ITE / code / H_I_D_A_C / base_estimators / HRenyi_MST_initialization.m

function [co] = HRenyi_MST_initialization(mult)
%Initialization of the minimum spanning tree (MST) based Renyi entropy estimator.
%   1)The estimator is treated as a cost object (co).
%   2)We use the naming convention 'H<name>_initialization' to ease embedding new entropy estimation methods.
%   mult: is a multiplicative constant relevant (needed) in the estimation; '=1' means yes, '=0' no.
%   co: cost object (structure).
%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 <>.

%mandatory fields: = 'Renyi_MST';
    co.mult = mult;
%other fields:
    co.alpha = 0.99; %alpha \ne 1. The Renyi entropy (H_{R,alpha}) equals to the Shannon differential entropy (H) in limit: H_{R,alpha} -> H, as alpha -> 1.
    %Possibilites for the MST (minimum spanning tree) method:
        co.MSTmethod = 'MatlabBGL_Prim';
        %co.MSTmethod = 'MatlabBGL_Kruskal';
        %co.MSTmethod = 'pmtk3_Prim';
        %co.MSTmethod = 'pmtk3_Kruskal';    
    co.additive_constant_is_relevant = 0; %1:additive constant is relevant (you can precompute it via 'estimate_HRenyi_constant.m'), 0:not relevant