ITE / code / H_I_D_A_C / base_estimators / DHellinger_kNN_k_estimation.m

function [D] = DHellinger_kNN_k_estimation(Y1,Y2,co)
%Estimates the Hellinger distance of Y1 and Y2 using the kNN method (S={k}).
%We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
%  Y1: Y1(:,t) is the t^th sample from the first distribution.
%  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.
%  co: divergence estimator object.
%   Barnabas Poczos and Liang Xiong and Dougal Sutherland and Jeff Schneider. Support Distribution Machines. Technical Report, 2012. "" (estimation)
%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.');

%D_ab (Bhattacharyya coefficient):
	if co.p %[p(x)dx]
		D_ab = estimate_Dtemp2(Y1,Y2,co);
	else %[q(x)dx]
		D_ab = estimate_Dtemp2(Y2,Y1,co);

%D = sqrt(1-D_ab);%theoretically
D = sqrt(abs(1-D_ab));%abs() to avoid possible 'sqrt(negative)' values due to the finite number of samples