cupy.random.RandomState

class cupy.random.RandomState(seed=None, method=None)

Portable container of a pseudo-random number generator.

An instance of this class holds the state of a random number generator. The state is available only on the device which has been current at the initialization of the instance.

Functions of cupy.random use global instances of this class. Different instances are used for different devices. The global state for the current device can be obtained by the cupy.random.get_random_state() function.

Parameters:

• seed (None or int) – Seed of the random number generator. See the seed() method for detail.

• method (int) –

  Method of the random number generator. Following values are available:

  cupy.cuda.curand.CURAND_RNG_PSEUDO_DEFAULT
  cupy.cuda.curand.CURAND_RNG_PSEUDO_XORWOW
  cupy.cuda.curand.CURAND_RNG_PSEUDO_MRG32K3A
  cupy.cuda.curand.CURAND_RNG_PSEUDO_MTGP32
  cupy.cuda.curand.CURAND_RNG_PSEUDO_MT19937
  cupy.cuda.curand.CURAND_RNG_PSEUDO_PHILOX4_32_10
  

Methods

beta(a, b, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the beta distribution.

See also

• cupy.random.beta() for full documentation

• numpy.random.RandomState.beta()

binomial(n, p, size=None, dtype=<class 'int'>)

Returns an array of samples drawn from the binomial distribution.

See also

• cupy.random.binomial() for full documentation

• numpy.random.RandomState.binomial()

chisquare(df, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the chi-square distribution.

See also

• cupy.random.chisquare() for full documentation

• numpy.random.RandomState.chisquare()

choice(a, size=None, replace=True, p=None)

Returns an array of random values from a given 1-D array.

See also

• cupy.random.choice() for full documentation

• numpy.random.choice()

dirichlet(alpha, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the dirichlet distribution.

See also

• cupy.random.dirichlet() for full documentation

• numpy.random.RandomState.dirichlet()

exponential(scale=1.0, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from a exponential distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.exponential() for full documentation

• numpy.random.RandomState.exponential()

f(dfnum, dfden, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the f distribution.

See also

• cupy.random.f() for full documentation

• numpy.random.RandomState.f()

gamma(shape, scale=1.0, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from a gamma distribution.

See also

• cupy.random.gamma() for full documentation

• numpy.random.RandomState.gamma()

geometric(p, size=None, dtype=<class 'int'>)

Returns an array of samples drawn from the geometric distribution.

See also

• cupy.random.geometric() for full documentation

• numpy.random.RandomState.geometric()

gumbel(loc=0.0, scale=1.0, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from a Gumbel distribution.

See also

• cupy.random.gumbel() for full documentation

• numpy.random.RandomState.gumbel()

hypergeometric(ngood, nbad, nsample, size=None, dtype=<class 'int'>)

Returns an array of samples drawn from the hypergeometric distribution.

See also

• cupy.random.hypergeometric() for full documentation

• numpy.random.RandomState.hypergeometric()

laplace(loc=0.0, scale=1.0, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the laplace distribution.

See also

• cupy.random.laplace() for full documentation

• numpy.random.RandomState.laplace()

logistic(loc=0.0, scale=1.0, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the logistic distribution.

See also

• cupy.random.logistic() for full documentation

• numpy.random.RandomState.logistic()

lognormal(mean=0.0, sigma=1.0, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from a log normal distribution.

See also

• cupy.random.lognormal() for full documentation

• numpy.random.RandomState.lognormal()

logseries(p, size=None, dtype=<class 'int'>)

Returns an array of samples drawn from a log series distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.logseries() for full documentation

• numpy.random.RandomState.logseries()

multivariate_normal(mean, cov, size=None, check_valid='ignore', tol=1e-08, method='cholesky', dtype=<class 'float'>)

Returns an array of samples drawn from the multivariate normal distribution.

Warning

This function calls one or more cuSOLVER routine(s) which may yield invalid results if input conditions are not met. To detect these invalid results, you can set the linalg configuration to a value that is not ignore in cupyx.errstate() or cupyx.seterr().

See also

• cupy.random.multivariate_normal() for full documentation

• numpy.random.RandomState.multivariate_normal()

negative_binomial(n, p, size=None, dtype=<class 'int'>)

Returns an array of samples drawn from the negative binomial distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.negative_binomial() for full documentation

• numpy.random.RandomState.negative_binomial()

noncentral_chisquare(df, nonc, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the noncentral chi-square distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.noncentral_chisquare() for full documentation

• numpy.random.RandomState.noncentral_chisquare()

noncentral_f(dfnum, dfden, nonc, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the noncentral F distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.noncentral_f() for full documentation

• numpy.random.RandomState.noncentral_f()

normal(loc=0.0, scale=1.0, size=None, dtype=<class 'float'>)

Returns an array of normally distributed samples.

