Example usage for weka.clusterers AbstractClusterer makeCopy

Introduction

In this page you can find the example usage for weka.clusterers AbstractClusterer makeCopy.

Prototype

public static Clusterer makeCopy(Clusterer model) throws Exception 

Source Link

Document

Creates a deep copy of the given clusterer using serialization.

Usage

From source file:com.actelion.research.orbit.imageAnalysis.models.ClassifierWrapper.java

License:Open Source License

public static ClassifierWrapper makeCopy(ClassifierWrapper classifierWrapper) throws Exception {
    if (classifierWrapper == null)
        return null;
    if (classifierWrapper.getWrapperType() == WRAPPERTYPE_CLASSIFIER) {
        //return new ClassifierWrapper(Classifier.makeCopy(classifierWrapper.getClassifier()));
        ClassifierWrapper cw = new ClassifierWrapper(
                (AbstractClassifier.makeCopy(classifierWrapper.getClassifier()))); // weka 3.7.1
        cw.setBinaryClassification(classifierWrapper.getBinaryClassification());
        cw.setBuild(classifierWrapper.isBuild);
        return cw;
    } else {//from  ww w . j a v a 2 s.  c om
        ClassifierWrapper cw = new ClassifierWrapper(
                AbstractClusterer.makeCopy(classifierWrapper.getClusterer()));
        cw.setBinaryClassification(classifierWrapper.getBinaryClassification());
        cw.setBuild(classifierWrapper.isBuild);
        return cw;
    }
}

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From source file:com.spread.experiment.tempuntilofficialrelease.ClassificationViaClustering108.java

License:Open Source License

/**
 * builds the classifier/*w w  w. j ava 2s . c o m*/
 * 
 * @param data the training instances
 * @throws Exception if something goes wrong
 */
@Override
public void buildClassifier(Instances data) throws Exception {

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

    // save original header (needed for clusters to classes output)
    m_OriginalHeader = data.stringFreeStructure();

    // remove class attribute for clusterer
    Instances clusterData = new Instances(data);
    clusterData.setClassIndex(-1);
    clusterData.deleteAttributeAt(data.classIndex());
    m_ClusteringHeader = clusterData.stringFreeStructure();

    if (m_ClusteringHeader.numAttributes() == 0) {
        System.err.println("Data contains only class attribute, defaulting to ZeroR model.");
        m_ZeroR = new ZeroR();
        m_ZeroR.buildClassifier(data);
    } else {
        m_ZeroR = null;

        // build clusterer
        m_ActualClusterer = AbstractClusterer.makeCopy(m_Clusterer);
        m_ActualClusterer.buildClusterer(clusterData);

        if (!getLabelAllClusters()) {

            // determine classes-to-clusters mapping
            ClusterEvaluation eval = new ClusterEvaluation();
            eval.setClusterer(m_ActualClusterer);
            eval.evaluateClusterer(clusterData);
            double[] clusterAssignments = eval.getClusterAssignments();
            int[][] counts = new int[eval.getNumClusters()][m_OriginalHeader.numClasses()];
            int[] clusterTotals = new int[eval.getNumClusters()];
            double[] best = new double[eval.getNumClusters() + 1];
            double[] current = new double[eval.getNumClusters() + 1];
            for (int i = 0; i < data.numInstances(); i++) {
                Instance instance = data.instance(i);
                if (!instance.classIsMissing()) {
                    counts[(int) clusterAssignments[i]][(int) instance.classValue()]++;
                    clusterTotals[(int) clusterAssignments[i]]++;
                }
            }
            best[eval.getNumClusters()] = Double.MAX_VALUE;
            ClusterEvaluation.mapClasses(eval.getNumClusters(), 0, counts, clusterTotals, current, best, 0);
            m_ClustersToClasses = new double[best.length];
            System.arraycopy(best, 0, m_ClustersToClasses, 0, best.length);
        } else {
            m_ClusterClassProbs = new double[m_ActualClusterer.numberOfClusters()][data.numClasses()];
            for (int i = 0; i < data.numInstances(); i++) {
                Instance clusterInstance = clusterData.instance(i);
                Instance originalInstance = data.instance(i);
                if (!originalInstance.classIsMissing()) {
                    double[] probs = m_ActualClusterer.distributionForInstance(clusterInstance);
                    for (int j = 0; j < probs.length; j++) {
                        m_ClusterClassProbs[j][(int) originalInstance.classValue()] += probs[j];
                    }
                }
            }
            for (int i = 0; i < m_ClusterClassProbs.length; i++) {
                Utils.normalize(m_ClusterClassProbs[i]);
            }
        }
    }
}

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