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

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

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    this.c = c;
  }

  public OutlierResult run(Relation<V> relation) throws IllegalStateException {
    // 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
      // anomalous set and is flagged as outlier
      final double loglikeGain = currentLogLike - logLike;
      oscores.put(curid, loglikeGain);
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  @Override
  protected void objectsInserted(DBIDs ids) {
    StepProgress stepprog = getLogger().isVerbose() ? new StepProgress(3) : null;

    ArrayDBIDs aids = DBIDUtil.ensureArray(ids);
    // materialize the new kNNs and RkNNs
    if(stepprog != null) {
      stepprog.beginStep(1, "New insertions ocurred, materialize their new kNNs and RkNNs.", getLogger());
    }
    materializeKNNAndRKNNs(aids, null);

    // update the old kNNs and RkNNs
    if(stepprog != null) {
      stepprog.beginStep(2, "New insertions ocurred, update the affected kNNs and RkNNs.", getLogger());
    }
    ArrayDBIDs rkNN_ids = updateKNNsAndRkNNs(ids);

    // inform listener
    if(stepprog != null) {
      stepprog.beginStep(3, "New insertions ocurred, inform listeners.", getLogger());
    }
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  @Override
  protected void objectsRemoved(DBIDs ids) {
    StepProgress stepprog = getLogger().isVerbose() ? new StepProgress(3) : null;

    ArrayDBIDs aids = DBIDUtil.ensureArray(ids);
    // delete the materialized (old) kNNs and RkNNs
    if(stepprog != null) {
      stepprog.beginStep(1, "New deletions ocurred, remove their materialized kNNs and RkNNs.", getLogger());
    }
    List<KNNResult<D>> kNNs = new ArrayList<KNNResult<D>>(ids.size());
    List<List<DistanceResultPair<D>>> rkNNs = new ArrayList<List<DistanceResultPair<D>>>(ids.size());
    for(DBID id : aids) {
      kNNs.add(storage.get(id));
      storage.delete(id);
      rkNNs.add(new ArrayList<DistanceResultPair<D>>(materialized_RkNN.get(id)));
      materialized_RkNN.delete(id);
    }
    ArrayDBIDs kNN_ids = extractAndRemoveIDs(kNNs, aids);
    ArrayDBIDs rkNN_ids = extractAndRemoveIDs(rkNNs, aids);

    // update the affected kNNs and RkNNs
    if(stepprog != null) {
      stepprog.beginStep(2, "New deletions ocurred, update the affected kNNs and RkNNs.", getLogger());
    }
    // update the kNNs of the RkNNs
    List<KNNResult<D>> kNNList = knnQuery.getKNNForBulkDBIDs(rkNN_ids, k);
    for(int i = 0; i < rkNN_ids.size(); i++) {
      DBID id = rkNN_ids.get(i);
      storage.put(id, kNNList.get(i));
      for(DistanceResultPair<D> kNN : kNNList.get(i)) {
        Set<DistanceResultPair<D>> rknns = materialized_RkNN.get(kNN.getDBID());
        rknns.add(new GenericDistanceResultPair<D>(kNN.getDistance(), id));
      }
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    testKNNQueries(rep, lin_knn_query, preproc_knn_query, k);
    testRKNNQueries(rep, lin_rknn_query, preproc_rknn_query, k);
  }

  private void testKNNQueries(Relation<DoubleVector> rep, KNNQuery<DoubleVector, DoubleDistance> lin_knn_query, KNNQuery<DoubleVector, DoubleDistance> preproc_knn_query, int k) {
    ArrayDBIDs sample = DBIDUtil.ensureArray(rep.getDBIDs());
    List<KNNResult<DoubleDistance>> lin_knn_ids = lin_knn_query.getKNNForBulkDBIDs(sample, k);
    List<KNNResult<DoubleDistance>> preproc_knn_ids = preproc_knn_query.getKNNForBulkDBIDs(sample, k);
    for(int i = 0; i < rep.size(); i++) {
      KNNResult<DoubleDistance> lin_knn = lin_knn_ids.get(i);
      KNNResult<DoubleDistance> pre_knn = preproc_knn_ids.get(i);
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    }
    System.out.println("knns ok");
  }

