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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_gen
.. autoclass:: Generator
:exclude-members:
Accessing the BitGenerator
==========================
.. autosummary::
:toctree: generated/
~Generator.bit_generator
Simple random data
==================
.. autosummary::
:toctree: generated/
~Generator.integers
~Generator.random
~Generator.choice
~Generator.bytes
Permutations
============
.. autosummary::
:toctree: generated/
~Generator.shuffle
~Generator.permutation
Distributions
=============
.. autosummary::
:toctree: generated/
~Generator.beta
~Generator.binomial
~Generator.chisquare
~Generator.dirichlet
~Generator.exponential
~Generator.f
~Generator.gamma
~Generator.geometric
~Generator.gumbel
~Generator.hypergeometric
~Generator.laplace
~Generator.logistic
~Generator.lognormal
~Generator.logseries
~Generator.multinomial
~Generator.multivariate_normal
~Generator.negative_binomial
~Generator.noncentral_chisquare
~Generator.noncentral_f
~Generator.normal
~Generator.pareto
~Generator.poisson
~Generator.power
~Generator.rayleigh
~Generator.standard_cauchy
~Generator.standard_exponential
~Generator.standard_gamma
~Generator.standard_normal
~Generator.standard_t
~Generator.triangular
~Generator.uniform
~Generator.vonmises
~Generator.wald
~Generator.weibull
~Generator.zipf
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