Constructor and Description |
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BiDiagonalizationByGolubKahanLanczos(Matrix A,
double epsilon,
RandomLongGenerator rlg)
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
|
BiDiagonalizationByGolubKahanLanczos(Matrix A,
RandomLongGenerator rlg)
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
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Modifier and Type | Method and Description |
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static CSRSparseMatrix |
MatrixFactory.randomCSRSparseMatrix(int nRows,
int nCols,
int nNonZero,
RandomLongGenerator uniform)
Constructs a random CSRSparseMatrix.
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static DOKSparseMatrix |
MatrixFactory.randomDOKSparseMatrix(int nRows,
int nCols,
int nNonZero,
RandomLongGenerator uniform)
Constructs a random DOKSparseMatrix.
|
static LILSparseMatrix |
MatrixFactory.randomLILSparseMatrix(int nRows,
int nCols,
int nNonZero,
RandomLongGenerator uniform)
Constructs a random LILSparseMatrix.
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Modifier and Type | Method and Description |
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static Vector |
VectorMathOperation.rbinom(int n,
int nTrials,
Vector p,
RandomLongGenerator uniform)
Generates
n random binomial numbers. |
Constructor and Description |
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BoxGeneralizedSimulatedAnnealingMinimizer(int dim,
double initialTemperature,
double qv,
double qa,
StopCondition stopCondition,
RandomLongGenerator rlg)
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
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BoxGeneralizedSimulatedAnnealingMinimizer(int dim,
double initialTemperature,
StopCondition stopCondition,
RandomLongGenerator rlg)
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
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Modifier and Type | Field and Description |
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protected RandomLongGenerator |
GeneticAlgorithm.uniform
This is a uniform random number generator.
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Constructor and Description |
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GeneticAlgorithm(RandomLongGenerator uniform)
Construct an instance of this implementation of genetic algorithm.
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Constructor and Description |
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Best1Bin(double Cr,
double F,
RandomLongGenerator uniform)
Construct an instance of
Best1Bin . |
Best2Bin(double Cr,
double F,
RandomLongGenerator uniform)
Construct an instance of
Best2Bin . |
DEOptim(DEOptim.NewCellFactory factoryCtor,
RandomLongGenerator uniform,
double epsilon,
int maxIterations,
int nStableIterations)
Construct a
DEOptim to solve unconstrained minimization problems. |
DEOptim(double Cr,
double F,
RandomLongGenerator uniform,
double epsilon,
int maxIterations,
int nStableIterations)
Construct a
DEOptim to solve unconstrained minimization problems. |
DEOptimCellFactory(double Cr,
double F,
RandomLongGenerator uniform)
Construct an instance of a
DEOptimCellFactory . |
Rand1Bin(double Cr,
double F,
RandomLongGenerator uniform)
Construct an instance of
Rand1Bin . |
Constructor and Description |
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GlobalSearchByLocalMinimizer(LocalSearchCellFactory.MinimizerFactory factory,
RandomLongGenerator uniform,
double epsilon,
int maxIterations,
int nStableIterations)
Construct a
GlobalSearchByLocalMinimizer to solve unconstrained minimization
problems. |
GlobalSearchByLocalMinimizer(RandomLongGenerator uniform,
double epsilon,
int maxIterations)
Construct a
GlobalSearchByLocalMinimizer to solve unconstrained minimization
problems. |
LocalSearchCellFactory(LocalSearchCellFactory.MinimizerFactory<T> factory,
RandomLongGenerator uniform)
Construct an instance of a
LocalSearchCellFactory . |
Modifier and Type | Field and Description |
---|---|
protected RandomLongGenerator |
SimpleGridMinimizer.uniform |
protected RandomLongGenerator |
SimpleCellFactory.uniform
the uniform random number generator
|
Constructor and Description |
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SimpleCellFactory(double rate,
RandomLongGenerator uniform)
Construct an instance of a
SimpleCellFactory . |
SimpleGridMinimizer(RandomLongGenerator uniform,
double epsilon,
int maxIterations)
Construct a
SimpleGridMinimizer to solve unconstrained minimization problems. |
SimpleGridMinimizer(SimpleGridMinimizer.NewCellFactoryCtor factoryCtor,
RandomLongGenerator uniform,
double epsilon,
int maxIterations,
int nStableIterations)
Construct a
SimpleGridMinimizer to solve unconstrained minimization problems. |
Constructor and Description |
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UniformMeshOverRegion(RealScalarFunction f,
SimpleCellFactory factory,
RandomLongGenerator uniform,
int minDiscretization,
Vector[] initials0,
double epsilon)
Generate an initial pool of chromosomes by putting a uniform mesh/grid/net over the entire
region.
