%Script file:  Iris classification using Matlab's RBF routines.
% AFTER IrisClass1, we see that a best width is 0.1314


numtrain=70; %Number of training points to use
load IrisData  %From earlier, loads X and Y, 150 points
eg=0.1;             %Error goal
sp=0.131;  %Gaussian Widths

[NumPoints,Dim]=size(X);
temp=randperm(NumPoints);
P=X(temp(1:numtrain),:)';
T=Y(temp(1:numtrain),:)';
Ptest=X(temp(numtrain+1:end),:)';
Ttest=Y(temp(numtrain+1:end),:)';
net=newrb(P,T,eg,sp);
z=sim(net,X');
[dim,npts]=size(z);
%Build a confusion matrix:
C=zeros(3,3);
Class=zeros(3,1);
for k=1:npts
   [x,j]=max(z(:,k));
   [h,hh]=max(Y(k,:));
   Class(hh)=Class(hh)+1;
   C(hh,j)=C(hh,j)+1;
end