Example usage for weka.core Instances instance

Introduction

In this page you can find the example usage for weka.core Instances instance.

Prototype

publicInstance instance(int index) 

Source Link

Document

Returns the instance at the given position.

Usage

From source file:lu.lippmann.cdb.lab.mds.MDSViewBuilder.java

License:Open Source License

/**
 * /*  w  ww . j  a va 2  s .c o  m*/
 * @param clusters
 */
public static void buildKMeansChart(final List<Instances> clusters) {
    final XYSeriesCollection dataset = new XYSeriesCollection();

    final JFreeChart chart = ChartFactory.createScatterPlot("", // title 
            "X", "Y", // axis labels 
            dataset, // dataset 
            PlotOrientation.VERTICAL, true, // legend? yes 
            true, // tooltips? yes 
            false // URLs? no 
    );

    final XYPlot xyPlot = (XYPlot) chart.getPlot();

    ((NumberAxis) xyPlot.getDomainAxis()).setTickUnit(new NumberTickUnit(2.0));
    ((NumberAxis) xyPlot.getRangeAxis()).setTickUnit(new NumberTickUnit(2.0));

    Attribute clsAttribute = null;
    int nbClass = 1;
    Instances cluster0 = clusters.get(0);
    if (cluster0.classIndex() != -1) {
        clsAttribute = cluster0.classAttribute();
        nbClass = clsAttribute.numValues();
    }
    if (nbClass <= 1) {
        dataset.addSeries(new XYSeries("Serie #1", false));
    } else {
        for (int i = 0; i < nbClass; i++) {
            dataset.addSeries(new XYSeries(clsAttribute.value(i), false));
        }
    }

    final XYToolTipGenerator gen = new XYToolTipGenerator() {
        @Override
        public String generateToolTip(XYDataset dataset, int series, int item) {
            return "TODO";
        }
    };

    for (int i = 0; i < nbClass; i++) {
        dataset.getSeries(i).clear();
        xyPlot.getRenderer().setSeriesToolTipGenerator(i, gen);
    }

    final int nbClusters = clusters.size();
    for (int i = 0; i < nbClusters; i++) {
        Instances instances = clusters.get(i);
        final int nbInstances = instances.numInstances();
        for (int j = 0; j < nbInstances; j++) {
            final Instance oInst = instances.instance(j);
            dataset.getSeries(i).add(oInst.value(0), oInst.value(1));
        }
    }

    final TitledBorder titleBorder = new TitledBorder("Kmeans of projection");
    ChartPanel chartPanel = new ChartPanel(chart);
    chartPanel.setMouseWheelEnabled(true);
    chartPanel.setPreferredSize(new Dimension(1200, 900));
    chartPanel.setBorder(titleBorder);
    chartPanel.setBackground(Color.WHITE);

    JXFrame frame = new JXFrame();
    frame.getContentPane().add(chartPanel);
    frame.setVisible(true);
    frame.pack();

}

---

From source file:lu.lippmann.cdb.lab.mds.UniversalMDS.java

License:Open Source License

public JXPanel buildMDSViewFromDataSet(Instances ds, MDSTypeEnum type) throws Exception {

    final XYSeriesCollection dataset = new XYSeriesCollection();

    final JFreeChart chart = ChartFactory.createScatterPlot("", // title 
            "X", "Y", // axis labels 
            dataset, // dataset 
            PlotOrientation.VERTICAL, true, // legend? yes 
            true, // tooltips? yes 
            false // URLs? no 
    );/*from www.  j a v  a2 s  .co m*/

    final XYPlot xyPlot = (XYPlot) chart.getPlot();

    chart.setTitle(type.name() + " MDS");

    Attribute clsAttribute = null;
    int nbClass = 1;
    if (ds.classIndex() != -1) {
        clsAttribute = ds.classAttribute();
        nbClass = clsAttribute.numValues();
    }

    final List<XYSeries> lseries = new ArrayList<XYSeries>();
    if (nbClass <= 1) {
        lseries.add(new XYSeries("Serie #1", false));
    } else {
        for (int i = 0; i < nbClass; i++) {
            lseries.add(new XYSeries(clsAttribute.value(i), false));
        }
    }
    dataset.removeAllSeries();

