Example usage for org.apache.commons.math3.random RandomDataGenerator nextPoisson

Introduction

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

Prototype

public long nextPoisson(double mean) throws NotStrictlyPositiveException 

Source Link

Document

<p> <strong>Algorithm Description</strong>: <ul><li> For small means, uses simulation of a Poisson process using Uniform deviates, as described <a href="http://irmi.epfl.ch/cmos/Pmmi/interactive/rng7.htm"> here.</a> The Poisson process (and hence value returned) is bounded by 1000 * mean.</li> <li> For large means, uses the rejection algorithm described in <br/> Devroye, Luc.

Usage

From source file:gdsc.smlm.model.StandardFluorophoreSequenceModel.java

/**
 * Get the number of blinks using the specified random data generator using a Poisson or Geometric distribution.
 * @param useGeometricBlinkingDistribution
 * @param rand//w  w w . jav a 2s. c  o m
 * @param mean
 * @return The number of blinks
 */
public static int getBlinks(boolean useGeometricBlinkingDistribution, RandomDataGenerator rand, double mean) {
    if (mean > 0) {
        return (useGeometricBlinkingDistribution) ? nextGeometric(rand, mean) : (int) rand.nextPoisson(mean);
    }
    return 0;
}

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

private float[] createBackground(RandomDataGenerator random) {
    float[] pixels2 = null;

    if (settings.background > 0) {
        if (random == null)
            random = new RandomDataGenerator();
        createBackgroundPixels();/*  w ww  .j  a va  2  s  .  c o m*/
        pixels2 = Arrays.copyOf(backgroundPixels, backgroundPixels.length);

        // Add Poisson noise.
        if (uniformBackground) {
            // We can do N random samples thus ensuring the background average is constant.
            // Note: The number of samples must be Poisson distributed.
            final int samples = (int) random.nextPoisson(pixels2[0] * pixels2.length);

            // Only do sampling if the number of samples is valid
            if (samples >= 1) {
                pixels2 = new float[pixels2.length];
                final int upper = pixels2.length - 1;
                for (int i = 0; i < samples; i++) {
                    pixels2[random.nextInt(0, upper)] += 1;
                }
            } else {
                // If using a uniform illumination then we can use a fixed Poisson distribution
                PoissonDistribution dist = new PoissonDistribution(random.getRandomGenerator(), pixels2[0],
                        PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
                for (int i = 0; i < pixels2.length; i++) {
                    pixels2[i] = dist.sample();
                }
            }
        } else {
            for (int i = 0; i < pixels2.length; i++) {
                pixels2[i] = random.nextPoisson(pixels2[i]);
            }
        }
    } else {
        pixels2 = new float[settings.size * settings.size];
    }

    return pixels2;
}

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From source file:org.meteoinfo.math.RandomUtil.java

/**
 * Get random data from a Poisson distribution
 * @param mean Poisson mean//from   w  w w .j a  v a2s  .  co m
 * @return Random value
 */
public static double poisson(double mean) {
    RandomDataGenerator rdg = new RandomDataGenerator();
    return rdg.nextPoisson(mean);
}

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From source file:org.meteoinfo.math.RandomUtil.java

/**
 * Get random data from a Poisson distribution
 *
 * @param mean Poisson mean/*from w w w  . java  2  s.co  m*/
 * @param n Array length
 * @return Array Result array
 */
public static Array poisson(double mean, int n) {
    Array a = Array.factory(DataType.INT, new int[] { n });
    RandomDataGenerator rd = new RandomDataGenerator();
    for (int i = 0; i < a.getSize(); i++) {
        a.setDouble(i, rd.nextPoisson(mean));
    }

    return a;
}

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From source file:org.meteoinfo.math.RandomUtil.java

/**
 * Get random data from a Poisson distribution
 *
 * @param mean Poisson mean//from   ww w .  j a  va 2 s  . c om
 * @param shape Shape
 * @return Array Result array
 */
public static Array poisson(double mean, List<Integer> shape) {
    int[] ashape = new int[shape.size()];
    for (int i = 0; i < shape.size(); i++) {
        ashape[i] = shape.get(i);
    }
    Array a = Array.factory(DataType.INT, ashape);
    RandomDataGenerator rd = new RandomDataGenerator();
    for (int i = 0; i < a.getSize(); i++) {
        a.setDouble(i, rd.nextPoisson(mean));
    }

    return a;
}

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