MLFG(1279, 861, *) Randomstate

Random generator

class randomstate.prng.mlfg_1279_861.RandomState(seed=None)

Container for a Multiplicative Lagged Fibonacci Generator (MLFG).

LFG(1279, 861, *) is a 64-bit implementation of an MLFG that uses lags 1279 and 861 where random numbers are determined by

\[x_n = (x_{n-k} * x_{n-l}) \mathrm{Mod} 2^M\]

where k=861, l=1279 and M=64. The period of the generator is 2**1340 - 2**61. Even though the PRNG uses 64 bits, only 63 are random since all numbers in x must be odd.

mlfg_1279_861.RandomState 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.

No Compatibility Guarantee

mlfg_1279_861.RandomState does not make a guarantee that a fixed seed and a fixed series of calls to mlfg_1279_861.RandomState methods using the same parameters will always produce the same results. This is different from numpy.random.RandomState guarantee. This is done to simplify improving random number generators. To ensure identical results, you must use the same release version.

Parameters:seed ({None, int, array_like}, optional) – Random seed initializing the pseudo-random number generator. Can be an integer in [0, 2**64-1], array of integers in [0, 2**64-1] or None (the default). If seed is None, then mlfg_1279_861.RandomState will try to read entropy from /dev/urandom (or the Windows analog) if available to produce a 64-bit seed. If unavailable, a 64-bit hash of the time and process ID is used.

Notes

The state of the MLFG(1279,861,*) PRNG is represented by 1279 64-bit unsigned integers as well as a two 32-bit integers representing the location in the state array of the current and lagged values.

State and Seeding

The mlfg_1279_861.RandomState state vector consists of a 1279 element array of 64-bit unsigned integers plus a two integers value between 0 and 1278 indicating the current position and the position of the lagged value within the main array required to produce the next random. All 1279 elements of the state array must be odd.

mlfg_1279_861.RandomState is seeded using either a single 64-bit unsigned integer or a vector of 64-bit unsigned integers. In either case, the input seed is used as an input (or inputs) for another simple random number generator, Splitmix64, and the output of this PRNG function is used as the initial state. Using a single 64-bit value for the seed can only initialize a small range of the possible initial state values. When using an array, the SplitMix64 state for producing the ith component of the initial state is XORd with the ith value of the seed array until the seed array is exhausted. When using an array the initial state for the SplitMix64 state is 0 so that using a single element array and using the same value as a scalar will produce the same initial state.

seed([seed])	Seed the generator.
get_state()	Return a dict containing the internal state of the generator.
set_state(state)	Set the internal state of the generator from a tuple.

Simple random data

rand(d0, d1, …, dn[, dtype])	Random values in a given shape.
randn(d0, d1, …, dn[, method, dtype])	Return a sample (or samples) from the “standard normal” distribution.
randint(low[, high, size, dtype])	Return random integers from low (inclusive) to high (exclusive).
random_integers(low[, high, size])	Random integers of type np.int between low and high, inclusive.
random_sample([size, dtype, out])	Return random floats in the half-open interval [0.0, 1.0).
random	random_sample(size=None, dtype=’d’, out=None)
ranf	random_sample(size=None, dtype=’d’, out=None)
sample	random_sample(size=None, dtype=’d’, out=None)
choice(a[, size, replace, p])	Generates a random sample from a given 1-D array
bytes(length)	Return random bytes.
random_uintegers([size, bits])	Return random unsigned integers
random_raw(self[, size])	Return randoms as generated by the underlying PRNG

Permutations

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

Distributions

beta(a, b[, size])	Draw samples from a Beta distribution.
binomial(n, p[, size])	Draw samples from a binomial distribution.
chisquare(df[, size])	Draw samples from a chi-square distribution.
complex_normal([loc, gamma, relation, size, …])	Draw random samples from a complex normal (Gaussian) distribution.
dirichlet(alpha[, size])	Draw samples from the Dirichlet distribution.
exponential([scale, size])	Draw samples from an exponential distribution.
f(dfnum, dfden[, size])	Draw samples from an F distribution.
gamma(shape[, scale, size])	Draw samples from a Gamma distribution.
geometric(p[, size])	Draw samples from the geometric distribution.
gumbel([loc, scale, size])	Draw samples from a Gumbel distribution.
hypergeometric(ngood, nbad, nsample[, size])	Draw samples from a Hypergeometric distribution.
laplace([loc, scale, size])	Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
logistic([loc, scale, size])	Draw samples from a logistic distribution.
lognormal([mean, sigma, size])	Draw samples from a log-normal distribution.
logseries(p[, size])	Draw samples from a logarithmic series distribution.
multinomial(n, pvals[, size])	Draw samples from a multinomial distribution.
multivariate_normal(mean, cov[, size, …)	Draw random samples from a multivariate normal distribution.
negative_binomial(n, p[, size])	Draw samples from a negative binomial distribution.
noncentral_chisquare(df, nonc[, size])	Draw samples from a noncentral chi-square distribution.
noncentral_f(dfnum, dfden, nonc[, size])	Draw samples from the noncentral F distribution.
normal([loc, scale, size, method])	Draw random samples from a normal (Gaussian) distribution.
pareto(a[, size])	Draw samples from a Pareto II or Lomax distribution with specified shape.
poisson([lam, size])	Draw samples from a Poisson distribution.
power(a[, size])	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rayleigh([scale, size])	Draw samples from a Rayleigh distribution.
standard_cauchy([size])	Draw samples from a standard Cauchy distribution with mode = 0.
standard_exponential([size, dtype, method, out])	Draw samples from the standard exponential distribution.
standard_gamma(shape[, size, dtype, method, out])	Draw samples from a standard Gamma distribution.
standard_normal([size, dtype, method, out])	Draw samples from a standard Normal distribution (mean=0, stdev=1).
standard_t(df[, size])	Draw samples from a standard Student’s t distribution with df degrees of freedom.
triangular(left, mode, right[, size])	Draw samples from the triangular distribution over the interval [left, right].
uniform([low, high, size])	Draw samples from a uniform distribution.
vonmises(mu, kappa[, size])	Draw samples from a von Mises distribution.
wald(mean, scale[, size])	Draw samples from a Wald, or inverse Gaussian, distribution.
weibull(a[, size])	Draw samples from a Weibull distribution.
zipf(a[, size])	Draw samples from a Zipf distribution.