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

Example 56 with MeanVariance

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

the class GlobalPrincipalComponentAnalysisTransformTest method defaultParameters.

/**
 * Test with default parameters.
 */
@Test
public void defaultParameters() {
    String filename = UNITTEST + "transformation-test-1.csv";
    GlobalPrincipalComponentAnalysisTransform<DoubleVector> filter = new ELKIBuilder<GlobalPrincipalComponentAnalysisTransform<DoubleVector>>(GlobalPrincipalComponentAnalysisTransform.class).build();
    MultipleObjectsBundle bundle = readBundle(filename, filter);
    int dim = getFieldDimensionality(bundle, 0, TypeUtil.NUMBER_VECTOR_FIELD);
    // We verify that the result has mean 0 and variance 1 in each column.
    // We also expect that covariances of any two columns are 0.
    CovarianceMatrix cm = new CovarianceMatrix(dim);
    MeanVariance[] mvs = MeanVariance.newArray(dim);
    for (int row = 0; row < bundle.dataLength(); row++) {
        DoubleVector d = get(bundle, row, 0, DoubleVector.class);
        cm.put(d);
        for (int col = 0; col < dim; col++) {
            final double v = d.doubleValue(col);
            if (v > Double.NEGATIVE_INFINITY && v < Double.POSITIVE_INFINITY) {
                mvs[col].put(v);
            }
        }
    }
    double[][] ncm = cm.destroyToPopulationMatrix();
    for (int col = 0; col < dim; col++) {
        for (int row = 0; row < dim; row++) {
            assertEquals("Unexpected covariance", col == row ? 1. : 0., ncm[row][col], 1e-15);
        }
        assertEquals("Mean not as expected", 0., mvs[col].getMean(), 1e-15);
        assertEquals("Variance not as expected", 1., mvs[col].getNaiveVariance(), 1e-15);
    }
}

Example 57 with MeanVariance

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

the class InstanceMeanVarianceNormalizationTest method defaultParameters.

/**
 * Test with default parameters.
 */
@Test
public void defaultParameters() {
    String filename = UNITTEST + "normalization-test-1.csv";
    InstanceMeanVarianceNormalization<DoubleVector> filter = new ELKIBuilder<>(InstanceMeanVarianceNormalization.class).build();
    MultipleObjectsBundle bundle = readBundle(filename, filter);
    int dim = getFieldDimensionality(bundle, 0, TypeUtil.NUMBER_VECTOR_FIELD);
    // Verify that the resulting data has mean 0 and variance 1 in each row.
    MeanVariance mvs = new MeanVariance();
    for (int row = 0; row < bundle.dataLength(); row++) {
        mvs.reset();
        DoubleVector d = get(bundle, row, 0, DoubleVector.class);
        for (int col = 0; col < dim; col++) {
            final double v = d.doubleValue(col);
            if (v > Double.NEGATIVE_INFINITY && v < Double.POSITIVE_INFINITY) {
                mvs.put(v);
            }
        }
        assertEquals("Mean is not 0", 0., mvs.getMean(), 1e-14);
        assertEquals("Variance is not 1", 1., mvs.getNaiveVariance(), 1e-14);
    }
}

Example 58 with MeanVariance

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

the class KNNJoinTest method doKNNJoin.

