%% Script file:  Online Training (Hebbian Learning)
% Example 1:  T, G and F.

%% Load the Data and Graph the Results.

T1=[1 1 1 -1;-1 1 -1 -1;-1 1 -1 -1;-1 1 -1 -1];
T2=[-1 1 1 1;-1 -1 1 -1;-1 -1 1 -1;-1 -1 1 -1];
G1=[1 1 1 -1;1 -1 -1 -1; 1 1 1 -1 ; 1 1 1 -1];
G2=[-1 1 1 1;-1 1 -1 -1; -1 1 1 1; -1 1 1 1];
F1=[1 1 1 -1;1 1 -1 -1;1 -1 -1 -1;1 -1 -1 -1];
F2=[-1 1 1 1;-1 1 1 -1;-1 1 -1 -1;-1 1 -1 -1];

gg=colormap(gray); gg=gg(end:-1:1,:);  %I'm reversing the usual grayscale values

X=[T1(:) T2(:) G1(:) G2(:) F1(:) F2(:)]; %X is 16 x 6
T=[1 1 0 0 0 0;0 0 1 1 0 0;0 0 0 0 1 1];

figure(1)
for j=1:6
    subplot(2,3,j);
    imagesc(reshape(X(:,j),4,4));
    colormap(gg);
end

%% This part is the linear neural network

% Main code start
NumPoints=6;
NumEpochs=60;

[W,b,EpochErr]=WidHoff2(X,T,NumEpochs);

%% Output results

Z=W*X+b*ones(1,NumPoints)

figure(2)
plot(EpochErr);

% There is a better way to do this- I need to go from vector form of output 
%   to the integer form: class 1, class 2, or class 3 for the confusion matrix code.

for i=1:6
  [v,idxT(i)]=max(T(:,i));
  [v,idxZ(i)]=max(Z(:,i));
end

%
% This is the confusion matrix code from the k-NN material.
%

C=conf_matrix(idxT,idxZ,3);