Examples with DoubleRelation de.lmu.ifi.dbs.elki.database.relation.DoubleRelation used on opensource projects

Example 86 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation in project elki by elki-project.

the class LogRankingPseudoOutlierScaling method prepare.

@Override
public void prepare(OutlierResult or) {
    // collect all outlier scores
    DoubleRelation oscores = or.getScores();
    scores = new double[oscores.size()];
    int pos = 0;
    if (or.getOutlierMeta() instanceof InvertedOutlierScoreMeta) {
        inverted = true;
    }
    for (DBIDIter iditer = oscores.iterDBIDs(); iditer.valid(); iditer.advance()) {
        scores[pos] = oscores.doubleValue(iditer);
        pos++;
    }
    if (pos != oscores.size()) {
        throw new AbortException("Database size is incorrect!");
    }
    // sort them
    Arrays.sort(scores);
}

Example 87 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation in project elki by elki-project.

the class MinusLogStandardDeviationScaling method prepare.

@Override
public void prepare(OutlierResult or) {
    if (fixedmean == null) {
        MeanVariance mv = new MeanVariance();
        DoubleRelation scores = or.getScores();
        for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
            double val = -FastMath.log(scores.doubleValue(id));
            if (!Double.isNaN(val) && !Double.isInfinite(val)) {
                mv.put(val);
            }
        }
        mean = mv.getMean();
        factor = lambda * mv.getSampleStddev() * MathUtil.SQRT2;
    } else {
        mean = fixedmean;
        Mean sqsum = new Mean();
        DoubleRelation scores = or.getScores();
        for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
            double val = -FastMath.log(scores.doubleValue(id));
            if (!Double.isNaN(val) && !Double.isInfinite(val)) {
                sqsum.put((val - mean) * (val - mean));
            }
        }
        factor = lambda * FastMath.sqrt(sqsum.getMean()) * MathUtil.SQRT2;
    }
}

Example 88 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation 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);
}

Example 89 with DoubleRelation

use of de.lmu.ifi.dbs.elki.database.relation.DoubleRelation in project elki by elki-project.

the class GaussianModel method run.

/**
 * Run the algorithm
 *
 * @param relation Data relation
 * @return Outlier result
 */
public OutlierResult run(Relation<V> relation) {
    DoubleMinMax mm = new DoubleMinMax();
    // resulting scores
    WritableDoubleDataStore oscores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT);
    // Compute mean and covariance Matrix
    CovarianceMatrix temp = CovarianceMatrix.make(relation);
    double[] mean = temp.getMeanVector(relation).toArray();
    // debugFine(mean.toString());
    double[][] covarianceMatrix = temp.destroyToPopulationMatrix();
    // debugFine(covarianceMatrix.toString());
    double[][] covarianceTransposed = inverse(covarianceMatrix);
    // Normalization factors for Gaussian PDF
    double det = new LUDecomposition(covarianceMatrix).det();
    final double fakt = 1.0 / FastMath.sqrt(MathUtil.powi(MathUtil.TWOPI, RelationUtil.dimensionality(relation)) * det);
    // for each object compute Mahalanobis distance
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        double[] x = minusEquals(relation.get(iditer).toArray(), mean);
        // Gaussian PDF
        final double mDist = transposeTimesTimes(x, covarianceTransposed, x);
        final double prob = fakt * FastMath.exp(-mDist * .5);
        mm.put(prob);
        oscores.putDouble(iditer, prob);
    }
    final OutlierScoreMeta meta;
    if (invert) {
        double max = mm.getMax() != 0 ? mm.getMax() : 1.;
        for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
            oscores.putDouble(iditer, (max - oscores.doubleValue(iditer)) / max);
        }
        meta = new BasicOutlierScoreMeta(0.0, 1.0);
    } else {
        meta = new InvertedOutlierScoreMeta(mm.getMin(), mm.getMax(), 0.0, Double.POSITIVE_INFINITY);
    }
    DoubleRelation res = new MaterializedDoubleRelation("Gaussian Model Outlier Score", "gaussian-model-outlier", oscores, relation.getDBIDs());
    return new OutlierResult(meta, res);
}