CPU random number generator. More...

#include <random.h>

Collaboration diagram for mshadow::Random< cpu, DType >:

Public Member Functions
	Random (int seed)
	constructor of random engine More...

	~Random (void)

void	Seed (int seed)
	seed random number generator using this seed More...

unsigned	GetSeed () const
	get random seed used in random generator More...

void	set_stream (Stream< cpu > *stream)
	set the stream of computation More...

unsigned	GetRandInt ()
	get some random integer More...

void	GetRandInt (const Tensor< cpu, 1, unsigned > &dst)
	get a set of random integers More...

template<int dim, class Sampler >
void	SampleDistribution (Tensor< cpu, dim, DType > *dst, Sampler sampler)
	generate data from a distribution More...

template<int dim, typename PType >
void	SampleUniform (Tensor< cpu, dim, DType > *dst, PType a=0.0f, PType b=1.0f)
	generate data from uniform [a,b) More...

template<int dim, typename PType >
void	SampleGaussian (Tensor< cpu, dim, DType > *dst, PType mu=0.0f, PType sigma=1.0f)
	generate data from standard gaussian More...

template<int dim, typename PType >
void	SampleGamma (Tensor< cpu, dim, DType > *dst, PType alpha, PType beta)
	generate data from a gamma distribution More...

template<int dim, typename PType >
void	SampleExponential (Tensor< cpu, dim, DType > *dst, PType lambda)
	generate data from an exponential distribution More...

template<int dim, typename PType >
void	SamplePoisson (Tensor< cpu, dim, DType > *dst, PType lambda)
	generate data from a poisson distribution More...

template<int dim, typename PType1 , typename PType2 >
void	SampleNegativeBinomial (Tensor< cpu, dim, DType > *dst, PType1 k, PType2 p)
	generate data from a negative binomial distribution More...

template<int dim, typename PType >
void	SampleGeneralizedNegativeBinomial (Tensor< cpu, dim, DType > *dst, PType mu, PType alpha)
	generate data from a generalized negative binomial distribution More...

template<int dim>
expr::ReshapeExp< Tensor< cpu, 1, DType >, DType, dim, 1 >	gaussian (Shape< dim > shape)
	return a temporal expression storing standard gaussian random variables the temporal tensor is only valid before next call of gaussian or uniform can be used as part of expression Caution: this means expression such as A = gaussian(s1) * gaussian(s2) will give invalid result, since second call of gaussian(s2) makes gaussian(s1) invalid A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression More...

template<int dim>
expr::ReshapeExp< Tensor< cpu, 1, DType >, DType, dim, 1 >	uniform (Shape< dim > shape)
	return a temporal expression storing standard uniform [0,1) the temporal tensor is only valid before next call of gaussian or uniform can be used as part of expression Caution: this means expression such as A = uniform(s1) * uniform(s2) will give invalid result, since second call of gaussian(s2) makes gaussian(s1) invalid A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression More...

std::mt19937 &	GetRndEngine ()

Detailed Description

template<typename DType>
class mshadow::Random< cpu, DType >

CPU random number generator.

Constructor & Destructor Documentation

◆ Random()

template<typename DType >

mshadow::Random< cpu, DType >::Random ( int seed )

inlineexplicit

constructor of random engine

Parameters

seed	random number seed

◆ ~Random()

template<typename DType >

mshadow::Random< cpu, DType >::~Random ( void )

inline

Member Function Documentation

◆ gaussian()

template<typename DType >

template<int dim>

expr::ReshapeExp<Tensor<cpu, 1, DType>, DType, dim, 1> mshadow::Random< cpu, DType >::gaussian ( Shape< dim > shape )

inline

return a temporal expression storing standard gaussian random variables the temporal tensor is only valid before next call of gaussian or uniform can be used as part of expression Caution: this means expression such as A = gaussian(s1) * gaussian(s2) will give invalid result, since second call of gaussian(s2) makes gaussian(s1) invalid A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression

Parameters

shape shape of the tensor

Returns: a temporal expression storing standard gaussian random variables

Template Parameters

dim	dimension of tensor

◆ GetRandInt() [1/2]

template<typename DType >

unsigned mshadow::Random< cpu, DType >::GetRandInt ( )

inline

get some random integer

Returns: integer as unsigned

◆ GetRandInt() [2/2]

template<typename DType >

void mshadow::Random< cpu, DType >::GetRandInt ( const Tensor< cpu, 1, unsigned > & dst )

inline

get a set of random integers

◆ GetRndEngine()

template<typename DType >

std::mt19937& mshadow::Random< cpu, DType >::GetRndEngine ( )

inline

◆ GetSeed()

template<typename DType >

unsigned mshadow::Random< cpu, DType >::GetSeed ( ) const

inline

get random seed used in random generator

Returns: seed in unsigned

◆ SampleDistribution()

template<typename DType >

template<int dim, class Sampler >

void mshadow::Random< cpu, DType >::SampleDistribution	(	Tensor< cpu, dim, DType > *	dst,
		Sampler	sampler
	)

