namespace for EigenRand More...

Classes
class	BalancedGen
	Generator of reals in a range `[-1, 1]` More...

class	BetaGen
	Generator of reals on a beta distribution. More...

class	BinomialGen
	Generator of integers on a binomial distribution. More...

class	CauchyGen
	Generator of reals on a Cauchy distribution. More...

class	ChiSquaredGen
	Generator of reals on a chi-squared distribution. More...

class	DirichletGen
	Generator of reals on a Dirichlet distribution. More...

class	DiscreteGen
	Generator of integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)` More...

class	DiscreteGen< _Scalar, double >
	`DiscreteGen` with `double` precision More...

class	DiscreteGen< _Scalar, float >
	`DiscreteGen` with `float` precision More...

class	DiscreteGen< _Scalar, int32_t >
	`DiscreteGen` with `int32_t` precision More...

class	ExponentialGen
	Generator of reals on an exponential distribution. More...

class	ExtremeValueGen
	Generator of reals on an extreme value distribution. More...

class	FisherFGen
	Generator of reals on a Fisher's f distribution. More...

class	GammaGen
	Generator of reals on a gamma distribution. More...

class	GenBase
	Base class of all univariate random generators. More...

class	GeometricGen
	Generator of integers on a geometric distribution. More...

class	InvWishartGen
	Generator of real matrices on a inverse Wishart distribution. More...

class	LognormalGen
	Generator of reals on a lognormal distribution. More...

class	MersenneTwister
	A vectorized version of Mersenne Twister Engine. More...

class	MultinomialGen
	Generator of real vectors on a multinomial distribution. More...

class	MvMatGenBase
	Base class of all multivariate random matrix generators. More...

class	MvNormalGen
	Generator of real vectors on a multivariate normal distribution. More...

class	MvVecGenBase
	Base class of all multivariate random vector generators. More...

class	NegativeBinomialGen
	Generator of integers on a negative binomial distribution. More...

class	NormalGen
	Generator of reals on a normal distribution. More...

class	PoissonGen
	Generator of integers on a Poisson distribution. More...

class	RandbitsGen
	Generator of random bits for integral scalars. More...

class	StdNormalGen
	Generator of reals on the standard normal distribution. More...

class	StudentTGen
	Generator of reals on a Student's t distribution. More...

class	UniformIntGen
	Generator of integers with a given range `[min, max]` More...

class	UniformRealGen
	Generator of reals in a range `[0, 1)` More...

class	WeibullGen
	Generator of reals on a Weibull distribution. More...

class	WishartGen
	Generator of real matrices on a Wishart distribution. More...

Typedefs
using	OptCacheStore = CacheStore< EIGEN_MAX_ALIGN_BYTES >

template<typename Derived , typename Urng >
using	RandBitsType = CwiseNullaryOp< internal::scalar_rng_adaptor< RandbitsGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	BalancedType = CwiseNullaryOp< internal::scalar_rng_adaptor< BalancedGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	UniformRealType = CwiseNullaryOp< internal::scalar_rng_adaptor< UniformRealGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	UniformIntType = CwiseNullaryOp< internal::scalar_rng_adaptor< UniformIntGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	DiscreteFType = CwiseNullaryOp< internal::scalar_rng_adaptor< DiscreteGen< typename Derived::Scalar, float >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	DiscreteDType = CwiseNullaryOp< internal::scalar_rng_adaptor< DiscreteGen< typename Derived::Scalar, double >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	DiscreteType = CwiseNullaryOp< internal::scalar_rng_adaptor< DiscreteGen< typename Derived::Scalar, int32_t >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	PoissonType = CwiseNullaryOp< internal::scalar_rng_adaptor< PoissonGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	BinomialType = CwiseNullaryOp< internal::scalar_rng_adaptor< BinomialGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	GeometricType = CwiseNullaryOp< internal::scalar_rng_adaptor< GeometricGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	NegativeBinomialType = CwiseNullaryOp< internal::scalar_rng_adaptor< NegativeBinomialGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	BetaType = CwiseNullaryOp< internal::scalar_rng_adaptor< BetaGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	CauchyType = CwiseNullaryOp< internal::scalar_rng_adaptor< CauchyGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	NormalType = CwiseNullaryOp< internal::scalar_rng_adaptor< StdNormalGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	Normal2Type = CwiseNullaryOp< internal::scalar_rng_adaptor< NormalGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	LognormalType = CwiseNullaryOp< internal::scalar_rng_adaptor< LognormalGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	StudentTType = CwiseNullaryOp< internal::scalar_rng_adaptor< StudentTGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	ExponentialType = CwiseNullaryOp< internal::scalar_rng_adaptor< ExponentialGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	GammaType = CwiseNullaryOp< internal::scalar_rng_adaptor< GammaGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	WeibullType = CwiseNullaryOp< internal::scalar_rng_adaptor< WeibullGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	ExtremeValueType = CwiseNullaryOp< internal::scalar_rng_adaptor< ExtremeValueGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	ChiSquaredType = CwiseNullaryOp< internal::scalar_rng_adaptor< ChiSquaredGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Derived , typename Urng >
using	FisherFType = CwiseNullaryOp< internal::scalar_rng_adaptor< FisherFGen< typename Derived::Scalar >, typename Derived::Scalar, Urng, true >, const Derived >

template<typename Packet >
using	Pmt19937_64 = MersenneTwister< Packet, 312, 156, 31, 0xb5026f5aa96619e9, 29, 0x5555555555555555, 17, 0x71d67fffeda60000, 37, 0xfff7eee000000000, 43, 6364136223846793005 >
	Alias of Eigen::Rand::MersenneTwister, equivalent to std::mt19937_64. More...

template<typename UIntType , typename BaseRng >
using	PacketRandomEngineAdaptor = ParallelRandomEngineAdaptor< UIntType, BaseRng, sizeof(typename BaseRng::result_type)/sizeof(uint64_t)>
	Scalar adaptor for random engines which generates packet. More...

