## Uses of Class
cern.jet.random.engine.RandomEngine

- Packages that use RandomEngine
  Package | Description
  --- | ---
  cern.colt.matrix.doublealgo | Double matrix algorithms such as print formatting, sorting, partitioning and statistics.
  cern.jet.random | Large variety of probability distributions featuring high performance generation of random numbers, CDF's and PDF's.
  cern.jet.random.engine | Engines generating strong uniformly distributed pseudo-random numbers; Needed by all JET probability distributions since they rely on uniform random numbers to generate random numbers from their own distribution.
  cern.jet.random.sampling | Samples (picks) random subsets of data sequences.
  cern.jet.stat.quantile | Scalable algorithms and data structures to compute approximate quantiles over very large data sequences.
  hep.aida.bin | Multisets (bags) with efficient statistics operations defined upon; This package requires the Colt distribution.

- - ### Uses of RandomEngine in cern.colt.matrix.doublealgo

    Methods in cern.colt.matrix.doublealgo with parameters of type RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `static DoubleMatrix1D` | Statistic.`viewSample(DoubleMatrix1D matrix, double fraction, RandomEngine randomGenerator)` Constructs and returns a sampling view with a size of `round(matrix.size() * fraction)`.
    `static DoubleMatrix2D` | Statistic.`viewSample(DoubleMatrix2D matrix, double rowFraction, double columnFraction, RandomEngine randomGenerator)` Constructs and returns a sampling view with `round(matrix.rows() * rowFraction)` rows and `round(matrix.columns() * columnFraction)` columns.
    `static DoubleMatrix3D` | Statistic.`viewSample(DoubleMatrix3D matrix, double sliceFraction, double rowFraction, double columnFraction, RandomEngine randomGenerator)` Constructs and returns a sampling view with `round(matrix.slices() * sliceFraction)` slices and `round(matrix.rows() * rowFraction)` rows and `round(matrix.columns() * columnFraction)` columns.

  - ### Uses of RandomEngine in cern.jet.random

    Methods in cern.jet.random that return RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `static RandomEngine` | AbstractDistribution.`makeDefaultGenerator()` Constructs and returns a new uniform random number generation engine seeded with the current time.

    Methods in cern.jet.random with parameters of type RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `static double` | Distributions.`nextBurr1(double r, int nr, RandomEngine randomGenerator)` Returns a random number from the Burr II, VII, VIII, X Distributions.
    `static double` | Distributions.`nextBurr2(double r, double k, int nr, RandomEngine randomGenerator)` Returns a random number from the Burr III, IV, V, VI, IX, XII distributions.
    `static double` | Distributions.`nextCauchy(RandomEngine randomGenerator)` Returns a cauchy distributed random number from the standard Cauchy distribution C(0,1).
    `static double` | Distributions.`nextErlang(double variance, double mean, RandomEngine randomGenerator)` Returns an erlang distributed random number with the given variance and mean.
    `static int` | Distributions.`nextGeometric(double p, RandomEngine randomGenerator)` Returns a discrete geometric distributed random number; Definition.
    `static double` | Distributions.`nextLambda(double l3, double l4, RandomEngine randomGenerator)` Returns a lambda distributed random number with parameters l3 and l4.
    `static double` | Distributions.`nextLaplace(RandomEngine randomGenerator)` Returns a Laplace (Double Exponential) distributed random number from the standard Laplace distribution L(0,1).
    `static double` | Distributions.`nextLogistic(RandomEngine randomGenerator)` Returns a random number from the standard Logistic distribution Log(0,1).
    `static double` | Distributions.`nextPowLaw(double alpha, double cut, RandomEngine randomGenerator)` Returns a power-law distributed random number with the given exponent and lower cutoff.
    `static double` | Distributions.`nextTriangular(RandomEngine randomGenerator)` Returns a random number from the standard Triangular distribution in (-1,1).
    `static double` | Distributions.`nextWeibull(double alpha, double beta, RandomEngine randomGenerator)` Returns a weibull distributed random number.
    `static int` | Distributions.`nextZipfInt(double z, RandomEngine randomGenerator)` Returns a zipfian distributed random number with the given skew.
    `static void` | Uniform.`staticSetRandomEngine(RandomEngine randomGenerator)` Sets the uniform random number generation engine shared by all static methods.

