Examples with WritableDBIDDataStore de.lmu.ifi.dbs.elki.database.datastore.WritableDBIDDataStore used on opensource projects

Example 1 with WritableDBIDDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableDBIDDataStore in project elki by elki-project.

the class SLINKHDBSCANLinearMemory method run.

/**
 * Run the algorithm
 *
 * @param db Database
 * @param relation Relation
 * @return Clustering hierarchy
 */
public PointerDensityHierarchyRepresentationResult run(Database db, Relation<O> relation) {
    final DistanceQuery<O> distQ = db.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<O> knnQ = db.getKNNQuery(distQ, minPts);
    // We need array addressing later.
    final ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
    // Compute the core distances
    // minPts + 1: ignore query point.
    final WritableDoubleDataStore coredists = computeCoreDists(ids, knnQ, minPts);
    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);
    FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("Running HDBSCAN*-SLINK", ids.size(), LOG) : null;
    // has to be an array for monotonicity reasons!
    ModifiableDBIDs processedIDs = DBIDUtil.newArray(ids.size());
    for (DBIDIter id = ids.iter(); id.valid(); id.advance()) {
        // Steps 1,3,4 are exactly as in SLINK
        step1(id, pi, lambda);
        // Step 2 is modified to use a different distance
        step2(id, processedIDs, distQ, coredists, m);
        step3(id, pi, lambda, processedIDs, m);
        step4(id, pi, lambda, processedIDs);
        processedIDs.add(id);
        LOG.incrementProcessed(progress);
    }
    LOG.ensureCompleted(progress);
    return new PointerDensityHierarchyRepresentationResult(ids, pi, lambda, distQ.getDistanceFunction().isSquared(), coredists);
}

Example 2 with WritableDBIDDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableDBIDDataStore in project elki by elki-project.

the class HDBSCANLinearMemory method run.

/**
 * Run the algorithm
 *
 * @param db Database
 * @param relation Relation
 * @return Clustering hierarchy
 */
public PointerDensityHierarchyRepresentationResult run(Database db, Relation<O> relation) {
    final DistanceQuery<O> distQ = db.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<O> knnQ = db.getKNNQuery(distQ, minPts);
    // We need array addressing later.
    final ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
    // 1. Compute the core distances
    // minPts + 1: ignore query point.
    final WritableDoubleDataStore coredists = computeCoreDists(ids, knnQ, minPts);
    final int numedges = ids.size() - 1;
    DoubleLongHeap heap = new DoubleLongMinHeap(numedges);
    // 2. Build spanning tree.
    FiniteProgress mprog = LOG.isVerbose() ? new FiniteProgress("Computing minimum spanning tree (n-1 edges)", numedges, LOG) : null;
    // 
    PrimsMinimumSpanningTree.processDense(// 
    ids, // 
    new HDBSCANAdapter(ids, coredists, distQ), new HeapMSTCollector(heap, mprog, LOG));
    LOG.ensureCompleted(mprog);
    // Storage for pointer representation:
    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);
    convertToPointerRepresentation(ids, heap, pi, lambda);
    return new PointerDensityHierarchyRepresentationResult(ids, pi, lambda, distQ.getDistanceFunction().isSquared(), coredists);
}

Example 3 with WritableDBIDDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableDBIDDataStore 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 4 with WritableDBIDDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableDBIDDataStore in project elki by elki-project.

the class KNNDD method run.

/**
 * Runs the algorithm in the timed evaluation part.
 *
 * @param relation Data relation
 */
public OutlierResult run(Relation<O> relation) {
    final DistanceQuery<O> distanceQuery = relation.getDistanceQuery(getDistanceFunction());
    final KNNQuery<O> knnQuery = relation.getKNNQuery(distanceQuery, k);
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("kNN distance for objects", relation.size(), LOG) : null;
    WritableDoubleDataStore knnDist = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    WritableDBIDDataStore neighbor = DataStoreUtil.makeDBIDStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    DBIDVar var = DBIDUtil.newVar();
    // Find nearest neighbors, and store the distances.
    for (DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
        final KNNList knn = knnQuery.getKNNForDBID(it, k);
        knnDist.putDouble(it, knn.getKNNDistance());
        neighbor.put(it, knn.assignVar(knn.size() - 1, var));
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    prog = LOG.isVerbose() ? new FiniteProgress("kNN distance descriptor", relation.size(), LOG) : null;
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_DB);
    DoubleMinMax minmax = new DoubleMinMax();
    for (DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
        // Distance
        double d = knnDist.doubleValue(it);
        // Distance of neighbor
        double nd = knnDist.doubleValue(neighbor.assignVar(it, var));
        double knndd = nd > 0 ? d / nd : d > 0 ? Double.POSITIVE_INFINITY : 1.;
        scores.put(it, knndd);
        minmax.put(knndd);
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    DoubleRelation scoreres = new MaterializedDoubleRelation("kNN Data Descriptor", "knndd-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0., Double.POSITIVE_INFINITY, 1.);
    return new OutlierResult(meta, scoreres);
}