Examples with DoubleMinMax de.lmu.ifi.dbs.elki.math.DoubleMinMax used on opensource projects

Example 16 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class CTLuMedianAlgorithm method run.

/**
 * Main method.
 *
 * @param database Database
 * @param nrel Neighborhood relation
 * @param relation Data relation (1d!)
 * @return Outlier detection result
 */
public OutlierResult run(Database database, Relation<N> nrel, Relation<? extends NumberVector> relation) {
    final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(database, nrel);
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
    MeanVariance mv = new MeanVariance();
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        DBIDs neighbors = npred.getNeighborDBIDs(iditer);
        final double median;
        {
            double[] fi = new double[neighbors.size()];
            // calculate and store Median of neighborhood
            int c = 0;
            for (DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
                if (DBIDUtil.equal(iditer, iter)) {
                    continue;
                }
                fi[c] = relation.get(iter).doubleValue(0);
                c++;
            }
            if (c > 0) {
                median = QuickSelect.median(fi, 0, c);
            } else {
                median = relation.get(iditer).doubleValue(0);
            }
        }
        double h = relation.get(iditer).doubleValue(0) - median;
        scores.putDouble(iditer, h);
        mv.put(h);
    }
    // Normalize scores
    final double mean = mv.getMean();
    final double stddev = mv.getNaiveStddev();
    DoubleMinMax minmax = new DoubleMinMax();
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        double score = Math.abs((scores.doubleValue(iditer) - mean) / stddev);
        minmax.put(score);
        scores.putDouble(iditer, score);
    }
    DoubleRelation scoreResult = new MaterializedDoubleRelation("MO", "Median-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0);
    OutlierResult or = new OutlierResult(scoreMeta, scoreResult);
    or.addChildResult(npred);
    return or;
}

Example 17 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class DWOF method run.

/**
 * Performs the Generalized DWOF_SCORE algorithm on the given database by
 * calling all the other methods in the proper order.
 *
 * @param database Database to query
 * @param relation Data to process
 * @return new OutlierResult instance
 */
public OutlierResult run(Database database, Relation<O> relation) {
    final DBIDs ids = relation.getDBIDs();
    DistanceQuery<O> distFunc = database.getDistanceQuery(relation, getDistanceFunction());
    // Get k nearest neighbor and range query on the relation.
    KNNQuery<O> knnq = database.getKNNQuery(distFunc, k, DatabaseQuery.HINT_HEAVY_USE);
    RangeQuery<O> rnnQuery = database.getRangeQuery(distFunc, DatabaseQuery.HINT_HEAVY_USE);
    StepProgress stepProg = LOG.isVerbose() ? new StepProgress("DWOF", 2) : null;
    // DWOF output score storage.
    WritableDoubleDataStore dwofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB | DataStoreFactory.HINT_HOT, 0.);
    if (stepProg != null) {
        stepProg.beginStep(1, "Initializing objects' Radii", LOG);
    }
    WritableDoubleDataStore radii = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, 0.);
    // Find an initial radius for each object:
    initializeRadii(ids, knnq, distFunc, radii);
    WritableIntegerDataStore oldSizes = DataStoreUtil.makeIntegerStorage(ids, DataStoreFactory.HINT_HOT, 1);
    WritableIntegerDataStore newSizes = DataStoreUtil.makeIntegerStorage(ids, DataStoreFactory.HINT_HOT, 1);
    int countUnmerged = relation.size();
    if (stepProg != null) {
        stepProg.beginStep(2, "Clustering-Evaluating Cycles.", LOG);
    }
    IndefiniteProgress clusEvalProgress = LOG.isVerbose() ? new IndefiniteProgress("Evaluating DWOFs", LOG) : null;
    while (countUnmerged > 0) {
        LOG.incrementProcessed(clusEvalProgress);
        // Increase radii
        for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
            radii.putDouble(iter, radii.doubleValue(iter) * delta);
        }
        // stores the clustering label for each object
        WritableDataStore<ModifiableDBIDs> labels = DataStoreUtil.makeStorage(ids, DataStoreFactory.HINT_TEMP, ModifiableDBIDs.class);
        // Cluster objects based on the current radius
        clusterData(ids, rnnQuery, radii, labels);
        // simple reference swap
        WritableIntegerDataStore temp = newSizes;
        newSizes = oldSizes;
        oldSizes = temp;
        // Update the cluster size count for each object.
        countUnmerged = updateSizes(ids, labels, newSizes);
        labels.destroy();
        // Update DWOF scores.
        for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
            double newScore = (newSizes.intValue(iter) > 0) ? ((double) (oldSizes.intValue(iter) - 1) / (double) newSizes.intValue(iter)) : 0.0;
            dwofs.putDouble(iter, dwofs.doubleValue(iter) + newScore);
        }
    }
    LOG.setCompleted(clusEvalProgress);
    LOG.setCompleted(stepProg);
    // Build result representation.
    DoubleMinMax minmax = new DoubleMinMax();
    for (DBIDIter iter = relation.iterDBIDs(); iter.valid(); iter.advance()) {
        minmax.put(dwofs.doubleValue(iter));
    }
    OutlierScoreMeta meta = new InvertedOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY);
    DoubleRelation rel = new MaterializedDoubleRelation("Dynamic-Window Outlier Factors", "dwof-outlier", dwofs, ids);
    return new OutlierResult(meta, rel);
}

Example 18 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class GaussianUniformMixture method run.

