Example usage for weka.classifiers.lazy KStar KStar

Introduction

In this page you can find the example usage for weka.classifiers.lazy KStar KStar.

Prototype

KStar

Source Link

Usage

From source file:au.edu.usyd.it.yangpy.snp.Ensemble.java

License:Open Source License

public void ensemble(String mode) throws Exception {

    numInstances = test.numInstances();//from ww  w .ja v  a 2  s. com
    numClasses = test.numClasses();
    givenValue = new double[numInstances];
    predictDistribution = new double[numClassifiers][numInstances][numClasses];
    predictValue = new double[numClassifiers][numInstances];
    voteValue = new double[numInstances][numClasses];

    // Setting the given class values of the test instances.
    for (int i = 0; i < numInstances; i++) {
        givenValue[i] = test.instance(i).classValue();
    }

    // Calculating the predicted class values using each classifier respectively.
    // J48 coverTree1NN KStar coverTree3NN coverTree5NN

    J48 tree = new J48();
    tree.setUnpruned(true);
    aucClassifiers[0] = classify(tree, 0);

    KStar kstar = new KStar();
    aucClassifiers[1] = classify(kstar, 1);

    IBk ctnn1 = new IBk(1);
    CoverTree search = new CoverTree();
    ctnn1.setNearestNeighbourSearchAlgorithm(search);
    aucClassifiers[2] = classify(ctnn1, 2);

    IBk ctnn3 = new IBk(3);
    ctnn3.setNearestNeighbourSearchAlgorithm(search);
    aucClassifiers[3] = classify(ctnn3, 3);

    IBk ctnn5 = new IBk(5);
    ctnn5.setNearestNeighbourSearchAlgorithm(search);
    aucClassifiers[4] = classify(ctnn5, 4);

    // Print the classification results if in print mode.
    if (mode.equals("v")) {
        System.out.println("J48   AUC: " + aucClassifiers[0]);
        System.out.println("KStar AUC: " + aucClassifiers[1]);
        System.out.println("CTNN1 AUC: " + aucClassifiers[2]);
        System.out.println("CTNN3 AUC: " + aucClassifiers[3]);
        System.out.println("CTNN5 AUC: " + aucClassifiers[4]);
        System.out.println("   -         -   ");
    }
}

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From source file:LeerArchivo.Leer.java

public void leerArchivoArff() {
    try {//from ww w . j  av a 2s .co  m
        // create J48
        Classifier cls = new KStar();
        // train
        Instances inst = new Instances(new BufferedReader(new FileReader("../datos.arff")));

        inst.setClassIndex(inst.numAttributes() - 1);
        cls.buildClassifier(inst);
        // serialize model
        ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream("./KStar.model"));
        oos.writeObject(cls);
        oos.flush();
        oos.close();
    } catch (IOException ex) {
        Logger.getLogger(Leer.class.getName()).log(Level.SEVERE, null, ex);
    } catch (Exception ex) {
        Logger.getLogger(Leer.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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