exnuclass1.m

支持向量机

%
%  SVM Classification 2D examples
%  with different kernels (including wavelets) and different penalization settings
%
%  05/05/03 AR


clear all
close all

n = 100; 

sigma=0.4;
[Xapp,yapp,xtest,ytest]=dataset('gaussian',n,0,sigma);
[Xapp]=normalizemeanstd(Xapp);


 

 kernel='gaussian';
 kerneloption=4;
 nu=0.1;
lambda = 1e-12;   


[xsup,w,w0,rho,pos,tps,alpha] = svmnuclass(Xapp,yapp,nu,lambda,kernel,kerneloption,1); 
ypredapp = svmval(Xapp,xsup,w,w0,kernel,kerneloption,1);

%------- Building a 2D Grid for function evaluation -------------------------
[xtest1 xtest2]  = meshgrid([-1:.05:1]*3.5,[-1:0.05:1]*3); 
nn = length(xtest1); 
Xtest = [reshape(xtest1 ,nn*nn,1) reshape(xtest2 ,nn*nn,1)]; 



%--------------  Evaluating the decision function
ypred = svmval(Xtest,xsup,w,w0,kernel,kerneloption,[ones(length(Xtest),1)]);
ypred = reshape(ypred,nn,nn); 
 

%--------------- plotting
figure(1); 
clf; 
%contourf(xtest1,xtest2,ypred,50);shading flat;
hold on
[cc,hh]=contour(xtest1,xtest2,ypred,[0 0],'k');
clabel(cc,hh); 
set(hh,'LineWidth',2);
h1=plot(Xapp(yapp==1,1),Xapp(yapp==1,2),'+r'); 
set(h1,'LineWidth',2);

h2=plot(Xapp(yapp==-1,1),Xapp(yapp==-1,2),'db'); 
set(h2,'LineWidth',2);
h3=plot(xsup(:,1),xsup(:,2),'ok'); 
set(h3,'LineWidth',2);




axis([-3.5 3.5 -3 3]);