Examples with SeededTest uk.ac.sussex.gdsc.test.junit5.SeededTest used on opensource projects

Example 71 with SeededTest

use of uk.ac.sussex.gdsc.test.junit5.SeededTest in project GDSC-SMLM by aherbert.

the class PeakResultDigestTest method sameEmptyResultsAreEqual.

@SeededTest
void sameEmptyResultsAreEqual(RandomSeed seed) {
    final UniformRandomProvider r = RngUtils.create(seed.getSeed());
    final PeakResult[] r1 = createResults(r, 0, 5, false, false, false, false);
    final PeakResultsDigest digest = new PeakResultsDigest(r1);
    Assertions.assertTrue(digest.matches(r1));
    Assertions.assertTrue(digest.matches(digest));
}

Example 72 with SeededTest

use of uk.ac.sussex.gdsc.test.junit5.SeededTest in project GDSC-SMLM by aherbert.

the class PeakResultDigestTest method sameResultsAreEqualWithId.

@SeededTest
void sameResultsAreEqualWithId(RandomSeed seed) {
    final UniformRandomProvider r = RngUtils.create(seed.getSeed());
    final PeakResult[] r1 = createResults(r, 10, 5, false, true, false, false);
    final PeakResultsDigest digest = new PeakResultsDigest(r1);
    Assertions.assertTrue(digest.matches(r1));
    Assertions.assertTrue(digest.matches(digest));
}

Example 73 with SeededTest

use of uk.ac.sussex.gdsc.test.junit5.SeededTest in project GDSC-SMLM by aherbert.

the class PeakResultDigestTest method digestMatchesPeakResultDigest.

@SeededTest
void digestMatchesPeakResultDigest(RandomSeed seed) {
    final UniformRandomProvider r = RngUtils.create(seed.getSeed());
    for (int size = 1; size < 5; size++) {
        final PeakResult[] r1 = createResults(r, size, 5, false, false, false, false);
        final PeakResultsDigest digest = new PeakResultsDigest(r1);
        final PeakResultDigest d = new PeakResultDigest();
        for (final PeakResult rr : r1) {
            d.update(rr);
        }
        Assertions.assertEquals(d.digest(), digest.getDigest());
    }
}

Example 74 with SeededTest

use of uk.ac.sussex.gdsc.test.junit5.SeededTest in project GDSC-SMLM by aherbert.

the class PeakResultsReaderTest method canConvertMalkToNmAndPhotons.

@SeededTest
void canConvertMalkToNmAndPhotons(RandomSeed seed) {
    final UniformRandomProvider rg = RngUtils.create(seed.getSeed());
    final MemoryPeakResults out = createResults(rg, 200, false, false, false, false, false);
    // Output in pixel and count
    final CalibrationWriter cal = new CalibrationWriter(out.getCalibration());
    cal.setDistanceUnit(DistanceUnit.PIXEL);
    cal.setIntensityUnit(IntensityUnit.COUNT);
    out.setCalibration(cal.getCalibration());
    out.setPsf(PsfHelper.create(PSFType.CUSTOM));
    final String filename = createFile();
    writeFile(false, ResultsFileFormat.MALK, false, false, false, false, false, false, out, filename);
    final MemoryPeakResults in = readFile(filename, false);
    // Change to nm and photon for the validation
    out.convertToUnits(DistanceUnit.NM, IntensityUnit.PHOTON, null);
    checkEqual(ResultsFileFormat.MALK, false, false, false, false, false, false, out, in);
}

Example 75 with SeededTest

use of uk.ac.sussex.gdsc.test.junit5.SeededTest in project GDSC-SMLM by aherbert.

the class MultivariateGaussianMixtureExpectationMaximizationTest method testExpectationMaximizationSpeedWithDifferentNumberOfComponents.

/**
 * Test the speed of implementations of the expectation maximization algorithm with a mixture of n
 * 2D Gaussian distributions.
 *
 * @param seed the seed
 */
@SpeedTag
@SeededTest
void testExpectationMaximizationSpeedWithDifferentNumberOfComponents(RandomSeed seed) {
    Assumptions.assumeTrue(TestSettings.allow(TestComplexity.HIGH));
    // Create data
    final UniformRandomProvider rng = RngUtils.create(seed.getSeed());
    for (int n = 2; n <= 4; n++) {
        final double[][][] data = new double[10][][];
        for (int i = 0; i < data.length; i++) {
            final double[] sampleWeights = createWeights(n, rng);
            final double[][] sampleMeans = create(n, 2, rng, -5, 5);
            final double[][] sampleStdDevs = create(n, 2, rng, 1, 10);
            final double[] sampleCorrelations = create(n, rng, -0.9, 0.9);
            data[i] = createData2d(1000, rng, sampleWeights, sampleMeans, sampleStdDevs, sampleCorrelations);
        }
        final int numComponents = n;
        // Time initial estimation and fitting
        final TimingService ts = new TimingService();
        ts.execute(new FittingSpeedTask("Commons n=" + n + " 2D", data) {

            @Override
            Object run(double[][] data) {
                final MultivariateNormalMixtureExpectationMaximization fitter = new MultivariateNormalMixtureExpectationMaximization(data);
                fitter.fit(MultivariateNormalMixtureExpectationMaximization.estimate(data, numComponents));
                return fitter.getLogLikelihood();
            }
        });
        ts.execute(new FittingSpeedTask("GDSC n=" + n + " 2D", data) {

            @Override
            Object run(double[][] data) {
                final MultivariateGaussianMixtureExpectationMaximization fitter = new MultivariateGaussianMixtureExpectationMaximization(data);
                fitter.fit(MultivariateGaussianMixtureExpectationMaximization.estimate(data, numComponents));
                return fitter.getLogLikelihood();
            }
        });
        if (logger.isLoggable(Level.INFO)) {
            logger.info(ts.getReport());
        }
        // More than twice as fast
        Assertions.assertTrue(ts.get(-1).getMean() < ts.get(-2).getMean() / 2);
    }
}