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DOC: adjust documentation for namespace change
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‎doc/source/reference/randomgen/brng/dsfmt.rst renamed to ‎doc/source/reference/random/brng/dsfmt.rst

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Double SIMD Mersenne Twister (dSFMT)
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------------------------------------
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.. module:: numpy.random.randomgen.dsfmt
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.. module:: numpy.random.dsfmt
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.. currentmodule:: numpy.random.randomgen.dsfmt
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.. currentmodule:: numpy.random.dsfmt
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.. autoclass:: DSFMT

‎doc/source/reference/randomgen/brng/index.rst renamed to ‎doc/source/reference/random/brng/index.rst

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Basic Random Number Generators
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------------------------------
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The random values produced by :class:`~randomgen.generator.RandomGenerator`
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.. currentmodule:: numpy.random
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The random values produced by :class:`~RandomGenerator`
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are produced by a basic RNG. These basic RNGs do not directly provide
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random numbers and only contains methods used for seeding, getting or
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setting the state, jumping or advancing the state, and for accessing

‎doc/source/reference/randomgen/brng/mt19937.rst renamed to ‎doc/source/reference/random/brng/mt19937.rst

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

‎doc/source/reference/randomgen/brng/pcg32.rst renamed to ‎doc/source/reference/random/brng/pcg32.rst

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Parallel Congruent Generator (32-bit, PCG32)
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--------------------------------------------
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.. module:: numpy.random.randomgen.pcg32
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.. module:: numpy.random.pcg32
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.. currentmodule:: numpy.random.randomgen.pcg32
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.. currentmodule:: numpy.random.pcg32
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.. autoclass:: PCG32
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:exclude-members:

‎doc/source/reference/randomgen/brng/pcg64.rst renamed to ‎doc/source/reference/random/brng/pcg64.rst

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

‎doc/source/reference/randomgen/brng/philox.rst renamed to ‎doc/source/reference/random/brng/philox.rst

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

‎doc/source/reference/randomgen/brng/threefry.rst renamed to ‎doc/source/reference/random/brng/threefry.rst

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ThreeFry Counter-based RNG
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--------------------------
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.. module:: numpy.random.randomgen.threefry
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.. module:: numpy.random.threefry
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.. currentmodule:: numpy.random.randomgen.threefry
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.. currentmodule:: numpy.random.threefry
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.. autoclass:: ThreeFry
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:exclude-members:

‎doc/source/reference/randomgen/brng/threefry32.rst renamed to ‎doc/source/reference/random/brng/threefry32.rst

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ThreeFry32 Counter-based RNG
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----------------------------
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.. module:: numpy.random.randomgen.threefry32
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.. module:: numpy.random.threefry32
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.. currentmodule:: numpy.random.randomgen.threefry32
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.. currentmodule:: numpy.random.threefry32
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.. autoclass:: ThreeFry32
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:exclude-members:

‎doc/source/reference/randomgen/brng/xoroshiro128.rst renamed to ‎doc/source/reference/random/brng/xoroshiro128.rst

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Xoroshiro128+
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-------------
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.. module:: numpy.random.randomgen.xoroshiro128
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.. module:: numpy.random.xoroshiro128
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.. currentmodule:: numpy.random.randomgen.xoroshiro128
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.. currentmodule:: numpy.random.xoroshiro128
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.. autoclass:: Xoroshiro128
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:exclude-members:

‎doc/source/reference/randomgen/brng/xorshift1024.rst renamed to ‎doc/source/reference/random/brng/xorshift1024.rst

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Xorshift1024*φ
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--------------
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.. module:: numpy.random.randomgen.xorshift1024
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.. module:: numpy.random.xorshift1024
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.. currentmodule:: numpy.random.randomgen.xorshift1024
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.. currentmodule:: numpy.random.xorshift1024
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.. autoclass:: Xorshift1024
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:exclude-members:

‎doc/source/reference/randomgen/brng/xoshiro256starstar.rst renamed to ‎doc/source/reference/random/brng/xoshiro256starstar.rst

