function model = oaosvm(data,options)% OAOSVM Multi-class SVM using One-Against-One decomposition.% % Synopsis:%  model = oaosvm( data )%  model = oaosvm( data, options )%% Description:%  model = oaosvm( data ) uses one-agains-one deconposition%   to train the multi-class Support Vector Machines (SVM)%   classifier. The classification into nclass classes %   is decomposed into nrule = (nclass-1)*nclass/2 binary %   problems.%%  model = oaosvm( 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:%   .solver [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 majority voting classifier:%   .Alpha [nsv x nrule] Weights (Lagrangeans).%   .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.%   .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',1000,'verb',1);%  model = oaosvm( data, options );%  figure; %  ppatterns(data); ppatterns(model.sv.X,'ok',13);%  pboundary( model );%  % See also %  MVSVMCLASS, OAASVM.%% 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:% 26-may-2004, VF% 4-feb-2004, VF% 9-Feb-2003, VF% Process inputs%-----------------------------if nargin < 2, options = []; else options=c2s(options); endif ~isfield(options,'verb'), options.verb = 0; endif ~isfield(options,'solver'), options.solver = '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);nrule = (nclass-1)*nclass/2;% display info%---------------------if options.verb == 1,  fprintf('Binary rules: %d\n', nrule);  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,nrule);b = zeros(nrule,1);bin_y = zeros(2,nrule);kercnt = 0;% One-Against-One decomposition%-----------------------------------rule = 0;for class1 = 1:nclass-1,  for class2 = class1+1:nclass,      rule = rule + 1;        if options.verb == 1,      fprintf('building rule %d-%d (%d of %d)', ...        class1, class2, rule, nrule );    end        % set binary subtask    %---------------------------------------------    bin_y(1,rule) = class1;    bin_y(2,rule) = class2;    data_inx = find(data.y==class1 | data.y==class2);    bin_data.X = data.X(:, data_inx);    bin_data.y = data.y(data_inx);    bin_data.y(find(bin_data.y == class1)) = 1;    bin_data.y(find(bin_data.y == class2)) = 2;        % solve binary subtask    %---------------------------------------------    bin_model = feval( options.solver, bin_data, options );    Alpha(data_inx(bin_model.sv.inx),rule) = bin_model.Alpha(:);    b(rule) = 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');    end    endend% set output model%---------------------------------% indices of all support vectorsinx = find(sum(abs(Alpha),2)~= 0);model.Alpha = Alpha(inx,:);model.b = b;model.bin_y = bin_y;model.sv.X = data.X(:,inx);model.sv.y = data.y(inx);model.sv.inx = inx;model.nsv = length(inx);model.kercnt = kercnt;model.options = options;model.fun = 'mvsvmclass';model.trnerr = cerror( mvsvmclass(data.X, model), data.y );% display info%--------------------if options.verb == 1,  fprintf('Total training error = %.4f\n', model.trnerr);endreturn;% EOF