Example usage for org.apache.commons.math3.distribution PoissonDistribution PoissonDistribution

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution PoissonDistribution PoissonDistribution.

Prototype

public PoissonDistribution(RandomGenerator rng, double p, double epsilon, int maxIterations)
        throws NotStrictlyPositiveException 

Source Link

Document

Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations.

Usage

From source file:com.cloudera.oryx.ml.serving.als.model.LoadTestALSModelFactory.java

public static ALSServingModel buildTestModel() {

    log.info("Building load test model...");

    System.gc();/*from   w  w w.  j  ava2  s .  co m*/
    long startMemory = JVMUtils.getUsedMemory();

    RandomGenerator random = RandomManager.getRandom();
    PoissonDistribution itemPerUserDist = new PoissonDistribution(random, AVG_ITEMS_PER_USER,
            PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
    ALSServingModel model = new ALSServingModel(FEATURES, true);

    long totalEntries = 0;
    for (int user = 0; user < USERS; user++) {
        String userID = "U" + user;
        model.setUserVector(userID, randomVector(random));
        int itemsPerUser = itemPerUserDist.sample();
        totalEntries += itemsPerUser;
        Collection<String> knownIDs = new ArrayList<>(itemsPerUser);
        for (int i = 0; i < itemsPerUser; i++) {
            knownIDs.add("I" + random.nextInt(ITEMS));
        }
        model.addKnownItems(userID, knownIDs);
    }

    for (int item = 0; item < ITEMS; item++) {
        model.setItemVector("I" + item, randomVector(random));
    }

    System.gc();
    long endMemory = JVMUtils.getUsedMemory();

    log.info("Built model over {} users, {} items, {} features, {} entries, using {}MB", USERS, ITEMS, FEATURES,
            totalEntries, (endMemory - startMemory) / 1_000_000);
    return model;
}

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From source file:com.ibm.bi.dml.runtime.util.PoissonPRNGenerator.java

public void setup(double mean, long sd) {
    seed = sd;//ww  w .j  a v a  2 s  . com

    SynchronizedRandomGenerator srg = new SynchronizedRandomGenerator(new Well1024a());
    srg.setSeed(seed);
    _pdist = new PoissonDistribution(srg, _mean, PoissonDistribution.DEFAULT_EPSILON,
            PoissonDistribution.DEFAULT_MAX_ITERATIONS);
}

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From source file:com.cloudera.oryx.app.serving.als.model.LoadTestALSModelFactory.java

public static ALSServingModel buildTestModel() {

    log.info("Building load test model...");

    System.gc();/*from ww w. j av a 2 s. c o  m*/
    long startMemory = JVMUtils.getUsedMemory();

    ALSServingModel model = new ALSServingModel(FEATURES, true, LSH_SAMPLE_RATE, new TestALSRescorerProvider());
    AtomicLong totalEntries = new AtomicLong();

    int numCores = Runtime.getRuntime().availableProcessors();
    log.info("Adding {} users", USERS);
    AtomicInteger userCount = new AtomicInteger();
    ExecUtils.doInParallel(numCores, i -> {
        RandomGenerator random = RandomManager.getRandom(((long) i << 32) ^ System.nanoTime());
        PoissonDistribution itemPerUserDist = new PoissonDistribution(random, AVG_ITEMS_PER_USER,
                PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
        for (int user = userCount.getAndIncrement(); user < USERS; user = userCount.getAndIncrement()) {
            String userID = "U" + user;
            model.setUserVector(userID, VectorMath.randomVectorF(FEATURES, random));
            int itemsPerUser = itemPerUserDist.sample();
            totalEntries.addAndGet(itemsPerUser);
            Collection<String> knownIDs = new ArrayList<>(itemsPerUser);
            for (int item = 0; item < itemsPerUser; item++) {
                knownIDs.add("I" + random.nextInt(ITEMS));
            }
            model.addKnownItems(userID, knownIDs);
        }
    });

    log.info("Adding {} items", ITEMS);
    AtomicInteger itemCount = new AtomicInteger();
    ExecUtils.doInParallel(numCores, i -> {
        RandomGenerator random = RandomManager.getRandom(((long) i << 32) ^ System.nanoTime());
        for (int item = itemCount.getAndIncrement(); item < ITEMS; item = itemCount.getAndIncrement()) {
            model.setItemVector("I" + item, VectorMath.randomVectorF(FEATURES, random));
        }
    });

