/*
* 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.som;
import java.util.ArrayList;
import java.util.List;
import javax.swing.JFrame;
import org.encog.mathutil.randomize.RangeRandomizer;
import org.encog.mathutil.rbf.RBFEnum;
import org.encog.ml.data.MLData;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.neural.som.SOM;
import org.encog.neural.som.training.basic.BasicTrainSOM;
import org.encog.neural.som.training.basic.neighborhood.NeighborhoodRBF;
public class SomColors extends JFrame implements Runnable {
/**
*
*/
private static final long serialVersionUID = -6762179069967224817L;
private MapPanel map;
private SOM network;
private Thread thread;
private BasicTrainSOM train;
private NeighborhoodRBF gaussian;
public SomColors() {
this.setSize(640, 480);
this.setDefaultCloseOperation(EXIT_ON_CLOSE);
this.network = createNetwork();
this.getContentPane().add(map = new MapPanel(this));
this.gaussian = new NeighborhoodRBF(RBFEnum.Gaussian,MapPanel.WIDTH,
MapPanel.HEIGHT);
this.train = new BasicTrainSOM(this.network, 0.01, null, gaussian);
train.setForceWinner(false);
this.thread = new Thread(this);
thread.start();
}
public SOM getNetwork() {
return this.network;
}
private SOM createNetwork() {
SOM result = new SOM(3,MapPanel.WIDTH * MapPanel.HEIGHT);
result.reset();
return result;
}
public static void main(String[] args) {
SomColors frame = new SomColors();
frame.setVisible(true);
}
public void run() {
List<MLData> samples = new ArrayList<MLData>();
for (int i = 0; i < 15; i++) {
MLData data = new BasicMLData(3);
data.setData(0, RangeRandomizer.randomize(-1, 1));
data.setData(1, RangeRandomizer.randomize(-1, 1));
data.setData(2, RangeRandomizer.randomize(-1, 1));
samples.add(data);
}
this.train.setAutoDecay(1000, 0.8, 0.003, 30, 5);
for (int i = 0; i < 1000; i++) {
int idx = (int) (Math.random() * samples.size());
MLData c = samples.get(idx);
this.train.trainPattern(c);
this.train.autoDecay();
this.map.repaint();
System.out.println("Iteration " + i + "," + this.train.toString());
}
}
}