cupy.random.RandomState¶

class cupy.random.RandomState(seed=None, method=100)[source]¶

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_XORWOW
cupy.cuda.curand.CURAND_RNG_MRG32K3A
cupy.cuda.curand.CURAND_RNG_MTGP32
cupy.cuda.curand.CURAND_RNG_MT19937
cupy.cuda.curand.CURAND_RNG_PHILOX4_32_10

Methods

beta(a, b, size=None, dtype=<class 'float'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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)[source]¶: Returns an array of random values from a given 1-D array.

See also

cupy.random.choice() for full document, numpy.random.choice

dirichlet(alpha, size=None, dtype=<class 'float'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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, dtype=<class 'float'>)[source]¶: (experimental) Returns an array of samples drawn from the multivariate normal distribution.

See also

cupy.random.multivariate_normal() for full documentation, numpy.random.RandomState.multivariate_normal

negative_binomial(n, p, size=None, dtype=<class 'int'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: Returns an array of samples drawn from the pareto II distribution.

See also

cupy.random.pareto_kernel() for full documentation, numpy.random.RandomState.pareto

permutation(a)[source]¶: Returns a permuted range or a permutation of an array.

poisson(lam=1.0, size=None, dtype=<class 'int'>)[source]¶: 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'>)[source]¶: 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)[source]¶: 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='l')[source]¶: 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)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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)[source]¶: 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)[source]¶: Returns a shuffled array.

See also

cupy.random.shuffle() for full document, numpy.random.shuffle

standard_cauchy(size=None, dtype=<class 'float'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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'>)[source]¶: 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)[source]¶

Draws integers between 0 and max integer inclusive.

Parameters:	size (int or tuple of ints) – Output shape.
Returns:	Drawn samples.
Return type:	cupy.ndarray