Mersenne Twister (MT19937)#
 class numpy.random.MT19937(seed=None)#
Container for the Mersenne Twister pseudorandom number generator.
 Parameters:
 seed{None, int, array_like[ints], SeedSequence}, optional
A seed to initialize the
BitGenerator
. If None, then fresh, unpredictable entropy will be pulled from the OS. If anint
orarray_like[ints]
is passed, then it will be passed toSeedSequence
to derive the initialBitGenerator
state. One may also pass in aSeedSequence
instance.
Notes
MT19937
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
MT19937
state vector consists of a 624element array of 32bit unsigned integers plus a single integer value between 0 and 624 that indexes the current position within the main array.The input seed is processed by
SeedSequence
to fill the whole state. The first element is reset such that only its most significant bit is set.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, MT19937, SeedSequence >>> sg = SeedSequence(1234) >>> rg = [Generator(MT19937(s)) for s in sg.spawn(10)]
Another method is to use
MT19937.jumped
which advances the state asif \(2^{128}\) random numbers have been generated ([1], [2]). This allows the original sequence to be split so that distinct segments can be used in each worker process. All generators should be chained to ensure that the segments come from the same sequence.>>> from numpy.random import Generator, MT19937, SeedSequence >>> sg = SeedSequence(1234) >>> bit_generator = MT19937(sg) >>> rg = [] >>> for _ in range(10): ... rg.append(Generator(bit_generator)) ... # Chain the BitGenerators ... bit_generator = bit_generator.jumped()
Compatibility Guarantee
MT19937
makes a guarantee that a fixed seed will always produce the same random integer stream.References
[1] (1,2)Hiroshi Haramoto, Makoto Matsumoto, and Pierre L’Ecuyer, “A Fast Jump Ahead Algorithm for Linear Recurrences in a Polynomial Space”, Sequences and Their Applications  SETA, 290–298, 2008.
[2]Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François Panneton, Pierre L’Ecuyer, “Efficient Jump Ahead for F2Linear Random Number Generators”, INFORMS JOURNAL ON COMPUTING, Vol. 20, No. 3, Summer 2008, pp. 385390.
 Attributes:
 lock: threading.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.
State#
Get or set the PRNG state 
Parallel generation#

Returns a new bit generator with the state jumped 
Extending#
CFFI interface 

ctypes interface 