Examples with CSVSinkFilter org.iobserve.analysis.behavior.clustering.hierarchical.CSVSinkFilter used on opensource projects

Example 1 with CSVSinkFilter

use of org.iobserve.analysis.behavior.clustering.hierarchical.CSVSinkFilter 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);
}