https://github.com/numpy/bitgenerators

Additional bitgenerators for the random generators within numpy.
https://github.com/numpy/bitgenerators

Last synced: 3 months ago
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Additional bitgenerators for the random generators within numpy.

• Host: GitHub
• URL: https://github.com/numpy/bitgenerators
• Owner: numpy
• License: bsd-3-clause
• Created: 2019-06-19T18:10:01.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2022-09-29T14:05:29.000Z (about 2 years ago)
• Last Synced: 2024-04-12T04:13:33.609Z (8 months ago)
• Language: C
• Size: 346 KB
• Stars: 9
• Watchers: 18
• Forks: 5
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# BitGenerators for `numpy.random.Generator`

The NumPy random number generator accepts a BitGenerator that provides

a bitstream. This package provides a variety of BitGenerators.

* MT19937 - The standard Python BitGenerator. Produces identical results to

  Python using the same seed/state. Adds a MT19937.jumped function

  that returns a new generator with state as-if ``2**128`` draws have been made.

* `DSFMT - SSE2 enabled versions of the MT19937 generator. Probably behind as

  many papers as any other generator. Good performance on any CPU with SSE2 or

  Altivec. See the [dSFMT authors'

  page](http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/).

* Xoshiro256 and Xoshiro512 - The most recently introduced XOR, shift, and

  rotate generator. Fast and popular bit generator, despite some reservations

  in rare corner cases. More information about these bit generators is

  available at the xorshift, xoroshiro and xoshiro [authors'

  page](http://xoroshiro.di.unimi.it).

* ThreeFry and Philox - counter-based generators capable of being advanced an

  arbitrary number of steps or generating independent streams. Very popular in

  machine learning. See the [Random123

  page](https://www.deshawresearch.com/resources_random123.html) for more

  details about this class of bit generators.

* PCG32 and PCG64 are permutation-congruential generators with very good

  statistical properties.  More information is available on the [PCG authors'

  page](http://www.pcg-random.org/).

* GJrand, SFC64, JSF64 - Fast chaotic 256-bit BitGenerators architected fairly

  similarly, based on random invertible mappings. They are very well-tested.

  JSF64 has been analyzed for a long time. SFC64 seems to be inspired by that

  work; it was written by the author of

  [PractRand](http://pracrand.sourceforge.net/) so it too has been pretty

  thoroughly tested.

## Installation from source

`pip install .`

## Building

`

`python setup.py bdist_wheel`

Then upload the wheel

## Testing

```

pip install . --target /tmp/tmpsite

PYTHONPATH=/tmp/tmpsite pytest tests

```