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**128draws 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.
- 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.
- 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 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.
- 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 so it too has been pretty thoroughly tested.
pip install .
python setup.py bdist_wheel`
Then upload the wheel
pip install . --target /tmp/tmpsite
PYTHONPATH=/tmp/tmpsite pytest tests