function model = oaasvm(data,options)% OAASVM Multi-class SVM using One-Agains-All decomposition.% % Synopsis:%  model = oaasvm( data )%  model = oaasvm( data, options)%% Description:%  model = oaasvm( data ) uses one-agains-all deconposition%   to train the multi-class Support Vector Machines (SVM)%   classifier. The classification into nclass classes %   is decomposed to nclass binary problems.%%  model = oaasvm( data, options) allows to specify the%   binary SVM solver and its paramaters.%% Input:%  data [struct] Training data:%   .X [dim x num_data] Training vectors.%   .y [1 x num_data] Labels of training data (1,2,...,nclass). %%  options [struct] Control parameters:%   .bin_svm [string] Function which implements the binary SVM %     solver; (default 'smo').%   .verb [1x1] If 1 then a progress info is displayed (default 0).%  The other fileds of options specifies the options of the binary%  solver (e.g., ker, arg, C). See help of the selected solver.%  % Output:%  model [struct] Multi-class SVM classifier:%   .Alpha [nsv x nclass] Weights (Lagrangians).%   .b [nclass x 1] Biases of discriminant functions.%   .sv.X [dim x nsv] Support vectors.%   .nsv [1x1] Number of support vectors.%   .trnerr [1x1] Training error.%   .kercnt [1x1] Number of kernel evaluations.%   .options [struct[ Copy of input argument options.%% Example:%  data = load('pentagon');%  options = struct('ker','rbf','arg',1,'C',10,'verb',1);%  model = oaasvm(data,options);%  figure; %  ppatterns(data); ppatterns( model.sv.X, 'ok',13);%  pboundary( model );%% See also %  SVMCLASS, OAOSVM.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2005, 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:% 25-jan-2005, VF, option solver replaced by bin_svm % 27-may-2004, VF, completely re-programed% 18-sep-2001, V. Franc, created% Process inputs%-----------------------------if nargin < 2, options = []; else options=c2s(options); endif ~isfield(options,'verb'), options.verb = 0; endif ~isfield(options,'bin_svm'), options.bin_svm = 'smo'; endif ~isfield(options,'ker'), options.ker = 'linear'; endif ~isfield(options,'arg'), options.arg = 1; endif ~isfield(options,'C'), options.C = inf; end[dim,num_data] = size(data.X);nclass = max(data.y);% display info%---------------------if options.verb == 1,  fprintf('Binary rules: %d\n', nclass);  fprintf('Training data: %d\n', num_data);  fprintf('Dimension: %d\n', dim);  if isfield( options, 'ker'), fprintf('Kernel: %s\n', options.ker); end  if isfield( options, 'arg'), fprintf('arg: %f\n', options.arg(1)); end  if isfield( options, 'C'), fprintf('C: %f\n', options.C); endend%----------------------------------------Alpha = zeros(num_data,nclass);b = zeros(nclass,1);orig_labels = data.y;kercnt = 0;% One-Against-All decomposition%----------------------------------------for i=1:nclass,   if options.verb==1,     fprintf('Training rule %d', i);   end   % set binary subtask   %---------------------------------------------   bin_labels = zeros(1,num_data);   bin_labels(find( orig_labels==i)) = 1;   bin_labels(find( orig_labels~=i)) = 2;      data.y = bin_labels;   % solve binary subtask   %-------------------------------------   bin_model = feval( options.bin_svm, data, options );   Alpha(bin_model.sv.inx,i) = bin_model.Alpha(:);   b(i) = bin_model.b;   kercnt = kercnt + bin_model.kercnt;      % progress info    %----------------------------   if options.verb ==1,     if isfield(bin_model, 'trnerr'),       fprintf(': trnerr = %.4f', bin_model.trnerr);     end     if isfield(bin_model, 'margin'),       fprintf(', margin = %f', bin_model.margin );     end     fprintf('\n');   endend% set output model%---------------------------------% indices of all support vectorsinx = find(sum(abs(Alpha),2)~= 0);model.Alpha = Alpha(inx,:);model.b = b;model.sv.X = data.X(:,inx);model.sv.y = orig_labels(inx);model.sv.inx = inx;model.nsv = length(inx);model.kercnt = kercnt;model.options = options;model.fun = 'svmclass';model.trnerr = cerror( svmclass(data.X, model), orig_labels );if strcmp(options.ker,'linear') == 1,  model.W = model.sv.X*model.Alpha;end% display info%--------------------if options.verb == 1,  fprintf('Total training error = %.4f\n', model.trnerr);endreturn;% EOF