function [y,dfce] = svmclass(X,model)% SVMCLASS Support Vector Machines Classifier.%% Synopsis:%  [y,dfce] = svmclass( X, model )%% Description:%  [y,dfce] = svmclass( X, model ) classifies input vectors X%    into classes using the multi-class SVM classifier%      y(i) = argmax f_j(X(:,i))%            j=1..nfun%    where f_j are linear functions in the feature space given %    by the prescribed kernel function (options.ker, options.arg). %    The discriminant functions f_j are determined by %      .Alpha [nsv x nfun] ... multipliers associated to SV%      .b [nclass] ... biases of discriminant functions.%      .sv.X [dim x nsv] ... support vectors.% %    See 'help kernelproj' for more info about valuation of the %    discriminant functions f_j.%%    In the binary case nfun=1 the binary SVM classifier is used%      y(i) = 1 if f(X(:,i) >= 0%           = 2 if f(X(:,i) < 0%    where f is the disrimiant function given by Alpha [nsv x 1],%    b [1x1] and support vectors sv.X.%      % Input:%  X [dim x num_data] Input vectors to be classified.%%  model [struct] SVM classifier:%   .Alpha [nsv x nfun] Multipliers associated to suport vectors.%   .b [nfun x 1] Biases.%   .sv.X [dim x nsv] Support vectors.%   .options.ker [string] Kernel identifier.%   .options.arg [1 x nargs] Kernel argument(s).%% Output:%  y [1 x num_data] Predicted labels.%  dfce [nfun x num_data] Values of discriminant functions.%% Example:%  trn = load('riply_trn');%  model = smo(trn,struct('ker','rbf','arg',1,'C',10));%  tst = load('riply_tst');%  ypred = svmclass( tst.X, model );%  cerror( ypred, tst.y )% % See also %  SMO, SVMLIGHT, SVMQUADPROG, KFD, KFDQP, MVSVMCLASS.  %% 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:% 14-may-2004, VF% 09-May-2003, VF% 14-Jan-2003, VF% allows model to be given in cellmodel=c2s(model);dfce = kernelproj(X, model);nfun = size(dfce,1);if nfun == 1,  % Binary case  %-------------------------------  y = ones(size(dfce));  y( find( dfce < 0 )) = 2;else    % Multi-class case  %-------------------------------  [dummy,y] = max( dfce );endreturn;% EOF