    /**
     * Initialize filtered series
     */
    final List<Instances> filteredInstances = new ArrayList<Instances>();
    for (int i = 0; i < lseries.size(); i++) {
        filteredInstances.add(new Instances(ds, 0));
    }

    for (int i = 0; i < ds.numInstances(); i++) {
        final Instance oInst = ds.instance(i);
        int indexOfSerie = 0;
        if (oInst.classIndex() != -1) {
            indexOfSerie = (int) oInst.value(oInst.classAttribute());
        }
        lseries.get(indexOfSerie).add(coordinates[i][0], coordinates[i][1]);
        filteredInstances.get(indexOfSerie).add(oInst);
    }

    final List<Paint> colors = new ArrayList<Paint>();

    for (final XYSeries series : lseries) {
        dataset.addSeries(series);
    }

    final XYToolTipGenerator gen = new XYToolTipGenerator() {
        @Override
        public String generateToolTip(XYDataset dataset, int series, int item) {
            return InstanceFormatter.htmlFormat(filteredInstances.get(series).instance(item), true);
        }
    };

    final Shape shape = new Ellipse2D.Float(0f, 0f, 5f, 5f);

    ((XYLineAndShapeRenderer) xyPlot.getRenderer()).setUseOutlinePaint(true);

    for (int p = 0; p < nbClass; p++) {
        xyPlot.getRenderer().setSeriesToolTipGenerator(p, gen);
        ((XYLineAndShapeRenderer) xyPlot.getRenderer()).setLegendShape(p, shape);
        xyPlot.getRenderer().setSeriesOutlinePaint(p, Color.BLACK);
    }

    for (int ii = 0; ii < nbClass; ii++) {
        colors.add(xyPlot.getRenderer().getItemPaint(ii, 0));
    }

    final ChartPanel chartPanel = new ChartPanel(chart);
    chartPanel.setMouseWheelEnabled(true);
    chartPanel.setPreferredSize(new Dimension(1200, 900));
    chartPanel.setBorder(new TitledBorder("MDS Projection"));
    chartPanel.setBackground(Color.WHITE);

    final JXPanel allPanel = new JXPanel();
    allPanel.setLayout(new BorderLayout());
    allPanel.add(chartPanel, BorderLayout.CENTER);

    return allPanel;
}

---

From source file:LVCoref.WekaWrapper.java

License:Open Source License

public static void main(String[] args) {
    try {//from www . j  a v  a 2  s. c o  m
        List<Document> docs = new LinkedList<Document>();
        Document d = new Document();
        d.readCONLL("data/pipeline/interview_16.lvsem.conll");
        d.addAnnotationMMAX("data/interview_16_coref_level.xml");
        d.useGoldMentions();
        docs.add(d);
        d = new Document();
        d.readCONLL("data/pipeline/interview_23.lvsem.conll");
        d.addAnnotationMMAX("data/interview_23_coref_level.xml");
        d.useGoldMentions();
        docs.add(d);
        d = new Document();
        d.readCONLL("data/pipeline/interview_27.lvsem.conll");
        d.addAnnotationMMAX("data/interview_27_coref_level.xml");
        d.useGoldMentions();
        docs.add(d);
        d = new Document();
        d.readCONLL("data/pipeline/interview_38.lvsem.conll");
        d.addAnnotationMMAX("data/interview_38_coref_level.xml");
        d.useGoldMentions();
        docs.add(d);

        Instances train = toArff2(docs);
        train.setClassIndex(train.numAttributes() - 1);
        String[] options = { "-U" };//, "-C", "0.5"};
        Classifier cls = new J48();
        cls.setOptions(options);
        cls.buildClassifier(train);

        docs = new LinkedList<Document>();
        d = new Document();
        d.readCONLL("data/pipeline/interview_43.lvsem.conll");
        d.addAnnotationMMAX("data/interview_43_coref_level.xml");
        d.useGoldMentions();
        docs.add(d);
        d = new Document();
        d.readCONLL("data/pipeline/interview_46.lvsem.conll");
        d.addAnnotationMMAX("data/interview_46_coref_level.xml");
        d.useGoldMentions();
        docs.add(d);

        Evaluation eval = new Evaluation(train);

        Instances data = toArff2(docs);
        data.setClassIndex(data.numAttributes() - 1);
        for (int i = 0; i < data.numInstances(); i++) {
            double clsLabel = cls.classifyInstance(data.instance(i));
            //System.out.println(clsLabel);
            data.instance(i).setClassValue(clsLabel);
            System.out.println(data.instance(i).toString(data.classIndex()));
        }

