Package de.lmu.ifi.dbs.elki.database.ids

Examples of de.lmu.ifi.dbs.elki.database.ids.DBIDs

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    if(cur instanceof NeighborSetPredicate) {
      if(parts.length == partpos + 1) {
        NeighborSetPredicate pred = (NeighborSetPredicate) cur;
        DBID id = stringToDBID(parts[partpos]);
        if(id != null) {
          DBIDs neighbors = pred.getNeighborDBIDs(id);
          re.appendKeyValue("DBID", id);
          re.appendKeyArray("neighbors");
          for(DBID nid : neighbors) {
            re.appendString(nid.toString());
          }
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   * @return the RkNNs of the specified ids, i.e. the kNNs which have been
   *         updated
   */
  private ArrayDBIDs updateKNNsAfterInsertion(DBIDs ids) {
    ArrayDBIDs rkNN_ids = DBIDUtil.newArray();
    DBIDs oldids = DBIDUtil.difference(relation.getDBIDs(), ids);
    for(DBID id1 : oldids) {
      List<DistanceResultPair<D>> kNNs = storage.get(id1);
      D knnDist = kNNs.get(kNNs.size() - 1).getDistance();
      // look for new kNNs
      List<DistanceResultPair<D>> newKNNs = new ArrayList<DistanceResultPair<D>>();
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      // logger.warning("No results found for "+JudgeOutlierScores.class.getSimpleName());
      return;
    }

    ModifiableDBIDs ids = DBIDUtil.newHashSet(ors.iterator().next().getScores().getDBIDs());
    DBIDs outlierIds = DatabaseUtil.getObjectsByLabelMatch(db, positiveClassName);
    ids.removeDBIDs(outlierIds);

    for(OutlierResult or : ors) {
      db.getHierarchy().add(or, computeScore(ids, outlierIds, or));
    }
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  public CorrelationAnalysisSolution<V> run(Database database, Relation<V> relation) throws IllegalStateException {
    if(logger.isVerbose()) {
      logger.verbose("retrieving database objects...");
    }
    V centroidDV = DatabaseUtil.centroid(relation);
    DBIDs ids;
    if(this.sampleSize > 0) {
      if(randomsample) {
        ids = DBIDUtil.randomSample(relation.getDBIDs(), this.sampleSize, 1);
      }
      else {
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    // FIXME: Ensure were looking at the right relation!
    SpatialIndexTree<N, E> index = indexes.iterator().next();
    SpatialPrimitiveDistanceFunction<V, D> distFunction = (SpatialPrimitiveDistanceFunction<V, D>) getDistanceFunction();
    DistanceQuery<V, D> distq = database.getDistanceQuery(relation, distFunction);

    DBIDs ids = relation.getDBIDs();

    WritableDataStore<KNNHeap<D>> knnHeaps = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, KNNHeap.class);

    try {
      // data pages of s
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    Collection<Individuum> individuums = (new EvolutionarySearch(relation, ranges, m, seed)).run();

    WritableDataStore<Double> outlierScore = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, Double.class);
    for(Individuum ind : individuums) {
      DBIDs ids = computeSubspaceForGene(ind.getGene(), ranges);
      double sparsityC = sparsity(ids.size(), dbsize, k);
      for(DBID id : ids) {
        Double prev = outlierScore.get(id);
        if(prev == null || sparsityC < prev) {
          outlierScore.put(id, sparsityC);
        }
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     *
     * @param gene Gene to evaluate
     * @return new individuum
     */
    private Individuum makeIndividuum(int[] gene) {
      final DBIDs ids = computeSubspaceForGene(gene, ranges);
      final double fitness = (ids.size() > 0) ? sparsity(ids.size(), dbsize, k) : Double.MAX_VALUE;
      return new Individuum(fitness, gene);
    }
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    for(Iterator<DBID> iter = relation.iterDBIDs(); iter.hasNext();) {
      DBID queryObject = iter.next();
      if(progress != null) {
        progress.incrementProcessed(logger);
      }
      DBIDs knnList = getKNN(relation, snnInstance, queryObject).asDBIDs();
      SODModel<V> model = new SODModel<V>(relation, knnList, alpha, relation.get(queryObject));
      sod_models.put(queryObject, model);
      minmax.put(model.getSod());
    }
    if(progress != null) {
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   */
  public OutlierResult run(Database database, Relation<O> relation) {
    DistanceQuery<O, D> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
    KNNQuery<O, D> knnQuery = database.getKNNQuery(distQuery, minpts);
    RangeQuery<O, D> rangeQuery = database.getRangeQuery(distQuery);
    DBIDs ids = relation.getDBIDs();

    WritableDataStore<List<DistanceResultPair<D>>> nMinPts = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, List.class);
    WritableDataStore<Double> coreDistance = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, Double.class);
    WritableDataStore<Integer> minPtsNeighborhoodSize = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, Integer.class);

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   * @return range map
   */
  protected ArrayList<ArrayList<DBIDs>> buildRanges(Relation<V> database) {
    final int dim = DatabaseUtil.dimensionality(database);
    final int size = database.size();
    final DBIDs allids = database.getDBIDs();
    final ArrayList<ArrayList<DBIDs>> ranges = new ArrayList<ArrayList<DBIDs>>();

    // Temporary projection storage of the database
    final ArrayList<ArrayList<FCPair<Double, DBID>>> dbAxis = new ArrayList<ArrayList<FCPair<Double, DBID>>>(dim);
    for(int i = 0; i < dim; i++) {
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Related Classes of de.lmu.ifi.dbs.elki.database.ids.DBIDs

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  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ORCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.KMeans
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  • de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyChosenInitialMeans
  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.TrivialAllInOne
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