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

Example 11 with InvertedOutlierScoreMeta

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

the class AggarwalYuNaive method run.

/**
 * Run the algorithm on the given relation.
 *
 * @param relation Relation
 * @return Outlier detection result
 */
public OutlierResult run(Relation<V> relation) {
    final int dimensionality = RelationUtil.dimensionality(relation);
    final int size = relation.size();
    ArrayList<ArrayList<DBIDs>> ranges = buildRanges(relation);
    ArrayList<ArrayList<IntIntPair>> Rk;
    // Build a list of all subspaces
    {
        // R1 initial one-dimensional subspaces.
        Rk = new ArrayList<>();
        // Set of all dim*phi ranges
        ArrayList<IntIntPair> q = new ArrayList<>();
        for (int i = 0; i < dimensionality; i++) {
            for (int j = 0; j < phi; j++) {
                IntIntPair s = new IntIntPair(i, j);
                q.add(s);
                // Add to first Rk
                ArrayList<IntIntPair> v = new ArrayList<>();
                v.add(s);
                Rk.add(v);
            }
        }
        // build Ri
        for (int i = 2; i <= k; i++) {
            ArrayList<ArrayList<IntIntPair>> Rnew = new ArrayList<>();
            for (int j = 0; j < Rk.size(); j++) {
                ArrayList<IntIntPair> c = Rk.get(j);
                for (IntIntPair pair : q) {
                    boolean invalid = false;
                    for (int t = 0; t < c.size(); t++) {
                        if (c.get(t).first == pair.first) {
                            invalid = true;
                            break;
                        }
                    }
                    if (!invalid) {
                        ArrayList<IntIntPair> neu = new ArrayList<>(c);
                        neu.add(pair);
                        Rnew.add(neu);
                    }
                }
            }
            Rk = Rnew;
        }
    }
    WritableDoubleDataStore sparsity = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
    // calculate the sparsity coefficient
    for (ArrayList<IntIntPair> sub : Rk) {
        DBIDs ids = computeSubspace(sub, ranges);
        final double sparsityC = sparsity(ids.size(), size, k, phi);
        if (sparsityC < 0) {
            for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
                double prev = sparsity.doubleValue(iter);
                if (Double.isNaN(prev) || sparsityC < prev) {
                    sparsity.putDouble(iter, sparsityC);
                }
            }
        }
    }
    DoubleMinMax minmax = new DoubleMinMax();
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        double val = sparsity.doubleValue(iditer);
        if (Double.isNaN(val)) {
            sparsity.putDouble(iditer, 0.0);
            val = 0.0;
        }
        minmax.put(val);
    }
    DoubleRelation scoreResult = new MaterializedDoubleRelation("AggarwalYuNaive", "aggarwal-yu-outlier", sparsity, relation.getDBIDs());
    OutlierScoreMeta meta = new InvertedOutlierScoreMeta(minmax.getMin(), minmax.getMax(), Double.NEGATIVE_INFINITY, 0.0);
    return new OutlierResult(meta, scoreResult);
}

Example 12 with InvertedOutlierScoreMeta

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

the class FastABOD method run.

/**
 * Run Fast-ABOD on the data set.
 *
 * @param relation Relation to process
 * @return Outlier detection result
 */
@Override
public OutlierResult run(Database db, Relation<V> relation) {
    DBIDs ids = 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();
    KNNHeap nn = DBIDUtil.newHeap(k);
    for (DBIDIter pA = ids.iter(); pA.valid(); pA.advance()) {
        final double simAA = kernelMatrix.getSimilarity(pA, pA);
        // Choose the k-min nearest
        nn.clear();
        for (DBIDIter nB = relation.iterDBIDs(); nB.valid(); nB.advance()) {
            if (DBIDUtil.equal(nB, pA)) {
                continue;
            }
            double simBB = kernelMatrix.getSimilarity(nB, nB);
            double simAB = kernelMatrix.getSimilarity(pA, nB);
            double sqdAB = simAA + simBB - simAB - simAB;
            if (!(sqdAB > 0.)) {
                continue;
            }
            nn.insert(sqdAB, nB);
        }
        KNNList nl = nn.toKNNList();
        s.reset();
        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 div = 1. / (sqdAB * sqdAC);
                s.put(numerator * div, FastMath.sqrt(div));
            }
        }
        // Sample variance probably would probably be better, but the ABOD
        // publication uses the naive variance.
        final double abof = s.getNaiveVariance();
        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 13 with InvertedOutlierScoreMeta

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

the class RankingPseudoOutlierScaling 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 14 with InvertedOutlierScoreMeta

use of de.lmu.ifi.dbs.elki.result.outlier.InvertedOutlierScoreMeta 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 15 with InvertedOutlierScoreMeta

use of de.lmu.ifi.dbs.elki.result.outlier.InvertedOutlierScoreMeta 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);
}