Example usage for weka.classifiers.trees RandomForest setOptions

Introduction

In this page you can find the example usage for weka.classifiers.trees RandomForest setOptions.

Prototype

@Override
public void setOptions(String[] options) throws Exception 

Source Link

Document

Parses a given list of options.

Usage

From source file:de.tudarmstadt.ukp.dkpro.spelling.experiments.hoo2012.featureextraction.AllFeaturesExtractor.java

License:Apache License

private Classifier getClassifier() throws Exception {
    Classifier cl = null;//from  ww  w  .  jav a2  s  . c o  m
    // Build and evaluate classifier
    // The options given correspond to the default settings in the WEKA GUI
    if (classifier.equals("smo")) {
        SMO smo = new SMO();
        smo.setOptions(Utils.splitOptions(
                "-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
        cl = smo;
    } else if (classifier.equals("j48")) {
        J48 j48 = new J48();
        j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
        cl = j48;
    } else if (classifier.equals("naivebayes")) {
        cl = new NaiveBayes();
    } else if (classifier.equals("randomforest")) {
        RandomForest rf = new RandomForest();
        rf.setOptions(Utils.splitOptions("-I 10 -K 0 -S 1"));
        cl = rf;
    }
    return cl;
}

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From source file:my.randomforestui.RandomForestUI.java

public static double doRandomForest(Instances training, Instances testing) throws Exception {
    double accuracy;

    //inisialisasi random forest
    String[] options = new String[1];
    // set tree random forest unpruned tree
    options[0] = "-U";
    // new instance of tree
    RandomForest tree = new RandomForest();
    // set the options
    tree.setOptions(options);
    // build classifier using training data
    tree.buildClassifier(training);/*from w w  w.  j  av  a  2  s.  c om*/

    Evaluation eval = new Evaluation(testing);
    eval.evaluateModel(tree, testing);
    //System.out.println((eval.correct()/56)*100);

    accuracy = (eval.correct() / 56) * 100;

    return accuracy;
}

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From source file:predictors.HelixIndexer.java

License:Open Source License

/**
 * Trains the Weka Classifer./* w w  w. j  a v  a2s. c om*/
 */
public void trainClassifier() {
    try {
        RandomForest classifier = new weka.classifiers.trees.RandomForest();
        Instances data = this.dataset;

        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        data.randomize(new Random(data.size()));

        String[] optClassifier = weka.core.Utils.splitOptions("-I 100 -K 9 -S 1 -num-slots 3");

        classifier.setOptions(optClassifier);
        classifier.setSeed(data.size());

        classifier.buildClassifier(data);

        this.classifier = classifier;
        this.isTrained = true;
    } catch (Exception e) {
        ErrorUtils.printError(HelixIndexer.class, "Training failed", e);
    }
}

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From source file:predictors.TopologyPredictor.java

License:Open Source License

/**
 * Trains the Weka Classifer.//from  w  w w . j  a  v  a2 s. co  m
 */
public void trainClassifier() {
    try {
        RandomForest classifier = new weka.classifiers.trees.RandomForest();
        Instances data = this.dataset;

        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        data.randomize(new Random(data.size()));

        String[] optClassifier = weka.core.Utils.splitOptions("-I 100 -K 7 -S 1 -num-slots 1");

        classifier.setOptions(optClassifier);
        classifier.setSeed(data.size());

        classifier.buildClassifier(data);

        this.classifier = classifier;
        this.isTrained = true;
    } catch (Exception e) {
        ErrorUtils.printError(TopologyPredictor.class, "Training failed", e);
    }
}

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