/*
* Encog(tm) Examples v2.4
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
*
* Copyright 2008-2010 by Heaton Research Inc.
*
* Released under the LGPL.
*
* This is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of
* the License, or (at your option) any later version.
*
* This software 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
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this software; if not, write to the Free
* Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
* 02110-1301 USA, or see the FSF site: http://www.fsf.org.
*
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* For information on Heaton Research trademarks, visit:
*
* http://www.heatonresearch.com/copyright.html
*/
package org.encog.examples.neural.adaline;
import org.encog.neural.data.NeuralData;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralData;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.Train;
import org.encog.neural.networks.training.simple.TrainAdaline;
import org.encog.neural.pattern.ADALINEPattern;
public class AdalineDigits {
public final static int CHAR_WIDTH = 5;
public final static int CHAR_HEIGHT = 7;
public static String[][] DIGITS = {
{ " OOO ",
"O O",
"O O",
"O O",
"O O",
"O O",
" OOO " },
{ " O ",
" OO ",
"O O ",
" O ",
" O ",
" O ",
" O " },
{ " OOO ",
"O O",
" O",
" O ",
" O ",
" O ",
"OOOOO" },
{ " OOO ",
"O O",
" O",
" OOO ",
" O",
"O O",
" OOO " },
{ " O ",
" OO ",
" O O ",
"O O ",
"OOOOO",
" O ",
" O " },
{ "OOOOO",
"O ",
"O ",
"OOOO ",
" O",
"O O",
" OOO " },
{ " OOO ",
"O O",
"O ",
"OOOO ",
"O O",
"O O",
" OOO " },
{ "OOOOO",
" O",
" O",
" O ",
" O ",
" O ",
"O " },
{ " OOO ",
"O O",
"O O",
" OOO ",
"O O",
"O O",
" OOO " },
{ " OOO ",
"O O",
"O O",
" OOOO",
" O",
"O O",
" OOO " } };
public static NeuralDataSet generateTraining()
{
NeuralDataSet result = new BasicNeuralDataSet();
for(int i=0;i<DIGITS.length;i++)
{
BasicNeuralData ideal = new BasicNeuralData(DIGITS.length);
// setup input
NeuralData input = image2data(DIGITS[i]);
// setup ideal
for(int j=0;j<DIGITS.length;j++)
{
if( j==i )
ideal.setData(j,1);
else
ideal.setData(j,-1);
}
// add training element
result.add(input,ideal);
}
return result;
}
public static NeuralData image2data(String[] image)
{
NeuralData result = new BasicNeuralData(CHAR_WIDTH*CHAR_HEIGHT);
for(int row = 0; row<CHAR_HEIGHT; row++)
{
for(int col = 0; col<CHAR_WIDTH; col++)
{
int index = (row*CHAR_WIDTH) + col;
char ch = image[row].charAt(col);
result.setData(index,ch=='O'?1:-1 );
}
}
return result;
}
public static void main(String args[])
{
int inputNeurons = CHAR_WIDTH * CHAR_HEIGHT;
int outputNeurons = DIGITS.length;
ADALINEPattern pattern = new ADALINEPattern();
pattern.setInputNeurons(inputNeurons);
pattern.setOutputNeurons(outputNeurons);
BasicNetwork network = pattern.generate();
// train it
NeuralDataSet training = generateTraining();
Train train = new TrainAdaline(network,training,0.01);
int epoch = 1;
do {
train.iteration();
System.out
.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while(train.getError() > 0.01);
//
System.out.println("Error:" + network.calculateError(training));
// test it
for(int i=0;i<DIGITS.length;i++)
{
int output = network.winner(image2data(DIGITS[i]));
for(int j=0;j<CHAR_HEIGHT;j++)
{
if( j==CHAR_HEIGHT-1 )
System.out.println(DIGITS[i][j]+" -> "+output);
else
System.out.println(DIGITS[i][j]);
}
System.out.println();
}
}
}