Java Code Examples for org.jfree.chart.ChartFrame#setVisible()

public void makeChartByMap(Map<String, Object> map,String title) {
    DefaultPieDataset dpd = new DefaultPieDataset(); //建立一个默认的饼图
    System.out.println(map);

    dpd.setValue("优", Integer.parseInt(map.get("优").toString()));
    dpd.setValue("良", Integer.parseInt(map.get("良").toString()));
    dpd.setValue("中", Integer.parseInt(map.get("中").toString()));
    dpd.setValue("差", Integer.parseInt(map.get("差").toString()));
    dpd.setValue("不及格", Integer.parseInt(map.get("不及格").toString()));

    JFreeChart chart = ChartFactory.createPieChart(title, dpd, true, true, false);
    PiePlot piePlot = (PiePlot) chart.getPlot();
    piePlot.setLabelGenerator(new StandardPieSectionLabelGenerator(("{0}:({2})"), NumberFormat.getNumberInstance(), new DecimalFormat("0.00%")));
    //可以查具体的API文档,第一个参数是标题，第二个参数是一个数据集，第三个参数表示是否显示Legend，第四个参数表示是否显示提示，第五个参数表示图中是否存在URL
    ChartFrame chartFrame = new ChartFrame(title, chart);
    //chart要放在Java容器组件中，ChartFrame继承自java的Jframe类。该第一个参数的数据是放在窗口左上角的，不是正中间的标题。
    chartFrame.pack(); //以合适的大小展现图形
    chartFrame.setVisible(true);//图形是否可见
}
 

public Grafico(String frametitle, String charttitle, String xlabel, String ylabel, XYSeriesCollection dataset){
	JFreeChart chart = ChartFactory.createXYLineChart(charttitle, xlabel, ylabel, dataset, PlotOrientation.VERTICAL, true, true, false);

	XYPlot plot = (XYPlot) chart.getPlot();

	plot.setBackgroundPaint(Color.lightGray);
	plot.setAxisOffset(new RectangleInsets(5.0, 5.0, 5.0, 5.0));
	plot.setDomainGridlinePaint(Color.white);
	plot.setRangeGridlinePaint(Color.white);
	XYLineAndShapeRenderer renderer = (XYLineAndShapeRenderer) plot.getRenderer();
	renderer.setShapesVisible(true);
	renderer.setShapesFilled(true);

	setXyplot(plot);
	setChart(chart);

	ChartFrame frame= new ChartFrame(frametitle,chart);

	frame.pack();
	frame.setVisible(true);
}
 

private void imagejLabelMouseClicked(java.awt.event.MouseEvent evt) {//GEN-FIRST:event_imagejLabelMouseClicked

    if (this.chart != null) {
        this.chart.setTitle(((VisualizePanel) this.getParent().getParent()).getData().getAttributeIndex(
                this.tableInfojTable.getSelectedRow()));

        ChartFrame frame = new ChartFrame("Attribute chart", chart, true);

        frame.pack();
        frame.setBackground(new Color(225, 225, 225));

        Dimension screenSize = Toolkit.getDefaultToolkit().getScreenSize();
        Dimension frameSize = frame.getSize();
        if (frameSize.height > screenSize.height) {
            frameSize.height = screenSize.height;
        }
        if (frameSize.width > screenSize.width) {
            frameSize.width = screenSize.width;
        }
        frame.setLocation((screenSize.width - frameSize.width) / 2,
                (screenSize.height - frameSize.height) / 2);
        frame.setIconImage(Toolkit.getDefaultToolkit().getImage(this.getClass().getResource("/keel/GraphInterKeel/resources/ico/logo/logo.gif")));
        frame.setVisible(true);
    }
}
 

private void imagejLabelMouseClicked(java.awt.event.MouseEvent evt) {//GEN-FIRST:event_imagejLabelMouseClicked
    if (this.chart2 != null) {
        ChartFrame frame = new ChartFrame("Attribute comparison", chart2,
                true);
        frame.pack();
        frame.setBackground(new Color(225, 225, 225));
        Dimension screenSize = Toolkit.getDefaultToolkit().getScreenSize();
        Dimension frameSize = frame.getSize();
        if (frameSize.height > screenSize.height) {
            frameSize.height = screenSize.height;
        }
        if (frameSize.width > screenSize.width) {
            frameSize.width = screenSize.width;
        }
        frame.setLocation((screenSize.width - frameSize.width) / 2,
                (screenSize.height - frameSize.height) / 2);
        frame.setIconImage(Toolkit.getDefaultToolkit().getImage(this.getClass().getResource("/keel/GraphInterKeel/resources/ico/logo/logo.gif")));
        frame.setVisible(true);
    }
}
 

public static void main(String[] args) {
	ArrayList<Double> dados1 = new ArrayList<Double>();
	dados1.add(1.0);
	dados1.add(2.0);
	dados1.add(4.0);
	dados1.add(8.0);
	dados1.add(16.0);
	dados1.add(32.0);
	dados1.add(64.0);
	dados1.add(128.0);
	
