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+5 0CHANGELOG.txt

+4 4README.md

+1 1code/estimators/base_estimators/DChiSquare_kNN_k_estimation.m

+1 1code/estimators/base_estimators/DChiSquare_kNN_k_initialization.m

+2 2code/estimators/base_estimators/DMMD_Ustat_estimation.m

+57 0code/estimators/base_estimators/HShannon_spacing_Vplin2_estimation.m

+29 0code/estimators/base_estimators/HShannon_spacing_Vplin2_initialization.m

+34 0code/estimators/meta_estimators/IChiSquare_DChiSquare_estimation.m

+45 0code/estimators/meta_estimators/IChiSquare_DChiSquare_initialization.m

+1 0code/estimators/quick_tests/quick_test_HShannon.m

+1 0code/estimators/quick_tests/quick_test_Iimreg.m

+1 0code/estimators/quick_tests/quick_test_Iindependence.m
CHANGELOG.txt
+Chisquare mutual information estimation based on Pearson chisquare divergence: added; see 'IChiSquare_DChiSquare_initialization', 'IChiSquare_DChiSquare_estimation.m'.
+Shannon entropy estimation based on an alternative linearly corrected spacing method: added; see 'HShannon_spacing_Vplin2_initialization.m', 'HShannon_spacing_Vplin2_estimation.m'.
+Quick tests updated with the new estimators, see 'quick_test_HShannon.m', 'quick_test_Iimreg.m', 'quick_test_Iindependence.m'.
Phientropy (fentropy) estimation based on the spacing method: added; see 'HPhi_spacing_initialization.m', 'HPhi_spacing_estimation.m'.
Pearson chi square divergence (chi square distance) estimation based on knearest neighbors: added; see 'DChiSquare_kNN_k_initialization.m', 'DChiSquare_kNN_k_estimation.m'.
README.md
 ITE has been accepted at [NIPS2013: MLOSS workshop](http://mloss.org/workshop/nips13/), [PDF](http://www.gatsby.ucl.ac.uk/~szabo/publications/szabo13information.pdf).
+ ITE has been accepted for presentation at [NIPS2013: MLOSS workshop](http://mloss.org/workshop/nips13/), [PDF](http://www.gatsby.ucl.ac.uk/~szabo/publications/szabo13information.pdf).
 `entropy (H)`: Shannon entropy, R�nyi entropy, Tsallis entropy (Havrda and Charv�t entropy), complex entropy, Phientropy (fentropy),
 `mutual information (I)`: generalized variance, kernel canonical correlation analysis, kernel generalized variance, HilbertSchmidt independence criterion, Shannon mutual information (total correlation, multiinformation), L2 mutual information, R�nyi mutual information, Tsallis mutual information, copulabased kernel dependency, multivariate version of Hoeffding's Phi, SchweizerWolff's sigma and kappa, complex mutual information, CauchySchwartz quadratic mutual information, Euclidean distance based quadratic mutual information, distance covariance, distance correlation, approximate correntropy independence measure,
+ `mutual information (I)`: generalized variance, kernel canonical correlation analysis, kernel generalized variance, HilbertSchmidt independence criterion, Shannon mutual information (total correlation, multiinformation), L2 mutual information, R�nyi mutual information, Tsallis mutual information, copulabased kernel dependency, multivariate version of Hoeffding's Phi, SchweizerWolff's sigma and kappa, complex mutual information, CauchySchwartz quadratic mutual information, Euclidean distance based quadratic mutual information, distance covariance, distance correlation, approximate correntropy independence measure, chisquare mutual information (HilbertSchmidt norm of the normalized crosscovariance operator, squaredloss mutual information, mean square contingency),
 `divergence (D)`: KullbackLeibler divergence (relative entropy, I directed divergence), L2 divergence, R�nyi divergence, Tsallis divergence, Hellinger distance, Bhattacharyya distance, maximum mean discrepancy (kernel distance), Jdistance (symmetrised KullbackLeibler divergence, J divergence), CauchySchwartz divergence, Euclidean distance based divergence, energy distance (specially the CramerVon Mises distance), JensenShannon divergence, JensenR�nyi divergence, K divergence, L divergence, certain fdivergences (Csisz�rMorimoto divergence, AliSilvey distance), nonsymmetric Bregman distance (Bregman divergence), JensenTsallis divergence, symmetric Bregman distance, Pearson chi square divergence (chi square distance),
 `association measures (A)`, including `measures of concordance`: multivariate extensions of Spearman's rho (Spearman's rank correlation coefficient, grade correlation coefficient), correntropy, centered correntropy, correntropy coefficient, correntropy induced metric, centered correntropy induced metric, multivariate extension of Blomqvist's beta (medial correlation coefficient), multivariate conditional version of Spearman's rho, lower/upper tail dependence via conditional Spearman's rho,
 code: [zip](https://bitbucket.org/szzoli/ite/downloads/ITE0.46_code.zip), [tar.bz2](https://bitbucket.org/szzoli/ite/downloads/ITE0.46_code.tar.bz2),
+ code: [zip](https://bitbucket.org/szzoli/ite/downloads/ITE0.47_code.zip), [tar.bz2](https://bitbucket.org/szzoli/ite/downloads/ITE0.47_code.tar.bz2),
