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

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

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   *
   * @throws ParameterException on errors.
   */
  @Test
  public void testCASHResults() {
    ListParameterization inp = new ListParameterization();
    // CASH input
    inp.addParameter(FileBasedDatabaseConnection.PARSER_ID, ParameterizationFunctionLabelParser.class);
    // Input
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-3d2d1d.csv", 600, inp, null);

    // CASH parameters
    ListParameterization params = new ListParameterization();
    params.addParameter(CASH.JITTER_ID, 0.7);
    params.addParameter(CASH.MINPTS_ID, 50);
    params.addParameter(CASH.MAXLEVEL_ID, 25);
    params.addFlag(CASH.ADJUST_ID);

    // setup algorithm
    CASH cash = ClassGenericsUtil.parameterizeOrAbort(CASH.class, params);
    testParameterizationOk(params);

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  @Test
  public void testKMeansLloyd() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(AbstractKMeans.K_ID, 5);
    params.addParameter(AbstractKMeans.SEED_ID, 3);
    AbstractKMeans<DoubleVector, DoubleDistance> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansLloyd.class, params);
    testParameterizationOk(params);

    // run KMeans on database
    Clustering<MeanModel<DoubleVector>> result = kmeans.run(db);
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  @Test
  public void testKMeansMacQueen() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(AbstractKMeans.K_ID, 5);
    params.addParameter(AbstractKMeans.SEED_ID, 3);
    AbstractKMeans<DoubleVector, DoubleDistance> kmeans = ClassGenericsUtil.parameterizeOrAbort(KMeansMacQueen.class, params);
    testParameterizationOk(params);

    // run KMeans on database
    Clustering<MeanModel<DoubleVector>> result = kmeans.run(db);
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  @Test
  public void testOPTICSResults() {
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(OPTICS.MINPTS_ID, 18);
    params.addParameter(OPTICSXi.XI_ID, 0.038);
    params.addParameter(OPTICSXi.XIALG_ID, OPTICS.class);
    OPTICSXi<DoubleDistance> opticsxi = ClassGenericsUtil.parameterizeOrAbort(OPTICSXi.class, params);
    testParameterizationOk(params);

    // run OPTICS on database
    Clustering<?> clustering = opticsxi.run(db);
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   * @throws ParameterException on errors.
   */
  @Test
  public void testCASHEmbedded() {
    // CASH input
    ListParameterization inp = new ListParameterization();
    inp.addParameter(FileBasedDatabaseConnection.PARSER_ID, ParameterizationFunctionLabelParser.class);
    Database db = makeSimpleDatabase(UNITTEST + "correlation-embedded-2-4d.ascii", 600, inp, null);

    // CASH parameters
    ListParameterization params = new ListParameterization();
    params.addParameter(CASH.JITTER_ID, 0.7);
    params.addParameter(CASH.MINPTS_ID, 160);
    params.addParameter(CASH.MAXLEVEL_ID, 40);

    // setup algorithm
    CASH cash = ClassGenericsUtil.parameterizeOrAbort(CASH.class, params);
    testParameterizationOk(params);

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  @Test
  public void testEMResults() {
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(AbstractKMeans.SEED_ID, 1);
    params.addParameter(EM.K_ID, 5);
    EM<DoubleVector> em = ClassGenericsUtil.parameterizeOrAbort(EM.class, params);
    testParameterizationOk(params);

    // run EM on database
    Clustering<EMModel<DoubleVector>> result = em.run(db);
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  @Test
  public void testERiCResults() {
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-3d2d1d.csv", 600);

    // ERiC
    ListParameterization params = new ListParameterization();
    params.addParameter(COPAC.PARTITION_ALGORITHM_ID, DBSCAN.class);
    params.addParameter(DBSCAN.MINPTS_ID, 30);
    params.addParameter(DBSCAN.EPSILON_ID, 0);
    // ERiC Distance function in DBSCAN:
    params.addParameter(COPAC.PARTITION_DISTANCE_ID, ERiCDistanceFunction.class);
    params.addParameter(ERiCDistanceFunction.DELTA_ID, 0.20);
    params.addParameter(ERiCDistanceFunction.TAU_ID, 0.04);
    // Preprocessing via Local PCA:
    params.addParameter(COPAC.PREPROCESSOR_ID, KNNQueryFilteredPCAIndex.Factory.class);
    params.addParameter(KNNQueryFilteredPCAIndex.Factory.K_ID, 50);
    // PCA
    params.addParameter(PCARunner.PCA_COVARIANCE_MATRIX, WeightedCovarianceMatrixBuilder.class);
    params.addParameter(WeightedCovarianceMatrixBuilder.WEIGHT_ID, ErfcWeight.class);
    params.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, RelativeEigenPairFilter.class);
    params.addParameter(RelativeEigenPairFilter.EIGENPAIR_FILTER_RALPHA, 1.60);

    ERiC<DoubleVector> eric = ClassGenericsUtil.parameterizeOrAbort(ERiC.class, params);
    testParameterizationOk(params);

    // run ERiC on database
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  @Test
  public void testSLINKResults() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(SLINK.SLINK_MINCLUSTERS_ID, 3);
    SLINK<DoubleVector, DoubleDistance> slink = ClassGenericsUtil.parameterizeOrAbort(SLINK.class, params);
    testParameterizationOk(params);

    // run SLINK on database
    Result result = slink.run(db);
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   */
  @Test
  public void testPROCLUSResults() {
    Database db = makeSimpleDatabase(UNITTEST + "subspace-simple.csv", 600);

    ListParameterization params = new ListParameterization();
    params.addParameter(PROCLUS.L_ID, 1);
    params.addParameter(PROCLUS.K_ID, 4);
    params.addParameter(PROCLUS.SEED_ID, 1);

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

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  @Test
  public void testERiCOverlap() {
    Database db = makeSimpleDatabase(UNITTEST + "correlation-overlap-3-5d.ascii", 650);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    // ERiC
    params.addParameter(COPAC.PARTITION_ALGORITHM_ID, DBSCAN.class);
    params.addParameter(DBSCAN.MINPTS_ID, 15);
    params.addParameter(DBSCAN.EPSILON_ID, 0);
    // ERiC Distance function in DBSCAN:
    params.addParameter(COPAC.PARTITION_DISTANCE_ID, ERiCDistanceFunction.class);
    params.addParameter(ERiCDistanceFunction.DELTA_ID, 1.0);
    params.addParameter(ERiCDistanceFunction.TAU_ID, 1.0);
    // Preprocessing via Local PCA:
    params.addParameter(COPAC.PREPROCESSOR_ID, KNNQueryFilteredPCAIndex.Factory.class);
    params.addParameter(KNNQueryFilteredPCAIndex.Factory.K_ID, 45);
    // PCA
    params.addParameter(PCARunner.PCA_COVARIANCE_MATRIX, WeightedCovarianceMatrixBuilder.class);
    params.addParameter(WeightedCovarianceMatrixBuilder.WEIGHT_ID, ErfcWeight.class);
    params.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, PercentageEigenPairFilter.class);
    params.addParameter(PercentageEigenPairFilter.ALPHA_ID, 0.6);

    ERiC<DoubleVector> eric = ClassGenericsUtil.parameterizeOrAbort(ERiC.class, params);
    testParameterizationOk(params);

    // run ERiC on database
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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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