Package de.lmu.ifi.dbs.elki.algorithm.clustering.trivial

Examples of de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering$Parameterizer

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   * @param clustering Clustering result
   * @param expected Expected score
   */
  protected <O> void testFMeasure(Database database, Clustering<?> clustering, double expected) {
    // Run by-label as reference
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<Model> rbl = bylabel.run(database);

    double score = PairCountingFMeasure.compareClusterings(clustering, rbl, 1.0);
    if(logger.isVerbose()) {
      logger.verbose(this.getClass().getSimpleName() + " score: " + score + " expect: " + expected);
    }
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    // run DeLiClu on database
    Clustering<?> clustering = opticsxi.run(db);

    // Test F-Measure   
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<Model> rbl = bylabel.run(db);
    ClusterContingencyTable ct = new ClusterContingencyTable(true, false);
    ct.process(clustering, rbl);
    double score = ct.getPaircount().f1Measure();
    // We cannot test exactly - due to Hashing, DeLiClu sequence is not
    // identical each time, the results will vary slightly.
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    testParameterizationOk(params);

    // run CLIQUE on database
    Clustering<SubspaceModel<DoubleVector>> result = clique.run(db);
    // Run by-label as reference
    ByLabelClustering bylabel = new ByLabelClustering(true, null);
    Clustering<Model> rbl = bylabel.run(db);

    double score = PairCountingFMeasure.compareClusterings(result, rbl, 1.0);
    org.junit.Assert.assertEquals(this.getClass().getSimpleName() + ": Score does not match.", 0.9882, score, 0.0001);
    testClusterSizes(result, new int[] { 200, 200, 216, 400 });
  }
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    // run all-in-noise
    TrivialAllNoise allinnoise = new TrivialAllNoise();
    Clustering<Model> ran = allinnoise.run(db);

    // run by-label
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<?> rbl = bylabel.run(db);

    assertEquals(1.0, computeFMeasure(rai, rai, false), Double.MIN_VALUE);
    assertEquals(1.0, computeFMeasure(ran, ran, false), Double.MIN_VALUE);
    assertEquals(1.0, computeFMeasure(rbl, rbl, false), Double.MIN_VALUE);
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   * @param clustering Clustering result
   * @param expected Expected score
   */
  protected <O> void testFMeasure(Database database, Clustering<?> clustering, double expected) {
    // Run by-label as reference
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<Model> rbl = bylabel.run(database);

    ClusterContingencyTable ct = new ClusterContingencyTable(true, false);
    ct.process(clustering, rbl);
    double score = ct.getPaircount().f1Measure();
    if(logger.isVerbose()) {
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    if(logger.isVerbose()) {
      logger.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelClustering()).run(database).getAllClusters();

    AggregatingHistogram<Double, Double> hist = AggregatingHistogram.DoubleSumHistogram(numbins, 0.0, 1.0);

    if(logger.isVerbose()) {
      logger.verbose("Processing points...");
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    // determine binning ranges.
    DoubleMinMax gminmax = new DoubleMinMax();

    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelClustering()).run(database).getAllClusters();

    // global in-cluster min/max
    DoubleMinMax giminmax = new DoubleMinMax();
    // global other-cluster min/max
    DoubleMinMax gominmax = new DoubleMinMax();
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    if(logger.isVerbose()) {
      logger.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelClustering()).run(database).getAllClusters();

    // Compute cluster averages and covariance matrix
    HashMap<Cluster<?>, V> averages = new HashMap<Cluster<?>, V>(split.size());
    HashMap<Cluster<?>, Matrix> covmats = new HashMap<Cluster<?>, Matrix>(split.size());
    for(Cluster<?> clus : split) {
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    if(logger.isVerbose()) {
      logger.verbose("Preprocessing clusters...");
    }
    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelClustering()).run(database).getAllClusters();

    AggregatingHistogram<Double, Double> hist = AggregatingHistogram.DoubleSumHistogram(numbins, 0.0, 1.0);

    if(logger.isVerbose()) {
      logger.verbose("Processing points...");
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    // determine binning ranges.
    DoubleMinMax gminmax = new DoubleMinMax();

    // Cluster by labels
    Collection<Cluster<Model>> split = (new ByLabelClustering()).run(database).getAllClusters();

    // global in-cluster min/max
    DoubleMinMax giminmax = new DoubleMinMax();
    // global other-cluster min/max
    DoubleMinMax gominmax = new DoubleMinMax();
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Related Classes of de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering$Parameterizer

  • de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest
  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.TestCLIQUEResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.TestDeLiCluResults
  • de.lmu.ifi.dbs.elki.algorithm.statistics.DistanceStatisticsWithClasses
  • de.lmu.ifi.dbs.elki.algorithm.statistics.EvaluateRankingQuality
  • de.lmu.ifi.dbs.elki.algorithm.statistics.RankingQualityHistogram
  • de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs
  • de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance
  • de.lmu.ifi.dbs.elki.evaluation.paircounting.TestClusterContingencyTable
  • de.lmu.ifi.dbs.elki.evaluation.TestPairCountingFMeasure
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