/*
* 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.gui.hopfield;
import java.awt.Color;
import java.awt.Graphics;
import java.awt.event.MouseEvent;
import java.awt.event.MouseListener;
import javax.swing.JPanel;
import org.encog.ml.data.specific.BiPolarNeuralData;
import org.encog.neural.thermal.HopfieldNetwork;
import org.encog.util.EngineArray;
public class HopfieldPanel extends JPanel implements MouseListener {
public HopfieldNetwork hopfield;
private boolean grid[];
private int margin;
private int gridX;
private int gridY;
private int cellWidth;
private int cellHeight;
public HopfieldPanel(int gridX,int gridY) {
this.gridX = gridX;
this.gridY = gridY;
this.grid = new boolean[this.gridX * this.gridY];
this.addMouseListener(this);
this.hopfield = new HopfieldNetwork(this.gridX * this.gridY);
}
/**
* Clear the grid.
*/
public void clear() {
int index = 0;
for (int y = 0; y < this.gridY; y++) {
for (int x = 0; x < this.gridX; x++) {
this.grid[index++] = false;
}
}
repaint();
}
/**
* Clear the weight matrix.
*/
public void clearMatrix() {
EngineArray.fill(this.hopfield.getWeights(),0);
}
/**
* Run the neural network.
*/
public void go() {
for(int i=0;i<this.grid.length;i++) {
this.hopfield.getCurrentState().setData(i, grid[i]);
}
this.hopfield.run();
for(int i=0;i<this.grid.length;i++) {
grid[i] = this.hopfield.getCurrentState().getBoolean(i);
}
repaint();
}
public void mouseReleased(final MouseEvent e) {
final int x = ((e.getX() - this.margin) / this.cellWidth);
final int y = e.getY() / this.cellHeight;
if (((x >= 0) && (x < this.gridY)) && ((y >= 0) && (y < this.gridY))) {
final int index = (y * this.gridX) + x;
this.grid[index] = !this.grid[index];
}
repaint();
}
@Override
public void paint(final Graphics g) {
int width = this.getWidth();
int height = this.getHeight();
this.cellHeight = height/this.gridY;
this.cellWidth = width/this.gridX;
g.setColor(Color.WHITE);
g.fillRect(0,0, width, height);
g.setColor(Color.BLACK);
this.margin = (this.getWidth() - (this.cellWidth * this.gridX)) / 2;
int index = 0;
for (int y = 0; y < this.gridY; y++) {
for (int x = 0; x < this.gridX; x++) {
if (this.grid[index++]) {
g.fillRect(this.margin + (x * this.cellWidth), y
* this.cellHeight, this.cellWidth,
this.cellHeight);
} else {
g.drawRect(this.margin + (x * this.cellWidth), y
* this.cellHeight, this.cellWidth,
this.cellHeight);
}
}
}
}
/**
* Train the neural network.
*/
public void train() {
BiPolarNeuralData pattern = new BiPolarNeuralData(this.grid.length);
for(int i=0;i<this.grid.length;i++) {
pattern.setData(i, grid[i]);
}
this.hopfield.addPattern(pattern);
}
@Override
public void mouseClicked(MouseEvent e) {
// TODO Auto-generated method stub
}
@Override
public void mousePressed(MouseEvent e) {
// TODO Auto-generated method stub
}
@Override
public void mouseEntered(MouseEvent e) {
// TODO Auto-generated method stub
}
@Override
public void mouseExited(MouseEvent e) {
// TODO Auto-generated method stub
}
}