Examples with NumberVector de.lmu.ifi.dbs.elki.data.NumberVector used on opensource projects

Example 51 with NumberVector

use of de.lmu.ifi.dbs.elki.data.NumberVector in project elki by elki-project.

the class TriangularDistanceFunctionTest method testTriganular.

@Test
public void testTriganular() {
    DoubleVector v0 = DoubleVector.wrap(new double[] { 0.8, 0.1, 0.1 });
    DoubleVector v1 = DoubleVector.wrap(new double[] { 0.1, 0.8, 0.1 });
    DoubleVector v2 = DoubleVector.wrap(new double[] { 0.1, 0.1, 0.8 });
    DoubleVector v3 = DoubleVector.wrap(new double[] { 1. / 3, 1. / 3, 1. / 3 });
    DoubleVector v4 = DoubleVector.wrap(new double[] { 0.6, 0.2, 0.2 });
    DoubleVector[] vecs = { v0, v1, v2, v3, v4 };
    // Manual computation of correct distances:
    double d10 = .49 / .9 + .49 / .9 + 0.;
    double d30 = // 
    (.8 - 1. / 3) * (.8 - 1. / 3) / (.8 + 1. / 3) + 2 * (.1 - 1. / 3) * (.1 - 1. / 3) / (.1 + 1. / 3);
    double d40 = .04 / 1.4 + .01 / .3 + .01 / .3;
    double d41 = .25 / 0.7 + .36 / 1. + .01 / .3;
    double d43 = // 
    (.6 - 1. / 3) * (.6 - 1. / 3) / (.6 + 1. / 3) + 2 * (.2 - 1. / 3) * (.2 - 1. / 3) / (.2 + 1. / 3);
    double[][] distances = { // 
    { 0., d10, d10, d30, d40 }, // 
    { d10, 0., d10, d30, d41 }, // 
    { d10, d10, 0., d30, d41 }, // 
    { d30, d30, d30, 0., d43 }, // 
    { d40, d41, d41, d43, 0. } };
    PrimitiveDistanceFunction<NumberVector> df = TriangularDistanceFunction.STATIC;
    for (int i = 0; i < vecs.length; i++) {
        for (int j = 0; j < vecs.length; j++) {
            assertEquals("Distance " + i + "," + j + " incorrect.", Math.sqrt(distances[i][j]), df.distance(vecs[i], vecs[j]), 1e-15);
        }
    }
}

Example 52 with NumberVector

use of de.lmu.ifi.dbs.elki.data.NumberVector in project elki by elki-project.

the class ManhattanDistanceFunction method minDist.

@Override
public double minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
    final int dim1 = mbr1.getDimensionality(), dim2 = mbr2.getDimensionality();
    final int mindim = (dim1 < dim2) ? dim1 : dim2;
    final NumberVector v1 = (mbr1 instanceof NumberVector) ? (NumberVector) mbr1 : null;
    final NumberVector v2 = (mbr2 instanceof NumberVector) ? (NumberVector) mbr2 : null;
    double agg = // 
    (v1 != null) ? // 
    (v2 != null) ? preDistance(v1, v2, 0, mindim) : preDistanceVM(v1, mbr2, 0, mindim) : (v2 != null) ? preDistanceVM(v2, mbr1, 0, mindim) : preDistanceMBR(mbr1, mbr2, 0, mindim);
    // first object has more dimensions.
    if (dim1 > mindim) {
        agg += (v1 != null) ? preNorm(v1, mindim, dim1) : preNormMBR(mbr1, mindim, dim1);
    }
    // second object has more dimensions.
    if (dim2 > mindim) {
        agg += (v2 != null) ? preNorm(v2, mindim, dim2) : preNormMBR(mbr2, mindim, dim2);
    }
    return agg;
}

Example 53 with NumberVector

use of de.lmu.ifi.dbs.elki.data.NumberVector in project elki by elki-project.

the class WeightedManhattanDistanceFunction method minDist.

