%% Sample RBF script.  
%First define the data

X=randn(1500,2);
Y=exp(-(X(:,1).^2+X(:,2).^2)/4)+0.15*randn(1500,1);  %Add some noise

%% Split the data into training and testing sets
% In this case, we'll use 300 points for training

temp=randperm(1500);
Xtrain=X(temp(1:300),:);
Xtest=X(temp(301:end),:);

Ytrain=Y(temp(1:300),:);
Ytest=Y(temp(301:end),:);

%% Create the Radial Basis Function.  
% For this example, we will use 10 points chosen at random from the data as
% our set of centers

temp=randperm(1500);
Centers=X(temp(1:10),:);

A=edm(Xtrain,Centers);
Phi=rbf1(A,1,3);

alpha=pinv(Phi)*Ytrain;

%% The error is measured by the error on the test set:

%Compute the new EDM:
A=edm(Xtest,Centers);
Phi=rbf1(A,1,3);

Yout=Phi*alpha;
[m,n]=size(Ytest);
%The error is the norm of the difference:
for j=1:m
    Err(j)=norm(Ytest(j,:)-Yout(j,:));
end

%% Plot in 3d

plot3(Xtest(:,1),Xtest(:,2),Yout,'.');