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+4 0CHANGELOG.txt

+1 1README.md

+44 0code/H_I_D_A_C/base_estimators/DsymBregman_kNN_k_estimation.m

+52 0code/H_I_D_A_C/base_estimators/DsymBregman_kNN_k_initialization.m

+38 0code/H_I_D_A_C/meta_estimators/DsymBregman_DBregman_estimation.m

+37 0code/H_I_D_A_C/meta_estimators/DsymBregman_DBregman_initialization.m

+2 4doc/ITE_documentation.txt
CHANGELOG.txt
+Symmetric Bregman distance estimation based on nonsymmetric Bregman distance: added; see 'DsymBregman_DBregman_initialization.m', 'DsymBregman_DBregman_estimation.m'.
+Symmetric Bregman distance estimation based on knearest neighbors: added; see 'DsymBregman_kNN_k_initialization.m', 'DsymBregman_kNN_k_estimation.m'.
JensenTsallis divergence estimation: added; see 'DJensenTsallis_HTsallis_initialization.m' and 'DJensenTsallis_HTsallis_estimation.m'.
Bregman distance estimation: added; see 'DBregman_kNN_k_initialization.m' and 'DBregman_kNN_k_estimation.m'.
README.md
 `entropy (H)`: Shannon entropy, Rényi entropy, Tsallis entropy (Havrda and Charvát entropy), complex entropy,
 `mutual information (I)`: generalized variance, kernel canonical correlation analysis, kernel generalized variance, HilbertSchmidt independence criterion, Shannon mutual information, L2 mutual information, Rényi 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,
 `divergence (D)`: KullbackLeibler divergence (relative entropy, I directed divergence), L2 divergence, Rényi 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, JensenRényi divergence, K divergence, L divergence, certain fdivergences (CsiszárMorimoto divergence, AliSilvey distance), nonsymmetric Bregman distance (Bregman divergence), JensenTsallis divergence,
+ `divergence (D)`: KullbackLeibler divergence (relative entropy, I directed divergence), L2 divergence, Rényi 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, JensenRényi divergence, K divergence, L divergence, certain fdivergences (CsiszárMorimoto divergence, AliSilvey distance), nonsymmetric Bregman distance (Bregman divergence), JensenTsallis divergence, symmetric Bregman 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/H_I_D_A_C/base_estimators/DsymBregman_kNN_k_estimation.m
+%We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
+% Y2: Y2(:,t) is the t^th sample from the second distribution. Note: the number of samples in Y1 [=size(Y1,2)] and Y2 [=size(Y2,2)] can be different.
+% Nikolai Leonenko, Luc Pronzato, and Vippal Savani. A class of Renyi information estimators for multidimensional densities. Annals of Statistics, 36(5):21532182, 2008.
+% Imre Csiszar. Generalized projections for nonnegative functions. Acta Mathematica Hungarica, 68:161185, 1995.
+% Lev M. Bregman. The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Mathematical Physics, 7:200217, 1967.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/DsymBregman_kNN_k_initialization.m
+% 2)We use the naming convention 'D<name>_initialization' to ease embedding new divergence estimation methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
+ %III: 'knnsearch' (Matlab Statistics Toolbox): parameters: co.k, co.NSmethod ('kdtree' or 'exhaustive').
+ %co.epsi = 0; %=0: exact kNN; >0: approximate kNN, the true (not squared) distances can not exceed the real distance more than a factor of (1+epsi).
code/H_I_D_A_C/meta_estimators/DsymBregman_DBregman_estimation.m
+%Estimates the symmetric Bregman distance (D) of Y1 and Y2 using the relation: D_S = (D_NS(f1,f2) + D_NS (f2,f1)) / alpha, where D_NS is the nonsymmetric Bregman distance.
+% 1)We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
+% Y2: Y2(:,t) is the t^th sample from the second distribution (f2). Note: the number of samples in Y1 [=size(Y1,2)] and Y2 [=size(Y2,2)] can be different.
+% Nikolai Leonenko, Luc Pronzato, and Vippal Savani. A class of Renyi information estimators for multidimensional densities. Annals of Statistics, 36(5):21532182, 2008.
+% Imre Csiszar. Generalized projections for nonnegative functions. Acta Mathematica Hungarica, 68:161185, 1995.
+% Lev M. Bregman. The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Mathematical Physics, 7:200217, 1967.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/meta_estimators/DsymBregman_DBregman_initialization.m
+%Initialization of the symmetric Bregman distance estimator using the relation: D_S = (D_NS(f1,f2) + D_NS (f2,f1)) / alpha, where D_NS is the nonsymmetric Bregman distance.
+% 2)We use the naming convention 'D<name>_initialization' to ease embedding new divergence estimation methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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.member_name = 'Bregman_kNN_k'; %you can change it to any (nonsymmetric) Bregman distance estimator
+ co.member_co.alpha = co.alpha; %automatism for setting the parameters (co.alpha) of member_co (co_2) in a <=2 deep meta construction (co_1 > co_2); otherwise, please set the parameters (co.alpha) in the member_cos.
doc/ITE_documentation.txt
+From v0.20, the documentation of a given release is available at 'https://bitbucket.org/szzoli/ite/downloads': Downloads tab: 'ITE<release>_documentation.pdf'.