function dist = codedist_bay(C1,C2,Py)% compute the distance using a Bayesian metric between 'C1' and  'C2'%%    dist = codedist_bay(C1,C2,{Py})%% 'C1' contains the result code, % 'C2' contains the codebooks prototype. '0' or  an infine small number 'eps'  represents the don't care% 'Py' is a matrix containing the moderated outputs of the binary classifiers%%  % An example:%  >> Ye = [1 1; 1 1];%  >> codebook = [1 2 3];%  >> old_codebook = [1 1 -1; 1 -1 -1];%  >> Py = [.9 -.1; -.1 .13];%  >> code(Ye, codebook, [],old_codebook,'codedist_bay',{Py})% in this call, 'Ye' is not used explicitly.%% To use this distance measure in LS-SVMlab, the following% procedure is to be followed, assume input data 'X' and multiclass% output 'Y':%%  >> [Ycode,codebook,old_codebook] = code(Y,'code_MOC');%  >> [alpha,b] = trainlssvm({X,Yc,'c',gam,sig2});%  >> Yhc = simlssvm({X,Yc,'c',gam,sig2},{alpha,b},Xt);%%  The moderated output for the LS-SVM can be computed using the bayesian inference%  framework for the LS-SVM: %  >> Ymod = bay_modoutClass(model,Xt);%  >> Yh = code(Yhc,old_codebook,[],codebook,'codedist_bay',{Ymod});%  % see also:%   bay_modoutClass, codedist_hamming, code_ECOC% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab% % encode for training% >> model = initlssvm(X,Y,'classification',gam,sig2,'preprocess','RBF_kernel');% >> model = changelssvm(model,'codetype','code_MOC');% >> model = changelssvm(model,'codedist_fct','codedist_hamming');% >> model = trainlssvm(model);% % % decode for simulating% >> model = changelssvm(model,'codedist_fct','codedist_bay');% >> model = changelssvm(model,'codedist_args',{bay_modoutClass(model,Xt)});% >>   Yt  = simlssvm(model,Xt);if nargin<3,  error(['moderated output needed as function arguments' ...	 '(for LS-SVM, model.codedist_args =' ...	 ' bay_modoutClass(model,X)).']);end[nb,nbin] = size(Py);[nbin,dim] = size(C2);dist = zeros(nb,dim);for d = 1:dim,  for n= 1:nb,    dist(n,d) = sum((1-Py(n,:).*C2(:,d)'))-sum(C2(:,d)==eps);  endend