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Xoshiro256**
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------------
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.. module:: numpy.random.randomgen.xoshiro256starstar
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.. module:: numpy.random.xoshiro256starstar
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.. currentmodule:: numpy.random.randomgen.xoshiro256starstar
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.. currentmodule:: numpy.random.xoshiro256starstar
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.. autoclass:: Xoshiro256StarStar
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:exclude-members:

‎doc/source/reference/randomgen/brng/xoshiro512starstar.rst renamed to ‎doc/source/reference/random/brng/xoshiro512starstar.rst

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Xoshiro512**
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------------
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.. module:: numpy.random.randomgen.xoshiro512starstar
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.. module:: numpy.random.xoshiro512starstar
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.. currentmodule:: numpy.random.randomgen.xoshiro512starstar
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.. currentmodule:: numpy.random.xoshiro512starstar
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.. autoclass:: Xoshiro512StarStar
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:exclude-members:

‎doc/source/reference/randomgen/change-log.rst renamed to ‎doc/source/reference/random/change-log.rst

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Change Log
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----------
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Change Log for the original bashtage/randomgen repo
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---------------------------------------------------
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v1.16.1
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=======
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- Synchronized with upstream changes.
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- Fixed a bug in gamma generation if the shape parameters is 0.0.
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v1.16.0
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=======
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- Fixed a bug that affected :class:`~randomgen.dsfmt.DSFMT` when calling
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:func:`~randomgen.dsfmt.DSFMT.jump` or :func:`~randomgen.dsfmt.DSFMT.seed`
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- Fixed a bug that affected ``randomgen.dsfmt.DSFMT`` when calling
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``~randomgen.dsfmt.DSFMT.jump`` or ``randomgen.dsfmt.DSFMT.seed``
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that failed to reset the buffer. This resulted in upto 381 values from the
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previous state being used before the buffer was refilled at the new state.
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- Fixed bugs in :class:`~randomgen.xoshiro512starstar.Xoshiro512StarStar`
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and :class:`~randomgen.xorshift1024.Xorshift1024` where the fallback
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- Fixed bugs in ``randomgen.xoshiro512starstar.Xoshiro512StarStar``
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and ``randomgen.xorshift1024.Xorshift1024`` where the fallback
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entropy initialization used too few bytes. This bug is unlikely to be
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encountered since this path is only encountered if the system random
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number generator fails.

‎doc/source/reference/randomgen/entropy.rst renamed to ‎doc/source/reference/random/entropy.rst

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System Entropy
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==============
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.. module:: numpy.random.randomgen.entropy
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.. module:: numpy.random.entropy
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.. autofunction:: random_entropy

‎doc/source/reference/randomgen/extending.rst renamed to ‎doc/source/reference/random/extending.rst

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.. currentmodule:: numpy.random.randomgen
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.. currentmodule:: numpy.random
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Extending
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---------
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.. code-block:: python
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from randomgen import Xoroshiro128
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from numpy.random import Xoroshiro128
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import numpy as np
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import numba as nb
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cimport numpy as np
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cimport cython
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from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
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from randomgen.common cimport *
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from randomgen.distributions cimport random_gauss_zig
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from randomgen.xoroshiro128 import Xoroshiro128
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from numpy.random.common cimport *
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from numpy.random.distributions cimport random_gauss_zig
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from numpy.random.xoroshiro128 import Xoroshiro128
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@cython.boundscheck(False)

‎doc/source/reference/randomgen/generator.rst renamed to ‎doc/source/reference/random/generator.rst

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.. currentmodule:: numpy.random.randomgen
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.. currentmodule:: numpy.random
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Random Generator
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----------------

‎doc/source/reference/randomgen/index.rst renamed to ‎doc/source/reference/random/index.rst

