/*
* 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.hopfield;
import org.encog.ml.data.specific.BiPolarNeuralData;
import org.encog.neural.thermal.HopfieldNetwork;
/**
* Simple class to recognize some patterns with a Hopfield Neural Network.
* This is very loosely based on a an example by Karsten Kutza,
* written in C on 1996-01-30.
* http://www.neural-networks-at-your-fingertips.com/hopfield.html
*
* I translated it to Java and adapted it to use Encog for neural
* network processing. I mainly kept the patterns from the
* original example.
*
*/
public class HopfieldAssociate {
final static int HEIGHT = 10;
final static int WIDTH = 10;
/**
* The neural network will learn these patterns.
*/
public static final String[][] PATTERN = { {
"O O O O O ",
" O O O O O",
"O O O O O ",
" O O O O O",
"O O O O O ",
" O O O O O",
"O O O O O ",
" O O O O O",
"O O O O O ",
" O O O O O" },
{ "OO OO OO",
"OO OO OO",
" OO OO ",
" OO OO ",
"OO OO OO",
"OO OO OO",
" OO OO ",
" OO OO ",
"OO OO OO",
"OO OO OO" },
{ "OOOOO ",
"OOOOO ",
"OOOOO ",
"OOOOO ",
"OOOOO ",
" OOOOO",
" OOOOO",
" OOOOO",
" OOOOO",
" OOOOO" },
{ "O O O O",
" O O O ",
" O O O ",
"O O O O",
" O O O ",
" O O O ",
"O O O O",
" O O O ",
" O O O ",
"O O O O" },
{ "OOOOOOOOOO",
"O O",
"O OOOOOO O",
"O O O O",
"O O OO O O",
"O O OO O O",
"O O O O",
"O OOOOOO O",
"O O",
"OOOOOOOOOO" } };
/**
* The neural network will be tested on these patterns, to see
* which of the last set they are the closest to.
*/
public static final String[][] PATTERN2 = { {
" ",
" ",
" ",
" ",
" ",
" O O O O O",
"O O O O O ",
" O O O O O",
"O O O O O ",
" O O O O O" },
{ "OOO O O",
" O OOO OO",
" O O OO O",
" OOO O ",
"OO O OOO",
" O OOO O",
"O OO O O",
" O OOO ",
"OO OOO O ",
" O O OOO" },
{ "OOOOO ",
"O O OOO ",
"O O OOO ",
"O O OOO ",
"OOOOO ",
" OOOOO",
" OOO O O",
" OOO O O",
" OOO O O",
" OOOOO" },
{ "O OOOO O",
"OO OOOO ",
"OOO OOOO ",
"OOOO OOOO",
" OOOO OOO",
" OOOO OO",
"O OOOO O",
"OO OOOO ",
"OOO OOOO ",
"OOOO OOOO" },
{ "OOOOOOOOOO",
"O O",
"O O",
"O O",
"O OO O",
"O OO O",
"O O",
"O O",
"O O",
"OOOOOOOOOO" } };
public BiPolarNeuralData convertPattern(String[][] data, int index)
{
int resultIndex = 0;
BiPolarNeuralData result = new BiPolarNeuralData(WIDTH*HEIGHT);
for(int row=0;row<HEIGHT;row++)
{
for(int col=0;col<WIDTH;col++)
{
char ch = data[index][row].charAt(col);
result.setData(resultIndex++, ch=='O');
}
}
return result;
}
public void display(BiPolarNeuralData pattern1,BiPolarNeuralData pattern2)
{
int index1 = 0;
int index2 = 0;
for(int row = 0;row<HEIGHT;row++)
{
StringBuilder line = new StringBuilder();
for(int col = 0;col<WIDTH;col++)
{
if(pattern1.getBoolean(index1++))
line.append('O');
else
line.append(' ');
}
line.append(" -> ");
for(int col = 0;col<WIDTH;col++)
{
if(pattern2.getBoolean(index2++))
line.append('O');
else
line.append(' ');
}
System.out.println(line.toString());
}
}
public void evaluate(HopfieldNetwork hopfieldLogic, String[][] pattern)
{
for(int i=0;i<pattern.length;i++)
{
BiPolarNeuralData pattern1 = convertPattern(pattern,i);
hopfieldLogic.setCurrentState(pattern1);
int cycles = hopfieldLogic.runUntilStable(100);
BiPolarNeuralData pattern2 = (BiPolarNeuralData)hopfieldLogic.getCurrentState();
System.out.println("Cycles until stable(max 100): " + cycles + ", result=");
display( pattern1, pattern2);
System.out.println("----------------------");
}
}
public void run()
{
/*HopfieldPattern pattern = new HopfieldPattern();
pattern.setInputNeurons(WIDTH*HEIGHT);
BasicNetwork hopfield = pattern.generate();
HopfieldLogic hopfieldLogic = (HopfieldLogic)hopfield.getLogic();*/
HopfieldNetwork hopfieldLogic = new HopfieldNetwork(WIDTH*HEIGHT);
for(int i=0;i<PATTERN.length;i++)
{
hopfieldLogic.addPattern(convertPattern(PATTERN,i));
}
evaluate(hopfieldLogic,PATTERN);
evaluate(hopfieldLogic,PATTERN2);
}
public static void main(String[] args)
{
HopfieldAssociate program = new HopfieldAssociate();
program.run();
}
}