        //     eval.crossValidateModel(cls, train, 10, new Random(1));
        //            // generate curve
        //     ThresholdCurve tc = new ThresholdCurve();
        //     //int classIndex = test.numAttributes()-1;
        //     Instances result = tc.getCurve(eval.predictions());//, classIndex);
        // 
        //     // plot curve
        //     ThresholdVisualizePanel vmc = new ThresholdVisualizePanel();
        //     vmc.setROCString("(Area under ROC = " + 
        //         weka.core.Utils.doubleToString(tc.getROCArea(result), 4) + ")");
        //     vmc.setName(result.relationName());
        //     PlotData2D tempd = new PlotData2D(result);
        //     tempd.setPlotName(result.relationName());
        //     tempd.addInstanceNumberAttribute();
        //     // specify which points are connected
        //     boolean[] cp = new boolean[result.numInstances()];
        //     for (int n = 1; n < cp.length; n++)
        //       cp[n] = true;
        //     tempd.setConnectPoints(cp);
        //     // add plot
        //     vmc.addPlot(tempd);
        // 
        //     // display curve
        //     String plotName = vmc.getName(); 
        //     final javax.swing.JFrame jf = 
        //       new javax.swing.JFrame("Weka Classifier Visualize: "+plotName);
        //     jf.setSize(500,400);
        //     jf.getContentPane().setLayout(new BorderLayout());
        //     jf.getContentPane().add(vmc, BorderLayout.CENTER);
        //     jf.addWindowListener(new java.awt.event.WindowAdapter() {
        //       public void windowClosing(java.awt.event.WindowEvent e) {
        //       jf.dispose();
        //       }
        //     });
        //     jf.setVisible(true);

        //            Instances test = toArff2(docs);
        //            test.setClassIndex(test.numAttributes()-1);
        //            
        //            
        //           Evaluation evals = new Evaluation(train); 
        //
        //            evals.evaluateModel(cls, test);
        //            System.out.println(evals.toSummaryString("\nResults\n======\n", false));
        //             System.out.println(evals.toMatrixString());
        //              System.out.println(evals.toClassDetailsString());
        //            
        //            System.out.println(cls);
        //            //System.out.println(toArff2(docs));

    } catch (Exception ex) {
        Logger.getLogger(WekaWrapper.class.getName()).log(Level.SEVERE, null, ex);
    }

}

---

From source file:machine_learing_clasifier.MyC45.java

@Override
public void buildClassifier(Instances i) throws Exception {
    if (!i.classAttribute().isNominal()) {
        throw new Exception("Class not nominal");
    }/* w  w w .j  av a 2  s  .  c  om*/

    //penanganan missing value
    for (int j = 0; j < i.numAttributes(); j++) {
        Attribute attr = i.attribute(j);
        for (int k = 0; k < i.numInstances(); k++) {
            Instance inst = i.instance(k);
            if (inst.isMissing(attr)) {
                inst.setValue(attr, fillMissingValue(i, attr));
                //bisa dituning lagi performancenya
            }
        }
    }

    i = new Instances(i);
    i.deleteWithMissingClass();
    makeTree(i);
}

---

From source file:machine_learing_clasifier.MyC45.java

public double fillMissingValue(Instances i, Attribute att) {
    int[] jumlahvalue = new int[att.numValues()];
    for (int k = 0; k < i.numInstances(); k++) {
        jumlahvalue[(int) i.instance(k).value(att)]++;
    }//from   w  w w  .  ja v  a 2s  .co  m
    return jumlahvalue[Utils.maxIndex(jumlahvalue)];
}

---

From source file:machine_learing_clasifier.MyC45.java

public double computeEntropy(Instances inst) {
    double[] classCount = new double[inst.numClasses()];
    for (int i = 0; i < inst.numInstances(); i++) {
        int temp = (int) inst.instance(i).classValue();
        classCount[temp]++;/*  w  ww . j a v a2  s .  co  m*/
    }
    double entropy = 0;
    for (int i = 0; i < inst.numClasses(); i++) {
        if (classCount[i] > 0) {
            entropy -= classCount[i] * Utils.log2(classCount[i] / inst.numInstances());
        }
    }
    entropy /= (double) inst.numInstances();
    return entropy;
}