template<typename UIntType , typename Rng >
using	UniversalRandomEngine = typename std::conditional< IsPacketRandomEngine< typename std::remove_reference< Rng >::type >::value, PacketRandomEngineAdaptor< UIntType, typename std::remove_reference< Rng >::type >, typename std::conditional< IsScalarRandomEngine< typename std::remove_reference< Rng >::type >::value, RandomEngineWrapper< typename std::remove_reference< Rng >::type >, void >::type >::type

using	Vmt19937_64 = std::mt19937_64
	same as std::mt19937_64 when EIGEN_DONT_VECTORIZE, Pmt19937_64<internal::Packet4i> when SSE2 enabled and Pmt19937_64<internal::Packet8i> when AVX2 enabled More...

template<typename UIntType = uint64_t>
using	P8_mt19937_64 = ParallelRandomEngineAdaptor< UIntType, Vmt19937_64, 8 >
	a vectorized mt19937_64 which generates 8 integers of 64bit simultaneously. It always yields the same value regardless of SIMD ISA.

Enumerations
enum	RandomEngineType { none, scalar, packet }

Functions
template<typename Derived , typename Urng >
const RandBitsType< Derived, Urng >	randBits (Index rows, Index cols, Urng &&urng)
	generates integers with random bits More...

template<typename Derived , typename Urng >
const RandBitsType< Derived, Urng >	randBitsLike (Derived &o, Urng &&urng)
	generates integers with random bits More...

template<typename Derived , typename Urng >
const BalancedType< Derived, Urng >	balanced (Index rows, Index cols, Urng &&urng)
	generates reals in a range `[-1, 1]` More...

template<typename Derived , typename Urng >
const BalancedType< Derived, Urng >	balancedLike (const Derived &o, Urng &&urng)
	generates reals in a range `[-1, 1]` More...

template<typename Derived , typename Urng >
const UniformRealType< Derived, Urng >	uniformReal (Index rows, Index cols, Urng &&urng)
	generates reals in a range `[0, 1)` More...

template<typename Derived , typename Urng >
const UniformRealType< Derived, Urng >	uniformRealLike (Derived &o, Urng &&urng)
	generates reals in a range `[0, 1)` More...

template<typename Derived , typename Urng >
const UniformIntType< Derived, Urng >	uniformInt (Index rows, Index cols, Urng &&urng, typename Derived::Scalar min, typename Derived::Scalar max)
	generates integers with a given range `[min, max]` More...

template<typename Derived , typename Urng >
const UniformIntType< Derived, Urng >	uniformIntLike (Derived &o, Urng &&urng, typename Derived::Scalar min, typename Derived::Scalar max)
	generates integers with a given range `[min, max]` More...

template<typename Derived , typename Urng , typename RealIter >
const DiscreteFType< Derived, Urng >	discreteF (Index rows, Index cols, Urng &&urng, RealIter first, RealIter last)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is float(23bit precision). More...

template<typename Derived , typename Urng , typename RealIter >
const DiscreteFType< Derived, Urng >	discreteFLike (Derived &o, Urng &&urng, RealIter first, RealIter last)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is float(23bit precision). More...

template<typename Derived , typename Urng , typename Real >
const DiscreteFType< Derived, Urng >	discreteF (Index rows, Index cols, Urng &&urng, const std::initializer_list< Real > &il)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is float(23bit precision). More...

template<typename Derived , typename Urng , typename Real >
const DiscreteFType< Derived, Urng >	discreteFLike (Derived &o, Urng &&urng, const std::initializer_list< Real > &il)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is float(23bit precision). More...

template<typename Derived , typename Urng , typename RealIter >
const DiscreteDType< Derived, Urng >	discreteD (Index rows, Index cols, Urng &&urng, RealIter first, RealIter last)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is double(52bit precision). More...

template<typename Derived , typename Urng , typename RealIter >
const DiscreteDType< Derived, Urng >	discreteDLike (Derived &o, Urng &&urng, RealIter first, RealIter last)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is double(52bit precision). More...

template<typename Derived , typename Urng , typename Real >
const DiscreteDType< Derived, Urng >	discreteD (Index rows, Index cols, Urng &&urng, const std::initializer_list< Real > &il)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is double(52bit precision). More...

template<typename Derived , typename Urng , typename Real >
const DiscreteDType< Derived, Urng >	discreteDLike (Derived &o, Urng &&urng, const std::initializer_list< Real > &il)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is double(52bit precision). More...

template<typename Derived , typename Urng , typename RealIter >
const DiscreteType< Derived, Urng >	discrete (Index rows, Index cols, Urng &&urng, RealIter first, RealIter last)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is int32(32bit precision). More...

template<typename Derived , typename Urng , typename RealIter >
const DiscreteType< Derived, Urng >	discreteLike (Derived &o, Urng &&urng, RealIter first, RealIter last)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is int32(32bit precision). More...

template<typename Derived , typename Urng , typename Real >
const DiscreteType< Derived, Urng >	discrete (Index rows, Index cols, Urng &&urng, const std::initializer_list< Real > &il)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is int32(32bit precision). More...

template<typename Derived , typename Urng , typename Real >
const DiscreteType< Derived, Urng >	discreteLike (Derived &o, Urng &&urng, const std::initializer_list< Real > &il)
	generates random integers on the interval `[0, n)`, where the probability of each individual integer `i` is proportional to `w(i)`. The data type used for calculation of probabilities is int32(32bit precision). More...

template<typename Derived , typename Urng >
const PoissonType< Derived, Urng >	poisson (Index rows, Index cols, Urng &&urng, double mean=1)
	generates reals on the Poisson distribution. More...

template<typename Derived , typename Urng >
const PoissonType< Derived, Urng >	poissonLike (Derived &o, Urng &&urng, double mean=1)
	generates reals on the Poisson distribution. More...

template<typename Derived , typename Urng >
const BinomialType< Derived, Urng >	binomial (Index rows, Index cols, Urng &&urng, typename Derived::Scalar trials=1, double p=0.5)
	generates reals on the binomial distribution. More...

