.fh.ginput = @gpcf_intcov_ginput; - gpcf.fh.cov = @gpcf_intcov_cov; - gpcf.fh.trcov = @gpcf_intcov_trcov; - gpcf.fh.trvar = @gpcf_intcov_trvar; - gpcf.fh.recappend = @gpcf_intcov_recappend; - - % help parameters for storing intermediate results - w0 = []; - w1 = []; - datahash0=0; - datahash1=0; - Ctrcov = []; - Ccov = []; - end - - % Initialize parameters - if init || ~ismember('intArea',ip.UsingDefaults) - gpcf.intArea=ip.Results.intArea; - end - - % Initialize parameters - if init || ~ismember('NintPoints',ip.UsingDefaults) - gpcf.NintPoints=ip.Results.NintPoints; - end - - function [w,s,h] = gpcf_intcov_pak(gpcf) - %GPCF_INTCOV_PAK Combine GP covariance function parameters into - % one vector - % - % Description - % W = GPCF_INTCOV_PAK(GPCF) takes a covariance function - % structure GPCF and combines the covariance function - % parameters and their hyperparameters into a single row - % vector W. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % See also - % GPCF_INTCOV_UNPAK - - ncf = length(gpcf.cf); - w = []; s = {}; h=[]; - - for i=1:ncf - cf = gpcf.cf{i}; - [wi, si, hi] = cf.fh.pak(cf); - w = [w wi]; - s = [s; si]; - h = [h 1+hi]; - end - - end - - function [gpcf, w] = gpcf_intcov_unpak(gpcf, w) - %GPCF_INTCOV_UNPAK Sets the covariance function parameters into - % the structure - % - % Description - % [GPCF, W] = GPCF_INTCOV_UNPAK(GPCF, W) takes a covariance - % function structure GPCF and a hyper-parameter vector W, - % and returns a covariance function structure identical - % to the input, except that the covariance hyper-parameters - % have been set to the values in W. Deletes the values set to - % GPCF from W and returns the modified W. This is a mandatory - % subfunction used for example in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(gpcf.magnSigma2) - % (hyperparameters of gpcf.magnSigma2) - % log(gpcf.lengthScale(:)) - % (hyperparameters of gpcf.lengthScale)]' - % - % See also - % GPCF_INTCOV_PAK - - ncf = length(gpcf.cf); - - for i=1:ncf - cf = gpcf.cf{i}; - [cf, w] = cf.fh.unpak(cf, w); - gpcf.cf{i} = cf; - end - - end - - function lp = gpcf_intcov_lp(gpcf) - %GPCF_INTCOV_LP Evaluate the log prior of covariance function parameters - % - % Description - % LP = GPCF_INTCOV_LP(GPCF, X, T) takes a covariance function - % structure GPCF and returns log(p(th)), where th collects the - % parameters. This is a mandatory subfunction used for example - % in energy computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LPG, GP_E - - lp = 0; - ncf = length(gpcf.cf); - for i=1:ncf - cf = gpcf.cf{i}; - lp = lp + cf.fh.lp(cf); - end - end - - function lpg = gpcf_intcov_lpg(gpcf) - %GPCF_INTCOV_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = GPCF_INTCOV_LPG(GPCF) takes a covariance function - % structure GPCF and returns LPG = d log (p(th))/dth, where th - % is the vector of parameters. This is a mandatory subfunction - % used for example in gradient computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - lpg = []; - ncf = length(gpcf.cf); - - % Evaluate the gradients - for i=1:ncf - cf = gpcf.cf{i}; - lpg=[lpg cf.fh.lpg(cf)]; - end - end - - function DKff = gpcf_intcov_cfg(gpcf, x, x2, mask) - %GPCF_INTCOV_CFG Evaluate gradient of covariance function - % with respect to the parameters - % - % Description - % DKff = GPCF_INTCOV_CFG(GPCF, X) takes a covariance function - % structure GPCF, a matrix X of input vectors and returns - % DKff, the gradients of covariance matrix Kff = k(X,X) with - % respect to th (cell array with matrix elements). This is a - % mandatory subfunction used for example in gradient computations. - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, [], MASK) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the diagonal of gradients of covariance matrix - % Kff = k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - [n, m] =size(x); - - DKff = {}; - % Evaluate: DKff{1} = d Kff / d magnSigma2 - % DKff{2} = d Kff / d lengthScale - % NOTE! Here we have already taken into account that the parameters are transformed - % through log() and thus dK/dlog(p) = p * dK/dp - - % evaluate the gradient for training covariance - if nargin == 2 - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - % point-point covariance - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1)); - for j1 = 1:length(temp) - [I,J,R] = find(temp{j1}); - DKff{end+1} = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - end - end - - % point-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1),intpoints); - for k1 = 1:length(temp) - temp2{ii1}(:,j1) = mean(temp{k1},2); - ii1=ii1+1; - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp + temp'; - end - - % area-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, intpoints1, intpoints2); - for l1 = 1:length(temp) - temp2{ii1}(j1,k1) = mean(mean(temp{l1})); - ii1=ii1+1; - end - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp; % + temp' - end - - % Evaluate the gradient of non-symmetric covariance (e.g. K_fu) - elseif nargin == 3 - if size(x,2) ~= size(x2,2) - error('gpcf_intcov -> _ghyper: The number of columns in x and x2 has to be the same. ') - end - - % Evaluate: DKff{1} = d mask(Kff,I) / d magnSigma2 - % DKff{2...} = d mask(Kff,I) / d lengthScale - elseif nargin == 4 - - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - for i1=1:length(DKff) - DKff{i1}=full(DKff{i1}); - end - end - - end - - function DKff = gpcf_intcov_ginput(gpcf, x, x2) - %GPCF_INTCOV_GINPUT Evaluate gradient of covariance function with - % respect to x - % - % Description - % DKff = GPCF_INTCOV_GINPUT(GPCF, X) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = GPCF_INTCOV_GINPUT(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - end - - - function C = gpcf_intcov_cov(gpcf, x1, x2, varargin) - %GP_INTCOV_COV Evaluate covariance matrix between two input vectors - % - % Description - % C = GP_INTCOV_COV(GP, TX, X) takes in covariance function of - % a Gaussian process GP and two matrixes TX and X that contain - % input vectors to GP. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i in TX - % and j in X. This is a mandatory subfunction used for example in - % prediction and energy computations. - % - % See also - % GPCF_INTCOV_TRCOV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - if isempty(x2) - x2=x1; - end - [n1,m1]=size(x1); - [n2,m2]=size(x2); - - if m1~=m2 - error('the number of columns of X1 and X2 has to be same') - end - - ncf = length(gpcf.cf); - ww = []; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512([x1, x2]); - fromMem = false; - if ~isempty(w1) - if all(size(ww)==size(w1)) && all(abs(ww-w1)<1e-8) && isequal(datahash,datahash1) - fromMem = true; - end - end - - if fromMem - C = Ccov; - else - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - intInd1 = find(x1(:,end)==1); - intInd2 = find(x2(:,end)==1); - pointInd1 = find(x1(:,end)==0); - pointInd2 = find(x2(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - C = sparse(n1,n2); - - % point-point covariance - if any(x1(:,end)==0) && any(x2(:,end)==0) - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.cov(cf, x1(pointInd1,1:end-1),x2(pointInd2,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd2(J),R,n1,n2); - end - - % point-area covariance - if any(x1(:,end)==0) && any(x2(:,end)==1) - temp=sparse(length(pointInd1),length(intInd2)); - for j1=1:length(intInd2) - %N=600; - %[tmp1, tmp2] = meshgrid( linspace(x2(intInd2(j1),1),x2(intInd2(j1),1)+intArea(1,1),N) , linspace(x2(intInd2(j1),2),x2(intInd2(j1),2)+intArea(1,2),N)); - %intpoints = [tmp1(:),tmp2(:)]; - intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,m2-1); - %intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*hammersley(m2-1,numPoints)'; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x1(pointInd1,1:end-1),intpoints),2); - end - end - [I,J,R] = find(temp); - C = C + sparse(pointInd1(I),intInd2(J),R,n1,n2); - end - - % area-point covariance - if any(x1(:,end)==1) && any(x2(:,end)==0) - temp=sparse(length(pointInd2),length(intInd1)); - for j1=1:length(intInd1) - intpoints = repmat(x1(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x2(pointInd2,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp'); - C = C + sparse(intInd1(I),pointInd2(J),R,n1,n2); - end - - % area-area covariance - if any(x1(:,end)==1) && any(x2(:,end)==1) - temp=sparse(length(intInd1),length(intInd2)); - for j1=1:length(intInd1) - intpoints1 = repmat(x1(intInd1(j1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd2) - intpoints2 = repmat(x2(intInd2(k1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - [I,J,R] = find(temp); - C = C + sparse(intInd1(I),intInd2(J),R,n1,n2); - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ccov=C; - datahash1=datahash; - w1=ww; - end - - end - - function C = gpcf_intcov_trcov(gpcf, x) - %GP_INTCOV_TRCOV Evaluate training covariance matrix of inputs - % - % Description - % C = GP_INTCOV_TRCOV(GP, TX) takes in covariance function of a - % Gaussian process GP and matrix TX that contains training - % input vectors. Returns covariance matrix C. Every element ij - % of C contains covariance between inputs i and j in TX. This is - % a mandatory subfunction used for example in prediction and - % energy computations. - % - % See also - % GPCF_INTCOV_COV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - ncf = length(gpcf.cf); - ww=[]; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512(x); - fromMem = false; - if ~isempty(w0) - if all(size(ww)==size(w0)) && all(abs(ww-w0)<1e-8) && isequal(datahash,datahash0) - fromMem = true; - end - end - - if fromMem - C = Ctrcov; - else - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - %intMethod = gpcf.intMethod; - - % point-point covariance - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trcov(cf, x(pointInd1,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - - % point-area covariance - temp=sparse(length(pointInd1),length(intInd1)); - randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x(pointInd1,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp'; - - % % area-area covariance - % temp=sparse(length(intInd1),length(intInd1)); - % for j1=1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for k1 = 1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for i1=1:ncf - % cf = gpcf.cf{i1}; - % temp(j1,k1) = temp(j1,k1) + mean(mean(feval(cf.fh.cov, cf, intpoints1, intpoints2))); - % end - % end - % end - % [I,J,R] = find(temp); - % C = C + sparse(intInd1(I),intInd1(J),R,n1,n1); - - % area-area covariance - temp=sparse(length(intInd1),length(intInd1)); - temp2=zeros(n1,1); - randpoints = [intArea intArea].*hammersley((dimInt)*2,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = j1+1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - % The covariance matrix seems to get non positive definite. Try to fix - % it by evaluating all the diagonal elements with same random numbers. - %randpoints1 = intArea.*rand(numPoints,dimInt); - %randpoints2 = intArea.