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

Packages that use RandomEngine
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
`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
`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
`static void` | `Uniform.staticSetRandomEngine(RandomEngine randomGenerator)` Sets the uniform random number generation engine shared by all static methods.
`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.

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

Uses of RandomEngine in cern.jet.random.engine

Subclasses of RandomEngine in cern.jet.random.engine
`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
`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
`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
`RandomEngine` | `RandomSamplingAssistant.getRandomGenerator()` Returns the used random generator.

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

Uses of RandomEngine in cern.jet.stat.quantile

Methods in cern.jet.stat.quantile with parameters of type RandomEngine
`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
`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
`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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