reference: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:

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()

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

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