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

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

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   *        materialized kNNs and RkNNs
   * @return the RkNNs of the specified ids, i.e. the kNNs which have been
   *         updated
   */
  private ArrayDBIDs updateKNNsAndRkNNs(DBIDs ids) {
    ArrayModifiableDBIDs rkNN_ids = DBIDUtil.newArray();
    DBIDs oldids = DBIDUtil.difference(relation.getDBIDs(), ids);
    for(DBID id1 : oldids) {
      KNNResult<D> kNNs = storage.get(id1);
      D knnDist = kNNs.getKNNDistance();
      // look for new kNNs
      KNNHeap<D> heap = null;
      for(DBID id2 : ids) {
        D dist = distanceQuery.distance(id1, id2);
        if(dist.compareTo(knnDist) <= 0) {
          if(heap == null) {
            heap = new KNNHeap<D>(k);
            heap.addAll(kNNs);
          }
          heap.add(dist, id2);
        }
      }
      if(heap != null) {
        KNNList<D> newKNNs = heap.toKNNList();
        storage.put(id1, newKNNs);

        // get the difference
        int i = 0;
        int j = 0;
        List<DistanceResultPair<D>> added = new ArrayList<DistanceResultPair<D>>();
        List<DistanceResultPair<D>> removed = new ArrayList<DistanceResultPair<D>>();
        while(i < kNNs.size() && j < newKNNs.size()) {
          DistanceResultPair<D> drp1 = kNNs.get(i);
          DistanceResultPair<D> drp2 = newKNNs.get(j);
          if(!drp1.equals(drp2)) {
            added.add(drp2);
            j++;
          }
          else {
            i++;
            j++;
          }
        }
        if(i != j) {
          for(; i < kNNs.size(); i++)
            removed.add(kNNs.get(i));
        }
        // add new RkNN
        for(DistanceResultPair<D> drp : added) {
          Set<DistanceResultPair<D>> rknns = materialized_RkNN.get(drp.getDBID());
          rknns.add(new GenericDistanceResultPair<D>(drp.getDistance(), id1));
        }
        // remove old RkNN
        for(DistanceResultPair<D> drp : removed) {
          Set<DistanceResultPair<D>> rknns = materialized_RkNN.get(drp.getDBID());
          rknns.remove(new GenericDistanceResultPair<D>(drp.getDistance(), id1));
        }

        rkNN_ids.add(id1);
      }
    }
    return rkNN_ids;
  }
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   *
   * @deprecated Use DBIDs, not integers!
   */
  @Deprecated
  public Bicluster(int[] rowIDs, int[] colIDs, Relation<V> database) {
    ArrayModifiableDBIDs ids = DBIDUtil.newArray(rowIDs.length);
    for(int rowid : rowIDs) {
      ids.add(DBIDUtil.importInteger(rowid));
    }
    this.rowIDs = ids;
    this.colIDs = colIDs;
    this.database = database;
  }
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      // Build DBID array
      if(idrepnr == -1) {
        this.ids = DBIDUtil.generateStaticDBIDRange(objpackages.dataLength());
      }
      else {
        final ArrayModifiableDBIDs newids = DBIDUtil.newArray(objpackages.dataLength());
        for(int j = 0; j < objpackages.dataLength(); j++) {
          DBID newid = (DBID) objpackages.data(j, idrepnr);
          newids.add(newid);
        }
        this.ids = newids;
      }
      // Replace id representation.
      // TODO: this is an ugly hack
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    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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   *        materialized kNNs
   * @return the RkNNs of the specified ids, i.e. the kNNs which have been
   *         updated
   */
  private ArrayDBIDs updateKNNsAfterInsertion(DBIDs ids) {
    ArrayModifiableDBIDs rkNN_ids = DBIDUtil.newArray();
    DBIDs oldids = DBIDUtil.difference(relation.getDBIDs(), ids);
    for(DBID id1 : oldids) {
      KNNResult<D> kNNs = storage.get(id1);
      D knnDist = kNNs.get(kNNs.size() - 1).getDistance();
      // look for new kNNs
      KNNHeap<D> heap = null;
      for(DBID id2 : ids) {
        D dist = distanceQuery.distance(id1, id2);
        if(dist.compareTo(knnDist) <= 0) {
          if(heap == null) {
            heap = new KNNHeap<D>(k);
            heap.addAll(kNNs);
          }
          heap.add(dist, id2);
        }
      }
      if(heap != null) {
        kNNs = heap.toKNNList();
        storage.put(id1, kNNs);
        rkNN_ids.add(id1);
      }
    }
    return rkNN_ids;
  }
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   * @return the RkNNs of the specified ids, i.e. the kNNs which have been
   *         updated
   */
  private ArrayDBIDs updateKNNsAfterDeletion(DBIDs ids) {
    SetDBIDs idsSet = DBIDUtil.ensureSet(ids);
    ArrayModifiableDBIDs rkNN_ids = DBIDUtil.newArray();
    for(DBID id1 : relation.iterDBIDs()) {
      KNNResult<D> kNNs = storage.get(id1);
      for(DistanceResultPair<D> kNN : kNNs) {
        if(idsSet.contains(kNN.getDBID())) {
          rkNN_ids.add(id1);
          break;
        }
      }
    }

