Permuted Congruential Generator (32-bit, PCG32)¶

class randomgen.pcg32.PCG32(seed=None, inc=0, *, mode=None)¶

Container for the PCG-32 pseudo-random number generator.

Parameters:¶

seed=None¶

Random seed initializing the pseudo-random number generator. Can be an integer in [0, 2**64], a SeedSequence instance or None (the default). If seed is None, then PCG32 will try to read data from /dev/urandom (or the Windows analog) if available. If unavailable, a 64-bit hash of the time and process ID is used.

inc=0¶

The increment in the LCG. Can be an integer in [0, 2**64] or None. The default is 0. If inc is None, then it is initialized using entropy.

mode=None¶

The seeding mode to use. “legacy” uses the legacy SplitMix64-based initialization. “sequence” uses a SeedSequence to transforms the seed into an initial state. None defaults to “sequence”.

lock¶

Lock instance that is shared so that the same bit git generator can be used in multiple Generators without corrupting the state. Code that generates values from a bit generator should hold the bit generator’s lock.

Type:¶

threading.Lock

seed_seq¶

The SeedSequence instance used to initialize the generator if mode is “sequence” or is seed is a SeedSequence. None if mode is “legacy”.

Type:¶

{None, SeedSequence}

Notes

PCG-32 is a 64-bit implementation of O’Neill’s permuted congruential generator ([1], [2]). PCG-32 has a period of \(2^{64}\) and supports advancing an arbitrary number of steps.

PCG32 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 a Generator or similar object that supports low-level access.

Supports the method advance to advance the RNG an arbitrary number of steps. The state of the PCG-32 PRNG is represented by 2 64-bit unsigned integers.

See PCG64 for a similar implementation with a smaller period.

State and Seeding

The PCG32 state vector consists of 2 unsigned 64-bit values. PCG32 is seeded using a single 64-bit unsigned integer.

Parallel Features

PCG32 can be used in parallel applications using advance with a different value in each instance to produce non-overlapping sequences.

>>> from numpy.random import Generator
>>> from randomgen import PCG32
>>> rg = [Generator(PCG32(1234, i + 1)) for i in range(10)]
>>> for i in range(10):
...     rg[i].bit_generator.advance(i * 2**32)

Compatibility Guarantee

PCG32 makes a guarantee that a fixed seed and will always produce the same random integer stream.

References

Seeding and State¶

Parallel generation¶

Extending¶

Testing¶