Examples with RandomGeneratorAdapter uk.ac.sussex.gdsc.core.utils.rng.RandomGeneratorAdapter used on opensource projects

Example 6 with RandomGeneratorAdapter

use of uk.ac.sussex.gdsc.core.utils.rng.RandomGeneratorAdapter in project GDSC-SMLM by aherbert.

the class MultivariateGaussianMixtureExpectationMaximizationTest method createData2d.

/**
 * Creates the data from a mixture of n 2D Gaussian distributions. The length of the weights array
 * (and all other arrays) is the number of mixture components.
 *
 * @param sampleSize the sample size
 * @param rng the random generator
 * @param weights the weights for each component
 * @param means the means for the x and y dimensions
 * @param stdDevs the std devs for the x and y dimensions
 * @param correlations the correlations between the x and y dimensions
 * @return the double[][]
 */
private static double[][] createData2d(int sampleSize, UniformRandomProvider rng, double[] weights, double[][] means, double[][] stdDevs, double[] correlations) {
    // Use Commons Math for sampling
    final ArrayList<Pair<Double, MultivariateNormalDistribution>> components = new ArrayList<>();
    for (int i = 0; i < weights.length; i++) {
        // Create covariance matrix
        final double sx = stdDevs[i][0];
        final double sy = stdDevs[i][1];
        final double sxsy = correlations[i] * sx * sy;
        final double[][] covar = new double[][] { { sx * sx, sxsy }, { sxsy, sy * sy } };
        components.add(new Pair<>(weights[i], new MultivariateNormalDistribution(means[i], covar)));
    }
    final MixtureMultivariateNormalDistribution dist = new MixtureMultivariateNormalDistribution(new RandomGeneratorAdapter(rng), components);
    return dist.sample(sampleSize);
}

Example 7 with RandomGeneratorAdapter

use of uk.ac.sussex.gdsc.core.utils.rng.RandomGeneratorAdapter in project GDSC-SMLM by aherbert.

the class CreateData method createPhotonDistribution.

/**
 * Creates the photon distribution.
 *
 * @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.getPhotonDistribution())) {
        // Get the distribution file
        final String filename = ImageJUtils.getFilename("Photon_distribution", settings.getPhotonDistributionFile());
        if (filename != null) {
            settings.setPhotonDistributionFile(filename);
            try (BufferedReader in = new BufferedReader(new UnicodeReader(new FileInputStream(new File(settings.getPhotonDistributionFile())), null))) {
                final StoredDataStatistics stats = new StoredDataStatistics();
                String str = in.readLine();
                double val = 0.0d;
                while (str != null) {
                    val = Double.parseDouble(str);
                    stats.add(val);
                    str = in.readLine();
                }
                if (stats.getSum() > 0) {
                    // Update the statistics to the desired mean.
                    final double scale = settings.getPhotonsPerSecond() / stats.getMean();
                    final 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;
                    final EmpiricalDistribution dist = new EmpiricalDistribution(binCount, new RandomGeneratorAdapter(createRandomGenerator()));
                    dist.load(values);
                    return dist;
                }
            } catch (final IOException | NullArgumentException | NumberFormatException ex) {
            // Ignore
            }
        }
        ImageJUtils.log("Failed to load custom photon distribution from file: %s. Default to fixed.", settings.getPhotonDistributionFile());
    } else if (PHOTON_DISTRIBUTION[PHOTON_UNIFORM].equals(settings.getPhotonDistribution())) {
        if (settings.getPhotonsPerSecond() < settings.getPhotonsPerSecondMaximum()) {
            return new UniformRealDistribution(new RandomGeneratorAdapter(createRandomGenerator()), settings.getPhotonsPerSecond(), settings.getPhotonsPerSecondMaximum());
        }
    } else if (PHOTON_DISTRIBUTION[PHOTON_GAMMA].equals(settings.getPhotonDistribution())) {
        final double scaleParameter = settings.getPhotonsPerSecond() / settings.getPhotonShape();
        return new GammaDistribution(new RandomGeneratorAdapter(createRandomGenerator()), settings.getPhotonShape(), scaleParameter, ExponentialDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    } else if (PHOTON_DISTRIBUTION[PHOTON_CORRELATED].equals(settings.getPhotonDistribution())) {
        // No distribution required
        return null;
    }
    settings.setPhotonDistribution(PHOTON_DISTRIBUTION[PHOTON_FIXED]);
    return null;
}