	Chart c = new Chart();
	
	c.plot(dados1, "Line plot", "X axis", "Y axis");
               
           int numberOfInputs=2;
           int numberOfNeurons=10;
           int numberOfPoints=100;
       
           double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -10.0, 10.0);
           
           String[] seriesNames = {"Scatter Plot"};
           Paint[] seriesColor = {Color.WHITE};
           
           Chart chart = new Chart("Scatter Plot",rndDataSet,seriesNames,0,seriesColor,Chart.SeriesType.DOTS);
           ChartFrame frame = new ChartFrame("Scatter Plot", chart.scatterPlot("X Axis", "Y Axis"));
           frame.pack();
           frame.setVisible(true);
	
}
 

/**
 * Wrap chart into a frame with specific title and display the frame at provided
 * location.
 *
 * @param title    frame title
 * @param location frame location
 */
public void display (String title,
                     Point location)
{
    ChartFrame frame = new ChartFrame(title, chart, true);
    frame.pack();
    frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
    frame.setLocation(location);
    frame.setVisible(true);
}
 

public static void main(String[] args){
    
    RandomNumberGenerator.seed=0;
    
    int numberOfInputs=2;
    int numberOfNeurons=10;
    int numberOfPoints=100;
    
    double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -10.0, 10.0);
    
    Kohonen kn0 = new Kohonen(numberOfInputs,numberOfNeurons,new UniformInitialization(-1.0,1.0),0);
    
    NeuralDataSet neuralDataSet = new NeuralDataSet(rndDataSet,2);
    
    CompetitiveLearning complrn=new CompetitiveLearning(kn0,neuralDataSet,LearningAlgorithm.LearningMode.ONLINE);
    complrn.show2DData=true;
    complrn.printTraining=true;
    complrn.setLearningRate(0.003);
    complrn.setMaxEpochs(10000);
    complrn.setReferenceEpoch(3000);
    
    try{
        String[] seriesNames = {"Training Data"};
        Paint[] seriesColor = {Color.WHITE};
        
        Chart chart = new Chart("Training",rndDataSet,seriesNames,0,seriesColor);
        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){
        
    }
}
 

public static void main(String[] args) {
	ArrayList<Double> dados1 = new ArrayList<Double>();
	dados1.add(1.0);
	dados1.add(2.0);
	dados1.add(4.0);
	dados1.add(8.0);
	dados1.add(16.0);
	dados1.add(32.0);
	dados1.add(64.0);
	dados1.add(128.0);
	
	Chart c = new Chart();
	
	c.plot(dados1, "Line plot", "X axis", "Y axis");
               
           int numberOfInputs=2;
           int numberOfNeurons=10;
           int numberOfPoints=100;
       
           double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -10.0, 10.0);
           
           String[] seriesNames = {"Scatter Plot"};
           Paint[] seriesColor = {Color.WHITE};
           
           Chart chart = new Chart("Scatter Plot",rndDataSet,seriesNames,0,seriesColor,Chart.SeriesType.DOTS);
           ChartFrame frame = new ChartFrame("Scatter Plot", chart.scatterPlot("X Axis", "Y Axis"));
           frame.pack();
           frame.setVisible(true);
	
}
 

public static void main(String[] args){
    
    RandomNumberGenerator.seed=0;
    
    int numberOfInputs=2;
    int numberOfNeurons=10;
    int numberOfPoints=100;
    
    double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -10.0, 10.0);
    
    Kohonen kn0 = new Kohonen(numberOfInputs,numberOfNeurons,new UniformInitialization(-1.0,1.0),0);
    
    NeuralDataSet neuralDataSet = new NeuralDataSet(rndDataSet,2);
    
    CompetitiveLearning complrn=new CompetitiveLearning(kn0,neuralDataSet,LearningAlgorithm.LearningMode.ONLINE);
    complrn.show2DData=true;
    complrn.printTraining=true;
    complrn.setLearningRate(0.003);
    complrn.setMaxEpochs(10000);
    complrn.setReferenceEpoch(3000);
    
    try{
        String[] seriesNames = {"Training Data"};
        Paint[] seriesColor = {Color.WHITE};
        
        Chart chart = new Chart("Training",rndDataSet,seriesNames,0,seriesColor);
        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){
        
