randomstate 1.11.0

Included Pseudo Random Number Generators

This modules 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

• xorshift128+ and xorshift1024*

• PCG32 and PCG64

• MRG32K3A

• A multiplicative lagged fibonacci generator (LFG(63, 1279, 861, *))

Differences from numpy.random.RandomState

Status

• Complete drop-in replacement for 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.

• Builds and passes all tests on:

• Linux 32/64 bit, Python 2.6, 2.7, 3.3, 3.4, 3.5

• PC-BSD (FreeBSD) 64-bit, Python 2.7

• OSX 64-bit, Python 2.7

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

Version

The version matched the latest version of NumPy where randomstate.prng.mt19937 passes all NumPy test.

Documentation

An occasionally updated build of the documentation is available on my github pages.

Plans

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

• Stream support for MLFG

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

Requirements

Building requires:

• Numpy (1.9, 1.10)

• Cython (0.22, 0.23)

• Python (2.6, 2.7, 3.3, 3.4, 3.5)

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.

Installing

python setup.py install

python setup.py install --no-sse2

Using

The separate generators are importable from randomstate.prng.

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

import randomstate
randomstate.standard_normal()
randomstate.exponential(1.0, 1.0, size=10)

will work.

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 (Box-Muller)
************************************************************
randomstate.prng-dsfmt-standard_normal             70.5%
randomstate.prng-mlfg_1279_861-standard_normal     26.9%
randomstate.prng-mrg32k3a-standard_normal         -18.7%
randomstate.prng-mt19937-standard_normal           13.5%
randomstate.prng-pcg32-standard_normal             26.1%
randomstate.prng-pcg64-standard_normal             26.2%
randomstate.prng-xorshift1024-standard_normal      27.2%
randomstate.prng-xorshift128-standard_normal       30.0%

Speed-up relative to NumPy (Ziggurat)
************************************************************
randomstate.prng-dsfmt-standard_normal            316.1%
randomstate.prng-mlfg_1279_861-standard_normal    247.0%
randomstate.prng-mrg32k3a-standard_normal          51.2%
randomstate.prng-mt19937-standard_normal          175.9%
randomstate.prng-pcg32-standard_normal            255.9%
randomstate.prng-pcg64-standard_normal            329.1%
randomstate.prng-xorshift1024-standard_normal     362.0%
randomstate.prng-xorshift128-standard_normal      513.7%