    System.gc();
    long endMemory = JVMUtils.getUsedMemory();

    log.info("Built model over {} users, {} items, {} features, {} entries, using {}MB", USERS, ITEMS, FEATURES,
            totalEntries, (endMemory - startMemory) / 1_000_000);
    log.info("Model: {}", model);
    return model;
}

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From source file:edu.cmu.tetrad.util.RandomUtil.java

/**
 * Returns the next random number drawn from a Poisson distribution with the given mean.
 *
 * @param lambda A positive real number equal to the expected number of occurrences during a given interval. See
 *               Wikipedia./*from  w ww  .j a  va2s  . c o  m*/
 * @return Ibid.
 */
public double nextPoisson(double lambda) {
    return new PoissonDistribution(randomGenerator, lambda, 1.0E-12D, 100000).sample();
}

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From source file:com.cloudera.oryx.app.serving.als.model.ALSServingModelTest.java

@Test
public void testLSHEffect() {
    RandomGenerator random = RandomManager.getRandom();
    PoissonDistribution itemPerUserDist = new PoissonDistribution(random, 20,
            PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
    int features = 20;
    ALSServingModel mainModel = new ALSServingModel(features, true, 1.0, null);
    ALSServingModel lshModel = new ALSServingModel(features, true, 0.5, null);

    int userItemCount = 20000;
    for (int user = 0; user < userItemCount; user++) {
        String userID = "U" + user;
        float[] vec = VectorMath.randomVectorF(features, random);
        mainModel.setUserVector(userID, vec);
        lshModel.setUserVector(userID, vec);
        int itemsPerUser = itemPerUserDist.sample();
        Collection<String> knownIDs = new ArrayList<>(itemsPerUser);
        for (int i = 0; i < itemsPerUser; i++) {
            knownIDs.add("I" + random.nextInt(userItemCount));
        }//from ww  w  .  ja  v a 2  s . co m
        mainModel.addKnownItems(userID, knownIDs);
        lshModel.addKnownItems(userID, knownIDs);
    }

    for (int item = 0; item < userItemCount; item++) {
        String itemID = "I" + item;
        float[] vec = VectorMath.randomVectorF(features, random);
        mainModel.setItemVector(itemID, vec);
        lshModel.setItemVector(itemID, vec);
    }

    int numRecs = 10;
    Mean meanMatchLength = new Mean();
    for (int user = 0; user < userItemCount; user++) {
        String userID = "U" + user;
        List<Pair<String, Double>> mainRecs = mainModel
                .topN(new DotsFunction(mainModel.getUserVector(userID)), null, numRecs, null)
                .collect(Collectors.toList());
        List<Pair<String, Double>> lshRecs = lshModel
                .topN(new DotsFunction(lshModel.getUserVector(userID)), null, numRecs, null)
                .collect(Collectors.toList());
        int i = 0;
        while (i < lshRecs.size() && i < mainRecs.size() && lshRecs.get(i).equals(mainRecs.get(i))) {
            i++;
        }
        meanMatchLength.increment(i);
    }
    log.info("Mean matching prefix: {}", meanMatchLength.getResult());
    assertGreaterOrEqual(meanMatchLength.getResult(), 4.0);

    meanMatchLength.clear();
    for (int item = 0; item < userItemCount; item++) {
        String itemID = "I" + item;
        List<Pair<String, Double>> mainRecs = mainModel
                .topN(new CosineAverageFunction(mainModel.getItemVector(itemID)), null, numRecs, null)
                .collect(Collectors.toList());
        List<Pair<String, Double>> lshRecs = lshModel
                .topN(new CosineAverageFunction(lshModel.getItemVector(itemID)), null, numRecs, null)
                .collect(Collectors.toList());
        int i = 0;
        while (i < lshRecs.size() && i < mainRecs.size() && lshRecs.get(i).equals(mainRecs.get(i))) {
            i++;
        }
        meanMatchLength.increment(i);
    }
    log.info("Mean matching prefix: {}", meanMatchLength.getResult());
    assertGreaterOrEqual(meanMatchLength.getResult(), 5.0);
}