    }
}
 

public static void main(String[] args) {
	ArrayList<Double> dados1 = new ArrayList<Double>();
	dados1.add(1.0);
	dados1.add(2.0);
	dados1.add(4.0);
	dados1.add(8.0);
	dados1.add(16.0);
	dados1.add(32.0);
	dados1.add(64.0);
	dados1.add(128.0);
	
	Chart c = new Chart();
	
	c.plot(dados1, "Line plot", "X axis", "Y axis");
               
           int numberOfInputs=2;
           int numberOfNeurons=10;
           int numberOfPoints=100;
       
           double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -10.0, 10.0);
           
           String[] seriesNames = {"Scatter Plot"};
           Paint[] seriesColor = {Color.WHITE};
           
           Chart chart = new Chart("Scatter Plot",rndDataSet,seriesNames,0,seriesColor,Chart.SeriesType.DOTS);
           ChartFrame frame = new ChartFrame("Scatter Plot", chart.scatterPlot("X Axis", "Y Axis"));
           frame.pack();
           frame.setVisible(true);
	
}
 

public static void main(String[] args){
    
    RandomNumberGenerator.seed=0;
    
    int numberOfInputs=2;
    int numberOfNeurons=10;
    int numberOfPoints=100;
    
    double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -10.0, 10.0);
    
    Kohonen kn0 = new Kohonen(numberOfInputs,numberOfNeurons,new UniformInitialization(-1.0,1.0),0);
    
    NeuralDataSet neuralDataSet = new NeuralDataSet(rndDataSet,2);
    
    CompetitiveLearning complrn=new CompetitiveLearning(kn0,neuralDataSet,LearningAlgorithm.LearningMode.ONLINE);
    complrn.show2DData=true;
    complrn.printTraining=true;
    complrn.setLearningRate(0.003);
    complrn.setMaxEpochs(10000);
    complrn.setReferenceEpoch(3000);
    
    try{
        String[] seriesNames = {"Training Data"};
        Paint[] seriesColor = {Color.WHITE};
        
        Chart chart = new Chart("Training",rndDataSet,seriesNames,0,seriesColor);
        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){
        
    }
}
 

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){
            
        }
    }
 

public static void main(String[] args){
    
    RandomNumberGenerator.seed=0;
    
    int numberOfInputs=2;
    int numberOfNeurons=20;
    int numberOfPoints=1000;
    
    double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -100.0, 100.0);
    
    for (int i=0;i<numberOfPoints;i++){
        rndDataSet[i][0]=i;            
        rndDataSet[i][0]+=RandomNumberGenerator.GenerateNext();
        rndDataSet[i][1]=Math.cos(i/100.0)*1000;            
        rndDataSet[i][1]+=RandomNumberGenerator.GenerateNext()*400;
    }
    
    Kohonen kn1 = new Kohonen(numberOfInputs,numberOfNeurons,new UniformInitialization(0.0,1000.0),1);
    
    NeuralDataSet neuralDataSet = new NeuralDataSet(rndDataSet,2);
    
    CompetitiveLearning complrn=new CompetitiveLearning(kn1,neuralDataSet,LearningAlgorithm.LearningMode.ONLINE);
    complrn.show2DData=true;
    complrn.printTraining=true;
    complrn.setLearningRate(0.3);
    complrn.setMaxEpochs(10000);
    complrn.setReferenceEpoch(3000);
    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);
        
        complrn.setPlot2DFrame(frame);
        complrn.showPlot2DData();
        System.in.read();
        
        complrn.train();
    }
    catch(Exception ne){
        
    }
}
 

public static void main(String[] args){
    
    RandomNumberGenerator.seed=0;
    
    int numberOfInputs=2;
    int numberOfNeurons=20;
    int numberOfPoints=1000;
    
    double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -100.0, 100.0);
    
    for (int i=0;i<numberOfPoints;i++){
        rndDataSet[i][0]=i;            
        rndDataSet[i][0]+=RandomNumberGenerator.GenerateNext();
        rndDataSet[i][1]=Math.cos(i/100.0)*1000;            
        rndDataSet[i][1]+=RandomNumberGenerator.GenerateNext()*400;
    }
    
    Kohonen kn1 = new Kohonen(numberOfInputs,numberOfNeurons,new UniformInitialization(0.0,1000.0),1);
    
    NeuralDataSet neuralDataSet = new NeuralDataSet(rndDataSet,2);
    
