Source

ITE / code / H_I_D / base_estimators / HqRenyi_CDSS_estimation.m

function [H] = HqRenyi_CDSS_estimation(Y,co)
%Estimates the quadratic Renyi entropy (H) of Y (Y(:,t) is the t^th sample) based on continuously differentiable sample spacing. 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: Umut Ozertem, Ismail Uysal, and Deniz Erdogmus. Continuously differentiable sample-spacing entropy estimation. IEEE Transactions on Neural Networks, 19:1978-1984, 2008.
%
%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 = size(Y,1);

%verification:
    if d~=1
        error('The samples must be one-dimensional for this estimator.');
    end

Y_sorted = sort(Y);
H = compute_CDSS(Y_sorted);
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