*rand(numPoints,dimInt); - %randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - intpoints2 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints1; - %intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp2(j1) = temp2(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - %temp2(j1) = temp2(j1) + mean(mean(feval(cf.fh.trcov, cf, intpoints))); - end - end - [I,J,R] = find(temp); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp' + sparse(1:n1,1:n1,temp2); - - C = (C+C')/2; - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ctrcov=C; - datahash0=datahash; - w0=ww; - end - - end - - function C = gpcf_intcov_trvar(gpcf, x) - %GP_INTCOV_TRVAR Evaluate training variance vector - % - % Description - % C = GP_INTCOV_TRVAR(GPCF, TX) takes in covariance function of - % a Gaussian process GPCF and matrix TX that contains training - % inputs. Returns variance vector C. Every element i of C - % contains variance of input i in TX. This is a mandatory - % subfunction used for example in prediction and energy - % computations. - % - % See also - % GPCF_INTCOV_COV, GP_COV, GP_TRCOV - - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - C = zeros(n1,1); - - % point-point covariance - temp = 0; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trvar(cf, x(pointInd1,1:end-1)); - end - C(pointInd1) = temp; - - % area-area covariance - temp=zeros(size(intInd1)); - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1) = temp(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - C(intInd1) = temp; - end - - function reccf = gpcf_intcov_recappend(reccf, ri, gpcf) - %RECAPPEND Record append - % - % Description - % RECCF = GPCF_INTCOV_RECAPPEND(RECCF, RI, GPCF) takes a - % covariance function record structure RECCF, record index RI - % and covariance function structure GPCF with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from GPCF. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - if nargin == 2 - % Initialize record - reccf.type = 'gpcf_intcov'; - reccf.NintPoints = ri.NintPoints; - reccf.intArea = ri.intArea; - - % Initialize parameters - ncf = length(ri.cf); - for i=1:ncf - cf = ri.cf{i}; - reccf.cf{i} = cf.fh.recappend([], ri.cf{i}); - end - - % Set the function handles - reccf.fh.pak = @gpcf_intcov_pak; - reccf.fh.unpak = @gpcf_intcov_unpak; - reccf.fh.e = @gpcf_intcov_lp; - reccf.fh.lpg = @gpcf_intcov_lpg; - reccf.fh.cfg = @gpcf_intcov_cfg; - reccf.fh.cov = @gpcf_intcov_cov; - reccf.fh.trcov = @gpcf_intcov_trcov; - reccf.fh.trvar = @gpcf_intcov_trvar; - reccf.fh.recappend = @gpcf_intcov_recappend; - else - % Append to the record - - %loop over all of the covariance functions - ncf = length(gpcf.cf); - reccf.NintPoints(ri,:) = gpcf.NintPoints; - reccf.intArea(ri,:) = gpcf.intArea; - for i=1:ncf - cf = gpcf.cf{i}; - reccf.cf{i} = cf.fh.recappend(reccf.cf{i}, ri, cf); - end - end - end -end - diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m deleted file mode 100644 index aaaf22d3..00000000 --- a/gp/lik_gaussianbl.m +++ /dev/null @@ -1,372 +0,0 @@ -function lik = lik_gaussianbl(varargin) -%LIK_GAUSSIAN Create a Gaussian likelihood structure -% -% Description -% LIK = LIK_GAUSSIANBL('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a Gaussian likelihood structure in which the named -% parameters have the specified values. Any unspecified -% parameters are set to default values. -% -% LIK = LIK_GAUSSIANBL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood function structure with the named -% parameters altered with the specified values. -% -% Parameters for Gaussian likelihood function [default] -% sigma2 - variance of the independent noise [0.1] for each -% block of inputs. If noiseSigma2 is a vector each -% entry of the vector specifies noise variance for -% a block of inputs defined by the last column of -% the input matrix X. The variances are set for blocks -% according to 'bl_indic' field so that sigma2(i) is a -% noise variance of the inputs whose last column equals -% bl_indic(i). -% bl_indic - block indicator vector [empty matrix]. If -% length(sigma2)>1 bl_indic has to be the same length -% as sigma2. -% sigma2_prior - prior for sigma2 [prior_logunif] -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% See also -% GP_SET, PRIOR_*, LIK_* - -% Internal note: Because Gaussian noise can be combined -% analytically to the covariance matrix, lik_gaussian is internally -% little between lik_* and gpcf_* functions. -% -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_GAUSSIANBL'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'addParamValue','bl_indic',0.1, @(x) isvector(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.type = 'GaussianBL'; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'GaussianBL') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('sigma2',ip.UsingDefaults) - lik.sigma2 = ip.Results.sigma2; - lik.bl_indic = ip.Results.bl_indic; - end - - if length(lik.sigma2)> 1 || length(lik.bl_indic) > 1 - if length(lik.sigma2) ~= length(lik.bl_indic) - error('sigma2 and bl_indic has to be same length') - end - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('sigma2_prior',ip.UsingDefaults) - lik.p.sigma2=ip.Results.sigma2_prior; - end - if init - % Set the function handles to the nested functions - lik.fh.pak = @lik_gaussianbl_pak; - lik.fh.unpak = @lik_gaussianbl_unpak; - lik.fh.lp = @lik_gaussianbl_lp; - lik.fh.lpg = @lik_gaussianbl_lpg; - lik.fh.cfg = @lik_gaussianbl_cfg; - lik.fh.trcov = @lik_gaussianbl_trcov; - lik.fh.trvar = @lik_gaussianbl_trvar; - lik.fh.recappend = @lik_gaussianbl_recappend; - end -end - - function [w s,h] = lik_gaussianbl_pak(lik) - %LIK_GAUSSIANBL_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_GAUSSIANBL_PAK(LIK) takes a likelihood structure LIK - % and combines the parameters into a single row vector W. - % This is a mandatory subfunction used for example in energy - % and gradient computations. - % - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_UNPAK - - w = []; s = {}; h=[]; - if ~isempty(lik.p.sigma2) - w = log(lik.sigma2); - if numel(lik.sigma2)>1 - s = [s; sprintf('log(gaussian.sigma2 x %d)',numel(lik.sigma2))]; - else - s = [s; 'log(gaussian.sigma2)']; - end - h = [h zeros(1,numel(lik.sigma))]; - % Hyperparameters of noiseSigma2 - [wh, sh,hh] = lik.p.sigma2.fh.pak(lik.p.sigma2); - w = [w wh]; - s = [s sh]; - h = [h hh]; - end - end - - function [lik, w] = lik_gaussianbl_unpak(lik, w) - %LIK_GAUSSIANBL_UNPAK Extract likelihood parameters from the vector. - % - % Description - % W = LIK_GAUSSIANBL_UNPAK(W, LIK) takes a likelihood structure - % LIK and extracts the parameters from the vector W to the LIK - % structure. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_PAK - - if ~isempty(lik.p.sigma2) - i2=length(lik.sigma2); - lik.sigma2 = exp(w(1:i2)); - w = w(i2+1:end); - - % Hyperparameters of sigma2 - [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); - lik.p.sigma2 = p; - end - end - - function lp = lik_gaussianbl_lp(lik) - %LIK_GAUSSIANBL_LP Evaluate the log prior of likelihood parameters - % - % Description - % LP = LIK_T_LP(LIK) takes a likelihood structure LIK and - % returns log(p(th)), where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_G, GP_E - - lp = 0; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + sum(log(lik.sigma2)); - end - end - - function lpg = lik_gaussianbl_lpg(lik) - %LIK_GAUSSIANBL_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = LIK_GAUSSIANBL_LPG(LIK) takes a Gaussian likelihood - % function structure LIK and returns LPG = d log (p(th))/dth, - % where th is the vector of parameters. This subfunction is - % needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - lpg = []; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - i2=length(lik.sigma2); - - lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); - lpg = lpgs(1:i2).*lik.sigma2 + 1; - if length(lpgs) > 1 - lpg = [lpg lpgs(i2+1:end)]; - end - end - end - - function DKff = lik_gaussianbl_cfg(lik, x, x2) - %LIK_GAUSSIANBL_CFG Evaluate gradient of covariance with respect to - % Gaussian noise - % - % Description - % Gaussian likelihood is a special case since it can be - % analytically combined with covariance functions and thus we - % compute gradient of covariance instead of gradient of likelihood. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X) takes a Gaussian likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of Gaussian noise covariance - % matrix Kff = k(X,X) with respect to th (cell array with - % matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X, X2) takes a Gaussian - % likelihood function structure LIK, a matrix X of input - % vectors and returns DKff, the gradients of Gaussian noise - % covariance matrix Kff = k(X,X) with respect to th (cell - % array with matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - [n, m] =size(x); - - if length(lik.sigma2)==1 - DKff{1} = lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - ind = find(x(:,end)==lik.bl_indic(i1)); - DKff{i1} = sparse(ind,ind,lik.sigma2(i1),n,n); - end - end - - end - - function DKff = lik_gaussianbl_ginput(lik, x, t, g_ind, gdata_ind, gprior_ind, varargin) - %LIK_GAUSSIANBL_GINPUT Evaluate gradient of likelihood function with - % respect to x. - % - % Description - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X, X2) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - end - - function C = lik_gaussianbl_trcov(lik, x) - %LIK_GAUSSIANBL_TRCOV Evaluate training covariance matrix - % corresponding to Gaussian noise - % Description - % C = LIK_GAUSSIANBL_TRCOV(GP, TX) takes in covariance function - % of a Gaussian process GP and matrix TX that contains - % training input vectors. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i and j - % in TX. This subfunction is needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, LIK_GAUSSIANBL_TRVAR, GP_COV, GP_TRCOV - - [n, m] =size(x); - - s2 = zeros(n,1); - if length(lik.sigma2)==1 - s2 = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - s2(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - C = sparse(1:n,1:n,s2,n,n); - - end - - function C = lik_gaussianbl_trvar(lik, x) - %LIK_GAUSSIANBL_TRVAR Evaluate training variance vector - % corresponding to Gaussian noise - % - % Description - % C = LIK_GAUSSIANBL_TRVAR(LIK, TX) takes in covariance function - % of a Gaussian process LIK and matrix TX that contains - % training inputs. Returns variance vector C. Every element i - % of C contains variance of input i in TX. This subfunction is - % needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, GP_COV, GP_TRCOV - - [n, m] =size(x); - - C = zeros(n,1); - if length(lik.sigma2)==1 - C = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - C(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - end - - function reccf = lik_gaussianbl_recappend(reccf, ri, lik) - %RECAPPEND Record append - % - % Description - % RECCF = LIK_GAUSSIANBL_RECAPPEND(RECCF, RI, LIK) takes a - % likelihood function record structure RECCF, record index RI - % and likelihood function structure LIK with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from LIK. