function model = new_bsvm2( data, options )% NEW_BSVM2 Multi-class BSVM with L2-soft margin.%% Synopsis:%  model = new_bsvm2( data, options ) %% Description:%  This function trains the multi-class SVM classifier based%  on BSVM formulation (bias added to the objective function) and%  L2-soft margin penalization of misclassifications [Franc02][Hsu02].%  The quadratic programming criterion can be optimized by one of the%  following algorithms:%    mdm  ... Mitchell-Demyanov-Malozemov%    imdm  ... Mitchell-Demyanov-Malozemov Improved 1.%    iimdm  ... Mitchell-Demyanov-Malozemov Improved 2.%    kozinec ... Kozinec algorithm.%    keerthi ... NPA algorithm by Keerthi et al.%    kowalczyk ... Based on Kowalczyk's maximal margin perceptron.%% Input:%  data [struct] Training data:%   .X [dim x num_data] Training vectors.%   .y [1 x num_data] Labels (1,2,...,nclass).%%  options [struct] Control parameters:%   .ker [string] Kernel identifier. See 'help kernel'.%   .arg [1 x nargs] Kernel argument(s).%   .C [1x1] Regularization constant.%   .qp [string] QP solver to use: 'mdm', 'imdm', 'iimdm' (default), %     'kozinec', 'kowalczyk','keerthi'.%   .tmax [1x1] Maximal number of iterations.%   .tolabs [1x1] Absolute tolerance stopping condition (default 0.0).%   .tolrel [1x1] Relative tolerance stopping condition (default 0.001).%   .cache [1x1] Number of columns of kernel matrix to be cached.%   .verb [1x1] If 1 then info about training process is printed.%% Output:%  model [struct] Multi-class SVM classifier:%   .Alpha [nsv x nclass] Weights.%   .b [nclass x 1] Biases.%   .sv.X [dim x nsv] Support vectors.%   .nsv [1x1] Number of support vectors.%   .options [struct] Copy of input options.%   .t [1x1] Number of iterations.%   .UB [1x1] Upper bound on the optimal solution.%   .LB [1x1] Lower bound on the optimal solution.%   .History [2 x (t+1)] UB and LB with respect to t.%   .trnerr [1x1] Training classification error.%   .kercnt [1x1] Number of kernel evaluations.%   .cputime [1x1] CPU time (measured by tic-toc).%   .qp_stat [struct] Statistics about QP optimization:%     .access [1x1] Access to matrix H.%     .t [1x1] Number of iterations.%     .UB [1x1] Upper bound on optimal criterion.%     .LB [1x1] Lower bound on optimal criterion.%     .LB_History [1x(t+1)] LB with respect to t.%     .UB_History [1x(t+1)] UB with respect to t.%     .NA [1x1] Number of non-zero elements in solution.%% Example:%  data = load('pentagon');%  options = struct('ker','rbf','arg',1,'C',10);%  model = bsvm2(data,options )%  figure; %  ppatterns(data); ppatterns(model.sv.X,'ok',12);%  pboundary(model);%% See also %  SVMCLASS, OAASVM, OAOSVM.%% 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:% 29-nov-2004, VF% 26-nov-2004, VF% 16-Nov-2004, VF% 31-may-2004, VF% 23-jan-2003, VFtic;% process inputs %-------------------------------------------------------data=c2s(data);if nargin < 2, options=[]; else options=c2s(options); endif ~isfield(options,'ker'), options.ker='linear'; endif ~isfield(options,'arg'), options.arg=1; endif ~isfield(options,'C'), options.C=inf; endif ~isfield(options,'tmax'), options.tmax=inf; endif ~isfield(options,'tolabs'), options.tolabs=0; endif ~isfield(options,'tolrel'), options.tolrel=0.001; endif ~isfield(options,'qp'), options.qp='iimdm'; endif ~isfield(options,'cache'), options.cache = 1000; endif ~isfield(options,'verb'), options.verb=0; endif ~isfield(options,'qp_verb'), options.qp_verb=0; 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); end  fprintf('QP solver: %s\n', options.qp);end% call MEX implementation[Alpha,b,exitflag,kercnt,access,trnerr,t,NA,UB,LB,History] = bsvm2_mex(...    data.X,...    data.y,...    options.ker,...    options.arg,...    options.C,...    options.qp,...    options.tmax,...    options.tolabs, ...    options.tolrel,...    options.cache, ...    options.qp_verb );% set up model%-------------------------sv_inx = find( sum(abs(Alpha),1) ~= 0 );Alpha = Alpha(:,sv_inx)';for i = 1:size(Alpha,2),  inx = find( data.y(sv_inx) ~= i);  Alpha(inx,i) = -Alpha(inx,i);endmodel.Alpha = Alpha;model.b = b;model.sv.X = data.X(:,sv_inx);model.sv.y = data.y(sv_inx);model.sv.inx = sv_inx;model.nsv = length(sv_inx);model.options = options;model.exitflag = exitflag;model.trnerr = trnerr;model.kercnt = kercnt;model.qp_stat.access = access;model.qp_stat.t = t;model.qp_stat.UB = UB;model.qp_stat.LB = LB;model.qp_stat.LB_History = History(1,:);model.qp_stat.UB_History = History(2,:);model.qp_stat.NA = NA;model.cputime = toc;model.fun = 'svmclass';return;% EOF