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randomgen <https://github.com/bashtage/randomgen>__End-of-life notification
This library was designed to bring alternative generators to the NumPy
infrastructure. It as been successful in advancing the conversation for
a future implementation of a new random number API in NumPy which will
allow new algorithms and/or generators. The next step in this process is
to separate the basic (or core RNG) from the functions that transform
random bits into useful random numbers. This has been implemented in a
successor project randomgen available on
GitHub <https://github.com/bashtage/randomgen>__ or
PyPi <https://pypi.org/project/randomstate/>__.
randomgen <https://github.com/bashtage/randomgen>__ has a slightly
different API, so please see the randomgen documentation <https://bashtage.github.io/randomgen>__.
This is a library and generic interface for alternative random generators in Python and NumPy.
.. code:: python
# import numpy.random as rnd
import randomstate as rnd
x = rnd.standard_normal(100)
y = rnd.random_sample(100)
z = rnd.randn(10,10)
.. code:: python
import randomstate as rnd
w = rnd.standard_normal(10000, method='zig')
x = rnd.standard_exponential(10000, method='zig')
y = rnd.standard_gamma(5.5, 10000, method='zig')
Support for 32-bit floating randoms for core generators. Currently supported:
random_sample)standard_exponential, both Inverse CDF and
Ziggurat)standard_normal, both Box-Muller and Ziggurat)standard_gamma, both Inverse CDF and
Ziggurat)WARNING: The 32-bit generators are experimental and subject to change.
Note: There are no plans to extend the alternative precision generation to all random number types.
Support for filling existing arrays using out keyword argument.
Currently supported in (both 32- and 64-bit outputs)
random_sample)standard_exponential)standard_normal)standard_gamma)This modules includes a number of alternative random number generators in addition to the MT19937 that is included in NumPy. The RNGs include:
MT19937 <https://github.com/numpy/numpy/blob/master/numpy/random/mtrand/>__,
the NumPy rngdSFMT <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/>__ a
SSE2-aware version of the MT19937 generator that is especially fast
at generating doublesSFMT <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/>__ a
SSE2-aware version of the MT19937 generator that is optimized for
integer valuesxorshift128+ <http://xorshift.di.unimi.it/>,
xoroshiro128+ <http://xoroshiro.di.unimi.it/> and
xorshift1024\* <http://xorshift.di.unimi.it/>__PCG32 <http://www.pcg-random.org/>__ and
PCG64 <http:w//www.pcg-random.org/>__MRG32K3A <http://simul.iro.umontreal.ca/rng>__numpy.random.RandomStateNew Features
- ``standard_normal``, ``normal``, ``randn`` and
``multivariate_normal`` all support an additional ``method`` keyword
argument which can be ``bm`` or ``zig`` where ``bm`` corresponds to
the current method using the Box-Muller transformation and ``zig``
uses the much faster (100%+) Ziggurat method.
- ``standard_exponential`` and ``standard_gamma`` both support an
additional ``method`` keyword argument which can be ``inv`` or
``zig`` where ``inv`` corresponds to the current method using the
inverse CDF and ``zig`` uses the much faster (100%+) Ziggurat method.
- Core random number generators can produce either single precision
(``np.float32``) or double precision (``np.float64``, the default)
using an the optional keyword argument ``dtype``
- Core random number generators can fill existing arrays using the
``out`` keyword argument
New Functions
random_entropy - Read from the system entropy provider, which is
commonly used in cryptographic applicationsrandom_raw - Direct access to the values produced by the
underlying PRNG. The range of the values returned depends on the
specifics of the PRNG implementation.random_uintegers - unsigned integers, either 32-
([0, 2**32-1]) or 64-bit ([0, 2**64-1])jump - Jumps RNGs that support it. jump moves the state a
great distance. Only available if supported by the RNG.advance - Advanced the core RNG 'as-if' a number of draws were
made, without actually drawing the numbers. Only available if
supported by the RNG.numpy.random.RandomState. The
mt19937 generator is identical to numpy.random.RandomState,
and will produce an identical sequence of random numbers for a given
seed.The version matched the latest version of NumPy where
randomstate.prng.mt19937 passes all NumPy test.
An occasionally updated build of the documentation is available on my github pages <http://bashtage.github.io/ng-numpy-randomstate/>__.
