randomstate.prng.dsfmt.RandomState(seed=None)¶Container for the SIMD-based Mersenne Twister pseudo-random number generator.
dSFMT.RandomState exposes a number of methods for generating random
numbers drawn from a variety of probability distributions [1] . 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
dSFMT.RandomState does not make a guarantee that a fixed seed and a
fixed series of calls to dSFMT.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**32-1], array of integers in
[0, 2**32-1] or None (the default). If seed is None,
then dSFMT.RandomState will try to read entropy from
/dev/urandom (or the Windows analog) if available to
produce a 64-bit seed. If unavailable, the a 64-bit hash of the time
and process ID is used. |
|---|
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 RandomState. The RandomState object, besides
being NumPy-aware, also has the advantage that it provides a much larger
number of probability distributions to choose from.
Parallel Features
dsfmt.RandomState can be used in parallel applications by
calling the method jump which advances the state as-if \(2^{128}\)
random numbers have been generated [2]. This allows the original sequence to
be split so that distinct segments can be used in each worker process. All
generators should be initialized with the same seed to ensure that the
segments come from the same sequence.
>>> from randomstate.entropy import random_entropy
>>> import randomstate.prng.dsfmt as rnd
>>> seed = random_entropy()
>>> rs = [rnd.RandomState(seed) for _ in range(10)]
# Advance rs[i] by i jumps
>>> for i in range(10):
rs[i].jump(i)
State and Seeding
The dsfmt.RandomState state vector consists of a 764 element array of
32-bit unsigned integers plus a single integer value between 0 and 382
indicating the current position within the main array. The implementation
used here augments this with a 384 element array of doubles which are used
to efficiently access the random numbers produced by the dSFMT generator.
dsfmt.RandomState is seeded using either a single 32-bit unsigned integer
or a vector of 32-bit unsigned integers. In either case, the input seed is
used as an input (or inputs) for a hashing function, and the output of the
hashing function is used as the initial state. Using a single 32-bit value
for the seed can only initialize a small range of the possible initial
state values.
References
| [1] | Mutsuo Saito and Makoto Matsumoto, “SIMD-oriented Fast Mersenne Twister: a 128-bit Pseudorandom Number Generator.” Monte Carlo and Quasi-Monte Carlo Methods 2006, Springer, pp. 607 – 622, 2008. |
| [2] | Hiroshi Haramoto, Makoto Matsumoto, and Pierre L’Ecuyer, “A Fast Jump Ahead Algorithm for Linear Recurrences in a Polynomial Space”, Sequences and Their Applications - SETA, 290–298, 2008. |
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. |
jump(iter = 1) |
Jumps the state of the random number generator as-if 2**128 random numbers have been generated. |
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 |
shuffle(x) |
Modify a sequence in-place by shuffling its contents. |
permutation(x) |
Randomly permute a sequence, or return a permuted range. |
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. |