Next-gen RandomState supporting multiple PRNGs
This is a library and generic interface for alternative random generators in Python and NumPy.
- Immediate drop in replacement for NumPy’s RandomState
# import numpy.random as rnd import randomstate as rnd x = rnd.standard_normal(100) y = rnd.random_sample(100) z = rnd.randn(10,10)
- Default random generator is identical to NumPy’s RandomState (i.e., same seed, same random numbers).
- 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
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
- Uniforms (random_sample)
- Exponentials (standard_exponential, both Inverse CDF and Ziggurat)
- Normals (standard_normal, both Box-Muller and Ziggurat)
- Standard Gammas (via 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)
- Uniforms (random_sample)
- Exponentials (standard_exponential)
- Normals (standard_normal)
- Standard Gammas (via standard_gamma)
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
- SFMT a SSE2-aware version of the MT19937 generator that is optimized for integer values
- xorshift128+, xoroshiro128+ and xorshift1024*
- PCG32 and PCG64
- A multiplicative lagged fibonacci generator (LFG(63, 1279, 861, *))
Differences from numpy.random.RandomState
- 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
- 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 core RNG ‘as-if’ a number of draws were made, without actually drawing the numbers. Only available if supported by the RNG.
- 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 2.7, 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 2.7
- Windows 32/64 bit (only tested on Python 2.7, 3.5 and 3.6, but should work on 3.3/3.4)
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.
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)
- Python (2.7, 3.4, 3.5, 3.6)
- NumPy (1.9, 1.10, 1.11, 1.12)
- Cython (0.22, not 0.23, 0.24, 0.25)
- 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
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.
python setup.py install
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
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.
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)
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%
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