Examples with ListParameterization de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization used on opensource projects

Example 6 with ListParameterization

use of de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization in project elki by elki-project.

the class LogLogisticDistributionTest method testParameterizer.

@Test
public void testParameterizer() throws ClassInstantiationException {
    load("loglogistic.ascii.gz");
    ListParameterization params = new ListParameterization();
    params.addParameter(LogLogisticDistribution.Parameterizer.SHAPE_ID, 2.);
    params.addParameter(LogLogisticDistribution.Parameterizer.LOCATION_ID, 0.);
    params.addParameter(LogLogisticDistribution.Parameterizer.SCALE_ID, .5);
    Distribution dist = ClassGenericsUtil.parameterizeOrAbort(LogLogisticDistribution.class, params);
    checkPDF(dist, "pdf_scipy_2_05", 1e-15);
}

Example 7 with ListParameterization

use of de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization in project elki by elki-project.

the class TrimmedEstimatorTest method testNormalDistribution.

@Test
public void testNormalDistribution() {
    final double trim = .01;
    NormalMOMEstimator mom = NormalMOMEstimator.STATIC;
    // We could instantiate directly, but we also want to cover the
    // parameterizer class.
    ListParameterization config = new ListParameterization();
    config.addParameter(TrimmedEstimator.Parameterizer.INNER_ID, mom);
    config.addParameter(TrimmedEstimator.Parameterizer.TRIM_ID, trim);
    TrimmedEstimator<NormalDistribution> est = ClassGenericsUtil.parameterizeOrAbort(TrimmedEstimator.class, config);
    Random r = new Random(0L);
    double[] data = new double[10000];
    final int corrupt = (int) Math.floor(data.length * trim * .5);
    for (int i = 0; i < data.length; i++) {
        data[i] = i < corrupt ? 1e10 : r.nextGaussian();
    }
    NormalDistribution bad = mom.estimate(data, DoubleArrayAdapter.STATIC);
    NormalDistribution good = est.estimate(data, DoubleArrayAdapter.STATIC);
    assertEquals("Mean not as expected from naive estimator.", 5e7, bad.getMean(), 1e-2);
    assertEquals("Stddev not as expected from naive estimator.", 7e8, bad.getStddev(), 1e7);
    assertEquals("Mean not as expected from trimmed estimator.", 0, good.getMean(), 1e-2);
    assertEquals("Stddev not as expected from trimmed estimator.", 1.0, good.getStddev(), 3e-2);
}

Example 8 with ListParameterization

use of de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization in project elki by elki-project.

the class Log1PlusNormalizationTest method parameters.

/**
 * Test with non-default parameters to ensure that both branches of the filter
 * are tested.
 */
@Test
public void parameters() {
    String filename = UNITTEST + "normalization-test-1.csv";
    // Use the value of b as the boost value.
    double b = 15.;
    ListParameterization config = new ListParameterization();
    config.addParameter(Log1PlusNormalization.Parameterizer.BOOST_ID, b);
    Log1PlusNormalization<DoubleVector> filter = ClassGenericsUtil.parameterizeOrAbort(Log1PlusNormalization.class, config);
    MultipleObjectsBundle filteredBundle = readBundle(filename, filter);
    // Load the test data again without a filter.
    MultipleObjectsBundle unfilteredBundle = readBundle(filename);
    // Check dimensionalities
    int dim = getFieldDimensionality(filteredBundle, 0, TypeUtil.NUMBER_VECTOR_FIELD);
    assertEquals("Dimensionality changed", dim, getFieldDimensionality(unfilteredBundle, 0, TypeUtil.NUMBER_VECTOR_FIELD));
    // Verify that the filtered and unfiltered bundles have the same length.
    assertEquals("Length changed", filteredBundle.dataLength(), unfilteredBundle.dataLength());
    // method.
    for (int row = 0; row < filteredBundle.dataLength(); row++) {
        DoubleVector dFil = get(filteredBundle, row, 0, DoubleVector.class);
        DoubleVector dUnfil = get(unfilteredBundle, row, 0, DoubleVector.class);
        for (int col = 0; col < dim; col++) {
            final double vFil = dFil.doubleValue(col);
            final double vUnfil = dUnfil.doubleValue(col);
            assertEquals("Value not as expected", vFil, FastMath.log1p(Math.abs(vUnfil) * b) / FastMath.log1p(b), 1e-15);
        }
    }
}

