Examples with RandomGenerator org.apache.commons.math3.random.RandomGenerator used on opensource projects

Example 61 with RandomGenerator

use of org.apache.commons.math3.random.RandomGenerator in project GDSC-SMLM by aherbert.

the class ConfigurationTemplateTest method canLoadTemplateImageFromFile.

@Test
public void canLoadTemplateImageFromFile() throws IOException {
    ConfigurationTemplate.clearTemplates();
    Assert.assertEquals(0, ConfigurationTemplate.getTemplateNamesWithImage().length);
    // Create a dummy image
    int size = 20;
    float[] pixels = new float[size * size];
    RandomGenerator r = new Well19937c();
    for (int i = pixels.length; i-- > 0; ) pixels[i] = r.nextFloat();
    ImagePlus imp = new ImagePlus("test", new FloatProcessor(size, size, pixels));
    File tmpFile = File.createTempFile("tmp", ".tif");
    tmpFile.deleteOnExit();
    IJ.save(imp, tmpFile.getPath());
    String name = "canLoadTemplateImageFromFile";
    File file = new File(Utils.replaceExtension(tmpFile.getPath(), ".xml"));
    ConfigurationTemplate.saveTemplate(name, new GlobalSettings(), file);
    Assert.assertEquals(1, ConfigurationTemplate.getTemplateNamesWithImage().length);
    ImagePlus imp2 = ConfigurationTemplate.getTemplateImage(name);
    Assert.assertNotNull(imp2);
    float[] data = (float[]) imp2.getProcessor().toFloat(0, null).getPixels();
    Assert.assertArrayEquals(pixels, data, 0);
}

Example 62 with RandomGenerator

use of org.apache.commons.math3.random.RandomGenerator in project GDSC-SMLM by aherbert.

the class PulseActivationAnalysisTest method canLinearlyUnmix2Channels.

private void canLinearlyUnmix2Channels(int n, int m) {
    RandomGenerator r = new Well19937c(30051977);
    try {
        for (int loop = 0; loop < 10; loop++) {
            // A rough mix of each channel
            double[] d = create(2, r, 1, 100);
            // Crosstalk should be below 50%
            double[] c = create(2, r, 0, 0.5);
            // Enumerate
            Iterator<int[]> it = CombinatoricsUtils.combinationsIterator(2, n);
            while (it.hasNext()) {
                final int[] channels = it.next();
                double[] dd = new double[2];
                for (int i : channels) dd[i] = d[i];
                Iterator<int[]> it2 = CombinatoricsUtils.combinationsIterator(2, m);
                while (it2.hasNext()) {
                    final int[] crosstalk = it2.next();
                    double[] cc = new double[2];
                    for (int i : crosstalk) cc[i] = c[i];
                    canLinearlyUnmix2Channels(dd[0], dd[1], cc[0], cc[1]);
                }
            }
        }
    } catch (AssertionError e) {
        throw new AssertionError(String.format("channels=%d, crosstalk=%d", n, m), e);
    }
}

Example 63 with RandomGenerator

use of org.apache.commons.math3.random.RandomGenerator in project GDSC-SMLM by aherbert.

the class SimpleRecombiner method recombine.

