Example usage for org.apache.commons.math3.random EmpiricalDistribution EmpiricalDistribution

Introduction

In this page you can find the example usage for org.apache.commons.math3.random EmpiricalDistribution EmpiricalDistribution.

Prototype

private EmpiricalDistribution(int binCount, RandomDataGenerator randomData) 

Source Link

Document

Private constructor to allow lazy initialisation of the RNG contained in the #randomData instance variable.

Usage

From source file:cz.cuni.mff.d3s.spl.utils.DistributionUtils.java

/** Create empirical distribution from given samples.
 * //from w w  w  .  j  av  a 2s. c  o m
 * @param samples Samples (does not need to be distinct) to use.
 * @return Empirical distribution built from the samples.
 */
public static EmpiricalDistribution makeEmpirical(double[] samples) {
    /* Be deterministic for now. */
    RandomGenerator gen = new JDKRandomGenerator();
    gen.setSeed(0);

    EmpiricalDistribution result = new EmpiricalDistribution(samples.length / 10 + 1, gen);
    result.load(samples);

    return result;
}

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From source file:gdsc.smlm.ij.plugins.CreateData.java

/**
 * @return A photon distribution loaded from a file of floating-point values with the specified population mean.
 *//* www .j ava2 s.  co m*/
private RealDistribution createPhotonDistribution() {
    if (settings.customPhotonDistribution) {
        // Get the distribution file
        String[] path = Utils.decodePath(settings.photonDistribution);
        OpenDialog chooser = new OpenDialog("Photon_distribution", path[0], path[1]);
        if (chooser.getFileName() != null) {
            String newFilename = chooser.getDirectory() + chooser.getFileName();
            settings.photonDistribution = newFilename;
            try {
                InputStream is = new FileInputStream(new File(settings.photonDistribution));
                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
            }
        }
    }
    // Fall back to a non-custom distribution
    settings.customPhotonDistribution = false;
    return null;
}

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