8000 DOC: fix doc linking, was referencing private submodules. · numpy/numpy@307c679 · GitHub
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DOC: fix doc linking, was referencing private submodules.
Closes gh-14359
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‎doc/source/reference/random/bit_generators/mt19937.rst

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Mersenne Twister (MT19937)
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Mersenne Twister (MT19937)
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--------------------------
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.. module:: numpy.random.mt19937
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.. currentmodule:: numpy.random.mt19937
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.. currentmodule:: numpy.random
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.. autoclass:: MT19937
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:exclude-members:

‎doc/source/reference/random/bit_generators/pcg64.rst

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Parallel Congruent Generator (64-bit, PCG64)
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--------------------------------------------
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.. module:: numpy.random.pcg64
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.. currentmodule:: numpy.random.pcg64
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.. currentmodule:: numpy.random
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.. autoclass:: PCG64
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:exclude-members:

‎doc/source/reference/random/bit_generators/philox.rst

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Philox Counter-based RNG
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------------------------
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.. module:: numpy.random.philox
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.. currentmodule:: numpy.random.philox
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.. currentmodule:: numpy.random
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.. autoclass:: Philox
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:exclude-members:

‎doc/source/reference/random/bit_generators/sfc64.rst

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SFC64 Small Fast Chaotic PRNG
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-----------------------------
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.. module:: numpy.random.sfc64
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.. currentmodule:: numpy.random.sfc64
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.. currentmodule:: numpy.random
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.. autoclass:: SFC64
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‎doc/source/reference/random/index.rst

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:maxdepth: 1
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generator
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legacy mtrand <legacy>
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Legacy generator and functions <legacy>
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BitGenerators, SeedSequences <bit_generators/index>
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Features

‎doc/source/reference/random/legacy.rst

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Legacy Random Generation
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------------------------
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The `~mtrand.RandomState` provides access to
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The `RandomState` provides access to
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legacy generators. This generator is considered frozen and will have
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no further improvements. It is guaranteed to produce the same values
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as the final point release of NumPy v1.16. These all depend on Box-Muller
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normals or inverse CDF exponentials or gammas. This class should only be used
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if it is essential to have randoms that are identical to what
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would have been produced by previous versions of NumPy.
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`~mtrand.RandomState` adds additional information
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`RandomState` adds additional information
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to the state which is required when using Box-Muller normals since these
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are produced in pairs. It is important to use
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`~mtrand.RandomState.get_state`, and not the underlying bit generators
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`RandomState.get_state`, and not the underlying bit generators
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`state`, when accessing the state so that these extra values are saved.
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Although we provide the `~mt19937.MT19937` BitGenerator for use independent of
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`~mtrand.RandomState`, note that its default seeding uses `~SeedSequence`
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rather than the legacy seeding algorithm. `~mtrand.RandomState` will use the
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Although we provide the `MT19937` BitGenerator for use independent of
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`RandomState`, note that its default seeding uses `SeedSequence`
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rather than the legacy seeding algorithm. `RandomState` will use the
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legacy seeding algorithm. The methods to use the legacy seeding algorithm are
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currently private as the main reason to use them is just to implement
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`~mtrand.RandomState`. However, one can reset the state of `~mt19937.MT19937`
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using the state of the `~mtrand.RandomState`:
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`RandomState`. However, one can reset the state of `MT19937`
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using the state of the `RandomState`:
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.. code-block:: python
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rs2.standard_exponential()
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.. currentmodule:: numpy.random.mtrand
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.. autoclass:: RandomState
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:exclude-members:
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‎numpy/random/mtrand.pyx

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See Also
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--------
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Generator
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mt19937.MT19937
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Bit_Generators
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MT19937
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:ref:`bit_generator`
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"""
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cdef public object _bit_generator

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