Example usage for org.apache.commons.math3.random RandomGenerator nextDouble

Introduction

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

Prototype

double nextDouble();

Source Link

Document

Returns the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence.

Usage

From source file:com.fpuna.preproceso.TestApacheMathLibDemo.java

/**
 * @param args//  www  .  j av a 2  s  .  c  o m
 */
public static void main(String[] args) {

    RandomGenerator randomGenerator = new JDKRandomGenerator();
    System.out.println(randomGenerator.nextInt());
    System.out.println(randomGenerator.nextDouble());

    /**
     * Descriptive Statistics like MEAN,GP,SD,MAX
    *
     */
    DescriptiveStatistics stats = new DescriptiveStatistics();
    stats.addValue(1);
    stats.addValue(2);
    stats.addValue(3);
    stats.addValue(4);
    stats.addValue(5);
    stats.addValue(6);
    stats.addValue(7);
    System.out.print("Mean : " + stats.getMean() + "\n");
    System.out.print("Standard deviation : " + stats.getStandardDeviation() + "\n");
    System.out.print("Max : " + stats.getMax() + "\n");

    /**
     * Complex number format a+bi
    *
     */
    Complex c1 = new Complex(1, 2);
    Complex c2 = new Complex(2, 3);
    System.out.print("Absolute of c1 " + c1.abs() + "\n");
    System.out.print("Addition : " + (c1.add(c2)) + "\n");
}

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From source file:math.test.TestDimpleRandom.java

/**
 * Measures speed of Apache vs Colt generators
 *///from w w  w.j a va2  s.  co m
public static void main(String[] args) {
    RandomGenerator apacheGenerator = new MersenneTwister(42);
    cern.jet.random.engine.RandomEngine cernGenerator = new cern.jet.random.engine.MersenneTwister();

    final int N = 100000;
    long start, end;

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        apacheGenerator.nextDouble();
    }
    end = System.nanoTime();
    long apacheTime = end - start;

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        cernGenerator.nextDouble();
    }
    end = System.nanoTime();

    long cernTime = end - start;

    System.out.format("MersenneTwister.nextDouble() x %d apache/cern %f\n", N, (double) apacheTime / cernTime);

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        BetaDistribution apacheBeta = new BetaDistribution(apacheGenerator, 1.0, 1.0);
        apacheBeta.sample();
    }
    end = System.nanoTime();
    apacheTime = end - start;

    cern.jet.random.Beta cernBeta = new cern.jet.random.Beta(1, 1, cernGenerator);

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        cernBeta.nextDouble();
    }
    end = System.nanoTime();
    apacheTime = end - start;

    System.out.format("Beta x %d apache/cern %f\n", N, (double) apacheTime / cernTime);

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        GammaDistribution apacheGamma = new GammaDistribution(apacheGenerator, 1.0, 1.0);
        apacheGamma.sample();
    }
    end = System.nanoTime();
    apacheTime = end - start;

    cern.jet.random.Gamma cernGamma = new cern.jet.random.Gamma(1, 1, cernGenerator);

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        cernGamma.nextDouble();
    }
    end = System.nanoTime();
    apacheTime = end - start;

    System.out.format("Gamma x %d apache/cern %f\n", N, (double) apacheTime / cernTime);

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        BinomialDistribution apacheBinomial = new BinomialDistribution(apacheGenerator, 1, .5);
        apacheBinomial.sample();
    }
    end = System.nanoTime();
    apacheTime = end - start;

    cern.jet.random.Binomial cernBinomial = new cern.jet.random.Binomial(1, .5, cernGenerator);

    start = System.nanoTime();
    for (int i = N; --i >= 0;) {
        cernBinomial.nextInt();
    }
    end = System.nanoTime();
    apacheTime = end - start;

    System.out.format("Binomial x %d apache/cern %f\n", N, (double) apacheTime / cernTime);
}

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From source file:com.anhth12.util.MathUtils.java

public static int binomial(RandomGenerator rng, int n, double p) {
    if ((p < 0) || (p > 1)) {
        return 0;
    }//from   w  ww  . ja va  2 s  .c  o m
    int c = 0;
    for (int i = 0; i < n; i++) {
        if (rng.nextDouble() < p) {
            c++;
        }
    }
    return c;
}

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From source file:com.cloudera.oryx.common.math.SolverLoadIT.java

private static RealMatrix randomSymmetricMatrix(int dimension) {
    RandomGenerator random = RandomManager.getRandom();
    RealMatrix symmetric = new Array2DRowRealMatrix(dimension, dimension);
    for (int j = 0; j < dimension; j++) {
        // Diagonal
        symmetric.setEntry(j, j, random.nextDouble());
        for (int k = j + 1; k < dimension; k++) {
            // Off-diagonal
            double d = random.nextDouble();
            symmetric.setEntry(j, k, d);
            symmetric.setEntry(k, j, d);
        }//from w w  w  .  j  ava 2 s.  c om
    }
    return symmetric;
}

