function [Alpha,bias,nsv,kercnt,trnerr,margin]=svmlight(data,labels,ker,arg,C,eps)% SVMLIGHT interface to the SVM^light software.% % [Alpha,bias,nsv,kercnt,trnerr,margin]=svmlight(data,labels,ker,arg,C,eps)%% The programs 'svm_learn' and 'svm_classify' must be in the path.%% Inputs:%  data [dim x N] training patterns%  labels [1 x N] labels of training patterns%  ker [string] kernel, see 'help kernel'.%  arg [...] argument of given kernel, see 'help kernel'.%  C [real] trade-off between margin and training error.%  eps [real] KKT stopping condiiton.%  verb [int] if 1 then progress info is displayed. % % Outputs:%  Alpha [1 x N] found Lagrangeian multipliers.%  bias [real] found bias.%  nsv [real] number of Support Vectors (number of Alpha > ZERO_LIM).%  kercnt [int] number of kernel evalutions.%  trnerr [real] classification error on training data.%  margin [real] margin between classes and the found hyperplane.%%  Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac%  (c) Czech Technical University Prague, http://cmp.felk.cvut.cz%  Written Vojtech Franc (diploma thesis) 02.11.1999, 13.4.2000%% Modifications%  26-sep-2002, VF%  3-Jun-2002, V.Franc[dim,num_data ] = size(data);if nargin < 6,  eps=0.001;endif nargin < 5,  error('Not enough input arguments.');end%--------------------------------switch ker  case 'linear'    ker='-t 0';  case 'rbf'    ker=['-t 2 -g ' num2str(1/(2*arg^2))];   case 'poly'     ker=['-t 1 -r 1 -s 1 -d ' num2str(arg)];  endcommand=['svm_learn ' ...         '-c ' num2str(C) ' '...         ker ' '...         '-v 1' ' ' ...         '-m 1' ' ' ...         '-e ' num2str(eps) ' '...         '-a tmp_alpha.txt tmp_examples.txt tmp_model.txt > tmp_verb.txt'];       xi2svmlight(data,labels,'tmp_examples.txt');        % call SVM_LIGHT%    evalc(command);    [a,b]=unix(command);        [lines]=textread('tmp_model.txt','%s');    for i=1:size(lines,1)      if strcmpi( lines(i), 'threshold' )==1,        bias=-str2num( lines{i-2});        break;      end    end        Alpha=textread('tmp_alpha.txt','%f');    Alpha=Alpha(:)'.*itosgn(labels);    [lines]=textread('tmp_verb.txt','%s');    for i=1:size(lines,1)      if strcmpi( lines{i}, 'misclassified,' ),        trnerr=str2num( lines{i-1}(2:end));        trnerr=trnerr/length(Alpha);      end      if strcmpi( lines(i), 'vector:' ) & strcmpi( lines(i-1), 'weight' )==1,        margin=1/str2num( lines{i+1}(5:end));      end      if strcmpi( lines(i), 'SV:' )==1,        nsv=str2num( lines{i+1});      end      if strcmpi( lines(i), 'evaluations:' )==1,        kercnt=str2num( lines{i+1});      end    end    % Alpha;% bias;% kercnt;% margin;    nsv = length(find(Alpha~=0));% trnerr;return;%EOF