Example 9 with ListParameterization

use of de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization in project elki by elki-project.

the class CosineHashFunctionFamilyTest method createCosineHashFunction.

private LocalitySensitiveHashFunction<? super NumberVector> createCosineHashFunction(int numberOfProjections) {
    ListParameterization params = new ListParameterization();
    params.addParameter(CosineHashFunctionFamily.Parameterizer.RANDOM_ID, 0L);
    params.addParameter(CosineHashFunctionFamily.Parameterizer.NUMPROJ_ID, numberOfProjections);
    CosineHashFunctionFamily cosineFamily = ClassGenericsUtil.parameterizeOrAbort(CosineHashFunctionFamily.class, params);
    LocalitySensitiveHashFunction<? super NumberVector> hashFunction = cosineFamily.generateHashFunctions(mockRelation(5), numberOfProjections).get(0);
    return hashFunction;
}

Example 10 with ListParameterization

use of de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization in project elki by elki-project.

the class OnlineLOF method getKNNAndRkNNQueries.

/**
 * Get the kNN and rkNN queries for the algorithm.
 *
 * @param relation Data
 * @param stepprog Progress logger
 * @return the kNN and rkNN queries
 */
private Pair<Pair<KNNQuery<O>, KNNQuery<O>>, Pair<RKNNQuery<O>, RKNNQuery<O>>> getKNNAndRkNNQueries(Database database, Relation<O> relation, StepProgress stepprog) {
    DistanceQuery<O> drefQ = database.getDistanceQuery(relation, referenceDistanceFunction);
    // Use "HEAVY" flag, since this is an online algorithm
    KNNQuery<O> kNNRefer = database.getKNNQuery(drefQ, krefer, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    RKNNQuery<O> rkNNRefer = database.getRKNNQuery(drefQ, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    // No optimized kNN query or RkNN query - use a preprocessor!
    if (kNNRefer == null || rkNNRefer == null) {
        if (stepprog != null) {
            stepprog.beginStep(1, "Materializing neighborhood w.r.t. reference neighborhood distance function.", LOG);
        }
        MaterializeKNNAndRKNNPreprocessor<O> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, referenceDistanceFunction, krefer);
        kNNRefer = preproc.getKNNQuery(drefQ, krefer, DatabaseQuery.HINT_HEAVY_USE);
        rkNNRefer = preproc.getRKNNQuery(drefQ, krefer, DatabaseQuery.HINT_HEAVY_USE);
        // add as index
        database.getHierarchy().add(relation, preproc);
    } else {
        if (stepprog != null) {
            stepprog.beginStep(1, "Optimized neighborhood w.r.t. reference neighborhood distance function provided by database.", LOG);
        }
    }
    DistanceQuery<O> dreachQ = database.getDistanceQuery(relation, reachabilityDistanceFunction);
    KNNQuery<O> kNNReach = database.getKNNQuery(dreachQ, kreach, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    RKNNQuery<O> rkNNReach = database.getRKNNQuery(dreachQ, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
    if (kNNReach == null || rkNNReach == null) {
        if (stepprog != null) {
            stepprog.beginStep(2, "Materializing neighborhood w.r.t. reachability distance function.", LOG);
        }
        ListParameterization config = new ListParameterization();
        config.addParameter(AbstractMaterializeKNNPreprocessor.Factory.DISTANCE_FUNCTION_ID, reachabilityDistanceFunction);
        config.addParameter(AbstractMaterializeKNNPreprocessor.Factory.K_ID, kreach);
        MaterializeKNNAndRKNNPreprocessor<O> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, reachabilityDistanceFunction, kreach);
        kNNReach = preproc.getKNNQuery(dreachQ, kreach, DatabaseQuery.HINT_HEAVY_USE);
        rkNNReach = preproc.getRKNNQuery(dreachQ, kreach, DatabaseQuery.HINT_HEAVY_USE);
        // add as index
        database.getHierarchy().add(relation, preproc);
    }
    Pair<KNNQuery<O>, KNNQuery<O>> kNNPair = new Pair<>(kNNRefer, kNNReach);
    Pair<RKNNQuery<O>, RKNNQuery<O>> rkNNPair = new Pair<>(rkNNRefer, rkNNReach);
    return new Pair<>(kNNPair, rkNNPair);
}