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - % Initialize record - if nargin == 2 - reccf.type = 'lik_gaussianbl'; - - % Initialize parameters - reccf.sigma2 = []; - reccf.bl_indic = []; - - % Set the function handles - reccf.fh.pak = @lik_gaussianbl_pak; - reccf.fh.unpak = @lik_gaussianbl_unpak; - reccf.fh.lp = @lik_gaussianbl_lp; - reccf.fh.lpg = @lik_gaussianbl_lpg; - reccf.fh.cfg = @lik_gaussianbl_cfg; - reccf.fh.trcov = @lik_gaussianbl_trcov; - reccf.fh.trvar = @lik_gaussianbl_trvar; - reccf.fh.recappend = @lik_gaussianbl_recappend; - reccf.p=[]; - reccf.p.sigma2=[]; - if ~isempty(ri.p.sigma2) - reccf.p.sigma2 = ri.p.sigma2; - end - return - end - - likp = lik.p; - - % record sigma - if ~isempty(lik.sigma2) - reccf.sigma2(ri,:)=lik.sigma2; - reccf.bl_indic(ri,:)=lik.bl_indic; - if ~isempty(lik.p.sigma2) - reccf.p.sigma2 = likp.sigma2.fh.recappend(reccf.p.sigma2, ri, likp.sigma2); - end - elseif ri==1 - reccf.sigma2=[]; - end - end - - From ac59d0b170042bbe1fde48bb3c970ecda64fb6d7 Mon Sep 17 00:00:00 2001 From: Aki Vehtari Date: Wed, 30 Apr 2014 14:16:08 +0300 Subject: [PATCH 51/64] Don't add xunit to the path in Octave --- gpstuff_install.m | 1 - 1 file changed, 1 deletion(-) diff --git a/gpstuff_install.m b/gpstuff_install.m index fe0f6759..1ccc809f 100644 --- a/gpstuff_install.m +++ b/gpstuff_install.m @@ -73,7 +73,6 @@ function gpstuff_install(SuiteSparse_path) S{4} = [PP '/mc']; S{5} = [PP '/misc']; S{6} = [PP '/optim']; - S{7} = [PP '/xunit']; fprintf ('\n The following paths have been added. You may wish to add them\n') ; fprintf ('permanently, using the MATLAB pathtool command or copying the below\n') ; From 9968dd8a74812a67abb25f6adf9d52754b75dd44 Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Mon, 21 Jul 2014 10:52:07 +0300 Subject: [PATCH 52/64] Update changelog --- ChangeLog.txt | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/ChangeLog.txt b/ChangeLog.txt index cf00709c..e358ee3c 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -1,3 +1,40 @@ +2014-07-xx Version 4.5 + +New features + - Input dependent noise and signal variance. + + Tolvanen, V., Jylänki, P. and Vehtari, A. (2014). Approximate Inference for + Nonstationary Heteroscedastic Gaussian Process Regression. In Proceedings of IEEE + International Workshop on Machine Learning for Signal Processing, accepted for + publication. Preprint arXiv:1404.5443 + + - Sparse stochastic variational inference model. + + Hensman, J., Fusi, N. and Lawrence, N. D. (2013). Gaussian processes + for big data. arXiv preprint arXiv:1309.6835. + + - Option 'autoscale' in the gp_rnd.m to get split normal approximated samples from the + posterior predictive distribution of the latent variable. + + Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo + Integration. Econometrica, 57(6):1317-1339. + + Villani, M. and Larsson, R. (2006). The Multivariate Split Normal Distribution + and Asymmetric Principal Components Analysis. Communications in + Statistics - Theory and Methods, 35(6):1123-1140. + +Improvements + - New unit test environment using the Matlab built-in test framework (the old Xunit + package is still also supported). + - Precomputed demo results (including the figures) are now available in the folder + tests/realValues. + - New demos demonstrating new features etc. + - demo_epinf, demonstrating the input dependent noise and signal variance model + - demo_svi_regression, demo_svi_classification + - demo_modelcomparison2, demo_survival_comparison + +Several minor bugfixes + 2014-04-11 Version 4.4 New features From fd063ed88721545b0a222e96e0ecf672693e8cb5 Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Mon, 21 Jul 2014 12:32:51 +0300 Subject: [PATCH 53/64] Fix forgotten merge conflict in README.txt --- README.txt | 21 +-------------------- 1 file changed, 1 insertion(+), 20 deletions(-) diff --git a/README.txt b/README.txt index 6c0e726c..3ec14461 100644 --- a/README.txt +++ b/README.txt @@ -1,11 +1,7 @@ Last modified: 2014-05-16 09:58:33 EEST ----------------------------------------------------------------- -<<<<<<< HEAD -GPstuff: Gaussian process models for Bayesian analysis 4.3.2 -======= GPstuff: Gaussian process models for Bayesian analysis 4.4 ->>>>>>> origin/develop Maintainer: Aki Vehtari @@ -89,12 +85,7 @@ Table of contents: 3. CONTENTS The GPstuff packge contains the following subdirectories: -<<<<<<< HEAD diag dist gp mc misc optim test_gpstuff SuiteSparse -======= - diag dist gp mc misc optim tests* SuiteSparse* - (* not in Octave) ->>>>>>> origin/develop Each folder contains Contents.m, which summarizes the functions in the folder. @@ -104,19 +95,9 @@ Table of contents: 4. TESTING THE INSTALLATION -<<<<<<< HEAD Installation can be tested by running command run_tests, which runs specific demos and compares the computed results to pre-saved - results. -======= - Installation can be tested by running command runtestset('fast'), which - runs a collection of demos and compares the computed results to pre-saved - results. Running this takes about one hour and it requires Matlab version - 2013b or greater for the unit test framework. Alternatively, the - 'xunit' package can be used instead. The xunit package can be downloaded - from - http://www.mathworks.com/matlabcentral/fileexchange/22846-matlab-xunit-test-framework ->>>>>>> origin/develop + results. 5. USER QUIDE (VERY SHORT) From fe34f12549bae809eb26023f53dd1508d7941abb Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Tue, 22 Jul 2014 10:49:21 +0300 Subject: [PATCH 54/64] Update real values for octave --- test_gpstuff/octave/realValues_periodic.mat | 5420 +++++++++-------- .../octave/realValues_survival_aft.mat | 914 +-- 2 files changed, 3373 insertions(+), 2961 deletions(-) diff --git a/test_gpstuff/octave/realValues_periodic.mat b/test_gpstuff/octave/realValues_periodic.mat index 0053fd88..147904b8 100644 --- a/test_gpstuff/octave/realValues_periodic.mat +++ b/test_gpstuff/octave/realValues_periodic.mat @@ -1,2529 +1,2941 @@ -# Created by Octave 3.8.0, Fri Mar 07 15:23:30 2014 EET -# name: Eft_full1 +# Created by Octave 3.8.0, Tue Jul 22 09:54:18 2014 EEST +# name: Eft_full # type: matrix -# rows: 557 +# rows: 132 # columns: 1 - -26.96164424234975 - -26.90679741662922 - -26.8518535826277 - -26.7416851983964 - -26.68646516294919 - -26.63115715362853 - -26.52028348323432 - 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b/test_gpstuff/octave/realValues_survival_aft.mat @@ -1,463 +1,463 @@ -# Created by Octave 3.8.0, Fri Mar 07 16:07:15 2014 EET +# Created by Octave 3.8.0, Tue Jul 22 09:41:41 2014 EEST # name: pmu # type: matrix # rows: 456 # columns: 3 - 0.186339873203285 0.3566512684628107 0.6725664454409377 - 0.3152864195406144 0.6405165538061871 1.209860070024493 - 0.4523886398550208 0.8847345200460948 1.666664199646072 - 0.4575032837249244 0.8977429143389408 1.689119989065154 - 0.535086764223615 1.026692394297916 1.935831748966438 - 0.607363371155875 1.145209892262637 2.172331832100323 - 0.683442172455262 1.260922759735573 2.381316859044639 - 0.7552410284974433 1.367144741238785 2.573087796299157 - 0.8158611493181469 1.467066129089893 2.739670984730375 - 0.8798441059991857 1.555729440741873 2.860186330895346 - 0.9324613743492076 1.632595565604633 3.000914225839108 - 0.9781463746312516 1.70676798846149 3.117189155770727 - 1.015344845626411 1.748967932842595 3.180882304235811 - 1.026851812178834 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7190122ee790824ac330fc2760d9dc4e05402439 Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Tue, 22 Jul 2014 12:29:31 +0300 Subject: [PATCH 55/64] Ensure the remove of unnecesssary files for the release --- gp/gp_cvlcriterion.m | 59 ---- gp/gp_lcriterion.m | 68 ---- gp/gp_refpred.m | 467 -------------------------- gp/gpcf_intcov.m | 755 ------------------------------------------- gp/lik_gaussianbl.m | 372 --------------------- 5 files changed, 1721 deletions(-) delete mode 100644 gp/gp_cvlcriterion.m delete mode 100644 gp/gp_lcriterion.m delete mode 100644 gp/gp_refpred.m delete mode 100644 gp/gpcf_intcov.m delete mode 100644 gp/lik_gaussianbl.m diff --git a/gp/gp_cvlcriterion.m b/gp/gp_cvlcriterion.m deleted file mode 100644 index c304c911..00000000 --- a/gp/gp_cvlcriterion.m +++ /dev/null @@ -1,59 +0,0 @@ -function PE2 = gp_cvlcriterion(gp, x, y, varargin) -%GP_CVLCRITERION cross-validation version of L-criterion -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns cross-validation -% version of L-criterion PE2 given a Gaussian process model GP, -% training inputs X and training outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Marriott, J. M., Spencer, N. M. and Pettitt, A. N. (2001). A -% Bayesian Approach to Selecting Covariates for -% Prediction. Scandinavian Journal of Statistics 28 87–97. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_LCRITERION -% -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_CVLCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_loopred(gp, x, y); - PE2 = mean((Ey-y).^2 + Vary); - - else - % Non-Gaussian likelihood - error('cvlcriterion not sensible for non-gaussian likelihoods'); - end - -end - diff --git a/gp/gp_lcriterion.m b/gp/gp_lcriterion.m deleted file mode 100644 index 20136998..00000000 --- a/gp/gp_lcriterion.m +++ /dev/null @@ -1,68 +0,0 @@ -function L2 = gp_lcriterion(gp, x, y, varargin) -%GP_LCRITERION L-criterion for model selection. -% -% Description -% PE2 = GP_CVLCRITERION(GP, X, Y, OPTIONS) returns L-criterion L2 -% given a Gaussian process model GP, training inputs X and training -% outputs Y. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% -% References -% Gelfand, A. E. and Ghosh, S. K. (1998). Model Choice: A Minimum -% Posterior Predictive Loss Approach. Biometrika 85 1–11. -% -% Ibrahim, J. G., Chen, M.-H. and Sinha, D. (2001). Criterion-based -% methods for Bayesian model assessment. Statistica Sinica 11 -% 419–443. -% -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In Statistics Surveys, 6:142-228. -% -% -% See also -% GP_CVLCRITERION -% -% -% Copyright (c) 2011 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - - ip=inputParser; - ip.FunctionName = 'GP_LCRITERION'; - ip=iparser(ip,'addRequired','gp',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'parse',gp, x, y, varargin{:}); - ip.parse(gp, x, y, varargin{:}); - % pass these forward - options=struct(); - z = ip.Results.z; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - [tn, nin] = size(x); - if ((isstruct(gp) && isfield(gp.lik.fh, 'trcov')) || (iscell(gp) && isfield(gp{1}.lik.fh,'trcov'))) - % Gaussian likelihood - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y); - L2 = sum((y-Ey).^2 + Vary); - - else - % Non-Gaussian likelihood - warning('L-criterion not sensible for non-gaussian likelihoods'); - [tmp,tmp,tmp,Ey,Vary] = gp_pred(gp, x, y, x, 'yt', y, options); - L2 = sum((y-Ey).^2 + Vary); - - end - -end - diff --git a/gp/gp_refpred.m b/gp/gp_refpred.m deleted file mode 100644 index fc73cf73..00000000 --- a/gp/gp_refpred.m +++ /dev/null @@ -1,467 +0,0 @@ -function u_g = gp_refpred(gp1, gp2, x, y, varargin) -% GP_REFPRED Reference predictive approximation to the expected utility of -% single predictions. -% -% Description -% u = GP_REFPRED(GP1, GP2, X, Y, OPTIONS) evaluates reference -% predictive approximation between models GP1 and GP2. Here GP1 is the -% reference model and GP2 is the candidate model. -% -% OPTIONS is optional parameter-value pair -% z - optional observed quantity in triplet (x_i,y_i,z_i) -% Some likelihoods may use this. For example, in case of -% Poisson likelihood we have z_i=E_i, that is, expected value -% for ith case. -% method - method for inference, 'posterior' (default) uses posterior -% predictive density, 'loo' uses leave-one-out predictive -% density (approximative), 'kfcv' uses loo cross-validation -% posterior predictive density, 'joint' uses joint -% posterior predictive density for latent values -% (non-Gaussian likelihood) or observations (Gaussian -% likelihood) -% x2,y2,z2 - Optional values for candidate model gp2. -% If only subset of these is specified, remaining variables -% are set from x,y,z. -% -% See also -% GP_LOOPRED, GP_KFCV -% -% References -% Vehtari & Ojanen (2011). Bayesian preditive methods for model -% assesment and selection. In preparation. -% -% Copyright (c) 2011-2012 Ville Tolvanen - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'GP_REFPRED'; - ip=iparser(ip,'addRequired','gp1',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','gp2',@(x) isstruct(x) || iscell(x)); - ip=iparser(ip,'addRequired','x', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addRequired','y', @(x) ~isempty(x) && isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','x2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','y2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','z2', [], @(x) isreal(x) && all(isfinite(x(:)))); - ip=iparser(ip,'addParamValue','method', 'posterior', @(x) ismember(x,{'posterior' 'kfcv' 'loo' 'joint'})); - ip=iparser(ip,'addParamValue','form', 'mean', @(x) ismember(x,{'mean','all'})); - ip=iparser(ip,'parse',gp1, gp2, x, y, varargin{:}); - % pass these forward - options=struct(); - x2 = ip.Results.x2; - y2 = ip.Results.y2; - z2 = ip.Results.z2; - z = ip.Results.z; - method = ip.Results.method; - form = ip.Results.form; - if ~isempty(ip.Results.z) - options.zt=ip.Results.z; - options.z=ip.Results.z; - end - if ~isempty(ip.Results.z2) - options2.zt=ip.Results.z2; - options2.z=ip.Results.z2; - else - options2 = options; - z2 = z; - end - [tn, nin] = size(x); - u_g = zeros(size(y)); - opt = optimset('TolX', 1e-4, 'TolFun', 1e-4); - if isempty(x2) - x2 = x; - end - if isempty(y2) - y2 = y; - end - - if isstruct(gp1) - % Single gp or MCMC - - switch gp1.