Examples with Mean org.apache.commons.math3.stat.descriptive.moment.Mean used on opensource projects

Example 61 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project gatk-protected by broadinstitute.

the class CoverageDropoutDetector method retrieveGaussianMixtureModelForFilteredTargets.

/** <p>Produces a Gaussian mixture model based on the difference between targets and segment means.</p>
     * <p>Filters targets to populations where more than the minProportion lie in a single segment.</p>
     * <p>Returns null if no pass filtering.  Please note that in these cases,
     * in the rest of this class, we use this to assume that a GMM is not a good model.</p>
     *
     * @param segments  -- segments with segment mean in log2 copy ratio space
     * @param targets -- targets with a log2 copy ratio estimate
     * @param minProportion -- minimum proportion of all targets that a given segment must have in order to be used
     *                      in the evaluation
     * @param numComponents -- number of components to use in the GMM.  Usually, this is 2.
     * @return  never {@code null}.  Fitting result with indications whether it converged or was even attempted.
     */
private MixtureMultivariateNormalFitResult retrieveGaussianMixtureModelForFilteredTargets(final List<ModeledSegment> segments, final TargetCollection<ReadCountRecord.SingleSampleRecord> targets, double minProportion, int numComponents) {
    // For each target in a segment that contains enough targets, normalize the difference against the segment mean
    //  and collapse the filtered targets into the copy ratio estimates.
    final List<Double> filteredTargetsSegDiff = getNumProbeFilteredTargetList(segments, targets, minProportion);
    if (filteredTargetsSegDiff.size() < numComponents) {
        return new MixtureMultivariateNormalFitResult(null, false, false);
    }
    // Assume that Apache Commons wants data points in the first dimension.
    // Note that second dimension of length 2 (instead of 1) is to wrok around funny Apache commons API.
    final double[][] filteredTargetsSegDiff2d = new double[filteredTargetsSegDiff.size()][2];
    // Convert the filtered targets into 2d array (even if second dimension is length 1).  The second dimension is
    //  uncorrelated Gaussian.  This is only to get around funny API in Apache Commons, which will throw an
    //  exception if the length of the second dimension is < 2
    final RandomGenerator rng = RandomGeneratorFactory.createRandomGenerator(new Random(RANDOM_SEED));
    final NormalDistribution nd = new NormalDistribution(rng, 0, .1);
    for (int i = 0; i < filteredTargetsSegDiff.size(); i++) {
        filteredTargetsSegDiff2d[i][0] = filteredTargetsSegDiff.get(i);
        filteredTargetsSegDiff2d[i][1] = nd.sample();
    }
    final MixtureMultivariateNormalDistribution estimateEM0 = MultivariateNormalMixtureExpectationMaximization.estimate(filteredTargetsSegDiff2d, numComponents);
    final MultivariateNormalMixtureExpectationMaximization multivariateNormalMixtureExpectationMaximization = new MultivariateNormalMixtureExpectationMaximization(filteredTargetsSegDiff2d);
    try {
        multivariateNormalMixtureExpectationMaximization.fit(estimateEM0);
    } catch (final MaxCountExceededException | ConvergenceException | SingularMatrixException e) {
        //  did not converge.  Include the model as it was when the exception was thrown.
        return new MixtureMultivariateNormalFitResult(multivariateNormalMixtureExpectationMaximization.getFittedModel(), false, true);
    }
    return new MixtureMultivariateNormalFitResult(multivariateNormalMixtureExpectationMaximization.getFittedModel(), true, true);
}

Example 62 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project gatk-protected by broadinstitute.

the class AdaptiveMetropolisSamplerUnitTest method testBeta.

@Test
public void testBeta() {
    final RandomGenerator rng = RandomGeneratorFactory.createRandomGenerator(new Random(RANDOM_SEED));
    for (final double a : Arrays.asList(10, 20, 30)) {
        for (final double b : Arrays.asList(10, 20, 30)) {
            final double theoreticalMean = a / (a + b);
            final double theoreticalVariance = a * b / ((a + b) * (a + b) * (a + b + 1));
            //Note: this is the theoretical standard deviation of the sample mean given uncorrelated
            //samples.  The sample mean will have a greater variance here because samples are correlated.
            final double standardDeviationOfMean = Math.sqrt(theoreticalVariance / NUM_SAMPLES);
            final Function<Double, Double> logPDF = x -> (a - 1) * Math.log(x) + (b - 1) * Math.log(1 - x);
            final AdaptiveMetropolisSampler sampler = new AdaptiveMetropolisSampler(INITIAL_BETA_GUESS, INITIAL_STEP_SIZE, 0, 1);
            final List<Double> samples = sampler.sample(rng, logPDF, NUM_SAMPLES, NUM_BURN_IN_STEPS);
            final double sampleMean = samples.stream().mapToDouble(x -> x).average().getAsDouble();
            final double sampleMeanSquare = samples.stream().mapToDouble(x -> x * x).average().getAsDouble();
            final double sampleVariance = (sampleMeanSquare - sampleMean * sampleMean) * NUM_SAMPLES / (NUM_SAMPLES - 1);
            Assert.assertEquals(sampleMean, theoreticalMean, 10 * standardDeviationOfMean);
            Assert.assertEquals(sampleVariance, theoreticalVariance, 10e-4);
        }
    }
}

Example 63 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project gatk-protected by broadinstitute.

the class GibbsSamplerCopyRatioUnitTest method testRunMCMCOnCopyRatioSegmentedGenome.

