%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CSC411: Machine Learning and Data Mining 
%     Tutorial 4 (Febuary 9th): Neural Network Toolbox - XOR problem
% TA: Rui Yan
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all;
close all;

%Input Data P
P = [1 1 0 0; 1 0 1 0];
%Target Data T
T = [0 1 1 0];

%Construct the Neural Network
%net = newff(PR,[S1..Sn],{TF1 ...TFn},BTF, BLF, PF);
net=newff([0 1; 0 1], [4 1],{'tansig' 'purelin'},'trainlm');
 
%Update the parameters for the training 
net.trainParam.epochs=10000;
net.trainParam.show=5;

%Train the neural network
net=train(net,P,T);
 
%Simulate the neural network
Y=sim(net,P);

%Plot the output
plot3(P(1,:),P(2,:),T,'+',P(1,:),P(2,:),Y,'o');
hold on;
plot3(P(1,:),P(2,:),Y-T,'b');