ITE / code / H_I_D / base_estimators / HShannon_spacing_Vb_estimation.m

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function [H] = HShannon_spacing_Vb_estimation(Y,co)
%Estimates the Shannon entropy (H) of Y (Y(:,t) is the t^th sample) using Vasicek's spacing method with a bias 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.
%   Bert Van Es. Estimating Functionals Related to a Density by a Class of Statistics Based on Spacings. Scandinavian Journal of Statistics, 19:61-72, 1992.
%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);
if d~=1
    disp('Error: samples must be one-dimensional for this estimator.');
    m = floor(sqrt(num_of_samples));%m/num_of_samples->0, m,num_of_samples->infty; m: can also be fixed
    Y_sorted = sort(Y);
    diffs = Y_sorted(1+m:num_of_samples) - Y_sorted(1:num_of_samples-m);
    b = sum(1./[m:num_of_samples]) + log(m/(num_of_samples+1));%bias correction
    H = mean(log((num_of_samples+1)/m*diffs)) + b;