Example usage for weka.classifiers.trees HoeffdingTree HoeffdingTree

Introduction

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

Prototype

HoeffdingTree

Source Link

Usage

From source file:fcul.viegas.ml.learners.NetworkStreamLearningClassifierMapFunction.java

public void open(Configuration parameters) throws Exception {

    try {/*from   w  w  w .  jav a 2s . co  m*/
        BufferedReader reader = new BufferedReader(new FileReader(NetworkPacketsDefinitions.arffPath));
        ArffLoader.ArffReader arff = new ArffLoader.ArffReader(reader);
        weka.core.Instances dataTrain = arff.getData();
        dataTrain.setClassIndex(dataTrain.numAttributes() - 1);

        Random rand = new Random();
        rand.setSeed(this.hashCode() ^ 83484856847L);
        ArrayList<Integer> listaFeaturesChoosen = new ArrayList<>();
        for (int j = 1; j < dataTrain.numAttributes(); j++) {
            listaFeaturesChoosen.add(j);
        }

        //chose features to remove
        int numberOfFeaturesToBeRemoved = (int) (listaFeaturesChoosen.size()
                * (1.0f - percentOfSubsetFeatures));
        for (int j = 0; j < numberOfFeaturesToBeRemoved; j++) {
            int numberOfFeature = rand.nextInt(listaFeaturesChoosen.size());
            listaFeaturesChoosen.remove(numberOfFeature);
        }
        System.out.println("LEARNING: featureSubset: " + NetworkStreamLearningClassifierMapFunction
                .printFeatures(listaFeaturesChoosen, dataTrain.numAttributes() - 1));
        mappingFeatures = "featureSubset: " + NetworkStreamLearningClassifierMapFunction
                .printFeatures(listaFeaturesChoosen, dataTrain.numAttributes() - 1);
        FastVector attributes = new FastVector();
        int indexLista = 0;
        for (int i = 0; i < NetworkPacketsDefinitions.numberOfFeatures; i++) {
            if (indexLista < listaFeaturesChoosen.size() && listaFeaturesChoosen.get(indexLista) == (i + 1)) {
                indexLista++;
                attributes.addElement(new Attribute("att" + (i + 1)));
            }
        }
        FastVector classLabels = new FastVector();
        for (int i = 0; i < NetworkPacketsDefinitions.numberOfClasses; i++) {
            classLabels.addElement("class" + (i + 1));
        }
        attributes.addElement(new Attribute("class", classLabels));

        this.streamLearningInstanceHeader = new InstancesHeader(new Instances("FlinkPhd", attributes, 0));

        this.streamLearningInstanceHeader.setClassIndex(attributes.size());

        listaFeaturesChoosen.add(dataTrain.numAttributes());

        String[] options = new String[2];
        options[0] = "-R";

        String optRemove = "";
        for (int j = 0; j < listaFeaturesChoosen.size() - 1; j++) {
            optRemove = optRemove + listaFeaturesChoosen.get(j) + ",";
        }
        optRemove = optRemove + listaFeaturesChoosen.get(listaFeaturesChoosen.size() - 1);
        options[1] = optRemove;

        Remove remove = new Remove();
        remove.setOptions(options);
        remove.setInvertSelection(true);
        remove.setInputFormat(dataTrain);

        weka.core.Instances newdataFeat = Filter.useFilter(dataTrain, remove);

        HoeffdingTree tree = new HoeffdingTree();

        weka.classifiers.misc.InputMappedClassifier classifier = new weka.classifiers.misc.InputMappedClassifier();
        classifier.setModelHeader(newdataFeat);
        classifier.setClassifier(tree);
        classifier.buildClassifier(newdataFeat);
        this.classifier = classifier;
        this.coreInstances = dataTrain;
        this.coreInstances.clear();

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

---

From source file:fcul.viegas.ml.learners.NetworkStreamLearningClassifierUpdateMapFunction.java

public void open(Configuration parameters) throws Exception {

    try {//from  w  w  w. j a  v  a  2s.co m
        BufferedReader reader = new BufferedReader(new FileReader(NetworkPacketsDefinitions.arffPath));
        ArffLoader.ArffReader arff = new ArffLoader.ArffReader(reader);
        weka.core.Instances dataTrain = arff.getData();
        dataTrain.setClassIndex(dataTrain.numAttributes() - 1);