/**
     * Tests Bayesian inference of a toy copy-ratio model via MCMC.
     * <p>
     *     Recovery of input values for the variance global parameter and the segment-level mean parameters is checked.
     *     In particular, the mean and standard deviation of the posterior for the variance must be recovered to within
     *     a relative error of 1% and 5%, respectively, in 500 samples (after 250 burn-in samples have been discarded).
     * </p>
     * <p>
     *     Furthermore, the number of truth values for the segment-level means falling outside confidence intervals of
     *     1-sigma, 2-sigma, and 3-sigma given by the posteriors in each segment should be roughly consistent with
     *     a normal distribution (i.e., ~32, ~5, and ~0, respectively; we allow for errors of 10, 5, and 2).
     *     Finally, the mean of the standard deviations of the posteriors for the segment-level means should be
     *     recovered to within a relative error of 5%.
     * </p>
     * <p>
     *     With these specifications, this unit test is not overly brittle (i.e., it should pass for a large majority
     *     of randomly generated data sets), but it is still brittle enough to check for correctness of the sampling
     *     (for example, specifying a sufficiently incorrect likelihood will cause the test to fail).
     * </p>
     */
@Test
public void testRunMCMCOnCopyRatioSegmentedGenome() {
    //Create new instance of the Modeller helper class, passing all quantities needed to initialize state and data.
    final CopyRatioModeller modeller = new CopyRatioModeller(VARIANCE_INITIAL, MEAN_INITIAL, COVERAGES_FILE, NUM_TARGETS_PER_SEGMENT_FILE);
    //Create a GibbsSampler, passing the total number of samples (including burn-in samples)
    //and the model held by the Modeller.
    final GibbsSampler<CopyRatioParameter, CopyRatioState, CopyRatioDataCollection> gibbsSampler = new GibbsSampler<>(NUM_SAMPLES, modeller.model);
    //Run the MCMC.
    gibbsSampler.runMCMC();
    //Check that the statistics---i.e., the mean and standard deviation---of the variance posterior
    //agree with those found by emcee/analytically to a relative error of 1% and 5%, respectively.
    final double[] varianceSamples = Doubles.toArray(gibbsSampler.getSamples(CopyRatioParameter.VARIANCE, Double.class, NUM_BURN_IN));
    final double variancePosteriorCenter = new Mean().evaluate(varianceSamples);
    final double variancePosteriorStandardDeviation = new StandardDeviation().evaluate(varianceSamples);
    Assert.assertEquals(relativeError(variancePosteriorCenter, VARIANCE_TRUTH), 0., RELATIVE_ERROR_THRESHOLD_FOR_CENTERS);
    Assert.assertEquals(relativeError(variancePosteriorStandardDeviation, VARIANCE_POSTERIOR_STANDARD_DEVIATION_TRUTH), 0., RELATIVE_ERROR_THRESHOLD_FOR_STANDARD_DEVIATIONS);
    //Check statistics---i.e., the mean and standard deviation---of the segment-level mean posteriors.
    //In particular, check that the number of segments where the true mean falls outside confidence intervals
    //is roughly consistent with a normal distribution.
    final List<Double> meansTruth = loadList(MEANS_TRUTH_FILE, Double::parseDouble);
    final int numSegments = meansTruth.size();
    final List<SegmentMeans> meansSamples = gibbsSampler.getSamples(CopyRatioParameter.SEGMENT_MEANS, SegmentMeans.class, NUM_BURN_IN);
    int numMeansOutsideOneSigma = 0;
    int numMeansOutsideTwoSigma = 0;
    int numMeansOutsideThreeSigma = 0;
    final List<Double> meanPosteriorStandardDeviations = new ArrayList<>();
    for (int segment = 0; segment < numSegments; segment++) {
        final int j = segment;
        final double[] meanInSegmentSamples = Doubles.toArray(meansSamples.stream().map(s -> s.get(j)).collect(Collectors.toList()));
        final double meanPosteriorCenter = new Mean().evaluate(meanInSegmentSamples);
        final double meanPosteriorStandardDeviation = new StandardDeviation().evaluate(meanInSegmentSamples);
        meanPosteriorStandardDeviations.add(meanPosteriorStandardDeviation);
        final double absoluteDifferenceFromTruth = Math.abs(meanPosteriorCenter - meansTruth.get(segment));
        if (absoluteDifferenceFromTruth > meanPosteriorStandardDeviation) {
            numMeansOutsideOneSigma++;
        }
        if (absoluteDifferenceFromTruth > 2 * meanPosteriorStandardDeviation) {
            numMeansOutsideTwoSigma++;
        }
        if (absoluteDifferenceFromTruth > 3 * meanPosteriorStandardDeviation) {
            numMeansOutsideThreeSigma++;
        }
    }
    final double meanPosteriorStandardDeviationsMean = new Mean().evaluate(Doubles.toArray(meanPosteriorStandardDeviations));
    Assert.assertEquals(numMeansOutsideOneSigma, 100 - 68, DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_1_SIGMA);
    Assert.assertEquals(numMeansOutsideTwoSigma, 100 - 95, DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_2_SIGMA);
    Assert.assertTrue(numMeansOutsideThreeSigma <= DELTA_NUMBER_OF_MEANS_ALLOWED_OUTSIDE_3_SIGMA);
    Assert.assertEquals(relativeError(meanPosteriorStandardDeviationsMean, MEAN_POSTERIOR_STANDARD_DEVIATION_MEAN_TRUTH), 0., RELATIVE_ERROR_THRESHOLD_FOR_STANDARD_DEVIATIONS);
}

