Examples of de.lmu.ifi.dbs.elki.data.synthetic.bymodel.GeneratorMain

de.lmu.ifi.dbs.elki.data.synthetic.bymodel.GeneratorMain

Generate a data set according to a given model. Key idea of this generator is to re-generate points if they are more likely to belong to a different cluster than the one they were generated for. The benefit is that we should end up with a data set that follows closely the model that we specified. The drawbacks are that on one hand, specifications might be unsatisfiable. For this a retry count is kept and an {@link UnableToComplyException} isthrown when the maximum number of retries is exceeded. On the other hand, the model might not be exactly as specified. When the generator reports an "Density correction factor estimation" that differs from 1.0 this is an indication that the result is not exact. On the third hand, rejecting points introduces effects where one generator can influence others, so random generator results will not be stable with respect to the addition of new dimensions and similar if there are any rejects involved. So this generator is not entirely optimal for generating data sets for scalability tests on the number of dimensions, although if clusters overlap little enough (so that no rejects happen) the results should be as expected. @author Erich Schubert @apiviz.has GeneratorInterface

  @Override
  public MultipleObjectsBundle loadData() {
    if(logger.isVerbose()) {
      logger.verbose("Loading specification ...");
    }
    GeneratorMain gen;
    try {
      gen = loadXMLSpecification();
    }
    catch(UnableToComplyException e) {
      throw new AbortException("Cannot load XML specification", e);
    }
    if(logger.isVerbose()) {
      logger.verbose("Generating clusters ...");
    }
    if(testAgainstModel != null) {
      gen.setTestAgainstModel(testAgainstModel);
    }
    try {
      return gen.generate();
    }
    catch(UnableToComplyException e) {
      throw new AbortException("Data generation failed. ", e);
    }
  }

        logger.warning("Could not set up XML Schema validation for speciciation file.");
      }
      Document doc = dbf.newDocumentBuilder().parse(in);
      Node root = doc.getDocumentElement();
      if(root.getNodeName() == "dataset") {
        GeneratorMain gen = new GeneratorMain();
        processElementDataset(gen, root);
        return gen;
      }
      else {
        throw new UnableToComplyException("Experiment specification has incorrect document element: " + root.getNodeName());

Examples of de.lmu.ifi.dbs.elki.data.synthetic.bymodel.GeneratorMain

Related Classes of de.lmu.ifi.dbs.elki.data.synthetic.bymodel.GeneratorMain