numpy.random.
Philox
Container for the Philox (4x64) pseudo-random number generator.
A seed to initialize the BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS. If an int or array_like[ints] is passed, then it will be passed to SeedSequence to derive the initial BitGenerator state. One may also pass in a SeedSequence instance.
BitGenerator
int
array_like[ints]
SeedSequence
Counter to use in the Philox state. Can be either a Python int (long in 2.x) in [0, 2**256) or a 4-element uint64 array. If not provided, the RNG is initialized at 0.
Key to use in the Philox state. Unlike seed, the value in key is directly set. Can be either a Python int in [0, 2**128) or a 2-element uint64 array. key and seed cannot both be used.
seed
Notes
Philox is a 64-bit PRNG that uses a counter-based design based on weaker (and faster) versions of cryptographic functions [1]. Instances using different values of the key produce independent sequences. Philox has a period of and supports arbitrary advancing and jumping the sequence in increments of . These features allow multiple non-overlapping sequences to be generated.
Philox 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.
Generator
State and Seeding
The Philox state vector consists of a 256-bit value encoded as a 4-element uint64 array and a 128-bit value encoded as a 2-element uint64 array. The former is a counter which is incremented by 1 for every 4 64-bit randoms produced. The second is a key which determined the sequence produced. Using different keys produces independent sequences.
The input seed is processed by SeedSequence to generate the key. The counter is set to 0.
Alternately, one can omit the seed parameter and set the key and counter directly.
key
counter
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:
SeedSequence.spawn
>>> from numpy.random import Generator, Philox, SeedSequence >>> sg = SeedSequence(1234) >>> rg = [Generator(Philox(s)) for s in sg.spawn(10)]
Philox can be used in parallel applications by calling the jumped method to advances the state as-if random numbers have been generated. Alternatively, advance can be used to advance the counter for any positive step in [0, 2**256). When using jumped, all generators should be chained to ensure that the segments come from the same sequence.
jumped
advance
>>> from numpy.random import Generator, Philox >>> bit_generator = Philox(1234) >>> rg = [] >>> for _ in range(10): ... rg.append(Generator(bit_generator)) ... bit_generator = bit_generator.jumped()
Alternatively, Philox can be used in parallel applications by using a sequence of distinct keys where each instance uses different key.
>>> key = 2**96 + 2**33 + 2**17 + 2**9 >>> rg = [Generator(Philox(key=key+i)) for i in range(10)]
Compatibility Guarantee
Philox makes a guarantee that a fixed seed will always produce the same random integer stream.
References
John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, “Parallel Random Numbers: As Easy as 1, 2, 3,” Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC11), New York, NY: ACM, 2011.
Examples
>>> from numpy.random import Generator, Philox >>> rg = Generator(Philox(1234)) >>> rg.standard_normal() 0.123 # random
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.
state
Get or set the PRNG state
advance(delta)
Advance the underlying RNG as-if delta draws have occurred.
jumped([jumps])
Returns a new bit generator with the state jumped
cffi
CFFI interface
ctypes
ctypes interface