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

Example 1 with UniformRealDistribution

use of org.apache.commons.math3.distribution.UniformRealDistribution in project GDSC-SMLM by aherbert.

the class CreateData method createPhotonDistribution.

/**
	 * @return A photon distribution loaded from a file of floating-point values with the specified population mean.
	 */
private RealDistribution createPhotonDistribution() {
    if (PHOTON_DISTRIBUTION[PHOTON_CUSTOM].equals(settings.photonDistribution)) {
        // Get the distribution file
        String filename = Utils.getFilename("Photon_distribution", settings.photonDistributionFile);
        if (filename != null) {
            settings.photonDistributionFile = filename;
            try {
                InputStream is = new FileInputStream(new File(settings.photonDistributionFile));
                BufferedReader in = new BufferedReader(new UnicodeReader(is, null));
                StoredDataStatistics stats = new StoredDataStatistics();
                try {
                    String str = null;
                    double val = 0.0d;
                    while ((str = in.readLine()) != null) {
                        val = Double.parseDouble(str);
                        stats.add(val);
                    }
                } finally {
                    in.close();
                }
                if (stats.getSum() > 0) {
                    // Update the statistics to the desired mean.
                    double scale = (double) settings.photonsPerSecond / stats.getMean();
                    double[] values = stats.getValues();
                    for (int i = 0; i < values.length; i++) values[i] *= scale;
                    // TODO - Investigate the limits of this distribution. 
                    // How far above and below the input data will values be generated.
                    // Create the distribution using the recommended number of bins
                    final int binCount = stats.getN() / 10;
                    EmpiricalDistribution dist = new EmpiricalDistribution(binCount, createRandomGenerator());
                    dist.load(values);
                    return dist;
                }
            } catch (IOException e) {
            // Ignore
            } catch (NullArgumentException e) {
            // Ignore 
            } catch (NumberFormatException e) {
            // Ignore
            }
        }
        Utils.log("Failed to load custom photon distribution from file: %s. Default to fixed.", settings.photonDistributionFile);
    } else if (PHOTON_DISTRIBUTION[PHOTON_UNIFORM].equals(settings.photonDistribution)) {
        if (settings.photonsPerSecond < settings.photonsPerSecondMaximum) {
            UniformRealDistribution dist = new UniformRealDistribution(createRandomGenerator(), settings.photonsPerSecond, settings.photonsPerSecondMaximum);
            return dist;
        }
    } else if (PHOTON_DISTRIBUTION[PHOTON_GAMMA].equals(settings.photonDistribution)) {
        final double scaleParameter = settings.photonsPerSecond / settings.photonShape;
        GammaDistribution dist = new GammaDistribution(createRandomGenerator(), settings.photonShape, scaleParameter, ExponentialDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        return dist;
    } else if (PHOTON_DISTRIBUTION[PHOTON_CORRELATED].equals(settings.photonDistribution)) {
        // No distribution required
        return null;
    }
    settings.photonDistribution = PHOTON_DISTRIBUTION[PHOTON_FIXED];
    return null;
}

Example 2 with UniformRealDistribution

use of org.apache.commons.math3.distribution.UniformRealDistribution in project gatk-protected 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 3 with UniformRealDistribution

use of org.apache.commons.math3.distribution.UniformRealDistribution 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 4 with UniformRealDistribution

use of org.apache.commons.math3.distribution.UniformRealDistribution in project druid by druid-io.

the class ColumnValueGenerator method initDistribution.

