Example usage for weka.clusterers ClusterEvaluation getClassesToClusters

Introduction

In this page you can find the example usage for weka.clusterers ClusterEvaluation getClassesToClusters.

Prototype

public int[] getClassesToClusters() 

Source Link

Document

Return the array (ordered by cluster number) of minimum error class to cluster mappings

Usage

From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java

License:Open Source License

public Instances clusteredInstances(Instances data) {
    if (data == null) {
        throw new NullPointerException("Data is null at clusteredInstances method");
    }/*from ww w. j a  v a  2 s . com*/
    Instances sampled_data = data;
    for (int i = 0; i < sampled_data.numInstances(); i++) {
        sampled_data.remove(i);
    }

    SimpleKMeans sKmeans = new SimpleKMeans();
    data.setClassIndex(data.numAttributes() - 1);
    Remove filter = new Remove();
    filter.setAttributeIndices("" + (data.classIndex() + 1));
    List assignments = new ArrayList();

    try {
        filter.setInputFormat(data);
        Instances dataClusterer = Filter.useFilter(data, filter);
        String[] options = new String[3];
        options[0] = "-I"; // max. iterations
        options[1] = "500";
        options[2] = "-O";
        sKmeans.setNumClusters(data.numClasses());
        sKmeans.setOptions(options);
        sKmeans.buildClusterer(dataClusterer);
        System.out.println("Kmeans\n:" + sKmeans);
        System.out.println(Arrays.toString(sKmeans.getAssignments()));
        assignments = Arrays.asList(sKmeans.getAssignments());
    } catch (Exception e) {
        e.printStackTrace();
    }
    System.out.println("Assignments\n: " + assignments);
    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(sKmeans);
    try {
        eval.evaluateClusterer(data);
    } catch (Exception e) {
        e.printStackTrace();
    }
    int classesToClustersMap[] = eval.getClassesToClusters();
    for (int i = 0; i < classesToClustersMap.length; i++) {
        if (assignments.get(i).equals(((Integer) classesToClustersMap[(int) data.get(i).classValue()]))) {
            ((Instances) sampled_data).add(data.get(i));
        }
    }
    return ((Instances) sampled_data);
}

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From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java

License:Open Source License

public static Instances clusterInstances(Instances data) {
    XMeans xmeans = new XMeans();
    Remove filter = new Remove();
    Instances dataClusterer = null;/*from w w w.  j a v a  2s  . c  o m*/
    if (data == null) {
        throw new NullPointerException("Data is null at clusteredInstances method");
    }
    //Get the attributes from the data for creating the sampled_data object

    ArrayList<Attribute> attrList = new ArrayList<Attribute>();
    Enumeration attributes = data.enumerateAttributes();
    while (attributes.hasMoreElements()) {
        attrList.add((Attribute) attributes.nextElement());
    }

    Instances sampled_data = new Instances(data.relationName(), attrList, 0);
    data.setClassIndex(data.numAttributes() - 1);
    sampled_data.setClassIndex(data.numAttributes() - 1);
    filter.setAttributeIndices("" + (data.classIndex() + 1));
    data.remove(0);//In Wavelet Stream of MOA always the first element comes without class

    try {
        filter.setInputFormat(data);
        dataClusterer = Filter.useFilter(data, filter);
        String[] options = new String[4];
        options[0] = "-L"; // max. iterations
        options[1] = Integer.toString(noOfClassesInPool - 1);
        if (noOfClassesInPool > 2) {
            options[1] = Integer.toString(noOfClassesInPool - 1);
            xmeans.setMinNumClusters(noOfClassesInPool - 1);
        } else {
            options[1] = Integer.toString(noOfClassesInPool);
            xmeans.setMinNumClusters(noOfClassesInPool);
        }
        xmeans.setMaxNumClusters(data.numClasses() + 1);
        System.out.println("No of classes in the pool: " + noOfClassesInPool);
        xmeans.setUseKDTree(true);
        //xmeans.setOptions(options);
        xmeans.buildClusterer(dataClusterer);
        System.out.println("Xmeans\n:" + xmeans);
    } catch (Exception e) {
        e.printStackTrace();
    }
    //System.out.println("Assignments\n: " + assignments);
    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(xmeans);
    try {
        eval.evaluateClusterer(data);
        int classesToClustersMap[] = eval.getClassesToClusters();
        //check the classes to cluster map
        int clusterNo = 0;
        for (int i = 0; i < data.size(); i++) {
            clusterNo = xmeans.clusterInstance(dataClusterer.get(i));
            //Check if the class value of instance and class value of cluster matches
            if ((int) data.get(i).classValue() == classesToClustersMap[clusterNo]) {
                sampled_data.add(data.get(i));
            }
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
    return ((Instances) sampled_data);
}

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