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1.tr_index; - end - if ~isfield(gp1,'etr') - model1 = 1; - if isfield(gp1.lik.fh, 'trcov') - switch method - case 'joint' - - otherwise - fh1 = @(f,Ey,Vary) norm_pdf(f,Ey,sqrt(Vary)); - end - else - fh1 = @(gp,Ef,Varf,f,z) exp(predvec(gp,Ef,(Varf),f,z)); - end - - switch method - case 'posterior' - if ~isequal(gp1.lik.type, 'Coxph') - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - else - [Ef1, Varf1] = gp_pred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - case 'loo' - if ~isfield(gp1.lik.fh, 'trcov') && ~isfield(gp1.lik, 'type_nd') - gp1 = gp_set(gp1, 'latent_method', 'EP'); - end - [Ef1, Varf1, tmp, Ey1, Vary1] = gp_loopred(gp1,x,y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp1, x, y, 'tstindex', tstind, 'opt', opt, 'display', 'iter', 'k', tn, options); - [Ef1, Varf1, Ey1, Vary1] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef1, Covf1] = gp_jpred(gp1,x,y,x,'yt',y, 'tstind', tstind, options); - end - - else - model1 = 2; - if isfield(gp1.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) mean(multi_npdf(f,Ey,(Vary)),1); - else - fh1 = @(gp,Ef,Varf,f,z) mean(exp(predvec(gp,Ef,(Varf),f,z)),1); - end - nsamples = length(gp1.edata); - if strcmp(gp1.type, 'PIC') - tr_index = gp1.tr_index; - gp1 = rmfield(gp1, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp1,j); - if strcmp(gp1.type, 'FIC') | strcmp(gp1.type, 'PIC') || strcmp(gp1.type, 'CS+FIC') || strcmp(gp1.type, 'VAR') || strcmp(gp1.type, 'DTC') || strcmp(gp1.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array1{j} = Gp; - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gpmc_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_loopred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, pred] = gp_kfcv(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - gp1 = gp_array1; - end - else - % GP IA - switch gp1{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp1{1}.tr_index; - end - model1 = 3; - nsamples = length(gp1); - for j = 1:nsamples - Gp = gp1{j}; - weight1(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, x, 'yt', y, 'tstind', tstind, options); - case 'loo' - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = gp_pred(Gp, x, y, 'z', z); - case 'kfcv' - [tmp, preds] = gp_pred(Gp, x, y, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options); - [Ef1(:,j), Varf1(:,j), tmp, Ey1(:,j), Vary1(:,j)] = deal(preds.Eft, preds.Varft, preds.Eyt, preds.Varyt); - end - end - if isequal(method, 'joint') - [Ef1, Covf1] = gp_jpred(gp1, x, y, x, 'yt', y, 'tstind', tstind, options); - end - if isfield(gp1{1}.lik.fh, 'trcov') - fh1 = @(f,Ey,Vary) sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight1'),1); - else - fh1 = @(gp,Ef,Varf,f,z) (sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight1'),1)); - end - - end - - if isstruct(gp2) - % Single gp or MCMC - switch gp2.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2.tr_index; - end - if ~isfield(gp2,'etr') - model2 = 1; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) norm_lpdf(f,Ey,sqrt(Vary)); - else - fh2 = @(gp,Ef,Varf,f,z) predvec(gp,Ef,(Varf),f,z); - end - switch method - case 'posterior' - if ~isequal(gp2.lik.type, 'Coxph') - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - else - [Ef2, Varf2] = gp_pred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - case 'loo' - if ~isfield(gp2.lik.fh, 'trcov') && ~isfield(gp2.lik, 'type_nd') - gp1 = gp_set(gp2, 'latent_method', 'EP'); - end - [Ef2, Varf2, tmp, Ey2, Vary2] = gp_loopred(gp2,x2,y2, 'z', z2); - case 'kfcv' - [tmp, preds] = gp_kfcv(gp2, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - [Ef2, Varf2, Ey2, Vary2] = deal(preds.Eft,preds.Varft,preds.Eyt,preds.Varyt); - case 'joint' - [Ef2, Covf2] = gp_jpred(gp2,x2,y2,x2,'yt',y2, 'tstind', tstind, options2); - end - else - model2 = 2; - if isfield(gp2.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(mean(multi_npdf(f,Ey,(Vary)),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(mean(exp(predvec(gp,Ef,(Varf),f,z)),1)); - end - nsamples = length(gp2.edata); - if strcmp(gp2.type, 'PIC') - tr_index = gp2.tr_index; - gp2 = rmfield(gp2, 'tr_index'); - else - tr_index = []; - end - - for j = 1:nsamples - Gp = take_nth(gp2,j); - if strcmp(gp2.type, 'FIC') | strcmp(gp2.type, 'PIC') || strcmp(gp2.type, 'CS+FIC') || strcmp(gp2.type, 'VAR') || strcmp(gp2.type, 'DTC') || strcmp(gp2.type, 'SOR') - Gp.X_u = reshape(Gp.X_u,length(Gp.X_u)/nin,nin); - end - Gp.tr_index = tr_index; - gp_array2{j} = Gp; - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gpmc_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_kfcv(Gp, x2, y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'iter', options2); - end - - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - gp2 = gp_array2; - end - else - % GP IA - switch gp2{1}.type - case {'FULL' 'VAR' 'DTC' 'SOR'} - tstind = []; - case {'FIC' 'CS+FIC'} - tstind = 1:tn; - case 'PIC' - tstind = gp2{1}.tr_index; - end - model2 = 3; - nsamples = length(gp2); - for j = 1:nsamples - Gp = gp2{j}; - weight2(j) = Gp.ia_weight; - w(j,:) = gp_pak(Gp); - switch method - case 'posterior' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - case 'loo' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_loopred(Gp, x2, y2, 'z', z2); - case 'kfcv' - [Ef2(:,j), Varf2(:,j), tmp, Ey2(:,j), Vary2(:,j)] = gp_pred(Gp, x2, y2, x2, 'yt', y2, 'tstindex', tstind, 'k', tn, 'opt', opt, 'display', 'off', options2); - end - end - if isequal(method, 'joint') - [Ef2, Covf2] = gp_jpred(gp2, x2, y2, x2, 'yt', y2, 'tstind', tstind, options2); - end - if isfield(gp2{1}.lik.fh, 'trcov') - fh2 = @(f,Ey,Vary) log(sum(bsxfun(@times, multi_npdf(f,Ey,(Vary)),weight2'),1)); - else - fh2 = @(gp,Ef,Varf,f,z) log(sum(bsxfun(@times, exp(predvec(gp,Ef,(Varf),f,z)),weight2'),1)); - end - - end - - if ((isstruct(gp1) && isfield(gp1.lik.fh, 'trcov')) || (iscell(gp1) && isfield(gp1{1}.lik.fh,'trcov'))) - % Gaussian likelihood - - switch method - case 'joint' - u_g = -0.5.*((Ey1 - Ey2)'*(Covy2\(Ey1-Ey2)) + sum(sum(inv(Covy2).*Covy1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covy2))))); - otherwise - for i=1:tn - m1 = Ey1(i,:); m2=Ey1(i,:).^2 + Vary1(i,:); - u_g(i) = mean(-1./(2.*Vary2(i,:))*m2 + Ey2(i,:)./Vary2(i,:)*m1 - Ey2(i,:).^2./(2.*Vary2(i,:)) - 0.5*log(2*pi*Vary2(i,:))); - end - end - - else - % Non-Gaussian likelihood - - switch method - case 'joint' - % Joint refpred of latent values - u_g = -0.5.*((Ef1 - Ef2)'*(Covf2\(Ef1-Ef2)) + sum(sum(inv(Covf2).*Covf1)) ... - + tn*log(2*pi) + 2*sum(log(diag(chol(Covf2))))); - - otherwise - if ismember(gp1.lik.type, {'Binomial', 'Poisson', 'Probit', 'Logit', 'Negbin', 'Negbinztr'}) - % Discrete likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - [tmp, tmp, int] = int_limits(gp1, Ef1(i,:), z1); - else - [minf maxf] = int_limits(gp1,Ef1(i,:),z1); - minf = sum(minf.*weight1); - maxf = sum(maxf.*weight1); - int = minf:maxf; - end - u_g(i) = sum(fh1(gp1,Ef1(i,:),Varf1(i,:),int,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),int,z12)); - end - else - % Continuous likelihoods - for i=1:tn - if ~isempty(z) - z1 = z(i); - z12 = z2(i); - else - z1 = []; - z12 = []; - end - if model1~=3 - if ismember(gp1.lik.type, {'Student-t', 'Weibull', 'Coxph'}) - [minf, maxf] = int_limits(gp1, Ef1(i), z1); - else - minf = mean(Ey1(i) - 12.*sqrt(Vary1(i)),2); - minf(minf<0)=0; - maxf = mean(Ey1(i) + 12.*sqrt(Vary1(i)),2); - end - else - minf = sum(bsxfun(@times, weight1, Ey1(i,:)-12.*sqrt(Vary1(i,:))),2); - maxf = sum(bsxfun(@times, weight1, Ey1(i,:)+12.*sqrt(Vary1(i,:))),2); - end - if ~isequal(gp1.lik.type, 'Coxph') - u_g(i) = quadgk(@(f) fh1(gp1,Ef1(i,:),Varf1(i,:),f,z1).*fh2(gp2,Ef2(i,:),Varf2(i,:),f,z12), minf, maxf, 'absTol', 1e-3); - else - ntime1=size(gp1.lik.xtime,1); - ntime2=size(gp2.lik.xtime,1); - u_g(i) = quadgk(@(f) fh1(gp1,Ef1([1:ntime1 i],:),Varf1([1:ntime1 i+ntime1],[1:ntime1 i+ntime1]),f,z1).*fh2(gp2,Ef2([1:ntime2 i],:),Varf2([1:ntime2 i+ntime2],[1:ntime2 i+ntime2]),f,z12), minf, maxf, 'absTol', 1e-3); - end - end - end - end - end - if isequal(form, 'mean') - u_g = mean(u_g); - end -end - -function predvec = predvec(gp, Ef, Varf, f, z) - % Compute vector of lpyts from lik.fh.predy when numel(Ef)=numel(Varf)=1 - % and numel(f) != 1. - if isstruct(gp) - if ~isfield(gp, 'etr') - % single gp - predvec=zeros(size(f)); - for i1=1:numel(f) - predvec(i1)=gp.lik.fh.predy(gp.lik,Ef,Varf,f(i1),z); - end - end - else - % ia & mc - predvec=zeros(length(gp), length(f)); - for i=1:numel(f) - for j=1:numel(gp) - predvec(j,i) = gp{j}.lik.fh.predy(gp{j}.lik, Ef(j), Varf(j), f(i), z); - end - end - end -end - -function mpdf = multi_npdf(f, mean, sigma2) -% for every element in f, compute means calculated with -% norm_pdf(f(i), mean, sqrt(sigma2)). If mean and sigma2 -% are vectors, returns length(mean) x length(f) matrix. - - mpdf = zeros(length(mean), length(f)); - for i=1:length(f) - mpdf(:,i) = norm_pdf(f(i), mean, sqrt(sigma2)); - end -end - -function [minf, maxf, interval] = int_limits(gp, Ef, z) -% Return integration limits for quadgk and interval for discrete integration. - if isstruct(gp) - gplik = gp.lik; - else - gplik = gp{1}.lik; - end - switch gplik.type - - case 'Binomial' - p = exp(Ef)./(1+exp(Ef)); - minf = binoinv(0.0001, z, p); - maxf = binoinv(0.9999, z, p); - interval = minf:maxf; - case 'Poisson' - lambda = z.*exp(Ef); - minf = poissinv(0.0001, lambda); - maxf = poissinv(0.9999, lambda); - interval=minf:maxf; - case {'Probit' 'Logit'} - minf = -1*ones(size(Ef)); - maxf = ones(size(Ef)); - interval = [-1 1]; - case {'Negbin' 'Negbinztr'} - r = gplik.disper; - p = z.