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 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 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 testCOPACOverlap() {
    Database db = makeSimpleDatabase(UNITTEST + "correlation-overlap-3-5d.ascii", 650);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    params.addParameter(COPAC.PARTITION_ALGORITHM_ID, DBSCAN.class);
    params.addParameter(DBSCAN.EPSILON_ID, 0.5);
    params.addParameter(DBSCAN.MINPTS_ID, 20);
    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, 0.8);

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

    Clustering<Model> result = copac.run(db);
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  @Test
  public void testFourCResults() {
    Database db = makeSimpleDatabase(UNITTEST + "hierarchical-3d2d1d.csv", 600);

    // Setup 4C
    ListParameterization params = new ListParameterization();
    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 0.30);
    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 20);
    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 5);

    FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
    testParameterizationOk(params);

    // run 4C on database
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  @Test
  public void testFourCOverlap() {
    Database db = makeSimpleDatabase(UNITTEST + "correlation-overlap-3-5d.ascii", 650);

    // Setup algorithm
    ListParameterization params = new ListParameterization();
    // 4C
    params.addParameter(AbstractProjectedDBSCAN.EPSILON_ID, 1.2);
    params.addParameter(AbstractProjectedDBSCAN.MINPTS_ID, 5);
    params.addParameter(AbstractProjectedDBSCAN.LAMBDA_ID, 3);

    FourC<DoubleVector> fourc = ClassGenericsUtil.parameterizeOrAbort(FourC.class, params);
    testParameterizationOk(params);

    // run 4C on database
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    DiSHDistanceFunction.Instance<V> dishDistanceQuery = dishDistance.instantiate(relation);
    // Configure and run OPTICS.
    if(logger.isVerbose()) {
      logger.verbose("*** Run OPTICS algorithm.");
    }
    ListParameterization opticsconfig = new ListParameterization(opticsAlgorithmParameters);
    opticsconfig.addParameter(OPTICS.DISTANCE_FUNCTION_ID, ProxyDistanceFunction.proxy(dishDistanceQuery));

    Class<OPTICS<V, PreferenceVectorBasedCorrelationDistance>> cls = ClassGenericsUtil.uglyCastIntoSubclass(OPTICS.class);
    OPTICS<V, PreferenceVectorBasedCorrelationDistance> optics = null;
    optics = opticsconfig.tryInstantiate(cls);
    ClusterOrderResult<PreferenceVectorBasedCorrelationDistance> opticsResult = optics.run(database, relation);

    if(logger.isVerbose()) {
      logger.verbose("*** Compute Clusters.");
    }
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      configOPTICS(config, mu, dishDistance);
    }

    public void configDiSHDistance(Parameterization config, double epsilon, int minpts) {
      ListParameterization dishParameters = new ListParameterization();
      dishParameters.addParameter(DiSHDistanceFunction.EPSILON_ID, epsilon);
      dishParameters.addParameter(IndexBasedDistanceFunction.INDEX_ID, DiSHPreferenceVectorIndex.Factory.class);
      dishParameters.addParameter(DiSHPreferenceVectorIndex.Factory.EPSILON_ID, Double.toString(epsilon));
      dishParameters.addParameter(DiSHPreferenceVectorIndex.Factory.MINPTS_ID, minpts);
      ChainedParameterization dishchain = new ChainedParameterization(dishParameters, config);
      dishchain.errorsTo(config);

      dishDistance = dishchain.tryInstantiate(DiSHDistanceFunction.class);
    }
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     * @param minpts MinPts value
     * @param dishDistance DiSH distance function
     */
    public void configOPTICS(Parameterization config, final int minpts, final DiSHDistanceFunction dishDistance) {
      // Configure OPTICS. Tracked parameters
      ListParameterization opticsParameters = new ListParameterization();
      opticsParameters.addParameter(OPTICS.EPSILON_ID, AbstractDistance.INFINITY_PATTERN);
      opticsParameters.addParameter(OPTICS.MINPTS_ID, minpts);
      // Configure OPTICS. Untracked parameters
      ListParameterization opticsUntrackedParameters = new ListParameterization();
      opticsUntrackedParameters.addParameter(OPTICS.DISTANCE_FUNCTION_ID, dishDistance);
      ChainedParameterization optchain = new ChainedParameterization(opticsParameters, config);
      TrackParameters trackpar = new TrackParameters(optchain);

      ChainedParameterization optchain2 = new ChainedParameterization(opticsUntrackedParameters, trackpar);
      optchain2.errorsTo(config);
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      if (config.grab(alphaP)) {
        alpha = alphaP.getValue();
      }

      // Configure Distance function
      ListParameterization opticsParameters = new ListParameterization();
      // preprocessor
      opticsParameters.addParameter(IndexBasedDistanceFunction.INDEX_ID, KNNQueryFilteredPCAIndex.Factory.class);
      opticsParameters.addParameter(KNNQueryFilteredPCAIndex.Factory.K_ID, k);
      opticsParameters.addParameter(PercentageEigenPairFilter.ALPHA_ID, alpha);
      opticsParameters.addParameter(PCABasedCorrelationDistanceFunction.DELTA_ID, delta);

      ChainedParameterization chain = new ChainedParameterization(opticsParameters, config);
      chain.errorsTo(config);
      distance = chain.tryInstantiate(PCABasedCorrelationDistanceFunction.class);
    }
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      if(config.grab(alphaP)) {
        alpha = alphaP.getValue();
      }

      // Configure HiSC distance function
      ListParameterization opticsParameters = new ListParameterization();

      // distance function
      opticsParameters.addParameter(HiSCDistanceFunction.EPSILON_ID, alpha);
      // preprocessor
      opticsParameters.addParameter(IndexBasedDistanceFunction.INDEX_ID, HiSCPreferenceVectorIndex.Factory.class);
      opticsParameters.addParameter(HiSCPreferenceVectorIndex.Factory.ALPHA_ID, alpha);

      ChainedParameterization chain = new ChainedParameterization(opticsParameters, config);
      chain.errorsTo(config);
      Class<HiSCDistanceFunction<V>> cls = ClassGenericsUtil.uglyCastIntoSubclass(HiSCDistanceFunction.class);
      distanceFunction = chain.tryInstantiate(cls);
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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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