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

/**
 * Randomly generate a histogram from poisson-gamma-gaussian samples
 * /*from  w ww. ja v  a2 s  . co  m*/
 * @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;
}

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From source file:hivemall.anomaly.ChangeFinder2DTest.java

public void testSota5D() throws HiveException {
    final int DIM = 5;
    final int EXAMPLES = 20001;

    final Double[] x = new Double[DIM];
    final List<Double> xList = Arrays.asList(x);

    Parameters params = new Parameters();
    params.set(LossFunction.logloss);//w ww  .ja  v a 2  s  . c  om
    params.r1 = 0.01d;
    params.k = 10;
    params.T1 = 10;
    params.T2 = 10;
    PrimitiveObjectInspector oi = PrimitiveObjectInspectorFactory.javaDoubleObjectInspector;
    ListObjectInspector listOI = ObjectInspectorFactory.getStandardListObjectInspector(oi);
    final ChangeFinder2D cf = new ChangeFinder2D(params, listOI);
    final double[] outScores = new double[2];

    RandomGenerator rng1 = new Well19937c(31L);
    final UniformIntegerDistribution uniform = new UniformIntegerDistribution(rng1, 0, 10);
    RandomGenerator rng2 = new Well19937c(41L);
    final PoissonDistribution poissonEvent = new PoissonDistribution(rng2, 1000.d,
            PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
    final StringBuilder buf = new StringBuilder(256);

    println("# time x0 x1 x2 x3 x4 mean0 mean1 mean2 mean3 mean4 outlier change");
    FIN: for (int i = 0; i < EXAMPLES;) {
        int len = poissonEvent.sample();
        double data[][] = new double[DIM][len];
        double mean[] = new double[DIM];
        double sd[] = new double[DIM];
        for (int j = 0; j < DIM; j++) {
            mean[j] = uniform.sample() * 5.d;
            sd[j] = uniform.sample() / 10.d * 5.d + 1.d;
            if (i % 5 == 0) {
                mean[j] += 50.d;
            }
            NormalDistribution normDist = new NormalDistribution(new Well19937c(i + j), mean[j], sd[j]);
            data[j] = normDist.sample(len);
            data[j][len / (j + 2) + DIM % (j + 1)] = mean[j] + (j + 4) * sd[j];
        }
        for (int j = 0; j < len; j++) {
            if (i >= EXAMPLES) {
                break FIN;
            }
            x[0] = data[0][j];
            x[1] = data[1][j];
            x[2] = data[2][j];
            x[3] = data[3][j];
            x[4] = data[4][j];
            cf.update(xList, outScores);
            buf.append(i).append(' ').append(x[0].doubleValue()).append(' ').append(x[1].doubleValue())
                    .append(' ').append(x[2].doubleValue()).append(' ').append(x[3].doubleValue()).append(' ')
                    .append(x[4].doubleValue()).append(' ').append(mean[0]).append(' ').append(mean[1])
                    .append(' ').append(mean[2]).append(' ').append(mean[3]).append(' ').append(mean[4])
                    .append(' ').append(outScores[0]).append(' ').append(outScores[1]);
            println(buf.toString());
            StringUtils.clear(buf);
            i++;
        }
    }
}

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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();/*from  w  w w .  java  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.apache.mahout.math.random.PoissonSampler.java

public PoissonSampler(double lambda) {
    limit = 1;/*from   w w  w .  jav a 2  s  .  com*/
    gen = RandomUtils.getRandom();
    pd = new PoissonDistribution(gen.getRandomGenerator(), lambda, PoissonDistribution.DEFAULT_EPSILON,
            PoissonDistribution.DEFAULT_MAX_ITERATIONS);
}

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From source file:org.apache.mahout.math.random.PoissonSamplerTest.java

private static void checkDistribution(Sampler<Double> pd, double alpha) {
    int[] count = new int[(int) Math.max(10, 5 * alpha)];
    for (int i = 0; i < 10000; i++) {
        count[pd.sample().intValue()]++;
    }/*  w ww .ja  va2 s .  c om*/

    IntegerDistribution ref = new PoissonDistribution(RandomUtils.getRandom().getRandomGenerator(), alpha,
            PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
    for (int i = 0; i < count.length; i++) {
        assertEquals(ref.probability(i), count[i] / 10000.0, 2.0e-2);
    }
}

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