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

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

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  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
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    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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  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
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    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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  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
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    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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  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);
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    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
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    // 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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  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);
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