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

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

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    for(Cluster<Model> clus : copacResult.getAllClusters()) {
      DBIDs group = clus.getIDs();
      if(clus.getModel() != null && clus.getModel() instanceof DimensionModel) {
        int correlationDimension = ((DimensionModel) clus.getModel()).getDimension();

        ListParameterization parameters = pcaParameters(correlationDimension);
        Class<PCAFilteredRunner<V>> cls = ClassGenericsUtil.uglyCastIntoSubclass(PCAFilteredRunner.class);
        PCAFilteredRunner<V> pca = parameters.tryInstantiate(cls);
        for(ParameterException e : parameters.getErrors()) {
          logger.warning("Error in internal parameterization: " + e.getMessage());
        }

        // get cluster list for this dimension.
        List<Cluster<CorrelationModel<V>>> correlationClusters = clusterMap.get(correlationDimension);
        if(correlationClusters == null) {
          correlationClusters = new ArrayList<Cluster<CorrelationModel<V>>>();
          clusterMap.put(correlationDimension, correlationClusters);
        }

        PCAFilteredResult pcares = pca.processIds(group, database);

        V centroid = DatabaseUtil.centroid(database, group);
        Cluster<CorrelationModel<V>> correlationCluster = new Cluster<CorrelationModel<V>>("[" + correlationDimension + "_" + correlationClusters.size() + "]", group, new CorrelationModel<V>(pcares, centroid), new ArrayList<Cluster<CorrelationModel<V>>>(), new ArrayList<Cluster<CorrelationModel<V>>>());
        correlationClusters.add(correlationCluster);
      }
      // partition containing noise
      else if(clus.getModel() != null && clus.isNoise()) {
        if(noise == null) {
          noise = clus;
        }
        else {
          ModifiableDBIDs merged = DBIDUtil.newHashSet(noise.getIDs());
          merged.addAll(clus.getIDs().asCollection());
          noise.setIDs(merged);
        }
      }
      else {
        throw new IllegalStateException("Unexpected group returned: " + clus.getClass().getName());
      }
    }

    if(noise != null && noise.size() > 0) {
      // get cluster list for this dimension.
      List<Cluster<CorrelationModel<V>>> correlationClusters = clusterMap.get(dimensionality);
      if(correlationClusters == null) {
        correlationClusters = new ArrayList<Cluster<CorrelationModel<V>>>();
        clusterMap.put(dimensionality, correlationClusters);
      }
      ListParameterization parameters = pcaParameters(dimensionality);
      Class<PCAFilteredRunner<V>> cls = ClassGenericsUtil.uglyCastIntoSubclass(PCAFilteredRunner.class);
      PCAFilteredRunner<V> pca = parameters.tryInstantiate(cls);
      for(ParameterException e : parameters.getErrors()) {
        logger.warning("Error in internal parameterization: " + e.getMessage());
      }
      PCAFilteredResult pcares = pca.processIds(noise.getIDs(), database);

      V centroid = DatabaseUtil.centroid(database, noise.getIDs());
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   *
   * @param correlationDimension the correlation dimension
   * @return the parameters for the PCA for the specified correlation dimension
   */
  private ListParameterization pcaParameters(int correlationDimension) {
    ListParameterization parameters = new ListParameterization();

    parameters.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class);
    parameters.addParameter(FirstNEigenPairFilter.EIGENPAIR_FILTER_N, correlationDimension);

    return parameters;
  }
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   * @param filename File to load
   * @param expectedSize Expected size in records
   * @return Database
   */
  protected <T> Database makeSimpleDatabase(String filename, int expectedSize) {
    return makeSimpleDatabase(filename, expectedSize, new ListParameterization(), null);
  }
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   * @param positive Positive class name
   * @param result Outlier result to process
   * @param expected Expected AUC value
   */
  protected void testAUC(Database db, String positive, OutlierResult result, double expected) {
    ListParameterization params = new ListParameterization();
    params.addParameter(ComputeROCCurve.POSITIVE_CLASS_NAME_ID, positive);
    ComputeROCCurve rocCurve = ClassGenericsUtil.parameterizeOrAbort(ComputeROCCurve.class, params);

    // Ensure the result has been added to the hierarchy:
    if(db.getHierarchy().getParents(result).size() < 1) {
      db.getHierarchy().add(db, result);
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  // variance
  double var2nnManhattan = 0.83157894;

  @Test
  public void testLinearScan() {
    ListParameterization inputparams = new ListParameterization();
    inputparams.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
    List<Class<?>> filters = Arrays.asList(new Class<?>[] { FixedDBIDsFilter.class });
    inputparams.addParameter(FileBasedDatabaseConnection.FILTERS_ID, filters);
    inputparams.addParameter(FixedDBIDsFilter.IDSTART_ID, 1);

    // get database
    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, inputparams);
    inputparams.failOnErrors();

    db.initialize();
    Relation<NumberVector<?, ?>> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    // verify data set size.
    org.junit.Assert.assertEquals("Database size does not match.", shoulds, relation.size());
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   *
   * @throws ParameterException on errors.
   */
  @Test
  public void testKNNJoinRtreeMini() {
    ListParameterization spatparams = new ListParameterization();
    spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
    spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 200);

    doKNNJoin(spatparams);
  }
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   *
   * @throws ParameterException on errors.
   */
  @Test
  public void testKNNJoinRtreeMaxi() {
    ListParameterization spatparams = new ListParameterization();
    spatparams.addParameter(StaticArrayDatabase.INDEX_ID, RStarTreeFactory.class);
    spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 2000);

    doKNNJoin(spatparams);
  }
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   *
   * @throws ParameterException on errors.
   */
  @Test
  public void testKNNJoinDeLiCluTreeMini() {
    ListParameterization spatparams = new ListParameterization();
    spatparams.addParameter(StaticArrayDatabase.INDEX_ID, DeLiCluTreeFactory.class);
    spatparams.addParameter(TreeIndexFactory.PAGE_SIZE_ID, 200);

    doKNNJoin(spatparams);
  }
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  @Test
  public void testGaussianUniformMixture() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-fire.ascii", 1025);

    // Parameterization
    ListParameterization params = new ListParameterization();

    // setup Algorithm
    GaussianUniformMixture<DoubleVector> gaussianUniformMixture = ClassGenericsUtil.parameterizeOrAbort(GaussianUniformMixture.class, params);
    testParameterizationOk(params);
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  @Test
  public void testOPTICSOF() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-parabolic.ascii", 530);

    // Parameterization
    ListParameterization params = new ListParameterization();
    params.addParameter(OPTICS.MINPTS_ID, 22);

    // setup Algorithm
    OPTICSOF<DoubleVector, DoubleDistance> opticsof = ClassGenericsUtil.parameterizeOrAbort(OPTICSOF.class, params);
    testParameterizationOk(params);
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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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