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, int, SeedSequence}, optional

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{None, int}, optional

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, “sequence”, “legacy”}, optional

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”.

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.

>>> 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

1

“PCG, A Family of Better Random Number Generators”, https://www.pcg-random.org/

2

O’Neill, Melissa E. “PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation”

Attributes
lockthreading.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.

seed_seq{None, SeedSequence}

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

Seeding and State

seed([seed, inc])

Seed the generator

state

Get or set the PRNG state

Parallel generation

advance(delta)

Advance the underlying RNG as-if delta draws have occurred.

jump([iter])

Jump the state a fixed increment

jumped([iter])

Returns a new bit generator with the state jumped

Extending

cffi

CFFI interface

ctypes

ctypes interface

Testing

random_raw([size, output])

Return randoms as generated by the underlying BitGenerator