[f07ddc]: inst / mvn_div_js.m  Maximize  Restore  History

 ``` 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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55``` ```function d = mvn_div_js(m1, m2, use_kl) %MVN_DIV_JS Compute the Jensen-Shannon (JS) divergence between two multivariate normals. % % [d] = MVN_DIV_JS(m1, m2) computes the JS divergence between two % multivariate normals. d is never negative. % % The JS divergence is defined as: % d = 0.5*KL(m1, m1+m2) + 0.5*KL(m2, m1+m2) % (c) 2010-2011, Dominik Schnitzer, % http://www.ofai.at/~dominik.schnitzer/mvn % % This file is part of the MVN Octave/Matlab Toolbox % MVN 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. % % MVN 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 MVN. If not, see . % Speedup: % % m12 = mvn_bregmancentroid_kl_left([m1 m2]); % % using Cholesky & more Optimization m12.m = 0.5*m1.m + 0.5*m2.m; m12.cov = 0.5*(m1.cov + m1.m*m1.m') + 0.5*(m2.cov + m2.m*m2.m') ... - m12.m*m12.m'; m12_chol = chol(m12.cov); m12.logdet = 2*sum(log(diag(m12_chol))); if ((nargin > 2) && (use_kl == 1)) m12_ui = m12_chol\eye(length(m12.m)); m12.icov = m12_ui*m12_ui'; d = 0.5*mvn_div_kl(m1, m12) + 0.5*mvn_div_kl(m2, m12); else % Speedup original (entropy): % % d = mvn_entropy(m12) - 0.5*mvn_entropy(m1) - 0.5*mvn_entropy(m2); % % faster: d = 0.5*m12.logdet - 0.25*m1.logdet - 0.25*m2.logdet; end d = max(d, 0); end ```