Améliorations / Corrections

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Description des améliorations :

Nom de la classe	Description
BitGenerator
Generator
MT19937
PCG64
PCG64DXSM
Philox
RandomState
SeedSequence
SFC64

Signature de la fonction	Description
beta(a, b, size=None)
binomial(n, p, size=None)
bytes(length)
chisquare(df, size=None)
choice(a, size=None, replace=True, p=None)
default_rng(seed=None)	default_rng(seed=None) _{[extrait de default_rng.__doc__]}
dirichlet(alpha, size=None)
exponential(scale=1.0, size=None)
f(dfnum, dfden, size=None)
gamma(shape, scale=1.0, size=None)
geometric(p, size=None)
get_bit_generator()
get_state(legacy=True)
gumbel(loc=0.0, scale=1.0, size=None)
hypergeometric(ngood, nbad, nsample, size=None)
laplace(loc=0.0, scale=1.0, size=None)
logistic(loc=0.0, scale=1.0, size=None)
lognormal(mean=0.0, sigma=1.0, size=None)
logseries(p, size=None)
multinomial(n, pvals, size=None)
multivariate_normal(mean, cov, size=None, check_valid='warn', tol=1e-08)
negative_binomial(n, p, size=None)
noncentral_chisquare(df, nonc, size=None)
noncentral_f(dfnum, dfden, nonc, size=None)
normal(loc=0.0, scale=1.0, size=None)
pareto(a, size=None)
permutation(x)
poisson(lam=1.0, size=None)
power(a, size=None)
rand(*args)
randint(low, high=None, size=None, dtype=<class 'int'>)
randn(*args)
random(size=None)
random_integers(low, high=None, size=None)
random_sample(size=None)
ranf(args, *kwargs)
rayleigh(scale=1.0, size=None)
sample(args, *kwargs)
seed(seed=None)
set_bit_generator(bitgen)
set_state(state)
shuffle(x)
standard_cauchy(size=None)
standard_exponential(size=None)
standard_gamma(shape, size=None)
standard_normal(size=None)
standard_t(df, size=None)
test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, durations=-1, tests=None)
triangular(left, mode, right, size=None)
uniform(low=0.0, high=1.0, size=None)
vonmises(mu, kappa, size=None)
wald(mean, scale, size=None)
weibull(a, size=None)
zipf(a, size=None)

Améliorations / Corrections

Contenu du module « numpy.random »

Liste des classes du module numpy.random

Liste des fonctions du module numpy.random