exc2.m

svm classification and regression code

X=[-0.4 0;0.5 0.5;0.5 0;0.5 -0.5;0 0.5;0 -0.5;-0.5 0.5;-0.5 -0.5;0.5 0; 0.1 0.4];
N=size(X,1);
X=[X;X+rand(N,2).*0.05;X.*1.5;X.*1.55+rand(N,2).*0.05]
N=size(X,1);

Y=zeros(N./2,1)+1;
Y=[Y;zeros(N./2,1)-1];
global p1;
global Yt;
p1=5.0;
ker='rbf';
[nsv, alpha, bias,Xs,Ys,alphas] = svc(X,Y,ker);
[h] = svcplot(X,Y,ker,alpha,bias);
trnX=X;
trnY=Y;
tstX=X;
predictedY =svcoutput(trnX,trnY,tstX,ker,alpha,bias);

figure(2)
xmin = min(X(:,1));, xmax = max(X(:,1)); 
ymin = min(X(:,2));, ymax = max(X(:,2)); 
xa = (xmax - xmin);, ya = (ymax - ymin);
ex=xa./100;ey=ya./100;
P=[];
for i=xmin:ex:xmax
    for j=ymin:ey:ymax
        P=[P;i,j];
    end
end
preYp =svcoutput(trnX,trnY,P,ker,alpha,bias,1);
svp=find(abs(preYp)<=0.001);
Pl=P(svp,:);

hold on
plot(X(1:N./2,1),X(1:N./2,2),'rO');
plot(X(N./2+1:N,1),X(N./2+1:N,2),'b*');
plot(Pl(:,1),Pl(:,2),'k:')