template<typename Derived , typename Urng >
const BinomialType< Derived, Urng >	binomialLike (Derived &o, Urng &&urng, typename Derived::Scalar trials=1, double p=0.5)
	generates reals on the binomial distribution. More...

template<typename Derived , typename Urng >
const GeometricType< Derived, Urng >	geometric (Index rows, Index cols, Urng &&urng, double p=0.5)
	generates reals on the geometric distribution. More...

template<typename Derived , typename Urng >
const GeometricType< Derived, Urng >	geometricLike (Derived &o, Urng &&urng, double p=0.5)
	generates reals on the geometric distribution. More...

template<typename Derived , typename Urng >
const NegativeBinomialType< Derived, Urng >	negativeBinomial (Index rows, Index cols, Urng &&urng, typename Derived::Scalar trials=1, double p=0.5)
	generates reals on the negative binomial distribution. More...

template<typename Derived , typename Urng >
const NegativeBinomialType< Derived, Urng >	negativeBinomialLike (Derived &o, Urng &&urng, typename Derived::Scalar trials=1, double p=0.5)
	generates reals on the negative binomial distribution. More...

template<typename Derived , typename Urng >
const BetaType< Derived, Urng >	beta (Index rows, Index cols, Urng &&urng, typename Derived::Scalar a=1, typename Derived::Scalar b=1)
	generates reals on the beta distribution. More...

template<typename Derived , typename Urng >
const BetaType< Derived, Urng >	betaLike (Derived &o, Urng &&urng, typename Derived::Scalar a=1, typename Derived::Scalar b=1)
	generates reals on the beta distribution. More...

template<typename Derived , typename Urng >
const CauchyType< Derived, Urng >	cauchy (Index rows, Index cols, Urng &&urng, typename Derived::Scalar a=0, typename Derived::Scalar b=1)
	generates reals on the Cauchy distribution. More...

template<typename Derived , typename Urng >
const CauchyType< Derived, Urng >	cauchyLike (Derived &o, Urng &&urng, typename Derived::Scalar a=0, typename Derived::Scalar b=1)
	generates reals on the Cauchy distribution. More...

template<typename Derived , typename Urng >
const NormalType< Derived, Urng >	normal (Index rows, Index cols, Urng &&urng)
	generates reals on a standard normal distribution (`mean` = 0, `stdev`=1) More...

template<typename Derived , typename Urng >
const NormalType< Derived, Urng >	normalLike (Derived &o, Urng &&urng)
	generates reals on a standard normal distribution (`mean` = 0, `stdev`=1) More...

template<typename Derived , typename Urng >
const Normal2Type< Derived, Urng >	normal (Index rows, Index cols, Urng &&urng, typename Derived::Scalar mean, typename Derived::Scalar stdev=1)
	generates reals on a normal distribution with arbitrary `mean` and `stdev`. More...

template<typename Derived , typename Urng >
const Normal2Type< Derived, Urng >	normalLike (Derived &o, Urng &&urng, typename Derived::Scalar mean, typename Derived::Scalar stdev=1)
	generates reals on a normal distribution with arbitrary `mean` and `stdev`. More...

template<typename Derived , typename Urng >
const LognormalType< Derived, Urng >	lognormal (Index rows, Index cols, Urng &&urng, typename Derived::Scalar mean=0, typename Derived::Scalar stdev=1)
	generates reals on a lognormal distribution with arbitrary `mean` and `stdev`. More...

template<typename Derived , typename Urng >
const LognormalType< Derived, Urng >	lognormalLike (Derived &o, Urng &&urng, typename Derived::Scalar mean=0, typename Derived::Scalar stdev=1)
	generates reals on a lognormal distribution with arbitrary `mean` and `stdev`. More...

template<typename Derived , typename Urng >
const StudentTType< Derived, Urng >	studentT (Index rows, Index cols, Urng &&urng, typename Derived::Scalar n=1)
	generates reals on the Student's t distribution with arbirtrary degress of freedom. More...

template<typename Derived , typename Urng >
const StudentTType< Derived, Urng >	studentTLike (Derived &o, Urng &&urng, typename Derived::Scalar n=1)
	generates reals on the Student's t distribution with arbirtrary degress of freedom. More...

template<typename Derived , typename Urng >
const ExponentialType< Derived, Urng >	exponential (Index rows, Index cols, Urng &&urng, typename Derived::Scalar lambda=1)
	generates reals on an exponential distribution with arbitrary scale parameter. More...

template<typename Derived , typename Urng >
const ExponentialType< Derived, Urng >	exponentialLike (Derived &o, Urng &&urng, typename Derived::Scalar lambda=1)
	generates reals on an exponential distribution with arbitrary scale parameter. More...

template<typename Derived , typename Urng >
const GammaType< Derived, Urng >	gamma (Index rows, Index cols, Urng &&urng, typename Derived::Scalar alpha=1, typename Derived::Scalar beta=1)
	generates reals on a gamma distribution with arbitrary shape and scale parameter. More...

template<typename Derived , typename Urng >
const GammaType< Derived, Urng >	gammaLike (Derived &o, Urng &&urng, typename Derived::Scalar alpha=1, typename Derived::Scalar beta=1)
	generates reals on a gamma distribution with arbitrary shape and scale parameter. More...

template<typename Derived , typename Urng >
const WeibullType< Derived, Urng >	weibull (Index rows, Index cols, Urng &&urng, typename Derived::Scalar a=1, typename Derived::Scalar b=1)
	generates reals on a Weibull distribution with arbitrary shape and scale parameter. More...

template<typename Derived , typename Urng >
const WeibullType< Derived, Urng >	weibullLike (Derived &o, Urng &&urng, typename Derived::Scalar a=1, typename Derived::Scalar b=1)
	generates reals on a Weibull distribution with arbitrary shape and scale parameter. More...

template<typename Derived , typename Urng >
const ExtremeValueType< Derived, Urng >	extremeValue (Index rows, Index cols, Urng &&urng, typename Derived::Scalar a=0, typename Derived::Scalar b=1)
	generates reals on an extreme value distribution (a.k.a Gumbel Type I, log-Weibull, Fisher-Tippett Type I) with arbitrary shape and scale parameter. More...