*exp(Ef); - minf = nbininv(0.0001, r, p); - maxf = nbininv(0.9999, r, p); - interval = minf:maxf; - case 'Student-t' - [n, n2] = size(Ef); - nu = gp.lik.nu; - minf = repmat(tinv(0.01, nu), n, n2); - maxf = repmat(tinv(0.99, nu), n, n2); - interval = []; - case 'Weibull' - % Probably not very sensible... - minf = 1e-5; - maxf = 1e5; - interval = maxf; - case 'Coxph' - minf = 0; - maxf = 1; - interval = maxf; - end - -end diff --git a/gp/gpcf_intcov.m b/gp/gpcf_intcov.m deleted file mode 100644 index de559bab..00000000 --- a/gp/gpcf_intcov.m +++ /dev/null @@ -1,755 +0,0 @@ -function gpcf = gpcf_intcov(varargin) -%GPCF_INTCOV Create an integrated covariance function -% -% Description -% GPCF = GPCF_INTCOV('nin',nin,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates an integrated covariance function structure in which the named -% parameters have the specified values. Any unspecified parameters are -% set to default values. Obligatory parameters are 'intArea', which -% defines the integration areas, and 'cf', which defines the covariance -% function(s) to be integrated. -% -% Notes of usage: -% -% The input matrix X can contain point locations and "lower-left" -% corners of integrated areas (areas are always intervals, cells, cubes -% etc.). Last column of the input X has to contain 1:s for integration -% areas and 0 for point location. For example, if x(3,end) = 1, then the -% third row of x tells the lower-left corner of an integrated area. -% -% Field gpcf.intArea tells the lengths of the integration path along -% each axis. -% -% GPCF = GPCF_INTCOV(GPCF,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a covariance function structure with the named -% parameters altered with the specified values. -% -% Parameters for piece wise polynomial (q=2) covariance function [default] -% IntArea - Integration path lengths per input dimension. -% cf - covariance functions to be integrated -% NintPoints - number of samples for areal integration -% approximation -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% NOTES, WARNINGS! -% * This function is still experimental. It should return correct -% answers in 1 and 2 dimensional problems -% * Evaluation of the integrated covariance is currently implemented -% using stochastic integration. This is awfully slow -% -> in 1d speed up could be obtained using quadrature -% -> in 2d and higher dimensions speed up is perhaps possible with -% extensions of quadrature -% -> Quasi-Monte Carlo has been tried and helps only little -% * Stochastic integration is highly variable -% -> gradients can not be evaluated accurately enough for which reason -% the inference has to be conducted with MCMC (HMC can not be used) -% -% See also -% GP_SET, GPCF_*, PRIOR_*, METRIC_* -% -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - if nargin>0 && ischar(varargin{1}) && ismember(varargin{1},{'init' 'set'}) - % remove init and set - varargin(1)=[]; - end - - ip=inputParser; - ip.FunctionName = 'GPCF_INTCOV'; - ip=iparser(ip,'addOptional','gpcf', [], @isstruct); - ip=iparser(ip,'addParamValue','intArea',[], @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','cf',[], @iscell); - ip=iparser(ip,'addParamValue','NintPoints',200, @(x) isscalar(x) && x>0); - ip=iparser(ip,'parse',varargin{:}); - gpcf=ip.Results.gpcf; - - if isempty(gpcf) - % Check that SuiteSparse is available - init=true; - gpcf.intArea=ip.Results.intArea; - if isempty(gpcf.intArea) - error('intArea has to be given for intcov: gpcf_intcov(''intArea'',INTAREA,...)') - end - gpcf.type = 'gpcf_intcov'; - else - if ~isfield(gpcf,'type') && ~isequal(gpcf.type,'gpcf_intcov') - error('First argument does not seem to be a valid covariance function structure') - end - init=false; - end - - if init || ~ismember('cf',ip.UsingDefaults) - % Initialize parameters - gpcf.cf = {}; - cfs=ip.Results.cf; - if ~isempty(cfs) - for i = 1:length(cfs) - gpcf.cf{i} = cfs{i}; - end - else - error('At least one covariance function has to be given in cf'); - end - end - - if init - % Set the function handles to the nested functions - gpcf.fh.pak = @gpcf_intcov_pak; - gpcf.fh.unpak = @gpcf_intcov_unpak; - gpcf.fh.lp = @gpcf_intcov_lp; - gpcf.fh.lpg = @gpcf_intcov_lpg; - gpcf.fh.cfg = @gpcf_intcov_cfg; - gpcf.fh.ginput = @gpcf_intcov_ginput; - gpcf.fh.cov = @gpcf_intcov_cov; - gpcf.fh.trcov = @gpcf_intcov_trcov; - gpcf.fh.trvar = @gpcf_intcov_trvar; - gpcf.fh.recappend = @gpcf_intcov_recappend; - - % help parameters for storing intermediate results - w0 = []; - w1 = []; - datahash0=0; - datahash1=0; - Ctrcov = []; - Ccov = []; - end - - % Initialize parameters - if init || ~ismember('intArea',ip.UsingDefaults) - gpcf.intArea=ip.Results.intArea; - end - - % Initialize parameters - if init || ~ismember('NintPoints',ip.UsingDefaults) - gpcf.NintPoints=ip.Results.NintPoints; - end - - function [w,s,h] = gpcf_intcov_pak(gpcf) - %GPCF_INTCOV_PAK Combine GP covariance function parameters into - % one vector - % - % Description - % W = GPCF_INTCOV_PAK(GPCF) takes a covariance function - % structure GPCF and combines the covariance function - % parameters and their hyperparameters into a single row - % vector W. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % See also - % GPCF_INTCOV_UNPAK - - ncf = length(gpcf.cf); - w = []; s = {}; h=[]; - - for i=1:ncf - cf = gpcf.cf{i}; - [wi, si, hi] = cf.fh.pak(cf); - w = [w wi]; - s = [s; si]; - h = [h 1+hi]; - end - - end - - function [gpcf, w] = gpcf_intcov_unpak(gpcf, w) - %GPCF_INTCOV_UNPAK Sets the covariance function parameters into - % the structure - % - % Description - % [GPCF, W] = GPCF_INTCOV_UNPAK(GPCF, W) takes a covariance - % function structure GPCF and a hyper-parameter vector W, - % and returns a covariance function structure identical - % to the input, except that the covariance hyper-parameters - % have been set to the values in W. Deletes the values set to - % GPCF from W and returns the modified W. This is a mandatory - % subfunction used for example in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(gpcf.magnSigma2) - % (hyperparameters of gpcf.magnSigma2) - % log(gpcf.lengthScale(:)) - % (hyperparameters of gpcf.lengthScale)]' - % - % See also - % GPCF_INTCOV_PAK - - ncf = length(gpcf.cf); - - for i=1:ncf - cf = gpcf.cf{i}; - [cf, w] = cf.fh.unpak(cf, w); - gpcf.cf{i} = cf; - end - - end - - function lp = gpcf_intcov_lp(gpcf) - %GPCF_INTCOV_LP Evaluate the log prior of covariance function parameters - % - % Description - % LP = GPCF_INTCOV_LP(GPCF, X, T) takes a covariance function - % structure GPCF and returns log(p(th)), where th collects the - % parameters. This is a mandatory subfunction used for example - % in energy computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LPG, GP_E - - lp = 0; - ncf = length(gpcf.cf); - for i=1:ncf - cf = gpcf.cf{i}; - lp = lp + cf.fh.lp(cf); - end - end - - function lpg = gpcf_intcov_lpg(gpcf) - %GPCF_INTCOV_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = GPCF_INTCOV_LPG(GPCF) takes a covariance function - % structure GPCF and returns LPG = d log (p(th))/dth, where th - % is the vector of parameters. This is a mandatory subfunction - % used for example in gradient computations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - lpg = []; - ncf = length(gpcf.cf); - - % Evaluate the gradients - for i=1:ncf - cf = gpcf.cf{i}; - lpg=[lpg cf.fh.lpg(cf)]; - end - end - - function DKff = gpcf_intcov_cfg(gpcf, x, x2, mask) - %GPCF_INTCOV_CFG Evaluate gradient of covariance function - % with respect to the parameters - % - % Description - % DKff = GPCF_INTCOV_CFG(GPCF, X) takes a covariance function - % structure GPCF, a matrix X of input vectors and returns - % DKff, the gradients of covariance matrix Kff = k(X,X) with - % respect to th (cell array with matrix elements). This is a - % mandatory subfunction used for example in gradient computations. - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % DKff = GPCF_INTCOV_CFG(GPCF, X, [], MASK) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the diagonal of gradients of covariance matrix - % Kff = k(X,X2) with respect to th (cell array with matrix - % elements). This subfunction is needed when using sparse - % approximations (e.g. FIC). - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - [n, m] =size(x); - - DKff = {}; - % Evaluate: DKff{1} = d Kff / d magnSigma2 - % DKff{2} = d Kff / d lengthScale - % NOTE! Here we have already taken into account that the parameters are transformed - % through log() and thus dK/dlog(p) = p * dK/dp - - % evaluate the gradient for training covariance - if nargin == 2 - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - % point-point covariance - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1)); - for j1 = 1:length(temp) - [I,J,R] = find(temp{j1}); - DKff{end+1} = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - end - end - - % point-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, x(pointInd1,1:end-1),intpoints); - for k1 = 1:length(temp) - temp2{ii1}(:,j1) = mean(temp{k1},2); - ii1=ii1+1; - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp + temp'; - end - - % area-area covariance - temp2={}; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - ii1=1; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = cf.fh.cfg(cf, intpoints1, intpoints2); - for l1 = 1:length(temp) - temp2{ii1}(j1,k1) = mean(mean(temp{l1})); - ii1=ii1+1; - end - end - end - end - for i1=1:length(temp2) - [I,J,R] = find(temp2{i1}); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - DKff{i1} = DKff{i1} + temp; % + temp' - end - - % Evaluate the gradient of non-symmetric covariance (e.g. K_fu) - elseif nargin == 3 - if size(x,2) ~= size(x2,2) - error('gpcf_intcov -> _ghyper: The number of columns in x and x2 has to be the same. ') - end - - % Evaluate: DKff{1} = d mask(Kff,I) / d magnSigma2 - % DKff{2...} = d mask(Kff,I) / d lengthScale - elseif nargin == 4 - - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - for i1=1:length(DKff) - DKff{i1}=full(DKff{i1}); - end - end - - end - - function DKff = gpcf_intcov_ginput(gpcf, x, x2) - %GPCF_INTCOV_GINPUT Evaluate gradient of covariance function with - % respect to x - % - % Description - % DKff = GPCF_INTCOV_GINPUT(GPCF, X) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = GPCF_INTCOV_GINPUT(GPCF, X, X2) takes a covariance - % function structure GPCF, a matrix X of input vectors and - % returns DKff, the gradients of covariance matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % GPCF_INTCOV_PAK, GPCF_INTCOV_UNPAK, GPCF_INTCOV_LP, GP_G - - end - - - function C = gpcf_intcov_cov(gpcf, x1, x2, varargin) - %GP_INTCOV_COV Evaluate covariance matrix between two input vectors - % - % Description - % C = GP_INTCOV_COV(GP, TX, X) takes in covariance function of - % a Gaussian process GP and two matrixes TX and X that contain - % input vectors to GP. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i in TX - % and j in X. This is a mandatory subfunction used for example in - % prediction and energy computations. - % - % See also - % GPCF_INTCOV_TRCOV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - if isempty(x2) - x2=x1; - end - [n1,m1]=size(x1); - [n2,m2]=size(x2); - - if m1~=m2 - error('the number of columns of X1 and X2 has to be same') - end - - ncf = length(gpcf.cf); - ww = []; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512([x1, x2]); - fromMem = false; - if ~isempty(w1) - if all(size(ww)==size(w1)) && all(abs(ww-w1)<1e-8) && isequal(datahash,datahash1) - fromMem = true; - end - end - - if fromMem - C = Ccov; - else - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - intInd1 = find(x1(:,end)==1); - intInd2 = find(x2(:,end)==1); - pointInd1 = find(x1(:,end)==0); - pointInd2 = find(x2(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - C = sparse(n1,n2); - - % point-point covariance - if any(x1(:,end)==0) && any(x2(:,end)==0) - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.cov(cf, x1(pointInd1,1:end-1),x2(pointInd2,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd2(J),R,n1,n2); - end - - % point-area covariance - if any(x1(:,end)==0) && any(x2(:,end)==1) - temp=sparse(length(pointInd1),length(intInd2)); - for j1=1:length(intInd2) - %N=600; - %[tmp1, tmp2] = meshgrid( linspace(x2(intInd2(j1),1),x2(intInd2(j1),1)+intArea(1,1),N) , linspace(x2(intInd2(j1),2),x2(intInd2(j1),2)+intArea(1,2),N)); - %intpoints = [tmp1(:),tmp2(:)]; - intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,m2-1); - %intpoints = repmat(x2(intInd2(j1),1:end-1),numPoints,1) + intArea.