Parallel Congruent Generator (64bit, PCG64)¶

class
numpy.random.pcg64.
PCG64
(seed_seq=None)¶ BitGenerator for the PCG64 pseudorandom number generator.
Parameters:  seed : {None, int, array_like[ints], ISeedSequence}, optional
A seed to initialize the BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS. If an
int
orarray_like[ints]
is passed, then it will be passed to SeedSequence to derive the initial BitGenerator state. One may also pass in an implementor of the ISeedSequence interface like SeedSequence.
Notes
PCG64 is a 128bit implementation of O’Neill’s permutation congruential generator ([1], [2]). PCG64 has a period of and supports advancing an arbitrary number of steps as well as streams. The specific member of the PCG family that we use is PCG XSL RR 128/64 as described in the paper ([2]).
PCG64
provides a capsule containing function pointers that produce doubles, and unsigned 32 and 64 bit integers. These are not directly consumable in Python and must be consumed by aGenerator
or similar object that supports lowlevel access.Supports the method
advance
to advance the RNG an arbitrary number of steps. The state of the PCG64 RNG is represented by 2 128bit unsigned integers.State and Seeding
The
PCG64
state vector consists of 2 unsigned 128bit values, which are represented externally as Python ints. One is the state of the PRNG, which is advanced by a linear congruential generator (LCG). The second is a fixed odd increment used in the LCG.The input seed is processed by SeedSequence to generate both values. The increment is not independently settable.
Parallel Features
The preferred way to use a BitGenerator in parallel applications is to use the SeedSequence.spawn method to obtain entropy values, and to use these to generate new BitGenerators:
>>> from numpy.random import Generator, PCG64, SeedSequence >>> sg = SeedSequence(1234) >>> rg = [Generator(PCG64(s)) for s in sg.spawn(10)]
Compatibility Guarantee
PCG64
makes a guarantee that a fixed seed and will always produce the same random integer stream.References
[1] “PCG, A Family of Better Random Number Generators” [2] (1, 2) O’Neill, Melissa E. “PCG: A Family of Simple Fast SpaceEfficient Statistically Good Algorithms for Random Number Generation”