function [model]=msvm2( data, options )% MSVM2 Multiclass SVM with 2-soft margin trained by single-class optimizer.%% Synopsis:%  model = mssvm( data, options) %% Description:%  This function implements transformation of multi-class SVM problem%  to the single-class SVM which is consequently solved by a seleceted%  single-class SVM optimizers (e.g., Kozinec, SMO, KOZSMO).%  %  The transformation is based on the Kersler's construction [DHS01] which %  can be simply expresed in terms of kernel functions [Franc02a]. %  The resulting multi-class SVM has the same architecture as the one %  yieled by one-against-all decomposition. In constrast to the one-against-all %  decomposition all rules (discriminant functions) are trained simultanously.%%  See 'help oaasvmclass' for info about classification.%% Inputs:%  data.X [dim x num_data] Training patterns.%  data.y [1 x num_data] Labels of training patterns. The integers from%   1 to max_class are assumed as the labels.%  options.ker [string] Kernel identifier. See 'help kernel'.%  options.arg [...] Arguments of selected kernel.%  options.C [positiv real] Regularization constant.%  options.solver [string] KOZINEC, SMO, KOZSMO%  options.tmax [int]%  options.tolabs [real] %  options.tolrel [real]%% Output:%  model.rule [cell num_class x 1] Description of SVM discriminat functions.%  model.num_classes [int] Number of classes.%  model.num_rules [int] Number of classification rules == num_classes.%  model.kercnt [int] Total number of used kernel evaluations.%  model.cputime [int] Used CPU time.%  model.options [struct] Copy of used options.%  model.nsv [int] Number of unique support vectors.%  model.sv.X [dim x nsv] Support vectors stored as columns.%  model.sv.y [1 x nsv] Labels of Support vectors (not used for %   classification).%  model.classifier [string] Name of classifier.%% Example:%  data=load('threeclusters1');%  model=msvm2(data,{'ker','linear','C',10})%  ytrn = oaasvmclass( data.X, model);%  trnerr=cerror(data.y,ytrn)%  figure; ppatterns(data); pmsvm(model);%% See also %  OAASVMCLASS, OAOSVM, OAOSVMCLASS, PMSVM.%% 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:% 23-jan-2003, VFtic;model.classifier = 'oaasvmclass';% process input argumensdata=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,'solver'), options.solver='kozinec'; end%---------------------------------[dim,num_data]=size(data.X);num_classes=max(data.y);%---------------------------------model.name = 'One-Against-All SVM classifier';[ker,arg]=kerarg(options.ker,options.arg);switch upper(options.solver)   case 'KOZINEC', solver = 0;      case 'SMO', solver = 1;   case 'KOZSMO', solver = 2;   case 'NPA', solver = 3;   otherwise error('Unknown solver.');end[Alpha,b,exitflag,kercnt,trnerr,t,UB,LB,History] = msvm2ccode(data.X,data.y,...    ker,arg,options.C,solver,options.tmax,options.tolabs, options.tolrel); 
model.num_classes = num_classes;model.num_rules = num_classes;model.rule = cell(model.num_rules,1);
model.exitflag = exitflag;
model.trnerr = trnerr;model.kercnt = kercnt;
model.t = t;
model.UB = UB;
model.LB = LB;
model.LB_History = History(1,:);model.UB_History = History(2,:);for i=1:num_classes,  model.rule{i}.b = b(i);  inx=find(Alpha(i,:)~=0);  model.rule{i}.Alpha = Alpha(i,inx);  model.rule{i}.sv.inx = inx;  lab=data.y(inx);  lab2=lab; lab2(find(lab==i))=1;lab2(find(lab~=i))=2;  model.rule{i}.Alpha = model.rule{i}.Alpha(:).*label12topm(lab2(:));  model.rule{i}.options = options;endmodel.options = options;model.sv = data;model.nsv = length(find(sum(Alpha)));  inx_sv = find(sum(abs(Alpha)));  % takes only support vectors (maintain only one copy od support % vectors) and change indicesmodel.nsv = length(inx_sv);inx_tmp = zeros(1,num_data);inx_tmp(inx_sv) = [1:model.nsv];model.sv.X = data.X(:,inx_sv);model.sv.y = data.y(:,inx_sv);for i=1:model.num_rules,  model.rule{i}.sv.inx = inx_tmp(model.rule{i}.sv.inx);endmodel.cputime = toc;return;% EOF