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ITE / code / H_I_D / base_estimators / HShannon_spacing_Vpconst_estimation.m

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function [H] = HShannon_spacing_Vpconst_estimation(Y,co)
%Estimates the Shannon entropy (H) of Y (Y(:,t) is the t^th sample) using Vasicek's spacing method with piecewise constant correction. 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.
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%REFERENCE: Nader Ebrahimi, Kurt Pflughoeft, and Ehsan S. Soofi. Two measures of sample entropy. Statistics and Probability Letters, 20:225-234, 1994.
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%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/>.

[d,num_of_samples] = size(Y);
if d~=1
    disp('Error: samples must be one-dimensional for this estimator.');
else
    %as a base estimator:
        m = floor(sqrt(num_of_samples));%m/num_of_samples->0, m,num_of_samples->infty
        Y_sorted = sort(Y);
        Y_sorted = [repmat(Y_sorted(1),1,m),Y_sorted,repmat(Y_sorted(end),1,m)];
        diffs = Y_sorted(2*m+1:num_of_samples+2*m) - Y_sorted(1:num_of_samples);
        c = [ones(1,m),2*ones(1,num_of_samples-2*m),ones(1,m)]; %piecewise constant correction
        H = mean(log (num_of_samples / m * diffs./c));
    %as a meta estimator:
        %HV = H_estimation(Y,co.member_co);
        %H = HV + 2*m/num_of_samples * log(2);
end