Examples with ProbabilisticOutlierScore de.lmu.ifi.dbs.elki.result.outlier.ProbabilisticOutlierScore used on opensource projects

Example 11 with ProbabilisticOutlierScore

use of de.lmu.ifi.dbs.elki.result.outlier.ProbabilisticOutlierScore in project elki by elki-project.

the class TrivialAllOutlier method run.

/**
 * Run the actual algorithm.
 *
 * @param relation Relation
 * @return Result
 */
public OutlierResult run(Relation<?> relation) {
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        scores.putDouble(iditer, 1.0);
    }
    DoubleRelation scoreres = new MaterializedDoubleRelation("Trivial all-outlier score", "all-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta meta = new ProbabilisticOutlierScore();
    return new OutlierResult(meta, scoreres);
}

Example 12 with ProbabilisticOutlierScore

use of de.lmu.ifi.dbs.elki.result.outlier.ProbabilisticOutlierScore in project elki by elki-project.

the class TrivialNoOutlier method run.

/**
 * Run the actual algorithm.
 *
 * @param relation Relation
 * @return Result
 */
public OutlierResult run(Relation<?> relation) {
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT);
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        scores.putDouble(iditer, 0.0);
    }
    DoubleRelation scoreres = new MaterializedDoubleRelation("Trivial no-outlier score", "no-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta meta = new ProbabilisticOutlierScore();
    return new OutlierResult(meta, scoreres);
}

Example 13 with ProbabilisticOutlierScore

use of de.lmu.ifi.dbs.elki.result.outlier.ProbabilisticOutlierScore in project elki by elki-project.

the class ISOS method run.

/**
 * Run the algorithm.
 *
 * @param relation data relation.
 * @return outlier detection result
 */
public OutlierResult run(Relation<O> relation) {
    // Query size
    final int k1 = k + 1;
    final double perplexity = k / 3.;
    KNNQuery<O> knnq = relation.getKNNQuery(getDistanceFunction(), k1);
    final double logPerp = FastMath.log(perplexity);
    double[] p = new double[k + 10];
    ModifiableDoubleDBIDList dists = DBIDUtil.newDistanceDBIDList(k + 10);
    DoubleDBIDListIter di = dists.iter();
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("ISOS scores", relation.size(), LOG) : null;
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_DB, 1.);
    for (DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
        KNNList knns = knnq.getKNNForDBID(it, k1);
        if (p.length < knns.size() + 1) {
            p = new double[knns.size() + 10];
        }
        final DoubleDBIDListIter ki = knns.iter();
        try {
            double id = estimateID(it, ki, p);
            adjustDistances(it, ki, knns.getKNNDistance(), id, dists);
            // We now continue with the modified distances:
            // Compute affinities
            SOS.computePi(it, di, p, perplexity, logPerp);
            // Normalization factor:
            double s = SOS.sumOfProbabilities(it, di, p);
            if (s > 0) {
                nominateNeighbors(it, di, p, 1. / s, scores);
            }
        } catch (ArithmeticException e) {
            // ID estimation failed, supposedly constant values.
            if (knns.size() > 1) {
                Arrays.fill(p, 1. / (knns.size() - 1));
                nominateNeighbors(it, di, p, 1., scores);
            }
        }
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    DoubleMinMax minmax = transformScores(scores, relation.getDBIDs(), logPerp, phi);
    DoubleRelation scoreres = new MaterializedDoubleRelation("Intrinsic Stoachastic Outlier Selection", "isos-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta meta = new ProbabilisticOutlierScore(minmax.getMin(), minmax.getMax(), 0.);
    return new OutlierResult(meta, scoreres);
}