 function [H] = HRenyi_MST_estimation(Y,co)
%Estimates the Renyi entropy (H) of Y (Y(:,t) is the t^th sample)
%using the minimum spanning tree (MST). 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:
% Joseph E. Yukich. Probability Theory of Classical Euclidean Optimization Problems, Lecture Notes in Mathematics, 1998, vol. 1675.
%
%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/>.
%co.mult:OK.
[d,num_of_samples] = size(Y);%dimension, number of samples
%length (L):
L = compute_length_HRenyi_MST(Y,co);
%estimation:
if co.additive_constant_is_relevant %the additive constant is relevant in the Renyi entropy estimation
FN = filename_of_HRenyi_constant(d,co);
if exist(FN)
load(FN,'constant');
H = log( L / (constant*num_of_samples^co.alpha) ) / (1co.alpha);
else
disp('Error: the file containing the additive constant does not exist. You can precompute the additive constant via estimate_HRenyi_constant.m.');
end
else %estimation up to an additive constant
H = log( L / num_of_samples^co.alpha ) / (1co.alpha);
end
