Example usage for weka.classifiers.rules ZeroR ZeroR

Introduction

In this page you can find the example usage for weka.classifiers.rules ZeroR ZeroR.

Prototype

ZeroR

Source Link

Usage

From source file:mulan.regressor.transformation.TransformationBasedMultiTargetRegressor.java

License:Open Source License

/**
 * Creates a new instance of {@link TransformationBasedMultiTargetRegressor} with default {@link ZeroR}
 * base regressor.// w  ww  . ja v a2s. c  om
 */
public TransformationBasedMultiTargetRegressor() {
    this(new ZeroR());
}

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From source file:nl.bioinf.roelen.thema11.classifier_tools.BoundaryClassifier.java

License:Open Source License

/**
 * method to build a classifier//from w w  w.  j a  v a  2  s. c  om
 * @param fileLocation the arrf file our attributes are in
 * @param method the method to use for building our classifier
 * @return the classifier object that was built
 */
public static Classifier build(String fileLocation, String method) {
    //init classifier object
    Classifier classifier;
    classifier = null;

    try {
        //get data
        ConverterUtils.DataSource source = new ConverterUtils.DataSource(fileLocation);
        //SET DATA AND OPTIONS
        Instances data = source.getDataSet();

        //remove the name and position entries, these are not important for classifying
        data.deleteAttributeAt(data.numAttributes() - 2);
        data.deleteAttributeAt(data.numAttributes() - 2);
        data.setClassIndex(data.numAttributes() - 1);

        //prepare data for classifying
        String[] options = new String[1];
        //unpruned
        options[0] = "-U"; // unpruned tree
        //see what method was given
        switch (method.toUpperCase()) {
        case "J48":
            //Build J48 classifier
            classifier = new J48(); // new instance of tree
            break;
        case "OneR":
            //Build OneR classifier
            classifier = new OneR();
            break;
        case "ZeroR":
            //build (useless) ZeroR classifier
            classifier = new ZeroR();
            break;
        default:
            //default is building OneR
            classifier = new OneR();
            break;
        }
        //set the options and build that thing
        classifier.setOptions(options); // set the options
        classifier.buildClassifier(data); // build classifier

    } catch (Exception ex) {
        Logger.getLogger(BoundaryClassifier.class.getName()).log(Level.SEVERE, null, ex);
    }
    return classifier;
}

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From source file:sg.edu.nus.comp.nlp.ims.classifiers.CMultiClassesSVM.java

License:Open Source License

@Override
public void buildClassifier(Instances p_Instances) throws Exception {
    Instances newInsts = null;// w  w  w. ja  v  a 2s .  co  m
    if (this.m_Classifier == null) {
        throw new IllegalStateException("No base classifier has been set!");
    }

    this.m_ZeroR = new ZeroR();
    this.m_ZeroR.buildClassifier(p_Instances);

    this.m_ClassAttribute = p_Instances.classAttribute();
    this.getOutputFormat(p_Instances);
    int numClassifiers = p_Instances.numClasses();
    switch (numClassifiers) {
    case 1:
        this.m_Classifiers = null;
        break;
    case 2:
        this.m_Classifiers = Classifier.makeCopies(this.m_Classifier, 1);
        newInsts = new Instances(this.m_OutputFormat, 0);
        for (int i = 0; i < p_Instances.numInstances(); i++) {
            Instance inst = this.filterInstance(p_Instances.instance(i));
            inst.setDataset(newInsts);
            newInsts.add(inst);
        }
        this.m_Classifiers[0].buildClassifier(newInsts);
        break;
    default:
        this.m_Classifiers = Classifier.makeCopies(this.m_Classifier, numClassifiers);
        Hashtable<String, ArrayList<Double>> id2Classes = null;
        if (this.m_IndexOfID >= 0) {
            id2Classes = new Hashtable<String, ArrayList<Double>>();
            for (int i = 0; i < p_Instances.numInstances(); i++) {
                Instance inst = p_Instances.instance(i);
                String id = inst.stringValue(this.m_IndexOfID);
                if (!id2Classes.containsKey(id)) {
                    id2Classes.put(id, new ArrayList<Double>());
                }
                id2Classes.get(id).add(inst.classValue());
            }
        }
        for (int classIdx = 0; classIdx < this.m_Classifiers.length; classIdx++) {
            newInsts = this.genInstances(p_Instances, classIdx, id2Classes);
            this.m_Classifiers[classIdx].buildClassifier(newInsts);
        }
    }
}

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From source file:test.org.moa.opencl.IBk.java

License:Open Source License

/**
 * Generates the classifier./*from w  w w .j a v  a2s .  co  m*/
 *
 * @param instances set of instances serving as training data 
 * @throws Exception if the classifier has not been generated successfully
 */
public void buildClassifier(Instances instances) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(instances);

    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();

    m_NumClasses = instances.numClasses();
    m_ClassType = instances.classAttribute().type();
    m_Train = new Instances(instances, 0, instances.numInstances());

    // Throw away initial instances until within the specified window size
    if ((m_WindowSize > 0) && (instances.numInstances() > m_WindowSize)) {
        m_Train = new Instances(m_Train, m_Train.numInstances() - m_WindowSize, m_WindowSize);
    }

    m_NumAttributesUsed = 0.0;
    for (int i = 0; i < m_Train.numAttributes(); i++) {
        if ((i != m_Train.classIndex())
                && (m_Train.attribute(i).isNominal() || m_Train.attribute(i).isNumeric())) {
            m_NumAttributesUsed += 1.0;
        }
    }

    m_NNSearch.setInstances(m_Train);

    // Invalidate any currently cross-validation selected k
    m_kNNValid = false;

    m_defaultModel = new ZeroR();
    m_defaultModel.buildClassifier(instances);
}

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