/**
 * Actual test routine.
 *
 * @param inputparams
 */
void doKNNJoin(ListParameterization inputparams) {
    Database db = AbstractSimpleAlgorithmTest.makeSimpleDatabase(dataset, shoulds, inputparams);
    Relation<NumberVector> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    // Euclidean
    {
        KNNJoin<DoubleVector, ?, ?> knnjoin = new KNNJoin<DoubleVector, RStarTreeNode, SpatialEntry>(EuclideanDistanceFunction.STATIC, 2);
        Relation<KNNList> result = knnjoin.run(db);
        MeanVariance meansize = new MeanVariance();
        for (DBIDIter id = relation.getDBIDs().iter(); id.valid(); id.advance()) {
            meansize.put(result.get(id).size());
        }
        org.junit.Assert.assertEquals("Euclidean mean 2NN set size", mean2nnEuclid, meansize.getMean(), 0.00001);
        org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
    }
    // Manhattan
    {
        KNNJoin<DoubleVector, ?, ?> knnjoin = new KNNJoin<DoubleVector, RStarTreeNode, SpatialEntry>(ManhattanDistanceFunction.STATIC, 2);
        Relation<KNNList> result = knnjoin.run(db);
        MeanVariance meansize = new MeanVariance();
        for (DBIDIter id = relation.getDBIDs().iter(); id.valid(); id.advance()) {
            meansize.put(result.get(id).size());
        }
        org.junit.Assert.assertEquals("Manhattan mean 2NN", mean2nnManhattan, meansize.getMean(), 0.00001);
        org.junit.Assert.assertEquals("Manhattan variance 2NN", var2nnManhattan, meansize.getSampleVariance(), 0.00001);
    }
}

Example 59 with MeanVariance

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

the class KNNJoinTest method testLinearScan.

@Test
public void testLinearScan() {
    Database db = AbstractSimpleAlgorithmTest.makeSimpleDatabase(dataset, shoulds);
    Relation<NumberVector> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
    // Euclidean
    {
        DistanceQuery<NumberVector> dq = db.getDistanceQuery(relation, EuclideanDistanceFunction.STATIC);
        KNNQuery<NumberVector> knnq = QueryUtil.getLinearScanKNNQuery(dq);
        MeanVariance meansize = new MeanVariance();
        for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
            meansize.put(knnq.getKNNForDBID(iditer, 2).size());
        }
        org.junit.Assert.assertEquals("Euclidean mean 2NN", mean2nnEuclid, meansize.getMean(), 0.00001);
        org.junit.Assert.assertEquals("Euclidean variance 2NN", var2nnEuclid, meansize.getSampleVariance(), 0.00001);
    }
    // Manhattan
    {
        DistanceQuery<NumberVector> dq = db.getDistanceQuery(relation, ManhattanDistanceFunction.STATIC);
        KNNQuery<NumberVector> knnq = QueryUtil.getLinearScanKNNQuery(dq);
        MeanVariance meansize = new MeanVariance();
        for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
            meansize.put(knnq.getKNNForDBID(iditer, 2).size());
        }
        org.junit.Assert.assertEquals("Manhattan mean 2NN", mean2nnManhattan, meansize.getMean(), 0.00001);
        org.junit.Assert.assertEquals("Manhattan variance 2NN", var2nnManhattan, meansize.getSampleVariance(), 0.00001);
    }
}

Example 60 with MeanVariance

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

the class AttributeWiseVarianceNormalizationTest method defaultParameters.

/**
 * Test with default parameters.
 */
@Test
public void defaultParameters() {
    String filename = UNITTEST + "normalization-test-1.csv";
    AttributeWiseVarianceNormalization<DoubleVector> filter = new ELKIBuilder<AttributeWiseVarianceNormalization<DoubleVector>>(AttributeWiseVarianceNormalization.class).build();
    MultipleObjectsBundle bundle = readBundle(filename, filter);
    int dim = getFieldDimensionality(bundle, 0, TypeUtil.NUMBER_VECTOR_FIELD);
    // We verify that the resulting data has mean 0 and variance 1 in each
    // column:
    MeanVariance[] mvs = MeanVariance.newArray(dim);
    for (int row = 0; row < bundle.dataLength(); row++) {
        DoubleVector d = get(bundle, row, 0, DoubleVector.class);
        for (int col = 0; col < dim; col++) {
            final double v = d.doubleValue(col);
            if (v > Double.NEGATIVE_INFINITY && v < Double.POSITIVE_INFINITY) {
                mvs[col].put(v);
            }
        }
    }
    for (int col = 0; col < dim; col++) {
        assertEquals("Mean not as expected", 0., mvs[col].getMean(), 1e-14);
        assertEquals("Variance not as expected", 1., mvs[col].getNaiveVariance(), 1e-14);
    }
}