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From source file:com.milaboratory.core.mutations.generator.MutationModels.java

public static NucleotideMutationModel getEmpiricalNucleotideMutationModelWithNoise(RandomGenerator rd,
        double minFactor, double maxFactor) {
    double l = maxFactor - minFactor;
    return new GenericNucleotideMutationModel(
            getEmpiricalNucleotideSubstitutionModelWithNoise(rd, minFactor, maxFactor),
            0.00522 * (minFactor + l * rd.nextDouble()), 0.00198 * (minFactor + l * rd.nextDouble()),
            rd.nextLong());/*w w w. j av  a2 s  .c  om*/
}

---

From source file:com.milaboratory.core.mutations.generator.SubstitutionModels.java

public static SubstitutionModel getEmpiricalNucleotideSubstitutionModelWithNoise(RandomGenerator rd,
        double minFactor, double maxFactor) {
    SubstitutionModelBuilder builder = new SubstitutionModelBuilder(4);
    double l = maxFactor - minFactor;
    builder.setProbability(A, G, 0.00267 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(A, C, 0.00009 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(A, T, 0.0019 * (minFactor + l * rd.nextDouble()));

    builder.setProbability(G, A, 0.00079 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(G, C, 0.0001 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(G, T, 0.00234 * (minFactor + l * rd.nextDouble()));

    builder.setProbability(C, A, 0.00001 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(C, G, 0.00017 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(C, T, 0.00162 * (minFactor + l * rd.nextDouble()));

    builder.setProbability(T, A, 0.00006 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(T, G, 0.00019 * (minFactor + l * rd.nextDouble()));
    builder.setProbability(T, C, 0.00119 * (minFactor + l * rd.nextDouble()));

    return builder.build();
}

---

From source file:com.github.rinde.rinsim.scenario.ScenarioTestUtil.java

public static Scenario create(long seed) {
    final int endTime = 3 * 60 * 60 * 1000;
    Scenario.Builder b = Scenario.builder()
            .addModel(PlaneRoadModel.supplier(new Point(0, 0), new Point(10, 10), SI.KILOMETER,
                    Measure.valueOf(50d, NonSI.KILOMETERS_PER_HOUR)))
            .addModel(DefaultPDPModel.supplier(TimeWindowPolicies.LIBERAL)).addEvents(Collections.nCopies(10,
                    new AddVehicleEvent(-1, VehicleDTO.builder().startPosition(new Point(5, 5)).build())));

    RandomGenerator rng = new MersenneTwister(seed);
    for (int i = 0; i < 20; i++) {
        long announceTime = rng.nextInt(DoubleMath.roundToInt(endTime * .8, RoundingMode.FLOOR));
        b.addEvent(new AddParcelEvent(ParcelDTO
                .builder(new Point(rng.nextDouble() * 10, rng.nextDouble() * 10),
                        new Point(rng.nextDouble() * 10, rng.nextDouble() * 10))
                .orderAnnounceTime(announceTime).pickupTimeWindow(new TimeWindow(announceTime, endTime))
                .deliveryTimeWindow(new TimeWindow(announceTime, endTime)).neededCapacity(0).build()));
    }//from  w  w  w  .j a  v a2 s  .  c o  m

    b.addEvent(new TimedEvent(PDPScenarioEvent.TIME_OUT, endTime)).scenarioLength(endTime)
            .stopCondition(new EndTimeStopCondition(endTime));

    b.addEventType(PDPScenarioEvent.ADD_DEPOT);

    return b.build();
}

---

From source file:edu.byu.nlp.stats.GammaDistribution.java

/**
 * self-contained gamma generator. Multiply result with scale parameter (or
 * divide by rate parameter). After Teh (npbayes).
 * /*from www.  ja va  2 s. c  o  m*/
 * Taken From knowceans.
 */
public static double sample(double shape, RandomGenerator rnd) {
    Preconditions.checkArgument(shape > 0.0);
    Preconditions.checkNotNull(rnd);

    if (shape == 1.0) {
        /* Exponential */
        return -Math.log(rnd.nextDouble());
    } else if (shape < 1.0) {
        /* Use Johnk's generator */
        double cc = 1.0 / shape;
        double dd = 1.0 / (1.0 - shape);
        while (true) {
            double xx = Math.pow(rnd.nextDouble(), cc);
            double yy = xx + Math.pow(rnd.nextDouble(), dd);
            if (yy <= 1.0) {
                // FIXME: assertion error for rr = 0.010817814317923407
                // assert yy != 0 && xx / yy > 0 : "rr = " + rr;
                // INFO: this if is a hack
                if (yy != 0 && xx / yy > 0) {
                    return -Math.log(rnd.nextDouble()) * xx / yy;
                }
            }
        }
    } else { /* rr > 1.0 */
        /* Use bests algorithm */
        double bb = shape - 1.0;
        double cc = 3.0 * shape - 0.75;
        while (true) {
            double uu = rnd.nextDouble();
            double vv = rnd.nextDouble();
            double ww = uu * (1.0 - uu);
            double yy = Math.sqrt(cc / ww) * (uu - 0.5);
            double xx = bb + yy;
            if (xx >= 0) {
                double zz = 64.0 * ww * ww * ww * vv * vv;
                assert zz > 0 && bb != 0 && xx / bb > 0;
                if ((zz <= (1.0 - 2.0 * yy * yy / xx))
                        || (Math.log(zz) <= 2.0 * (bb * Math.log(xx / bb) - yy))) {
                    return xx;
                }
            }
        }
    }
}

