Example usage for weka.clusterers SimpleKMeans getAssignments

Introduction

In this page you can find the example usage for weka.clusterers SimpleKMeans getAssignments.

Prototype

public int[] getAssignments() throws Exception 

Source Link

Document

Gets the assignments for each instance.

Usage

From source file:org.montp2.m1decol.ter.clustering.KMeansClustering.java

License:Open Source License

public Map<Integer, List<Integer>> computeInstanceByCluster(String arffFilter, String inModel,
        Map<Integer, Integer> arffToIdUser) throws Exception {

    SimpleKMeans kmeans = WekaUtils.loadModel(inModel);

    Instances data = new Instances(WekaUtils.loadARFF(arffFilter));

    int[] clusters = kmeans.getAssignments();

    Map<Integer, List<Integer>> idUserByCluster = new HashMap<Integer, List<Integer>>();

    for (int i = 0; i < data.numInstances(); i++) {
        int ind = clusters[i];

        List<Integer> users = idUserByCluster.get(ind);
        if (users == null) {
            users = new ArrayList<Integer>();
            idUserByCluster.put(ind, users);
        }//from w ww  .j a v a2s  .  com
        users.add(arffToIdUser.get(i));
    }

    return idUserByCluster;

}

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From source file:org.montp2.m1decol.ter.clustering.NearestNeighbor.java

License:Open Source License

public Map<Integer, List<DistanceUser>> computeNearestNeighbor(String arffData, String inModel,
        Map<Integer, Integer> arffToIdUser) throws Exception {

    SimpleKMeans kmeans = WekaUtils.loadModel(inModel);

    EuclideanDistance eclidean = (EuclideanDistance) kmeans.getDistanceFunction();

    Instances data = new Instances(WekaUtils.loadARFF(arffData));

    int[] clusters = kmeans.getAssignments();
    Instances clusterCentroid = kmeans.getClusterCentroids();

    Map<Integer, List<DistanceUser>> nearUser = new HashMap<Integer, List<DistanceUser>>();

    for (int i = 0; i < clusterCentroid.numInstances(); i++) {
        nearUser.put(i, new ArrayList<DistanceUser>());
    }//w  w w  . j a  v  a  2 s .co m

    for (int i = 0; i < data.numInstances(); i++) {
        int ind = clusters[i];
        double dist = eclidean.distance(clusterCentroid.instance(ind), data.instance(i));
        List<DistanceUser> nears = nearUser.get(ind);
        if (nears.size() < 10) {
            nears.add(new DistanceUser(i, dist));
        } else {
            DistanceUser max = Collections.max(nears);
            if (max.getDistance() > dist) {
                int maxIndex = nears.indexOf(max);
                nears.set(maxIndex, new DistanceUser(i, dist));
            }
        }
    }

    for (Map.Entry<Integer, List<DistanceUser>> item : nearUser.entrySet()) {
        for (DistanceUser user : item.getValue()) {
            user.setIdentifier(arffToIdUser.get(user.getIdentifier()));
        }
    }

    return nearUser;
}

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From source file:qoala.arff.java

public void SimpleKmeans(int numberOfCLuster) throws Exception {

    Instances train = new Instances(dataSet);

    SimpleKMeans skm = new SimpleKMeans();
    skm.setPreserveInstancesOrder(true);
    skm.setNumClusters(numberOfCLuster);
    skm.buildClusterer(train);/*from w  ww  .jav  a  2 s . c o m*/
    skm.setSeed(10);
    int[] ClusterSize = skm.getClusterSizes();

    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(skm);
    eval.evaluateClusterer(train);

    System.out.println("Cluster Evaluation:" + eval.clusterResultsToString());

    int[] assignments = skm.getAssignments();

    System.out.println("# - cluster - distribution");

    for (int j = 0; j < skm.getNumClusters(); j++) {
        int i = 0;
        for (int clusterNum : assignments) {

            if (clusterNum == j)

                System.out.println("Instance " + i + " -> Cluster number: " + clusterNum);

            i++;
        }
    }
}

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From source file:swm.project.mappings.UserToUserCluster.java

private void clusterUserHistoryWithKmeans() throws FileNotFoundException, IOException, Exception {
    Reader reader;/* www.  j  a  va 2  s  .  c  o  m*/
    userToUserClusterHistory = new HashMap<>();
    userClustersToUsersHistory = new HashMap<>();
    reader = new FileReader(MappingConstants.USER_MOVIE_CLUSTERS);
    Instances instanceValues = new Instances(reader);
    SimpleKMeans kmeans = new SimpleKMeans();

    kmeans.setNumClusters(20);
    kmeans.setPreserveInstancesOrder(true);
    kmeans.setDistanceFunction(new EuclideanDistance());
    kmeans.buildClusterer(instanceValues);

    int[] assignments = kmeans.getAssignments();
    int userid = 0;
    for (int clusterNo : assignments) {
        int user = (int) instanceValues.get(userid).value(0);
        userToUserClusterHistory.put(user, clusterNo);
        ArrayList<Integer> users = new ArrayList<>();
        if (userClustersToUsersHistory.containsKey(clusterNo)) {
            users = userClustersToUsersHistory.get(clusterNo);
            users.add(user);
        } else {
            users.add(user);
            userClustersToUsersHistory.put(clusterNo, users);
        }
        userid++;

    }
}

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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");
    }// w  w w. ja  v a2  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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