*hammersley(m2-1,numPoints)'; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x1(pointInd1,1:end-1),intpoints),2); - end - end - [I,J,R] = find(temp); - C = C + sparse(pointInd1(I),intInd2(J),R,n1,n2); - end - - % area-point covariance - if any(x1(:,end)==1) && any(x2(:,end)==0) - temp=sparse(length(pointInd2),length(intInd1)); - for j1=1:length(intInd1) - intpoints = repmat(x1(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x2(pointInd2,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp'); - C = C + sparse(intInd1(I),pointInd2(J),R,n1,n2); - end - - % area-area covariance - if any(x1(:,end)==1) && any(x2(:,end)==1) - temp=sparse(length(intInd1),length(intInd2)); - for j1=1:length(intInd1) - intpoints1 = repmat(x1(intInd1(j1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = 1:length(intInd2) - intpoints2 = repmat(x2(intInd2(k1),1:dimInt),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - [I,J,R] = find(temp); - C = C + sparse(intInd1(I),intInd2(J),R,n1,n2); - end - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ccov=C; - datahash1=datahash; - w1=ww; - end - - end - - function C = gpcf_intcov_trcov(gpcf, x) - %GP_INTCOV_TRCOV Evaluate training covariance matrix of inputs - % - % Description - % C = GP_INTCOV_TRCOV(GP, TX) takes in covariance function of a - % Gaussian process GP and matrix TX that contains training - % input vectors. Returns covariance matrix C. Every element ij - % of C contains covariance between inputs i and j in TX. This is - % a mandatory subfunction used for example in prediction and - % energy computations. - % - % See also - % GPCF_INTCOV_COV, GPCF_INTCOV_TRVAR, GP_COV, GP_TRCOV - - ncf = length(gpcf.cf); - ww=[]; - for i1=1:ncf - ww = [ww gpcf.cf{i1}.fh.pak(gpcf.cf{i1})]; - end - datahash=hash_sha512(x); - fromMem = false; - if ~isempty(w0) - if all(size(ww)==size(w0)) && all(abs(ww-w0)<1e-8) && isequal(datahash,datahash0) - fromMem = true; - end - end - - if fromMem - C = Ctrcov; - else - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - dimInt = numel(gpcf.intArea); - %intMethod = gpcf.intMethod; - - % point-point covariance - temp=sparse(0); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trcov(cf, x(pointInd1,1:end-1)); - end - [I,J,R] = find(temp); - C = sparse(pointInd1(I),pointInd1(J),R,n1,n1); - - % point-area covariance - temp=sparse(length(pointInd1),length(intInd1)); - randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(:,j1) = temp(:,j1) + mean(cf.fh.cov(cf, x(pointInd1,1:dimInt),intpoints),2); - end - end - [I,J,R] = find(temp); - temp = sparse(pointInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp'; - - % % area-area covariance - % temp=sparse(length(intInd1),length(intInd1)); - % for j1=1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for k1 = 1:length(intInd1) - % RandStream.setDefaultStream(RandStream('mt19937ar','seed',100)) - % intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - % for i1=1:ncf - % cf = gpcf.cf{i1}; - % temp(j1,k1) = temp(j1,k1) + mean(mean(feval(cf.fh.cov, cf, intpoints1, intpoints2))); - % end - % end - % end - % [I,J,R] = find(temp); - % C = C + sparse(intInd1(I),intInd1(J),R,n1,n1); - - % area-area covariance - temp=sparse(length(intInd1),length(intInd1)); - temp2=zeros(n1,1); - randpoints = [intArea intArea].*hammersley((dimInt)*2,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for k1 = j1+1:length(intInd1) - intpoints2 = repmat(x(intInd1(k1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints2 = repmat(x(intInd1(k1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1,k1) = temp(j1,k1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - end - % The covariance matrix seems to get non positive definite. Try to fix - % it by evaluating all the diagonal elements with same random numbers. - %randpoints1 = intArea.*rand(numPoints,dimInt); - %randpoints2 = intArea.*rand(numPoints,dimInt); - %randpoints = intArea.*hammersley(dimInt,numPoints)'; - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,1:dimInt); - intpoints2 = repmat(x(intInd1(j1),1:dimInt),numPoints,1) + randpoints(:,dimInt+1:2*dimInt); - %intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints1; - %intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - %intpoints = repmat(x(intInd1(j1),1:end-1),numPoints,1) + randpoints2; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp2(j1) = temp2(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - %temp2(j1) = temp2(j1) + mean(mean(feval(cf.fh.trcov, cf, intpoints))); - end - end - [I,J,R] = find(temp); - temp = sparse(intInd1(I),intInd1(J),R,n1,n1); - C = C + temp + temp' + sparse(1:n1,1:n1,temp2); - - C = (C+C')/2; - - % check if CS covariances are used. If not change C into full matrix - % for speed up - sp = false; - for i1=1:ncf - if isfield(gpcf.cf{i1}, 'cs') && gpcf.cf{i1} == 1 - sp=true; - end - end - if ~sp - C=full(C); - end - - % store in the memory - Ctrcov=C; - datahash0=datahash; - w0=ww; - end - - end - - function C = gpcf_intcov_trvar(gpcf, x) - %GP_INTCOV_TRVAR Evaluate training variance vector - % - % Description - % C = GP_INTCOV_TRVAR(GPCF, TX) takes in covariance function of - % a Gaussian process GPCF and matrix TX that contains training - % inputs. Returns variance vector C. Every element i of C - % contains variance of input i in TX. This is a mandatory - % subfunction used for example in prediction and energy - % computations. - % - % See also - % GPCF_INTCOV_COV, GP_COV, GP_TRCOV - - - [n1,m1]=size(x); - - intInd1 = find(x(:,end)==1); - pointInd1 = find(x(:,end)==0); - ncf = length(gpcf.cf); - numPoints = gpcf.NintPoints; - intArea = repmat(gpcf.intArea,numPoints,1); - - C = zeros(n1,1); - - % point-point covariance - temp = 0; - for i1=1:ncf - cf = gpcf.cf{i1}; - temp = temp + cf.fh.trvar(cf, x(pointInd1,1:end-1)); - end - C(pointInd1) = temp; - - % area-area covariance - temp=zeros(size(intInd1)); - for j1=1:length(intInd1) - intpoints1 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - intpoints2 = repmat(x(intInd1(j1),1:end-1),numPoints,1) + intArea.*rand(numPoints,dimInt); - for i1=1:ncf - cf = gpcf.cf{i1}; - temp(j1) = temp(j1) + mean(mean(cf.fh.cov(cf, intpoints1, intpoints2))); - end - end - C(intInd1) = temp; - end - - function reccf = gpcf_intcov_recappend(reccf, ri, gpcf) - %RECAPPEND Record append - % - % Description - % RECCF = GPCF_INTCOV_RECAPPEND(RECCF, RI, GPCF) takes a - % covariance function record structure RECCF, record index RI - % and covariance function structure GPCF with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from GPCF. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - if nargin == 2 - % Initialize record - reccf.type = 'gpcf_intcov'; - reccf.NintPoints = ri.NintPoints; - reccf.intArea = ri.intArea; - - % Initialize parameters - ncf = length(ri.cf); - for i=1:ncf - cf = ri.cf{i}; - reccf.cf{i} = cf.fh.recappend([], ri.cf{i}); - end - - % Set the function handles - reccf.fh.pak = @gpcf_intcov_pak; - reccf.fh.unpak = @gpcf_intcov_unpak; - reccf.fh.lp = @gpcf_intcov_lp; - reccf.fh.lpg = @gpcf_intcov_lpg; - reccf.fh.cfg = @gpcf_intcov_cfg; - reccf.fh.cov = @gpcf_intcov_cov; - reccf.fh.trcov = @gpcf_intcov_trcov; - reccf.fh.trvar = @gpcf_intcov_trvar; - reccf.fh.recappend = @gpcf_intcov_recappend; - else - % Append to the record - - %loop over all of the covariance functions - ncf = length(gpcf.cf); - reccf.NintPoints(ri,:) = gpcf.NintPoints; - reccf.intArea(ri,:) = gpcf.intArea; - for i=1:ncf - cf = gpcf.cf{i}; - reccf.cf{i} = cf.fh.recappend(reccf.cf{i}, ri, cf); - end - end - end -end - diff --git a/gp/lik_gaussianbl.m b/gp/lik_gaussianbl.m deleted file mode 100644 index aaaf22d3..00000000 --- a/gp/lik_gaussianbl.m +++ /dev/null @@ -1,372 +0,0 @@ -function lik = lik_gaussianbl(varargin) -%LIK_GAUSSIAN Create a Gaussian likelihood structure -% -% Description -% LIK = LIK_GAUSSIANBL('PARAM1',VALUE1,'PARAM2,VALUE2,...) -% creates a Gaussian likelihood structure in which the named -% parameters have the specified values. Any unspecified -% parameters are set to default values. -% -% LIK = LIK_GAUSSIANBL(LIK,'PARAM1',VALUE1,'PARAM2,VALUE2,...) -% modify a likelihood function structure with the named -% parameters altered with the specified values. -% -% Parameters for Gaussian likelihood function [default] -% sigma2 - variance of the independent noise [0.1] for each -% block of inputs. If noiseSigma2 is a vector each -% entry of the vector specifies noise variance for -% a block of inputs defined by the last column of -% the input matrix X. The variances are set for blocks -% according to 'bl_indic' field so that sigma2(i) is a -% noise variance of the inputs whose last column equals -% bl_indic(i). -% bl_indic - block indicator vector [empty matrix]. If -% length(sigma2)>1 bl_indic has to be the same length -% as sigma2. -% sigma2_prior - prior for sigma2 [prior_logunif] -% -% Note! If the prior is 'prior_fixed' then the parameter in -% question is considered fixed and it is not handled in -% optimization, grid integration, MCMC etc. -% -% See also -% GP_SET, PRIOR_*, LIK_* - -% Internal note: Because Gaussian noise can be combined -% analytically to the covariance matrix, lik_gaussian is internally -% little between lik_* and gpcf_* functions. -% -% Copyright (c) 2007-2011 Jarno Vanhatalo -% Copyright (c) 2010 Aki Vehtari - -% This software is distributed under the GNU General Public -% License (version 3 or later); please refer to the file -% License.txt, included with the software, for details. - - ip=inputParser; - ip.FunctionName = 'LIK_GAUSSIANBL'; - ip=iparser(ip,'addOptional','lik', [], @isstruct); - ip=iparser(ip,'addParamValue','sigma2',0.1, @(x) isvector(x) && all(x>0)); - ip=iparser(ip,'addParamValue','sigma2_prior',prior_logunif(), @(x) isstruct(x) || isempty(x)); - ip=iparser(ip,'addParamValue','bl_indic',0.1, @(x) isvector(x)); - ip=iparser(ip,'parse',varargin{:}); - lik=ip.Results.lik; - - if isempty(lik) - init=true; - lik.type = 'GaussianBL'; - else - if ~isfield(lik,'type') || ~isequal(lik.type,'GaussianBL') - error('First argument does not seem to be a valid likelihood function structure') - end - init=false; - end - - % Initialize parameters - if init || ~ismember('sigma2',ip.UsingDefaults) - lik.sigma2 = ip.Results.sigma2; - lik.bl_indic = ip.Results.bl_indic; - end - - if length(lik.sigma2)> 1 || length(lik.bl_indic) > 1 - if length(lik.sigma2) ~= length(lik.bl_indic) - error('sigma2 and bl_indic has to be same length') - end - end - % Initialize prior structure - if init - lik.p=[]; - end - if init || ~ismember('sigma2_prior',ip.UsingDefaults) - lik.p.sigma2=ip.Results.sigma2_prior; - end - if init - % Set the function handles to the nested functions - lik.fh.pak = @lik_gaussianbl_pak; - lik.fh.unpak = @lik_gaussianbl_unpak; - lik.fh.lp = @lik_gaussianbl_lp; - lik.fh.lpg = @lik_gaussianbl_lpg; - lik.fh.cfg = @lik_gaussianbl_cfg; - lik.fh.trcov = @lik_gaussianbl_trcov; - lik.fh.trvar = @lik_gaussianbl_trvar; - lik.fh.recappend = @lik_gaussianbl_recappend; - end -end - - function [w s,h] = lik_gaussianbl_pak(lik) - %LIK_GAUSSIANBL_PAK Combine likelihood parameters into one vector. - % - % Description - % W = LIK_GAUSSIANBL_PAK(LIK) takes a likelihood structure LIK - % and combines the parameters into a single row vector W. - % This is a mandatory subfunction used for example in energy - % and gradient