  private void testRKNNQueries(Relation<DoubleVector> rep, RKNNQuery<DoubleVector, DoubleDistance> lin_rknn_query, RKNNQuery<DoubleVector, DoubleDistance> preproc_rknn_query, int k) {
    ArrayDBIDs sample = DBIDUtil.ensureArray(rep.getDBIDs());
    List<List<DistanceResultPair<DoubleDistance>>> lin_rknn_ids = lin_rknn_query.getRKNNForBulkDBIDs(sample, k);
    List<List<DistanceResultPair<DoubleDistance>>> preproc_rknn_ids = preproc_rknn_query.getRKNNForBulkDBIDs(sample, k);
    for(int i = 0; i < rep.size(); i++) {
      List<DistanceResultPair<DoubleDistance>> lin_rknn = lin_rknn_ids.get(i);
      List<DistanceResultPair<DoubleDistance>> pre_rknn = preproc_rknn_ids.get(i);
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    MeanVariance ksize = new MeanVariance();
    if(logger.isVerbose()) {
      logger.verbose("Approximating nearest neighbor lists to database objects");
    }

    ArrayDBIDs aids = DBIDUtil.ensureArray(relation.getDBIDs());
    int minsize = (int) Math.floor(aids.size() / partitions);

    FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Processing partitions.", partitions, logger) : null;
    for(int part = 0; part < partitions; part++) {
      int size = (partitions * minsize + part >= aids.size()) ? minsize : minsize + 1;
      // Collect the ids in this node.
      ArrayModifiableDBIDs ids = DBIDUtil.newArray(size);
      for(int i = 0; i < size; i++) {
        assert (size * partitions + part < aids.size());
        ids.add(aids.get(i * partitions + part));
      }
      HashMap<DBIDPair, D> cache = new HashMap<DBIDPair, D>(size * size * 3 / 8);
      for(DBID id : ids) {
        KNNHeap<D> kNN = new KNNHeap<D>(k, distanceQuery.infiniteDistance());
        for(DBID id2 : ids) {
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  protected void preprocess() {
    DistanceQuery<O, D> distanceQuery = relation.getDatabase().getDistanceQuery(relation, distanceFunction);
    storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, KNNResult.class);
    FiniteProgress progress = getLogger().isVerbose() ? new FiniteProgress("Materializing random-sample k nearest neighbors (k=" + k + ")", relation.size(), getLogger()) : null;

    final ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
    final int samplesize = (int) (ids.size() * share);
    final long iseed = (seed != null) ? seed : (new Random()).nextLong();

    int i = 0;
    for(DBID id : ids) {
      KNNHeap<D> kNN = new KNNHeap<D>(k, distanceQuery.infiniteDistance());
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   */
  @Override
  protected void preprocess() {
    createStorage();

    ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
    FiniteProgress progress = getLogger().isVerbose() ? new FiniteProgress("Materializing k nearest neighbors (k=" + k + ")", ids.size(), getLogger()) : null;

    // Try bulk
    List<KNNResult<D>> kNNList = null;
    if(usebulk) {
      kNNList = knnQuery.getKNNForBulkDBIDs(ids, k);
      if(kNNList != null) {
        for(int i = 0; i < ids.size(); i++) {
          DBID id = ids.get(i);
          storage.put(id, kNNList.get(i));
          if(progress != null) {
            progress.incrementProcessed(getLogger());
          }
        }
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   * @param ids the ids of the newly inserted objects
   */
  protected void objectsInserted(DBIDs ids) {
    StepProgress stepprog = getLogger().isVerbose() ? new StepProgress(3) : null;

    ArrayDBIDs aids = DBIDUtil.ensureArray(ids);
    // materialize the new kNNs
    if(stepprog != null) {
      stepprog.beginStep(1, "New insertions ocurred, materialize their new kNNs.", getLogger());
    }
    List<KNNResult<D>> kNNList = knnQuery.getKNNForBulkDBIDs(aids, k);
    for(int i = 0; i < aids.size(); i++) {
      DBID id = aids.get(i);
      storage.put(id, kNNList.get(i));
    }

    // update the affected kNNs
    if(stepprog != null) {
      stepprog.beginStep(2, "New insertions ocurred, update the affected kNNs.", getLogger());
    }
    ArrayDBIDs rkNN_ids = updateKNNsAfterInsertion(ids);

    // inform listener
    if(stepprog != null) {
      stepprog.beginStep(3, "New insertions ocurred, inform listeners.", getLogger());
    }
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    // update the affected kNNs
    if(stepprog != null) {
      stepprog.beginStep(2, "New deletions ocurred, update the affected kNNs.", getLogger());
    }
    ArrayDBIDs rkNN_ids = updateKNNsAfterDeletion(ids);

    // inform listener
    if(stepprog != null) {
      stepprog.beginStep(3, "New deletions ocurred, inform listeners.", getLogger());
    }
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