MT19937(seed=None)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.
MT19937
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.
The Python stdlib module "random" also contains a Mersenne Twister pseudo-random number generator.
State and Seeding
The MT19937
state vector consists of a 624-element array of 32-bit 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 :None:None:`MT19937.jumped` which advances the state as-if $2^{128}$
random numbers have been generated (, ). 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 and will always produce the same random integer stream.
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.
Container for the Mersenne Twister pseudo-random number generator.
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.random.mtrand.RandomState
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