exr1.m

svm classification and regression code

load ('Data/sinx_data.mat')
load ('Data_noise/sinx/noise_04.mat')
global p1
p1=2;
ker='rbf';
[nsv, beta, bias] = svr(Xtr,Ytr,ker);
svrplot(Xtr,Ytr,ker,beta,bias)
Y = svroutput(Xtr,Xtr,ker,beta,bias);
Yt = svroutput(Xtr,Xte,ker,beta,bias);

train_mse=mean((Y-Ytr).^2);
test_mse=mean((Yt-Yte).^2);
J1=train_mse.^0.5./std(Ytr,1)
J2=test_mse.^0.5/std(Yte,1)


figure(2)
subplot(2,1,1)
plot(Ytr,'.k');
hold on 
plot(Y,'r');
subplot(2,1,2)
plot(Yte,'.k');
hold on 
plot(Yt,'r');