computations. - % - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_UNPAK - - w = []; s = {}; h=[]; - if ~isempty(lik.p.sigma2) - w = log(lik.sigma2); - if numel(lik.sigma2)>1 - s = [s; sprintf('log(gaussian.sigma2 x %d)',numel(lik.sigma2))]; - else - s = [s; 'log(gaussian.sigma2)']; - end - h = [h zeros(1,numel(lik.sigma))]; - % Hyperparameters of noiseSigma2 - [wh, sh,hh] = lik.p.sigma2.fh.pak(lik.p.sigma2); - w = [w wh]; - s = [s sh]; - h = [h hh]; - end - end - - function [lik, w] = lik_gaussianbl_unpak(lik, w) - %LIK_GAUSSIANBL_UNPAK Extract likelihood parameters from the vector. - % - % Description - % W = LIK_GAUSSIANBL_UNPAK(W, LIK) takes a likelihood structure - % LIK and extracts the parameters from the vector W to the LIK - % structure. This is a mandatory subfunction used for example - % in energy and gradient computations. - % - % Assignment is inverse of - % w = [ log(lik.sigma2) - % (hyperparameters of lik.magnSigma2)]' - % - % See also - % LIK_GAUSSIANBL_PAK - - if ~isempty(lik.p.sigma2) - i2=length(lik.sigma2); - lik.sigma2 = exp(w(1:i2)); - w = w(i2+1:end); - - % Hyperparameters of sigma2 - [p, w] = lik.p.sigma2.fh.unpak(lik.p.sigma2, w); - lik.p.sigma2 = p; - end - end - - function lp = lik_gaussianbl_lp(lik) - %LIK_GAUSSIANBL_LP Evaluate the log prior of likelihood parameters - % - % Description - % LP = LIK_T_LP(LIK) takes a likelihood structure LIK and - % returns log(p(th)), where th collects the parameters. This - % subfunction is needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_G, GP_E - - lp = 0; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - lp = likp.sigma2.fh.lp(lik.sigma2, likp.sigma2) + sum(log(lik.sigma2)); - end - end - - function lpg = lik_gaussianbl_lpg(lik) - %LIK_GAUSSIANBL_LPG Evaluate gradient of the log prior with respect - % to the parameters. - % - % Description - % LPG = LIK_GAUSSIANBL_LPG(LIK) takes a Gaussian likelihood - % function structure LIK and returns LPG = d log (p(th))/dth, - % where th is the vector of parameters. This subfunction is - % needed when there are likelihood parameters. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - lpg = []; - - if ~isempty(lik.p.sigma2) - likp=lik.p; - i2=length(lik.sigma2); - - lpgs = likp.sigma2.fh.lpg(lik.sigma2, likp.sigma2); - lpg = lpgs(1:i2).*lik.sigma2 + 1; - if length(lpgs) > 1 - lpg = [lpg lpgs(i2+1:end)]; - end - end - end - - function DKff = lik_gaussianbl_cfg(lik, x, x2) - %LIK_GAUSSIANBL_CFG Evaluate gradient of covariance with respect to - % Gaussian noise - % - % Description - % Gaussian likelihood is a special case since it can be - % analytically combined with covariance functions and thus we - % compute gradient of covariance instead of gradient of likelihood. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X) takes a Gaussian likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of Gaussian noise covariance - % matrix Kff = k(X,X) with respect to th (cell array with - % matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % DKff = LIK_GAUSSIANBL_CFG(LIK, X, X2) takes a Gaussian - % likelihood function structure LIK, a matrix X of input - % vectors and returns DKff, the gradients of Gaussian noise - % covariance matrix Kff = k(X,X) with respect to th (cell - % array with matrix elements). This subfunction is needed only in - % Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - [n, m] =size(x); - - if length(lik.sigma2)==1 - DKff{1} = lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - ind = find(x(:,end)==lik.bl_indic(i1)); - DKff{i1} = sparse(ind,ind,lik.sigma2(i1),n,n); - end - end - - end - - function DKff = lik_gaussianbl_ginput(lik, x, t, g_ind, gdata_ind, gprior_ind, varargin) - %LIK_GAUSSIANBL_GINPUT Evaluate gradient of likelihood function with - % respect to x. - % - % Description - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % DKff = LIK_GAUSSIANBL_GINPUT(LIK, X, X2) takes a likelihood - % function structure LIK, a matrix X of input vectors and - % returns DKff, the gradients of likelihood matrix Kff = - % k(X,X2) with respect to X (cell array with matrix elements). - % This subfunction is needed when computing gradients with - % respect to inducing inputs in sparse approximations. - % - % See also - % LIK_GAUSSIANBL_PAK, LIK_GAUSSIANBL_UNPAK, LIK_GAUSSIANBL_E, GP_G - - end - - function C = lik_gaussianbl_trcov(lik, x) - %LIK_GAUSSIANBL_TRCOV Evaluate training covariance matrix - % corresponding to Gaussian noise - % Description - % C = LIK_GAUSSIANBL_TRCOV(GP, TX) takes in covariance function - % of a Gaussian process GP and matrix TX that contains - % training input vectors. Returns covariance matrix C. Every - % element ij of C contains covariance between inputs i and j - % in TX. This subfunction is needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, LIK_GAUSSIANBL_TRVAR, GP_COV, GP_TRCOV - - [n, m] =size(x); - - s2 = zeros(n,1); - if length(lik.sigma2)==1 - s2 = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - s2(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - C = sparse(1:n,1:n,s2,n,n); - - end - - function C = lik_gaussianbl_trvar(lik, x) - %LIK_GAUSSIANBL_TRVAR Evaluate training variance vector - % corresponding to Gaussian noise - % - % Description - % C = LIK_GAUSSIANBL_TRVAR(LIK, TX) takes in covariance function - % of a Gaussian process LIK and matrix TX that contains - % training inputs. Returns variance vector C. Every element i - % of C contains variance of input i in TX. This subfunction is - % needed only in Gaussian likelihoods. - % - % See also - % LIK_GAUSSIANBL_COV, GP_COV, GP_TRCOV - - [n, m] =size(x); - - C = zeros(n,1); - if length(lik.sigma2)==1 - C = ones(n,1).*lik.sigma2; - else - for i1 = 1:length(lik.bl_indic) - C(x(:,end)==lik.bl_indic(i1)) = lik.sigma2(i1); - end - end - - end - - function reccf = lik_gaussianbl_recappend(reccf, ri, lik) - %RECAPPEND Record append - % - % Description - % RECCF = LIK_GAUSSIANBL_RECAPPEND(RECCF, RI, LIK) takes a - % likelihood function record structure RECCF, record index RI - % and likelihood function structure LIK with the current MCMC - % samples of the parameters. Returns RECCF which contains all - % the old samples and the current samples from LIK. This - % subfunction is needed when using MCMC sampling (gp_mc). - % - % See also - % GP_MC and GP_MC -> RECAPPEND - - % Initialize record - if nargin == 2 - reccf.type = 'lik_gaussianbl'; - - % Initialize parameters - reccf.sigma2 = []; - reccf.bl_indic = []; - - % Set the function handles - reccf.fh.pak = @lik_gaussianbl_pak; - reccf.fh.unpak = @lik_gaussianbl_unpak; - reccf.fh.lp = @lik_gaussianbl_lp; - reccf.fh.lpg = @lik_gaussianbl_lpg; - reccf.fh.cfg = @lik_gaussianbl_cfg; - reccf.fh.trcov = @lik_gaussianbl_trcov; - reccf.fh.trvar = @lik_gaussianbl_trvar; - reccf.fh.recappend = @lik_gaussianbl_recappend; - reccf.p=[]; - reccf.p.sigma2=[]; - if ~isempty(ri.p.sigma2) - reccf.p.sigma2 = ri.p.sigma2; - end - return - end - - likp = lik.p; - - % record sigma - if ~isempty(lik.sigma2) - reccf.sigma2(ri,:)=lik.sigma2; - reccf.bl_indic(ri,:)=lik.bl_indic; - if ~isempty(lik.p.sigma2) - reccf.p.sigma2 = likp.sigma2.fh.recappend(reccf.p.sigma2, ri, likp.sigma2); - end - elseif ri==1 - reccf.sigma2=[]; - end - end - - From 0b38861eae78508f0139e2c584902765a82c3fbc Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Tue, 22 Jul 2014 13:46:53 +0300 Subject: [PATCH 56/64] Forgotten date in ChangeLog.txt --- ChangeLog.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ChangeLog.txt b/ChangeLog.txt index e358ee3c..743493c8 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -1,4 +1,4 @@ -2014-07-xx Version 4.5 +2014-07-22 Version 4.5 New features - Input dependent noise and signal variance. From 037878614b117bc90c0cffdbbc8ed674da5ae0f2 Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Tue, 22 Jul 2014 13:46:53 +0300 Subject: [PATCH 57/64] Forgotten date in ChangeLog.txt --- ChangeLog.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ChangeLog.txt b/ChangeLog.txt index e358ee3c..743493c8 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -1,4 +1,4 @@ -2014-07-xx Version 4.5 +2014-07-22 Version 4.5 New features - Input dependent noise and signal variance. From b54eac88b6c64a628ddd3c8ef741e48190c17b48 Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Wed, 15 Jul 2015 13:38:31 +0300 Subject: [PATCH 58/64] Add gpinv for octave in psislw.m --- misc/psislw.m | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/misc/psislw.m b/misc/psislw.m index 982f9858..76cc7afe 100644 --- a/misc/psislw.m +++ b/misc/psislw.m @@ -80,3 +80,23 @@ % warning('Following indeces have estimated tail index k>1'); % disp(ksi) % end + + +function x = gpinv(p,k,sigma) +x = NaN(size(p)); +if sigma <= 0 + return +end +ok = (p>0) & (p<1); +if abs(k) < eps + x(ok) = -log1p(-p(ok)); +else + x(ok) = expm1(-k * log1p(-p(ok))) ./ k; +end +x = sigma*x; +if ~all(ok) + x(p==0) = 0; + x(p==1 & k>=0) = Inf; + x(p==1 & k<0) = -sigma/k; +end +end From 28aa7e74a0df2de94704c4af4a16298c42185dbd Mon Sep 17 00:00:00 2001 From: Tuomas Sivula Date: Wed, 15 Jul 2015 13:54:24 +0300 Subject: [PATCH 59/64] Fix psislw.m --- misc/psislw.m | 1 + 1 file changed, 1 insertion(+) diff --git a/misc/psislw.m b/misc/psislw.m index 76cc7afe..24b47317 100644 --- a/misc/psislw.m +++ b/misc/psislw.m @@ -81,6 +81,7 @@ % disp(ksi) % end +end function x = gpinv(p,k,sigma) x = NaN(size(p)); From cd2d9ce33103f2fd9d8e8a960b56bcfc6d12fe22 Mon Sep 17 00:00:00 2001 From: Aki Vehtari Date: Thu, 20 Aug 2015 22:07:35 +0300 Subject: [PATCH 60/64] New url for homepage --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 698df0c7..cf136bfa 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ Maintainer: Aki Vehtari ## HOME PAGE Additional information and illustrations of the features can be found at - and + and ## INTRODUCTION From 76171ef54e643174dfabdbccec2f761bfba613ca Mon Sep 17 00:00:00 2001 From: Aki Vehtari Date: Thu, 20 Aug 2015 22:07:35 +0300 Subject: [PATCH 61/64] New url for homepage --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 698df0c7..cf136bfa 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ Maintainer: Aki Vehtari ## HOME PAGE Additional information and illustrations of the features can be found at - and + and ## INTRODUCTION From 0e985570984200fad6528a34d143a59a7a137922 Mon Sep 17 00:00:00 2001 From: Markus Paasiniemi Date: Wed, 13 Jan 2016 22:49:09 +0200 Subject: [PATCH 62/64] Added fixFunctionHandles-function that recreates the functions handles broken when Octave saves and loads GP structures. --- misc/fixFunctionHandles.m | 73 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 misc/fixFunctionHandles.m diff --git a/misc/fixFunctionHandles.m b/misc/fixFunctionHandles.m new file mode 100644 index 00000000..942ea692 --- /dev/null +++ b/misc/fixFunctionHandles.m @@ -0,0 +1,73 @@ +function gpo = fixFunctionHandles(gpo) +%FIXFUNCTIONHANDLES +% +% Syntax: +% gpo = fixFunctionHandles(gpo); +% +% Description: +% Fix the function handles that are broken when +% when octatve saves and loads gp structures +% +% Copyright (c) 2016 Markus Paasiniemi +% +% This software is distributed under the GNU General Public +% License (version 3 or later); please refer to the file +% License.txt, included with the software, for details. + +% list all subfields of the struct gpo +gpFields = fieldnamesRec(gpo); +gpFields = cellstr(horzcat(repmat(['gpo.'],length(gpFields),1),char(gpFields))); + +%Fix all function handles for all execpt *_rec +for k = 1:length(gpFields) + + %check if gpFields{k} is a function/subfunction handle + if strcmp(typeinfo(eval(gpFields{k})),'function handle') + + spl2 = strsplit(functions(eval(gpFields{k})).file,{'\' '/','.'