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ITE / code / H_I_D_C / base_estimators / DRenyi_kNN_k_estimation.m

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function [D] = DRenyi_kNN_k_estimation(Y1,Y2,co)
%Estimates the Renyi divergence (D) of Y1 and Y2 (Y1(:,t), Y2(:,t) is the t^th sample)
%using the kNN method (S={k}). The number of samples in Y1 [=size(Y1,2)] and Y2 [=size(Y2,2)] can be different. 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: 
%   Barnabas Poczos, Zoltan Szabo, Jeff Schneider: Nonparametric divergence estimators for Independent Subspace Analysis. EUSIPCO-2011, pages 1849-1853.
%   Barnabas Poczos, Jeff Schneider: On the Estimation of alpha-Divergences. AISTATS-2011, pages 609-617.
%   Barnabas Poczos, Liang Xiong, Jeff Schneider. Nonparametric Divergence: Estimation with Applications to Machine Learning on Distributions. UAI-2011.
%
%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 <http://www.gnu.org/licenses/>.

%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

Dtemp1 = estimate_Dtemp1(Y1,Y2,co);
D = log(Dtemp1) / (co.alpha-1);