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

Example 61 with DoubleDBIDListIter

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

the class DWOF method clusterData.

/**
 * This method applies a density based clustering algorithm.
 *
 * It looks for an unclustered object and builds a new cluster for it, then
 * adds all the points within its radius to that cluster.
 *
 * nChain represents the points to be added to the cluster and its
 * radius-neighbors
 *
 * @param ids Database IDs to process
 * @param rnnQuery Data to process
 * @param radii Radii to cluster accordingly
 * @param labels Label storage.
 */
private void clusterData(DBIDs ids, RangeQuery<O> rnnQuery, WritableDoubleDataStore radii, WritableDataStore<ModifiableDBIDs> labels) {
    FiniteProgress clustProg = LOG.isVerbose() ? new FiniteProgress("Density-Based Clustering", ids.size(), LOG) : null;
    // Iterate over all objects
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        if (labels.get(iter) != null) {
            continue;
        }
        ModifiableDBIDs newCluster = DBIDUtil.newArray();
        newCluster.add(iter);
        labels.put(iter, newCluster);
        LOG.incrementProcessed(clustProg);
        // container of the points to be added and their radii neighbors to the
        // cluster
        ModifiableDBIDs nChain = DBIDUtil.newArray();
        nChain.add(iter);
        // iterate over nChain
        for (DBIDIter toGetNeighbors = nChain.iter(); toGetNeighbors.valid(); toGetNeighbors.advance()) {
            double range = radii.doubleValue(toGetNeighbors);
            DoubleDBIDList nNeighbors = rnnQuery.getRangeForDBID(toGetNeighbors, range);
            for (DoubleDBIDListIter iter2 = nNeighbors.iter(); iter2.valid(); iter2.advance()) {
                if (DBIDUtil.equal(toGetNeighbors, iter2)) {
                    continue;
                }
                if (labels.get(iter2) == null) {
                    newCluster.add(iter2);
                    labels.put(iter2, newCluster);
                    nChain.add(iter2);
                    LOG.incrementProcessed(clustProg);
                } else if (labels.get(iter2) != newCluster) {
                    ModifiableDBIDs toBeDeleted = labels.get(iter2);
                    newCluster.addDBIDs(toBeDeleted);
                    for (DBIDIter iter3 = toBeDeleted.iter(); iter3.valid(); iter3.advance()) {
                        labels.put(iter3, newCluster);
                    }
                    toBeDeleted.clear();
                }
            }
        }
    }
    LOG.ensureCompleted(clustProg);
}

Example 62 with DoubleDBIDListIter

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

the class MaterializeKNNAndRKNNPreprocessor method materializeKNNAndRKNNs.

/**
 * Materializes the kNNs and RkNNs of the specified object IDs.
 *
 * @param ids the IDs of the objects
 */
private void materializeKNNAndRKNNs(ArrayDBIDs ids, FiniteProgress progress) {
    // add an empty list to each rknn
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        if (materialized_RkNN.get(iter) == null) {
            materialized_RkNN.put(iter, new TreeSet<DoubleDBIDPair>());
        }
    }
    // knn query
    List<? extends KNNList> kNNList = knnQuery.getKNNForBulkDBIDs(ids, k);
    int i = 0;
    for (DBIDIter id = ids.iter(); id.valid(); id.advance(), i++) {
        KNNList kNNs = kNNList.get(i);
        storage.put(id, kNNs);
        for (DoubleDBIDListIter iter = kNNs.iter(); iter.valid(); iter.advance()) {
            TreeSet<DoubleDBIDPair> rknns = materialized_RkNN.get(iter);
            rknns.add(makePair(iter, id));
        }
        getLogger().incrementProcessed(progress);
    }
    getLogger().ensureCompleted(progress);
}

Example 63 with DoubleDBIDListIter

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

the class MaterializeKNNAndRKNNPreprocessor method updateKNNsAndRkNNs.