template<typename Derived , typename Urng >
const ExtremeValueType< Derived, Urng >	extremeValueLike (Derived &o, Urng &&urng, typename Derived::Scalar a=0, typename Derived::Scalar b=1)
	generates reals on an extreme value distribution (a.k.a Gumbel Type I, log-Weibull, Fisher-Tippett Type I) with arbitrary shape and scale parameter. More...

template<typename Derived , typename Urng >
const ChiSquaredType< Derived, Urng >	chiSquared (Index rows, Index cols, Urng &&urng, typename Derived::Scalar n=1)
	generates reals on the Chi-squared distribution with arbitrary degrees of freedom. More...

template<typename Derived , typename Urng >
const ChiSquaredType< Derived, Urng >	chiSquaredLike (Derived &o, Urng &&urng, typename Derived::Scalar n=1)
	generates reals on the Chi-squared distribution with arbitrary degrees of freedom. More...

template<typename Derived , typename Urng >
const FisherFType< Derived, Urng >	fisherF (Index rows, Index cols, Urng &&urng, typename Derived::Scalar m=1, typename Derived::Scalar n=1)
	generates reals on the Fisher's F distribution. More...

template<typename Derived , typename Urng >
const FisherFType< Derived, Urng >	fisherFLike (Derived &o, Urng &&urng, typename Derived::Scalar m=1, typename Derived::Scalar n=1)
	generates reals on the Fisher's F distribution. More...

template<typename IntTy , typename WeightTy >
auto	makeMultinomialGen (IntTy trials, const MatrixBase< WeightTy > &probs) -> MultinomialGen< IntTy, MatrixBase< WeightTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::MultinomialGen More...

template<typename AlphaTy >
auto	makeDirichletGen (const MatrixBase< AlphaTy > &alpha) -> DirichletGen< typename MatrixBase< AlphaTy >::Scalar, MatrixBase< AlphaTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::DirichletGen More...

template<typename MeanTy , typename CovTy >
auto	makeMvNormGen (const MatrixBase< MeanTy > &mean, const MatrixBase< CovTy > &cov) -> MvNormalGen< typename MatrixBase< MeanTy >::Scalar, MatrixBase< MeanTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::MvNormal More...

template<typename MeanTy , typename LTTy >
auto	makeMvNormGenFromLt (const MatrixBase< MeanTy > &mean, const MatrixBase< LTTy > &lt) -> MvNormalGen< typename MatrixBase< MeanTy >::Scalar, MatrixBase< MeanTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::MvNormal More...

template<typename ScaleTy >
auto	makeWishartGen (Index df, const MatrixBase< ScaleTy > &scale) -> WishartGen< typename MatrixBase< ScaleTy >::Scalar, MatrixBase< ScaleTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::WishartGen More...

template<typename LTTy >
auto	makeWishartGenFromLt (Index df, const MatrixBase< LTTy > &lt) -> WishartGen< typename MatrixBase< LTTy >::Scalar, MatrixBase< LTTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::WishartGen More...

template<typename ScaleTy >
auto	makeInvWishartGen (Index df, const MatrixBase< ScaleTy > &scale) -> InvWishartGen< typename MatrixBase< ScaleTy >::Scalar, MatrixBase< ScaleTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::InvWishartGen More...

template<typename ILTTy >
auto	makeInvWishartGenFromIlt (Index df, const MatrixBase< ILTTy > &ilt) -> InvWishartGen< typename MatrixBase< ILTTy >::Scalar, MatrixBase< ILTTy >::RowsAtCompileTime >
	helper function constructing Eigen::Rand::InvWishartGen More...

template<typename UIntType , typename Rng >
UniversalRandomEngine< UIntType, Rng >	makeUniversalRng (Rng &&rng)
	Helper function for making a UniversalRandomEngine. More...

Variables
constexpr detail::FullMatrix	full_matrix

constexpr detail::LowerTriangular	lower_triangular

constexpr detail::InvLowerTriangular	inv_lower_triangular

Detailed Description

namespace for EigenRand

Typedef Documentation

◆ PacketRandomEngineAdaptor

template<typename UIntType , typename BaseRng >

using Eigen::Rand::PacketRandomEngineAdaptor = typedef ParallelRandomEngineAdaptor<UIntType, BaseRng, sizeof(typename BaseRng::result_type) / sizeof(uint64_t)>

Scalar adaptor for random engines which generates packet.

Template Parameters

UIntType	scalar integer type for `result_type` of an adapted random number engine
BaseRng

◆ Pmt19937_64

template<typename Packet >

using Eigen::Rand::Pmt19937_64 = typedef MersenneTwister<Packet, 312, 156, 31, 0xb5026f5aa96619e9, 29, 0x5555555555555555, 17, 0x71d67fffeda60000, 37, 0xfff7eee000000000, 43, 6364136223846793005>

Alias of Eigen::Rand::MersenneTwister, equivalent to std::mt19937_64.

Template Parameters

Packet

◆ Vmt19937_64

using Eigen::Rand::Vmt19937_64 = typedef std::mt19937_64

same as std::mt19937_64 when EIGEN_DONT_VECTORIZE, Pmt19937_64<internal::Packet4i> when SSE2 enabled and Pmt19937_64<internal::Packet8i> when AVX2 enabled

Note: It yields the same random sequence only within the same seed and the same SIMD ISA. If you want to keep the same random sequence across different SIMD ISAs, use P8_mt19937_64.