private ChromosomePair<T> recombine(Chromosome<T> chromosome1, Chromosome<T> chromosome2, double[] s1, double[] s2) {
    int nCrossovers = 1;
    if (fraction > 0)
        nCrossovers = Math.max(1, (int) random.nextPoisson(fraction * chromosome1.length()));
    // Randomly select positions using a partial Fischer-Yates shuffle
    int[] positions = new int[s1.length];
    for (int i = 0; i < positions.length; i++) positions[i] = i;
    RandomGenerator ran = random.getRandomGenerator();
    for (int i = positions.length, n = nCrossovers; i-- > 1 && n-- > 0; ) {
        int j = (int) (ran.nextInt(i + 1));
        int tmp = positions[i];
        positions[i] = positions[j];
        positions[j] = tmp;
    }
    // Reverse the array because the end is random
    Sort.reverse(positions);
    positions = Arrays.copyOf(positions, nCrossovers);
    // Get the positions in order
    if (nCrossovers != 1)
        Arrays.sort(positions);
    int nextSwap = 0;
    // Create the children by copying the parent, swapping at each crossover position
    double[] n1 = new double[s1.length];
    double[] n2 = new double[n1.length];
    for (int i = 0; i < n1.length; i++) {
        if (positions[nextSwap] == i) {
            double[] tmp = s1;
            s1 = s2;
            s2 = tmp;
            nextSwap++;
            // Avoid index out of bounds
            if (nextSwap == nCrossovers)
                nextSwap--;
        }
        n1[i] = s1[i];
        n2[i] = s2[i];
    }
    // Create the new chromosome using the correct parent, i.e.
    // If the first swap position was at the start then reverse them.
    Chromosome<T> c1 = (positions[0] == 0) ? chromosome2 : chromosome1;
    Chromosome<T> c2 = (positions[0] == 0) ? chromosome1 : chromosome2;
    c1 = c1.newChromosome(n1);
    c2 = c2.newChromosome(n2);
    // Ensure the child order is random
    return (ran.nextDouble() < 0.5) ? new ChromosomePair<T>(c1, c2) : new ChromosomePair<T>(c2, c1);
}

Example 64 with RandomGenerator

use of org.apache.commons.math3.random.RandomGenerator in project GDSC-SMLM by aherbert.

the class EMGainAnalysis method simulateFromPoissonGammaGaussian.

/**
	 * Randomly generate a histogram from poisson-gamma-gaussian samples
	 * 
	 * @return The histogram
	 */
private int[] simulateFromPoissonGammaGaussian() {
    // Randomly sample
    RandomGenerator random = new Well44497b(System.currentTimeMillis() + System.identityHashCode(this));
    PoissonDistribution poisson = new PoissonDistribution(random, _photons, PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
    CustomGammaDistribution gamma = new CustomGammaDistribution(random, _photons, _gain, GammaDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    final int steps = simulationSize;
    int[] sample = new int[steps];
    for (int n = 0; n < steps; n++) {
        if (n % 64 == 0)
            IJ.showProgress(n, steps);
        // Poisson
        double d = poisson.sample();
        // Gamma
        if (d > 0) {
            gamma.setShapeUnsafe(d);
            d = gamma.sample();
        }
        // Gaussian
        d += _noise * random.nextGaussian();
        // Convert the sample to a count 
        sample[n] = (int) Math.round(d + _bias);
    }
    int max = Maths.max(sample);
    int[] h = new int[max + 1];
    for (int s : sample) h[s]++;
    return h;
}

Example 65 with RandomGenerator

use of org.apache.commons.math3.random.RandomGenerator in project GDSC-SMLM by aherbert.

the class EMGainAnalysis method simulateFromPDF.

/**
	 * Random sample from the cumulative probability distribution function that is used during fitting
	 * 
	 * @return The histogram
	 */
private int[] simulateFromPDF() {
    final double[] g = pdf(0, _photons, _gain, _noise, (int) _bias);
    // Debug this
    double[] x = Utils.newArray(g.length, 0, 1.0);
    Utils.display(TITLE + " PDF", new Plot(TITLE + " PDF", "ADU", "P", x, Arrays.copyOf(g, g.length)));
    // Get cumulative probability
    double sum = 0;
    for (int i = 0; i < g.length; i++) {
        final double p = g[i];
        g[i] += sum;
        sum += p;
    }
    for (int i = 0; i < g.length; i++) g[i] /= sum;
    // Ensure value of 1 at the end
    g[g.length - 1] = 1;
    // Randomly sample
    RandomGenerator random = new Well44497b(System.currentTimeMillis() + System.identityHashCode(this));
    int[] h = new int[g.length];
    final int steps = simulationSize;
    for (int n = 0; n < steps; n++) {
        if (n % 64 == 0)
            IJ.showProgress(n, steps);
        final double p = random.nextDouble();
        for (int i = 0; i < g.length; i++) if (p <= g[i]) {
            h[i]++;
            break;
        }
    }
    return h;
}