function model = svm2(data,options)% SVM2 Learning of binary SVM classifier with L2-soft margin.%% Synopsis:%  model = svm2(data)%  model = svm2(data,options)%% Description:%  This function learns binary Support Vector Machines%  classifier with L2-soft margin. The corresponding quadratic %  programming task is solved by one of the following %  algorithms:%    mdm  ... Mitchell-Demyanov-Malozemov (MDM) algorithm.%    imdm ... Improved MDM algorithm (IMDM) (defaut).%%  For more info refer to V.Franc: Optimization Algorithms for Kernel %  Methods. Research report. CTU-CMP-2005-22. CTU FEL Prague. 2005.%  ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-PhD.pdf .%% Input:%  data [struct] Training data:%   .X [dim x num_data] Training vectors.%   .y [1 x num_data] Labels must equal 1 and/or 2.%%  options [struct] Control parameters:%   .ker [string] Kernel identifier. See 'help kernel'.%   .arg [1 x nargs] Kernel argument(s).%   .C [1x1] Regularization constant.%   .solver [string] Solver to be used: 'mdm', 'imdm' (default).%   .tmax [1x1] Maximal number of iterations (default inf).%   .tolabs [1x1] Absolute tolerance stopping condition (default 0.0).%   .tolrel [1x1] Relative tolerance stopping condition (default 1e-3).%   .thlb [1x1] Threshold on lower bound (default inf).%   .cache [1x1] #of columns of kernel matrix to be cached (default 1000).%   .verb [1x1] If > 0 then some info is displayed (default 0).%% Output:%  model [struct] Binary SVM classifier:%   .Alpha [nsv x 1] Weights of support vectors.%   .b [1x1] Bias of decision function.%   .sv.X [dim x nsv] Support vectors.%   .sv.inx [1 x nsv] Indices of SVs (model.sv.X = data.X(:,inx)).%   .nsv [int] Number of Support Vectors.%   .kercnt [1x1] Number of kernel evaluations.%   .trnerr [1x1] Classification error on training data.%   .margin [1x1] Margin.%   .options [struct] Copy of used options.%   .cputime [1x1] Used CPU time in seconds (meassured by tic-toc).%   .stat [struct] Statistics about optimization:%     .access [1x1] Number of requested columns of matrix H.%     .t [1x1] Number of iterations.%     .UB [1x1] Upper bound on the optimal value of criterion. %     .LB [1x1] Lower bound on the optimal value of criterion. %     .LB_History [1x(t+1)] LB with respect to iteration.%     .UB_History [1x(t+1)] UB with respect to iteration.%     .NA [1x1] Number of non-zero entries in solution.%% Example:%  data = load('riply_trn');%  options = struct('ker','rbf','arg',1,'C',1);%  model = svm2(data,options )%  figure; ppatterns(data); psvm( model );%% See also%  SVMCLASS, SVMLIGHT, SMO, GNPP.%% 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:% 09-sep-2005, VF% 08-aug-2005, VF% 24-jan-2005, VF% 29-nov-2004, VF% restart clocktic;if nargin < 2, options = []; else options = c2s(options); endif ~isfield(options,'solver'), options.solver = 'imdm'; endif ~isfield(options,'tolabs'), options.tolabs = 0; endif ~isfield(options,'tolrel'), options.tolrel = 1e-3; endif ~isfield(options,'thlb'), options.thlb = inf; endif ~isfield(options,'tmax'), options.tmax = inf; endif ~isfield(options,'C'), options.C = inf; endif ~isfield(options,'ker'), options.ker = 'linear'; endif ~isfield(options,'arg'), options.arg = 1; endif ~isfield(options,'cache'), options.cache = 1000; endif ~isfield(options,'verb'), options.verb = 0; end% call MEX implementation of QPC2 solver[Alpha,b,exitflag,kercnt,access,errcnt,t,UB,LB,History] = svm2_mex(...    data.X,...    data.y,...    options.ker,...    options.arg,...    options.C,...    options.solver,...    options.tmax,...    options.tolabs, ...    options.tolrel,...    options.thlb, ...    options.cache, ...    options.verb );% remove non-support vectorsinx = find(Alpha ~=0 );% setup output modelmodel.Alpha = Alpha(inx);model.b = b;model.sv.X = data.X(:,inx);model.sv.inx = inx;model.sv.y = data.y(inx);model.nsv = length(inx);if strcmp( options.ker, 'linear'),  model.W = model.sv.X * model.Alpha;endmodel.options = options;model.kercnt = kercnt;model.trnerr = errcnt/size(data.X,2);model.errcnt = errcnt;model.margin = 1/sqrt(sum(abs(model.Alpha))-sum(model.Alpha.^2)...    /2/model.options.C);model.exitflag = exitflag;model.stat.access = access;model.stat.t = t;model.stat.UB = UB;model.stat.LB = LB;model.stat.LB_History = History(1,:);model.stat.UB_History = History(2,:);model.stat.NA = length(inx);model.cputime = toc;model.fun = 'svmclass';return;%EOF