        Random rand = new Random();
        rand.setSeed(this.hashCode() ^ 83484856847L);
        ArrayList<Integer> listaFeaturesChoosen = new ArrayList<>();
        for (int j = 1; j < dataTrain.numAttributes(); j++) {
            listaFeaturesChoosen.add(j);
        }

        //chose features to remove
        int numberOfFeaturesToBeRemoved = (int) (listaFeaturesChoosen.size()
                * (1.0f - percentOfSubsetFeatures));
        for (int j = 0; j < numberOfFeaturesToBeRemoved; j++) {
            int numberOfFeature = rand.nextInt(listaFeaturesChoosen.size());
            listaFeaturesChoosen.remove(numberOfFeature);
        }
        System.out.println("UPDATES featureSubset: " + NetworkStreamLearningClassifierMapFunction
                .printFeatures(listaFeaturesChoosen, dataTrain.numAttributes() - 1));
        mappingFeatures = "featureSubset: " + NetworkStreamLearningClassifierMapFunction
                .printFeatures(listaFeaturesChoosen, dataTrain.numAttributes() - 1);
        FastVector attributes = new FastVector();
        int indexLista = 0;
        for (int i = 0; i < NetworkPacketsDefinitions.numberOfFeatures; i++) {
            if (indexLista < listaFeaturesChoosen.size() && listaFeaturesChoosen.get(indexLista) == (i + 1)) {
                indexLista++;
                attributes.addElement(new Attribute("att" + (i + 1)));
            }
        }
        FastVector classLabels = new FastVector();
        for (int i = 0; i < NetworkPacketsDefinitions.numberOfClasses; i++) {
            classLabels.addElement("class" + (i + 1));
        }
        attributes.addElement(new Attribute("class", classLabels));

        this.streamLearningInstanceHeader = new InstancesHeader(new Instances("FlinkPhd", attributes, 0));

        this.streamLearningInstanceHeader.setClassIndex(attributes.size());

        listaFeaturesChoosen.add(dataTrain.numAttributes());

        String[] options = new String[2];
        options[0] = "-R";

        String optRemove = "";
        for (int j = 0; j < listaFeaturesChoosen.size() - 1; j++) {
            optRemove = optRemove + listaFeaturesChoosen.get(j) + ",";
        }
        optRemove = optRemove + listaFeaturesChoosen.get(listaFeaturesChoosen.size() - 1);
        options[1] = optRemove;

        Remove remove = new Remove();
        remove.setOptions(options);
        remove.setInvertSelection(true);
        remove.setInputFormat(dataTrain);

        weka.core.Instances newdataFeat = Filter.useFilter(dataTrain, remove);

        HoeffdingTree tree = new HoeffdingTree();

        weka.classifiers.misc.InputMappedClassifier classifier = new weka.classifiers.misc.InputMappedClassifier();
        classifier.setModelHeader(newdataFeat);
        classifier.setClassifier(tree);
        classifier.buildClassifier(newdataFeat);
        this.classifier = classifier;
        this.coreInstances = dataTrain;

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

---

From source file:meka.classifiers.multilabel.incremental.BRUpdateable.java

License:Open Source License

public BRUpdateable() {
    // default classifier for GUI
    this.m_Classifier = new HoeffdingTree();
}

---

From source file:meka.classifiers.multilabel.incremental.CCUpdateable.java

License:Open Source License

public CCUpdateable() {
    // default classifier for GUI
    this.m_Classifier = new HoeffdingTree();
}

---

From source file:meka.classifiers.multilabel.incremental.PSUpdateable.java

License:Open Source License

public PSUpdateable() {
    // default classifier for GUI
    this.m_Classifier = new HoeffdingTree();
}

---

From source file:meka.classifiers.multilabel.incremental.RTUpdateable.java

License:Open Source License

public RTUpdateable() {
    // default classifier for GUI
    this.m_Classifier = new HoeffdingTree();
}

---