% % SVM Classification 2D examplesclear all close all clcn = 100; sigma=0.3;[Xapp,yapp,xtest,ytest]=datasets('gaussian',n,0,sigma);%產生n個高斯分布的隨機樣本數據 Xapp=single(Xapp); [Xapp]=normalizemeanstd(Xapp);%對數據進行歸一化 plot(Xapp(:,1),Xapp(:,2),'.'); axis([-3.5 3.5 -3.5 3.5]) %%lambda = 1e-7; C = 10;kernel='gaussian'; %支持向量機的核函數kerneloption=1;[xsup,w,w0,pos,tps,alpha] = svmclass(Xapp,yapp,C,lambda,kernel,kerneloption,1); %[xsup,w,w0,pos,tps,alpha] = svmclassLS(Xapp,yapp,C,lambda,kernel,kerneloption,1,1,100); 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,[-1 0 1],'k'); clabel(cc,hh); set(hh,'LineWidth',1); h1=plot(Xapp(yapp==1,1),Xapp(yapp==1,2),'+r'); set(h1,'LineWidth',1);h2=plot(Xapp(yapp==-1,1),Xapp(yapp==-1,2),'db'); set(h2,'LineWidth',1); h3=plot(xsup(:,1),xsup(:,2),'ok'); set(h3,'LineWidth',1);axis([-3.5 3.5 -3 3]);

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