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Constructor and Description |
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UniformDistributionOverBox1(RandomLongGenerator uniform,
int N,
RealInterval... bounds)
Construct a generator to uniformly sample points over a feasible region.
|
UniformDistributionOverBox2(double scale,
RealInterval[] bounds,
int[] discretizations,
RandomLongGenerator uniform)
Construct a generator to uniformly sample points over a feasible region.
|
UniformDistributionOverBox2(double scale,
RealInterval[] bounds,
int discretization,
RandomLongGenerator uniform)
Construct a generator to uniformly sample points over a feasible region.
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Constructor and Description |
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GeneralizedSimulatedAnnealingMinimizer(int dim,
double initialTemperature,
double qv,
double qa,
StopCondition stopCondition,
RandomLongGenerator uniform)
Constructs a new instance of the Generalized Simulated Annealing minimizer.
|
GeneralizedSimulatedAnnealingMinimizer(int dim,
double initialTemperature,
StopCondition stopCondition,
RandomLongGenerator uniform)
Constructs a new instance of the Generalized Simulated Annealing minimizer with the
recommended visiting and acceptance parameter.
|
SimulatedAnnealingMinimizer(int dim,
double initialTemperature,
StopCondition stopCondition,
RandomLongGenerator uniform)
Constructs a new instance to use
BoltzTemperatureFunction , BoltzAnnealingFunction
and MetropolisAcceptanceProbabilityFunction. |
SimulatedAnnealingMinimizer(TemperatureFunction temperatureFunction,
AnnealingFunction annealingFunction,
TemperedAcceptanceProbabilityFunction probabilityFunction,
int markovLength,
StopCondition stopCondition,
RandomLongGenerator uniform)
Constructs a new instance.
|
Constructor and Description |
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BoxGSAAnnealingFunction(Vector lower,
Vector upper,
double qv,
RandomLongGenerator uniform)
Constructs a boxed annealing function.
|
GSAAnnealingFunction(double qv,
RandomLongGenerator rlg,
RandomStandardNormalGenerator rnorm)
Constructs a GSA annealing function.
|
Constructor and Description |
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ProbabilityMassSampler(List<ProbabilityMassFunction.Mass<X>> outcomes,
RandomLongGenerator uniformRNG)
Creates an instance with the probable values and an RNG.
|
Modifier and Type | Field and Description |
---|---|
static RandomLongGenerator |
RNGUtils.SYNC_UNIFORM |
static RandomLongGenerator |
RNGUtils.UNIFORM |
Modifier and Type | Method and Description |
---|---|
static RandomLongGenerator |
RNGUtils.synchronizedRLG(RandomLongGenerator uniform)
Returns a synchronized (thread-safe)
RandomLongGenerator
backed by a specified generator. |
Modifier and Type | Method and Description |
---|---|
static RandomLongGenerator |
RNGUtils.synchronizedRLG(RandomLongGenerator uniform)
Returns a synchronized (thread-safe)
RandomLongGenerator
backed by a specified generator. |
Modifier and Type | Class and Description |
---|---|
class |
ConcurrentCachedRLG
This is a fast thread-safe wrapper for random long generators.
|
Constructor and Description |
---|
ConcurrentCachedRLG(RandomLongGenerator uniform)
Construct a new instance which wraps the given random long generator and
uses a cache which has 1000 entries per available core.
|
ConcurrentCachedRLG(RandomLongGenerator uniform,
int cacheSize)
Constructs a new instance which wraps the given random long generator and
uses a cache of the specified size.
|
Modifier and Type | Class and Description |
---|---|
class |
ThreadIDRLG
This uniform number generator generates independent sequences of random numbers per thread, hence
thread-safe.
|
Constructor and Description |
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MultinomialRVG(int N,
double[] prob,
RandomLongGenerator uniform)
Constructs a multinomial random vector generator.
