# ITE / code / H_I_D / meta_estimators / IShannon_HShannon_estimation.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 36 37 38 39 function [I] = IShannon_HShannon_estimation(Y,ds,co) %Estimates Shannon mutual information (I) making use of an estimator for Shannon differential entropy; co is the cost object. %This is a "meta" method, using the relation: I(y^1,...,y^M) = \sum_{m=1}^MH(y^m) - H([y^1,...,y^M]). % %We make use of the naming convention 'I_estimation', to ease embedding new mutual information estimation methods. % %INPUT: % Y: Y(:,t) is the t^th sample. % ds: subspace dimensions. % co: mutual information estimator object. %REFERENCE: % Thomas M. Cover, Joy A. Thomas. Elements of Information Theory, John Wiley and Sons, New York, USA (1991). % %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 . %initialization: num_of_comps = length(ds);%number of subspaces=:M cum_ds = cumsum([1;ds(1:end-1)]);%1,d_1+1,d_1+d_2+1,...,d_1+...+d_{M-1}+1 = starting indices of the subspaces %I = - H([y^1,...,y^M]): I = -H_estimation(Y,co.member_co); %I = I + \sum_{m=1}^MH(y^m): for k = 1 : num_of_comps idx = [cum_ds(k) : cum_ds(k)+ds(k)-1]; I = I + H_estimation(Y(idx,:),co.member_co); end 
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