Example usage for weka.clusterers SimpleKMeans SimpleKMeans

Introduction

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

Prototype

public SimpleKMeans() 

Source Link

Document

the default constructor.

Usage

From source file:soccer.core.ASimplePractice.java

public void evaluate() throws IOException, Exception {
    Instances data = loader.getInstances();
    SimpleKMeans cluster = new SimpleKMeans();
    cluster.setNumClusters(4);/*from   w  w  w .ja  v a2  s  . c o m*/
    cluster.buildClusterer(data);
    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(cluster);
    eval.evaluateClusterer(data);
    System.out.println(eval.clusterResultsToString());
}

---

From source file:soccer.core.classifiers.BookKeeperConsistencyClassifier.java

public static void main(String[] args) throws Exception {
    BookKeeperConsistency bkc = new BookKeeperConsistency();
    Instances data = bkc.getInstances();

    RemoveWithValues rwv = new RemoveWithValues();
    rwv.setOptions(new String[] { "-C", "4", "-S", "6", "-V" });
    rwv.setInputFormat(data);//from  w  ww . j a va2 s .  com
    data = Filter.useFilter(data, rwv);
    RemoveWithValues rwv1 = new RemoveWithValues();
    rwv1.setOptions(new String[] { "-C", "6", "-S", "6", "-V" });
    rwv1.setInputFormat(data);
    data = Filter.useFilter(data, rwv1);

    //        Normalize nm = new Normalize();
    //        nm.setOptions(new String[]{
    //            "-S", "100"
    //        });
    //        nm.setInputFormat(data);
    //        data = Filter.useFilter(data, nm);

    Remove rm = new Remove();
    rm.setOptions(new String[] { "-R", "2-last" });
    rm.setInputFormat(data);
    Instances newData = Filter.useFilter(data, rm);

    SimpleKMeans cluster = new SimpleKMeans();
    cluster.setOptions(new String[] { "-N", "2", "-A", "weka.core.ManhattanDistance" });

    cluster.buildClusterer(newData);
    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(cluster);
    eval.evaluateClusterer(newData);
    System.out.println(eval.clusterResultsToString());
    //        for (int i = 0; i < newData.size(); i++) {
    //            Instance instance = newData.get(i);
    //            if (cluster.clusterInstance(instance) == 0) {
    //                System.out.println(data.get(i).toString());
    //            }
    //        }
}

---

From source file:soccer.core.models.PlayerModel.java

public static void main(String[] args) throws Exception {
    PlayerModel pm = new PlayerModel();
    Instances data = pm.buildInstance();
    SimpleKMeans cluster = new SimpleKMeans();
    cluster.setNumClusters(4);//w  w w . j  ava 2 s.c o  m
    cluster.buildClusterer(data);
    ClusterEvaluation eval = new ClusterEvaluation();
    eval.setClusterer(cluster);
    eval.evaluateClusterer(data);
    System.out.println(eval.clusterResultsToString());
}

---

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

private void clusterUserHistoryWithKmeans() throws FileNotFoundException, IOException, Exception {
    Reader reader;/*from w ww .  j a  v a  2s  .  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++;

    }
}

---

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   w ww  .j  av a2  s  . co  m*/
    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);
}

---

From source file:tubes2.Main.java

/**
 * @param args the command line arguments
 */// ww w  .  j av  a  2 s .  c o m
public static void main(String[] args) throws IOException, Exception {
    // TODO code application logic here
    String filename = "weather";

    //Masih belum mengerti tipe .csv yang dapat dibaca seperti apa
    //CsvToArff convert = new CsvToArff(filename+".csv");

    //LOAD FILE
    BufferedReader datafile = readDataFile("data/" + filename + ".arff");
    Instances data = new Instances(datafile);

    ArrayList<Integer> numericIdx = new ArrayList<Integer>();
    for (int i = 0; i < data.numAttributes(); i++) {
        if (data.attribute(i).isNumeric()) {
            numericIdx.add(i);
        }
    }
    System.out.println();
    System.out.println("\n----SEBELUM NORMALISASI-----");
    System.out.println(data);
    normalizeData(data, numericIdx);
    System.out.println("\n----SETELAH NORMALISASI-----");
    System.out.println(data);

    //END OF LOAD FILE

    SimpleKMeans simpleK = new SimpleKMeans();
    simpleK.setNumClusters(4);
    Clusterer[] clusterers = { simpleK, new myKMeans(4), new myAgnes(4) };

    boolean first = true;
    for (Clusterer clusterer : clusterers) {
        try {
            clusterer.buildClusterer(data);
            System.out.println("\n\n----HASIL CLUSTERING-----");
            System.out.println(clusterer);
        } catch (Exception ex) {
            Logger.getLogger(Main.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
}

---

From source file:wekimini.InputGenerator.java

public void selectKmClusters(int numClusters) throws Exception {
    buildDataset();/*from w  w  w . jav  a2 s  . c  om*/

    SimpleKMeans km = new SimpleKMeans();
    km.setNumClusters(numClusters);
    km.buildClusterer(dataset);

    clusters = km.getClusterCentroids();

    addKmClustersToTraining();
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• »