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

Example 11 with WritableDoubleDataStore

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

the class IDOS method computeIDOS.

/**
 * Computes all IDOS scores.
 *
 * @param ids the DBIDs to process
 * @param knnQ the KNN query
 * @param intDims Precomputed intrinsic dimensionalities
 * @param idosminmax Output of minimum and maximum, for metadata
 * @return ID scores
 */
protected DoubleDataStore computeIDOS(DBIDs ids, KNNQuery<O> knnQ, DoubleDataStore intDims, DoubleMinMax idosminmax) {
    WritableDoubleDataStore ldms = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("ID Outlier Scores for objects", ids.size(), LOG) : null;
    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        final KNNList neighbors = knnQ.getKNNForDBID(iter, k_r);
        double sum = 0.;
        int cnt = 0;
        for (DoubleDBIDListIter neighbor = neighbors.iter(); neighbor.valid(); neighbor.advance()) {
            if (DBIDUtil.equal(iter, neighbor)) {
                continue;
            }
            final double id = intDims.doubleValue(neighbor);
            sum += id > 0 ? 1.0 / id : 0.;
            if (++cnt == k_r) {
                // Always stop after at most k_r elements.
                break;
            }
        }
        final double id_q = intDims.doubleValue(iter);
        final double idos = id_q > 0 ? id_q * sum / cnt : 0.;
        ldms.putDouble(iter, idos);
        idosminmax.put(idos);
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    return ldms;
}

Example 12 with WritableDoubleDataStore

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

the class IntrinsicDimensionalityOutlier method run.

/**
 * Run the algorithm
 *
 * @param database Database
 * @param relation Data relation
 * @return Outlier result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
    final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, k + 1);
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("kNN distance for objects", relation.size(), LOG) : null;
    DoubleMinMax minmax = new DoubleMinMax();
    WritableDoubleDataStore id_score = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        double id = 0.;
        try {
            id = estimator.estimate(knnQuery, iditer, k + 1);
        } catch (ArithmeticException e) {
            id = 0.;
        }
        id_score.putDouble(iditer, id);
        minmax.put(id);
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    DoubleRelation scoreres = new MaterializedDoubleRelation("Intrinsic dimensionality", "id-score", id_score, relation.getDBIDs());
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0.0);
    return new OutlierResult(meta, scoreres);
}

Example 13 with WritableDoubleDataStore

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

the class ALOCI method run.

public OutlierResult run(Database database, Relation<O> relation) {
    final int dim = RelationUtil.dimensionality(relation);
    final Random random = rnd.getSingleThreadedRandom();
    FiniteProgress progressPreproc = LOG.isVerbose() ? new FiniteProgress("Build aLOCI quadtress", g, LOG) : null;
    // Compute extend of dataset.
    double[] min, max;
    {
        double[][] hbbs = RelationUtil.computeMinMax(relation);
        min = hbbs[0];
        max = hbbs[1];
        double maxd = 0;
        for (int i = 0; i < dim; i++) {
            maxd = MathUtil.max(maxd, max[i] - min[i]);
        }
        // Enlarge bounding box to have equal lengths.
        for (int i = 0; i < dim; i++) {
            double diff = (maxd - (max[i] - min[i])) * .5;
            min[i] -= diff;
            max[i] += diff;
        }
    }
    List<ALOCIQuadTree> qts = new ArrayList<>(g);
    double[] nshift = new double[dim];
    ALOCIQuadTree qt = new ALOCIQuadTree(min, max, nshift, nmin, relation);
    qts.add(qt);
    LOG.incrementProcessed(progressPreproc);
    /*
     * create the remaining g-1 shifted QuadTrees. This not clearly described in
     * the paper and therefore implemented in a way that achieves good results
     * with the test data.
     */
    for (int shift = 1; shift < g; shift++) {
        double[] svec = new double[dim];
        for (int i = 0; i < dim; i++) {
            svec[i] = random.nextDouble() * (max[i] - min[i]);
        }
        qt = new ALOCIQuadTree(min, max, svec, nmin, relation);
        qts.add(qt);
        LOG.incrementProcessed(progressPreproc);
    }
    LOG.ensureCompleted(progressPreproc);
    // aLOCI main loop: evaluate
    FiniteProgress progressLOCI = LOG.isVerbose() ? new FiniteProgress("Compute aLOCI scores", relation.size(), LOG) : null;
    WritableDoubleDataStore mdef_norm = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
    DoubleMinMax minmax = new DoubleMinMax();
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        final O obj = relation.get(iditer);
        double maxmdefnorm = 0;
        // For each level
        for (int l = 0; ; l++) {
            // Find the closest C_i
            Node ci = null;
            for (int i = 0; i < g; i++) {
                Node ci2 = qts.get(i).findClosestNode(obj, l);
                if (ci2.getLevel() != l) {
                    continue;
                }
                // TODO: always use manhattan?
                if (ci == null || distFunc.distance(ci, obj) > distFunc.distance(ci2, obj)) {
                    ci = ci2;
                }
            }
            // LOG.debug("level:" + (ci != null ? ci.getLevel() : -1) +" l:"+l);
            if (ci == null) {
                // no matching tree for this level.
                break;
            }
            // Find the closest C_j
            Node cj = null;
            for (int i = 0; i < g; i++) {
                Node cj2 = qts.get(i).findClosestNode(ci, l - alpha);
                // TODO: allow higher levels or not?
                if (cj != null && cj2.getLevel() < cj.getLevel()) {
                    continue;
                }
                // TODO: always use manhattan?
                if (cj == null || distFunc.distance(cj, ci) > distFunc.distance(cj2, ci)) {
                    cj = cj2;
                }
            }
            // LOG.debug("level:" + (cj != null ? cj.getLevel() : -1) +" l:"+l);
            if (cj == null) {
                // no matching tree for this level.
                continue;
            }
            double mdefnorm = calculate_MDEF_norm(cj, ci);
            // LOG.warning("level:" + ci.getLevel() + "/" + cj.getLevel() +
            // " mdef: " + mdefnorm);
            maxmdefnorm = MathUtil.max(maxmdefnorm, mdefnorm);
        }
        // Store results
        mdef_norm.putDouble(iditer, maxmdefnorm);
        minmax.put(maxmdefnorm);
        LOG.incrementProcessed(progressLOCI);
    }
    LOG.ensureCompleted(progressLOCI);
    DoubleRelation scoreResult = new MaterializedDoubleRelation("aLOCI normalized MDEF", "aloci-mdef-outlier", mdef_norm, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY);
    OutlierResult result = new OutlierResult(scoreMeta, scoreResult);
    return result;
}

