# ITE / code / H_I_D / base_estimators / IHSIC_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 40 function [I] = IHSIC_estimation(Y,ds,co) %Estimates mutual information (I) using the HSIC (Hilbert-Schmidt independence criterion) method. % %INPUT: % Y: Y(:,t) is the t^th sample. % ds: subspace dimensions. % co: initialized mutual information estimator object. %REFERENCE: % Arthur Gretton, Olivier Bousquet, Alexander Smola and Bernhard Schölkopf: Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005, 63-78. % %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 . %co.mult:OK. %initialization: num_of_samples = size(Y,2); num_of_comps = length(ds); 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 (M=number of subspaces). Gs = {}; %Gs: for k = 1 : num_of_comps ind = [cum_ds(k):cum_ds(k)+ds(k)-1]; %Cholesky decomposition: [G,p] = chol_gauss(Y(ind,:),co.sigma,num_of_samples*co.eta); [temp,p] = sort(p); %p:=inverse of p Gs{k} = G(p,:); end I = hsicChol(Gs,num_of_samples,num_of_comps);