%The Matlab Code for pages 46-48 in our text, for the Associative Memory
%example.

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)

%**************************************
%The main code starts here!
%**************************************

X=[T1(:) T2(:) G1(:) G2(:) F1(:) F2(:)];
T=[60 60 0 0 -60 -60];
alpha=0.03;
W=randn(1,16);
b=0;
TotalErr=0;
NumPoints=6;

for k=1:60
    idx=randperm(6);

    for j=1:NumPoints
        ThisOut=W*X(:,idx(j))+b;
        ThisErr=T(idx(j))-ThisOut;
    %Update the weights and biases
        W=W+alpha*ThisErr*X(:,idx(j))';
        b=b+alpha*ThisErr;
    end
    EpochErr(k)=norm((W*X+b*ones(1,6))-T);
end
figure(2)
plot(EpochErr);