randomgen 1.14.2

Random generator supporting multiple PRNGs

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

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

Compatibility Warning

RandomGen no longer supports 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.

Features

• Replacement for NumPy’s RandomState

# import numpy.random as rnd
from randomgen import RandomGenerator, MT19937
rnd = RandomGenerator(MT19937())
x = rnd.standard_normal(100)
y = rnd.random_sample(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 RandomGenerator
# Use Xoroshiro128
rnd = RandomGenerator()
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:

  • Uniforms (random_sample)

  • 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_sample)

  • Exponentials (standard_exponential)

  • Normals (standard_normal)

  • Standard Gammas (via standard_gamma)

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:

• MT19937, the NumPy rng

• dSFMT a SSE2-aware version of the MT19937 generator that is especially fast at generating doubles

• xoroshiro128+ and xorshift1024*φ

• PCG64

• ThreeFry and Philox from Random123 ## Differences from numpy.random.RandomState

New Features

• 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.

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, 3.4, 3.5, 3.6 (probably works on 2.6 and 3.3)

• PC-BSD (FreeBSD) 64-bit, Python 2.7

• OSX 64-bit, Python 3.6

• Windows 32/64 bit (only tested on Python 2.7, 3.5 and 3.6, but should work on 3.3/3.4)

Version

The version matched the latest version of NumPy where RandoMGenerator(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.

Plans

This module is essentially complete. There are a few rough edges that need to be smoothed.

• Creation of additional streams from where supported (i.e. a next_stream() method)

Requirements

Building requires:

• Python (2.7, 3.4, 3.5, 3.6)

• NumPy (1.10, 1.11, 1.12, 1.13, 1.14)

• Cython (0.26+)

• tempita (0.5+), if not provided by Cython

Testing requires pytest (3.0+).

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) and Appveyor (Windows). The library is occasionally tested on Linux 32-bit and Free BSD 11.1.

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 where still relevant.

Installing

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 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.

Using

The separate generators are importable from randomgen

from randomgen import RandomGenerator, ThreeFry, PCG64, MT19937
rg = RandomGenerator(ThreeFry())
rg.random_sample(100)

rg = RandomGenerator(PCG64())
rg.random_sample(100)

# Identical to NumPy
rg = RandomGenerator(MT19937())
rg.random_sample(100)

License

Standard 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           137.1%
MT19937          21.0%
PCG32           101.2%
PCG64           110.7%
Philox           -2.7%
ThreeFry        -11.4%
ThreeFry32      -62.3%
Xoroshiro128    181.4%
Xorshift1024    141.8%

Speed-up relative to NumPy (64-bit unsigned integers)
************************************************************
DSFMT            24.8%
MT19937          15.0%
PCG32            92.6%
PCG64            99.0%
Philox          -20.4%
ThreeFry        -21.7%
ThreeFry32      -64.4%
Xoroshiro128    164.2%
Xorshift1024    120.8%

Speed-up relative to NumPy (Standard normals)
************************************************************
DSFMT           299.4%
MT19937         271.2%
PCG32           364.5%
PCG64           364.2%
Philox          256.9%
ThreeFry        236.0%
ThreeFry32       97.0%
Xoroshiro128    477.4%
Xorshift1024    360.7%