mtalg.random.MultithreadedRNG

class MultithreadedRNG(seed: int | None = None, num_threads: int | None = None, bit_generator: ~numpy.random.bit_generator.BitGenerator = <class 'numpy.random._pcg64.PCG64'>)

Multithreaded random number generator.

Parameters:

• seed (int) – Random seed.

• num_threads (int) – Number of threads to be used, overrides threads as set by set_num_threads().

• bit_generator (np.random.BitGenerator) – Bit generator, defaults to PCG64.

Examples

Instantiate a multithreaded random number generator which uses 4 threads, setting a seed to derive the initial BitGenerator state.

>>> from mtalg.random import MultithreadedRNG
>>> mrng = MultithreadedRNG(seed=1, num_threads=4)

Create a 10000 x 5000 matrix with numbers, drawing from the standard normal distribution.

>>> a = mrng.standard_normal(size=(10_000, 5_000))

Create a 10000 x 5000 matrix with numbers, drawing from the uniform distribution.

>>> b = mrng.uniform(size=(10_000, 5_000), low=0, high=10)

Note

For more information on distributions, see Random generator distributions.

Methods

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