The RandomState provides access to
legacy generators. This generator is considered frozen and will have
no further improvements. It is guaranteed to produce the same values
as the final point release of NumPy v1.16. These all depend on Box-Muller
normals or inverse CDF exponentials or gammas. This class should only be used
if it is essential to have randoms that are identical to what
would have been produced by previous versions of NumPy.
RandomState adds additional information
to the state which is required when using Box-Muller normals since these
are produced in pairs. It is important to use
RandomState.get_state, and not the underlying bit generators
state, when accessing the state so that these extra values are saved.
Although we provide the MT19937 BitGenerator for use independent of
RandomState, note that its default seeding uses SeedSequence
rather than the legacy seeding algorithm. RandomState will use the
legacy seeding algorithm. The methods to use the legacy seeding algorithm are
currently private as the main reason to use them is just to implement
RandomState. However, one can reset the state of MT19937
using the state of the RandomState:
from numpy.random import MT19937
from numpy.random import RandomState
rs = RandomState(12345)
mt19937 = MT19937()
mt19937.state = rs.get_state()
rs2 = RandomState(mt19937)
# Same output
Container for the slow Mersenne Twister pseudo-random number generator.
Consider using a different BitGenerator with the Generator container
RandomState and Generator expose 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.
A fixed bit generator using a fixed seed and a fixed series of calls to
‘RandomState’ methods using the same parameters will always produce the
same results up to roundoff error except when the values were incorrect.
RandomState is effectively frozen and will only receive updates that
are required by changes in the the internals of Numpy. More substantial
changes, including algorithmic improvements, are reserved for
Random seed used to initialize the pseudo-random number generator or
an instantized BitGenerator. If an integer or array, used as a seed for
the MT19937 BitGenerator. Values can be any integer between 0 and
2**32 - 1 inclusive, an array (or other sequence) of such integers,
or None (the default). If seed is None, then the MT19937
BitGenerator is initialized by reading data from /dev/urandom
(or the Windows analogue) if available or seed from the clock
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. RandomState, besides being
NumPy-aware, has the advantage that it provides a much larger number
of probability distributions to choose from.
Return a tuple representing the internal state of the generator.
Set the internal state of the generator from a tuple.
Reseed a legacy MT19937 BitGenerator
rand(d0, d1, …, dn)
Random values in a given shape.
randn(d0, d1, …, dn)
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.
Return random floats in the half-open interval [0.0, 1.0).
choice(a[, size, replace, p])
Generates a random sample from a given 1-D array
Return random bytes.
Modify a sequence in-place by shuffling its contents.
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.
Draw samples from a chi-square distribution.
Draw samples from the Dirichlet distribution.
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.
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.
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])
Draw random samples from a normal (Gaussian) distribution.
Draw samples from a Pareto II or Lomax distribution with specified shape.
Draw samples from a Poisson distribution.
Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
Draw samples from a Rayleigh distribution.
Draw samples from a standard Cauchy distribution with mode = 0.
Draw samples from the standard exponential distribution.
Draw samples from a standard Gamma distribution.
Draw samples from a standard Normal distribution (mean=0, stdev=1).
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.
Draw samples from a Weibull distribution.
Draw samples from a Zipf distribution.
Many of the RandomState methods above are exported as functions in
numpy.random This usage is discouraged, as it is implemented via a global
RandomState instance which is not advised on two counts:
It uses global state, which means results will change as the code changes
It uses a RandomState rather than the more modern Generator.
For backward compatible legacy reasons, we cannot change this. See
This is an alias of random_sample.