Package de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization

Examples of de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization

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  @Test
  public void testPROCLUSSubspaceOverlapping() {
    Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(PROCLUS.L_ID, 2);
    params.addParameter(PROCLUS.K_ID, 3);
    params.addParameter(PROCLUS.SEED_ID, 2);
    PROCLUS<DoubleVector> proclus = ClassGenericsUtil.parameterizeOrAbort(PROCLUS.class, params);
    testParameterizationOk(params);

    // run PROCLUS on database
    Clustering<Model> result = proclus.run(db);
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   * @throws ParameterException
   */
  @Test
  public void testPreDeConResults() {
    // Additional input parameters
    ListParameterization inp = new ListParameterization();
    inp.addParameter(ClassLabelFilter.CLASS_LABEL_INDEX_ID, 1);
    Class<?>[] filters = new Class<?>[] { ClassLabelFilter.class };
    // FIXME: makeSimpleDatabase currently does also add FILTERS, this doesn't
    // work.
    Database db = makeSimpleDatabase(UNITTEST + "axis-parallel-subspace-clusters-6d.csv.gz", 2500, inp, filters);

    ListParameterization params = new ListParameterization();
    // PreDeCon
    // FIXME: These parameters do NOT work...
    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 50);
    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 50);
    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 2);

    // setup algorithm
    PreDeCon<DoubleVector> predecon = ClassGenericsUtil.parameterizeOrAbort(PreDeCon.class, params);
    testParameterizationOk(params);

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  @Test
  public void testPreDeConSubspaceOverlapping() {
    Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    // PreDeCon
    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 2.0);
    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 7);
    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 4);
    params.addParameter(Factory.DELTA_ID, 0.04);
    PreDeCon<DoubleVector> predecon = ClassGenericsUtil.parameterizeOrAbort(PreDeCon.class, params);
    testParameterizationOk(params);

    // run PredeCon on database
    Clustering<Model> result = predecon.run(db);
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   */
  @Test
  public void testORCLUSResults() {
    Database db = makeSimpleDatabase(UNITTEST + "correlation-hierarchy.csv", 450);

    ListParameterization params = new ListParameterization();
    // these parameters are not picked too smartly - room for improvement.
    params.addParameter(ORCLUS.K_ID, 3);
    params.addParameter(ORCLUS.L_ID, 1);
    params.addParameter(ORCLUS.SEED_ID, 2);

    // setup algorithm
    ORCLUS<DoubleVector> orclus = ClassGenericsUtil.parameterizeOrAbort(ORCLUS.class, params);
    testParameterizationOk(params);

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  @Test
  public void testORCLUSSkewedDisjoint() {
    Database db = makeSimpleDatabase(UNITTEST + "correlation-skewed-disjoint-3-5d.ascii", 601);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(ORCLUS.K_ID, 3);
    params.addParameter(ORCLUS.L_ID, 4);
    params.addParameter(ORCLUS.SEED_ID, 9);

    ORCLUS<DoubleVector> orclus = ClassGenericsUtil.parameterizeOrAbort(ORCLUS.class, params);
    testParameterizationOk(params);

    // run ORCLUS on database
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   */
  @Test
  public void testDiSHResults() {
    Database db = makeSimpleDatabase(UNITTEST + "subspace-hierarchy.csv", 450);

    ListParameterization params = new ListParameterization();
    params.addParameter(DiSH.EPSILON_ID, 0.005);
    params.addParameter(DiSH.MU_ID, 50);

    // setup algorithm
    DiSH<DoubleVector> dish = ClassGenericsUtil.parameterizeOrAbort(DiSH.class, params);
    testParameterizationOk(params);

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  @Test
  public void testDiSHSubspaceOverlapping() {
    Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-4-5d.ascii", 1100);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(DiSH.EPSILON_ID, 0.1);
    params.addParameter(DiSH.MU_ID, 30);
    DiSH<DoubleVector> dish = ClassGenericsUtil.parameterizeOrAbort(DiSH.class, params);
    testParameterizationOk(params);

    // run DiSH on database
    Clustering<SubspaceModel<DoubleVector>> result = dish.run(db);
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   */
  @Test
  public void testCLIQUEResults() {
    Database db = makeSimpleDatabase(UNITTEST + "subspace-simple.csv", 600);

    ListParameterization params = new ListParameterization();
    params.addParameter(CLIQUE.TAU_ID, "0.1");
    params.addParameter(CLIQUE.XSI_ID, 20);

    // setup algorithm
    CLIQUE<DoubleVector> clique = ClassGenericsUtil.parameterizeOrAbort(CLIQUE.class, params);
    testParameterizationOk(params);

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  @Test
  public void testCOPACResults() {
    Database db = makeSimpleDatabase(UNITTEST + "correlation-hierarchy.csv", 450);

    // these parameters are not picked too smartly - room for improvement.
    ListParameterization params = new ListParameterization();
    params.addParameter(COPAC.PARTITION_ALGORITHM_ID, DBSCAN.class);
    params.addParameter(DBSCAN.EPSILON_ID, 0.02);
    params.addParameter(DBSCAN.MINPTS_ID, 50);
    params.addParameter(COPAC.PREPROCESSOR_ID, KNNQueryFilteredPCAIndex.Factory.class);
    params.addParameter(KNNQueryFilteredPCAIndex.Factory.K_ID, 15);

    COPAC<DoubleVector, DoubleDistance> copac = ClassGenericsUtil.parameterizeOrAbort(COPAC.class, params);
    testParameterizationOk(params);

    // run COPAC on database
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  @Test
  public void testCLIQUESubspaceOverlapping() {
    Database db = makeSimpleDatabase(UNITTEST + "subspace-overlapping-3-4d.ascii", 850);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(CLIQUE.TAU_ID, 0.2);
    params.addParameter(CLIQUE.XSI_ID, 6);
    CLIQUE<DoubleVector> clique = ClassGenericsUtil.parameterizeOrAbort(CLIQUE.class, params);
    testParameterizationOk(params);

    // run CLIQUE on database
    Clustering<SubspaceModel<DoubleVector>> result = clique.run(db);
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Related Classes of de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization

  • de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest
  • de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedDBSCAN$Parameterizer
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.CASH
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC$Parameterizer
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO$Parameterizer
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestCASHResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestCOPACResults
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.TestERiCResults
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