---

From source file:com.github.rinde.rinsim.pdptw.common.ScenarioTestUtil.java

/**
 * Creates a random scenario.// w  w  w  . j av a 2s  . c o m
 * @param seed The seed to use.
 * @param models Additional models (optional).
 * @return A new random scenario.
 */
public static Scenario createRandomScenario(long seed, ModelBuilder<?, ?>... models) {
    final int endTime = 3 * 60 * 60 * 1000;
    final Scenario.Builder b = Scenario.builder().addModel(RoadModelBuilders.plane())
            .addModel(DefaultPDPModel.builder()).addModels(asList(models)).addEvents(Collections.nCopies(10,
                    AddVehicleEvent.create(-1, VehicleDTO.builder().startPosition(new Point(5, 5)).build())));

    final RandomGenerator rng = new MersenneTwister(seed);
    for (int i = 0; i < 20; i++) {
        final long announceTime = rng.nextInt(DoubleMath.roundToInt(endTime * .8, RoundingMode.FLOOR));
        b.addEvent(AddParcelEvent.create(Parcel
                .builder(new Point(rng.nextDouble() * 10, rng.nextDouble() * 10),
                        new Point(rng.nextDouble() * 10, rng.nextDouble() * 10))
                .orderAnnounceTime(announceTime).pickupTimeWindow(TimeWindow.create(announceTime, endTime))
                .deliveryTimeWindow(TimeWindow.create(announceTime, endTime)).neededCapacity(0).buildDTO()));
    }

    b.addEvent(TimeOutEvent.create(endTime)).scenarioLength(endTime)
            .setStopCondition(StopConditions.limitedTime(endTime));

    return b.build();
}

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From source file:edu.byu.nlp.crowdsourcing.models.meanfield.MeanFieldMomRespModelTest.java

private static MeanFieldMomRespModel buildModel(RandomGenerator rnd, int numClasses, int numAnnotators,
        int numInstances, int numFeatures) {

    // priors//from w  w w.  j  a v  a 2s  . c o  m
    double bTheta = rnd.nextDouble();
    double bMu = rnd.nextDouble();
    double cMu = rnd.nextDouble() * 10;
    double bAlpha = rnd.nextDouble();
    double cAlpha = rnd.nextDouble() * 10;
    double bPhi = rnd.nextDouble();
    PriorSpecification priors = new PriorSpecification(bTheta, bMu, cMu, bAlpha, cAlpha, bPhi, -1, false,
            numAnnotators);

    int[][][] a = new int[numInstances][numAnnotators][numClasses];
    for (int i = 0; i < numInstances; i++) {
        for (int j = 0; j < numAnnotators; j++) {
            for (int k = 0; k < numClasses; k++) {
                a[i][j][k] = rnd.nextInt(10);
            }
        }
    }
    double[][] countOfXAndF = new double[numInstances][numFeatures];
    for (int i = 0; i < numInstances; i++) {
        for (int f = 0; f < numFeatures; f++) {
            countOfXAndF[i][f] = rnd.nextDouble();
        }
    }
    double[][][] gammaParams = new double[numAnnotators][numClasses][numClasses];
    for (int j = 0; j < numAnnotators; j++) {
        CrowdsourcingUtils.initializeConfusionMatrixWithPrior(gammaParams[j], priors.getBGamma(j),
                priors.getCGamma());
    }
    Map<String, Integer> instanceIndices = Maps.newHashMap();
    for (int i = 0; i < numInstances; i++) {
        instanceIndices.put("" + i, i);
    }
    Dataset data = null;
    //    MeanFieldMomRespModel model = new MeanFieldMomRespModel(priors, a, countOfXAndF, gammaParams, instanceIndices, data, rnd);
    MeanFieldMomRespModel model = new MeanFieldMomRespModel(priors, a, gammaParams, instanceIndices, data, rnd);

    // init variables
    for (int i = 0; i < numInstances; i++) {
        for (int k = 0; k < numClasses; k++) {
            model.vars.logg[i][k] = rnd.nextDouble();
        }
        DoubleArrays.normalizeAndLogToSelf(model.vars.logg[i]);
    }
    for (int k = 0; k < numClasses; k++) {
        model.vars.pi[k] = rnd.nextDouble() * 10;
    }
    for (int j = 0; j < numAnnotators; j++) {
        for (int k = 0; k < numClasses; k++) {
            for (int k2 = 0; k2 < numClasses; k2++) {
                model.vars.nu[j][k][k2] = rnd.nextDouble() * 10;
            }
        }
    }
    for (int k = 0; k < numClasses; k++) {
        for (int f = 0; f < numFeatures; f++) {
            model.vars.lambda[k][f] = rnd.nextDouble() * 10;
        }
    }
    return model;
}

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