ITE / code / H_I_D_A_C / meta_estimators / DKL_CCE_HShannon_estimation.m

function [D] = DKL_CCE_HShannon_estimation(Y1,Y2,co)
%Estimates the Kullback-Leibler divergence (D) using the relation: D(f_1,f_2) = CE(f_1,f_2) - H(f_1). Here D denotes the Kullback-Leibler divergence, CE stands for cross-entropy and H is the Shannon differential entropy. 
%   1)We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
%   2)This is a meta method: the cross-entropy and Shannon entropy estimators can be arbitrary.
%  Y1: Y1(:,t) is the t^th sample from the first distribution.
%  Y2: Y2(:,t) is the t^th sample from the second distribution.
%  co: divergence estimator object.
%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 size(Y1,1)~=size(Y2,1)
        error('The dimension of the samples in Y1 and Y2 must be equal.');

CE = C_estimation(Y1,Y2,co.CE_member_co);
H = H_estimation(Y1,co.H_member_co);
D =  CE - H;