    Constructors in cern.jet.random with parameters of type RandomEngine
    Constructor and Description
    `Beta(double alpha, double beta, RandomEngine randomGenerator)` Constructs a Beta distribution.
    `Binomial(int n, double p, RandomEngine randomGenerator)` Constructs a binomial distribution.
    `BreitWigner(double mean, double gamma, double cut, RandomEngine randomGenerator)` Constructs a BreitWigner distribution.
    `BreitWignerMeanSquare(double mean, double gamma, double cut, RandomEngine randomGenerator)` Constructs a mean-squared BreitWigner distribution.
    `ChiSquare(double freedom, RandomEngine randomGenerator)` Constructs a ChiSquare distribution.
    `Empirical(double[] pdf, int interpolationType, RandomEngine randomGenerator)` Constructs an Empirical distribution.
    `EmpiricalWalker(double[] pdf, int interpolationType, RandomEngine randomGenerator)` Constructs an Empirical distribution.
    `Exponential(double lambda, RandomEngine randomGenerator)` Constructs a Negative Exponential distribution.
    `ExponentialPower(double tau, RandomEngine randomGenerator)` Constructs an Exponential Power distribution.
    `Gamma(double alpha, double lambda, RandomEngine randomGenerator)` Constructs a Gamma distribution.
    `Hyperbolic(double alpha, double beta, RandomEngine randomGenerator)` Constructs a Beta distribution.
    `HyperGeometric(int N, int s, int n, RandomEngine randomGenerator)` Constructs a HyperGeometric distribution.
    `Logarithmic(double p, RandomEngine randomGenerator)` Constructs a Logarithmic distribution.
    `NegativeBinomial(int n, double p, RandomEngine randomGenerator)` Constructs a Negative Binomial distribution.
    `Normal(double mean, double standardDeviation, RandomEngine randomGenerator)` Constructs a normal (gauss) distribution.
    `Poisson(double mean, RandomEngine randomGenerator)` Constructs a poisson distribution.
    `PoissonSlow(double mean, RandomEngine randomGenerator)` Constructs a poisson distribution.
    `StudentT(double freedom, RandomEngine randomGenerator)` Constructs a StudentT distribution.
    `Uniform(double min, double max, RandomEngine randomGenerator)` Constructs a uniform distribution with the given minimum and maximum.
    `Uniform(RandomEngine randomGenerator)` Constructs a uniform distribution with `min=0.0` and `max=1.0`.
    `VonMises(double freedom, RandomEngine randomGenerator)` Constructs a Von Mises distribution.
    `Zeta(double ro, double pk, RandomEngine randomGenerator)` Constructs a Zeta distribution.

  - ### Uses of RandomEngine in cern.jet.random.engine

    Subclasses of RandomEngine in cern.jet.random.engine
    Modifier and Type | Class and Description
    --- | ---
    `class` | `DRand` Quick medium quality uniform pseudo-random number generator.
    `class` | `MersenneTwister` MersenneTwister (MT19937) is one of the strongest uniform pseudo-random number generators known so far; at the same time it is quick.
    `class` | `MersenneTwister64` Same as `MersenneTwister` except that method `raw()` returns 64 bit random numbers instead of 32 bit random numbers.

    Methods in cern.jet.random.engine that return RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `static RandomEngine` | RandomEngine.`makeDefault()` Constructs and returns a new uniform random number engine seeded with the current time.

    Methods in cern.jet.random.engine with parameters of type RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `static void` | Benchmark.`test(int size, RandomEngine randomEngine)` Prints the first `size` random numbers generated by the given engine.

  - ### Uses of RandomEngine in cern.jet.random.sampling

    Methods in cern.jet.random.sampling that return RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `RandomEngine` | RandomSamplingAssistant.`getRandomGenerator()` Returns the used random generator.

    Methods in cern.jet.random.sampling with parameters of type RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `static void` | RandomSampler.`sample(long n, long N, int count, long low, long[] values, int fromIndex, RandomEngine randomGenerator)` Efficiently computes a sorted random set of `count` elements from the interval `[low,low+N-1]`.

    Constructors in cern.jet.random.sampling with parameters of type RandomEngine
    Constructor and Description
    `RandomSampler(long n, long N, long low, RandomEngine randomGenerator)` Constructs a random sampler that computes and delivers sorted random sets in blocks.
    `RandomSamplingAssistant(long n, long N, RandomEngine randomGenerator)` Constructs a random sampler that samples `n` random elements from an input sequence of `N` elements.
    `WeightedRandomSampler(int weight, RandomEngine randomGenerator)` Chooses exactly one random element from successive blocks of `weight` input elements each.

  - ### Uses of RandomEngine in cern.jet.stat.quantile

    Methods in cern.jet.stat.quantile with parameters of type RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `static DoubleQuantileFinder` | QuantileFinderFactory.`newDoubleQuantileFinder(boolean known_N, long N, double epsilon, double delta, int quantiles, RandomEngine generator)` Returns a quantile finder that minimizes the amount of memory needed under the user provided constraints.

  - ### Uses of RandomEngine in hep.aida.bin

    Methods in hep.aida.bin with parameters of type RandomEngine
    Modifier and Type | Method and Description
    --- | ---
    `void` | DynamicBin1D.`sample(int n, boolean withReplacement, RandomEngine randomGenerator, DoubleBuffer buffer)` Uniformly samples (chooses) `n` random elements with or without replacement from the contained elements and adds them to the given buffer.
    `DynamicBin1D` | DynamicBin1D.`sampleBootstrap(DynamicBin1D other, int resamples, RandomEngine randomGenerator, BinBinFunction1D function)` Generic bootstrap resampling.

    Constructors in hep.aida.bin with parameters of type RandomEngine
    Constructor and Description
    `QuantileBin1D(boolean known_N, long N, double epsilon, double delta, int quantiles, RandomEngine randomGenerator)` Equivalent to `new QuantileBin1D(known_N, N, epsilon, delta, quantiles, randomGenerator, false, false, 2)`.
    `QuantileBin1D(boolean known_N, long N, double epsilon, double delta, int quantiles, RandomEngine randomGenerator, boolean hasSumOfLogarithms, boolean hasSumOfInversions, int maxOrderForSumOfPowers)` Constructs and returns an empty bin that, under the given constraints, minimizes the amount of memory needed.

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