/*
* Encog(tm) Examples v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.examples.neural.adaline;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
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 MLDataSet generateTraining()
{
MLDataSet result = new BasicMLDataSet();
for(int i=0;i<DIGITS.length;i++)
{
BasicMLData ideal = new BasicMLData(DIGITS.length);
// setup input
MLData 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 MLData image2data(String[] image)
{
MLData result = new BasicMLData(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 = (BasicNetwork)pattern.generate();
// train it
MLDataSet training = generateTraining();
MLTrain 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();
}
}
}