Example usage for weka.classifiers.misc InputMappedClassifier setClassifier

Introduction

In this page you can find the example usage for weka.classifiers.misc InputMappedClassifier setClassifier.

Prototype

public void setClassifier(Classifier newClassifier) 

Source Link

Document

Set the base learner.

Usage

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

public void open(Configuration parameters) throws Exception {

    try {/*w  ww.j a  va2  s .c  o 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();
    }
}

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

public void open(Configuration parameters) throws Exception {

    try {/*from   w  ww .j  av a 2  s.  com*/
        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();
    }
}

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