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

Example 86 with DoubleMinMax

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

the class InstanceRankNormalizationTest method defaultParameters.

/**
 * Test with default parameters.
 */
@Test
public void defaultParameters() {
    String filename = UNITTEST + "normalization-test-1.csv";
    InstanceRankNormalization<DoubleVector> filter = new ELKIBuilder<>(InstanceRankNormalization.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(), 0);
    }
}

Example 87 with DoubleMinMax

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

the class Log1PlusNormalizationTest method defaultParameters.

/**
 * Test with default parameters.
 */
@Test
public void defaultParameters() {
    String filename = UNITTEST + "normalization-test-1.csv";
    InstanceMinMaxNormalization<DoubleVector> minMaxFilter = new ELKIBuilder<>(InstanceMinMaxNormalization.class).build();
    Log1PlusNormalization<DoubleVector> log1plusFilter = new ELKIBuilder<>(Log1PlusNormalization.class).build();
    MultipleObjectsBundle bundle = readBundle(filename, minMaxFilter, log1plusFilter);
    int dim = getFieldDimensionality(bundle, 0, TypeUtil.NUMBER_VECTOR_FIELD);
    // We verify that minimum and maximum values in each row are 0 and 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++) {
            final double val = d.doubleValue(col);
            if (val > Double.NEGATIVE_INFINITY && val < Double.POSITIVE_INFINITY) {
                mms.put(val);
            }
        }
        assertEquals("Minimum not expected", 0., mms.getMin(), 1e-15);
        assertEquals("Maximum not expected", 1., mms.getMax(), 1e-15);
    }
}

Example 88 with DoubleMinMax

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

the class SweepHullDelaunay2D method getHull.

/**
 * Get the convex hull only.
 *
 * Note: if you also want the Delaunay Triangulation, you should get that
 * first!
 *
 * @return Convex hull
 */
public Polygon getHull() {
    if (hull == null) {
        run(true);
    }
    DoubleMinMax minmaxX = new DoubleMinMax();
    DoubleMinMax minmaxY = new DoubleMinMax();
    List<double[]> hullp = new ArrayList<>(hull.size());
    for (IntIntPair pair : hull) {
        double[] v = points.get(pair.first);
        hullp.add(v);
        minmaxX.put(v[0]);
        minmaxY.put(v[1]);
    }
    return new Polygon(hullp, minmaxX.getMin(), minmaxX.getMax(), minmaxY.getMin(), minmaxY.getMax());
}

Example 89 with DoubleMinMax

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

the class AttributeWiseMinMaxNormalizationTest method defaultParameters.

/**
 * Test with default parameters.
 */
@Test
public void defaultParameters() {
    String filename = UNITTEST + "normalization-test-1.csv";
    AttributeWiseMinMaxNormalization<DoubleVector> filter = new ELKIBuilder<AttributeWiseMinMaxNormalization<DoubleVector>>(AttributeWiseMinMaxNormalization.class).build();
    MultipleObjectsBundle bundle = readBundle(filename, filter);
    int dim = getFieldDimensionality(bundle, 0, TypeUtil.NUMBER_VECTOR_FIELD);
    // We verify that minimum and maximum values in each column are 0 and 1:
    DoubleMinMax[] mms = DoubleMinMax.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 val = d.doubleValue(col);
            if (val > Double.NEGATIVE_INFINITY && val < Double.POSITIVE_INFINITY) {
                mms[col].put(val);
            }
        }
    }
    for (int col = 0; col < dim; col++) {
        assertEquals("Minimum not as expected", 0., mms[col].getMin(), 0.);
        assertEquals("Maximum not as expected", 1., mms[col].getMax(), 0.);
    }
}