code/estimators/base_estimators/DChiSquare_kNN_k_estimation.m
%We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
code/estimators/base_estimators/DChiSquare_kNN_k_initialization.m
%Initialization of the kNN (knearest neighbor, S={k}) based Pearson chisquare divergence estimator.
+%Initialization of the kNN (knearest neighbor, S={k}) based Pearson chisquare divergence estimator.
code/estimators/base_estimators/DMMD_Ustat_estimation.m
%Estimates divergence (D) of Y1 and Y2 using the MMD (maximum mean discrepancy) method, applyingVstatistics.
+%Estimates divergence (D) of Y1 and Y2 using the MMD (maximum mean discrepancy) method, applying Ustatistics.
%We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
code/estimators/base_estimators/HShannon_spacing_Vplin2_estimation.m
+%Estimates the Shannon entropy (H) of Y using Vasicek's spacing method with piecewise linear correction2.
+%We use the naming convention 'H<name>_estimation' to ease embedding new entropy estimation methods.
+% Nader Ebrahimi, Kurt Pflughoeft and Ehsan S. Soofi. Two measures of sample entropy. Statistics and Probability Letters, 20(3):225234, 1994.
+%Copyright (C) 2013 Zoltan Szabo ("http://www.gatsby.ucl.ac.uk/~szabo/", "zoltan (dot) szabo (at) gatsby (dot) ucl (dot) ac (dot) uk")
+%ITE 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 software 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 ITE. If not, see <http://www.gnu.org/licenses/>.
+%co.mult:OK. The information theoretical quantity of interest can be (and is!) estimated exactly [co.mult=1]; the computational complexity of the estimation is essentially the same as that of the 'up to multiplicative constant' case [co.mult=0].
+ Y_sorted = [repmat(Y_sorted(1),1,m),Y_sorted,repmat(Y_sorted(end),1,m)];%with the smallest (left) and largest (right) element
+ %Y_sorted = [a + ([1:m]1)/m*(Y_sorted(1)a), Y_sorted, b(num_of_samples[num_of_samplesm:num_of_samples])/m * (bY_sorted(end))]; %a>Y_sorted(1) linearly, Y_sorted, Y_sorted(end)>b linearly
+c = [1+([1:m]1)/m,2*ones(1,num_of_samples2*m),1+(num_of_samples[num_of_samplesm+1:num_of_samples])/m]; %piecewise linear correction2
code/estimators/base_estimators/HShannon_spacing_Vplin2_initialization.m
+%Initialization of the Shannon differential entropy (H) estimator of Vasicek's spacing technique with piecewise linear correction2.
+% 2)We use the naming convention 'H<name>_initialization' to ease embedding new entropy estimation methods.
+%Copyright (C) 2013 Zoltan Szabo ("http://www.gatsby.ucl.ac.uk/~szabo/", "zoltan (dot) szabo (at) gatsby (dot) ucl (dot) ac (dot) uk")
+%ITE 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 software 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 ITE. If not, see <http://www.gnu.org/licenses/>.
+%mandatory fields (following the template structure of the estimators to make uniform usage of the estimators possible):
code/estimators/meta_estimators/IChiSquare_DChiSquare_estimation.m
+%Estimates chisquare mutual information (I) based on Pearson chisquare divergence. The estimation is carried out according to the relation: I(y^1,...,y^M) = D(f_y,\prod_{m=1}^M f_{y^m}).
+% 1)We use the naming convention 'I<name>_estimation' to ease embedding new mutual information estimation methods.
+%Copyright (C) 2013 Zoltan Szabo ("http://www.gatsby.ucl.ac.uk/~szabo/", "zoltan (dot) szabo (at) gatsby (dot) ucl (dot) ac (dot) uk")
+%ITE 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 software 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 ITE. If not, see <http://www.gnu.org/licenses/>.
+%co.mult:OK. The information theoretical quantity of interest can be (and is!) estimated exactly [co.mult=1]; the computational complexity of the estimation is essentially the same as that of the 'up to multiplicative constant' case [co.mult=0].
code/estimators/meta_estimators/IChiSquare_DChiSquare_initialization.m
+%Initialization of the "meta" chisquare mutual information based on Pearson chisquare divergence.
+%Mutual information is estimated using the relation: I(y^1,...,y^M) = D(f_y,\prod_{m=1}^M f_{y^m}).
+% 2)We use the naming convention 'I<name>_initialization' to ease embedding new mutual information estimation methods.
+% post_init: {field_name1,field_value1,field_name2,field_value2,...}; cell array containing the names and the values of the cost object fields that are to be used
+%Copyright (C) 2013 Zoltan Szabo ("http://www.gatsby.ucl.ac.uk/~szabo/", "zoltan (dot) szabo (at) gatsby (dot) ucl (dot) ac (dot) uk")
+%ITE 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 software 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 ITE. If not, see <http://www.gnu.org/licenses/>.
+%mandatory fields (following the template structure of the estimators to make uniform usage of the estimators possible):
+ co.member_name = 'ChiSquare_kNN_k'; %you can change it to any Pearson chisquare divergence estimator