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.. current_module numpy.random.randomgen
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.. currentmodule:: numpy.random
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numpy.random.randomgen
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======================
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numpy.random
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============
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This package modernizes the legacy
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`~numpy.random.RandomState` object and allows changing the core random
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number generator (RNG). The `~numpy.random.randomgen.RandomGenerator` can
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be initialized with a number of different RNGs, and exposes many different
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probability distributions.
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A `~RandomGenerator` can
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be initialized with a number of different Random Number Generators (RNG)s, and
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exposes many different probability distributions.
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Quick Start
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-----------
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By default, `generator.RandomGenerator` uses normals provided by
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By default, `RandomGenerator` uses normals provided by
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`xoroshiro128.Xoroshiro128` which will be faster than the legacy methods in
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`numpy.random`
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`numpy.random.RandomState`
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.. code-block:: python
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# As replacement for numpy.random
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import randomgen.generator as random
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# As replacement for numpy.random.RandomState
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from numpy import random
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random.standard_normal()
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`numpy.random.randomgen.RandomGenerator` can also be used as a replacement for
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`~numpy.random.RandomState`, although the random values are generated by
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`~numpyrandom.randomgen.xoroshiro128.Xoroshiro128`. Since ``randomgen``
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separates the `~numpy.random.randomgen.RandomGenerator` from the RNG, it is not
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possible to directly seed the generator.
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`RandomGenerator` can be used as a direct replacement for
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`~RandomState`, although the random values are generated by
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`~xoroshiro128.Xoroshiro128`. The `RandomGenerator` holds an instance of a RNG.
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It is accessable as ``gen.brng``.
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.. code-block:: python
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# As replacement for RandomState()
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from randomgen import RandomGenerator
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from numpy.random import RandomGenerator
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rg = RandomGenerator()
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rg.standard_normal()
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Seeds can be passed to any of the basic RNGs. Here `numpy.random.randomgen.
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mt19937.MT19937` is used and the `~numpy.random.randomgen.RandomGenerator` is
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accessed via the attribute `~numpy.random.randomgen.mt19937.MT19937.generator`.
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Seeds can be passed to any of the basic RNGs. Here `mt19937.MT19937` is used
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and the ``RandomGenerator`` is accessed via the attribute `mt19937.MT19937.
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generator`.
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.. code-block:: python
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from randomgen import MT19937
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from numpy.random import MT19937
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rg = MT19937(12345).generator
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rg.standard_normal()
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random unsigned 32- and 64-bit values. The basic random generator also handles
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all seeding since this varies when using alternative basic RNGs.
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The `random generator <~numpy.random.randomgen.RandomGenerator>` takes the
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The `random generator <~RandomGenerator>` takes the
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basic RNG-provided functions and transforms them into more useful
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distributions, e.g., simulated normal random values. This structure allows
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alternative basic RNGs to be used without code duplication.
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The `~numpy.random.randomgen.RandomGenerator` is the user-facing object
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that is nearly identical to :class:`~numpy.random.RandomState`. The canonical
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method to initialize a generator passes a basic RNG -- `~numpy.random.
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randomgen.mt19937.MT19937`, the underlying RNG in NumPy -- as the
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sole argument. Note that the basic RNG must be instantized.
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The ``RandomGenerator`` is the user-facing object
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that is nearly identical to :class:`~.RandomState`. The canonical
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method to initialize a generator passes a basic RNG -- `~mt19937.MT19937`, the
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underlying RNG in NumPy -- as the sole argument. Note that the basic RNG must
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be instantized.
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.. code-block:: python
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from randomgen import RandomGenerator, MT19937
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from numpy.random import RandomGenerator, MT19937
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rg = RandomGenerator(MT19937())
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rg.random_sample()
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rg = RandomGenerator(MT19937(12345))
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rg.random_sample()
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A shorthand method is also available which uses the `~numpy.random.randomgen.
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mt19937.MT19937.generator` property from a basic RNG to access an embedded
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random generator.