See also

• cupy.random.normal() for full documentation

• numpy.random.RandomState.normal()

pareto(a, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the pareto II distribution.

See also

• cupy.random.pareto() for full documentation

• numpy.random.RandomState.pareto()

permutation(a)

Returns a permuted range or a permutation of an array.

poisson(lam=1.0, size=None, dtype=<class 'int'>)

Returns an array of samples drawn from the poisson distribution.

See also

• cupy.random.poisson() for full documentation

• numpy.random.RandomState.poisson()

power(a, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the power distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.power() for full documentation

• numpy.random.RandomState.power()

rand(*size, **kwarg)

Returns uniform random values over the interval [0, 1).

See also

• cupy.random.rand() for full documentation

• numpy.random.RandomState.rand()

randint(low, high=None, size=None, dtype=<class 'int'>)

Returns a scalar or an array of integer values over [low, high).

See also

• cupy.random.randint() for full documentation

• numpy.random.RandomState.randint()

randn(*size, **kwarg)

Returns an array of standard normal random values.

See also

• cupy.random.randn() for full documentation

• numpy.random.RandomState.randn()

random_sample(size=None, dtype=<class 'float'>)

Returns an array of random values over the interval [0, 1).

See also

• cupy.random.random_sample() for full documentation

• numpy.random.RandomState.random_sample()

rayleigh(scale=1.0, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from a rayleigh distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.rayleigh() for full documentation

• numpy.random.RandomState.rayleigh()

seed(seed=None)

Resets the state of the random number generator with a seed.

See also

• cupy.random.seed() for full documentation

• numpy.random.RandomState.seed()

shuffle(a)

Returns a shuffled array.

See also

• cupy.random.shuffle() for full documentation

• numpy.random.shuffle()

standard_cauchy(size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the standard cauchy distribution.

See also

• cupy.random.standard_cauchy() for full documentation

• numpy.random.RandomState.standard_cauchy()

standard_exponential(size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the standard exp distribution.

See also

• cupy.random.standard_exponential() for full documentation

• numpy.random.RandomState.standard_exponential()

standard_gamma(shape, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from a standard gamma distribution.

See also

• cupy.random.standard_gamma() for full documentation

• numpy.random.RandomState.standard_gamma()

standard_normal(size=None, dtype=<class 'float'>)

Returns samples drawn from the standard normal distribution.

See also

• cupy.random.standard_normal() for full documentation

• numpy.random.RandomState.standard_normal()

standard_t(df, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the standard t distribution.

See also

• cupy.random.standard_t() for full documentation

• numpy.random.RandomState.standard_t()

tomaxint(size=None)

Draws integers between 0 and max integer inclusive.

Return a sample of uniformly distributed random integers in the interval [0, np.iinfo(np.int_).max]. The np.int_ type translates to the C long integer type and its precision is platform dependent.

Parameters:

size (int or tuple of ints) – Output shape.

Returns:

Drawn samples.

Return type:

cupy.ndarray

See also

numpy.random.RandomState.tomaxint()

triangular(left, mode, right, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the triangular distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.triangular() for full documentation

• numpy.random.RandomState.triangular()

uniform(low=0.0, high=1.0, size=None, dtype=<class 'float'>)

Returns an array of uniformly-distributed samples over an interval.

See also

• cupy.random.uniform() for full documentation

• numpy.random.RandomState.uniform()

vonmises(mu, kappa, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the von Mises distribution.

See also

• cupy.random.vonmises() for full documentation

• numpy.random.RandomState.vonmises()

wald(mean, scale, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the Wald distribution.

See also

• cupy.random.wald() for full documentation

• numpy.random.RandomState.wald()

weibull(a, size=None, dtype=<class 'float'>)

Returns an array of samples drawn from the weibull distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.weibull() for full documentation

• numpy.random.RandomState.weibull()

zipf(a, size=None, dtype=<class 'int'>)

Returns an array of samples drawn from the Zipf distribution.

Warning

This function may synchronize the device.

See also

• cupy.random.zipf() for full documentation

• numpy.random.RandomState.zipf()

__eq__(value, /)

Return self==value.

__ne__(value, /)

Return self!=value.

__lt__(value, /)

Return self<value.

__le__(value, /)

Return self<=value.

__gt__(value, /)

Return self>value.

__ge__(value, /)

Return self>=value.