Function Documentation

◆ balanced()

template<typename Derived , typename Urng >

const BalancedType<Derived, Urng> Eigen::Rand::balanced	(	Index	rows,
		Index	cols,
		Urng &&	urng
	)

inline

generates reals in a range [-1, 1]

Template Parameters

Derived	a type of Eigen::DenseBase
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::BalancedGen

◆ balancedLike()

template<typename Derived , typename Urng >

const BalancedType<Derived, Urng> Eigen::Rand::balancedLike	(	const Derived &	o,
		Urng &&	urng
	)

inline

generates reals in a range [-1, 1]

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::BalancedGen

◆ beta()

template<typename Derived , typename Urng >

const BetaType<Derived, Urng> Eigen::Rand::beta	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	a = `1`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on the beta distribution.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
a,b	shape parameter

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::BetaGen

◆ betaLike()

template<typename Derived , typename Urng >

const BetaType<Derived, Urng> Eigen::Rand::betaLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	a = `1`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on the beta distribution.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
a,b	shape parameter

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::BetaGen

◆ binomial()

template<typename Derived , typename Urng >

const BinomialType<Derived, Urng> Eigen::Rand::binomial	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	trials = `1`,
		double	p = `0.5`
	)

inline

generates reals on the binomial distribution.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
trials	the number of trials
p	probability of a trial generating true

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::BinomialGen

◆ binomialLike()

template<typename Derived , typename Urng >

const BinomialType<Derived, Urng> Eigen::Rand::binomialLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	trials = `1`,
		double	p = `0.5`
	)

inline

generates reals on the binomial distribution.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
trials	the number of trials
p	probability of a trial generating true

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::BinomialGen

◆ cauchy()

template<typename Derived , typename Urng >

const CauchyType<Derived, Urng> Eigen::Rand::cauchy	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	a = `0`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on the Cauchy distribution.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
a	a location parameter of the distribution
b	a scale parameter of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::CauchyGen

◆ cauchyLike()

template<typename Derived , typename Urng >

const CauchyType<Derived, Urng> Eigen::Rand::cauchyLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	a = `0`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on the Cauchy distribution.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
a	a location parameter of the distribution
b	a scale parameter of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::CauchyGen

◆ chiSquared()

template<typename Derived , typename Urng >

const ChiSquaredType<Derived, Urng> Eigen::Rand::chiSquared	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	n = `1`
	)

inline

generates reals on the Chi-squared distribution with arbitrary degrees of freedom.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
n	the degrees of freedom of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::ChiSquaredGen

◆ chiSquaredLike()

template<typename Derived , typename Urng >

const ChiSquaredType<Derived, Urng> Eigen::Rand::chiSquaredLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	n = `1`
	)

inline

generates reals on the Chi-squared distribution with arbitrary degrees of freedom.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
n	the degrees of freedom of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::ChiSquaredGen

◆ discrete() [1/2]

template<typename Derived , typename Urng , typename Real >

const DiscreteType<Derived, Urng> Eigen::Rand::discrete	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		const std::initializer_list< Real > &	il
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is int32(32bit precision).

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
il	an instance of `initializer_list` containing the numbers to use as weights. The type of the elements referred by it must be convertible to `double`.

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::DiscreteGen

◆ discrete() [2/2]

template<typename Derived , typename Urng , typename RealIter >

const DiscreteType<Derived, Urng> Eigen::Rand::discrete	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		RealIter	first,
		RealIter	last
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is int32(32bit precision).

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
first,last	the range of elements defining the numbers to use as weights. The type of the elements referred by `RealIter` must be convertible to `double`.

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::DiscreteGen

◆ discreteD() [1/2]

template<typename Derived , typename Urng , typename Real >

const DiscreteDType<Derived, Urng> Eigen::Rand::discreteD	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		const std::initializer_list< Real > &	il
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is double(52bit precision).

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
il	an instance of `initializer_list` containing the numbers to use as weights. The type of the elements referred by it must be convertible to `double`.

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::DiscreteGen

◆ discreteD() [2/2]

template<typename Derived , typename Urng , typename RealIter >

const DiscreteDType<Derived, Urng> Eigen::Rand::discreteD	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		RealIter	first,
		RealIter	last
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is double(52bit precision).

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
first,last	the range of elements defining the numbers to use as weights. The type of the elements referred by `RealIter` must be convertible to `double`.

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::DiscreteGen

◆ discreteDLike() [1/2]

template<typename Derived , typename Urng , typename Real >

const DiscreteDType<Derived, Urng> Eigen::Rand::discreteDLike	(	Derived &	o,
		Urng &&	urng,
		const std::initializer_list< Real > &	il
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is double(52bit precision).

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
il	an instance of `initializer_list` containing the numbers to use as weights. The type of the elements referred by it must be convertible to `double`.

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::DiscreteGen

◆ discreteDLike() [2/2]

template<typename Derived , typename Urng , typename RealIter >

const DiscreteDType<Derived, Urng> Eigen::Rand::discreteDLike	(	Derived &	o,
		Urng &&	urng,
		RealIter	first,
		RealIter	last
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is double(52bit precision).

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
first,last	the range of elements defining the numbers to use as weights. The type of the elements referred by `RealIter` must be convertible to `double`.

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::DiscreteGen

◆ discreteF() [1/2]

template<typename Derived , typename Urng , typename Real >

const DiscreteFType<Derived, Urng> Eigen::Rand::discreteF	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		const std::initializer_list< Real > &	il
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is float(23bit precision).

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
il	an instance of `initializer_list` containing the numbers to use as weights. The type of the elements referred by it must be convertible to `double`.

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::DiscreteGen

◆ discreteF() [2/2]

template<typename Derived , typename Urng , typename RealIter >

const DiscreteFType<Derived, Urng> Eigen::Rand::discreteF	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		RealIter	first,
		RealIter	last
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is float(23bit precision).

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
first,last	the range of elements defining the numbers to use as weights. The type of the elements referred by `RealIter` must be convertible to `double`.

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::DiscreteGen

◆ discreteFLike() [1/2]

template<typename Derived , typename Urng , typename Real >

const DiscreteFType<Derived, Urng> Eigen::Rand::discreteFLike	(	Derived &	o,
		Urng &&	urng,
		const std::initializer_list< Real > &	il
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is float(23bit precision).

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
il	an instance of `initializer_list` containing the numbers to use as weights. The type of the elements referred by it must be convertible to `double`.

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::DiscreteGen

◆ discreteFLike() [2/2]

template<typename Derived , typename Urng , typename RealIter >

const DiscreteFType<Derived, Urng> Eigen::Rand::discreteFLike	(	Derived &	o,
		Urng &&	urng,
		RealIter	first,
		RealIter	last
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is float(23bit precision).

