ITE / code / H_I_D / base_estimators / HRenyi_CDSS_estimation.m

function [H] = HRenyi_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 ("", "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 = size(Y,1);
if d~=1
    disp('Error: samples must be one-dimensional for this estimator.');
    Y_sorted = sort(Y);
    H = compute_CDSS(Y_sorted);