/**
 * Run the algorithm
 *
 * @param relation Data relation
 * @return Outlier result
 */
public OutlierResult run(Relation<V> relation) {
    // Use an array list of object IDs for fast random access by an offset
    ArrayDBIDs objids = DBIDUtil.ensureArray(relation.getDBIDs());
    // A bit set to flag objects as anomalous, none at the beginning
    long[] bits = BitsUtil.zero(objids.size());
    // Positive masked collection
    DBIDs normalObjs = new MaskedDBIDs(objids, bits, true);
    // Positive masked collection
    DBIDs anomalousObjs = new MaskedDBIDs(objids, bits, false);
    // resulting scores
    WritableDoubleDataStore oscores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT);
    // compute loglikelihood
    double logLike = relation.size() * logml + loglikelihoodNormal(normalObjs, relation);
    // LOG.debugFine("normalsize " + normalObjs.size() + " anormalsize " +
    // anomalousObjs.size() + " all " + (anomalousObjs.size() +
    // normalObjs.size()));
    // LOG.debugFine(logLike + " loglike beginning" +
    // loglikelihoodNormal(normalObjs, database));
    DoubleMinMax minmax = new DoubleMinMax();
    DBIDIter iter = objids.iter();
    for (int i = 0; i < objids.size(); i++, iter.advance()) {
        // LOG.debugFine("i " + i);
        // Change mask to make the current object anomalous
        BitsUtil.setI(bits, i);
        // Compute new likelihoods
        double currentLogLike = normalObjs.size() * logml + loglikelihoodNormal(normalObjs, relation) + anomalousObjs.size() * logl + loglikelihoodAnomalous(anomalousObjs);
        // if the loglike increases more than a threshold, object stays in
        // anomalous set and is flagged as outlier
        final double loglikeGain = currentLogLike - logLike;
        oscores.putDouble(iter, loglikeGain);
        minmax.put(loglikeGain);
        if (loglikeGain > c) {
            // flag as outlier
            // LOG.debugFine("Outlier: " + curid + " " + (currentLogLike -
            // logLike));
            // Update best logLike
            logLike = currentLogLike;
        } else {
            // LOG.debugFine("Inlier: " + curid + " " + (currentLogLike - logLike));
            // undo bit set
            BitsUtil.clearI(bits, i);
        }
    }
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0);
    DoubleRelation res = new MaterializedDoubleRelation("Gaussian Mixture Outlier Score", "gaussian-mixture-outlier", oscores, relation.getDBIDs());
    return new OutlierResult(meta, res);
}

Example 19 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class ABOD method run.

/**
 * Run ABOD on the data set.
 *
 * @param relation Relation to process
 * @return Outlier detection result
 */
public OutlierResult run(Database db, Relation<V> relation) {
    ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
    // Build a kernel matrix, to make O(n^3) slightly less bad.
    SimilarityQuery<V> sq = db.getSimilarityQuery(relation, kernelFunction);
    KernelMatrix kernelMatrix = new KernelMatrix(sq, relation, ids);
    WritableDoubleDataStore abodvalues = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
    DoubleMinMax minmaxabod = new DoubleMinMax();
    MeanVariance s = new MeanVariance();
    DBIDArrayIter pA = ids.iter(), pB = ids.iter(), pC = ids.iter();
    for (; pA.valid(); pA.advance()) {
        final double abof = computeABOF(kernelMatrix, pA, pB, pC, s);
        minmaxabod.put(abof);
        abodvalues.putDouble(pA, abof);
    }
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Angle-Based Outlier Degree", "abod-outlier", abodvalues, relation.getDBIDs());
    OutlierScoreMeta scoreMeta = new InvertedOutlierScoreMeta(minmaxabod.getMin(), minmaxabod.getMax(), 0.0, Double.POSITIVE_INFINITY);
    return new OutlierResult(scoreMeta, scoreResult);
}

Example 20 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax in project elki by elki-project.

the class LBABOD method run.