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
first,last	the range of elements defining the numbers to use as weights. The type of the elements referred by `RealIter` must be convertible to `double`.

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::DiscreteGen

◆ discreteLike() [1/2]

template<typename Derived , typename Urng , typename Real >

const DiscreteType<Derived, Urng> Eigen::Rand::discreteLike	(	Derived &	o,
		Urng &&	urng,
		const std::initializer_list< Real > &	il
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is int32(32bit precision).

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
il	an instance of `initializer_list` containing the numbers to use as weights. The type of the elements referred by it must be convertible to `double`.

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::DiscreteGen

◆ discreteLike() [2/2]

template<typename Derived , typename Urng , typename RealIter >

const DiscreteType<Derived, Urng> Eigen::Rand::discreteLike	(	Derived &	o,
		Urng &&	urng,
		RealIter	first,
		RealIter	last
	)

inline

generates random integers on the interval [0, n), where the probability of each individual integer i is proportional to w(i). The data type used for calculation of probabilities is int32(32bit precision).

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
first,last	the range of elements defining the numbers to use as weights. The type of the elements referred by `RealIter` must be convertible to `double`.

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::DiscreteGen

◆ exponential()

template<typename Derived , typename Urng >

const ExponentialType<Derived, Urng> Eigen::Rand::exponential	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	lambda = `1`
	)

inline

generates reals on an exponential distribution with arbitrary scale parameter.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
lambda	a scale parameter of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::ExponentialGen

◆ exponentialLike()

template<typename Derived , typename Urng >

const ExponentialType<Derived, Urng> Eigen::Rand::exponentialLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	lambda = `1`
	)

inline

generates reals on an exponential distribution with arbitrary scale parameter.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
lambda	a scale parameter of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::ExponentialGen

◆ extremeValue()

template<typename Derived , typename Urng >

const ExtremeValueType<Derived, Urng> Eigen::Rand::extremeValue	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	a = `0`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on an extreme value distribution (a.k.a Gumbel Type I, log-Weibull, Fisher-Tippett Type I) with arbitrary shape and scale parameter.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
a	a location parameter of the distribution
b	a scale parameter of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::ExtremeValueGen

◆ extremeValueLike()

template<typename Derived , typename Urng >

const ExtremeValueType<Derived, Urng> Eigen::Rand::extremeValueLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	a = `0`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on an extreme value distribution (a.k.a Gumbel Type I, log-Weibull, Fisher-Tippett Type I) with arbitrary shape and scale parameter.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
a	a location parameter of the distribution
b	a scale parameter of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::ExtremeValueGen

◆ fisherF()

template<typename Derived , typename Urng >

const FisherFType<Derived, Urng> Eigen::Rand::fisherF	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	m = `1`,
		typename Derived::Scalar	n = `1`
	)

inline

generates reals on the Fisher's F distribution.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
m	degrees of freedom
n	degrees of freedom

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::FisherFGen

◆ fisherFLike()

template<typename Derived , typename Urng >

const FisherFType<Derived, Urng> Eigen::Rand::fisherFLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	m = `1`,
		typename Derived::Scalar	n = `1`
	)

inline

generates reals on the Fisher's F distribution.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
m	degrees of freedom
n	degrees of freedom

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::FisherFGen

◆ gamma()

template<typename Derived , typename Urng >

const GammaType<Derived, Urng> Eigen::Rand::gamma	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	alpha = `1`,
		typename Derived::Scalar	beta = `1`
	)

inline

generates reals on a gamma distribution with arbitrary shape and scale parameter.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
alpha	a shape parameter of the distribution
beta	a scale parameter of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::GammaGen

◆ gammaLike()

template<typename Derived , typename Urng >

const GammaType<Derived, Urng> Eigen::Rand::gammaLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	alpha = `1`,
		typename Derived::Scalar	beta = `1`
	)

inline

generates reals on a gamma distribution with arbitrary shape and scale parameter.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
alpha	a shape parameter of the distribution
beta	a scale parameter of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::GammaGen

◆ geometric()

template<typename Derived , typename Urng >

const GeometricType<Derived, Urng> Eigen::Rand::geometric	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		double	p = `0.5`
	)

inline

generates reals on the geometric distribution.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
p	probability of a trial generating true

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::GeometricGen

◆ geometricLike()

template<typename Derived , typename Urng >

const GeometricType<Derived, Urng> Eigen::Rand::geometricLike	(	Derived &	o,
		Urng &&	urng,
		double	p = `0.5`
	)

inline

generates reals on the geometric distribution.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
p	probability of a trial generating true

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::GeometricGen

◆ lognormal()

template<typename Derived , typename Urng >

const LognormalType<Derived, Urng> Eigen::Rand::lognormal	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	mean = `0`,
		typename Derived::Scalar	stdev = `1`
	)

inline

generates reals on a lognormal distribution with arbitrary mean and stdev.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
mean	a mean value of the distribution
stdev	a standard deviation value of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::LognormalGen

◆ lognormalLike()

template<typename Derived , typename Urng >

const LognormalType<Derived, Urng> Eigen::Rand::lognormalLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	mean = `0`,
		typename Derived::Scalar	stdev = `1`
	)

inline

generates reals on a lognormal distribution with arbitrary mean and stdev.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
mean	a mean value of the distribution
stdev	a standard deviation value of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::LognormalGen

◆ makeDirichletGen()

template<typename AlphaTy >

auto Eigen::Rand::makeDirichletGen ( const MatrixBase< AlphaTy > & alpha ) -> DirichletGen<typename MatrixBase<AlphaTy>::Scalar, MatrixBase<AlphaTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::DirichletGen

Template Parameters

AlphaTy

Parameters

alpha The concentration parameters with shape (Dim, 1) of matrix or vector. The number of entries determines the dimensionality of the distribution.

