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

Example 81 with DoubleMinMax

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

the class OutlierLinearScaling method prepare.

@Override
public void prepare(OutlierResult or) {
    if (usemean) {
        MeanVariance mv = new MeanVariance();
        DoubleMinMax mm = (max == null) ? new DoubleMinMax() : null;
        boolean skippedzeros = false;
        DoubleRelation scores = or.getScores();
        for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
            double val = scores.doubleValue(id);
            if (nozeros && val == 0.0) {
                skippedzeros = true;
                continue;
            }
            if (!Double.isNaN(val) && !Double.isInfinite(val)) {
                mv.put(val);
            }
            if (max == null) {
                mm.put(val);
            }
        }
        if (skippedzeros && mm.getMin() == mm.getMax()) {
            mm.put(0.0);
            mv.put(0.0);
        }
        min = mv.getMean();
        if (max == null) {
            max = mm.getMax();
        }
    } else {
        if (min == null || max == null) {
            boolean skippedzeros = false;
            DoubleMinMax mm = new DoubleMinMax();
            DoubleRelation scores = or.getScores();
            for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
                double val = scores.doubleValue(id);
                if (nozeros && val == 0.0) {
                    skippedzeros = true;
                    continue;
                }
                mm.put(val);
            }
            if (skippedzeros && mm.getMin() == mm.getMax()) {
                mm.put(0.0);
            }
            if (min == null) {
                min = mm.getMin();
            }
            if (max == null) {
                max = mm.getMax();
            }
        }
    }
    factor = (max - min);
}

Example 82 with DoubleMinMax

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

the class OutlierLinearScaling method prepare.

@Override
public <A> void prepare(A array, NumberArrayAdapter<?, A> adapter) {
    if (usemean) {
        MeanVariance mv = new MeanVariance();
        DoubleMinMax mm = (max == null) ? new DoubleMinMax() : null;
        boolean skippedzeros = false;
        final int size = adapter.size(array);
        for (int i = 0; i < size; i++) {
            double val = adapter.getDouble(array, i);
            if (nozeros && val == 0.0) {
                skippedzeros = true;
                continue;
            }
            if (!Double.isNaN(val) && !Double.isInfinite(val)) {
                mv.put(val);
            }
            if (max == null) {
                mm.put(val);
            }
        }
        if (skippedzeros && mm.getMin() == mm.getMax()) {
            mm.put(0.0);
            mv.put(0.0);
        }
        min = mv.getMean();
        if (max == null) {
            max = mm.getMax();
        }
    } else {
        if (min == null || max == null) {
            boolean skippedzeros = false;
            DoubleMinMax mm = new DoubleMinMax();
            final int size = adapter.size(array);
            for (int i = 0; i < size; i++) {
                double val = adapter.getDouble(array, i);
                if (nozeros && val == 0.0) {
                    skippedzeros = true;
                    continue;
                }
                mm.put(val);
            }
            if (skippedzeros && mm.getMin() == mm.getMax()) {
                mm.put(0.0);
            }
            if (min == null) {
                min = mm.getMin();
            }
            if (max == null) {
                max = mm.getMax();
            }
        }
    }
    factor = (max - min);
}

Example 83 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax 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 84 with DoubleMinMax

use of de.lmu.ifi.dbs.elki.math.DoubleMinMax 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);
}

Example 85 with DoubleMinMax

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

the class InstanceMinMaxNormalizationTest method defaultParameters.

/**
 * Test with default parameters.
 */
@Test
public void defaultParameters() {
    String filename = UNITTEST + "normalization-test-1.csv";
    InstanceMinMaxNormalization<DoubleVector> filter = new ELKIBuilder<>(InstanceMinMaxNormalization.class).build();
    MultipleObjectsBundle bundle = readBundle(filename, filter);
    int dim = getFieldDimensionality(bundle, 0, TypeUtil.NUMBER_VECTOR_FIELD);
    // Verify that, in each row, the min value is 0 and the max value 1.
    DoubleMinMax mms = new DoubleMinMax();
    for (int row = 0; row < bundle.dataLength(); row++) {
        mms.reset();
        DoubleVector d = get(bundle, row, 0, DoubleVector.class);
        for (int col = 0; col < dim; col++) {
            mms.put(d.doubleValue(col));
        }
        assertEquals("Min value is not 0", 0., mms.getMin(), 0);
        assertEquals("Max value is not 1", 1., mms.getMax(), 1e-15);
    }
}