function dist = codedist_loss(C1,C2,Ylat,loss)% compute the distance using a loss function metric between 'C1' and  'C2'%%    dist = codedist_loss(C1,C2,Ylatent)%    dist = codedist_loss(C1,C2,Ylatent, loss_fct)%% 'C1' contains the result code, % 'C2' contains the codebooks prototype. An infine small number 'eps'  represents the don't care% 'loss_fct' is the loss function used in order to compute the loss%            between 2 codewords. By default the sum of squares is used. %            One can do 'winner-takes-all' decoding by using the%            loss function 'max'.%% An example:%  >> Ye = [1 1; 1 1; -1 -1; 1 -1];%  >> codebook = [1 2 3];%  >> old_codebook = [1 1 -1; 1 -1 -1];%  >> code(Ye, codebook, [],old_codebook,'codedist_loss',{'Ylatent','mse'})%% To use this distance measure in LS-SVMlab, the following% procedure is to be followed, assume input data 'X' and multiclass% output 'Y'%% % encode for training% >> model = initlssvm(X,Y,'classification',gam,sig2,'preprocess','RBF_kernel');% >> model = changelssvm(model,'codetype','code_OneVsOne');% >> model = trainlssvm(model);% % % decode for simulating% >> [Yhamming, Ylatent] = simlssvm(model,Xt); % >> model = changelssvm(model,'codedist_fct','codedist_loss');% >> model = changelssvm(model,'codedist_args',{Ylatent,'sse'});% >>   Yt  = simlssvm(model,Xt);%% see also:%   bay_modoutClass, codedist_hamming, code_ECOC% (c) SCD-KULeuven, rights & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlabif nargin<3,  warning('The latent variables should be used, proceeding with the binary classifiers...');  Ylat = C1;endeval('loss;','loss=''sse'';');[nb,nbin] = size(Ylat);[nbin,dim] = size(C2);dist = zeros(nb,dim);for d = 1:dim,  for n= 1:nb,    nondontcare = find(Ylat(n,:)~=eps & C2(:,d)'~=eps);    dist(n,d) = feval(loss, Ylat(n,nondontcare),C2(nondontcare,d)');  endenddistfunction l = sse(X,Y)l = sum(sum((X-Y).^2));function l = winnertakesall(X,Y)p = find(Y>0);l = max(X(P));