Random sampling (
Numpy’s random number routines produce pseudo random numbers using
combinations of a BitGenerator to create sequences and a
to use those sequences to sample from different statistical distributions:
- BitGenerators: Objects that generate random numbers. These are typically unsigned integer words filled with sequences of either 32 or 64 random bits.
- Generators: Objects that transform sequences of random bits from a BitGenerator into sequences of numbers that follow a specific probability distribution (such as uniform, Normal or Binomial) within a specified interval.
Since Numpy version 1.17.0 the Generator can be initialized with a
number of different BitGenerators. It exposes many different probability
distributions. See NEP 19 for context on the updated random Numpy number
routines. The legacy
RandomState random number routines are still
available, but limited to a single BitGenerator.
# Uses the old numpy.random.RandomState from numpy import random random.standard_normal()
Generator can be used as a direct replacement for RandomState, although the random values are generated by
Generator holds an instance of a BitGenerator. It is accessible as
# As replacement for RandomState(); default_rng() instantiates Generator with # the default PCG64 BitGenerator. from numpy.random import default_rng rg = default_rng() rg.standard_normal() rg.bit_generator
Seeds can be passed to any of the BitGenerators. The provided value is mixed via
SeedSequence to spread a possible sequence of seeds across a wider range of initialization states for the BitGenerator. Here
PCG64 is used and is wrapped with a
from numpy.random import Generator, PCG64 rg = Generator(PCG64(12345)) rg.standard_normal()
The new infrastructure takes a different approach to producing random numbers
RandomState object. Random number generation is separated into
two components, a bit generator and a random generator.
The BitGenerator has a limited set of responsibilities. It manages state and provides functions to produce random doubles and random unsigned 32- and 64-bit values.
random generator takes the
bit generator-provided stream and transforms them into more useful
distributions, e.g., simulated normal random values. This structure allows
alternative bit generators to be used with little code duplication.
from numpy.random import default_rng rg = default_rng(12345) rg.random()
from numpy.random import Generator, MT19937 rg = Generator(MT19937(12345)) rg.random()
What’s New or Different
The Box-Muller method used to produce NumPy’s normals is no longer available
Generator. It is not possible to reproduce the exact random
values using Generator for the normal distribution or any other
distribution that relies on the normal such as the
RandomState.standard_t. If you require bitwise backward compatible
- The Generator’s normal, exponential and gamma functions use 256-step Ziggurat methods which are 2-10 times faster than NumPy’s Box-Muller or inverse CDF implementations.
dtypeargument that accepts
np.float64to produce either single or double prevision uniform random variables for select distributions
outargument that allows existing arrays to be filled for select distributions
random_entropyprovides access to the system source of randomness that is used in cryptographic applications (e.g.,
- All BitGenerators can produce doubles, uint64s and uint32s via CTypes
ctypes) and CFFI (
cffi). This allows the bit generators to be used in numba.
- The bit generators can be used in downstream projects via Cython.
integersis now the canonical way to generate integer random numbers from a discrete uniform distribution. The
randnmethods are only available through the legacy
endpointkeyword can be used to specify open or closed intervals. This replaces both
randintand the deprecated
randomis now the canonical way to generate floating-point random numbers, which replaces
RandomState.random_sample, RandomState.sample, and RandomState.ranf. This is consistent with Python’s
- All BitGenerators in numpy use
SeedSequenceto convert seeds into initialized states.
See What’s New or Different for a complete list of improvements and
differences from the traditional
The included generators can be used in parallel, distributed applications in one of three ways:
- Parallel Applications
- Multithreaded Generation
- What’s New or Different
- Comparing Performance
- Reading System Entropy
This package was developed independently of NumPy and was integrated in version 1.17.0. The original repo is at https://github.com/bashtage/randomgen.