Chris@87: """
Chris@87: ========================
Chris@87: Random Number Generation
Chris@87: ========================
Chris@87: 
Chris@87: ==================== =========================================================
Chris@87: Utility functions
Chris@87: ==============================================================================
Chris@87: random               Uniformly distributed values of a given shape.
Chris@87: bytes                Uniformly distributed random bytes.
Chris@87: random_integers      Uniformly distributed integers in a given range.
Chris@87: random_sample        Uniformly distributed floats in a given range.
Chris@87: random               Alias for random_sample
Chris@87: ranf                 Alias for random_sample
Chris@87: sample               Alias for random_sample
Chris@87: choice               Generate a weighted random sample from a given array-like
Chris@87: permutation          Randomly permute a sequence / generate a random sequence.
Chris@87: shuffle              Randomly permute a sequence in place.
Chris@87: seed                 Seed the random number generator.
Chris@87: ==================== =========================================================
Chris@87: 
Chris@87: ==================== =========================================================
Chris@87: Compatibility functions
Chris@87: ==============================================================================
Chris@87: rand                 Uniformly distributed values.
Chris@87: randn                Normally distributed values.
Chris@87: ranf                 Uniformly distributed floating point numbers.
Chris@87: randint              Uniformly distributed integers in a given range.
Chris@87: ==================== =========================================================
Chris@87: 
Chris@87: ==================== =========================================================
Chris@87: Univariate distributions
Chris@87: ==============================================================================
Chris@87: beta                 Beta distribution over ``[0, 1]``.
Chris@87: binomial             Binomial distribution.
Chris@87: chisquare            :math:`\\chi^2` distribution.
Chris@87: exponential          Exponential distribution.
Chris@87: f                    F (Fisher-Snedecor) distribution.
Chris@87: gamma                Gamma distribution.
Chris@87: geometric            Geometric distribution.
Chris@87: gumbel               Gumbel distribution.
Chris@87: hypergeometric       Hypergeometric distribution.
Chris@87: laplace              Laplace distribution.
Chris@87: logistic             Logistic distribution.
Chris@87: lognormal            Log-normal distribution.
Chris@87: logseries            Logarithmic series distribution.
Chris@87: negative_binomial    Negative binomial distribution.
Chris@87: noncentral_chisquare Non-central chi-square distribution.
Chris@87: noncentral_f         Non-central F distribution.
Chris@87: normal               Normal / Gaussian distribution.
Chris@87: pareto               Pareto distribution.
Chris@87: poisson              Poisson distribution.
Chris@87: power                Power distribution.
Chris@87: rayleigh             Rayleigh distribution.
Chris@87: triangular           Triangular distribution.
Chris@87: uniform              Uniform distribution.
Chris@87: vonmises             Von Mises circular distribution.
Chris@87: wald                 Wald (inverse Gaussian) distribution.
Chris@87: weibull              Weibull distribution.
Chris@87: zipf                 Zipf's distribution over ranked data.
Chris@87: ==================== =========================================================
Chris@87: 
Chris@87: ==================== =========================================================
Chris@87: Multivariate distributions
Chris@87: ==============================================================================
Chris@87: dirichlet            Multivariate generalization of Beta distribution.
Chris@87: multinomial          Multivariate generalization of the binomial distribution.
Chris@87: multivariate_normal  Multivariate generalization of the normal distribution.
Chris@87: ==================== =========================================================
Chris@87: 
Chris@87: ==================== =========================================================
Chris@87: Standard distributions
Chris@87: ==============================================================================
Chris@87: standard_cauchy      Standard Cauchy-Lorentz distribution.
Chris@87: standard_exponential Standard exponential distribution.
Chris@87: standard_gamma       Standard Gamma distribution.
Chris@87: standard_normal      Standard normal distribution.
Chris@87: standard_t           Standard Student's t-distribution.
Chris@87: ==================== =========================================================
Chris@87: 
Chris@87: ==================== =========================================================
Chris@87: Internal functions
Chris@87: ==============================================================================
Chris@87: get_state            Get tuple representing internal state of generator.
Chris@87: set_state            Set state of generator.
Chris@87: ==================== =========================================================
Chris@87: 
Chris@87: """
Chris@87: from __future__ import division, absolute_import, print_function
Chris@87: 
Chris@87: import warnings
Chris@87: 
Chris@87: # To get sub-modules
Chris@87: from .info import __doc__, __all__
Chris@87: 
Chris@87: 
Chris@87: with warnings.catch_warnings():
Chris@87:     warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
Chris@87:     from .mtrand import *
Chris@87: 
Chris@87: # Some aliases:
Chris@87: ranf = random = sample = random_sample
Chris@87: __all__.extend(['ranf', 'random', 'sample'])
Chris@87: 
Chris@87: def __RandomState_ctor():
Chris@87:     """Return a RandomState instance.
Chris@87: 
Chris@87:     This function exists solely to assist (un)pickling.
Chris@87: 
Chris@87:     Note that the state of the RandomState returned here is irrelevant, as this function's
Chris@87:     entire purpose is to return a newly allocated RandomState whose state pickle can set.
Chris@87:     Consequently the RandomState returned by this function is a freshly allocated copy
Chris@87:     with a seed=0.
Chris@87: 
Chris@87:     See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
Chris@87: 
Chris@87:     """
Chris@87:     return RandomState(seed=0)
Chris@87: 
Chris@87: from numpy.testing import Tester
Chris@87: test = Tester().test
Chris@87: bench = Tester().bench