|
UniformDistributionOverBox(RandomLongGenerator uniform,
RealInterval... bounds)
Construct a random vector generator to uniformly sample points over a box region.
|
Constructor and Description |
---|
ErgodicHybridMCMC(double a,
double b,
RandomLongGenerator uniform,
AbstractHybridMCMC hybridMCMC)
Constructs a new instance where dt is uniformly drawn from a given range.
|
HybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
Vector initialState,
RandomLongGenerator rlg)
Constructs a new instance with the given parameters.
|
MultipointHybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
int M,
Vector initialState,
RandomLongGenerator uniform)
Constructs a new instance with equal weights to the M configurations.
|
MultipointHybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
int M,
Vector w,
Vector initialState,
RandomLongGenerator uniform)
Constructs a new instance with the given parameters.
|
Modifier and Type | Method and Description |
---|---|
static boolean |
MetropolisUtils.isProposalAccepted(RealScalarFunction logf,
RandomLongGenerator uniform,
Vector currentState,
Vector proposedState)
Uses the given LOG density function to determine whether the given state transition should be
accepted.
|
Constructor and Description |
---|
Metropolis(RealScalarFunction logf,
RealVectorFunction proposalFunction,
Vector initialState,
RandomLongGenerator uniform)
Constructs a new instance with the given parameters.
|
Metropolis(RealScalarFunction logf,
Vector initialState,
double sigma,
RandomLongGenerator uniform)
Constructs a new instance, which draws the offset of the next proposed state from the
previous state from a standard Normal distribution, with the given variance and zero
covariance.
|
Metropolis(RealScalarFunction logf,
Vector initialState,
Matrix scale,
RandomLongGenerator uniform)
Constructs a new instance, which draws the offset of the next proposed state from the
previous state from a standard Normal distribution, multiplied by the given scale matrix.
|
RobustAdaptiveMetropolis(RealScalarFunction logf,
double targetAcceptance,
Vector initialState,
RandomLongGenerator uniform)
Constructs an instance which assumes an initial variance of 1 per variable, uses a gamma of
0.5.
|
RobustAdaptiveMetropolis(RealScalarFunction logf,
Matrix initialScale,
double gamma,
double targetAcceptance,
Vector initialState,
RandomStandardNormalGenerator rnorm,
RandomLongGenerator uniform)
Constructs a new instance with the given parameters.
|
Constructor and Description |
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GaussianProposalFunction(double[] sigma,
RandomLongGenerator uniform)
Constructs a Gaussian proposal function.
|
GaussianProposalFunction(double sigma,
int size,
RandomLongGenerator uniform)
Constructs a Gaussian proposal function.
|
GaussianProposalFunction(Matrix scale,
RandomLongGenerator uniform)
Constructs a Gaussian proposal function.
|
HybridMCMCProposalFunction(Vector m,
RandomLongGenerator uniform)
Constructs a hybrid MC proposal function.
|
Constructor and Description |
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BinomialRNG(int n,
double p,
RandomLongGenerator uniform)
Construct a random number generator to sample from the binomial distribution.
|
InverseTransformSampling(ProbabilityDistribution distribution,
RandomLongGenerator uniform)
Construct a random number generator to sample from a distribution.
|
Constructor and Description |
---|
Cheng1978(double aa,
double bb,
RandomLongGenerator uniform)
Construct a random number generator to sample from the beta distribution.
|
Constructor and Description |
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KunduGupta2007(double k,
double theta,
RandomLongGenerator uniform)
Construct a random number generator to sample from the gamma distribution.
|
MarsagliaTsang2000(double k,
double theta,
RandomStandardNormalGenerator normal,
RandomLongGenerator uniform)
Construct a random number generator to sample from the gamma distribution.
|
XiTanLiu2010a(double k,
RandomLongGenerator uniform)
Construct a random number generator to sample from the gamma distribution.
|
XiTanLiu2010b(double k,
RandomLongGenerator uniform)
Construct a random number generator to sample from the gamma distribution.
|
Constructor and Description |
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BoxMuller(RandomLongGenerator uniform)
Construct a random number generator to sample from the standard Normal distribution.
|
MarsagliaBray1964(RandomLongGenerator uniform)
Construct a random number generator to sample from the standard Normal distribution.
|
Ziggurat2000(RandomLongGenerator uniform)
Construct a Ziggurat random normal generator.
