## Copyright (C) 2006 Michel D. Schmid  <michaelschmid@users.sourceforge.net>

##

##

## This program is free software; you can redistribute it and/or modify it

## under the terms of the GNU General Public License as published by

## the Free Software Foundation; either version 2, or (at your option)

## any later version.

##

## This program is distributed in the hope that it will be useful, but

## WITHOUT ANY WARRANTY; without even the implied warranty of

## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU

## General Public License for more details.

##

## You should have received a copy of the GNU General Public License

## along with this program; see the file COPYING.  If not, write to the Free

## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA

## 02110-1301, USA.



## This is a test to train a 9-1-1 MLP (was a real project).



## author: Michel D. Schmid <michaelschmid@users.sourceforge.net>





## for debug purpose only

global DEBUG = 0;

## comments to DEBUG:

# 0 or not exist means NO DEBUG

# 1 means, DEBUG, write to command window

# 2 means, DEBUG, write to files...





## load data



mData = load("mData.txt","mData");

mData = mData.mData;

[nRows, nColumns] = size(mData);

# this file contains 13 columns. The first 12 columns are the inputs

# the last column is the output

# remove column 4, 8 and 12

# 89 rows



## first permute the whole matrix in row wise

## this won't be used right now for debug and test purpose

# order = randperm(nRows);

# mData(order,:) = mData;



mOutput = mData(:,end);

mInput = mData(:,1:end-1);

mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12



## now prepare data

mInput = mInput';

mOutput = mOutput';

%mOutput = [mOutput; mOutput*4];

# now split the data matrix in 3 pieces, train data, test data and validate data

# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data

# in this neural network we have 12 weights, for each weight at least 3 train sets..

# (that's a rule of thumb like 1/2, 1/3 and 1/6)

# 1/2 of 89 = 44.5; let's take 44 for training

nTrainSets = floor(nRows/2);

# now the rest of the sets are again 100%

# ==> 2/3 for test sets and 1/3 for validate sets

nTestSets = (nRows-nTrainSets)/3*2;

nValiSets = nRows-nTrainSets-nTestSets;



mValiInput = mInput(:,1:nValiSets);

mValliOutput = mOutput(:,1:nValiSets);

mInput(:,1:nValiSets) = [];

mOutput(:,1:nValiSets) = [];

mTestInput = mInput(:,1:nTestSets);

mTestOutput = mOutput(:,1:nTestSets);

mInput(:,1:nTestSets) = [];

mOutput(:,1:nTestSets) = [];

mTrainInput = mInput(:,1:nTrainSets);

mTrainOutput = mOutput(:,1:nTrainSets);



[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs



## comments: there is no reason to standardize the outputs because we have only

# one output ...



# define the max and min inputs for each row

mMinMaxElements = min_max(mTrainInputN); % input matrix with (R x 2)...



## define network

nHiddenNeurons = 2;

nOutputNeurons = 1;



MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","learngdm","mse");

## for test purpose, define weights by hand

MLPnet.IW{1,1}(1,:) = 0.5;

MLPnet.IW{1,1}(2,:) = 1.5;

MLPnet.LW{2,1}(:) = 0.5;

MLPnet.b{1,1}(1,:) = 0.5;

MLPnet.b{1,1}(2,:) = 1.5;

MLPnet.b{2,1}(:) = 0.5;



saveMLPStruct(MLPnet,"MLP3test.txt");

#disp("network structure saved, press any key to continue...")

#pause



## define validation data new, for matlab compatibility

VV.P = mValiInput;

VV.T = mValliOutput;



## standardize also the validate data

VV.P = trastd(VV.P,cMeanInput,cStdInput);



#[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);

[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);

# saveMLPStruct(net,"MLP3testNachTraining.txt");

# disp("network structure saved, press any key to continue...")

# pause

# % the names in matlab help, see "train" in help for more informations

# tr.perf(max(tr.epoch)+1);

# 

# % make preparations for net test and test MLPnet

#     % standardise input & output test data

[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);

# % [mTestOutputN] = trastd(mTestOutput,cMeanOutput,cStdOutput);

#     % define unused parameters to get E-variable of simulation

# Pi = zeros(6,0);

# Ai = zeros(6,0);

#     % simulate net

[simOut] = sim(net,mTestInputN);%,Pi,Ai,mTestOutput);

simOut