function [D_J] = DJdistance_estimation(Y1,Y2,co) %Estimates the J-distance of Y1 and Y2 using the relation: D_J(f_1,f_2) = D(f_1,f_2)+D(f_2,f_1), where D denotes the Kullback-Leibler divergence. % %Note: % 1)We use the naming convention 'D_estimation' to ease embedding new divergence estimation methods. % 2)This is a meta method: the Kullback-Leibler divergence estimator can be arbitrary. % %INPUT: % 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 ("http://nipg.inf.elte.hu/szzoli", "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 . %co.mult:OK. %verification: if size(Y1,1)~=size(Y2,1) error('The dimension of the samples in Y1 and Y2 must be equal.'); end D_J = D_estimation(Y1,Y2,co.member_co) + D_estimation(Y2,Y1,co.member_co);