123 lines
5.4 KiB
Python
123 lines
5.4 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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==================== =========================================================
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Utility functions
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==============================================================================
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random Uniformly distributed values of a given shape.
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bytes Uniformly distributed random bytes.
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random_integers Uniformly distributed integers in a given range.
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random_sample Uniformly distributed floats in a given range.
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random Alias for random_sample
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ranf Alias for random_sample
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sample Alias for random_sample
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choice Generate a weighted random sample from a given array-like
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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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seed Seed the random number generator.
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==================== =========================================================
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==================== =========================================================
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Compatibility functions
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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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randint Uniformly distributed integers in a given range.
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==================== =========================================================
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==================== =========================================================
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Univariate 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 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 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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from __future__ import division, absolute_import, print_function
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import warnings
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# To get sub-modules
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from .info import __doc__, __all__
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
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from .mtrand import *
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# Some aliases:
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ranf = random = sample = random_sample
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__all__.extend(['ranf', 'random', 'sample'])
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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 function's
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entire purpose is to return a newly allocated RandomState whose state pickle can set.
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Consequently the RandomState returned by this function is a freshly allocated copy
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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.testing import _numpy_tester
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test = _numpy_tester().test
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bench = _numpy_tester().bench
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