ITE / code / H_I_D_C / base_estimators / DCS_KDE_iChol_estimation.m

function [D] = DCS_KDE_iChol_estimation(Y1,Y2,co)
%Estimates the Cauchy-Schwartz divergence using Gaussian KDE (kernel density estimation) and incomplete Cholesky decomposition. The number of samples in Y1 [=size(Y1,2)] and Y2 [=size(Y2,2)] must be equal; otherwise their minimum is taken. Cost parameters are provided in the cost object co.
%We make use of the naming convention 'D<name>_estimation', to ease embedding new divergence estimation methods.
%REFERENCE: Sohan Seth and Jose C. Principe. On speeding up computation in information theoretic learning. In International Joint Conference on Neural Networks (IJCNN), pages 2883-2887, 2009.
%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 <>.


    [dY1,num_of_samplesY1] = size(Y1);
    [dY2,num_of_samplesY2] = size(Y2);

    if dY1~=dY2
        error('The dimension of the samples in Y1 and Y2 must be equal.');

    if num_of_samplesY1~=num_of_samplesY2
        warning('There must be equal number of samples in Y1 and Y2 for this estimator. Minimum of the sample numbers has been taken.');
        num_of_samples = min(num_of_samplesY1,num_of_samplesY2);
        Y1 = Y1(:,1:num_of_samples);
        Y2 = Y2(:,1:num_of_samples);

D = d_cs(Y1.',Y2.',co.sigma);