function [y,votes] = mvsvmclass(X,model)% MVSVMCLASS Majority voting multi-class SVM classifier.%% Synopsis:%  [y,votes] = mvsvmclass(X,model)%% Description:%  [y,votes] = mvsvmclass(X,model) multi-class SVM classifier %    based on majority voting. The classifier involves nrule%    binary rules each classifying into one of nclass labels.%    The final decision is make for the class with majority %    votes.%% Input:%  X [dim x num_data] Input vectors to be classified.%%  model [struct] Multi-class SVM majority voting classifier:%   .Alpha [nsv x nrule] Weights.%   .bin_y [2 x nrule] Translation between binary responses of%     the discriminant functions and class labels.%   .b [nrule x 1] Biases of discriminant functions.%   .sv.X [dim x nsv] Support vectors.%   .options.ker [string] Kernel identifier; see 'help kernel'.%   .options.arg [1 x nargs] Kernel agrument(s).%% Output:%  y [1 x num_data] Predicted labels.%  votes [nclass x num_data] Number of votes for each class.%% Example:%% See also %  OAOSVM, SVMCLASS.%% 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%  11-Feb-2003, VF %  8-Feb-2003, VF %  3-Jun-2002, V.Franc[dim,num_data] = size(X);nclass = max( model.bin_y(:) );nrule = size( model.Alpha, 2);votes = zeros(nclass, num_data );dfce = kernelproj( X, model );for i=1:nrule,    inx_pos = find( dfce(i,:) >= 0 );  inx_neg = find( dfce(i,:) < 0 );  votes( model.bin_y(1,i), inx_pos) = votes( model.bin_y(1,i), inx_pos) + 1;  votes( model.bin_y(2,i), inx_neg) = votes( model.bin_y(2,i), inx_neg) + 1;end[dummy, y] = max( votes );return;% EOF