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.. currentmodule:: numpy.random
Random Generator
----------------
The `~Generator` provides access to
a wide range of distributions, and served as a replacement for
:class:`~numpy.random.RandomState`. The main difference between
the two is that ``Generator`` relies on an additional BitGenerator to
manage state and generate the random bits, which are then transformed into
random values from useful distributions. The default BitGenerator used by
``Generator`` is `~PCG64`. The BitGenerator
can be changed by passing an instantized BitGenerator to ``Generator``.
.. autofunction:: default_rng
.. autoclass:: Generator
:exclude-members:
Accessing the BitGenerator
==========================
.. autosummary::
:toctree: generated/
~numpy.random.Generator.bit_generator
Simple random data
==================
.. autosummary::
:toctree: generated/
~numpy.random.Generator.integers
~numpy.random.Generator.random
~numpy.random.Generator.choice
~numpy.random.Generator.bytes
Permutations
============
.. autosummary::
:toctree: generated/
~numpy.random.Generator.shuffle
~numpy.random.Generator.permutation
Distributions
=============
.. autosummary::
:toctree: generated/
~numpy.random.Generator.beta
~numpy.random.Generator.binomial
~numpy.random.Generator.chisquare
~numpy.random.Generator.dirichlet
~numpy.random.Generator.exponential
~numpy.random.Generator.f
~numpy.random.Generator.gamma
~numpy.random.Generator.geometric
~numpy.random.Generator.gumbel
~numpy.random.Generator.hypergeometric
~numpy.random.Generator.laplace
~numpy.random.Generator.logistic
~numpy.random.Generator.lognormal
~numpy.random.Generator.logseries
~numpy.random.Generator.multinomial
~numpy.random.Generator.multivariate_hypergeometric
~numpy.random.Generator.multivariate_normal
~numpy.random.Generator.negative_binomial
~numpy.random.Generator.noncentral_chisquare
~numpy.random.Generator.noncentral_f
~numpy.random.Generator.normal
~numpy.random.Generator.pareto
~numpy.random.Generator.poisson
~numpy.random.Generator.power
~numpy.random.Generator.rayleigh
~numpy.random.Generator.standard_cauchy
~numpy.random.Generator.standard_exponential
~numpy.random.Generator.standard_gamma
~numpy.random.Generator.standard_normal
~numpy.random.Generator.standard_t
~numpy.random.Generator.triangular
~numpy.random.Generator.uniform
~numpy.random.Generator.vonmises
~numpy.random.Generator.wald
~numpy.random.Generator.weibull
~numpy.random.Generator.zipf
|