@Override
public double minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
    final int dim1 = mbr1.getDimensionality(), dim2 = mbr2.getDimensionality();
    final int mindim = (dim1 < dim2) ? dim1 : dim2;
    final NumberVector v1 = (mbr1 instanceof NumberVector) ? (NumberVector) mbr1 : null;
    final NumberVector v2 = (mbr2 instanceof NumberVector) ? (NumberVector) mbr2 : null;
    double agg = // 
    (v1 != null) ? // 
    (v2 != null) ? preDistance(v1, v2, 0, mindim) : preDistanceVM(v1, mbr2, 0, mindim) : (v2 != null) ? preDistanceVM(v2, mbr1, 0, mindim) : preDistanceMBR(mbr1, mbr2, 0, mindim);
    // first object has more dimensions.
    if (dim1 > mindim) {
        agg += (v1 != null) ? preNorm(v1, mindim, dim1) : preNormMBR(mbr1, mindim, dim1);
    }
    // second object has more dimensions.
    if (dim2 > mindim) {
        agg += (v2 != null) ? preNorm(v2, mindim, dim2) : preNormMBR(mbr2, mindim, dim2);
    }
    return agg;
}

Example 54 with NumberVector

use of de.lmu.ifi.dbs.elki.data.NumberVector in project elki by elki-project.

the class TrivialAverageCoordinateOutlier method run.

/**
 * Run the actual algorithm.
 *
 * @param relation Relation
 * @return Result
 */
public OutlierResult run(Relation<? extends NumberVector> relation) {
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT);
    DoubleMinMax minmax = new DoubleMinMax();
    Mean m = new Mean();
    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
        m.reset();
        NumberVector nv = relation.get(iditer);
        for (int i = 0; i < nv.getDimensionality(); i++) {
            m.put(nv.doubleValue(i));
        }
        final double score = m.getMean();
        scores.putDouble(iditer, score);
        minmax.put(score);
    }
    DoubleRelation scoreres = new MaterializedDoubleRelation("Trivial mean score", "mean-outlier", scores, relation.getDBIDs());
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax());
    return new OutlierResult(meta, scoreres);
}

Example 55 with NumberVector

use of de.lmu.ifi.dbs.elki.data.NumberVector in project elki by elki-project.

the class UncertainifyFilter method filter.

@Override
public MultipleObjectsBundle filter(MultipleObjectsBundle objects) {
    if (objects.dataLength() == 0) {
        return objects;
    }
    MultipleObjectsBundle bundle = new MultipleObjectsBundle();
    for (int r = 0; r < objects.metaLength(); r++) {
        SimpleTypeInformation<?> type = objects.meta(r);
        @SuppressWarnings("unchecked") final List<Object> column = (List<Object>) objects.getColumn(r);
        if (!TypeUtil.NUMBER_VECTOR_FIELD.isAssignableFromType(type)) {
            bundle.appendColumn(type, column);
            continue;
        }
        // Get the replacement type information
        @SuppressWarnings("unchecked") final VectorFieldTypeInformation<NumberVector> castType = (VectorFieldTypeInformation<NumberVector>) type;
        final int dim = castType.getDimensionality();
        if (keep) {
            bundle.appendColumn(type, column);
        }
        // Uncertain objects produced
        final List<UO> uos = new ArrayList<>(column.size());
        // Normalization scan
        FiniteProgress nprog = LOG.isVerbose() ? new FiniteProgress("Derive uncertain objects", objects.dataLength(), LOG) : null;
        for (int i = 0; i < objects.dataLength(); i++) {
            final NumberVector obj = (NumberVector) column.get(i);
            final UO normalizedObj = generator.newFeatureVector(rand, obj, ArrayLikeUtil.NUMBERVECTORADAPTER);
            uos.add(normalizedObj);
            LOG.incrementProcessed(nprog);
        }
        LOG.ensureCompleted(nprog);
        // Add column with uncertain objects
        bundle.appendColumn(new VectorFieldTypeInformation<UO>(generator.getFactory(), dim), uos);
    }
    return bundle;
}