%% Script file:  Associations with Widrow-Hoff
%% First, the data (and plots)
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,:);

subplot(2,3,1)
imagesc(T1)
colormap(gg)
subplot(2,3,2)
imagesc(G1)
subplot(2,3,3)
imagesc(F1)

subplot(2,3,4)
imagesc(T2)
subplot(2,3,5)
imagesc(G2)
subplot(2,3,6)
imagesc(F2)

%% Next, the actual training and results:
X=[T1(:) T2(:) G1(:) G2(:) F1(:) F2(:)];
X=X';  %Data should be number of pts (6) by dimension (16)
T=[60 60 0 0 -60 -60]; 
T=T';  %Targets should be a column.
lr=0.06;   % alpha is 0.03
iters=60;  %60 times through the data

[W,b,EpochErr]=wid_hoff1(X,T,lr,iters);

figure(2)
plot(EpochErr);

%% Example of using the network to classify the points. 
Ans1=W*X(3,:)'+b  %Should be zero for G
Ans2=W*X(6,:)'+b  %Should be about -60 for F