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 testLoOP() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(LoOP.KCOMP_ID, 15);

    // setup Algorithm
    LoOP<DoubleVector, DoubleDistance> loop = ClassGenericsUtil.parameterizeOrAbort(LoOP.class, params);
    testParameterizationOk(params);
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  @Test
  public void testABOD() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(ABOD.K_ID, 5);

    // setup Algorithm
    ABOD<DoubleVector> abod = ClassGenericsUtil.parameterizeOrAbort(ABOD.class, params);
    testParameterizationOk(params);
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  @Test
  public void testReferenceBasedOutlierDetection() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(ReferenceBasedOutlierDetection.K_ID, 11);
    params.addParameter(GridBasedReferencePoints.GRID_ID, 11);

    // setup Algorithm
    ReferenceBasedOutlierDetection<DoubleVector, DoubleDistance> referenceBasedOutlierDetection = ClassGenericsUtil.parameterizeOrAbort(ReferenceBasedOutlierDetection.class, params);
    testParameterizationOk(params);
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  @Test
  public void testGaussianModel() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-fire.ascii", 1025);

    // Parameterization
    ListParameterization params = new ListParameterization();

    // setup Algorithm
    GaussianModel<DoubleVector> gaussianModel = ClassGenericsUtil.parameterizeOrAbort(GaussianModel.class, params);
    testParameterizationOk(params);
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  @Test
  public void testAggarwalYuEvolutionary() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(AggarwalYuEvolutionary.K_ID, 2);
    params.addParameter(AggarwalYuEvolutionary.PHI_ID, 8);
    params.addParameter(AggarwalYuEvolutionary.M_ID, 5);
    params.addParameter(AggarwalYuEvolutionary.SEED_ID, 0);

    // setup Algorithm
    AggarwalYuEvolutionary<DoubleVector> aggarwalYuEvolutionary = ClassGenericsUtil.parameterizeOrAbort(AggarwalYuEvolutionary.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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  @Test
  public void testLDOF() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-fire.ascii", 1025);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(LDOF.K_ID, 25);

    // setup Algorithm
    LDOF<DoubleVector, DoubleDistance> ldof = ClassGenericsUtil.parameterizeOrAbort(LDOF.class, params);
    testParameterizationOk(params);
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  @Test
  public void testDBSCANResults() {
    Database db = makeSimpleDatabase(UNITTEST + "3clusters-and-noise-2d.csv", 330);

    // setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(DBSCAN.EPSILON_ID, 0.04);
    params.addParameter(DBSCAN.MINPTS_ID, 20);
    DBSCAN<DoubleVector, DoubleDistance> dbscan = ClassGenericsUtil.parameterizeOrAbort(DBSCAN.class, params);
    testParameterizationOk(params);

    // run DBSCAN on database
    Clustering<Model> result = dbscan.run(db);
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  @Test
  public void testDBSCANOnSingleLinkDataset() {
    Database db = makeSimpleDatabase(UNITTEST + "single-link-effect.ascii", 638);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(DBSCAN.EPSILON_ID, 11.5);
    params.addParameter(DBSCAN.MINPTS_ID, 120);
    DBSCAN<DoubleVector, DoubleDistance> dbscan = ClassGenericsUtil.parameterizeOrAbort(DBSCAN.class, params);
    testParameterizationOk(params);

    // run DBSCAN on database
    Clustering<Model> result = dbscan.run(db);
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  @Test
  public void testKMeansResults() {
    Database db = makeSimpleDatabase(UNITTEST + "different-densities-2d-no-noise.ascii", 1000);

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

    // run KMeans on database
    Clustering<MeanModel<DoubleVector>> result = kmeans.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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