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

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

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    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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      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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      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.
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        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);
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