214 lines
7.3 KiB
Python
214 lines
7.3 KiB
Python
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"""
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========================
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Random Number Generation
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========================
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Use ``default_rng()`` to create a `Generator` and call its methods.
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=============== =========================================================
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Generator
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--------------- ---------------------------------------------------------
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Generator Class implementing all of the random number distributions
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default_rng Default constructor for ``Generator``
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=============== =========================================================
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============================================= ===
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BitGenerator Streams that work with Generator
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--------------------------------------------- ---
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MT19937
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PCG64
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PCG64DXSM
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Philox
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SFC64
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============================================= ===
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============================================= ===
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Getting entropy to initialize a BitGenerator
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--------------------------------------------- ---
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SeedSequence
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============================================= ===
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Legacy
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------
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For backwards compatibility with previous versions of numpy before 1.17, the
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various aliases to the global `RandomState` methods are left alone and do not
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use the new `Generator` API.
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==================== =========================================================
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Utility functions
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-------------------- ---------------------------------------------------------
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random Uniformly distributed floats over ``[0, 1)``
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bytes Uniformly distributed random bytes.
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permutation Randomly permute a sequence / generate a random sequence.
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shuffle Randomly permute a sequence in place.
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choice Random sample from 1-D array.
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==================== =========================================================
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==================== =========================================================
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Compatibility
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functions - removed
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in the new API
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-------------------- ---------------------------------------------------------
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rand Uniformly distributed values.
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randn Normally distributed values.
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ranf Uniformly distributed floating point numbers.
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random_integers Uniformly distributed integers in a given range.
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(deprecated, use ``integers(..., closed=True)`` instead)
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random_sample Alias for `random_sample`
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randint Uniformly distributed integers in a given range
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seed Seed the legacy random number generator.
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==================== =========================================================
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==================== =========================================================
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Univariate
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distributions
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-------------------- ---------------------------------------------------------
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beta Beta distribution over ``[0, 1]``.
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binomial Binomial distribution.
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chisquare :math:`\\chi^2` distribution.
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exponential Exponential distribution.
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f F (Fisher-Snedecor) distribution.
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gamma Gamma distribution.
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geometric Geometric distribution.
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gumbel Gumbel distribution.
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hypergeometric Hypergeometric distribution.
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laplace Laplace distribution.
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logistic Logistic distribution.
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lognormal Log-normal distribution.
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logseries Logarithmic series distribution.
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negative_binomial Negative binomial distribution.
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noncentral_chisquare Non-central chi-square distribution.
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noncentral_f Non-central F distribution.
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normal Normal / Gaussian distribution.
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pareto Pareto distribution.
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poisson Poisson distribution.
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power Power distribution.
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rayleigh Rayleigh distribution.
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triangular Triangular distribution.
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uniform Uniform distribution.
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vonmises Von Mises circular distribution.
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wald Wald (inverse Gaussian) distribution.
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weibull Weibull distribution.
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zipf Zipf's distribution over ranked data.
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==================== =========================================================
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==================== ==========================================================
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Multivariate
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distributions
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-------------------- ----------------------------------------------------------
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dirichlet Multivariate generalization of Beta distribution.
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multinomial Multivariate generalization of the binomial distribution.
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multivariate_normal Multivariate generalization of the normal distribution.
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==================== ==========================================================
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==================== =========================================================
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Standard
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distributions
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-------------------- ---------------------------------------------------------
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standard_cauchy Standard Cauchy-Lorentz distribution.
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standard_exponential Standard exponential distribution.
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standard_gamma Standard Gamma distribution.
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standard_normal Standard normal distribution.
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standard_t Standard Student's t-distribution.
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==================== =========================================================
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==================== =========================================================
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Internal functions
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-------------------- ---------------------------------------------------------
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get_state Get tuple representing internal state of generator.
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set_state Set state of generator.
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==================== =========================================================
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"""
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__all__ = [
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'beta',
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'binomial',
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'bytes',
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'chisquare',
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'choice',
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'dirichlet',
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'exponential',
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'f',
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'gamma',
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'geometric',
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'get_state',
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'gumbel',
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'hypergeometric',
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'laplace',
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'logistic',
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'lognormal',
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'logseries',
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'multinomial',
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'multivariate_normal',
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'negative_binomial',
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'noncentral_chisquare',
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'noncentral_f',
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'normal',
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'pareto',
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'permutation',
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'poisson',
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'power',
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'rand',
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'randint',
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'randn',
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'random',
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'random_integers',
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'random_sample',
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'ranf',
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'rayleigh',
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'sample',
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'seed',
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'set_state',
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'shuffle',
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'standard_cauchy',
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'standard_exponential',
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'standard_gamma',
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'standard_normal',
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'standard_t',
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'triangular',
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'uniform',
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'vonmises',
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'wald',
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'weibull',
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'zipf',
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]
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# add these for module-freeze analysis (like PyInstaller)
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from . import _bounded_integers, _common, _pickle
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from ._generator import Generator, default_rng
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from ._mt19937 import MT19937
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from ._pcg64 import PCG64, PCG64DXSM
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from ._philox import Philox
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from ._sfc64 import SFC64
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from .bit_generator import BitGenerator, SeedSequence
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from .mtrand import *
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__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
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'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',
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'BitGenerator']
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def __RandomState_ctor():
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"""Return a RandomState instance.
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This function exists solely to assist (un)pickling.
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Note that the state of the RandomState returned here is irrelevant, as this
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function's entire purpose is to return a newly allocated RandomState whose
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state pickle can set. Consequently the RandomState returned by this function
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is a freshly allocated copy with a seed=0.
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See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
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"""
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return RandomState(seed=0)
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from numpy._pytesttester import PytestTester
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test = PytestTester(__name__)
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del PytestTester
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