Examples with NormalDistribution org.apache.commons.math3.distribution.NormalDistribution used on opensource projects

Example 6 with NormalDistribution

use of org.apache.commons.math3.distribution.NormalDistribution in project gatk by broadinstitute.

the class CoverageDropoutDetectorTest method getUnivariateGaussianTargetsWithDropout.

private Object[][] getUnivariateGaussianTargetsWithDropout(final double sigma, final double dropoutRate) {
    Random rng = new Random(337);
    final RandomGenerator randomGenerator = RandomGeneratorFactory.createRandomGenerator(rng);
    NormalDistribution n = new NormalDistribution(randomGenerator, 1, sigma);
    final int numDataPoints = 10000;
    final int numEventPoints = 2000;
    // Randomly select dropoutRate of targets and reduce by 25%-75% (uniformly distributed)
    UniformRealDistribution uniformRealDistribution = new UniformRealDistribution(randomGenerator, 0, 1.0);
    final List<ReadCountRecord.SingleSampleRecord> targetList = new ArrayList<>();
    for (int i = 0; i < numDataPoints; i++) {
        double coverage = n.sample() + (i < (numDataPoints - numEventPoints) ? 0.0 : 0.5);
        if (uniformRealDistribution.sample() < dropoutRate) {
            double multiplier = .25 + uniformRealDistribution.sample() / 2;
            coverage = coverage * multiplier;
        }
        targetList.add(new ReadCountRecord.SingleSampleRecord(new Target("arbitrary_name", new SimpleInterval("chr1", 100 + 2 * i, 101 + 2 * i)), coverage));
    }
    HashedListTargetCollection<ReadCountRecord.SingleSampleRecord> targets = new HashedListTargetCollection<>(targetList);
    List<ModeledSegment> segments = new ArrayList<>();
    segments.add(new ModeledSegment(new SimpleInterval("chr1", 100, 16050), 8000, 1));
    segments.add(new ModeledSegment(new SimpleInterval("chr1", 16100, 20200), 2000, 1.5));
    return new Object[][] { { targets, segments } };
}

Example 7 with NormalDistribution

use of org.apache.commons.math3.distribution.NormalDistribution in project gatk by broadinstitute.

the class CNLOHCaller method calculateDoubleGaussian.

/**
     * This function takes two half-Gaussian distributions and uses one for the values below the mode and the other for values above the
     *  mode.
     *
     *  If the mode is outside the credible interval, the calculation is done as if the interval boundary is
     *   {@link CNLOHCaller#MIN_DIST_FROM_MODE} above/below the mode.
     *
     *
     * @param val value to calculate the "pdf"
     * @param credibleMode mode of the presumed distribution
     * @param credibleLow 95% confidence interval on the low tail
     * @param credibleHigh 95% confidence interval on the high tail
     * @return pdf with a minimum value of {@link CNLOHCaller#MIN_L}
     */
@VisibleForTesting
static double calculateDoubleGaussian(final double val, final double credibleMode, final double credibleLow, final double credibleHigh) {
    final double hiDist = Math.max(credibleHigh - credibleMode, MIN_DIST_FROM_MODE);
    final double loDist = Math.max(credibleMode - credibleLow, MIN_DIST_FROM_MODE);
    return new NormalDistribution(null, credibleMode, (val >= credibleMode ? hiDist : loDist) / NUM_STD_95_CONF_INTERVAL_GAUSSIAN).density(val);
}

Example 8 with NormalDistribution

use of org.apache.commons.math3.distribution.NormalDistribution in project gatk by broadinstitute.

the class AdaptiveMetropolisSampler method sample.

public double sample(final RandomGenerator rng, final Function<Double, Double> logPDF) {
    Utils.nonNull(rng);
    Utils.nonNull(logPDF);
    final AbstractRealDistribution normal = new NormalDistribution(rng, 0, 1);
    final double proposal = xCurrent + stepSize * normal.sample();
    final double acceptanceProbability = (proposal < lowerBound || upperBound < proposal) ? 0 : Math.min(1, Math.exp(logPDF.apply(proposal) - logPDF.apply(xCurrent)));
    //adjust stepSize larger/smaller to decrease/increase the acceptance rate
    final double correctionFactor = (acceptanceProbability - optimalAcceptanceRate) * adjustmentRate * (timeScale / (timeScale + iteration));
    stepSize *= Math.exp(correctionFactor);
    iteration++;
    return rng.nextDouble() < acceptanceProbability ? proposal : xCurrent;
}

Example 9 with NormalDistribution

use of org.apache.commons.math3.distribution.NormalDistribution in project pyramid by cheng-li.

the class MultiLabelSynthesizer method independentNoise.

/**
     * y0: w=(0,1)
     * y1: w=(1,1)
     * y2: w=(1,0)
     * y3: w=(1,-1)
     * @return
     */
public static MultiLabelClfDataSet independentNoise() {
    int numData = 10000;
    int numClass = 4;
    int numFeature = 2;
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numFeatures(numFeature).numClasses(numClass).numDataPoints(numData).build();
    // generate weights
    Vector[] weights = new Vector[numClass];
    for (int k = 0; k < numClass; k++) {
        Vector vector = new DenseVector(numFeature);
        weights[k] = vector;
    }
    weights[0].set(0, 0);
    weights[0].set(1, 1);
    weights[1].set(0, 1);
    weights[1].set(1, 1);
    weights[2].set(0, 1);
    weights[2].set(1, 0);
    weights[3].set(0, 1);
    weights[3].set(1, -1);
    // generate features
    for (int i = 0; i < numData; i++) {
        for (int j = 0; j < numFeature; j++) {
            dataSet.setFeatureValue(i, j, Sampling.doubleUniform(-1, 1));
        }
    }
    NormalDistribution[] noises = new NormalDistribution[4];
    noises[0] = new NormalDistribution(0, 0.1);
    noises[1] = new NormalDistribution(0, 0.1);
    noises[2] = new NormalDistribution(0, 0.1);
    noises[3] = new NormalDistribution(0, 0.1);
    // assign labels
    int numFlipped = 0;
    for (int i = 0; i < numData; i++) {
        for (int k = 0; k < numClass; k++) {
            double dot = weights[k].dot(dataSet.getRow(i));
            double score = dot + noises[k].sample();
            if (score >= 0) {
                dataSet.addLabel(i, k);
            }
            if (dot * score < 0) {
                numFlipped += 1;
            }
        }
    }
    System.out.println("number of flipped = " + numFlipped);
    return dataSet;
}

Example 10 with NormalDistribution

use of org.apache.commons.math3.distribution.NormalDistribution in project pyramid by cheng-li.

the class RegressionSynthesizer method univarNormal.

public RegDataSet univarNormal() {
    NormalDistribution normalDistribution = new NormalDistribution(0, 1);
    RegDataSet dataSet = RegDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(1).dense(true).missingValue(false).build();
    for (int i = 0; i < numDataPoints; i++) {
        double featureValue = Sampling.doubleUniform(-1, 1);
        double label;
        label = normalDistribution.density(featureValue);
        label += noise.sample();
        dataSet.setFeatureValue(i, 0, featureValue);
        dataSet.setLabel(i, label);
    }
    return dataSet;
}