% Training nonlinear layers (general_trainbp.m).

clf;
figure(gcf)
setfsize(500,200);
echo on
clc

%    INITFF   - Initializes a feed-forward network.
%    TRAINBP  - Trains a feed-forward network with backpropagation.
%    SIMUFF   - Simulates a feed-forward network.

%    PATTERN ASSOCIATION WITH A LINEAR NEURON:

%    Using the above functions a neuron is trained
%    to respond to specific inputs with target outputs.

pause % Strike any key to continue...
clc
%    DEFINING A VECTOR ASSOCATION PROBLEM
%    ====================================

%    P defines four 2-element input vectors (column vectors):

load screen.dat ;
P = screen';

%    T defines the associated 1-element targets (column vectors):

load TS.dat ;
T = TS';

pause % Strike any key to design the network...
clc
%    DESIGN THE NETWORK
%    ==================

%    INITFF is used to initialize the weights and biases for
%    the LOGSIG neuron.
%    

[W1,b1,W2,b2] = initff(P,8,'logsig',T,'purelin')

clc
%    TRAINING THE NETWORK
%    ====================

%    TBP1 uses backpropagation to train 1-layer networks.

df = 10   ; % Frequency of display.
me = 100 ; % Maximum number of epochs to train.
eg = 0.01 ; % Sum-squared error goal.
lr = 0.0001 ; % Learning rate.

tp = [ df me eg lr ] ;

%    Training begins...please wait...

[W1,b1,W2,b2,ep,tr] = trainbp(W1,b1,'logsig',W2,b2,'purelin',P,T,tp);

%    ...and finishes.

%    TRAINBP has returned new weight and bias values, the number
%    of epochs trained EP, and a record of training errors TR.

pause % Strike any key to see a plot of errors...
clc
%    PLOTTING THE ERROR CURVE
%    ========================

%    Here the errors are plotted with respect to training epochs:

ploterr(tr,eg);

pause % Strike any key to use the classifier...
clc
%    USING THE PATTERN ASSOCIATOR
%    ============================

%    We can now test the associator with the origonal
%    inputs and see if it returns the target.

B = simuff(P,W1,b1,'logsig',W2,b2,'purelin')
A = round(B)
T
E = T - A

errors=sum(abs(E))

pause % strike any key to go on

load applns.dat;
PA=applns';
AA=round(simuff(PA,W1,b1,'logsig',W2,b2,'purelin'));
save predict2.dat AA -ascii

echo off
disp('End of general_trainbp.m')

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