function [y, dfce] = bayescls( X, model )% BAYESCLS Bayesian classifier with reject option.% % Synopsis:%  [y, dfce] = bayescls(X,model)%% Description:%  This function implements the classifier minimizing the Bayesian risk %  with 0/1-loss function. It corresponds to the minimization of %  probability of misclassification. The input vectors X are classified %  into classes with the highest a posterior probabilities computed from %  given model.% %  The model contains parameters of conditional class probabilities%  in model.Pclass [cell 1 x num_classes] and a priory probabilities%  in model.Prior [1 x num_classes]. %%  The function%    p = feval(model.Pclass{i}.fun, X, model.pclass{i})%  is called to evaluate the i-the class conditional probability of X.%  %  It returns class labels y [1 x num_data] for each input vector%  and matrix dfce [num_class x num_data] of unnormalized a posterior%  probabilities%    dfce(y,i) = Conditional_probability(X(:,i)|y)*Prior(y).%%  If the field model.eps exists then the Bayesian classifier %  with the reject option is used. The eps is penalty for the %  decision "don't know" which is indicated by label y = 0.%   % Input:%  X [dim x num_data] Vectors to be classified.%%  model [struct] Describes probabilistic model:%   .Pclass [cell 1 x num_classes] Class conditional probabilities.%   .Prior [1 x num_classes] A priory probabilities.%%   .eps [1x1] (optional) Penalty of decision "don't know". %% Output:%  y [1 x num_data] Labels (1 to num_classes); 0 for "don't know".%  dfce [num_classes x num_data] Unnormalized a posterior %   probabilities (see above).%% Example:%  trn = load('riply_trn');%  tst = load('riply_tst');%  inx1 = find(trn.y==1);%  inx2 = find(trn.y==2);%  model.Pclass{1} = mlcgmm(trn.X(:,inx1));%  model.Pclass{2} = mlcgmm(trn.X(:,inx2));%  model.Prior = [length(inx1) length(inx2)]/(length(inx1)+length(inx2));%  ypred = bayescls(tst.X,model);%  cerror(ypred,tst.y)% % See also %  BAYESDF, BAYESERR.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 09-jun-2004, VF% 01-may-2004, VF% 11-mar-2004, VF, "don't" know decision added.% 19-sep-2003, VF[dim,num_data]=size(X);num_classes = length( model.Pclass );dfce=zeros(num_classes,num_data);% compute unnormalized a posterior probabilitiesfor i=1:num_classes,  dfce(i,:) = model.Prior(i)*feval(model.Pclass{i}.fun,X,model.Pclass{i});end% take maximum[tmp,y] = max(dfce);% reject optionsif isfield(model, 'eps'),  perror = 1-tmp./sum(dfce,1);    inx = find( perror > model.eps);  y(inx) = 0;endreturn;% EOF