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A shorthand method is also available which uses the `~mt19937.MT19937.
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generator` property from a basic RNG to access an embedded random generator.
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.. code-block:: python
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.. warning::
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The Box-Muller method used to produce NumPy's normals is no longer available
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in `~numpy.random.randomgen.RandomGenerator`. It is not possible to
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reproduce the random values using `~numpy.random.randomgen.RandomGenerator`
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for the normal distribution or any other distribution that
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relies on the normal such as the gamma or student's t. If you require
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backward compatibility, a legacy generator, `~numpy.random.randomgen.legacy.
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in `~RandomGenerator`. It is not possible to reproduce the random values
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using ``RandomGenerator`` for the normal distribution or any other
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distribution that relies on the normal such as the gamma or student's t.
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If you require backward compatibility, a legacy generator, `~legacy.
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LegacyGenerator`, has been created which can fully reproduce the sequence
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produced by NumPy.
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select distributions
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* Optional ``out`` argument that allows existing arrays to be filled for
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select distributions
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* `~numpy.random.randomgen.entropy.random_entropy` provides access to the system
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* `~entropy.random_entropy` provides access to the system
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source of randomness that is used in cryptographic applications (e.g.,
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``/dev/urandom`` on Unix).
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* All basic random generators functions can produce doubles, uint64s and
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uint32s via CTypes (`~numpy.random.randomgen.xoroshiro128.Xoroshiro128.ctypes`)
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and CFFI (:meth:`~numpy.random.randomgen.xoroshiro128.Xoroshiro128.cffi`).
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uint32s via CTypes (`~xoroshiro128.Xoroshiro128.ctypes`)
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and CFFI (:meth:`~xoroshiro128.Xoroshiro128.cffi`).
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This allows these basic RNGs to be used in numba.
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* The basic random number generators can be used in downstream projects via
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:ref:`Cython <randomgen_cython>`.
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* Support for Lemire’s method [Lemire]_ of generating uniform integers on an
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arbitrary interval by setting ``use_masked=True`` in
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`~umpy.random.randomgen.generator.RandomGenerator.randint`.
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`~RandomGenerator.randint`.
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See :ref:`new-or-different` for a complete list of improvements and
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* MT19937 - The standard NumPy generator. Produces identical results to NumPy
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using the same seed/state. Adds a
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`~numpy.random.randomgen.mt19937.MT19937.jump` function that advances the
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`~mt19937.MT19937.jump` function that advances the
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generator as-if ``2**128`` draws have been made. See `numpy.random`.
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* dSFMT - SSE2 enabled versions of the MT19937 generator. Theoretically
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the same, but with a different state and so it is not possible to produce a
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* XoroShiro128+ - Improved version of XorShift128+ with better performance
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and statistical quality. Like the XorShift generators, it can be jumped
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to produce multiple streams in parallel applications. See
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`~numpy.random.randomgen.xoroshiro128.Xoroshiro128.jump` for details.
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`~xoroshiro128.Xoroshiro128.jump` for details.
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More information about this PRNG is available at the
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`xorshift, xoroshiro and xoshiro authors' page`_.
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* XorShift1024*φ - Fast fast generator based on the XSadd
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generator. Supports ``jump`` and so can be used in
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parallel applications. See the documentation for
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`~numpy.random.randomgen.xorshift1024.Xorshift1024.jump` for details. More
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`~xorshift1024.Xorshift1024.jump` for details. More
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information about these PRNGs is available at the
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`xorshift, xoroshiro and xoshiro authors' page`_.
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* Xorshiro256** and Xorshiro512** - The most recently introduced XOR,
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shift, and rotate generator. Supports ``jump`` and so can be used in
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parallel applications. See the documentation for
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`~numpy.random.randomgen.xoshiro256starstar.Xoshirt256StarStar.jump` for
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`~xoshiro256starstar.Xoshirt256StarStar.jump` for
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details. More information about these PRNGs is available at the
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`xorshift, xoroshiro and xoshiro authors' page`_.
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* PCG-64 - Fast generator that support many parallel streams and
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can be advanced by an arbitrary amount. See the documentation for
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`~numpy.random.randomgen.pcg64.PCG64.advance`. PCG-64 has a period of
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`~pcg64.PCG64.advance`. PCG-64 has a period of
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:math:`2^{128}`. See the `PCG author's page`_ for more details about
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this class of PRNG.
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* ThreeFry and Philox - counter-based generators capable of being advanced an

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