Coverage for /usr/lib/python3/dist-packages/numpy/random/__init__.py : 92%

""" 

======================== 

Random Number Generation 

======================== 

==================== ========================================================= 

Utility functions 

============================================================================== 

random_sample Uniformly distributed floats over ``[0, 1)``. 

random Alias for `random_sample`. 

bytes Uniformly distributed random bytes. 

random_integers Uniformly distributed integers in a given range. 

permutation Randomly permute a sequence / generate a random sequence. 

shuffle Randomly permute a sequence in place. 

seed Seed the random number generator. 

choice Random sample from 1-D array. 

==================== ========================================================= 

==================== ========================================================= 

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 

__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_integers', 

'random_sample', 

'rayleigh', 

'seed', 

'set_state', 

'shuffle', 

'standard_cauchy', 

'standard_exponential', 

'standard_gamma', 

'standard_normal', 

'standard_t', 

'triangular', 

'uniform', 

'vonmises', 

'wald', 

'weibull', 

'zipf' 

] 

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._pytesttester import PytestTester 

test = PytestTester(__name__) 

del PytestTester