This module is essentially complete. There are a few rough edges that need to be smoothed.
next_stream() method)Building requires:
Testing requires pytest (3.0+).
Note: it might work with other versions but only tested with these versions.
| All development has been on 64-bit Linux, and it is regularly tested on Travis-CI. The library is occasionally tested on Linux 32-bit, | OSX 10.10, PC-BSD 10.2 (should also work on Free BSD) and Windows (Python 2.7/3.5, both 32 and 64-bit).
Basic tests are in place for all RNGs. The MT19937 is tested against NumPy's implementation for identical results. It also passes NumPy's test suite.
.. code:: bash
python setup.py install
SSE2
``dSFTM`` makes use of SSE2 by default. If you have a very old computer
or are building on non-x86, you can install using:
.. code:: bash
python setup.py install --no-sse2
Windows
Either use a binary installer, or if building from scratch, use Python
3.5 with Visual Studio 2015 Community Edition. It can also be build
using Microsoft Visual C++ Compiler for Python 2.7 and Python 2.7,
although some modifications may be needed to distutils to find the
compiler.
The separate generators are importable from randomstate.prng.
.. code:: python
import randomstate
rs = randomstate.prng.xorshift128.RandomState()
rs.random_sample(100)
rs = randomstate.prng.pcg64.RandomState()
rs.random_sample(100)
# Identical to NumPy
rs = randomstate.prng.mt19937.RandomState()
rs.random_sample(100)
Like NumPy, randomstate also exposes a single instance of the
mt19937 generator directly at the module level so that commands like
.. code:: python
import randomstate
randomstate.standard_normal()
randomstate.exponential(1.0, 1.0, size=10)
will work.
Standard NCSA, plus sub licenses for components.
Performance is promising, and even the mt19937 seems to be faster than NumPy's mt19937.
::
Speed-up relative to NumPy (Uniform Doubles)
************************************************************
randomstate.prng-dsfmt-random_sample 313.5%
randomstate.prng-mlfg_1279_861-random_sample 459.4%
randomstate.prng-mrg32k3a-random_sample -57.6%
randomstate.prng-mt19937-random_sample 72.5%
randomstate.prng-pcg32-random_sample 232.8%
randomstate.prng-pcg64-random_sample 330.6%
randomstate.prng-xoroshiro128plus-random_sample 609.9%
randomstate.prng-xorshift1024-random_sample 348.8%
randomstate.prng-xorshift128-random_sample 489.7%
Speed-up relative to NumPy (Normals using Box-Muller)
************************************************************
randomstate.prng-dsfmt-standard_normal 26.8%
randomstate.prng-mlfg_1279_861-standard_normal 30.9%
randomstate.prng-mrg32k3a-standard_normal -14.8%
randomstate.prng-mt19937-standard_normal 17.7%
randomstate.prng-pcg32-standard_normal 24.5%
randomstate.prng-pcg64-standard_normal 26.2%
randomstate.prng-xoroshiro128plus-standard_normal 31.4%
randomstate.prng-xorshift1024-standard_normal 27.4%
randomstate.prng-xorshift128-standard_normal 30.3%
Speed-up relative to NumPy (Normals using Ziggurat)
************************************************************
randomstate.prng-dsfmt-standard_normal 491.7%
randomstate.prng-mlfg_1279_861-standard_normal 439.6%
randomstate.prng-mrg32k3a-standard_normal 101.2%
randomstate.prng-mt19937-standard_normal 354.4%
randomstate.prng-pcg32-standard_normal 531.0%
randomstate.prng-pcg64-standard_normal 517.9%
randomstate.prng-xoroshiro128plus-standard_normal 674.0%
randomstate.prng-xorshift1024-standard_normal 486.7%
randomstate.prng-xorshift128-standard_normal 617.0%
.. |Travis Build Status| image:: https://travis-ci.org/bashtage/ng-numpy-randomstate.svg?branch=master :target: https://travis-ci.org/bashtage/ng-numpy-randomstate .. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/odc5c4ukhru5xicl/branch/master?svg=true :target: https://ci.appveyor.com/project/bashtage/ng-numpy-randomstate/branch/master .. |PyPI version| image:: https://badge.fury.io/py/randomstate.svg :target: https://badge.fury.io/py/randomstate
FAQs
Next-gen RandomState supporting multiple PRNGs
We found that randomstate demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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