Examples with FastVector weka.core.FastVector used on opensource projects

Example 6 with FastVector

use of weka.core.FastVector in project iobserve-analysis by research-iobserve.

the class ClusterMerger method execute.

/*
     * (non-Javadoc)
     *
     * @see teetime.framework.AbstractConsumerStage#execute(java.lang.Object)
     */
@Override
protected void execute(final Map<Integer, List<Pair<Instance, Double>>> clustering) throws Exception {
    /**
     * simply pick the first instance of every cluster lookup attributes to build a new
     * instances Object
     */
    Instance instance = clustering.entrySet().iterator().next().getValue().get(0).getElement1();
    final FastVector attributes = new FastVector();
    for (int j = 0; j < instance.numAttributes(); j++) {
        attributes.addElement(instance.attribute(j));
    }
    final Instances result = new Instances("Clustering Result", attributes, clustering.size());
    for (final List<Pair<Instance, Double>> entry : clustering.values()) {
        if (!entry.isEmpty()) {
            instance = entry.get(0).getElement1();
            result.add(instance);
        }
    }
    this.printInstances(result);
    this.outputPort.send(result);
}

Example 7 with FastVector

use of weka.core.FastVector in project iobserve-analysis by research-iobserve.

the class JUnitTestsHierarchicalClustering method setupTestData.

/**
 * Create test data sets and their expected clustering results.
 *
 * @throws IOException
 *             when failing to write a CSV file to CSVOUTPUTPATH
 */
@Before
public void setupTestData() throws IOException {
    // Create Attribute names for the test data sets.
    final FastVector attVector = new FastVector(1);
    attVector.addElement(new Attribute("Attribute1"));
    attVector.addElement(new Attribute("Attribute2"));
    // Create a data set with two similar instances i1 and i2 with two attributes.
    final Instance i1one = new Instance(1.0, new double[] { 1.0, 2.0 });
    final Instance i2one = new Instance(1.0, new double[] { 1.5, 2.1 });
    final Instances instancesOne = new Instances("Instances", attVector, 2);
    instancesOne.add(i1one);
    instancesOne.add(i2one);
    i1one.setDataset(instancesOne);
    i2one.setDataset(instancesOne);
    this.setTestInstancesOneCluster(instancesOne);
    // Create expected result.
    final Map<Integer, List<Pair<Instance, Double>>> expResOne = new HashMap<>();
    expResOne.put(0, new LinkedList<Pair<Instance, Double>>());
    expResOne.get(0).add(new Pair<>(i1one, 1.0));
    expResOne.get(0).add(new Pair<>(i2one, 1.0));
    this.setExpectedResultsOneCluster(expResOne);
    /*
         * Create a data set with two similar instances i1 and i2 and one different instance i3 with
         * two attributes.
         */
    final Instance i1two = new Instance(1.0, new double[] { 1.0, 2.0 });
    final Instance i2two = new Instance(1.0, new double[] { 1.5, 2.1 });
    final Instance i3two = new Instance(1.0, new double[] { 1000.0, 1200.0 });
    final Instances instancesTwo = new Instances("Instances", attVector, 3);
    instancesTwo.add(i1two);
    instancesTwo.add(i2two);
    instancesTwo.add(i3two);
    i1two.setDataset(instancesTwo);
    i2two.setDataset(instancesTwo);
    i3two.setDataset(instancesTwo);
    this.setTestInstancesTwoClusters(instancesTwo);
    // Create expected result.
    final Map<Integer, List<Pair<Instance, Double>>> expResTwo = new HashMap<>();
    expResTwo.put(0, new LinkedList<Pair<Instance, Double>>());
    expResTwo.put(1, new LinkedList<Pair<Instance, Double>>());
    expResTwo.get(0).add(new Pair<>(i1two, 1.0));
    expResTwo.get(0).add(new Pair<>(i2two, 1.0));
    expResTwo.get(1).add(new Pair<>(i3two, 1.0));
    this.setExpectedResultsTwoCluster(expResTwo);
    // Write a CSV file for a clustering result which expects two clusters.
    final CSVSinkFilter csvFilter = new CSVSinkFilter();
    final Map<Double, List<Instance>> clusteringKVs = csvFilter.convertClusteringResultsToKVPair(expResTwo);
    csvFilter.createCSVFromClusteringResult(JUnitTestsHierarchicalClustering.CSVOUTPUTPATH, clusteringKVs);
}

Example 8 with FastVector

use of weka.core.FastVector in project iobserve-analysis by research-iobserve.

the class AbstractClustering method createInstances.

/**
 * It transforms the user sessions(userSessions in form of counts of their called operation
 * signatures) to Weka instances that can be used for the clustering.
 *
 * @param countModel
 *            contains the userSessions in form of counts of called operation signatures
 * @param listOfDistinctOperationSignatures
 *            contains the extracted distinct operation signatures of the input
 *            entryCallSequenceModel
 * @return the Weka instances that hold the data that is used for the clustering
 */
protected Instances createInstances(final List<UserSessionAsCountsOfCalls> countModel, final List<String> listOfDistinctOperationSignatures) {
    final int numberOfDistinctOperationSignatures = listOfDistinctOperationSignatures.size();
    final FastVector fvWekaAttributes = new FastVector(numberOfDistinctOperationSignatures);
    for (int i = 0; i < numberOfDistinctOperationSignatures; i++) {
        final String attributeName = "Attribute" + i;
        final Attribute attribute = new Attribute(attributeName);
        fvWekaAttributes.addElement(attribute);
    }
    final Instances clusterSet = new Instances("CallCounts", fvWekaAttributes, countModel.size());
    for (final UserSessionAsCountsOfCalls userSession : countModel) {
        int indexOfAttribute = 0;
        final Instance instance = new Instance(numberOfDistinctOperationSignatures);
        for (int row = 0; row < listOfDistinctOperationSignatures.size(); row++) {
            instance.setValue((Attribute) fvWekaAttributes.elementAt(indexOfAttribute), userSession.getAbsoluteCountOfCalls()[row]);
            indexOfAttribute++;
        }
        clusterSet.add(instance);
    }
    return clusterSet;
}