""" ======================== Random Number Generation ======================== ==================== ========================================================= Utility functions ============================================================================== random Uniformly distributed values of a given shape. bytes Uniformly distributed random bytes. random_integers Uniformly distributed integers in a given range. random_sample Uniformly distributed floats in a given range. random Alias for random_sample ranf Alias for random_sample sample Alias for random_sample choice Generate a weighted random sample from a given array-like permutation Randomly permute a sequence / generate a random sequence. shuffle Randomly permute a sequence in place. seed Seed the random number generator. ==================== ========================================================= ==================== ========================================================= Compatibility functions ============================================================================== rand Uniformly distributed values. randn Normally distributed values. ranf Uniformly distributed floating point numbers. randint Uniformly distributed integers in a given range. ==================== ========================================================= ==================== ========================================================= 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. ==================== ========================================================= """ from __future__ import division, absolute_import, print_function import warnings # To get sub-modules from .info import __doc__, __all__ with warnings.catch_warnings(): warnings.filterwarnings("ignore", message="numpy.ndarray size changed") from .mtrand import * # Some aliases: ranf = random = sample = random_sample __all__.extend(['ranf', 'random', 'sample']) 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.testing import Tester test = Tester().test bench = Tester().bench