Example 64 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project gatk-protected by broadinstitute.

the class GibbsSamplerSingleGaussianUnitTest method testRunMCMCOnSingleGaussianModel.

/**
     * Tests Bayesian inference of a Gaussian model via MCMC.  Recovery of input values for the variance and mean
     * global parameters is checked.  In particular, the mean and standard deviation of the posteriors for
     * both parameters must be recovered to within a relative error of 1% and 10%, respectively, in 250 samples
     * (after 250 burn-in samples have been discarded).
     */
@Test
public void testRunMCMCOnSingleGaussianModel() {
    //Create new instance of the Modeller helper class, passing all quantities needed to initialize state and data.
    final GaussianModeller modeller = new GaussianModeller(VARIANCE_INITIAL, MEAN_INITIAL, datapointsList);
    //Create a GibbsSampler, passing the total number of samples (including burn-in samples)
    //and the model held by the Modeller.
    final GibbsSampler<GaussianParameter, ParameterizedState<GaussianParameter>, GaussianDataCollection> gibbsSampler = new GibbsSampler<>(NUM_SAMPLES, modeller.model);
    //Run the MCMC.
    gibbsSampler.runMCMC();
    //Get the samples of each of the parameter posteriors (discarding burn-in samples) by passing the
    //parameter name, type, and burn-in number to the getSamples method.
    final double[] varianceSamples = Doubles.toArray(gibbsSampler.getSamples(GaussianParameter.VARIANCE, Double.class, NUM_BURN_IN));
    final double[] meanSamples = Doubles.toArray(gibbsSampler.getSamples(GaussianParameter.MEAN, Double.class, NUM_BURN_IN));
    //Check that the statistics---i.e., the means and standard deviations---of the posteriors
    //agree with those found by emcee/analytically to a relative error of 1% and 10%, respectively.
    final double variancePosteriorCenter = new Mean().evaluate(varianceSamples);
    final double variancePosteriorStandardDeviation = new StandardDeviation().evaluate(varianceSamples);
    Assert.assertEquals(relativeError(variancePosteriorCenter, VARIANCE_TRUTH), 0., RELATIVE_ERROR_THRESHOLD_FOR_CENTERS);
    Assert.assertEquals(relativeError(variancePosteriorStandardDeviation, VARIANCE_POSTERIOR_STANDARD_DEVIATION_TRUTH), 0., RELATIVE_ERROR_THRESHOLD_FOR_STANDARD_DEVIATIONS);
    final double meanPosteriorCenter = new Mean().evaluate(meanSamples);
    final double meanPosteriorStandardDeviation = new StandardDeviation().evaluate(meanSamples);
    Assert.assertEquals(relativeError(meanPosteriorCenter, MEAN_TRUTH), 0., RELATIVE_ERROR_THRESHOLD_FOR_CENTERS);
    Assert.assertEquals(relativeError(meanPosteriorStandardDeviation, MEAN_POSTERIOR_STANDARD_DEVIATION_TRUTH), 0., RELATIVE_ERROR_THRESHOLD_FOR_STANDARD_DEVIATIONS);
}

Example 65 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project gatk-protected by broadinstitute.

the class SliceSamplerUnitTest method testInitialPointOutOfRange.

@Test(expectedExceptions = IllegalArgumentException.class)
public void testInitialPointOutOfRange() {
    rng.setSeed(RANDOM_SEED);
    final double mean = 5.;
    final double standardDeviation = 0.75;
    final NormalDistribution normalDistribution = new NormalDistribution(mean, standardDeviation);
    final Function<Double, Double> normalLogPDF = normalDistribution::logDensity;
    final double xInitial = -10.;
    final double xMin = 0.;
    final double xMax = 1.;
    final double width = 0.5;
    final SliceSampler normalSampler = new SliceSampler(rng, normalLogPDF, xMin, xMax, width);
    normalSampler.sample(xInitial);
}