Example 14 with WritableDoubleDataStore

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

the class FlexibleLOF method doRunInTime.

/**
 * Performs the Generalized LOF_SCORE algorithm on the given database and
 * returns a {@link FlexibleLOF.LOFResult} encapsulating information that may
 * be needed by an OnlineLOF algorithm.
 *
 * @param ids Object ids
 * @param kNNRefer the kNN query w.r.t. reference neighborhood distance
 *        function
 * @param kNNReach the kNN query w.r.t. reachability distance function
 * @param stepprog Progress logger
 * @return LOF result
 */
protected LOFResult<O> doRunInTime(DBIDs ids, KNNQuery<O> kNNRefer, KNNQuery<O> kNNReach, StepProgress stepprog) {
    // Assert we got something
    if (kNNRefer == null) {
        throw new AbortException("No kNN queries supported by database for reference neighborhood distance function.");
    }
    if (kNNReach == null) {
        throw new AbortException("No kNN queries supported by database for reachability distance function.");
    }
    // Compute LRDs
    LOG.beginStep(stepprog, 2, "Computing LRDs.");
    WritableDoubleDataStore lrds = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
    computeLRDs(kNNReach, ids, lrds);
    // compute LOF_SCORE of each db object
    LOG.beginStep(stepprog, 3, "Computing LOFs.");
    WritableDoubleDataStore lofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
    // track the maximum value for normalization.
    DoubleMinMax lofminmax = new DoubleMinMax();
    computeLOFs(kNNRefer, ids, lrds, lofs, lofminmax);
    LOG.setCompleted(stepprog);
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Local Outlier Factor", "lof-outlier", lofs, ids);
    OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta(lofminmax.getMin(), lofminmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 1.0);
    OutlierResult result = new OutlierResult(scoreMeta, scoreResult);
    return new LOFResult<>(result, kNNRefer, kNNReach, lrds, lofs);
}

Example 15 with WritableDoubleDataStore

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

the class KDEOS method run.

/**
 * Run the KDEOS outlier detection algorithm.
 *
 * @param database Database to query
 * @param rel Relation to process
 * @return Outlier detection result
 */
public OutlierResult run(Database database, Relation<O> rel) {
    final DBIDs ids = rel.getDBIDs();
    LOG.verbose("Running kNN preprocessor.");
    KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, rel, getDistanceFunction(), kmax + 1);
    // Initialize store for densities
    WritableDataStore<double[]> densities = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, double[].class);
    estimateDensities(rel, knnq, ids, densities);
    // Compute scores:
    WritableDoubleDataStore kofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB);
    DoubleMinMax minmax = new DoubleMinMax();
    computeOutlierScores(knnq, ids, densities, kofs, minmax);
    DoubleRelation scoreres = new MaterializedDoubleRelation("Kernel Density Estimation Outlier Scores", "kdeos-outlier", kofs, ids);
    OutlierScoreMeta meta = new ProbabilisticOutlierScore(minmax.getMin(), minmax.getMax());
    return new OutlierResult(meta, scoreres);
}