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DOC: Spelling changes
Fix multiple spelling errors
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README.md

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## Compatibility Warning
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`RandomGenerator` does notsupports Box-Muller normal variates and so it not
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`RandomGenerator` does not support Box-Muller normal variates and so it not
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100% compatible with NumPy (or randomstate). Box-Muller normals are slow
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to generate and all functions which previously relied on Box-Muller
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normals now use the faster Ziggurat implementation. If you require backward
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## Version
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The version matched the latest version of NumPy where
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`RandoMGenerator(MT19937())` passes all NumPy test.
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`RandomGenerator(MT19937())` passes all NumPy test.
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## Documentation
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README.rst

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Compatibility Warning
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---------------------
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``RandomGenerator`` does notsupports Box-Muller normal variates and so
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``RandomGenerator`` does not support Box-Muller normal variates and so
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it not 100% compatible with NumPy (or randomstate). Box-Muller normals
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are slow to generate and all functions which previously relied on
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Box-Muller normals now use the faster Ziggurat implementation. If you
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-------
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The version matched the latest version of NumPy where
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``RandoMGenerator(MT19937())`` passes all NumPy test.
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``RandomGenerator(MT19937())`` passes all NumPy test.
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Documentation
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-------------

doc/source/change-log.rst

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Changes since v1.14
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===================
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- Added absolute_import to avoid import noise on Python 2.7
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- Add legacy generator which allows NumPy replication
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- Improve type handling of integers
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- Switch to array-fillers for 0 parameter distribution to improve performance

randomgen/dsfmt.pyx

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Notes
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-----
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``DSFMT`` directly provides generators for doubles, and unsigned 32 and 64-
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bit integers [1]_ . These are not firectly available and must be consumed
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bit integers [1]_ . These are not directly available and must be consumed
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via a ``RandomGenerator`` object.
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The Python stdlib module "random" also contains a Mersenne Twister
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@property
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def ctypes(self):
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"""
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Cytpes interface
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Ctypes interface
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Returns
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-------

randomgen/generator.pyx

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>>> from randomgen import MT19937
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>>> rg = RandomGenerator(MT19937())
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The generator is also directly avialable from basic RNGs
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The generator is also directly available from basic RNGs
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>>> rg = MT19937().generator
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>>> rg.standard_normal()
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.. math:: f(x; a,b) = \\frac{1}{B(\\alpha, \\beta)} x^{\\alpha - 1}
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(1 - x)^{\\beta - 1},
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where the normalisation, B, is the beta function,
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where the normalization, B, is the beta function,
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.. math:: B(\\alpha, \\beta) = \\int_0^1 t^{\\alpha - 1}
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(1 - t)^{\\beta - 1} dt.

randomgen/legacy/_legacy.pyx

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numbers for a set of distributions where the method used to produce random
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samples has changed. Three core generators have changed: normal, exponential
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and gamma. These have been replaced by faster Ziggurat-based methods in
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``RadnomGenerator``. ``_LegacyGenerator`` retains the slower methods
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``RandomGenerator``. ``_LegacyGenerator`` retains the slower methods
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to produce samples from these distributions as well as from distributions
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that depend on these such as the Chi-square, power or Weibull.
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randomgen/mt19937.pyx

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Notes
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-----
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``MT19937`` directly provides generators for doubles, and unsigned 32 and 64-
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bit integers [1]_ . These are not firectly available and must be consumed
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bit integers [1]_ . These are not directly available and must be consumed
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via a ``RandomGenerator`` object.
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The Python stdlib module "random" also contains a Mersenne Twister
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@property
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"""
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Cytpes interface
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Ctypes interface
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Returns
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-------

randomgen/pcg32.pyx

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@property
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def ctypes(self):
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"""
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Cytpes interface
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Ctypes interface
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Returns
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-------

randomgen/pcg64.pyx

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@property
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def ctypes(self):
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"""
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Cytpes interface
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Ctypes interface
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-------

randomgen/philox.pyx

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def ctypes(self):
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"""
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Cytpes interface
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Ctypes interface
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-------

randomgen/threefry.pyx

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``ThreeFry`` can be used in parallel applications by
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calling the method ``jump`` which advances the state as-if
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:math:`2^{128}` random numbers have been generated. Alternatively,
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``advance`` can be used to advance the counter for an abritrary number of
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positive steps in [0, 2**256). When using ``jump``, all generators should
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``advance`` can be used to advance the counter for an any
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positive step in [0, 2**256). When using ``jump``, all generators should
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be initialized with the same seed to ensure that the segments come from
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the same sequence. Alternatively, ``ThreeFry`` can be used
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in parallel applications by using a sequence of distinct keys where each
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"""
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Ctypes interface
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Returns
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-------

randomgen/threefry32.pyx

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def ctypes(self):
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"""
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Cytpes interface
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Ctypes interface
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Returns
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-------

randomgen/xoroshiro128.pyx

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@property
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def ctypes(self):
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"""
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Cytpes interface
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Ctypes interface
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Returns
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-------

randomgen/xorshift1024.pyx

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@property
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def ctypes(self):
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"""
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Cytpes interface
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Ctypes interface
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Returns
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-------

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