Package de.lmu.ifi.dbs.elki.distance.distancevalue

Examples of de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance

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      for(int i = 0; i < node.getNumEntries(); i++) {
        SpatialEntry p = node.getEntry(i);
        for(Entry<DBID, KNNHeap<DoubleDistance>> ent : knnLists.entrySet()) {
          final DBID q = ent.getKey();
          final KNNHeap<DoubleDistance> knns_q = ent.getValue();
          DoubleDistance knn_q_maxDist = knns_q.getKNNDistance();

          DBID pid = ((LeafEntry) p).getDBID();
          // FIXME: objects are NOT accessible by DBID in a plain rtree context!
          DoubleDistance dist_pq = distanceFunction.distance(relation.get(pid), relation.get(q));
          tree.distanceCalcs++;
          if(dist_pq.compareTo(knn_q_maxDist) <= 0) {
            knns_q.add(dist_pq, pid);
          }
        }
      }
    }
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   *
   * @return the reachability distance of this entry
   */
  @Override
  public DoubleDistance getReachability() {
    return new DoubleDistance(reachability);
  }
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    // compute the center distances of entries to the node and sort it
    // in decreasing order to their distances
    for(int i = 0; i < node.getNumEntries(); i++) {
      E entry = node.getEntry(i);
      DoubleDistance dist = distFunction.centerDistance(node, entry);
      reInsertEntries[i] = new DistanceEntry<DoubleDistance, E>(entry, dist, i);
    }
    Arrays.sort(reInsertEntries, Collections.reverseOrder());

    // define, how many entries will be reinserted
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    }

    // 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);
        DoubleDistance dist_old = distances.get(id).getDistance();

        DoubleDistance dist = dist_new.compareTo(dist_old) < 0 ? dist_new : dist_old;
        distances.put(id, new GenericDistanceResultPair<DoubleDistance>(dist, id));
      }

      if(logger.isDebugging()) {
        logger.debugFiner("medoids " + medoids);
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    Map<DBID, List<DistanceResultPair<DoubleDistance>>> result = new HashMap<DBID, List<DistanceResultPair<DoubleDistance>>>();

    for(DBID m : medoids) {
      // determine minimum distance between current medoid m and any other
      // medoid m_i
      DoubleDistance minDist = null;
      for(DBID m_i : medoids) {
        if(m_i == m) {
          continue;
        }
        DoubleDistance currentDist = distFunc.distance(m, m_i);
        if(minDist == null || currentDist.compareTo(minDist) < 0) {
          minDist = currentDist;
        }
      }

      // determine points in sphere centered at m with radius minDist
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      Pair<DoubleDistance, Integer> minDist = null;
      for(int i = 0; i < dimensions.size(); i++) {
        Pair<V, Set<Integer>> pair_i = dimensions.get(i);
        V c_i = pair_i.first;
        Set<Integer> dimensions_i = pair_i.second;
        DoubleDistance currentDist = manhattanSegmentalDistance(p, c_i, dimensions_i);
        if(minDist == null || currentDist.compareTo(minDist.first) < 0) {
          minDist = new Pair<DoubleDistance, Integer>(currentDist, i);
        }
      }
      // add p to cluster with mindist
      assert minDist != null;
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    double result = 0;
    for(Integer d : dimensions) {
      result += Math.abs(o1.doubleValue(d) - o2.doubleValue(d));
    }
    result /= dimensions.size();
    return new DoubleDistance(result);
  }
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    CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);

    Matrix weightMatrix = model.getSimilarityMatrix();
    DoubleVector centroid = new DoubleVector(model.getCentroid());
    DistanceQuery<DoubleVector, DoubleDistance> df = QueryUtil.getDistanceQuery(derivatorDB, new WeightedDistanceFunction(weightMatrix));
    DoubleDistance eps = df.getDistanceFactory().parseString("0.25");

    ids.addDBIDs(interval.getIDs());
    // Search for nearby vectors in original database
    for(DBID id : relation.iterDBIDs()) {
      DoubleVector v = new DoubleVector(relation.get(id).getColumnVector().getArrayRef());
      DoubleDistance d = df.distance(v, centroid);
      if(d.compareTo(eps) < 0) {
        ids.add(id);
      }
    }

    Matrix basis = model.getStrongEigenvectors();
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   *
   * @return the reachability distance of this entry
   */
  @Override
  public DoubleDistance getReachability() {
    return new DoubleDistance(reachability);
  }
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    Iterator<DBID> it = database.iterDBIDs();
    while(it.hasNext()) {
      DBID id = it.next();
      V o = database.get(id);

      DoubleDistance minDist = null;
      ORCLUSCluster minCluster = null;

      // determine projected distance between o and cluster
      for(int i = 0; i < clusters.size(); i++) {
        ORCLUSCluster c = clusters.get(i);
        V o_proj = projection(c, o, factory);
        DoubleDistance dist = distFunc.distance(o_proj, projectedCentroids.get(i));
        if(minDist == null || minDist.compareTo(dist) > 0) {
          minDist = dist;
          minCluster = c;
        }
      }
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Related Classes of de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance

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  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ORCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS
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  • de.lmu.ifi.dbs.elki.database.query.DoubleDistanceResultPair
  • de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction
  • de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceNorm
  • de.lmu.ifi.dbs.elki.distance.distancefunction.adapter.AbstractSimilarityAdapter$Instance
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