inline

generate data from a distribution

Parameters

dst	destination

Template Parameters

dim	dimension of tensor

Parameters

sampler sampler of the distribution

◆ SampleExponential()

template<typename DType >

template<int dim, typename PType >

void mshadow::Random< cpu, DType >::SampleExponential	(	Tensor< cpu, dim, DType > *	dst,
		PType	lambda
	)

inline

generate data from an exponential distribution

Parameters

dst	destination
lambda	parameter (rate) of the distribution

Template Parameters

dim	dimension of tensor

◆ SampleGamma()

template<typename DType >

template<int dim, typename PType >

void mshadow::Random< cpu, DType >::SampleGamma	(	Tensor< cpu, dim, DType > *	dst,
		PType	alpha,
		PType	beta
	)

inline

generate data from a gamma distribution

Parameters

dst	destination
alpha	(shape) parameter
beta	(scale) parameter

Template Parameters

dim	dimension of tensor

◆ SampleGaussian()

template<typename DType >

template<int dim, typename PType >

void mshadow::Random< cpu, DType >::SampleGaussian	(	Tensor< cpu, dim, DType > *	dst,
		PType	mu = `0.0f`,
		PType	sigma = `1.0f`
	)

inline

generate data from standard gaussian

Parameters

dst	destination
mu	mean variable
sigma	standard deviation

Template Parameters

dim	dimension of tensor

◆ SampleGeneralizedNegativeBinomial()

template<typename DType >

template<int dim, typename PType >

void mshadow::Random< cpu, DType >::SampleGeneralizedNegativeBinomial	(	Tensor< cpu, dim, DType > *	dst,
		PType	mu,
		PType	alpha
	)

inline

generate data from a generalized negative binomial distribution

Parameters

dst	destination
mu	parameter (mean) of the distribution
alpha	parameter (over dispersion) of the distribution (for alpha=0 this gives a Poisson)

Template Parameters

dim	dimension of tensor

◆ SampleNegativeBinomial()

template<typename DType >

template<int dim, typename PType1 , typename PType2 >

void mshadow::Random< cpu, DType >::SampleNegativeBinomial	(	Tensor< cpu, dim, DType > *	dst,
		PType1	k,
		PType2	p
	)

inline

generate data from a negative binomial distribution

Parameters

dst	destination
k	limit on number of failures
p	success probability

Template Parameters

dim	dimension of tensor

◆ SamplePoisson()

template<typename DType >

template<int dim, typename PType >

void mshadow::Random< cpu, DType >::SamplePoisson	(	Tensor< cpu, dim, DType > *	dst,
		PType	lambda
	)

inline

generate data from a poisson distribution

Parameters

dst	destination
lambda	parameter (rate) of the distribution

Template Parameters

dim	dimension of tensor

◆ SampleUniform()

template<typename DType >

template<int dim, typename PType >

void mshadow::Random< cpu, DType >::SampleUniform	(	Tensor< cpu, dim, DType > *	dst,
		PType	a = `0.0f`,
		PType	b = `1.0f`
	)

inline

generate data from uniform [a,b)

Parameters

dst	destination
a	lower bound of uniform
b	upper bound of uniform

Template Parameters

dim	dimension of tensor

◆ Seed()

template<typename DType >

void mshadow::Random< cpu, DType >::Seed ( int seed )

inline

seed random number generator using this seed

Parameters

seed	seed of prng

◆ set_stream()

template<typename DType >

void mshadow::Random< cpu, DType >::set_stream ( Stream< cpu > * stream )

inline

set the stream of computation

Parameters

stream computation stream

◆ uniform()

template<typename DType >

template<int dim>

expr::ReshapeExp<Tensor<cpu, 1, DType>, DType, dim, 1> mshadow::Random< cpu, DType >::uniform ( Shape< dim > shape )

inline

return a temporal expression storing standard uniform [0,1) the temporal tensor is only valid before next call of gaussian or uniform can be used as part of expression Caution: this means expression such as A = uniform(s1) * uniform(s2) will give invalid result, since second call of gaussian(s2) makes gaussian(s1) invalid A = gaussian(s1)*B+C; is correct; use one gaussian/uniform in each expression

Parameters

shape shape of the tensor

Returns: a temporal expression storing standard uniform [0,1)

Template Parameters

dim	dimension of tensor

The documentation for this class was generated from the following file:

/work/mxnet/3rdparty/mshadow/mshadow/random.h

Public Member Functions

Detailed Description

template<typename DType> class mshadow::Random< cpu, DType >

Constructor & Destructor Documentation

◆ Random()

◆ ~Random()

Member Function Documentation

◆ gaussian()

◆ GetRandInt() [1/2]

◆ GetRandInt() [2/2]

◆ GetRndEngine()

◆ GetSeed()

◆ SampleDistribution()

◆ SampleExponential()

◆ SampleGamma()

◆ SampleGaussian()

◆ SampleGeneralizedNegativeBinomial()

◆ SampleNegativeBinomial()

◆ SamplePoisson()

◆ SampleUniform()

◆ Seed()

◆ set_stream()

◆ uniform()

template<typename DType>
class mshadow::Random< cpu, DType >