Example usage for weka.classifiers.trees RandomTree setSeed

Introduction

In this page you can find the example usage for weka.classifiers.trees RandomTree setSeed.

Prototype

@Override
public void setSeed(int seed) 

Source Link

Document

Set the seed for random number generation.

Usage

From source file:controller.MineroControler.java

public String clasificardorArbolAleat(String atributo) {
    BufferedReader breader = null;
    Instances datos = null;/*from ww  w.  j a  v  a 2s. c  o  m*/
    breader = new BufferedReader(fuente_arff);
    try {
        datos = new Instances(breader);
        Attribute atr = datos.attribute(atributo);
        datos.setClass(atr);
        //datos.setClassIndex(0);
    } catch (IOException ex) {
        System.err.println("Problemas al intentar cargar los datos");
        return null;
    }

    RandomTree arbol = new RandomTree(); // Class for constructing a tree that considers K randomly chosen attributes at each node. 

    try {

        arbol.setNumFolds(100);
        arbol.setKValue(0);
        arbol.setMinNum(1);
        arbol.setMaxDepth(0);
        arbol.setSeed(1);
        arbol.buildClassifier(datos);

    } catch (Exception ex) {
        System.err.println("Problemas al ejecutar algorimo de clasificacion" + ex.getLocalizedMessage());
    }
    return arbol.toString();
}

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From source file:org.openml.webapplication.fantail.dc.landmarking.RandomTreeBasedLandmarker.java

License:Open Source License

public Map<String, Double> characterize(Instances data) {

    int numFolds = m_NumFolds;

    double score1 = 0.5;
    double score2 = 0.5;
    double score3 = 0.5;

    weka.classifiers.trees.RandomTree cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(m_Seed);
    cls.setMaxDepth(1);/* w ww.  j av a 2s .  co  m*/

    try {
        // ds.buildClassifier(data);
        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score1 = eval.weightedAreaUnderROC();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(m_Seed);
    cls.setMaxDepth(2);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score2 = eval.weightedAreaUnderROC();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(m_Seed);
    cls.setMaxDepth(3);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score3 = eval.weightedAreaUnderROC();

    } catch (Exception e) {
        e.printStackTrace();
    }

    Map<String, Double> qualities = new HashMap<String, Double>();
    qualities.put(ids[0], score1);
    qualities.put(ids[1], score2);
    qualities.put(ids[2], score3);
    return qualities;
}

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From source file:org.openml.webapplication.fantail.dc.landmarking.RandomTreeBasedLandmarker2.java

License:Open Source License

public Map<String, Double> characterize(Instances data) {

    int seed = m_Seed;
    Random r = new Random(seed);

    int numFolds = m_NumFolds;

    double score1 = 0.5;
    double score2 = 0.5;
    // double score3 = 0.5;

    double score3 = 0.5;
    double score4 = 0.5;
    // double score3 = 0.5;

    double score5 = 0.5;
    double score6 = 0.5;

    weka.classifiers.trees.RandomTree cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);//from   ww  w.  j a v  a  2 s  .  c  om
    // cls.setMaxDepth(1);

    try {
        // ds.buildClassifier(data);
        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score1 = eval.pctIncorrect();
        score2 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(2);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score3 = eval.pctIncorrect();
        score4 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(3);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score5 = eval.pctIncorrect();
        score6 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(4);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(5);

    try {
        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

    } catch (Exception e) {
        e.printStackTrace();
    }

    Map<String, Double> qualities = new HashMap<String, Double>();
    qualities.put(ids[0], score1);
    qualities.put(ids[1], score2);
    qualities.put(ids[2], score3);
    qualities.put(ids[3], score4);
    qualities.put(ids[4], score5);
    qualities.put(ids[5], score6);
    return qualities;
}

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