%%  SVM Classification 2D examples%  with different kernels (including wavelets) and different penalization settings%%  05/05/03 ARclear allclose alln = 100; sigma=0.4;[Xapp,yapp,xtest,ytest]=datasets('gaussian',n,0,sigma);[Xapp]=normalizemeanstd(Xapp);  kernel='gaussian'; kerneloption=10; nu=0.2;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 functionypred = svmval(Xtest,xsup,w,w0,kernel,kerneloption,[ones(length(Xtest),1)]);ypred = reshape(ypred,nn,nn);  %--------------- plottingfigure(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]);