/**
 * Run LB-ABOD on the data set.
 *
 * @param relation Relation to process
 * @return Outlier detection result
 */
@Override
public OutlierResult run(Database db, Relation<V> relation) {
    ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
    DBIDArrayIter pB = ids.iter(), pC = ids.iter();
    SimilarityQuery<V> sq = db.getSimilarityQuery(relation, kernelFunction);
    KernelMatrix kernelMatrix = new KernelMatrix(sq, relation, ids);
    // Output storage.
    WritableDoubleDataStore abodvalues = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
    DoubleMinMax minmaxabod = new DoubleMinMax();
    double max = 0.;
    // Storage for squared distances (will be reused!)
    WritableDoubleDataStore sqDists = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT);
    // Nearest neighbor heap (will be reused!)
    KNNHeap nn = DBIDUtil.newHeap(k);
    // Priority queue for candidates
    ModifiableDoubleDBIDList candidates = DBIDUtil.newDistanceDBIDList(relation.size());
    // get Candidate Ranking
    for (DBIDIter pA = relation.iterDBIDs(); pA.valid(); pA.advance()) {
        // Compute nearest neighbors and distances.
        nn.clear();
        double simAA = kernelMatrix.getSimilarity(pA, pA);
        // Sum of 1./(|AB|) and 1./(|AB|^2); for computing R2.
        double sumid = 0., sumisqd = 0.;
        for (pB.seek(0); pB.valid(); pB.advance()) {
            if (DBIDUtil.equal(pB, pA)) {
                continue;
            }
            double simBB = kernelMatrix.getSimilarity(pB, pB);
            double simAB = kernelMatrix.getSimilarity(pA, pB);
            double sqdAB = simAA + simBB - simAB - simAB;
            sqDists.putDouble(pB, sqdAB);
            final double isqdAB = 1. / sqdAB;
            sumid += FastMath.sqrt(isqdAB);
            sumisqd += isqdAB;
            // Update heap
            nn.insert(sqdAB, pB);
        }
        // Compute FastABOD approximation, adjust for lower bound.
        // LB-ABOF is defined via a numerically unstable formula.
        // Variance as E(X^2)-E(X)^2 suffers from catastrophic cancellation!
        // TODO: ensure numerical precision!
        double nnsum = 0., nnsumsq = 0., nnsumisqd = 0.;
        KNNList nl = nn.toKNNList();
        DoubleDBIDListIter iB = nl.iter(), iC = nl.iter();
        for (; iB.valid(); iB.advance()) {
            double sqdAB = iB.doubleValue();
            double simAB = kernelMatrix.getSimilarity(pA, iB);
            if (!(sqdAB > 0.)) {
                continue;
            }
            for (iC.seek(iB.getOffset() + 1); iC.valid(); iC.advance()) {
                double sqdAC = iC.doubleValue();
                double simAC = kernelMatrix.getSimilarity(pA, iC);
                if (!(sqdAC > 0.)) {
                    continue;
                }
                // Exploit bilinearity of scalar product:
                // <B-A, C-A> = <B, C-A> - <A,C-A>
                // = <B,C> - <B,A> - <A,C> + <A,A>
                double simBC = kernelMatrix.getSimilarity(iB, iC);
                double numerator = simBC - simAB - simAC + simAA;
                double sqweight = 1. / (sqdAB * sqdAC);
                double weight = FastMath.sqrt(sqweight);
                double val = numerator * sqweight;
                nnsum += val * weight;
                nnsumsq += val * val * weight;
                nnsumisqd += sqweight;
            }
        }
        // Remaining weight, term R2:
        double r2 = sumisqd * sumisqd - 2. * nnsumisqd;
        double tmp = (2. * nnsum + r2) / (sumid * sumid);
        double lbabof = 2. * nnsumsq / (sumid * sumid) - tmp * tmp;
        // Track maximum?
        if (lbabof > max) {
            max = lbabof;
        }
        abodvalues.putDouble(pA, lbabof);
        candidates.add(lbabof, pA);
    }
    // Put maximum from approximate values.
    minmaxabod.put(max);
    candidates.sort();
    // refine Candidates
    int refinements = 0;
    DoubleMinHeap topscores = new DoubleMinHeap(l);
    MeanVariance s = new MeanVariance();
    for (DoubleDBIDListIter pA = candidates.iter(); pA.valid(); pA.advance()) {
        // Stop refining
        if (topscores.size() >= k && pA.doubleValue() > topscores.peek()) {
            break;
        }
        final double abof = computeABOF(kernelMatrix, pA, pB, pC, s);
        // Store refined score:
        abodvalues.putDouble(pA, abof);
        minmaxabod.put(abof);
        // Update the heap tracking the top scores.
        if (topscores.size() < k) {
            topscores.add(abof);
        } else {
            if (topscores.peek() > abof) {
                topscores.replaceTopElement(abof);
            }
        }
        refinements += 1;
    }
    if (LOG.isStatistics()) {
        LoggingConfiguration.setVerbose(Level.VERYVERBOSE);
        LOG.statistics(new LongStatistic("lb-abod.refinements", refinements));
    }
    // Build result representation.
    DoubleRelation scoreResult = new MaterializedDoubleRelation("Angle-based Outlier Detection", "abod-outlier", abodvalues, ids);
    OutlierScoreMeta scoreMeta = new InvertedOutlierScoreMeta(minmaxabod.getMin(), minmaxabod.getMax(), 0.0, Double.POSITIVE_INFINITY);
    return new OutlierResult(scoreMeta, scoreResult);
}