X=randn(1500,2);
Y=exp(-(X(:,1).^2+X(:,2).^2)./4)+0.5*randn(1500,1);
temp=randperm(1500);
Xtrain=X(temp(1:300),:); Xtest=X(temp(301:end),:);
Ytrain=Y(temp(1:300),:); Ytest=Y(temp(301:end),:);
for k=1:30
for j=1:20
NumClusters=j;
temp=randperm(300);
C=Xtrain(temp(1:NumClusters),:);
A=edm(Xtrain,C);
Phi=rbf1(A,1,3);
alpha=pinv(Phi)*Ytrain;
TrainErr(k,j)=(1/length(Ytrain))*norm(Phi*alpha-Ytrain);
%Compute the error using all the data:
A=edm(Xtest,C);
Phi=rbf1(A,1,3);
Z=Phi*alpha;
Err(k,j)=(1/length(Ytest))*norm(Ytest-Z);
end
end
figure(1)
plot(mean(TrainErr));
title('Training error tends to always decrease');
figure(2)
plot(mean(Err));
title('Average error on test set by number of centers used');