Example usage for weka.clusterers MakeDensityBasedClusterer setClusterer

Introduction

In this page you can find the example usage for weka.clusterers MakeDensityBasedClusterer setClusterer.

Prototype

public void setClusterer(Clusterer toWrap) 

Source Link

Document

Sets the clusterer to wrap.

Usage

From source file:adams.flow.transformer.WekaCrossValidationClustererEvaluator.java

License:Open Source License

/**
 * Executes the flow item./*w  w w  .  j a v a  2  s  . c o  m*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    Instances data;
    weka.clusterers.Clusterer cls;
    int folds;
    MakeDensityBasedClusterer make;
    double log;

    result = null;

    try {
        // evaluate classifier
        cls = getClustererInstance();
        if (cls == null)
            throw new IllegalStateException("Clusterer '" + getClusterer() + "' not found!");

        data = (Instances) m_InputToken.getPayload();
        folds = m_Folds;
        if (folds == -1)
            folds = data.numInstances();
        if (!(cls instanceof DensityBasedClusterer)) {
            make = new MakeDensityBasedClusterer();
            make.setClusterer(cls);
            cls = make;
        }
        log = ClusterEvaluation.crossValidateModel((DensityBasedClusterer) cls, data, folds,
                new Random(m_Seed));
        m_OutputToken = new Token(new WekaClusterEvaluationContainer(log));
    } catch (Exception e) {
        m_OutputToken = null;
        result = handleException("Failed to cross-validate clusterer: ", e);
    }

    if (m_OutputToken != null)
        updateProvenance(m_OutputToken);

    return result;
}

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From source file:br.com.ufu.lsi.rebfnetwork.RBFNetwork.java

License:Open Source License

/**
 * Builds the classifier/*from   w w  w .ja  v a2 s .  co  m*/
 *
 * @param instances the training data
 * @throws Exception if the classifier could not be built 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();

    // only class? -> build ZeroR model
    if (instances.numAttributes() == 1) {
        System.err.println(
                "Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!");
        m_ZeroR = new weka.classifiers.rules.ZeroR();
        m_ZeroR.buildClassifier(instances);
        return;
    } else {
        m_ZeroR = null;
    }

    m_standardize = new Standardize();
    m_standardize.setInputFormat(instances);
    instances = Filter.useFilter(instances, m_standardize);

    SimpleKMeans sk = new SimpleKMeans();
    sk.setNumClusters(m_numClusters);
    sk.setSeed(m_clusteringSeed);
    MakeDensityBasedClusterer dc = new MakeDensityBasedClusterer();
    dc.setClusterer(sk);
    dc.setMinStdDev(m_minStdDev);
    m_basisFilter = new ClusterMembership();
    m_basisFilter.setDensityBasedClusterer(dc);
    m_basisFilter.setInputFormat(instances);
    Instances transformed = Filter.useFilter(instances, m_basisFilter);

    if (instances.classAttribute().isNominal()) {
        m_linear = null;
        m_logistic = new Logistic();
        m_logistic.setRidge(m_ridge);
        m_logistic.setMaxIts(m_maxIts);
        m_logistic.buildClassifier(transformed);
    } else {
        m_logistic = null;
        m_linear = new LinearRegression();
        m_linear.setAttributeSelectionMethod(
                new SelectedTag(LinearRegression.SELECTION_NONE, LinearRegression.TAGS_SELECTION));
        m_linear.setRidge(m_ridge);
        m_linear.buildClassifier(transformed);
    }
}

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From source file:milk.classifiers.MIRBFNetwork.java

License:Open Source License

public Exemplars transform(Exemplars ex) throws Exception {

    // Throw all the instances together
    Instances data = new Instances(ex.exemplar(0).getInstances());
    for (int i = 0; i < ex.numExemplars(); i++) {
        Exemplar curr = ex.exemplar(i);/*from  w w  w .j a v  a  2s  .c om*/
        double weight = 1.0 / (double) curr.getInstances().numInstances();
        for (int j = 0; j < curr.getInstances().numInstances(); j++) {
            Instance inst = (Instance) curr.getInstances().instance(j).copy();
            inst.setWeight(weight);
            data.add(inst);
        }
    }
    double factor = (double) data.numInstances() / (double) data.sumOfWeights();
    for (int i = 0; i < data.numInstances(); i++) {
        data.instance(i).setWeight(data.instance(i).weight() * factor);
    }

    SimpleKMeans kMeans = new SimpleKMeans();
    kMeans.setNumClusters(m_num_clusters);
    MakeDensityBasedClusterer clust = new MakeDensityBasedClusterer();
    clust.setClusterer(kMeans);
    m_clm.setDensityBasedClusterer(clust);
    m_clm.setIgnoredAttributeIndices("" + (ex.exemplar(0).idIndex() + 1));
    m_clm.setInputFormat(data);

    // Use filter and discard result
    Instances tempData = Filter.useFilter(data, m_clm);
    tempData = new Instances(tempData, 0);
    tempData.insertAttributeAt(ex.exemplar(0).getInstances().attribute(0), 0);

    // Go through exemplars and add them to new dataset
    Exemplars newExs = new Exemplars(tempData);
    for (int i = 0; i < ex.numExemplars(); i++) {
        Exemplar curr = ex.exemplar(i);
        Instances temp = Filter.useFilter(curr.getInstances(), m_clm);
        temp.insertAttributeAt(ex.exemplar(0).getInstances().attribute(0), 0);
        for (int j = 0; j < temp.numInstances(); j++) {
            temp.instance(j).setValue(0, curr.idValue());
        }
        newExs.add(new Exemplar(temp));
    }
    //System.err.println("Finished transforming");
    //System.err.println(newExs);
    return newExs;
}

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