Source

ITE / code / H_I_D / base_estimators / HShannon_spacing_LL_estimation.m

function [H] = HShannon_spacing_LL_estimation(Y,co)
%Estimates the Shannon entropy (H) of Y (Y(:,t) is the t^th sample) using Correa's spacing method (locally linear regression). 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. 
%
%REFERENCE:
%    	Juan C. Correa. A new estimator of entropy. Communications in Statistics - Theory and Methods, Volume 24, Issue 10, pp. 2439-2449, 1995.  
%      
%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
    m = floor(sqrt(num_of_samples));%m/num_of_samples->0, m,num_of_samples->infty; m<num_of_samples/2
    Y_sorted = sort(Y);
    Y_sorted = [repmat(Y_sorted(1),1,m),Y_sorted,repmat(Y_sorted(end),1,m)];
    b = colfilt(Y_sorted,[1,2*m+1],'sliding',@locally_linear_regression);
    b = b(m+1:end-m);%discard the superfluous values
    H = -mean(log(b/num_of_samples));
end