% Implementation of the Kullback-Leibler Divergence to use with pdist
% (cf. "The Earth Movers' Distance as a Metric for Image Retrieval",
% Y. Rubner, C. Tomasi, L.J. Guibas, 2000)
% @author: B. Schauerte
% @date: 2009
% @url: http://cvhci.anthropomatik.kit.edu/~bschauer/
% Copyright 2009 B. Schauerte. All rights reserved.
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% modification, are permitted provided that the following conditions are
% 1. Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% 2. Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the
% THIS SOFTWARE IS PROVIDED BY B. SCHAUERTE ''AS IS'' AND ANY EXPRESS OR
% IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
% WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
% DISCLAIMED. IN NO EVENT SHALL B. SCHAUERTE OR CONTRIBUTORS BE LIABLE
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% official policies, either expressed or implied, of B. Schauerte.
m=size(XJ,1); % number of samples of p
p=size(XI,2); % dimension of samples
assert(p == size(XJ,2)); % equal dimensions
assert(size(XI,1) == 1); % pdist requires XI to be a single sample
d=zeros(m,1); % initialize output array
%d(i,1) = d(i,1) + (XJ(i,j) * log(XJ(i,j) / XI(1,j))); % XI is the model!
if XI(1,j) ~= 0
d(i,1) = d(i,1) + (XI(1,j) * log(XI(1,j) / XJ(i,j))); % XJ is the model! makes it possible to determine each "likelihood" that XI was drawn from each of the models in XJ