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Random generator supporting multiple PRNGs

Project description

RandomGen

Random Number Generator using settable Basic RNG interface for future NumPy RandomState evolution.

Continuous Integration

Travis Build Status Appveyor Build Status Build Status FreeBSD Status on Cirrus

Coverage

Coverage Status codecov

Latest Release

PyPI version Anacnoda Cloud

License

NCSA License BSD License DOI

This is a library and generic interface for alternative random generators in Python and NumPy.

WARNINGS

Changes in v1.18

There are many changes between v1.16.x and v1.18.x. These reflect API decision taken in conjunction with NumPy in preparation of the core of randomgen being used as the preferred random number generator in NumPy. These all issue DeprecationWarnings except for BasicRNG.generator which raises NotImplementedError. The C-API has also changed to reflect the preferred naming the underlying Pseudo-RNGs, which are now known as bit generators (or BigGenerators).

Future Plans

A substantial portion of randomgen has been merged into NumPy. Revamping NumPy’s random number generation was always the goal of this project (and its predecessor NextGen NumPy RandomState), and so it has succeeded.

While I have no immediate plans to remove anything, after a 1.19 release I will:

  • Remove Generator and RandomState. These duplicate NumPy and will diverge over time. The versions in NumPy are authoritative.
  • Preserve novel methods of Generator in a new class, ExtendedGenerator.
  • Add some distributions that are not supported in NumPy.
  • Remove MT19937 PCG64 since these are duplicates of bit generators in NumPy.
  • Add any interesting bit generators I come across.

Python 2.7 Support

v1.16 is the final major version that supports Python 2.7. Any bugs in v1.16 will be patched until the end of 2019. All future releases are Python 3, with an initial minimum version of 3.5.

Compatibility Warning

Generator does not support Box-Muller normal variates and so it not 100% compatible with NumPy (or randomstate). Box-Muller normals are slow to generate and all functions which previously relied on Box-Muller normals now use the faster Ziggurat implementation. If you require backward compatibility, a legacy generator, RandomState, has been created which can fully reproduce the sequence produced by NumPy.

Features

  • Designed as a peplacement for NumPy’s 1.16’s RandomState

    from randomgen import Generator, MT19937
    rnd = Generator(MT19937())
    x = rnd.standard_normal(100)
    y = rnd.random(100)
    z = rnd.randn(10,10)
    
  • Default random generator is a fast generator called Xoroshiro128plus

  • Support for random number generators that support independent streams and jumping ahead so that sub-streams can be generated

  • Faster random number generation, especially for normal, standard exponential and standard gamma using the Ziggurat method

    from randomgen import Generator
    # Default bit generator is Xoroshiro128
    rnd = Generator()
    w = rnd.standard_normal(10000)
    x = rnd.standard_exponential(10000)
    y = rnd.standard_gamma(5.5, 10000)
    
  • Support for 32-bit floating randoms for core generators. Currently supported:

    • Uniforms (random)
    • Exponentials (standard_exponential, both Inverse CDF and Ziggurat)
    • Normals (standard_normal)
    • Standard Gammas (via standard_gamma)

    WARNING: The 32-bit generators are experimental and subject to change.

    Note: There are no plans to extend the alternative precision generation to all distributions.

  • Support for filling existing arrays using out keyword argument. Currently supported in (both 32- and 64-bit outputs)

    • Uniforms (random)
    • Exponentials (standard_exponential)
    • Normals (standard_normal)
    • Standard Gammas (via standard_gamma)
  • Support for Lemire’s method of generating uniform integers on an arbitrary interval by setting use_masked=True.

Included Pseudo Random Number Generators

This module includes a number of alternative random number generators in addition to the MT19937 that is included in NumPy. The RNGs include:

Differences from numpy.random.RandomState

Note

These comparrisons are relative to NumPy 1.16. The project has been substantially merged into NumPy 1.17+.

