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

Examples of de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering.run()


   * @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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    // 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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    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 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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    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, PairCountingFMeasure.compareClusterings(rai, rai), Double.MIN_VALUE);
    assertEquals(1.0, PairCountingFMeasure.compareClusterings(ran, ran), Double.MIN_VALUE);
    assertEquals(1.0, PairCountingFMeasure.compareClusterings(rbl, rbl), Double.MIN_VALUE);
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