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

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

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   */
  protected DBIDs computeSubspace(Vector<IntIntPair> subspace, ArrayList<ArrayList<DBIDs>> ranges) {
    HashSetModifiableDBIDs ids = DBIDUtil.newHashSet(ranges.get(subspace.get(0).first - 1).get(subspace.get(0).second));
    // intersect all selected dimensions
    for(int i = 1; i < subspace.size(); i++) {
      DBIDs current = ranges.get(subspace.get(i).first - 1).get(subspace.get(i).second);
      ids.retainAll(current);
      if(ids.size() == 0) {
        break;
      }
    }
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  protected DBIDs computeSubspaceForGene(int[] gene, ArrayList<ArrayList<DBIDs>> ranges) {
    HashSetModifiableDBIDs m = DBIDUtil.newHashSet(ranges.get(0).get(gene[0]));
    // intersect
    for(int i = 1; i < gene.length; i++) {
      if(gene[i] != DONT_CARE) {
        DBIDs current = ranges.get(i).get(gene[i]);
        m.retainAll(current);
      }
    }
    return m;
  }
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      OutlierScalingFunction oscaling = (OutlierScalingFunction) scaling;
      oscaling.prepare(or);
    }

    ModifiableDBIDs ids = DBIDUtil.newHashSet(or.getScores().getDBIDs());
    DBIDs outlierIds = DatabaseUtil.getObjectsByLabelMatch(database, positiveClassName);
    // first value for outliers, second for each object
    final AggregatingHistogram<Pair<Double, Double>, Pair<Double, Double>> hist;
    // If we have useful (finite) min/max, use these for binning.
    double min = scaling.getMin();
    double max = scaling.getMax();
    if(Double.isInfinite(min) || Double.isNaN(min) || Double.isInfinite(max) || Double.isNaN(max)) {
      hist = FlexiHistogram.DoubleSumDoubleSumHistogram(bins);
    }
    else {
      hist = AggregatingHistogram.DoubleSumDoubleSumHistogram(bins, min, max);
    }
    // first fill histogram only with values of outliers
    Pair<Double, Double> positive, negative;
    if(!splitfreq) {
      positive = new Pair<Double, Double>(0., 1. / ids.size());
      negative = new Pair<Double, Double>(1. / ids.size(), 0.);
    }
    else {
      positive = new Pair<Double, Double>(0., 1. / outlierIds.size());
      negative = new Pair<Double, Double>(1. / (ids.size() - outlierIds.size()), 0.);
    }
    ids.removeDBIDs(outlierIds);
    // fill histogram with values of each object
    for(DBID id : ids) {
      double result = or.getScores().get(id);
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   * @param cluster object
   */
  public PairGeneratorNoise(Cluster<?> cluster) {
    // build int array for the cluster
    // TODO: copy less.
    DBIDs dbids = cluster.getIDs();
    ids = new int[dbids.size()];
    int j = 0;
    for (DBID id : dbids) {
      ids[j] = id.getIntegerID();
      j++;
    }
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        }
      }
    }

    // build int array for the cluster
    DBIDs cids = cluster.getIDs();
    thisids = new int[cids.size()];
    {
      int j = 0;
      for(DBID id : cids) {
        thisids[j] = id.getIntegerID();
        j++;
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   *
   * @param candidates the map containing the sets
   * @return the set with the maximum size
   */
  private int max(Map<Integer, ModifiableDBIDs> candidates) {
    DBIDs maxSet = null;
    Integer maxDim = null;
    for(Integer nextDim : candidates.keySet()) {
      DBIDs nextSet = candidates.get(nextDim);
      if(maxSet == null || maxSet.size() < nextSet.size()) {
        maxSet = nextSet;
        maxDim = nextDim;
      }
    }

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   * @return the set with the maximum size
   */
  private int maxIntersection(Map<Integer, ModifiableDBIDs> candidates, DBIDs set, ModifiableDBIDs result) {
    Integer maxDim = null;
    for(Integer nextDim : candidates.keySet()) {
      DBIDs nextSet = candidates.get(nextDim);
      ModifiableDBIDs nextIntersection = DBIDUtil.intersection(set, nextSet);
      if(result.size() < nextIntersection.size()) {
        result = nextIntersection;
        maxDim = nextDim;
      }
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    // Use an array list of object IDs for fast random access by an offset
    ArrayDBIDs objids = DBIDUtil.ensureArray(relation.getDBIDs());
    // A bit set to flag objects as anomalous, none at the beginning
    BitSet bits = new BitSet(objids.size());
    // Positive masked collection
    DBIDs normalObjs = new MaskedDBIDs(objids, bits, true);
    // Positive masked collection
    DBIDs anomalousObjs = new MaskedDBIDs(objids, bits, false);
    // resulting scores
    WritableDataStore<Double> oscores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, Double.class);
    // compute loglikelihood
    double logLike = relation.size() * logml + loglikelihoodNormal(normalObjs, relation);
    // logger.debugFine("normalsize   " + normalObjs.size() + " anormalsize  " +
    // anomalousObjs.size() + " all " + (anomalousObjs.size() +
    // normalObjs.size()));
    // logger.debugFine(logLike + " loglike beginning" +
    // loglikelihoodNormal(normalObjs, database));
    DoubleMinMax minmax = new DoubleMinMax();
    for(int i = 0; i < objids.size(); i++) {
      // logger.debugFine("i     " + i);
      // Change mask to make the current object anomalous
      bits.set(i);
      // Compute new likelihoods
      double currentLogLike = normalObjs.size() * logml + loglikelihoodNormal(normalObjs, relation) + anomalousObjs.size() * logl + loglikelihoodAnomalous(anomalousObjs);

      // Get the actual object id
      DBID curid = objids.get(i);

      // if the loglike increases more than a threshold, object stays in
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    }

    WritableDataStore<Double> sparsity = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, Double.class);
    // calculate the sparsity coefficient
    for(Vector<IntIntPair> sub : Rk) {
      DBIDs ids = computeSubspace(sub, ranges);
      final double sparsityC = sparsity(ids.size(), size, k);

      if(sparsityC < 0) {
        for(DBID id : ids) {
          Double prev = sparsity.get(id);
          if(prev == null || sparsityC < prev) {
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   */
  public OutlierResult run(Relation<V> relation) {
    DistanceQuery<V, D> distFunc = relation.getDatabase().getDistanceQuery(relation, distanceFunction);
    Collection<V> refPoints = refp.getReferencePoints(relation);

    DBIDs ids = relation.getDBIDs();
    // storage of distance/score values.
    WritableDataStore<Double> rbod_score = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_STATIC, Double.class);
    // compute density for one reference point, to initialize the first density
    // value for each object

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