Examples with DoubleDBIDListIter de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter used on opensource projects

Example 6 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class WeightedCovarianceMatrixBuilder method processQueryResults.

/**
 * Compute Covariance Matrix for a QueryResult Collection.
 *
 * By default it will just collect the ids and run processIds
 *
 * @param results a collection of QueryResults
 * @param database the database used
 * @param k number of elements to process
 * @return Covariance Matrix
 */
@Override
public double[][] processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k) {
    final int dim = RelationUtil.dimensionality(database);
    final CovarianceMatrix cmat = new CovarianceMatrix(dim);
    // avoid bad parameters
    k = k <= results.size() ? k : results.size();
    // find maximum distance
    double maxdist = 0.0, stddev = 0.0;
    {
        int i = 0;
        for (DoubleDBIDListIter it = results.iter(); it.valid() && i < k; it.advance(), k++) {
            final double dist = it.doubleValue();
            stddev += dist * dist;
            if (dist > maxdist) {
                maxdist = dist;
            }
        }
        if (maxdist == 0.0) {
            maxdist = 1.0;
        }
        stddev = FastMath.sqrt(stddev / k);
    }
    // calculate weighted PCA
    int i = 0;
    for (DoubleDBIDListIter it = results.iter(); it.valid() && i < k; it.advance(), k++) {
        final double dist = it.doubleValue();
        NumberVector obj = database.get(it);
        double weight = weightfunction.getWeight(dist, maxdist, stddev);
        cmat.put(obj, weight);
    }
    return cmat.destroyToPopulationMatrix();
}

Example 7 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class MaterializeKNNAndRKNNPreprocessor method objectsRemoved.

@Override
protected void objectsRemoved(DBIDs ids) {
    StepProgress stepprog = getLogger().isVerbose() ? new StepProgress(3) : null;
    // For debugging: valid DBIDs still in the database.
    final DBIDs valid = DBIDUtil.ensureSet(distanceQuery.getRelation().getDBIDs());
    ArrayDBIDs aids = DBIDUtil.ensureArray(ids);
    // delete the materialized (old) kNNs and RkNNs
    getLogger().beginStep(stepprog, 1, "New deletions ocurred, remove their materialized kNNs and RkNNs.");
    // Temporary storage of removed lists
    List<KNNList> kNNs = new ArrayList<>(ids.size());
    List<TreeSet<DoubleDBIDPair>> rkNNs = new ArrayList<>(ids.size());
    for (DBIDIter iter = aids.iter(); iter.valid(); iter.advance()) {
        kNNs.add(storage.get(iter));
        for (DBIDIter it = storage.get(iter).iter(); it.valid(); it.advance()) {
            if (!valid.contains(it) && !ids.contains(it)) {
                LOG.warning("False kNN: " + it);
            }
        }
        storage.delete(iter);
        rkNNs.add(materialized_RkNN.get(iter));
        for (DoubleDBIDPair it : materialized_RkNN.get(iter)) {
            if (!valid.contains(it) && !ids.contains(it)) {
                LOG.warning("False RkNN: " + it);
            }
        }
        materialized_RkNN.delete(iter);
    }
    // Keep only those IDs not also removed
    ArrayDBIDs kNN_ids = affectedkNN(kNNs, aids);
    ArrayDBIDs rkNN_ids = affectedRkNN(rkNNs, aids);
    // update the affected kNNs and RkNNs
    getLogger().beginStep(stepprog, 2, "New deletions ocurred, update the affected kNNs and RkNNs.");
    // Recompute the kNN for affected objects (in rkNN lists)
    {
        List<? extends KNNList> kNNList = knnQuery.getKNNForBulkDBIDs(rkNN_ids, k);
        int i = 0;
        for (DBIDIter reknn = rkNN_ids.iter(); reknn.valid(); reknn.advance(), i++) {
            if (kNNList.get(i) == null && !valid.contains(reknn)) {
                LOG.warning("BUG in online kNN/RkNN maintainance: " + DBIDUtil.toString(reknn) + " no longer in database.");
                continue;
            }
            assert (kNNList.get(i) != null);
            storage.put(reknn, kNNList.get(i));
            for (DoubleDBIDListIter it = kNNList.get(i).iter(); it.valid(); it.advance()) {
                materialized_RkNN.get(it).add(makePair(it, reknn));
            }
        }
    }
    // remove objects from RkNNs of objects (in kNN lists)
    {
        SetDBIDs idsSet = DBIDUtil.ensureSet(ids);
        for (DBIDIter nn = kNN_ids.iter(); nn.valid(); nn.advance()) {
            TreeSet<DoubleDBIDPair> rkNN = materialized_RkNN.get(nn);
            for (Iterator<DoubleDBIDPair> it = rkNN.iterator(); it.hasNext(); ) {
                if (idsSet.contains(it.next())) {
                    it.remove();
                }
            }
        }
    }
    // inform listener
    getLogger().beginStep(stepprog, 3, "New deletions ocurred, inform listeners.");
    fireKNNsRemoved(ids, rkNN_ids);
    getLogger().ensureCompleted(stepprog);
}

