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

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

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   * Returns the partition algorithm.
   *
   * @return the specified partition algorithm
   */
  public ClusteringAlgorithm<Clustering<Model>> getPartitionAlgorithm(DistanceQuery<V, D> query) {
    ListParameterization reconfig = new ListParameterization(partitionAlgorithmParameters);
    ProxyDistanceFunction<V, D> dist = ProxyDistanceFunction.proxy(query);
    reconfig.addParameter(AbstractDistanceBasedAlgorithm.DISTANCE_FUNCTION_ID, dist);
    ClusteringAlgorithm<Clustering<Model>> instance = reconfig.tryInstantiate(partitionAlgorithm);
    reconfig.failOnErrors();
    return instance;
  }
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        indexI = indexP.instantiateClass(config);
      }

      ObjectParameter<FilteredLocalPCABasedDistanceFunction<V, ?, D>> pdistP = new ObjectParameter<FilteredLocalPCABasedDistanceFunction<V, ?, D>>(PARTITION_DISTANCE_ID, FilteredLocalPCABasedDistanceFunction.class, LocallyWeightedDistanceFunction.class);
      if(config.grab(pdistP)) {
        ListParameterization predefinedDist = new ListParameterization();
        predefinedDist.addParameter(IndexBasedDistanceFunction.INDEX_ID, indexI);
        ChainedParameterization chainDist = new ChainedParameterization(predefinedDist, config);
        chainDist.errorsTo(config);
        pdistI = pdistP.instantiateClass(chainDist);
        predefinedDist.reportInternalParameterizationErrors(config);
      }

      // Parameterize algorithm:
      ClassParameter<ClusteringAlgorithm<Clustering<Model>>> algP = new ClassParameter<ClusteringAlgorithm<Clustering<Model>>>(PARTITION_ALGORITHM_ID, ClusteringAlgorithm.class);
      if(config.grab(algP)) {
        ListParameterization predefined = new ListParameterization();
        predefined.addParameter(AbstractDistanceBasedAlgorithm.DISTANCE_FUNCTION_ID, pdistI);
        TrackParameters trackpar = new TrackParameters(config);
        ChainedParameterization chain = new ChainedParameterization(predefined, trackpar);
        chain.errorsTo(config);
        algP.instantiateClass(chain);
        algC = algP.getValue();
        algO = trackpar.getGivenParameters();
        predefined.reportInternalParameterizationErrors(chain);
      }
    }
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   *
   * @throws ParameterException on errors.
   */
  @Test
  public final void testFitDoubleArray() {
    ListParameterization config = new ListParameterization();
    // Input
    config.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);
    // This data was generated with a mean of 0.0 and stddev 1.23,

    // get database
    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, config);
    db.initialize();
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  private Matrix runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs ids) throws UnableToComplyException, ParameterException {
    // build database for derivator
    Database derivatorDB = buildDerivatorDB(relation, interval);

    // set the parameters
    ListParameterization parameters = new ListParameterization();
    parameters.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
    parameters.addParameter(FirstNEigenPairFilter.EIGENPAIR_FILTER_N, Integer.toString(dim - 1));
    DependencyDerivator<DoubleVector, DoubleDistance> derivator = null;
    Class<DependencyDerivator<DoubleVector, DoubleDistance>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DependencyDerivator.class);
    derivator = parameters.tryInstantiate(cls);

    CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);

    Matrix weightMatrix = model.getSimilarityMatrix();
    DoubleVector centroid = new DoubleVector(model.getCentroid());
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  private LinearEquationSystem runDerivator(Relation<ParameterizationFunction> relation, int dimensionality, DBIDs ids) {
    try {
      // build database for derivator
      Database derivatorDB = buildDerivatorDB(relation, ids);

      ListParameterization parameters = new ListParameterization();
      parameters.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
      parameters.addParameter(FirstNEigenPairFilter.EIGENPAIR_FILTER_N, Integer.toString(dimensionality));
      DependencyDerivator<DoubleVector, DoubleDistance> derivator = null;
      Class<DependencyDerivator<DoubleVector, DoubleDistance>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DependencyDerivator.class);
      derivator = parameters.tryInstantiate(cls);

      CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);
      LinearEquationSystem les = model.getNormalizedLinearEquationSystem(null);
      return les;
    }
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   *
   * @throws ParameterException on errors.
   */
  @Test
  public void testCompareDatabases() {
    ListParameterization params = new ListParameterization();
    // Input
    params.addParameter(FileBasedDatabaseConnection.INPUT_ID, dataset);

    // get database
    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, params);
    db.initialize();

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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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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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