Examples of NeighborSetPredicate

Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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  public OutlierResult run(Relation<N> spatial, Relation<O> attributes) {
    final int dim = DatabaseUtil.dimensionality(attributes);
    if(logger.isDebugging()) {
      logger.debug("Dimensionality: " + dim);
    }
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(spatial);

    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;
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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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   * @param nrel Neighborhood relation
   * @param relation Data relation (1d!)
   * @return Outlier detection result
   */
  public OutlierResult run(Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    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;
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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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   * @param spatial Spatial Relation to use.
   * @param relation Relation to use.
   * @return Outlier detection result
   */
  public OutlierResult run(Database database, Relation<N> spatial, Relation<O> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(spatial);
    DistanceQuery<O, D> distFunc = getNonSpatialDistanceFunction().instantiate(relation);

    WritableDataStore<Double> modifiedDistance = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, Double.class);
    // calculate D-Tilde
    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.put(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.put(id, maxDist);
      }
    }

    // Second step - compute actual SLOM values
    DoubleMinMax slomminmax = new DoubleMinMax();
    WritableDataStore<Double> sloms = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    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.get(neighbor);
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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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   * @param nrel Neighborhood relation
   * @param relation Data relation (1d!)
   * @return Outlier detection result
   */
  public OutlierResult run(Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDataStore<Double> means = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP, Double.class);

    // Calculate average of neighborhood for each object and perform a linear
    // regression using the covariance matrix
    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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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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   * @param spatial Relation for neighborhood
   * @param relation Attributes to evaluate
   * @return Outlier result
   */
  public OutlierResult run(Database database, Relation<N> spatial, Relation<O> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(spatial);
    DistanceQuery<O, D> distFunc = getNonSpatialDistanceFunction().instantiate(relation);

    WritableDataStore<Double> lrds = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, Double.class);
    WritableDataStore<Double> lofs = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);
    DoubleMinMax lofminmax = new DoubleMinMax();

    // Compute densities
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      double avg = 0;
      for(DBID n : neighbors) {
        avg += distFunc.distance(id, n).doubleValue();
      }
      double lrd = 1 / (avg / neighbors.size());
      if (Double.isNaN(lrd)) {
        lrd = 0;
      }
      lrds.put(id, lrd);
    }

    // Compute density quotients
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      double avg = 0;
      for(DBID n : neighbors) {
        avg += lrds.get(n);
      }
      final double lrd = (avg / neighbors.size()) / lrds.get(id);
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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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   * @param nrel Neighborhood relation
   * @param relation Data relation (1d!)
   * @return Outlier detection result
   */
  public OutlierResult run(Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);

    // Compute the global mean and variance
    MeanVariance globalmv = new MeanVariance();
    for(DBID id : relation.iterDBIDs()) {
      globalmv.put(relation.get(id).doubleValue(1));
    }

    DoubleMinMax minmax = new DoubleMinMax();
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    // calculate normalized attribute values
    // calculate neighborhood average of normalized attribute values.
    for(DBID id : relation.iterDBIDs()) {
      // Compute global z score
      final double globalZ = (relation.get(id).doubleValue(1) - globalmv.getMean()) / globalmv.getNaiveStddev();
      // Compute local average z score
      Mean localm = new Mean();
      for(DBID n : npred.getNeighborDBIDs(id)) {
        if(id.equals(n)) {
          continue;
        }
        localm.put((relation.get(n).doubleValue(1) - globalmv.getMean()) / globalmv.getNaiveStddev());
      }
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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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   * @param relation Data Relation (1 dimensional!)
   * @return Outlier detection result
   */
  public OutlierResult run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
    assert (DatabaseUtil.dimensionality(relation) == 1) : "TrimmedMean can only process one-dimensional data sets.";
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);

    WritableDataStore<Double> errors = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP, Double.class);
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Computing trimmed means", relation.size(), logger) : null;
    for(DBID id : relation.iterDBIDs()) {
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      int num = 0;
      double[] values = new double[neighbors.size()];
      // calculate trimmedMean
      for(DBID n : neighbors) {
        values[num] = relation.get(n).doubleValue(1);
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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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  public OutlierResult run(Relation<N> spatial, Relation<O> attributes) {
    if(logger.isDebugging()) {
      logger.debug("Dimensionality: " + DatabaseUtil.dimensionality(attributes));
    }
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(spatial);

    CovarianceMatrix covmaker = new CovarianceMatrix(DatabaseUtil.dimensionality(attributes));
    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);
      // TODO: remove object itself from neighbors?

      // Mean vector "g"
      Vector mean = Centroid.make(attributes, neighbors);
      // Delta vector "h"
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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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   * @param nrel Neighborhood relation
   * @param relation Data relation (1d!)
   * @return Outlier detection result
   */
  public OutlierResult run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
    WritableDataStore<Double> scores = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC, Double.class);

    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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Examples of de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate

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    }

    // Neighbor access
    if(cur instanceof NeighborSetPredicate) {
      if(parts.length == partpos + 1) {
        NeighborSetPredicate pred = (NeighborSetPredicate) cur;
        DBID id = stringToDBID(parts[partpos]);
        if(id != null) {
          DBIDs neighbors = pred.getNeighborDBIDs(id);
          re.appendKeyValue("DBID", id);
          re.appendKeyArray("neighbors");
          for(DBID nid : neighbors) {
            re.appendString(nid.toString());
          }
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