ITE / code / H_I_D / base_estimators / HRenyi_kNN_S_estimation.m

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function [H] = HRenyi_kNN_S_estimation(Y,co)
%Estimates the Renyi entropy (H) of Y (Y(:,t) is the t^th sample) using the generalized k-nearest neighbor (S\subseteq {1,...,k}) method. 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.
%   David Pal, Barnabas Poczos, Csaba Szepesvari: Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs. NIPS-2010, pages 1849-1857.
%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);%dimension, number of samples

%length (L):
    L = compute_length_HRenyi_kNN_S(Y,co);

    if co.additive_constant_is_relevant %the additive constant is relevant in the Renyi entropy estimation
        FN = filename_of_HRenyi_constant(d,co);
        if exist(FN)
            H = log( L / (constant*num_of_samples^co.alpha) ) / (1-co.alpha);
            error('The file containing the additive constant does not exist. You can precompute the additive constant via estimate_HRenyi_constant.m.');
    else %estimation up to an additive constant
        H = log( L / num_of_samples^co.alpha ) / (1-co.alpha);