API reference

Multithreading settings

get_num_threads()	Get number of threads for multithreaded RNGs and algebra functions.
set_num_threads(num_threads)	Set number of threads for subsequent multithreaded RNGs and algebra functions.

Linear algebra

add(a, b[, num_threads, direction])	Add multithreaded.
div(a, b[, num_threads, direction])	Divide multithreaded.
mul(a, b[, num_threads, direction])	Multiply multithreaded.
pow(a, b[, num_threads, direction])	Raise to power multithreaded.
sub(a, b[, num_threads, direction])	Subtract multithreaded.

Pseudorandom number generation

Constructor

random.MultithreadedRNG(seed, num_threads, ...)

Multithreaded random number generator.

Functions

The functions below are the same as the methods of the MultithreadedRNG defined above, as it comes from an instance of this class, instantiated with default arguments and seed as set by numpy.random.seed.

Note

For more information on distributions, see Random generator distributions.

random.beta(a, b[, size])	Draw from a beta distribution.
random.binomial(n, p[, size])	Draw from a binomial distribution.
random.chisquare(df[, size])	Draw from a chisquare distribution.
random.exponential([scale, size])	Draw from an exponential distribution.
random.f(dfnum, dfden[, size])	Draw from an F distribution.
random.gamma(shape[, scale, size])	Draw from a gamma distribution.
random.geometric(p[, size])	Draw from a geometric distribution.
random.gumbel([loc, scale, size])	Draw from a Gumbel distribution.
random.hypergeometric(ngood, nbad, nsample)	Draw from a hypergeometric distribution.
random.integers(low[, high, size, dtype, ...])	Draw random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive).
random.laplace([loc, scale, size])	Draw from a Laplace distribution.
random.logistic([loc, scale, size])	Draw from a logistic distribution.
random.lognormal([mean, sigma, size])	Draw from a lognormal distribution.
random.logseries(p[, size])	Draw from a logarithmic series distribution.
random.negative_binomial(n, p[, size])	Draw from a negative binomial distribution.
random.noncentral_chisquare(df, nonc[, size])	Draw from a noncentral chisquare distribution.
random.noncentral_f(dfnum, dfden, nonc[, size])	Draw from a noncentral F distribution.
random.normal([loc, scale, size])	Draw from the normal distribution.
random.pareto(a[, size])	Draw from a Pareto II or Lomax distribution.
random.poisson([lam, size])	Draw from a poisson distribution.
random.power(a[, size])	Draw from a power distribution.
random.random([size, dtype])	Return random floats in the half-open interval [0.0, 1.0).
random.rayleigh([scale, size])	Draw from a Rayleigh distribution.
random.standard_cauchy([size])	Draw from a standard Cauchy distribution.
random.standard_exponential([size, dtype, ...])	Draw from a standard exponential distribution.
random.standard_gamma(shape[, size, dtype])	Draw from a standard gamma distribution.
random.standard_normal([size, dtype])	Draw from a standard normal distribution.
random.standard_t(df[, size])	Draw from a standard Student’s t distribution.
random.triangular(left, mode, right[, size])	Draw from a triangular distribution.
random.uniform([low, high, size])	Draw from a uniform distribution.
random.vonmises(mu, kappa[, size])	Draw from a von Mises distribution.
random.wald(mean, scale[, size])	Draw from a Wald distribution.
random.weibull(a[, size])	Draw from a Weibull distribution.
random.zipf(a[, size])	Draw from a Zipf distribution.