edu.packt.neuralnet.som.Kohonen2DTest.java Source code

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Here is the source code for edu.packt.neuralnet.som.Kohonen2DTest.java

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package edu.packt.neuralnet.som;

import edu.packt.neuralnet.chart.Chart;
import edu.packt.neuralnet.data.NeuralDataSet;
import edu.packt.neuralnet.init.GaussianInitialization;
import edu.packt.neuralnet.init.UniformInitialization;
import edu.packt.neuralnet.learn.LearningAlgorithm;
import edu.packt.neuralnet.math.RandomNumberGenerator;
import java.awt.Color;
import java.awt.Paint;
import org.jfree.chart.ChartFrame;

/**
 *
 * @author fab
 */
public class Kohonen2DTest {

    public static void main(String[] args) {

        RandomNumberGenerator.seed = System.currentTimeMillis();

        int numberOfInputs = 2;
        int neuronsGridX = 12;
        int neuronsGridY = 12;
        int numberOfPoints = 1000;

        double[][] rndDataSet;
        rndDataSet = RandomNumberGenerator.GenerateMatrixGaussian(numberOfPoints, numberOfInputs, 100.0, 1.0);
        //rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, 100.0, 110.0);

        for (int i = 0; i < numberOfPoints; i++) {
            rndDataSet[i][0] *= Math.sin(i);
            rndDataSet[i][0] += RandomNumberGenerator.GenerateNext() * 50;
            rndDataSet[i][1] *= Math.cos(i);
            rndDataSet[i][1] += RandomNumberGenerator.GenerateNext() * 50;
        }

        //        for (int i=0;i<numberOfPoints;i++){
        //            rndDataSet[i][0]=i;            
        //            rndDataSet[i][0]+=RandomNumberGenerator.GenerateNext();
        //            rndDataSet[i][1]=Math.cos(i/100.0);            
        //            rndDataSet[i][1]+=RandomNumberGenerator.GenerateNext()*5;
        //        }        

        Kohonen kn2 = new Kohonen(numberOfInputs, neuronsGridX, neuronsGridY,
                new GaussianInitialization(500.0, 20.0));

        NeuralDataSet neuralDataSet = new NeuralDataSet(rndDataSet, 2);

        CompetitiveLearning complrn = new CompetitiveLearning(kn2, neuralDataSet,
                LearningAlgorithm.LearningMode.ONLINE);
        complrn.show2DData = true;
        complrn.printTraining = true;
        complrn.setLearningRate(0.5);
        complrn.setMaxEpochs(1000);
        complrn.setReferenceEpoch(300);
        complrn.sleep = -1;

        try {
            String[] seriesNames = { "Training Data" };
            Paint[] seriesColor = { Color.WHITE };

            Chart chart = new Chart("Training", rndDataSet, seriesNames, 0, seriesColor, Chart.SeriesType.DOTS);
            ChartFrame frame = new ChartFrame("Training", chart.scatterPlot("X", "Y"));
            frame.pack();
            frame.setVisible(true);
            //            //System.in.read();
            complrn.setPlot2DFrame(frame);
            complrn.showPlot2DData();
            //System.in.read();
            complrn.train();
        } catch (Exception ne) {

        }
    }

}