}); + fn = func2str(eval(gpFields{k})); + sfn = strsplit(gpFields{k},'.'){end}; + + %check if gpFields{k} is a function handle + if strcmp(spl2{end-1},fn) + eval(strcat(gpFields{k},'=@',fn,';')); + + elseif strcmp(strsplit(gpFields{k},'.'){end},'ne') + eval(strcat(gpFields{k},'=',spl2{end-1},'("init",gpo).fh.ne;')); + + %gpFields{k} is a subfunction handle + elseif isfield(eval(spl2{end-1}).fh,sfn) + eval(strcat(gpFields{k},'=',spl2{end-1},'.fh.',sfn,';')); + + %gpFields{k} is a subfunction handle of type *_rec + else + eval(strcat(gpFields{k},'=',spl2{end-1},'.fh.recappend(',spl2{end-1},',',spl2{end-1},').fh.',sfn,';')); + end + end +end + + +% Helper function to recurse through +% a struct and find all subfields + function fNames = fieldnamesRec(s) + + fNames = fieldnames(s); + for i = 1:length(fNames) + + if isstruct(s.(fNames{i})) + newFields = fieldnamesRec(s.(fNames{i})); + fNames(end+1:end+length(newFields)) = cellstr(horzcat(repmat([fNames{i} '.'], length(newFields), 1),char(newFields))); + + elseif iscell(s.(fNames{i})) + for j = 1:length(s.(fNames{i})) + + if isstruct(s.(fNames{i}){j}) + newFields = fieldnamesRec(s.(fNames{i}){j}); + fNames(end+1:end+length(newFields)) = cellstr(horzcat(repmat([fNames{i} '{' num2str(j) '}.'],length(newFields), 1),char(newFields))); + end + end + end + end + end + +end \ No newline at end of file From 3f2a5b9c8ef937610dd5327a11bd426de831063b Mon Sep 17 00:00:00 2001 From: Siivola Eero Date: Mon, 6 Jun 2016 13:44:34 +0300 Subject: [PATCH 63/64] Fixed one bug related to previous merge and updated test reference files for Octave tests --- gp/gp_cpred.m | 3 +- test_gpstuff/octave/realValues_classific.mat | 4822 +++++++------- .../octave/realValues_derivativeobs.mat | 970 +-- .../octave/realValues_neuralnetcov.mat | 1602 ++--- test_gpstuff/octave/realValues_periodic.mat | 5810 ++++++++--------- .../octave/realValues_survival_aft.mat | 914 +-- 6 files changed, 7060 insertions(+), 7061 deletions(-) diff --git a/gp/gp_cpred.m b/gp/gp_cpred.m index 07a992a2..1d8ac2f3 100644 --- a/gp/gp_cpred.m +++ b/gp/gp_cpred.m @@ -57,7 +57,6 @@ ip=iparser(ip,'addParamValue','tr', 0.25, @(x) isreal(x) && all(isfinite(x(:)))); ip=iparser(ip,'addParamValue','target', 'mu', @(x) ismember(x,{'f','mu','cdf'})); ip=iparser(ip,'addParamValue','prct', [5 50 95], @(x) isreal(x) && all(isfinite(x(:)))); -ip=iparser(ip,'addParamValue','normdata', struct(), @(x) isempty(x) || isstruct(x)); ip=iparser(ip,'addParamValue','xlabels', [], @(x) isempty(x) || iscell(x)); ip=iparser(ip,'parse',gp, x, y, xt, ind, varargin{:}); @@ -169,7 +168,7 @@ end if isequal(plot_results, 'on') if ind>0 - if ind>=1&numel(nd.xmean)>=ind + if ind>=1 && numel(nd.xmean)>=ind xtnn=denormdata(xtnn,nd.xmean(ind),nd.xstd(ind)); end deltadist=gp_finddeltadist(gp); diff --git a/test_gpstuff/octave/realValues_classific.mat b/test_gpstuff/octave/realValues_classific.mat index 6bb07902..c0601314 100644 --- a/test_gpstuff/octave/realValues_classific.mat +++ b/test_gpstuff/octave/realValues_classific.mat @@ -1,2437 +1,2437 @@ -# Created by Octave 3.8.0, Mon Mar 10 10:24:18 2014 EET -# name: Eft_la +# Created by Octave 3.8.1, Mon Jun 06 11:37:33 2016 EEST +# name: Efs_mc # type: matrix # rows: 400 -# columns: 1 - -3.751860146391613 - -4.535507420336021 - -5.109081868182438 - -5.356124335579072 - 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0.06581039696140312 0.1566410250436157 0.357129943003173 + 0.06289106188622655 0.1523331229627211 0.3515435631257566 + 0.06072580248675803 0.1484226656952905 0.3467111574213012 + 0.05668399940706513 0.1428680117657776 0.3395642632942216 + 0.05365756683743776 0.1383504769734602 0.3359065780019623 + 0.05244361517719451 0.1367740928963683 0.3334865606940431 + 0.05233440339210582 0.1365326540564767 0.3332043161306052 + 0.0428866490519088 0.1243776249217631 0.3229159196571897 + 0.03893354657318122 0.1184279864797626 0.3230302376303228 + 0.03822709158905624 0.117534655174859 0.3245663655633323 From 6edd306ebdf859de41ed7f62c18d427ed8f959b6 Mon Sep 17 00:00:00 2001 From: Siivola Eero Date: Tue, 7 Jun 2016 15:38:50 +0300 Subject: [PATCH 64/64] Added script (update_test_references) to update specific test reference files. Furthermore, updated all reference files since the ones updated in the commit (3f2a5b9c8ef937610dd5327a11bd426de831063b) were cretated without setting randomstream to 0. --- test_gpstuff/octave/realValues_binomial1.mat | 2402 +-- test_gpstuff/octave/realValues_classific.mat | 1602 +- .../octave/realValues_derivativeobs.mat | 970 +- test_gpstuff/octave/realValues_multinom.mat | 1446 +- .../octave/realValues_neuralnetcov.mat | 1602 +- test_gpstuff/octave/realValues_periodic.mat | 2 +- .../octave/realValues_regression1.mat | 16430 ++++++++-------- .../realValues_regression_additive1.mat | 6754 +++---- .../octave/realValues_regression_hier.mat | 62 +- .../octave/realValues_regression_sparse1.mat | 13692 ++++++------- .../octave/realValues_survival_aft.mat | 914 +- test_gpstuff/update_test_references.m | 34 + 12 files changed, 22972 insertions(+), 22938 deletions(-) create mode 100644 test_gpstuff/update_test_references.m diff --git a/test_gpstuff/octave/realValues_binomial1.mat b/test_gpstuff/octave/realValues_binomial1.mat index c9a8dbe3..2e04b934 100644 --- a/test_gpstuff/octave/realValues_binomial1.mat +++ b/test_gpstuff/octave/realValues_binomial1.mat @@ -1,1219 +1,1219 @@ -# Created by Octave 3.8.0, Fri Mar 07 16:08:41 2014 EET +# Created by Octave 3.8.1, Tue Jun 07 13:29:24 2016 EEST # name: Eyt_la # type: matrix # rows: 400 # columns: 1 - 44.20206073986993 - 46.44794086381489 - 58.32213514398789 - 44.82633934813244 - 70.72275172704173 - 56.25046780139917 - 58.92446433160838 - 65.31181124605307 - 51.88330812876021 - 55.63402111958408 - 58.59966653926348 - 60.04255569296325 - 51.98706751658647 - 62.08187905448571 - 45.654592390778 - 47.21500908718283 - 59.74629358329793 - 48.58799992648604 - 64.46386583822108 - 52.6025184529695 - 47.76106441614149 - 51.59011757468211 - 46.76657708714484 - 66.61956491719758 - 50.81367355215274 - 64.89225301406434 - 52.13990217595008 - 44.70521184015787 - 57.83451032449464 - 54.35715236309009 - 73.89104555542264 - 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0.001945064880032987 0.000869290684839541 0.0004688680662390254 0.005162925933905882 0.0007304990550167645 0.0006654289156742266 0.0007769050287862456 0.0004936492349543187 0.0008461710238947262 0.0001141614986011064 0.0001919320466043928 0.0003526301434035872 0.0003571508179120997 0.000741570009342496 0.00085249160625267 0.001450909872797723 0.0008521167842125976 # name: Varft_ep diff --git a/test_gpstuff/octave/realValues_derivativeobs.mat b/test_gpstuff/octave/realValues_derivativeobs.mat index 5da9b3b3..e8a81522 100644 --- a/test_gpstuff/octave/realValues_derivativeobs.mat +++ b/test_gpstuff/octave/realValues_derivativeobs.mat @@ -1,509 +1,509 @@ -# Created by Octave 3.8.1, Mon Jun 06 11:39:28 2016 EEST +# Created by Octave 3.8.1, Tue Jun 07 13:32:52 2016 EEST # name: Eft # type: matrix # rows: 121 # columns: 1 - -0.01665808448074832 - -0.02119597170738272 - -0.02663862173736712 - -0.03306736573058265 - -0.04054301235006359 - -0.0490975672172759 - -0.05872597982602629 - 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a/test_gpstuff/octave/realValues_regression_hier.mat +++ b/test_gpstuff/octave/realValues_regression_hier.mat @@ -1,37 +1,37 @@ -# Created by Octave 3.8.0, Mon Mar 10 10:15:48 2014 EET +# Created by Octave 3.8.1, Tue Jun 07 13:55:23 2016 EEST # name: Eff # type: matrix # rows: 30 # columns: 5 - 151.1549780644712 199.569618707705 242.188694649426 287.0096739153062 319.510473075381 - 143.8537466919393 199.8366369084666 250.2030154880229 302.7575135355449 342.7842583114276 - 157.3275463157208 209.0764882855522 255.0234934425549 303.0761009203998 338.6339444492094 - 154.9765400272429 197.8111762873663 234.7344039987359 273.8875513316054 300.8905724423598 - 134.965642596373 187.1690484040498 233.7231217064119 282.5283811272264 318.9606154013592 - 159.3407603983855 208.7104456723482 252.2647186122979 297.9760971205893 331.3012300505898 - 140.6533604875376 188.9861303478035 231.5715124214733 276.409580395914 308.9763500925989 - 155.6591060119099 205.1908129444658 248.9344595304981 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a/test_gpstuff/octave/realValues_regression_sparse1.mat +++ b/test_gpstuff/octave/realValues_regression_sparse1.mat @@ -1,6876 +1,6876 @@ -# Created by Octave 3.8.0, Fri Mar 07 15:40:06 2014 EET +# Created by Octave 3.8.1, Tue Jun 07 13:57:00 2016 EEST # name: Eft_fic # type: matrix # rows: 1369 # columns: 1 - -0.633114037974792 - -0.7723524745223329 - -0.9233066040805206 - -1.081723031420471 - -1.24183447753576 - -1.396443795401604 - -1.537165349768856 - -1.654836856845313 - -1.740096148773463 - -1.784094672329889 - -1.77929556544919 - -1.720282521455624 - -1.60449041081941 - -1.432763544767523 - -1.209655187207468 - -0.9434032718034226 - -0.6455507876060105 - -0.3302211456359369 - -0.01310330679101527 - 0.2897582182004051 - 0.563243807265685 - 0.7943713131216869 - 0.9733076695860766 - 1.094070046850721 - 1.154845864421574 - 1.157908973622078 - 1.109159177874954 - 1.01735646426968 - 0.8931535675014468 - 0.7480467238246523 - 0.5933634769365447 - 0.4393897926580748 - 0.2947104748551971 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0.07071835237906722 0.1654284715601424 0.3572988835268792 + 0.06767134338337338 0.1616101538378398 0.3539113633494149 + 0.0658653055228533 0.1594901236680711 0.351755205195146 + 0.06294247284733875 0.1551543912900627 0.3470275012697289 + 0.06043861786002882 0.1513004060223987 0.3413396680809865 + 0.05609819107237627 0.1450528535057662 0.3354989337224782 + 0.05227337922269128 0.1401112624206844 0.3325358403034011 + 0.05096116304432959 0.1385805198907011 0.3323655284416129 + 0.05084307080388557 0.1382170866752817 0.3321989470839741 + 0.0424668484480567 0.1251468261645505 0.3208173139525312 + 0.03803070838174732 0.1196997855810452 0.3207148384351949 + 0.03753429033217082 0.118837722543698 0.3201837501789481 diff --git a/test_gpstuff/update_test_references.m b/test_gpstuff/update_test_references.m new file mode 100644 index 00000000..c1093c3c --- /dev/null +++ b/test_gpstuff/update_test_references.m @@ -0,0 +1,34 @@ +% Run selected demos, compare results from demos to old results and save +% any error messages. + +demos={'demo_binomial1', ... + 'demo_classific', 'demo_derivativeobs', ... + 'demo_multinom', 'demo_neuralnetcov', ... + 'demo_periodic', 'demo_regression1', 'demo_regression_additive1', ... + 'demo_regression_hier', 'demo_regression_sparse1', 'demo_survival_aft'}; + +iter=0; +failed=0; +path=strrep(which('run_tests'), 'run_tests.m', ''); +for ii=1:length(demos) + iter=iter+1; + setrandstream(0); + if exist('OCTAVE_VERSION','builtin') + values.real=load([path 'octave/realValues_' strrep(demos{ii}, 'demo_', '')]); + else + values.real=load([path 'matlab/realValues_' strrep(demos{ii}, 'demo_', '')]); + end + field=fieldnames(values.real); + fprintf('\nRunning demo: %s\n\n', demos{ii}); + try + eval(demos{ii}); + delete([path 'realValues_' strrep(demos{ii}, 'demo_', '') '.mat']) + for jj=1:length(field) + save('-append', [path 'realValues_' strrep(demos{ii}, 'demo_', '') '.mat'], field{jj}) + end + catch err + fprintf('Failed %s\n s%', demos{ii}, err) + end + close all; +end +fprintf('Results saved to %s.\n', path);