Returns: an instance of MultinomialGen in the appropriate type

◆ makeInvWishartGen()

template<typename ScaleTy >

auto Eigen::Rand::makeInvWishartGen	(	Index	df,
		const MatrixBase< ScaleTy > &	scale
	)		-> InvWishartGen<typename MatrixBase<ScaleTy>::Scalar, MatrixBase<ScaleTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::InvWishartGen

Template Parameters

ScaleTy

Parameters

df	degrees of freedom
scale	scale matrix

◆ makeInvWishartGenFromIlt()

template<typename ILTTy >

auto Eigen::Rand::makeInvWishartGenFromIlt	(	Index	df,
		const MatrixBase< ILTTy > &	ilt
	)		-> InvWishartGen<typename MatrixBase<ILTTy>::Scalar, MatrixBase<ILTTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::InvWishartGen

Template Parameters

ILTTy

Parameters

df	degrees of freedom
ilt	lower triangular matrix of decomposed inverse scale

◆ makeMultinomialGen()

template<typename IntTy , typename WeightTy >

auto Eigen::Rand::makeMultinomialGen	(	IntTy	trials,
		const MatrixBase< WeightTy > &	probs
	)		-> MultinomialGen<IntTy, MatrixBase<WeightTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::MultinomialGen

Template Parameters

IntTy
WeightTy

Parameters

trials	the number of trials
probs	The weights of each category with shape `(Dim, 1)` of matrix or vector. The number of entries determines the dimensionality of the distribution

Returns: an instance of MultinomialGen in the appropriate type

◆ makeMvNormGen()

template<typename MeanTy , typename CovTy >

auto Eigen::Rand::makeMvNormGen	(	const MatrixBase< MeanTy > &	mean,
		const MatrixBase< CovTy > &	cov
	)		-> MvNormalGen<typename MatrixBase<MeanTy>::Scalar, MatrixBase<MeanTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::MvNormal

Template Parameters

MeanTy
CovTy

Parameters

mean	mean vector of the distribution
cov	covariance matrix (should be positive semi-definite)

◆ makeMvNormGenFromLt()

template<typename MeanTy , typename LTTy >

auto Eigen::Rand::makeMvNormGenFromLt	(	const MatrixBase< MeanTy > &	mean,
		const MatrixBase< LTTy > &	lt
	)		-> MvNormalGen<typename MatrixBase<MeanTy>::Scalar, MatrixBase<MeanTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::MvNormal

Template Parameters

MeanTy
LTTy

Parameters

mean	mean vector of the distribution
lt	lower triangular matrix of decomposed covariance

◆ makeUniversalRng()

template<typename UIntType , typename Rng >

UniversalRandomEngine<UIntType, Rng> Eigen::Rand::makeUniversalRng ( Rng && rng )

Helper function for making a UniversalRandomEngine.

Template Parameters

UIntType
Rng

Parameters

rng	any random number engine for either packet or scalar type

Returns: an instance of PacketRandomEngineAdaptor for UIntType

◆ makeWishartGen()

template<typename ScaleTy >

auto Eigen::Rand::makeWishartGen	(	Index	df,
		const MatrixBase< ScaleTy > &	scale
	)		-> WishartGen<typename MatrixBase<ScaleTy>::Scalar, MatrixBase<ScaleTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::WishartGen

Template Parameters

ScaleTy

Parameters

df	degrees of freedom
scale	scale matrix (should be positive definitive)

◆ makeWishartGenFromLt()

template<typename LTTy >

auto Eigen::Rand::makeWishartGenFromLt	(	Index	df,
		const MatrixBase< LTTy > &	lt
	)		-> WishartGen<typename MatrixBase<LTTy>::Scalar, MatrixBase<LTTy>::RowsAtCompileTime>

inline

helper function constructing Eigen::Rand::WishartGen

Template Parameters

LTTy

Parameters

df	degrees of freedom
lt	lower triangular matrix of decomposed scale

◆ negativeBinomial()

template<typename Derived , typename Urng >

const NegativeBinomialType<Derived, Urng> Eigen::Rand::negativeBinomial	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	trials = `1`,
		double	p = `0.5`
	)

inline

generates reals on the negative binomial distribution.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
trials	the number of trial successes
p	probability of a trial generating true

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::NegativeBinomialGen

◆ negativeBinomialLike()

template<typename Derived , typename Urng >

const NegativeBinomialType<Derived, Urng> Eigen::Rand::negativeBinomialLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	trials = `1`,
		double	p = `0.5`
	)

inline

generates reals on the negative binomial distribution.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
trials	the number of trial successes
p	probability of a trial generating true

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::NegativeBinomialGen

◆ normal() [1/2]

template<typename Derived , typename Urng >

const NormalType<Derived, Urng> Eigen::Rand::normal	(	Index	rows,
		Index	cols,
		Urng &&	urng
	)

inline

generates reals on a standard normal distribution (mean = 0, stdev=1)

Template Parameters

Derived	a type of Eigen::DenseBase
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::StdNormalGen

◆ normal() [2/2]

template<typename Derived , typename Urng >

const Normal2Type<Derived, Urng> Eigen::Rand::normal	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	mean,
		typename Derived::Scalar	stdev = `1`
	)

inline

generates reals on a normal distribution with arbitrary mean and stdev.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
mean	a mean value of the distribution
stdev	a standard deviation value of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::NormalGen

◆ normalLike() [1/2]

template<typename Derived , typename Urng >

const NormalType<Derived, Urng> Eigen::Rand::normalLike	(	Derived &	o,
		Urng &&	urng
	)

inline

generates reals on a standard normal distribution (mean = 0, stdev=1)

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::StdNormalGen

◆ normalLike() [2/2]

template<typename Derived , typename Urng >

const Normal2Type<Derived, Urng> Eigen::Rand::normalLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	mean,
		typename Derived::Scalar	stdev = `1`
	)

inline

generates reals on a normal distribution with arbitrary mean and stdev.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
mean	a mean value of the distribution
stdev	a standard deviation value of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::NormalGen

◆ poisson()

template<typename Derived , typename Urng >

const PoissonType<Derived, Urng> Eigen::Rand::poisson	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		double	mean = `1`
	)

inline

generates reals on the Poisson distribution.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
mean	rate parameter

