Example usage for weka.classifiers Evaluation toMatrixString

List of usage examples for weka.classifiers Evaluation toMatrixString

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Introduction

In this page you can find the example usage for weka.classifiers Evaluation toMatrixString.

Prototype

public String toMatrixString() throws Exception

Source Link

Document

Calls toMatrixString() with a default title.

Usage

From source file:tucil2ai.Tucil2AI.java

/**
 *
 * @param E/*from   w w  w.j  a  va 2  s  .c  om*/
 * @throws Exception
 */
public static void printEval(Evaluation E) throws Exception {
    System.out.println(E.toSummaryString("\nResults\n======\n", false));
    System.out.println(E.toClassDetailsString());
    System.out.println(E.toMatrixString());
}

From source file:view.centerPanels.ClassificationPnlCenterPredict.java

private void btnStartActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_btnStartActionPerformed

    //imate vec objekat instances i objekat naiveBayes u ovoj klasi
    //treba da izvucete ono sto je uetu u text fieldove preko liste i da napravite
    //objekat Instance (nema "s"). kad naravite objekat Instance samo ga dodate
    //na instances.add(  )..
    //onda koristite takav Instances i naiveBayes za evaluaciju i u textArea
    //ispisujete kojoj klasi pripada.. Sad ne trba matrica konfuzije potrebno
    //je da ispisete string kojoj klasi pripada
    //pogledajte klasu ClusterinPnlCenterPredict tu je odradjeno 90% onog sto se i ovde trazi
    //        instances.delete();
    for (int i = 0; i < textFields.size(); i++) {
        String text = textFields.get(i).getText().trim();

        //prekace prazna pollja jer za klasterizaciju znaci da se ona ignorisu
        //to za klasifikaciju nije slucaj
        if (!text.equals("")) {

            if (instances.attribute(i).isNominal()) {
                boolean correct = false;
                for (int j = 0; j < instances.attribute(i).numValues(); j++) {
                    if (text.equals(instances.attribute(i).value(j))) {
                        correct = true;/*from w w w .j  ava2 s  . c  o  m*/
                    }
                }
                if (!correct) {
                    JOptionPane.showMessageDialog(this,
                            "Incorrect format for attribute " + instances.attribute(i).name());
                    break;
                }
            }

            if (instances.attribute(i).isNumeric()) {
                try {
                    double value = Double.parseDouble(text);
                } catch (Exception e) {
                    JOptionPane.showMessageDialog(this,
                            "Incorrect format for attribute " + instances.attribute(i).name());
                    break;
                }
            }

        }
    }

    int numAttributes = instances.numAttributes();

    Instance instance = new Instance(numAttributes);

    for (int i = 0; i < textFields.size(); i++) {
        String text = textFields.get(i).getText().trim();

        try {
            double value = Double.parseDouble(text);
            instance.setValue(i, value);

        } catch (Exception e) {
            instance.setValue(i, text);
        }
    }

    String value = instances.attribute(numAttributes - 1).value(0);
    Attribute Att = instances.attribute(numAttributes - 1);

    instance.setValue(Att, value);
    //        instances.delete();
    //        instance.setValue((numAttributes - 1), instances.attribute(numAttributes - 1).value(0) );

    instances.add(instance);

    double[][] matrix = null;

    System.out.println(instances);
    System.out.println(naiveBayes);

    try {

        //                instances.setClassIndex(instances.numAttributes() - 1);
        Evaluation eval = new Evaluation(instances);

        eval.evaluateModel(naiveBayes, instances);

        textArea.setText(eval.toMatrixString());
        matrix = eval.confusionMatrix();

    } catch (Exception ex) {
        System.out.println("Greska: " + ex);
    }

    double[] array = new double[matrix.length];

    for (int i = 0; i < array.length; i++) {

        double sum = 0;
        for (int j = 0; j < array.length; j++) {

            sum = sum + matrix[j][i];

        }
        array[i] = sum;

    }

    String className = null;
    for (int i = 0; i < array.length; i++) {
        if (array[i] == 1) {
            className = instances.attribute(numAttributes - 1).value(i);
        }
    }

    textArea.setText("This instance belongs to class: \n\n== " + className + " ==");

    instances.delete();
}

From source file:view.centerPanels.ClassificationPnlCenterTrainNew.java

public ClassificationPnlCenterTrainNew() {
    initComponents();/*from   w w  w  .  ja v a 2  s.  co m*/

    Instances data = Data.getInstance().getInstances();

    try {
        nb = new NaiveBayes();

        data.setClassIndex(data.numAttributes() - 1);

        Evaluation eval = new Evaluation(data);

        nb.buildClassifier(data);

        eval.evaluateModel(nb, data);

        jTextArea1.setText(eval.toMatrixString());

        //                System.out.println(eval.toMatrixString());
    } catch (Exception ex) {
        JOptionPane.showMessageDialog(this, ex);
    }

    setSize(MainGUI.getInstance().getPnlCenter().getWidth(), MainGUI.getInstance().getPnlCenter().getHeight());
    setVisible(true);

}