exr5.m

svm classification and regression code

st1=cputime;
x1=-2:0.3:2;
x2=-2:0.3:2;
[X1,X2]=meshgrid(x1,x2);
X=[];
[m,n]=size(X1);
for i=1:m;
  for j=1:n;
      X=[X;X1(i,j),X2(i,j)];
  end
end
Yd=(1+sin(X1).^2+X2.^2).^2;
Y=(1+sin(X(:,1)).^2+X(:,2).^2).^2;


global p1
p1=1.2;
ker='rbf';
[nsv, beta, bias] = svr(X,Y,ker);
%svrplot(X,Y,ker,beta,bias)
mx=size(X,1);

x1t=-2:0.151:2;
x2t=-2:0.151:2;
[X1t,X2t]=meshgrid(x1t,x2t);
Xt=[];
[mt,nt]=size(X1t);
for i=1:mt;
  for j=1:nt;
      Xt=[Xt;X1t(i,j),X2t(i,j)];
  end
end
Yt = svroutput(X,Xt,ker,beta,bias);
Ytd=(1+sin(X1t).^2+X2t.^2).^2;

k=size(Yt,1);
for i=mt:-1:1
    for j=nt:-1:1
        Ys(i,j)=Yt(k);
        k=k-1;
    end
end
figure(2)
mesh(X1,X2,Yd)

figure(3)
mesh(X1t,X2t,Ys);
figure(4)
mesh(X1t,X2t,Ytd);
 

DY=Ys-Ytd;
ERR=sum(sum((DY).^2))./mt./nt

T=cputime-st1