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