MKL-powered RandomState

class mkl_random.MKLRandomState(seed=None, brng='MT19937')

Container for the Intel(R) MKL-powered (pseudo-)random number generators.

MKLRandomState exposes a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None. If size is None, then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.

Compatibility Notice While this class shares some similarities with the original RandomState, it has been rewritten to use MKL’s vector statistics functionality, that provides efficient implementation of the MT19937 and many other basic psuedo-random number generation algorithms as well as efficient sampling from other common statistical distributions. As a consequence this version is NOT seed-compatible with the original RandomState.

Parameters:

seed{None, int, array_like}, optional

Random seed initializing the pseudo-random number generator. Can be an integer, an array (or other sequence) of integers of any length, or None (the default). If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.

brngstr, optional

Name of the basic random number generation algorithm, or a non-deterministic hardware-based generator, provided by Intel MKL. One of ‘MT19937’, ‘SFMT19937’, ‘MT2203’, ‘R250’, ‘WH’, ‘MCG31’, ‘MCG59’, ‘MRG32K3A’, ‘PHILOX4X32X10’, ‘NONDETERM’, ‘ARS5’. The default is ‘MT19937’, the Mersenne Twister generator.

Notes

The Python stdlib module “random” also contains a Mersenne Twister pseudo-random number generator with a number of methods that are similar to the ones available in MKLRandomState. MKLRandomState, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.

References

MKL Documentation

Note

mkl_random.RandomState is a deprecated alias of mkl_random.MKLRandomState, kept for backward compatibility with legacy code. A NumPy-compatible drop-in replacement for numpy.random.RandomState is available in mkl_random.interfaces.numpy_random.

Seeding and state

seed	Seed the generator.
get_state	Return a tuple representing the internal state of the generator.
set_state	Set the internal state of the generator from a tuple.

Simple random data

rand	Random values in a given shape.
randn	Return a sample (or samples) from the "standard normal" distribution.
randint	Return random integers from low (inclusive) to high (exclusive).
random_integers	Random integers of type np.int between low and high, inclusive.
random_sample	Return random floats in the half-open interval [0.0, 1.0).
tomaxint	Return a sample of uniformly distributed random integers in the interval [0, np.iinfo("long").max].
choice	Generates a random sample from a given 1-D array
bytes	Return random bytes.

Permutations

shuffle	Modify a sequence in-place by shuffling its contents.
permutation	Randomly permute a sequence, or return a permuted range.

Distributions

beta	Draw samples from a Beta distribution.
binomial	Draw samples from a binomial distribution.
chisquare	Draw samples from a chi-square distribution.
dirichlet	Draw samples from the Dirichlet distribution.
exponential	Draw samples from an exponential distribution.
f	Draw samples from an F distribution.
gamma	Draw samples from a Gamma distribution.
geometric	Draw samples from the geometric distribution.
gumbel	Draw samples from a Gumbel distribution.
hypergeometric	Draw samples from a Hypergeometric distribution.
laplace	Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
logistic	Draw samples from a logistic distribution.
lognormal	Draw samples from a log-normal distribution.
logseries	Draw samples from a logarithmic series distribution.
multinomial	Draw samples from a multinomial distribution.
multivariate_normal	Draw random samples from a multivariate normal distribution.
negative_binomial	Draw samples from a negative binomial distribution.
noncentral_chisquare	Draw samples from a noncentral chi-square distribution.
noncentral_f	Draw samples from the noncentral F distribution.
normal	Draw random samples from a normal (Gaussian) distribution.
pareto	Draw samples from a Pareto II or Lomax distribution with specified shape.
poisson	Draw samples from a Poisson distribution.
power	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rayleigh	Draw samples from a Rayleigh distribution.
standard_cauchy	Draw samples from a standard Cauchy distribution with mode = 0.
standard_exponential	Draw samples from the standard exponential distribution.
standard_gamma	Draw samples from a standard Gamma distribution.
standard_normal	Draw samples from a standard Normal distribution (mean=0, stdev=1).
standard_t	Draw samples from a standard Student's t distribution with df degrees of freedom.
triangular	Draw samples from the triangular distribution.
uniform	Draw samples from a uniform distribution.
vonmises	Draw samples from a von Mises distribution.
wald	Draw samples from a Wald, or inverse Gaussian, distribution.
weibull	Draw samples from a Weibull distribution.
zipf	Draw samples from a Zipf distribution.

MKL-specific methods

leapfrog	Initializes the current state generator using leap-frog method, if supported for the basic random pseudo-random number generation algorithm.
skipahead	Initializes the current state generator using skip-ahead method, if supported for the basic random pseudo-random number generation algorithm.
randint_untyped	Return random integers from low (inclusive) to high (exclusive).
multinormal_cholesky	Draw random samples from a multivariate normal distribution.