Random Number Generation

Random Number Generation#

MatX provides the capability to generate random numbers on the host and device using the random() and randomi() operators. both host and device generation is supported through the cuRAND library.

  • random() only generates random distribution for float data types

  • randomi() only generates random disitrubtions for integral data types

Please see the documentation for each function for a full list of supported types

template<typename T, typename ShapeType, typename LowerType = typename inner_op_type_t<T>::type, std::enable_if_t<!std::is_array_v<remove_cvref_t<ShapeType>>, bool> = true>
__MATX_INLINE__ auto matx::random(ShapeType &&s, Distribution_t dist, uint64_t seed = 0, LowerType alpha = 1, LowerType beta = 0)#

Return a random number with a specified shape.

Supported Types: float, double, complex<float>, complex<double>

Template Parameters:

  • ShapeType – Shape type

  • T – Type of output

  • LowerType – Either T or the inner type of T if T is complex*

Parameters:

  • s – Shape of operator

  • dist – Distribution (either NORMAL or UNIFORM)

  • seed – Random number seed

  • alpha – Value to multiply by each number

  • beta – Value to add to each number

Returns:

Random number operator

template<typename T, int RANK, typename LowerType = typename inner_op_type_t<T>::type>
__MATX_INLINE__ auto matx::random(const index_t (&s)[RANK], Distribution_t dist, uint64_t seed = 0, LowerType alpha = 1, LowerType beta = 0)#

Return a random number with a specified shape.

Supported Types: float, double, complex<float>, complex<double>

Template Parameters:

  • RANK – Rank of operator

  • T – Type of output

  • LowerType – Either T or the inner type of T if T is complex

Parameters:

  • s – Array of dimensions

  • dist – Distribution (either NORMAL or UNIFORM)

  • seed – Random number seed

  • alpha – Value to multiply by each number

  • beta – Value to add to each number

Returns:

Random number operator

template<typename T, typename ShapeType, typename LowerType = typename inner_op_type_t<T>::type, std::enable_if_t<!std::is_array_v<remove_cvref_t<ShapeType>>, bool> = true>
__MATX_INLINE__ auto matx::randomi(ShapeType &&s, uint64_t seed = 0, LowerType min = 0, LowerType max = 100)#

Return a random number with a specified shape.

Supported types: uint32_t, int32_t, uint64_t, int64_t

Template Parameters:

  • ShapeType – Shape type

  • T – Type of output

  • LowerType – Either T or the inner type of T if T is complex*

Parameters:

  • s – Shape of operator

  • seed – Random number seed

  • min – min of generation range

  • max – max of generation range

Returns:

Random number operator

template<typename T, int RANK, typename LowerType = typename inner_op_type_t<T>::type>
__MATX_INLINE__ auto matx::randomi(const index_t (&s)[RANK], uint64_t seed = 0, LowerType min = 0, LowerType max = 100)#

Return a random number with a specified shape.

Supported types: uint32_t, int32_t, uint64_t, int64_t

Template Parameters:

  • RANK – Rank of operator

  • T – Type of output

  • LowerType – Either T or the inner type of T if T is complex

Parameters:

  • s – Array of dimensions

  • seed – Random number seed

  • min – min of generation range

  • max – max of generation range

Returns:

Random number operator

Examples#

index_t count = 50;

tensor_t<TestType, 3> t3f({count, count, count});

(t3f = (TestType)-1000000).run(this->exec);
(t3f = random<TestType>({count, count, count}, UNIFORM)).run(this->exec);

index_t count = 50;

tensor_t<TestType, 3> t3f({count, count, count});
TestType minBound = std::numeric_limits<TestType>::min(); 
TestType maxBound = std::numeric_limits<TestType>::max(); 

(t3f = (TestType)0).run(this->exec);
(t3f = randomi<TestType>({count, count, count}, 0, minBound, maxBound )).run(this->exec);