ITE / code / H_I_D / base_estimators / HRenyi_MST_initialization.m

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35``` ```function [co] = HRenyi_MST_initialization(mult) %Initialization of the minimum spanning tree (MST) based Renyi entropy estimator. % %Note: % 1)The estimator is treated as a cost object (co). % 2)We make use of the naming convention 'H_initialization', to ease embedding new entropy estimation methods. % %INPUT: % mult: is a multiplicative constant relevant (needed) in the estimation; '=1' means yes, '=0' no. %OUTPUT: % co: cost object (structure). % %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 . %mandatory fields: co.name = 'Renyi_MST'; co.mult = mult; %other fields: co.alpha = 0.99; %The Renyi entropy equals to the Shannon differential entropy, in limit, i.e., Renyi=H_{R,alpha} -> Shannon=H, provided that 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'; ```