/**
 * Updates the kNNs and RkNNs after insertion of the specified ids.
 *
 * @param ids the ids of newly inserted objects causing a change of
 *        materialized kNNs and RkNNs
 * @return the RkNNs of the specified ids, i.e. the kNNs which have been
 *         updated
 */
private ArrayDBIDs updateKNNsAndRkNNs(DBIDs ids) {
    ArrayModifiableDBIDs rkNN_ids = DBIDUtil.newArray();
    DBIDs oldids = DBIDUtil.difference(relation.getDBIDs(), ids);
    for (DBIDIter id = oldids.iter(); id.valid(); id.advance()) {
        KNNList oldkNNs = storage.get(id);
        double knnDist = oldkNNs.getKNNDistance();
        // look for new kNNs
        KNNHeap heap = null;
        for (DBIDIter newid = ids.iter(); newid.valid(); newid.advance()) {
            double dist = distanceQuery.distance(id, newid);
            if (dist <= knnDist) {
                // New id changes the kNNs of oldid.
                if (heap == null) {
                    heap = DBIDUtil.newHeap(oldkNNs);
                }
                heap.insert(dist, newid);
            }
        }
        // kNNs for oldid have changed:
        if (heap != null) {
            KNNList newkNNs = heap.toKNNList();
            storage.put(id, newkNNs);
            // get the difference
            int i = 0;
            int j = 0;
            ModifiableDoubleDBIDList added = DBIDUtil.newDistanceDBIDList();
            ModifiableDoubleDBIDList removed = DBIDUtil.newDistanceDBIDList();
            // TODO: use iterators.
            while (i < oldkNNs.size() && j < newkNNs.size()) {
                DoubleDBIDPair drp1 = oldkNNs.get(i);
                DoubleDBIDPair drp2 = newkNNs.get(j);
                // NOTE: we assume that on ties they are ordered the same way!
                if (!DBIDUtil.equal(drp1, drp2)) {
                    added.add(drp2);
                    j++;
                } else {
                    i++;
                    j++;
                }
            }
            if (i != j) {
                for (; i < oldkNNs.size(); i++) {
                    removed.add(oldkNNs.get(i));
                }
                for (; j < newkNNs.size(); i++) {
                    added.add(newkNNs.get(i));
                }
            }
            // add new RkNN
            for (DoubleDBIDListIter newnn = added.iter(); newnn.valid(); newnn.advance()) {
                TreeSet<DoubleDBIDPair> rknns = materialized_RkNN.get(newnn);
                rknns.add(makePair(newnn, id));
            }
            // remove old RkNN
            for (DoubleDBIDListIter oldnn = removed.iter(); oldnn.valid(); oldnn.advance()) {
                TreeSet<DoubleDBIDPair> rknns = materialized_RkNN.get(oldnn);
                rknns.remove(makePair(oldnn, id));
            }
            rkNN_ids.add(id);
        }
    }
    return rkNN_ids;
}

Example 64 with DoubleDBIDListIter

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

the class NNDescentTest method testKNNQueries.

private void testKNNQueries(Relation<DoubleVector> rep, KNNQuery<DoubleVector> lin_knn_query, KNNQuery<DoubleVector> preproc_knn_query, int k) {
    ArrayDBIDs sample = DBIDUtil.ensureArray(rep.getDBIDs());
    List<? extends KNNList> lin_knn_ids = lin_knn_query.getKNNForBulkDBIDs(sample, k);
    List<? extends KNNList> preproc_knn_ids = preproc_knn_query.getKNNForBulkDBIDs(sample, k);
    for (int i = 0; i < rep.size(); i++) {
        KNNList lin_knn = lin_knn_ids.get(i);
        KNNList pre_knn = preproc_knn_ids.get(i);
        DoubleDBIDListIter lin = lin_knn.iter(), pre = pre_knn.iter();
        for (; lin.valid() && pre.valid(); lin.advance(), pre.advance(), i++) {
            if (DBIDUtil.equal(lin, pre) || lin.doubleValue() == pre.doubleValue()) {
                continue;
            }
            fail(// 
            new StringBuilder(1000).append("Neighbor distances do not agree: ").append(lin_knn.toString()).append(" got: ").append(pre_knn.toString()).toString());
        }
        assertEquals("kNN sizes do not agree.", lin_knn.size(), pre_knn.size());
        for (int j = 0; j < lin_knn.size(); j++) {
            assertTrue("kNNs of linear scan and preprocessor do not match!", DBIDUtil.equal(lin_knn.get(j), pre_knn.get(j)));
            assertEquals("kNNs of linear scan and preprocessor do not match!", lin_knn.get(j).doubleValue(), pre_knn.get(j).doubleValue(), 0.);
        }
    }
}

Example 65 with DoubleDBIDListIter

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

the class MkMaxTree method reverseKNNQuery.

/**
 * Performs a reverse k-nearest neighbor query for the given object ID. In the
 * first step the candidates are chosen by performing a reverse k-nearest
 * neighbor query with k = {@link #getKmax()}. Then these candidates are refined
 * in a second step.
 */
@Override
public DoubleDBIDList reverseKNNQuery(DBIDRef id, int k) {
    if (k > this.getKmax()) {
        throw new IllegalArgumentException("Parameter k has to be equal or less than " + "parameter k of the MkMax-Tree!");
    }
    // get the candidates
    ModifiableDoubleDBIDList candidates = DBIDUtil.newDistanceDBIDList();
    doReverseKNNQuery(id, getRoot(), null, candidates);
    if (k == this.getKmax()) {
        candidates.sort();
        // rkNNStatistics.addResults(candidates.size());
        return candidates;
    }
    // refinement of candidates
    ModifiableDBIDs candidateIDs = DBIDUtil.newArray(candidates.size());
    for (DBIDIter candidate = candidates.iter(); candidate.valid(); candidate.advance()) {
        candidateIDs.add(candidate);
    }
    Map<DBID, KNNList> knnLists = batchNN(getRoot(), candidateIDs, k);
    ModifiableDoubleDBIDList result = DBIDUtil.newDistanceDBIDList();
    for (DBIDIter iter = candidateIDs.iter(); iter.valid(); iter.advance()) {
        DBID cid = DBIDUtil.deref(iter);
        KNNList cands = knnLists.get(cid);
        for (DoubleDBIDListIter iter2 = cands.iter(); iter2.valid(); iter2.advance()) {
            if (DBIDUtil.equal(id, iter2)) {
                result.add(iter2.doubleValue(), cid);
                break;
            }
        }
    }
    // FIXME: re-add statistics.
    // rkNNStatistics.addResults(result.size());
    // rkNNStatistics.addCandidates(candidates.size());
    result.sort();
    return result;
}