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

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

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

de.lmu.ifi.dbs.elki.database.ids.DBID
Database ID object. While this currently is just an Integer, it should be avoided to store the object IDs in regular integers to reduce problems if this API ever changes (for example if someone needs to do context tracking for debug purposes!) In particular, a developer should not make any assumption of these IDs being consistent across multiple results/databases. @author Erich Schubert @apiviz.landmark

   */
  public static <V extends NumberVector<?, ?>> double quickMedian(Relation<V> relation, ArrayDBIDs ids, int dimension, int numberOfSamples) {
    final int everyNthItem = (int) Math.max(1, Math.floor(ids.size() / (double) numberOfSamples));
    final double[] vals = new double[numberOfSamples];
    for(int i = 0; i < numberOfSamples; i++) {
      final DBID id = ids.get(i * everyNthItem);
      vals[i] = relation.get(id).doubleValue(dimension);
    }
    Arrays.sort(vals);
    if(vals.length % 2 == 1) {
      return vals[((vals.length + 1) / 2) - 1];

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   */
  private D processDataPages(DistanceQuery<V, D> distQ, N pr, N ps, WritableDataStore<KNNHeap<D>> knnLists, D pr_knn_distance) {
    // TODO: optimize for double?
    boolean infinite = pr_knn_distance.isInfiniteDistance();
    for(int i = 0; i < pr.getNumEntries(); i++) {
      DBID r_id = ((LeafEntry) pr.getEntry(i)).getDBID();
      KNNHeap<D> knnList = knnLists.get(r_id);


      for(int j = 0; j < ps.getNumEntries(); j++) {
        DBID s_id = ((LeafEntry) ps.getEntry(j)).getDBID();


        D distance = distQ.distance(r_id, s_id);
        if(knnList.add(distance, s_id)) {
          // set kNN distance of r
          if(infinite) {

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      return iter.hasNext();
    }


    @Override
    public O next() {
      DBID id = iter.next();
      return database.get(id);
    }

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    SimilarityQuery<V, IntegerDistance> snnInstance = similarityFunction.instantiate(relation);
    FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Assigning Subspace Outlier Degree", relation.size(), logger) : null;
    WritableDataStore<SODModel<?>> sod_models = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, SODModel.class);
    DoubleMinMax minmax = new DoubleMinMax();
    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));

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      for(int i = 0; i < relation.size(); i++) {
        // Iterating over the elements of a line (breadth-first).
        for(IterableIterator<DBID> iter : IDVectorOntoScoreVector.keySet()) {
          if(iter.hasNext()) { // Always true if every algorithm returns a
                               // complete result (one score for every DBID).
            DBID tmpID = iter.next();
            double score = IDVectorOntoScoreVector.get(iter).get(tmpID);
            if(scores.get(tmpID) == null) {
              scores.put(tmpID, score);
              minmax.put(score);
            }

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   */
  private KNNList<DoubleDistance> getKNN(Relation<V> database, SimilarityQuery<V, IntegerDistance> snnInstance, DBID queryObject) {
    // similarityFunction.getPreprocessor().getParameters();
    KNNHeap<DoubleDistance> kNearestNeighbors = new KNNHeap<DoubleDistance>(knn, new DoubleDistance(Double.POSITIVE_INFINITY));
    for(Iterator<DBID> iter = database.iterDBIDs(); iter.hasNext();) {
      DBID id = iter.next();
      if(!id.equals(queryObject)) {
        double distance = 1.0 / snnInstance.similarity(queryObject, id).doubleValue();
        kNearestNeighbors.add(new DoubleDistanceResultPair(distance, id));
      }
    }
    return kNearestNeighbors.toKNNList();

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    Iterator<DBID> iter = database.iterDBIDs();
    if(!iter.hasNext()) {
      throw new IllegalStateException(ExceptionMessages.DATABASE_EMPTY);
    }
    DBID firstid = iter.next();
    minhotset.add(new FCPair<Double, DBID>(Double.MAX_VALUE, firstid));
    maxhotset.add(new FCPair<Double, DBID>(Double.MIN_VALUE, firstid));
    while(iter.hasNext()) {
      DBID id1 = iter.next();
      // generate candidates for min distance.
      ArrayList<FCPair<Double, DBID>> np = new ArrayList<FCPair<Double, DBID>>(k * 2 + randomsize * 2);
      for(FCPair<Double, DBID> pair : minhotset) {
        DBID id2 = pair.getSecond();
        // skip the object itself
        if(id1.compareTo(id2) == 0) {
          continue;
        }
        double d = distFunc.distance(id1, id2).doubleValue();
        np.add(new FCPair<Double, DBID>(d, id1));
        np.add(new FCPair<Double, DBID>(d, id2));
      }
      for(DBID id2 : randomset) {
        double d = distFunc.distance(id1, id2).doubleValue();
        np.add(new FCPair<Double, DBID>(d, id1));
        np.add(new FCPair<Double, DBID>(d, id2));
      }
      minhotset.addAll(np);
      shrinkHeap(minhotset, k);


      // generate candidates for max distance.
      ArrayList<FCPair<Double, DBID>> np2 = new ArrayList<FCPair<Double, DBID>>(k * 2 + randomsize * 2);
      for(FCPair<Double, DBID> pair : minhotset) {
        DBID id2 = pair.getSecond();
        // skip the object itself
        if(id1.compareTo(id2) == 0) {
          continue;
        }
        double d = distFunc.distance(id1, id2).doubleValue();

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        cmem.add(new FCPair<Double, DBID>(d, i1));
      }
      Collections.sort(cmem);


      for(int ind = 0; ind < cmem.size(); ind++) {
        DBID i1 = cmem.get(ind).getSecond();
        List<DistanceResultPair<D>> knn = knnQuery.getKNNForDBID(i1, relation.size());
        double result = ROC.computeROCAUCDistanceResult(relation.size(), clus, knn);


        hist.aggregate(((double) ind) / clus.size(), result);

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    WritableDataStore<Double> lrds = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, Double.class);
    for(DBID id : relation.iterDBIDs()) {
      List<Double> core = new ArrayList<Double>();
      double lrd = 0;
      for(DistanceResultPair<D> neighPair : nMinPts.get(id)) {
        DBID idN = neighPair.getDBID();
        double coreDist = coreDistance.get(idN);
        double dist = distQuery.distance(id, idN).doubleValue();
        Double rd = Math.max(coreDist, dist);
        lrd = rd + lrd;
        core.add(rd);
      }
      lrd = (minPtsNeighborhoodSize.get(id) / lrd);
      reachDistance.put(id, core);
      lrds.put(id, lrd);
    }


    // Pass 3
    DoubleMinMax ofminmax = new DoubleMinMax();
    WritableDataStore<Double> ofs = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_STATIC, Double.class);
    for(DBID id : relation.iterDBIDs()) {
      double of = 0;
      for(DistanceResultPair<D> pair : nMinPts.get(id)) {
        DBID idN = pair.getDBID();
        double lrd = lrds.get(id);
        double lrdN = lrds.get(idN);
        of = of + lrdN / lrd;
      }
      of = of / minPtsNeighborhoodSize.get(id);

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    KNNQuery<V, DoubleDistance> knnQuery = QueryUtil.getKNNQuery(relation, EuclideanDistanceFunction.STATIC, k);


    Iterator<DBID> it = relation.iterDBIDs();
    while(it.hasNext()) {
      DBID id = it.next();


      if(logger.isDebugging()) {
        msg.append("\n\nid = ").append(id);
        ///msg.append(" ").append(database.getObjectLabelQuery().get(id));
        msg.append("\n knns: ");

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