%% Example 4 (Same as Example 3, but uses a subroutine for Widrow-Hoff)

%% First, set up the data and plot:
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, create the domain and targets:
X=[T1(:) T2(:) G1(:) G2(:) F1(:) F2(:)];
T=[60 60 0 0 -60 -60];
alpha=0.03;

%% Call our training function:
[W,b,err]=wid_hoff1(X',T',alpha,10)

% The matrix err is 10 x 6, we can plot mean(err') to see the mean error
% on the 10 trials:
figure; % Open a second figure
plot(mean(err'))