---

From source file:machine_learing_clasifier.MyC45.java

public Instances[] splitData(Instances inst, Attribute attr) {
    Instances[] split = new Instances[attr.numValues()];
    for (int i = 0; i < attr.numValues(); i++) {
        split[i] = new Instances(inst, inst.numInstances());
    }// w w w  .j  a v  a 2s  .  c om

    for (int i = 0; i < inst.numInstances(); i++) {
        int temp = (int) inst.instance(i).value(attr);
        split[temp].add(inst.instance(i));
    }

    return split;
}

---

From source file:machine_learing_clasifier.MyC45.java

public Instances[] splitDataContinous(Instances inst, Attribute attr, double threshold) {
    Instances[] split = new Instances[2];
    for (int i = 0; i < 2; i++) {
        split[i] = new Instances(inst, inst.numInstances());
    }/*ww w  .  ja va2 s.c om*/

    for (int i = 0; i < inst.numInstances(); i++) {
        double temp = inst.instance(i).value(attr);
        if (temp < threshold) {
            split[0].add(inst.instance(i));
        } else {
            split[1].add(inst.instance(i));
        }
    }

    return split;
}

---

From source file:machine_learing_clasifier.MyC45.java

public void makeTree(Instances data) throws Exception {
    if (data.numInstances() == 0) {
        return;//from ww w  . ja  va  2 s  . c o m
    }

    double[] infoGains = new double[data.numAttributes()];
    for (int i = 0; i < data.numAttributes(); i++) {
        Attribute att = data.attribute(i);
        if (data.classIndex() != att.index()) {
            if (att.isNominal()) {
                infoGains[att.index()] = computeInformationGain(data, att);
            } else {
                infoGains[att.index()] = computeInformationGainContinous(data, att,
                        BestContinousAttribute(data, att));
            }
        }
    }

    m_Attribute = data.attribute(Utils.maxIndex(infoGains));
    if (m_Attribute.isNumeric()) {
        numericAttThreshold = BestContinousAttribute(data, m_Attribute);
        System.out.println(" ini kalo continous dengan attribut : " + numericAttThreshold);
    }
    System.out.println("huhu = " + m_Attribute.toString());

    if (Utils.eq(infoGains[m_Attribute.index()], 0)) {
        m_Attribute = null;
        m_Distribution = new double[data.numClasses()];
        for (int i = 0; i < data.numInstances(); i++) {
            int inst = (int) data.instance(i).value(data.classAttribute());
            m_Distribution[inst]++;
        }
        Utils.normalize(m_Distribution);
        m_ClassValue = Utils.maxIndex(m_Distribution);
        m_ClassAttribute = data.classAttribute();
    } else {
        Instances[] splitData;
        if (m_Attribute.isNominal()) {
            splitData = splitData(data, m_Attribute);
        } else {
            splitData = splitDataContinous(data, m_Attribute, numericAttThreshold);
        }

        if (m_Attribute.isNominal()) {
            System.out.println("nominal");
            m_Successors = new MyC45[m_Attribute.numValues()];
            System.out.println(m_Successors.length);
            for (int j = 0; j < m_Attribute.numValues(); j++) {
                m_Successors[j] = new MyC45(head, this);
                m_Successors[j].buildClassifier(splitData[j]);
            }
        } else {
            System.out.println("numeric");
            m_Successors = new MyC45[2];
            System.out.println(m_Successors.length);
            for (int j = 0; j < 2; j++) {
                m_Successors[j] = new MyC45(head, this);
                m_Successors[j].buildClassifier(splitData[j]);
            }
        }
    }
}

---

From source file:machine_learing_clasifier.MyID3.java

@Override
public void buildClassifier(Instances i) throws Exception {
    if (!i.classAttribute().isNominal()) {
        throw new Exception("Class not nominal");
    }// ww  w .ja  v  a  2 s.  com

    for (int j = 0; j < i.numAttributes(); j++) {
        Attribute attr = i.attribute(j);
        if (!attr.isNominal()) {
            throw new Exception("Attribute not nominal");
        }

        for (int k = 0; k < i.numInstances(); k++) {
            Instance inst = i.instance(k);
            if (inst.isMissing(attr)) {
                throw new Exception("Missing value");
            }
        }
    }

    i = new Instances(i);
    i.deleteWithMissingClass();
    makeTree(i);
}

---

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