""" ======================== 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 PCG64DXSM 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, PCG64DXSM from ._philox import Philox from ._sfc64 import SFC64 from .mtrand import * __all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937', 'Philox', 'PCG64', 'PCG64DXSM', '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