private void initDistribution() {
    GeneratorColumnSchema.ValueDistribution distributionType = schema.getDistributionType();
    ValueType type = schema.getType();
    List<Object> enumeratedValues = schema.getEnumeratedValues();
    List<Double> enumeratedProbabilities = schema.getEnumeratedProbabilities();
    List<Pair<Object, Double>> probabilities = new ArrayList<>();
    switch(distributionType) {
        case SEQUENTIAL:
            // not random, just cycle through numbers from start to end, or cycle through enumerated values if provided
            distribution = new SequentialDistribution(schema.getStartInt(), schema.getEndInt(), schema.getEnumeratedValues());
            break;
        case UNIFORM:
            distribution = new UniformRealDistribution(schema.getStartDouble(), schema.getEndDouble());
            break;
        case DISCRETE_UNIFORM:
            if (enumeratedValues == null) {
                enumeratedValues = new ArrayList<>();
                for (int i = schema.getStartInt(); i < schema.getEndInt(); i++) {
                    Object val = convertType(i, type);
                    enumeratedValues.add(val);
                }
            }
            // give them all equal probability, the library will normalize probabilities to sum to 1.0
            for (Object enumeratedValue : enumeratedValues) {
                probabilities.add(new Pair<>(enumeratedValue, 0.1));
            }
            distribution = new EnumeratedTreeDistribution<>(probabilities);
            break;
        case NORMAL:
            distribution = new NormalDistribution(schema.getMean(), schema.getStandardDeviation());
            break;
        case ROUNDED_NORMAL:
            NormalDistribution normalDist = new NormalDistribution(schema.getMean(), schema.getStandardDeviation());
            distribution = new RealRoundingDistribution(normalDist);
            break;
        case ZIPF:
            int cardinality;
            if (enumeratedValues == null) {
                Integer startInt = schema.getStartInt();
                cardinality = schema.getEndInt() - startInt;
                ZipfDistribution zipf = new ZipfDistribution(cardinality, schema.getZipfExponent());
                for (int i = 0; i < cardinality; i++) {
                    probabilities.add(new Pair<>((Object) (i + startInt), zipf.probability(i)));
                }
            } else {
                cardinality = enumeratedValues.size();
                ZipfDistribution zipf = new ZipfDistribution(enumeratedValues.size(), schema.getZipfExponent());
                for (int i = 0; i < cardinality; i++) {
                    probabilities.add(new Pair<>(enumeratedValues.get(i), zipf.probability(i)));
                }
            }
            distribution = new EnumeratedTreeDistribution<>(probabilities);
            break;
        case LAZY_ZIPF:
            int lazyCardinality;
            Integer startInt = schema.getStartInt();
            lazyCardinality = schema.getEndInt() - startInt;
            distribution = new ZipfDistribution(lazyCardinality, schema.getZipfExponent());
            break;
        case LAZY_DISCRETE_UNIFORM:
            distribution = new UniformIntegerDistribution(schema.getStartInt(), schema.getEndInt());
            break;
        case ENUMERATED:
            for (int i = 0; i < enumeratedValues.size(); i++) {
                probabilities.add(new Pair<>(enumeratedValues.get(i), enumeratedProbabilities.get(i)));
            }
            distribution = new EnumeratedTreeDistribution<>(probabilities);
            break;
        default:
            throw new UnsupportedOperationException("Unknown distribution type: " + distributionType);
    }
    if (distribution instanceof AbstractIntegerDistribution) {
        ((AbstractIntegerDistribution) distribution).reseedRandomGenerator(seed);
    } else if (distribution instanceof AbstractRealDistribution) {
        ((AbstractRealDistribution) distribution).reseedRandomGenerator(seed);
    } else {
        ((EnumeratedDistribution) distribution).reseedRandomGenerator(seed);
    }
}

Example 5 with UniformRealDistribution

use of org.apache.commons.math3.distribution.UniformRealDistribution in project beam by apache.

the class SyntheticOptionsTest method testRealDistributionDeserializerWithUniformDistribution.

@Test
public void testRealDistributionDeserializerWithUniformDistribution() throws Exception {
    String syntheticOptions = "{\"seed\":12345," + "\"delayDistribution\":{\"type\":\"uniform\",\"lower\":0,\"upper\":100}}";
    SyntheticOptions sourceOptions = optionsFromString(syntheticOptions, SyntheticOptions.class);
    assertEquals(0, (long) ((UniformRealDistribution) sourceOptions.delayDistribution.getDistribution()).getSupportLowerBound());
    assertEquals(100, (long) ((UniformRealDistribution) sourceOptions.delayDistribution.getDistribution()).getSupportUpperBound());
}