|
Zignor2005(RandomLongGenerator uniform)
Construct an improved Ziggurat random normal generator.
|
Constructor and Description |
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InverseTransformSamplingTruncatedNormalRNG(double mu,
double sigma,
double a,
double b,
RandomLongGenerator uniform)
Construct a rng that samples from a truncated Normal distribution using inverse sampling
technique.
|
Constructor and Description |
---|
Knuth1969(double lambda,
RandomLongGenerator uniform)
Construct a random number generator to sample from the Poisson distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
MWC8222
Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.
|
class |
SHR0
SHR0 is a simple uniform random number generator.
|
class |
SHR3
SHR3 is a 3-shift-register generator with period 2^32-1.
|
class |
UniformRNG
A pseudo uniform random number generator samples numbers from the unit interval, [0, 1],
in such a way that there are equal probabilities of them falling in any same length sub-interval.
|
Modifier and Type | Interface and Description |
---|---|
interface |
LinearCongruentialGenerator
A linear congruential generator (LCG) produces a sequence of pseudo-random numbers
based on a linear recurrence relation.
|
Modifier and Type | Class and Description |
---|---|
class |
CompositeLinearCongruentialGenerator
A composite generator combines a number of simple
LinearCongruentialGenerator , such as Lehmer ,
to form one longer period generator by first summing values and then taking modulus. |
class |
LEcuyer
This is the uniform random number generator recommended by L'Ecuyer in 1996.
|
class |
Lehmer
Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in
[0, 1].
|
class |
MRG
A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form:
|
Modifier and Type | Class and Description |
---|---|
class |
MersenneTwister
Mersenne Twister is one of the best pseudo random number generators
available.
|
Modifier and Type | Method and Description |
---|---|
Iterator<RandomLongGenerator> |
DynamicCreator.iterator() |
Constructor and Description |
---|
MersenneTwisterParamSearcher(RandomLongGenerator rng)
Constructs a new instance which uses the given RNG to do the parameter search.
|
MersenneTwisterParamSearcher(RandomLongGenerator rng,
MersenneExponent p)
Constructs a new instance which uses the given RNG to do the parameter search, with the given
period parameter.
|
Constructor and Description |
---|
CaseResamplingReplacement(double[] sample,
RandomLongGenerator uniform)
Constructs a bootstrap sample generator.
|
CaseResamplingReplacementForObject(X[] sample,
Class<X> clazz,
RandomLongGenerator uniform)
Constructs a bootstrap sample generator.
|
Constructor and Description |
---|
CommonRandomNumbers(UnivariateRealFunction f,
UnivariateRealFunction g,
RandomLongGenerator X1)
Estimates \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random
variables.
|
CommonRandomNumbers(UnivariateRealFunction f,
UnivariateRealFunction g,
RandomLongGenerator X1,
UnivariateRealFunction X2)
Estimates \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random
variables.
|
Constructor and Description |
---|
MultivariateRandomRealizationOfRandomProcess(MultivariateRandomProcess process,
int size,
RandomLongGenerator uniform)
Construct a random realization generator from a multivariate random/stochastic process.
|
Constructor and Description |
---|
RandomRealizationOfRandomProcess(RandomProcess process,
int size,
RandomLongGenerator uniform)
Construct a random realization generator from a random/stochastic process.
|
Constructor and Description |
---|
AS159(int[] rowSums,
int[] colSums,
RandomLongGenerator uniform)
Constructs a random table generator according to the row and column totals.
|
FisherExactDistribution(int[] rowSums,
int[] colSums,
int nSims,
RandomLongGenerator uniform)
Construct the distribution for Fisher's exact test.
|
Constructor and Description |
---|
ARResamplerFactory(RandomLongGenerator uniform) |
GARCHResamplerFactory(RandomLongGenerator uniform) |
GARCHResamplerFactory2(RandomLongGenerator uniform) |
ModelResamplerFactory(RandomLongGenerator uniform) |
Constructor and Description |
---|
KnightSatchellTran1995(double mu,
double q,
double alpha0,
double rate0,
double p,
double alpha1,
double rate1,
RandomStandardNormalGenerator rnorm,
RandomLongGenerator rlg)
Constructs an instance of the Knight-Satchell-Tran model of returns.
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