    CompetitiveLearning complrn=new CompetitiveLearning(kn1,neuralDataSet,LearningAlgorithm.LearningMode.ONLINE);
    complrn.show2DData=true;
    complrn.printTraining=true;
    complrn.setLearningRate(0.3);
    complrn.setMaxEpochs(10000);
    complrn.setReferenceEpoch(3000);
    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);
        
        complrn.setPlot2DFrame(frame);
        complrn.showPlot2DData();
        System.in.read();
        
        complrn.train();
    }
    catch(Exception ne){
        
    }
}
 

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){
            
        }
    }
 

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){
            
        }
    }
 

public static void main(String[] args){
    
    RandomNumberGenerator.seed=0;
    
    int numberOfInputs=2;
    int numberOfNeurons=20;
    int numberOfPoints=1000;
    
    double[][] rndDataSet = RandomNumberGenerator.GenerateMatrixBetween(numberOfPoints, numberOfInputs, -100.0, 100.0);
    
    for (int i=0;i<numberOfPoints;i++){
        rndDataSet[i][0]=i;            
        rndDataSet[i][0]+=RandomNumberGenerator.GenerateNext();
        rndDataSet[i][1]=Math.cos(i/100.0)*1000;            
        rndDataSet[i][1]+=RandomNumberGenerator.GenerateNext()*400;
    }
    
    Kohonen kn1 = new Kohonen(numberOfInputs,numberOfNeurons,new UniformInitialization(0.0,1000.0),1);
    
    NeuralDataSet neuralDataSet = new NeuralDataSet(rndDataSet,2);
    
    CompetitiveLearning complrn=new CompetitiveLearning(kn1,neuralDataSet,LearningAlgorithm.LearningMode.ONLINE);
    complrn.show2DData=true;
    complrn.printTraining=true;
    complrn.setLearningRate(0.3);
    complrn.setMaxEpochs(10000);
    complrn.setReferenceEpoch(3000);
    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);
        
        complrn.setPlot2DFrame(frame);
        complrn.showPlot2DData();
        System.in.read();
        
        complrn.train();
    }
    catch(Exception ne){
        
    }
}
 

@Override
public synchronized void run() {

    DefaultCategoryDataset dataset = new DefaultCategoryDataset();

    final JFreeChart chart = ChartFactory.createBarChart(
            getTitle(),
            getEntity(),
            "Value",
            dataset,
            PlotOrientation.VERTICAL,
            true,
            true,
            false
    );

    final CategoryPlot plot = chart.getCategoryPlot();
    plot.setBackgroundPaint(Color.lightGray);
    plot.setDomainGridlinePaint(Color.white);
    plot.setRangeGridlinePaint(Color.white);
    chart.setBackgroundPaint(Color.lightGray);

    ChartFrame frame= new ChartFrame(getTitle(), chart);
    frame.pack();
    frame.setVisible(true);

    while (true) {
        ArrayList<Codelet> tempCodeletsList = new ArrayList<Codelet>();
        tempCodeletsList.addAll(this.getListOfMotivationalEntities());

        synchronized (tempCodeletsList) {

            for (Codelet co : tempCodeletsList) {
                dataset.addValue(co.getActivation(), co.getName(), "activation");
            }
            try {
                Thread.currentThread().sleep(getRefreshPeriod());
            } catch (InterruptedException e) {
                e.printStackTrace();
            }

        }
    }
}
 

/**
 * All params can be null.
 *
 * @param min - the minimum number on the left range axis (must be set if max is set)
 * @param max - the maximum number on the left range axis (must be set if min is set)
 * @param method2 - the method to run out of Record, and populates a bar series (right range axis)
 * @param highLightIndex - the index to highlight
 * @return
 * @throws Exception
 */
public JFreeChart makeTimeChart(Double min, Double max, String method2, Integer highLightIndex)
    throws Exception {
  startTms = System.currentTimeMillis();
  endTms = 0;
  XYBarRenderer.setDefaultShadowsVisible(false);
  TimeSeriesCollection data = new TimeSeriesCollection();
  Map<String, TimeSeries> timeSeriesMap = makeTimeSeriesMap(method, recordMap, keyNames);
  for (TimeSeries timeSeries : timeSeriesMap.values()) {
    data.addSeries(timeSeries);
  }
  String yAxisLabel = method;
  String denominator = Record.getDenominator(report.getRecordClass(), method.toLowerCase());
  if (denominator != null) {
    yAxisLabel += " (" + denominator + ")";
  }
  chart = ChartFactory.createTimeSeriesChart(title, "Time", yAxisLabel, data, true, true, true);
  XYPlot plot = (XYPlot) chart.getPlot();
  if (method2 != null) {
    Map<Key, R> recordMap2 = recordMap;
    TimeSeriesCollection data2 = new TimeSeriesCollection();
    if (keyNames.length > 0) {
      recordMap2 = report.getMapAggregatedOn();
    }
    Map<String, TimeSeries> timeSeriesMap2 = makeTimeSeriesMap(method2, recordMap2);
    if (timeSeriesMap2.get("Total (" + method2 + ")") != null) {
      data2.addSeries(timeSeriesMap2.get("Total (" + method2 + ")"));
    }
    String y2AxisLabel = method2;
    String demoninator2 = Record.getDenominator(report.getRecordClass(), method2.toLowerCase());
    if (demoninator2 != null) {
      y2AxisLabel += " (" + demoninator2 + ")";
    }
    NumberAxis axis2 = new NumberAxis(y2AxisLabel);
    XYBarRenderer renderer2 = new XYBarRenderer(0.20);
    plot.setRangeAxis(1, axis2);
    plot.setRangeAxisLocation(1, AxisLocation.BOTTOM_OR_RIGHT);
    plot.setDataset(1, data2);
    plot.setRenderer(1, renderer2);
    plot.mapDatasetToRangeAxis(1, 1);
    renderer2.setBarPainter(new StandardXYBarPainter());
    plot.setDatasetRenderingOrder(DatasetRenderingOrder.REVERSE);
  }
  if (min != null && max != null) {
    plot.getRangeAxis(0).setRange(min, max);
  }

