.. currentmodule:: numpy.random

.. _legacy:

Legacy random generation

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:

.. code-block:: python

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

rs.standard_normal() rs2.standard_normal()

rs.random() rs2.random()

rs.standard_exponential() rs2.standard_exponential()

.. autoclass:: RandomState :members: init :exclude-members: init

Seeding and state

.. autosummary:: :toctree: generated/

~RandomState.get_state ~RandomState.set_state ~RandomState.seed

Simple random data

.. autosummary:: :toctree: generated/

~RandomState.rand ~RandomState.randn ~RandomState.randint ~RandomState.random_integers ~RandomState.random_sample ~RandomState.choice ~RandomState.bytes

Permutations

.. autosummary:: :toctree: generated/

~RandomState.shuffle ~RandomState.permutation

Distributions

.. autosummary:: :toctree: generated/

~RandomState.beta ~RandomState.binomial ~RandomState.chisquare ~RandomState.dirichlet ~RandomState.exponential ~RandomState.f ~RandomState.gamma ~RandomState.geometric ~RandomState.gumbel ~RandomState.hypergeometric ~RandomState.laplace ~RandomState.logistic ~RandomState.lognormal ~RandomState.logseries ~RandomState.multinomial ~RandomState.multivariate_normal ~RandomState.negative_binomial ~RandomState.noncentral_chisquare ~RandomState.noncentral_f ~RandomState.normal ~RandomState.pareto ~RandomState.poisson ~RandomState.power ~RandomState.rayleigh ~RandomState.standard_cauchy ~RandomState.standard_exponential ~RandomState.standard_gamma ~RandomState.standard_normal ~RandomState.standard_t ~RandomState.triangular ~RandomState.uniform ~RandomState.vonmises ~RandomState.wald ~RandomState.weibull ~RandomState.zipf

.. _functions-in-numpy-random:

Functions in numpy.random

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 will not change this.

.. autosummary:: :toctree: generated/

beta
binomial
bytes
chisquare
choice
dirichlet
exponential
f
gamma
geometric
get_state
gumbel
hypergeometric
laplace
logistic
lognormal
logseries
multinomial
multivariate_normal
negative_binomial
noncentral_chisquare
noncentral_f
normal
pareto
permutation
poisson
power
rand
randint
randn
random
random_integers
random_sample
ranf
rayleigh
sample
seed
set_state
shuffle
standard_cauchy
standard_exponential
standard_gamma
standard_normal
standard_t
triangular
uniform
vonmises
wald
weibull
zipf