/*
* 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.predict;
import java.awt.BorderLayout;
import java.awt.Container;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import javax.swing.JButton;
import javax.swing.JFrame;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.arrayutil.TemporalWindowArray;
import org.encog.util.simple.EncogUtility;
public class PredictSIN extends JFrame implements ActionListener {
public final static int INPUT_WINDOW = 5;
public final static int PREDICT_WINDOW = 1;
private BasicNetwork network;
private GraphPanel graph;
private MLDataSet trainingData;
private MLTrain train;
private JButton btnTrain;
public PredictSIN()
{
this.setTitle("SIN Wave Predict");
this.setSize(640, 480);
Container content = this.getContentPane();
content.setLayout(new BorderLayout());
content.add(graph = new GraphPanel(), BorderLayout.CENTER);
network = EncogUtility.simpleFeedForward(INPUT_WINDOW, PREDICT_WINDOW*2, 0, 1, true);
network.reset();
graph.setNetwork(network);
this.trainingData = generateTraining();
this.train = new ResilientPropagation(this.network,this.trainingData);
btnTrain = new JButton("Train");
this.btnTrain.addActionListener(this);
content.add(btnTrain,BorderLayout.SOUTH);
graph.setError(network.calculateError(this.trainingData));
}
public void performTraining()
{
for(int i=0;i<10;i++) {
this.train.iteration();
}
graph.setError(train.getError());
}
public MLDataSet generateTraining()
{
TemporalWindowArray temp = new TemporalWindowArray(INPUT_WINDOW,PREDICT_WINDOW);
double[] a = new double[360];
for(int i = 0;i<360;i++)
{
a[i] = GraphPanel.obtainActual(i);
}
temp.analyze(a);
return temp.process(a);
}
public static void main(String[] args)
{
PredictSIN program = new PredictSIN();
program.setVisible(true);
}
public void actionPerformed(ActionEvent e) {
performTraining();
this.graph.refresh();
}
}