qoala.arff.java Source code

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Here is the source code for qoala.arff.java

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package qoala;

/*
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import java.io.File;
import java.io.IOException;
import java.util.*;
import java.io.*;
import java.util.logging.Level;
import java.util.logging.Logger;
import weka.clusterers.ClusterEvaluation;
import weka.clusterers.EM;
import weka.clusterers.SimpleKMeans;
import weka.clusterers.XMeans;

import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ArffSaver;
import weka.filters.Filter;
import weka.filters.unsupervised.instance.NonSparseToSparse;
import weka.core.DenseInstance;
import weka.core.BinarySparseInstance;
import weka.core.SparseInstance;

/**
 *
 * @author siavash
 */

//This Class comprises of methods to create instances  and arff file and clustering methods - SimpleKmeans,X-Means,EM// 
public class arff {

    FastVector attributes;
    Instances dataSet;
    int[] values;
    double[][] vals;
    int k = 0;
    setData cl = new setData();
    // NewJFrame Frame = new NewJFrame();

    //Generates a datasets with diffrent attributes//
    public void valueSession(setData f) {
        vals = new double[cl.Datapoints.size() / 5][6];
        //set up Attributes//
        attributes = new FastVector();

        attributes.addElement(new Attribute("att1 SessionID"));
        attributes.addElement(new Attribute("att2 NumberofClicks"));
        attributes.addElement(new Attribute("att3 NumberofQueriesInSession"));
        attributes.addElement(new Attribute("att4 NumberofQueryTermsInSession"));
        attributes.addElement(new Attribute("att5 NumberofAverageQueryTermsInSession"));
        attributes.addElement(new Attribute("att6 SessionDuration"));
        //Creates instances object//
        dataSet = new Instances("My weka", attributes, 0);

        // fills the instances with data//
        for (int k = 0; k < cl.Datapoints.get(cl.Datapoints.size() / 5).getSession(); k++) {

            vals[k][0] = (double) f.GetSessionRetrieval(k);

            vals[k][1] = (double) f.GetClick(k);

            vals[k][2] = (double) f.GetSessionNumber(k);

            vals[k][3] = (double) f.GetTerms(k);

            vals[k][4] = (double) f.GetAverageTerms(k);

            vals[k][5] = (double) f.sessionDurationRetrieval(k);

            Instance inst = new DenseInstance(6);
            inst.setValue(0, vals[k][0]);
            inst.setValue(1, vals[k][1]);
            inst.setValue(2, vals[k][2]);
            inst.setValue(3, vals[k][3]);
            inst.setValue(4, vals[k][4]);
            inst.setValue(5, vals[k][5]);

            dataSet.add(inst);

        }

    }

    //Generates the Arff File with diffrent attributes//    
    public void ArffCreator() {

        System.out.println(dataSet);

        ArffSaver arffSaverInstance = new ArffSaver();
        arffSaverInstance.setInstances(dataSet);
        try {
            arffSaverInstance.setFile(new File("MyProject.arff"));
            arffSaverInstance.writeBatch();
        } catch (IOException ex) {
            Logger.getLogger(arff.class.getName()).log(Level.SEVERE, null, ex);
        }

        System.out.println("Done");

    }

    //Performs SimpleKmeans by using WEKA library with user-specific number of Clusters//
    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);
        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++;
            }
        }
    }

    //Performs EMClustering by using WEKA library with user-specific number of Clusters// 
    public void EMClustering(int NumberOfCluster) throws Exception {

        Instances train = new Instances(dataSet);
        String[] options = new String[2];
        options[0] = "-I";
        options[1] = "100";

        EM em = new EM();
        em.setOptions(options);
        em.setNumClusters(NumberOfCluster);
        em.buildClusterer(train);

        ClusterEvaluation eval = new ClusterEvaluation();
        eval.setClusterer(em);
        eval.evaluateClusterer(train);
        eval.getNumClusters();
        System.out.println("Cluster Evaluation:" + eval.clusterResultsToString());

        System.out.println("# - cluster - distribution");
        for (int j = 0; j < eval.getNumClusters(); j++) {

            for (int i = 0; i < train.numInstances(); i++) {

                int cluster = em.clusterInstance(train.instance(i));
                if (cluster == j)
                    System.out.println("Instance " + i + " -> Cluster number: " + cluster);

            }
        }
    }

    //Performs XMeans by using WEKA library with user-specific number of Clusters//
    public void XMenas() throws Exception {

        Instances train = new Instances(dataSet);
        XMeans xm = new XMeans();

        xm.setMaxNumClusters(100);
        xm.setMinNumClusters(2);
        xm.buildClusterer(train);

        ClusterEvaluation eval = new ClusterEvaluation();
        eval.setClusterer(xm);
        eval.evaluateClusterer(train);
        eval.getNumClusters();
        System.out.println("Cluster Evaluation:" + eval.clusterResultsToString());
        System.out.println("# - cluster - distribution");
        for (int j = 0; j < eval.getNumClusters(); j++) {

            for (int i = 0; i < train.numInstances(); i++) {
                int cluster = xm.clusterInstance(train.instance(i));
                if (cluster == j)

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

    }

}