/*
 * 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.
 * 
 * Encog and Heaton Research are Trademarks of Heaton Research, Inc.
 * For information on Heaton Research trademarks, visit:
 * 
 * http://www.heatonresearch.com/copyright.html
 */

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.neural.data.NeuralData;
import org.encog.neural.data.basic.BasicNeuralData;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.competitive.CompetitiveTraining;
import org.encog.neural.networks.training.competitive.neighborhood.NeighborhoodRBF;
import org.encog.neural.pattern.SOMPattern;

public class SomColors extends JFrame implements Runnable {

	/**
	 * 
	 */
	private static final long serialVersionUID = -6762179069967224817L;
	private MapPanel map;
	private BasicNetwork network;
	private Thread thread;
	private CompetitiveTraining 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 CompetitiveTraining(this.network, 0.01, null, gaussian);
		train.setForceWinner(false);
		this.thread = new Thread(this);
		thread.start();
	}

	public BasicNetwork getNetwork() {
		return this.network;
	}

	private BasicNetwork createNetwork() {
		BasicNetwork result = new BasicNetwork();
		SOMPattern pattern = new SOMPattern();
		pattern.setInputNeurons(3);
		pattern.setOutputNeurons(MapPanel.WIDTH * MapPanel.HEIGHT);
		result = pattern.generate();
		result.reset();
		return result;
	}

	public static void main(String[] args) {
		SomColors frame = new SomColors();
		frame.setVisible(true);
	}

	public void run() {

		List<NeuralData> samples = new ArrayList<NeuralData>();
		for (int i = 0; i < 15; i++) {
			NeuralData data = new BasicNeuralData(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());
			NeuralData c = samples.get(idx);

			this.train.trainPattern(c);
			this.train.autoDecay();
			this.map.repaint();
			System.out.println("Iteration " + i + "," + this.train.toString());
		}
	}
}