    // 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));
    }

    return rkNN_ids;
  }
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  public DBIDs insert(ObjectBundle objpackages) {
    if(objpackages.dataLength() == 0) {
      return DBIDUtil.EMPTYDBIDS;
    }
    // insert into db
    ArrayModifiableDBIDs newids = DBIDUtil.newArray(objpackages.dataLength());
    Relation<?>[] targets = alignColumns(objpackages);

    int idrepnr = -1;
    for(int i = 0; i < targets.length; i++) {
      if(targets[i] == idrep) {
        idrepnr = i;
        break;
      }
    }

    for(int j = 0; j < objpackages.dataLength(); j++) {
      // insert object
      final DBID newid;
      if(idrepnr < 0) {
        newid = DBIDUtil.generateSingleDBID();
      }
      else {
        newid = (DBID) objpackages.data(j, idrepnr);
      }
      if(ids.contains(newid)) {
        throw new AbortException("Duplicate DBID conflict.");
      }
      ids.add(newid);
      for(int i = 0; i < targets.length; i++) {
        // DBIDs were handled above.
        if(i == idrepnr) {
          continue;
        }
        @SuppressWarnings("unchecked")
        final Relation<Object> relation = (Relation<Object>) targets[i];
        relation.set(newid, objpackages.data(j, i));
      }
      newids.add(newid);
    }

    // Notify indexes of insertions
    for(Index index : indexes) {
      index.insertAll(newids);
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          pos.add(d);
        }
      }
    }

    ArrayModifiableDBIDs ids = DBIDUtil.newArray(relation.getDBIDs());
    ids.remove(firstid);
    final int size = ids.size();

    double[][] data = new double[size][size];
    DoubleMinMax minmax = new DoubleMinMax();

    for(int a = 0; a < size; a++) {
      final NumberVector<?, ?> veca = relation.get(ids.get(a));
      // Direct AUC score:
      {
        for(int d = 0; d < dim; d++) {
          combined[d].first = veca.doubleValue(d + 1);
          combined[d].second = d;
        }
        Arrays.sort(combined, Collections.reverseOrder(DoubleIntPair.BYFIRST_COMPARATOR));
        double auc = ROC.computeAUC(ROC.materializeROC(dim, pos, Arrays.asList(combined).iterator()));
        data[a][a] = auc;
        // minmax.put(auc);
        // logger.verbose(auc + " " + labels.get(ids.get(a)));
      }
      // Compare to others, exploiting symmetry
      for(int b = a + 1; b < size; b++) {
        final NumberVector<?, ?> vecb = relation.get(ids.get(b));
        for(int d = 0; d < dim; d++) {
          combined[d].first = veca.doubleValue(d + 1) + vecb.doubleValue(d + 1);
          combined[d].second = d;
        }
        Arrays.sort(combined, Collections.reverseOrder(DoubleIntPair.BYFIRST_COMPARATOR));
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  }

  @Override
  public void getKNNForBulkHeaps(Map<DBID, KNNHeap<D>> heaps) {
    final int size = heaps.size();
    ArrayModifiableDBIDs ids = DBIDUtil.newArray(size);
    List<KNNHeap<D>> kheaps = new ArrayList<KNNHeap<D>>(size);
    for(Entry<DBID, KNNHeap<D>> ent : heaps.entrySet()) {
      ids.add(ent.getKey());
      kheaps.add(ent.getValue());
    }
    linearScanBatchKNN(ids, kheaps);
  }
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   * @param m the number of medoids to be returned
   * @param random random number generator
   * @return a piercing set of m medoids from the specified sample set
   */
  private ModifiableDBIDs greedy(DistanceQuery<V, DoubleDistance> distFunc, DBIDs sampleSet, int m, Random random) {
    ArrayModifiableDBIDs s = DBIDUtil.newArray(sampleSet);
    ModifiableDBIDs medoids = DBIDUtil.newHashSet();

    // m_1 is random point of S
    DBID m_i = s.remove(random.nextInt(s.size()));
    medoids.add(m_i);
    if(logger.isDebugging()) {
      logger.debugFiner("medoids " + medoids);
    }

    // compute distances between each point in S and m_i
    Map<DBID, DistanceResultPair<DoubleDistance>> distances = new HashMap<DBID, DistanceResultPair<DoubleDistance>>();
    for(DBID id : s) {
      DoubleDistance dist = distFunc.distance(id, m_i);
      distances.put(id, new GenericDistanceResultPair<DoubleDistance>(dist, id));
    }

    for(int i = 1; i < m; i++) {
      // choose medoid m_i to be far from prevois medoids
      List<DistanceResultPair<DoubleDistance>> d = new ArrayList<DistanceResultPair<DoubleDistance>>(distances.values());
      Collections.sort(d);

      m_i = d.get(d.size() - 1).getDBID();
      medoids.add(m_i);
      s.remove(m_i);
      distances.remove(m_i);

      // compute distances of each point to closest medoid
      for(DBID id : s) {
        DoubleDistance dist_new = distFunc.distance(id, m_i);
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