Double SIMD Mersenne Twister (dSFMT)¶

class randomgen.dsfmt.DSFMT(seed=
None
, *, mode=None
)¶ Container for the SIMDbased Mersenne Twister pseudo RNG.
 Parameters:¶
 seed=
None
¶ Random seed used to initialize the pseudorandom number generator. Can be any integer between 0 and 2**32  1 inclusive, an array (or other sequence) of unsigned 32bit integers, a SeedSequence instance or
None
(the default). If seed isNone
, then 764 32bit unsigned integers are read from/dev/urandom
(or the Windows analog) if available. If unavailable, a hash of the time and process ID is used. mode=
None
¶ The seeding mode to use. “legacy” uses the legacy SplitMix64based initialization. “sequence” uses a SeedSequence to transforms the seed into an initial state. None defaults to “sequence”.
 seed=
 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:¶
 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
DSFMT
provides a capsule containing function pointers that produce doubles, and unsigned 32 and 64 bit integers [1] . These are not directly consumable in Python and must be consumed by aGenerator
or similar object that supports lowlevel access.The Python stdlib module “random” also contains a Mersenne Twister pseudorandom number generator.
State and Seeding
The
DSFMT
state vector consists of a 384 element array of 64bit unsigned integers plus a single integer value between 0 and 382 indicating the current position within the main array. The implementation used here augments this with a 382 element array of doubles which are used to efficiently access the random numbers produced by the dSFMT generator.DSFMT
is seeded using either a single 32bit unsigned integer or a vector of 32bit unsigned integers. In either case, the input seed is used as an input (or inputs) for a hashing function, and the output of the hashing function is used as the initial state. Using a single 32bit value for the seed can only initialize a small range of the possible initial state values.Parallel Features
DSFMT
can be used in parallel applications by calling the methodjump
which advances the state asif \(2^{128}\) random numbers have been generated [2]. This allows the original sequence to be split so that distinct segments can be used in each worker process. All generators should be initialized with the same seed to ensure that the segments come from the same sequence.>>> from numpy.random import Generator >>> from randomgen.entropy import random_entropy >>> from randomgen import DSFMT >>> seed = random_entropy() >>> rs = [Generator(DSFMT(seed)) for _ in range(10)] # Advance each DSFMT instance by i jumps >>> for i in range(10): ... rs[i].bit_generator.jump()
Compatibility Guarantee
DSFMT
makes a guarantee that a fixed seed and will always produce the same random integer stream.References
Seeding and State¶

Seed the generator 
Get or set the PRNG state 
Parallel generation¶

Returns a new bit generator with the state jumped 

Jumps the state asif 2**128 random numbers have been generated. 
Extending¶
CFFI interface 

ctypes interface 
Testing¶

Return randoms as generated by the underlying BitGenerator 