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""" 

======================== 

Random Number Generation 

======================== 

 

Use ``default_rng()`` to create a `Generator` and call its methods. 

 

=============== ========================================================= 

Generator 

--------------- --------------------------------------------------------- 

Generator Class implementing all of the random number distributions 

default_rng Default constructor for ``Generator`` 

=============== ========================================================= 

 

============================================= === 

BitGenerator Streams that work with Generator 

--------------------------------------------- --- 

MT19937 

PCG64 

Philox 

SFC64 

============================================= === 

 

============================================= === 

Getting entropy to initialize a BitGenerator 

--------------------------------------------- --- 

SeedSequence 

============================================= === 

 

 

Legacy 

------ 

 

For backwards compatibility with previous versions of numpy before 1.17, the 

various aliases to the global `RandomState` methods are left alone and do not 

use the new `Generator` API. 

 

==================== ========================================================= 

Utility functions 

-------------------- --------------------------------------------------------- 

random Uniformly distributed floats over ``[0, 1)`` 

bytes Uniformly distributed random bytes. 

permutation Randomly permute a sequence / generate a random sequence. 

shuffle Randomly permute a sequence in place. 

choice Random sample from 1-D array. 

==================== ========================================================= 

 

==================== ========================================================= 

Compatibility 

functions - removed 

in the new API 

-------------------- --------------------------------------------------------- 

rand Uniformly distributed values. 

randn Normally distributed values. 

ranf Uniformly distributed floating point numbers. 

random_integers Uniformly distributed integers in a given range. 

(deprecated, use ``integers(..., closed=True)`` instead) 

random_sample Alias for `random_sample` 

randint Uniformly distributed integers in a given range 

seed Seed the legacy random number generator. 

==================== ========================================================= 

 

==================== ========================================================= 

Univariate 

distributions 

-------------------- --------------------------------------------------------- 

beta Beta distribution over ``[0, 1]``. 

binomial Binomial distribution. 

chisquare :math:`\\chi^2` distribution. 

exponential Exponential distribution. 

f F (Fisher-Snedecor) distribution. 

gamma Gamma distribution. 

geometric Geometric distribution. 

gumbel Gumbel distribution. 

hypergeometric Hypergeometric distribution. 

laplace Laplace distribution. 

logistic Logistic distribution. 

lognormal Log-normal distribution. 

logseries Logarithmic series distribution. 

negative_binomial Negative binomial distribution. 

noncentral_chisquare Non-central chi-square distribution. 

noncentral_f Non-central F distribution. 

normal Normal / Gaussian distribution. 

pareto Pareto distribution. 

poisson Poisson distribution. 

power Power distribution. 

rayleigh Rayleigh distribution. 

triangular Triangular distribution. 

uniform Uniform distribution. 

vonmises Von Mises circular distribution. 

wald Wald (inverse Gaussian) distribution. 

weibull Weibull distribution. 

zipf Zipf's distribution over ranked data. 

==================== ========================================================= 

 

==================== ========================================================== 

Multivariate 

distributions 

-------------------- ---------------------------------------------------------- 

dirichlet Multivariate generalization of Beta distribution. 

multinomial Multivariate generalization of the binomial distribution. 

multivariate_normal Multivariate generalization of the normal distribution. 

==================== ========================================================== 

 

==================== ========================================================= 

Standard 

distributions 

-------------------- --------------------------------------------------------- 

standard_cauchy Standard Cauchy-Lorentz distribution. 

standard_exponential Standard exponential distribution. 

standard_gamma Standard Gamma distribution. 

standard_normal Standard normal distribution. 

standard_t Standard Student's t-distribution. 

==================== ========================================================= 

 

==================== ========================================================= 

Internal functions 

-------------------- --------------------------------------------------------- 

get_state Get tuple representing internal state of generator. 

set_state Set state of generator. 

==================== ========================================================= 

 

 

""" 

__all__ = [ 

'beta', 

'binomial', 

'bytes', 

'chisquare', 

'choice', 

'dirichlet', 

'exponential', 

'f', 

'gamma', 

'geometric', 

'get_state', 

'gumbel', 

'hypergeometric', 

'laplace', 

'logistic', 

'lognormal', 

'logseries', 

'multinomial', 

'multivariate_normal', 

'negative_binomial', 

'noncentral_chisquare', 

'noncentral_f', 

'normal', 

'pareto', 

'permutation', 

'poisson', 

'power', 

'rand', 

'randint', 

'randn', 

'random', 

'random_integers', 

'random_sample', 

'ranf', 

'rayleigh', 

'sample', 

'seed', 

'set_state', 

'shuffle', 

'standard_cauchy', 

'standard_exponential', 

'standard_gamma', 

'standard_normal', 

'standard_t', 

'triangular', 

'uniform', 

'vonmises', 

'wald', 

'weibull', 

'zipf', 

] 

 

# add these for module-freeze analysis (like PyInstaller) 

from . import _pickle 

from . import _common 

from . import _bounded_integers 

 

from ._generator import Generator, default_rng 

from .bit_generator import SeedSequence, BitGenerator 

from ._mt19937 import MT19937 

from ._pcg64 import PCG64 

from ._philox import Philox 

from ._sfc64 import SFC64 

from .mtrand import * 

 

__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937', 

'Philox', 'PCG64', 'SFC64', 'default_rng', 'BitGenerator'] 

 

 

def __RandomState_ctor(): 

"""Return a RandomState instance. 

 

This function exists solely to assist (un)pickling. 

 

Note that the state of the RandomState returned here is irrelevant, as this 

function's entire purpose is to return a newly allocated RandomState whose 

state pickle can set. Consequently the RandomState returned by this function 

is a freshly allocated copy with a seed=0. 

 

See https://github.com/numpy/numpy/issues/4763 for a detailed discussion 

 

""" 

return RandomState(seed=0) 

 

 

from numpy._pytesttester import PytestTester 

test = PytestTester(__name__) 

del PytestTester