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randomstate's documentation

This package contains drop-in replacements for the NumPy RandomState object that change the core random number generator.

What's New or Different

  • random_uintegers produces either 32-bit or 64-bit unsigned integers. These are at the core of most PRNGs and so they are directly exposed.
  • randomstate.entropy.random_entropy provides access to the system source of randomness that is used in cryptographic applications (e.g., /dev/urandom on Unix).
  • The normal generator supports a 256-step Ziggurat method which is 2-6 times faster than NumPy's standard_normal. This generator can be accessed by passing the keyword argument method='zig'.
  • random_sample accepts the optional keyword argument dtype which accepts np.float32 or np.float64 to produce either single or double prevision uniform random variables
  • For changes since the previous release, see the :ref:`change-log`
from randomstate import standard_normal
standard_normal(100000, method='zig')

Parallel Generation

The included generators can be used in parallel, distributed applications in one of two ways:

Supported Generators

The main innovation is the inclusion of a number of alternative pseudo-random number generators, 'in addition' to the standard PRNG in NumPy. The included PRNGs are:

  • MT19937 - The standard NumPy generator. Produces identical results to NumPy using the same seed/state. See NumPy's documentation.
  • dSFMT - A SSE2 enables version of the MT19937 generator. Theoretically the same, but with a different state and so it is not possible to produce a sequence identical to MT19937. See the dSFMT authors' page.
  • XoroShiro128+ - Improved version of XorShift128+ with improved performance and better statistical quality. Like the XorShift generators, can be jumped to produce multiple streams in parallel applications. See :meth:`randomstate.prng.xoroshiro128plus.jump` for details. More information about this PRNG is available at the xorshift and xoroshiro authors' page.
  • XorShit128+ and XorShift1024* - Vast fast generators based on the XSadd generator. These generators can be rapidly 'jumped' and so can be used in parallel applications. See the documentation for :meth:`randomstate.prng.xorshift1024.jump` for details. More information about these PRNGs is available at the xorshift and xoroshiro authors' page.
  • PCG-32 and PCG-64 - Fast generators that support many parallel streams and can be advanced by an arbitrary amount. See the documentation for :meth:`randomstate.prng.pcg64.advance`. PCG-32 only as a period of 2^{64} while PCG-64 has a period of 2^{128}. See the PCG author's page for more details about this class of PRNG.
  • Multiplicative Lagged Fibonacci Generator MLFG(1279, 861, *) - A directly implemented multiplicative lagged Fibonacci generator with a very large period and good performance. Future plans include multiple stream support. See the wiki page on Fibonacci generators.
  • MRG32K3A - a classic and popular generator by L'Ecuyer. Future plans include multiple stream support. See the MRG32K3A author's page. Lower performance than more modern generators.

New Features

.. toctree::
   :maxdepth: 2

   Using in Parallel Applications <parallel>
   Reading System Entropy <entropy>

Individual Pseudo Random Number Generators

.. toctree::
   :maxdepth: 2

   MT19937 <mt19937>
   dSFMT <dsfmt>
   XorShift128+ <xorshift128>
   XoroShiro128+ <xoroshiro128plus>
   XorShift1024* <xorshift1024>
   PCG-32 <pcg32>
   PCG-64 <pcg64>
   MLFG <mlfg>
   MRG32K3A <mrg32k3a>

Changes

.. toctree::
   :maxdepth: 2

   Change Log <change-log>

Indices and tables