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::PoissonGen

◆ poissonLike()

template<typename Derived , typename Urng >

const PoissonType<Derived, Urng> Eigen::Rand::poissonLike	(	Derived &	o,
		Urng &&	urng,
		double	mean = `1`
	)

inline

generates reals on the Poisson distribution.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
mean	rate parameter

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::PoissonGen

◆ randBits()

template<typename Derived , typename Urng >

const RandBitsType<Derived, Urng> Eigen::Rand::randBits	(	Index	rows,
		Index	cols,
		Urng &&	urng
	)

inline

generates integers with random bits

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::RandbitsGen

◆ randBitsLike()

template<typename Derived , typename Urng >

const RandBitsType<Derived, Urng> Eigen::Rand::randBitsLike	(	Derived &	o,
		Urng &&	urng
	)

inline

generates integers with random bits

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::RandbitsGen

◆ studentT()

template<typename Derived , typename Urng >

const StudentTType<Derived, Urng> Eigen::Rand::studentT	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	n = `1`
	)

inline

generates reals on the Student's t distribution with arbirtrary degress of freedom.

Template Parameters

Derived	a type of Eigen::DenseBase
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
n	degrees of freedom

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::StudentTGen

◆ studentTLike()

template<typename Derived , typename Urng >

const StudentTType<Derived, Urng> Eigen::Rand::studentTLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	n = `1`
	)

inline

generates reals on the Student's t distribution with arbirtrary degress of freedom.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
n	degrees of freedom

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::StudentTGen

◆ uniformInt()

template<typename Derived , typename Urng >

const UniformIntType<Derived, Urng> Eigen::Rand::uniformInt	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	min,
		typename Derived::Scalar	max
	)

inline

generates integers with a given range [min, max]

Template Parameters

Derived	a type of Eigen::DenseBase
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
min,max	the range of integers being generated

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::UniformIntGen

◆ uniformIntLike()

template<typename Derived , typename Urng >

const UniformIntType<Derived, Urng> Eigen::Rand::uniformIntLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	min,
		typename Derived::Scalar	max
	)

inline

generates integers with a given range [min, max]

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
min,max	the range of integers being generated

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::UniformIntGen

◆ uniformReal()

template<typename Derived , typename Urng >

const UniformRealType<Derived, Urng> Eigen::Rand::uniformReal	(	Index	rows,
		Index	cols,
		Urng &&	urng
	)

inline

generates reals in a range [0, 1)

Template Parameters

Derived	a type of Eigen::DenseBase
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::UniformRealGen

◆ uniformRealLike()

template<typename Derived , typename Urng >

const UniformRealType<Derived, Urng> Eigen::Rand::uniformRealLike	(	Derived &	o,
		Urng &&	urng
	)

inline

generates reals in a range [0, 1)

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::UniformRealGen

◆ weibull()

template<typename Derived , typename Urng >

const WeibullType<Derived, Urng> Eigen::Rand::weibull	(	Index	rows,
		Index	cols,
		Urng &&	urng,
		typename Derived::Scalar	a = `1`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on a Weibull distribution with arbitrary shape and scale parameter.

Template Parameters

Derived
Urng

Parameters

rows	the number of rows being generated
cols	the number of columns being generated
urng	c++11-style random number generator
a	a shape parameter of the distribution
b	a scale parameter of the distribution

Returns: a random matrix expression with a shape (rows, cols)

See also: Eigen::Rand::WeibullGen

◆ weibullLike()

template<typename Derived , typename Urng >

const WeibullType<Derived, Urng> Eigen::Rand::weibullLike	(	Derived &	o,
		Urng &&	urng,
		typename Derived::Scalar	a = `1`,
		typename Derived::Scalar	b = `1`
	)

inline

generates reals on a Weibull distribution with arbitrary shape and scale parameter.

Template Parameters

Derived
Urng

Parameters

o	an instance of any type of Eigen::DenseBase
urng	c++11-style random number generator
a	a shape parameter of the distribution
b	a scale parameter of the distribution

Returns: a random matrix expression of the same shape as o

See also: Eigen::Rand::WeibullGen

Classes

Typedefs

Enumerations

Functions

Variables

Detailed Description

Typedef Documentation

◆ PacketRandomEngineAdaptor

◆ Pmt19937_64

◆ Vmt19937_64

Function Documentation

◆ balanced()

◆ balancedLike()

◆ beta()

◆ betaLike()

◆ binomial()

◆ binomialLike()

◆ cauchy()

◆ cauchyLike()

◆ chiSquared()

◆ chiSquaredLike()

◆ discrete() [1/2]

◆ discrete() [2/2]

◆ discreteD() [1/2]

◆ discreteD() [2/2]

◆ discreteDLike() [1/2]

◆ discreteDLike() [2/2]

◆ discreteF() [1/2]

◆ discreteF() [2/2]

◆ discreteFLike() [1/2]

◆ discreteFLike() [2/2]

◆ discreteLike() [1/2]

◆ discreteLike() [2/2]

◆ exponential()

◆ exponentialLike()

◆ extremeValue()

◆ extremeValueLike()

◆ fisherF()

◆ fisherFLike()

◆ gamma()

◆ gammaLike()

◆ geometric()

◆ geometricLike()

◆ lognormal()

◆ lognormalLike()

◆ makeDirichletGen()

◆ makeInvWishartGen()

◆ makeInvWishartGenFromIlt()

◆ makeMultinomialGen()

◆ makeMvNormGen()

◆ makeMvNormGenFromLt()

◆ makeUniversalRng()

◆ makeWishartGen()

◆ makeWishartGenFromLt()

◆ negativeBinomial()

◆ negativeBinomialLike()

◆ normal() [1/2]

◆ normal() [2/2]

◆ normalLike() [1/2]

◆ normalLike() [2/2]

◆ poisson()

◆ poissonLike()

◆ randBits()

◆ randBitsLike()

◆ studentT()

◆ studentTLike()

◆ uniformInt()

◆ uniformIntLike()

◆ uniformReal()

◆ uniformRealLike()

◆ weibull()

◆ weibullLike()