Example 8 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class PreprocessorKNNQuery method getSublist.

/**
 * Subset a knn list for a smaller k.
 *
 * @param knn k nearest neighbor list
 * @param k New k
 * @return Sublist
 */
protected static KNNList getSublist(KNNList knn, int k) {
    DoubleDBIDListIter it = knn.iter();
    // k-distance for the new k.
    final double kdist = it.seek(k - 1).doubleValue();
    if (kdist >= knn.getKNNDistance()) {
        return knn;
    }
    // Find ties:
    int subk = k;
    for (it.advance(); it.valid() && kdist < it.doubleValue() && subk < k; it.advance()) {
        subk++;
    }
    return subk < knn.size() ? DBIDUtil.subList(knn, subk) : knn;
}

Example 9 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class MkTabTree method kNNdistanceAdjustment.

@Override
protected void kNNdistanceAdjustment(MkTabEntry entry, Map<DBID, KNNList> knnLists) {
    MkTabTreeNode<O> node = getNode(entry);
    double[] knnDistances_node = initKnnDistanceList();
    if (node.isLeaf()) {
        for (int i = 0; i < node.getNumEntries(); i++) {
            MkTabEntry leafEntry = node.getEntry(i);
            KNNList knns = knnLists.get(getPageID(leafEntry));
            double[] distances = new double[knns.size()];
            int j = 0;
            for (DoubleDBIDListIter iter = knns.iter(); iter.valid(); iter.advance(), j++) {
                distances[j] = iter.doubleValue();
            }
            leafEntry.setKnnDistances(distances);
            // FIXME: save copy
            knnDistances_node = max(knnDistances_node, leafEntry.getKnnDistances());
        }
    } else {
        for (int i = 0; i < node.getNumEntries(); i++) {
            MkTabEntry dirEntry = node.getEntry(i);
            kNNdistanceAdjustment(dirEntry, knnLists);
            // FIXME: save copy
            knnDistances_node = max(knnDistances_node, dirEntry.getKnnDistances());
        }
    }
    entry.setKnnDistances(knnDistances_node);
}

Example 10 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class MkAppTree method getMeanKNNList.

private double[] getMeanKNNList(DBIDs ids, Map<DBID, KNNList> knnLists) {
    double[] means = new double[settings.kmax];
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        DBID id = DBIDUtil.deref(iter);
        KNNList knns = knnLists.get(id);
        int k = 0;
        for (DoubleDBIDListIter it = knns.iter(); k < settings.kmax && it.valid(); it.advance(), k++) {
            means[k] += it.doubleValue();
        }
    }
    for (int k = 0; k < settings.kmax; k++) {
        means[k] /= ids.size();
    }
    return means;
}