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

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


    if(spol instanceof ClassStylingPolicy) {
      ClassStylingPolicy cspol = (ClassStylingPolicy) spol;
      for(int cnum = cspol.getMinStyle(); cnum < cspol.getMaxStyle(); cnum++) {
        for(Iterator<DBID> iter = cspol.iterateClass(cnum); iter.hasNext();) {
          DBID cur = iter.next();
          try {
            final NumberVector<?, ?> vec = rel.get(cur);
            double[] v = proj.fastProjectDataToRenderSpace(vec);
            ml.useMarker(svgp, layer, v[0], v[1], cnum, marker_size);
          }
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    }

    // try to expand the cluster
    ModifiableDBIDs currentCluster = DBIDUtil.newArray();
    for(DistanceResultPair<DoubleDistance> seed : seeds) {
      DBID nextID = seed.getDBID();

      Integer nextID_corrDim = distFunc.getIndex().getLocalProjection(nextID).getCorrelationDimension();
      // nextID is not reachable from start object
      if(nextID_corrDim > lambda) {
        continue;
      }

      if(!processedIDs.contains(nextID)) {
        currentCluster.add(nextID);
        processedIDs.add(nextID);
      }
      else if(noise.contains(nextID)) {
        currentCluster.add(nextID);
        noise.remove(nextID);
      }
    }
    seeds.remove(0);

    while(seeds.size() > 0) {
      DBID q = seeds.remove(0).getDBID();
      Integer corrDim_q = distFunc.getIndex().getLocalProjection(q).getCorrelationDimension();
      // q forms no lambda-dim hyperplane
      if(corrDim_q > lambda) {
        continue;
      }
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      DBIDs selection = DBIDUtil.ensureSet(selContext.getSelectedIds());

      final double width = plotwidth / order.size();
      int begin = -1;
      for(int j = 0; j < order.size(); j++) {
        DBID id = order.get(j).getID();
        if(selection.contains(id)) {
          if(begin == -1) {
            begin = j;
          }
        }
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    else {
      selection = DBIDUtil.newHashSet(selContext.getSelectedIds());
    }

    for(int i = begin; i <= end; i++) {
      DBID id = order.get(i).getID();
      if(mode == Mode.INVERT) {
        if(!selection.contains(id)) {
          selection.add(id);
        }
        else {
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   * @param mtree Mtree to visualize
   * @param entry Current entry
   * @param depth Current depth
   */
  private void visualizeMTreeEntry(SVGPlot svgp, Element layer, Projection2D proj, AbstractMTree<?, D, N, E> mtree, E entry, int depth) {
    DBID roid = entry.getRoutingObjectID();
    if(roid != null) {
      NumberVector<?, ?> ro = rel.get(roid);
      D rad = entry.getCoveringRadius();

      final Element r;
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   */
  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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      return iter.hasNext();
    }

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

    // for each data point o do
    Iterator<DBID> it = database.iterDBIDs();
    while(it.hasNext()) {
      DBID id = it.next();
      V o = database.get(id);

      DoubleDistance minDist = null;
      ORCLUSCluster minCluster = null;
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  private Matrix findBasis(Relation<V> database, DistanceQuery<V, DoubleDistance> distFunc, ORCLUSCluster cluster, int dim) {
    // covariance matrix of cluster
    // Matrix covariance = Util.covarianceMatrix(database, cluster.objectIDs);
    List<DistanceResultPair<DoubleDistance>> results = new ArrayList<DistanceResultPair<DoubleDistance>>(cluster.objectIDs.size());
    for(Iterator<DBID> it = cluster.objectIDs.iterator(); it.hasNext();) {
      DBID id = it.next();
      DoubleDistance distance = distFunc.distance(cluster.centroid, database.get(id));
      DistanceResultPair<DoubleDistance> qr = new GenericDistanceResultPair<DoubleDistance>(distance, id);
      results.add(qr);
    }
    Collections.sort(results);
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      List<Pair<BitSet, ArrayModifiableDBIDs>> parallelClusters = clustersMap.get(pv);
      for(Pair<BitSet, ArrayModifiableDBIDs> cluster : parallelClusters) {
        if(cluster.second.isEmpty()) {
          continue;
        }
        DBID firstID = cluster.second.get(0);
        ClusterOrderEntry<PreferenceVectorBasedCorrelationDistance> entry = entryMap.get(firstID);
        DBID predecessorID = entry.getPredecessorID();
        if(predecessorID == null) {
          continue;
        }
        ClusterOrderEntry<PreferenceVectorBasedCorrelationDistance> predecessor = entryMap.get(predecessorID);
        // parallel cluster
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