www.gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\stprtool\svm\svmlight.m
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; end if 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)]; end command=['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