  if (highLightIndex != null) {
    chart.getXYPlot().getRenderer().setSeriesStroke(highLightIndex, new BasicStroke(5f));
  }

  XYLineAndShapeRenderer renderer = (XYLineAndShapeRenderer) plot.getRenderer();
  renderer.setDefaultShapesVisible(true);
  renderer.setUseFillPaint(true);
  renderer.setDefaultFillPaint(Color.white);

  long diff = endTms - startTms;
  String format;
  if (diff > NINTY_DAYS) {
    format = "MMM-yyyy";
  } else if (diff > TWO_DAYS) {
    format = "dd-MMM";
  } else if (diff > TWO_MINUTES) {
    format = "HH:mm";
  } else {
    format = "HH:mm:ss";
  }
  DateAxis axis = (DateAxis) plot.getDomainAxis();
  axis.setDateFormatOverride(new SimpleDateFormat(format));

  LegendTitle lt = chart.getLegend(0);
  lt.setPosition(RectangleEdge.RIGHT);

  if (displayFrame) {
    ChartFrame frame = new ChartFrame(title, chart);
    frame.pack();
    frame.setVisible(true);
  }

  return chart;
}
 

public void plot (Point upperLeft)
        {
            // All values, quorum line & spread line
            plotValues();
            plotQuorumLine();
            plotSpreadLine("", peak);

            // Second peak spread line?
            if (secondPeak != null) {
                plotSpreadLine("Second", secondPeak);
            }

            // Chart
//            JFreeChart chartLines = ChartFactory.createXYLineChart(
//                    sheet.getId() + " (" + name + " runs)", // Title
//                    "Lengths " + ((scale != null) ? scale : "*no scale*"), // X-Axis label
//                    "Counts", // Y-Axis label
//                    dataset, // Dataset
//                    PlotOrientation.VERTICAL, // orientation,
//                    true, // Show legend
//                    false, // Show tool tips
//                    false // urls
//                    );
            // use a histogram so we can see the actual buckets values
            // rather than being left to interpolate for any given length
	    JFreeChart chart = ChartFactory.createHistogram(
                    sheet.getId() + " (" + name + " runs)", // Title
                    "", // X-Axis label - already labeled in chartLines
                    "", // Y-Axis label - already labeled in chartLines
                    dataset, // Dataset
                    PlotOrientation.VERTICAL, // orientation,
                    true, // Show legend
                    false, // Show tool tips
                    false // urls
                    );
            // have the quorum and spread be lines rather than bars
            XYPlot xyPlot = (XYPlot) chart.getPlot();
            xyPlot.setDataset(1, datasetLines);
            XYLineAndShapeRenderer renderer1 = new XYLineAndShapeRenderer();
            renderer1.setSeriesPaint(0, Color.GREEN); 
            xyPlot.setRenderer(1, renderer1);
            // "FORWARD" causes the added dataset of 
            // lines to be overlayed on histogram bars
            xyPlot.setDatasetRenderingOrder(DatasetRenderingOrder.FORWARD);
            
            // Hosting frame
            ChartFrame frame = new ChartFrame(
                    sheet.getId() + " - " + name + " runs",
                    chart,
                    true);
            frame.pack();
            frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
            frame.setLocation(upperLeft);
            frame.setVisible(true);
        }