ITE / code / H_I_D / base_estimators / HRenyi_kNN_1tok_estimation.m

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function [H] = HRenyi_kNN_1tok_estimation(Y,co)
%Estimates the Renyi entropy (H) of Y (Y(:,t) is the t^th sample)
%using the kNN method (S={1,...,k}). Cost parameters are provided in the cost object co.
%We make use of the naming convention 'H<name>_estimation', to ease embedding new entropy estimation methods.
%   Barnabas Poczos, Andras Lorincz. Independent Subspace Analysis Using k-Nearest Neighborhood Estimates. ICANN-2005, pages 163-168.
%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 <>.

[d,num_of_samples] = size(Y);
squared_distances = kNN_squared_distances(Y,Y,co,1);
gam = d * (1-co.alpha);


%estimation up to an additive constant:
    L = sum(sum(squared_distances.^(gam/2),1));
    H = log( L / num_of_samples^co.alpha ) / (1-co.alpha);