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

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

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    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);

    MeanVariance zmv = new MeanVariance();
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      // Compute Mean of neighborhood
      Mean localmean = new Mean();
      for(DBID n : neighbors) {
        if(id.equals(n)) {
          continue;
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    Collection<Individuum> individuums = (new EvolutionarySearch(relation, ranges, m, seed)).run();

    WritableDoubleDataStore outlierScore = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
    for(Individuum ind : individuums) {
      DBIDs ids = computeSubspaceForGene(ind.getGene(), ranges);
      double sparsityC = sparsity(ids.size(), dbsize, k);
      for(DBID id : ids) {
        double prev = outlierScore.doubleValue(id);
        if(Double.isNaN(prev) || sparsityC < prev) {
          outlierScore.putDouble(id, sparsityC);
        }
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     *
     * @param gene Gene to evaluate
     * @return new individuum
     */
    private Individuum makeIndividuum(int[] gene) {
      final DBIDs ids = computeSubspaceForGene(gene, ranges);
      final double fitness = (ids.size() > 0) ? sparsity(ids.size(), dbsize, k) : Double.MAX_VALUE;
      return new Individuum(fitness, gene);
    }
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    for(DBID id : relation.iterDBIDs()) {
      double sum = 0;
      double maxDist = 0;
      int cnt = 0;

      final DBIDs neighbors = npred.getNeighborDBIDs(id);
      for(DBID neighbor : neighbors) {
        if(id.equals(neighbor)) {
          continue;
        }
        double dist = distFunc.distance(id, neighbor).doubleValue();
        sum += dist;
        cnt++;
        maxDist = Math.max(maxDist, dist);
      }
      if(cnt > 1) {
        modifiedDistance.putDouble(id, ((sum - maxDist) / (cnt - 1)));
      }
      else {
        // Use regular distance when the d-tilde trick is undefined.
        // Note: this can be 0 when there were no neighbors.
        modifiedDistance.putDouble(id, maxDist);
      }
    }

    // Second step - compute actual SLOM values
    DoubleMinMax slomminmax = new DoubleMinMax();
    WritableDoubleDataStore sloms = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);

    for(DBID id : relation.iterDBIDs()) {
      double sum = 0;
      int cnt = 0;

      final DBIDs neighbors = npred.getNeighborDBIDs(id);
      for(DBID neighbor : neighbors) {
        if(neighbor.equals(id)) {
          continue;
        }
        sum += modifiedDistance.doubleValue(neighbor);
        cnt++;
      }
      double slom;
      if(cnt > 0) {
        // With and without the object itself:
        double avgPlus = (sum + modifiedDistance.doubleValue(id)) / (cnt + 1);
        double avg = sum / cnt;

        double beta = 0;
        for(DBID neighbor : neighbors) {
          final double dist = modifiedDistance.doubleValue(neighbor);
          if(dist > avgPlus) {
            beta += 1;
          }
          else if(dist < avgPlus) {
            beta -= 1;
          }
        }
        // Include object itself
        if(!neighbors.contains(id)) {
          final double dist = modifiedDistance.doubleValue(id);
          if(dist > avgPlus) {
            beta += 1;
          }
          else if(dist < avgPlus) {
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      // Expand multiple steps
      FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Expanding neighborhoods", database.size(), logger) : null;
      for(final DBID id : database.iterDBIDs()) {
        HashSetModifiableDBIDs res = DBIDUtil.newHashSet(id);
        DBIDs todo = id;
        for(int i = 0; i < steps; i++) {
          ModifiableDBIDs ntodo = DBIDUtil.newHashSet();
          for(final DBID oid : todo) {
            DBIDs add = innerinst.getNeighborDBIDs(oid);
            if(add != null) {
              for(DBID nid : add) {
                if(res.contains(nid)) {
                  continue;
                }
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    this.store = store;
  }

  @Override
  public DBIDs getNeighborDBIDs(DBID reference) {
    DBIDs neighbors = store.get(reference);
    if(neighbors != null) {
      return neighbors;
    }
    else {
      // Use just the object itself.
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    // Add starting object
    result.add(new DoubleObjPair<DBID>(computeWeight(0), reference));
    seen.add(reference);
    // Extend.
    DBIDs cur = reference;
    for(int i = 1; i <= steps; i++) {
      final double weight = computeWeight(i);
      // Collect newly discovered IDs
      ModifiableDBIDs add = DBIDUtil.newHashSet();
      for(DBID id : cur) {
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    CovarianceMatrix covm = new CovarianceMatrix(2);
    for(DBID id : relation.iterDBIDs()) {
      final double local = relation.get(id).doubleValue(1);
      // Compute mean of neighbors
      Mean mean = new Mean();
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      for(DBID n : neighbors) {
        if(id.equals(n)) {
          continue;
        }
        mean.put(relation.get(n).doubleValue(1));
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    CovarianceMatrix covmaker = new CovarianceMatrix(dim);
    WritableDataStore<Vector> deltas = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_TEMP, Vector.class);
    for(DBID id : attributes.iterDBIDs()) {
      final O obj = attributes.get(id);
      final DBIDs neighbors = npred.getNeighborDBIDs(id);
      // Compute the median vector
      final Vector median;
      {
        double[][] data = new double[dim][neighbors.size()];
        int i = 0;
        // Load data
        for(DBID n : neighbors) {
          // TODO: skip object itself within neighbors?
          O nobj = attributes.get(n);
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    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);

    MeanVariance mv = new MeanVariance();
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      final double median;
      {
        double[] fi = new double[neighbors.size()];
        // calculate and store Median of neighborhood
        int c = 0;
        for(DBID n : neighbors) {
          if(id.equals(n)) {
            continue;
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Related Classes of de.lmu.ifi.dbs.elki.database.ids.DBIDs

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  • de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
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  • de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.RandomlyChosenInitialMeans
  • de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS
  • de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.TrivialAllInOne
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