ITE / code / H_I_D_A_C / meta_estimators / ITsallis_DTsallis_estimation.m

function [I] = ITsallis_DTsallis_estimation(Y,ds,co)
%Estimates Tsallis mutual information (I) based on Tsallis divergence. The estimation is carried out using the formula: 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.
%   2)This is a meta method: the Tsallis divergence estimator can be arbitrary.
%   Y: Y(:,t) is the t^th sample.
%  ds: subspace dimensions.
%  co: mutual information estimator object.
%   Barnabas Poczos, Zoltan Szabo, Jeff Schneider. Nonparametric divergence estimators for Independent Subspace Analysis. European Signal Processing Conference (EUSIPCO), pages 1849-1853, 2011.
%   Barnabas Poczos, Jeff Schneider. On the Estimation of alpha-Divergences. International conference on Artificial Intelligence and Statistics (AISTATS), pages 609-617, 2011.
%Copyright (C) 2012 Zoltan Szabo ("", "szzoli (at) cs (dot) elte (dot) hu")
%This file is part of the ITE (Information Theoretical Estimators) Matlab/Octave toolbox.
%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 <>.


    if sum(ds) ~= size(Y,1);
        error('The subspace dimensions are not compatible with Y.');

[Y1,Y2] = div_sample_generation(Y,ds);
I = D_estimation(Y1,Y2,co.member_co);
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