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

Example 66 with SeededTest

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

the class MultivariateGaussianMixtureExpectationMaximizationTest method testExpectationMaximizationSpeed.

/**
 * Test the speed of implementations of the expectation maximization algorithm with a mixture of n
 * ND Gaussian distributions.
 *
 * @param seed the seed
 */
@SpeedTag
@SeededTest
void testExpectationMaximizationSpeed(RandomSeed seed) {
    Assumptions.assumeTrue(TestSettings.allow(TestComplexity.HIGH));
    final MultivariateGaussianMixtureExpectationMaximization.DoubleDoubleBiPredicate relChecker = TestHelper.doublesAreClose(1e-6)::test;
    // Create data
    final UniformRandomProvider rng = RngUtils.create(seed.getSeed());
    for (int n = 2; n <= 3; n++) {
        for (int dim = 2; dim <= 4; dim++) {
            final double[][][] data = new double[10][][];
            final int nCorrelations = dim - 1;
            for (int i = 0; i < data.length; i++) {
                final double[] sampleWeights = createWeights(n, rng);
                final double[][] sampleMeans = create(n, dim, rng, -5, 5);
                final double[][] sampleStdDevs = create(n, dim, rng, 1, 10);
                final double[][] sampleCorrelations = IntStream.range(0, n).mapToObj(component -> create(nCorrelations, rng, -0.9, 0.9)).toArray(double[][]::new);
                data[i] = createDataNd(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 + " " + dim + "D", 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 + " " + dim + "D", data) {

                @Override
                Object run(double[][] data) {
                    final MultivariateGaussianMixtureExpectationMaximization fitter = new MultivariateGaussianMixtureExpectationMaximization(data);
                    fitter.fit(MultivariateGaussianMixtureExpectationMaximization.estimate(data, numComponents));
                    return fitter.getLogLikelihood();
                }
            });
            ts.execute(new FittingSpeedTask("GDSC rel 1e-6 n=" + n + " " + dim + "D", data) {

                @Override
                Object run(double[][] data) {
                    final MultivariateGaussianMixtureExpectationMaximization fitter = new MultivariateGaussianMixtureExpectationMaximization(data);
                    fitter.fit(MultivariateGaussianMixtureExpectationMaximization.estimate(data, numComponents), 1000, relChecker);
                    return fitter.getLogLikelihood();
                }
            });
            if (logger.isLoggable(Level.INFO)) {
                logger.info(ts.getReport());
            }
            // More than twice as fast
            Assertions.assertTrue(ts.get(-2).getMean() < ts.get(-3).getMean() / 2);
        }
    }
}

Example 67 with SeededTest

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

the class MultivariateGaussianMixtureExpectationMaximizationTest method canCreateMixedMultivariateGaussianDistribution.

@SeededTest
void canCreateMixedMultivariateGaussianDistribution(RandomSeed seed) {
    // Will be normalised
    final double[] weights = { 1, 1 };
    final double[][] means = new double[2][];
    final double[][][] covariances = new double[2][][];
    final double[][] data1 = { { 1, 2 }, { 2.5, 1.5 }, { 3.5, 1.0 } };
    final double[][] data2 = { { 4, 2 }, { 3.5, -1.5 }, { -3.5, 1.0 } };
    means[0] = getColumnMeans(data1);
    covariances[0] = getCovariance(data1);
    means[1] = getColumnMeans(data2);
    covariances[1] = getCovariance(data2);
    // Create components. This does not have to be zero based.
    final LocalList<double[]> list = new LocalList<>();
    list.addAll(Arrays.asList(data1));
    list.addAll(Arrays.asList(data2));
    final double[][] data = list.toArray(new double[0][]);
    final int[] components = { -1, -1, -1, 3, 3, 3 };
    // Randomise the data
    for (int n = 0; n < 3; n++) {
        final long start = n + seed.getSeedAsLong();
        // This relies on the shuffle being the same
        RandomUtils.shuffle(data, RngUtils.create(start));
        RandomUtils.shuffle(components, RngUtils.create(start));
        final MixtureMultivariateGaussianDistribution dist = MultivariateGaussianMixtureExpectationMaximization.createMixed(data, components);
        Assertions.assertArrayEquals(new double[] { 0.5, 0.5 }, dist.getWeights());
        final MultivariateGaussianDistribution[] distributions = dist.getDistributions();
        Assertions.assertEquals(weights.length, distributions.length);
        final DoubleDoubleBiPredicate test = TestHelper.doublesAreClose(1e-8);
        for (int i = 0; i < means.length; i++) {
            TestAssertions.assertArrayTest(means[i], distributions[i].getMeans(), test);
            TestAssertions.assertArrayTest(covariances[i], distributions[i].getCovariances(), test);
        }
    }
}

Example 68 with SeededTest

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

the class PeakResultDigestTest method sameSize1ResultsAreEqual.

@SeededTest
void sameSize1ResultsAreEqual(RandomSeed seed) {
    final UniformRandomProvider r = RngUtils.create(seed.getSeed());
    final PeakResult[] r1 = createResults(r, 1, 5, false, false, false, false);
    final PeakResultsDigest digest = new PeakResultsDigest(r1);
    Assertions.assertTrue(digest.matches(r1));
    Assertions.assertTrue(digest.matches(digest));
}

Example 69 with SeededTest

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

the class PeakResultDigestTest method sameResultsAreEqualWithDeviation.

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

Example 70 with SeededTest

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

the class PeakResultDigestTest method differentResultsAreNotEqual.

@SeededTest
void differentResultsAreNotEqual(RandomSeed seed) {
    final UniformRandomProvider r = RngUtils.create(seed.getSeed());
    final PeakResult[] r1 = createResults(r, 10, 5, false, false, false, false);
    final PeakResultsDigest digest = new PeakResultsDigest(r1);
    for (final int size : new int[] { 10, 1, 0 }) {
        final PeakResult[] r2 = createResults(r, size, 5, false, false, false, false);
        Assertions.assertFalse(digest.matches(r2));
        Assertions.assertFalse(digest.matches(new PeakResultsDigest(r2)));
    }
}