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

Example 16 with DoubleDBIDList

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

the class COPACNeighborPredicate method instantiate.

/**
 * Full instantiation method.
 *
 * @param database Database
 * @param relation Vector relation
 * @return Instance
 */
public COPACNeighborPredicate.Instance instantiate(Database database, Relation<V> relation) {
    DistanceQuery<V> dq = database.getDistanceQuery(relation, EuclideanDistanceFunction.STATIC);
    KNNQuery<V> knnq = database.getKNNQuery(dq, settings.k);
    WritableDataStore<COPACModel> storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, COPACModel.class);
    Duration time = LOG.newDuration(this.getClass().getName() + ".preprocessing-time").begin();
    FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress(this.getClass().getName(), relation.size(), LOG) : null;
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        DoubleDBIDList ref = knnq.getKNNForDBID(iditer, settings.k);
        storage.put(iditer, computeLocalModel(iditer, ref, relation));
        LOG.incrementProcessed(progress);
    }
    LOG.ensureCompleted(progress);
    LOG.statistics(time.end());
    return new Instance(relation.getDBIDs(), storage);
}

Example 17 with DoubleDBIDList

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

the class ERiCNeighborPredicate method instantiate.

/**
 * Full instantiation interface.
 *
 * @param database Database
 * @param relation Relation
 * @return Instance
 */
public Instance instantiate(Database database, Relation<V> relation) {
    DistanceQuery<V> dq = database.getDistanceQuery(relation, EuclideanDistanceFunction.STATIC);
    KNNQuery<V> knnq = database.getKNNQuery(dq, settings.k);
    WritableDataStore<PCAFilteredResult> storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, PCAFilteredResult.class);
    PCARunner pca = settings.pca;
    EigenPairFilter filter = settings.filter;
    Duration time = LOG.newDuration(this.getClass().getName() + ".preprocessing-time").begin();
    FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress(this.getClass().getName(), relation.size(), LOG) : null;
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        DoubleDBIDList ref = knnq.getKNNForDBID(iditer, settings.k);
        PCAResult pcares = pca.processQueryResult(ref, relation);
        storage.put(iditer, new PCAFilteredResult(pcares.getEigenPairs(), filter.filter(pcares.getEigenvalues()), 1., 0.));
        LOG.incrementProcessed(progress);
    }
    LOG.ensureCompleted(progress);
    LOG.statistics(time.end());
    return new Instance(relation.getDBIDs(), storage, relation);
}

Example 18 with DoubleDBIDList

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

the class AbstractFilteredPCAIndex method initialize.

@Override
public void initialize() {
    if (relation == null || relation.size() <= 0) {
        throw new EmptyDataException();
    }
    // recomputed for the partitions!
    if (storage != null) {
        return;
    }
    storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, PCAFilteredResult.class);
    long start = System.currentTimeMillis();
    FiniteProgress progress = getLogger().isVerbose() ? new FiniteProgress("Performing local PCA", relation.size(), getLogger()) : null;
    // TODO: use a bulk operation?
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        DoubleDBIDList objects = objectsForPCA(iditer);
        SortedEigenPairs epairs = pca.processIds(objects, relation).getEigenPairs();
        int numstrong = filter.filter(epairs.eigenValues());
        PCAFilteredResult pcares = new PCAFilteredResult(epairs, numstrong, 1., 0.);
        storage.put(iditer, pcares);
        getLogger().incrementProcessed(progress);
    }
    getLogger().ensureCompleted(progress);
    long end = System.currentTimeMillis();
    if (getLogger().isVerbose()) {
        long elapsedTime = end - start;
        getLogger().verbose(this.getClass().getName() + " runtime: " + elapsedTime + " milliseconds.");
    }
}

Example 19 with DoubleDBIDList

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDList 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);
}