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| 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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