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 testSNNClusteringResults() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d.ascii", 1200);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(SNNClustering.EPSILON_ID, 77);
    params.addParameter(SNNClustering.MINPTS_ID, 28);
    params.addParameter(SharedNearestNeighborPreprocessor.Factory.NUMBER_OF_NEIGHBORS_ID, 100);
    SNNClustering<DoubleVector> snn = ClassGenericsUtil.parameterizeOrAbort(SNNClustering.class, params);
    testParameterizationOk(params);

    // run SNN on database
    Clustering<Model> result = snn.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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   */
  @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 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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  @Test
  public void testEMResults() {
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(EM.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 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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   * @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 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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   *
   * @throws ParameterException
   */
  @Test
  public void testDeLiCluResults() {
    ListParameterization indexparams = new ListParameterization();
    // We need a special index for this algorithm:
    indexparams.addParameter(StaticArrayDatabase.INDEX_ID, DeLiCluTreeFactory.class);
    indexparams.addParameter(DeLiCluTreeFactory.PAGE_SIZE_ID, 1000);
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-2d.ascii", 710, indexparams, null);

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

    // run DeLiClu on database
    Clustering<?> clustering = opticsxi.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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