Example usage for weka.clusterers SimpleKMeans setSeed

List of usage examples for weka.clusterers SimpleKMeans setSeed

Introduction

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

Prototype

@Override
public void setSeed(int value) 

Source Link

Document

Set the seed for random number generation.

Usage

From source file:kmeansapps.Kmeans.java

public void startCluster(String path, int numOfCluster, JTable tableResult, JFrame apps) {
    try {/*from   w  w  w  .  jav  a 2 s  .c  o m*/
        // TODO code application logic here
        SimpleKMeans kmeans = new SimpleKMeans();
        String[] columnNames = new String[numOfCluster];

        kmeans.setSeed(10);
        kmeans.setPreserveInstancesOrder(true);
        kmeans.setNumClusters(numOfCluster);

        BufferedReader datafile = readDataFile(path);
        Instances data = new Instances(datafile);

        kmeans.buildClusterer(data);
        double SSE = kmeans.getSquaredError();
        // This array returns the cluster number (starting with 0) for each instance
        // The array has as many elements as the number of instances
        int[] assignments = kmeans.getAssignments();

        //            //setting columNames
        //            for (int i = 0; i < numOfCluster; i++) {
        //                columnNames[i] = "Cluster "+i+"";
        //            }

        // bikin arraylist 2 dimensi untuk menampung instance masuk ke cluster berapa.
        ArrayList<ArrayList<String>> listOfCluster = new ArrayList<ArrayList<String>>();
        ArrayList<String> listMemberOfCluster;

        //tambahkan list cluster
        for (int i = 0; i < numOfCluster; i++) {
            listMemberOfCluster = new ArrayList<>();
            listOfCluster.add(listMemberOfCluster);
        }
        //tambahkan anggota list ke cluster
        int j = 0;
        for (int clusterNum : assignments) {
            listOfCluster.get(clusterNum).add(j + "");
            j++;
        }

        for (int i = 0; i < listOfCluster.size(); i++) {
            System.out.print("Cluster - " + i + " -> ");
            for (String listMemberOfCluster1 : listOfCluster.get(i)) {
                System.out.print(listMemberOfCluster1 + " ");
            }
            System.out.println("");
        }

        //            int i=0;
        //            for(int clusterNum : assignments) {
        //                System.out.printf("Instance %d -> Cluster %d \n", i, clusterNum);
        //                i++;
        //                System.out.println(SSE);
        //            }

        //            //output to table
        //            tableResult.setModel(new DefaultTableModel(
        //            new Object[][]{
        //            },
        //            columnNames));
        //            apps.setVisible(true);
        //            
        //            int j=0;
        //            DefaultTableModel model = (DefaultTableModel) tableResult.getModel();
        //            for(int clusterNum : assignments) {
        //                if (clusterNum==0){
        //                    model.addRow(new Object[]{j, "", "", "", "", ""});
        //                }
        //                else if (clusterNum==1){
        //                    model.addRow(new Object[]{"", j, "", "", "", ""});
        //                }
        //                else if (clusterNum==2){
        //                    model.addRow(new Object[]{"", "", j, "", "", ""});
        //                }
        //                else if (clusterNum==3){
        //                    model.addRow(new Object[]{"", "", "", j, "", ""});
        //                }
        //                else if (clusterNum==4){
        //                    model.addRow(new Object[]{"", "", "", "", j, ""});
        //                }
        //                else if (clusterNum==5){
        //                    model.addRow(new Object[]{"", "", "", "", "", j});
        //                }
        //                
        //                j++;
        //            }
    } catch (Exception ex) {
        Logger.getLogger(Kmeans.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:kmeansapps.Kmeans.java

public void startCluster(String path, int numOfCluster, JTextArea textarea) {
    try {//w  w w  .  ja v a  2  s . c  om
        // TODO code application logic here
        SimpleKMeans kmeans = new SimpleKMeans();
        String[] columnNames = new String[numOfCluster];
        kmeans.setSeed(10);
        kmeans.setPreserveInstancesOrder(true);
        kmeans.setNumClusters(numOfCluster);

        BufferedReader datafile = readDataFile(path);
        Instances data = new Instances(datafile);

        kmeans.buildClusterer(data);
        double SSE = kmeans.getSquaredError();
        // This array returns the cluster number (starting with 0) for each instance
        // The array has as many elements as the number of instances
        int[] assignments = kmeans.getAssignments();

        // bikin arraylist 2 dimensi untuk menampung instance masuk ke cluster berapa.
        ArrayList<ArrayList<String>> listOfCluster = new ArrayList<ArrayList<String>>();
        ArrayList<String> listMemberOfCluster;

