Examples with PrimitiveDistanceFunction de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction used on opensource projects

Example 1 with PrimitiveDistanceFunction

use of de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction in project elki by elki-project.

the class PAMInitialMeans method chooseInitialMeans.

@Override
public <T extends NumberVector> double[][] chooseInitialMeans(Database database, Relation<T> relation, int k, NumberVectorDistanceFunction<? super T> distanceFunction) {
    if (relation.size() < k) {
        throw new AbortException("Database has less than k objects.");
    }
    // Ugly cast; but better than code duplication.
    @SuppressWarnings("unchecked") Relation<O> rel = (Relation<O>) relation;
    // Get a distance query
    @SuppressWarnings("unchecked") final PrimitiveDistanceFunction<? super O> distF = (PrimitiveDistanceFunction<? super O>) distanceFunction;
    final DistanceQuery<O> distQ = database.getDistanceQuery(rel, distF);
    DBIDs medids = chooseInitialMedoids(k, rel.getDBIDs(), distQ);
    double[][] medoids = new double[k][];
    DBIDIter iter = medids.iter();
    for (int i = 0; i < k; i++, iter.advance()) {
        medoids[i] = relation.get(iter).toArray();
    }
    return medoids;
}

Example 2 with PrimitiveDistanceFunction

use of de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction in project elki by elki-project.

the class SLINK method run.

/**
 * Performs the SLINK algorithm on the given database.
 *
 * @param database Database to process
 * @param relation Data relation to use
 */
public PointerHierarchyRepresentationResult run(Database database, Relation<O> relation) {
    DBIDs ids = relation.getDBIDs();
    WritableDBIDDataStore pi = DataStoreUtil.makeDBIDStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
    WritableDoubleDataStore lambda = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, Double.POSITIVE_INFINITY);
    // Temporary storage for m.
    WritableDoubleDataStore m = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    // To allow CLINK logger override
    final Logging log = getLogger();
    FiniteProgress progress = log.isVerbose() ? new FiniteProgress("Running SLINK", ids.size(), log) : null;
    ArrayDBIDs aids = DBIDUtil.ensureArray(ids);
    // First element is trivial/special:
    DBIDArrayIter id = aids.iter(), it = aids.iter();
    // Step 1: initialize
    for (; id.valid(); id.advance()) {
        // P(n+1) = n+1:
        pi.put(id, id);
    // L(n+1) = infinity already.
    }
    // First element is finished already (start at seek(1) below!)
    log.incrementProcessed(progress);
    // Optimized branch
    if (getDistanceFunction() instanceof PrimitiveDistanceFunction) {
        PrimitiveDistanceFunction<? super O> distf = (PrimitiveDistanceFunction<? super O>) getDistanceFunction();
        for (id.seek(1); id.valid(); id.advance()) {
            step2primitive(id, it, id.getOffset(), relation, distf, m);
            // SLINK or CLINK
            process(id, aids, it, id.getOffset(), pi, lambda, m);
            log.incrementProcessed(progress);
        }
    } else {
        // Fallback branch
        DistanceQuery<O> distQ = database.getDistanceQuery(relation, getDistanceFunction());
        for (id.seek(1); id.valid(); id.advance()) {
            step2(id, it, id.getOffset(), distQ, m);
            // SLINK or CLINK
            process(id, aids, it, id.getOffset(), pi, lambda, m);
            log.incrementProcessed(progress);
        }
    }
    log.ensureCompleted(progress);
    // We don't need m anymore.
    m.destroy();
    m = null;
    return new PointerHierarchyRepresentationResult(ids, pi, lambda, getDistanceFunction().isSquared());
}

Example 3 with PrimitiveDistanceFunction

use of de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction in project elki by elki-project.

the class BestOfMultipleKMeans method run.

@Override
public Clustering<M> run(Database database, Relation<V> relation) {
    if (!(innerkMeans.getDistanceFunction() instanceof PrimitiveDistanceFunction)) {
        throw new AbortException("K-Means results can only be evaluated for primitive distance functions, got: " + innerkMeans.getDistanceFunction().getClass());
    }
    @SuppressWarnings("unchecked") final NumberVectorDistanceFunction<? super NumberVector> df = (NumberVectorDistanceFunction<? super NumberVector>) innerkMeans.getDistanceFunction();
    Clustering<M> bestResult = null;
    double bestCost = Double.NaN;
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("K-means iterations", trials, LOG) : null;
    for (int i = 0; i < trials; i++) {
        Clustering<M> currentCandidate = innerkMeans.run(database, relation);
        double currentCost = qualityMeasure.quality(currentCandidate, df, relation);
        if (LOG.isVerbose()) {
            LOG.verbose("Cost of candidate " + i + ": " + currentCost);
        }
        if (qualityMeasure.isBetter(currentCost, bestCost)) {
            bestResult = currentCandidate;
            bestCost = currentCost;
        }
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    return bestResult;
}