New Features relative to NumPy 1.16

  • standard_normal, normal, randn and multivariate_normal all use the much faster (100%+) Ziggurat method.
  • standard_gamma and gamma both use the much faster Ziggurat method.
  • standard_exponential exponential 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 the optional keyword argument dtype
  • Core random number generators can fill existing arrays using the out keyword argument
  • Standardizes integer-values random values as int64 for all platforms.
  • randint supports generating using rejection sampling on masked values (the default) or Lemire’s method. Lemire’s method can be much faster when the required interval length is much smaller than the closes power of 2.

New Functions

  • random_entropy - Read from the system entropy provider, which is commonly used in cryptographic applications
  • random_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 RNG ‘as-if’ a number of draws were made, without actually drawing the numbers. Only available if supported by the RNG.

Status

  • Builds and passes all tests on:
    • Linux 32/64 bit, Python 2.7, 3.5, 3.6, 3.7
    • Linux (ARM/ARM64), Python 3.7
    • OSX 64-bit, Python 2.7, 3.5, 3.6, 3.7
    • Windows 32/64 bit, Python 2.7, 3.5, 3.6, 3.7
    • PC-BSD (FreeBSD) 64-bit, Python 2.7 (Occasional, no CI)

Version

The package version matches the latest version of NumPy where RandomState(MT19937()) passes all NumPy test.

Documentation

Documentation for the latest release is available on my GitHub pages. Documentation for the latest commit (unreleased) is available under devel.

Requirements

Building requires:

  • Python (3.5, 3.6, 3.7, 3.8)
  • NumPy (1.13, 1.14, 1.15, 1.16, 1.17, 1.18)
  • Cython (0.26+)
  • tempita (0.5+), if not provided by Cython

Testing requires pytest (4.0+).

Note: it might work with other versions but only tested with these versions.

Development and Testing

All development has been on 64-bit Linux, and it is regularly tested on Travis-CI (Linux/OSX), Appveyor (Windows), Cirrus (FreeBSD) and Drone.io (ARM/ARM64 Linux).

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 where still relevant.

Installing

Either install from PyPi using

pip install randomgen

or, if you want the latest version,

pip install git+https://github.com/bashtage/randomgen.git

or from a cloned repo,

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:

python setup.py install --no-sse2

Windows

Either use a binary installer, or if building from scratch, use Python 3.6/3.7 with Visual Studio 2015/2017 Community Edition. It can also be build using Microsoft Visual C++ Compiler for Python 2.7 and Python 2.7.

Using

The separate generators are importable from randomgen

from randomgen import Generator, ThreeFry, PCG64, MT19937
rg = Generator(ThreeFry())
rg.random(100)

rg = Generator(PCG64())
rg.random(100)

# Identical to NumPy
rg = Generator(MT19937())
rg.random(100)

License

Dual: BSD 3-Clause and NCSA, plus sub licenses for components.

Performance

Performance is promising, and even the mt19937 seems to be faster than NumPy’s mt19937.

Speed-up relative to NumPy (Uniform Doubles)
************************************************************
DSFMT                 184.9%
MT19937                17.3%
PCG32                  83.3%
PCG64                 108.3%
Philox                 -4.9%
ThreeFry              -12.0%
Xoroshiro128          159.5%
Xorshift1024          150.4%
Xoshiro256            145.7%
Xoshiro512            113.1%

Speed-up relative to NumPy (64-bit unsigned integers)
************************************************************
DSFMT                  17.4%
MT19937                 7.8%
PCG32                  60.3%
PCG64                  73.5%
Philox                -25.5%
ThreeFry              -30.5%
Xoroshiro128          124.0%
Xorshift1024          109.4%
Xoshiro256            100.3%
Xoshiro512             63.5%

Speed-up relative to NumPy (Standard normals)
************************************************************
DSFMT                 183.0%
MT19937               169.0%
PCG32                 240.7%
PCG64                 231.6%
Philox                131.3%
ThreeFry              118.3%
Xoroshiro128          332.1%
Xorshift1024          232.4%
Xoshiro256            306.6%
Xoshiro512            274.6%

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