        //tambahkan list cluster
        for (int i = 0; i < numOfCluster; i++) {
            listMemberOfCluster = new ArrayList<>();
            listOfCluster.add(listMemberOfCluster);
        }
        //tambahkan anggota list ke cluster
        int j = 0;
        for (int clusterNum : assignments) {
            listOfCluster.get(clusterNum).add(j + "");
            j++;
        }
        textarea.setText("");
        String result = "";
        for (int i = 0; i < listOfCluster.size(); i++) {
            result = result + ("Cluster - " + i + " ==> ");
            for (String listMemberOfCluster1 : listOfCluster.get(i)) {
                result = result + (listMemberOfCluster1 + " ");
            }
            result = result + ("\n");
        }
        result = result + ("\nSSE : ") + kmeans.getSquaredError();
        textarea.setText(result);
    } catch (Exception ex) {
        Logger.getLogger(Kmeans.class.getName()).log(Level.SEVERE, null, ex);
    }
}

From source file:nl.uva.sne.classifiers.Kmeans.java

@Override
public Map<String, String> cluster(String inDir) throws IOException, ParseException {
    try {//from   w w  w .  ja v a 2s.  c  o  m

        Instances data = ClusterUtils.terms2Instances(inDir, false);

        DistanceFunction df;
        //            SimpleKMeans currently only supports the Euclidean and Manhattan distances.
        switch (distanceFunction) {
        case "Euclidean":
            df = new EuclideanDistance(data);
            break;
        case "Manhattan":
            df = new ManhattanDistance(data);
            break;
        default:
            df = new EuclideanDistance(data);
            break;
        }

        SimpleKMeans clusterer = new SimpleKMeans();

        Random rand = new Random(System.currentTimeMillis());
        int seed = rand.nextInt((Integer.MAX_VALUE - 1000000) + 1) + 1000000;
        clusterer.setSeed(seed);
        clusterer.setMaxIterations(1000000000);
        Logger.getLogger(Kmeans.class.getName()).log(Level.INFO, "Start clusteing");
        clusterer.setPreserveInstancesOrder(true);

        clusterer.setNumClusters(numOfClusters);
        clusterer.setDistanceFunction(df);

        return ClusterUtils.bulidClusters(clusterer, data, inDir);

    } catch (Exception ex) {
        Logger.getLogger(Kmeans.class.getName()).log(Level.SEVERE, null, ex);
    }
    return null;
}

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

License:Open Source License

public Clusterer computeClustering(String inPath, String outPath, Properties propertiesCluster)
        throws Exception {
    Instances inputInstances = WekaUtils.loadARFF(inPath);

    EuclideanDistance euclideanDistance = new EuclideanDistance();
    euclideanDistance.setAttributeIndices("first-last");
    euclideanDistance.setDontNormalize(false);
    euclideanDistance.setInvertSelection(false);

    SimpleKMeans kmeans = new SimpleKMeans();
    kmeans.setPreserveInstancesOrder(/* ww w  .  j  ava 2s . c o  m*/
            Boolean.valueOf(propertiesCluster.getProperty(ClusterProperties.Kmeans.PERSERVE_INSTANCE)));
    kmeans.setDontReplaceMissingValues(Boolean
            .valueOf(propertiesCluster.getProperty(ClusterProperties.Kmeans.DONT_REPLACE_MISSING_VALUES)));
    kmeans.setDisplayStdDevs(
            Boolean.valueOf(propertiesCluster.getProperty(ClusterProperties.Kmeans.DISPLAY_STD_DEVS)));
    kmeans.setMaxIterations(
            Integer.valueOf(propertiesCluster.getProperty(ClusterProperties.Kmeans.MAX_ITERATIONS)));
    kmeans.setNumClusters(
            Integer.valueOf(propertiesCluster.getProperty(ClusterProperties.Kmeans.NUM_CLUSTERS)));
    kmeans.setSeed(10);
    //kmeans.setSeed(
    //      Integer.valueOf(propertiesCluster.getProperty(ClusterProperties.Kmeans.SEED)));
    kmeans.setDistanceFunction(euclideanDistance);
    kmeans.buildClusterer(inputInstances);

    WekaUtils.saveModel(kmeans, outPath);

    /*
    *
    * Pour obtenir les pourcentages de les clusters
    * ClusterEvaluation eval = new ClusterEvaluation();
    * eval.setClusterer(kmeans);
    * eval.evaluateClusterer(inputInstances);
    * System.out.println(eval.clusterResultsToString());
    *
    * */

    return kmeans;
}

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   ww w  .  ja v a  2  s.  co 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++;
        }
    }
}