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MT19937(seed=None)
Container for the Mersenne Twister pseudo-random 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 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.
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.
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 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 `MT19937.jumped` which advances the state as-if
:math:`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] 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 F2-Linear
Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20,
No. 3, Summer 2008, pp. 385-390.
Get or set the PRNG state
Returns
-------
state : dict
Dictionary containing the information required to describe the
state of the PRNG
name __loader__ loader __file__ origin __package__ parent __path__ submodule_search_locations Missing type object int an integer is required name '%U' is not defined %s (%s:%d) builtins cython_runtime __builtins__ does not match 4294967295 numpy.random.bit_generator BitGenerator numpy dtype flatiter broadcast ndarray generic number unsignedinteger inexact complexfloating flexible character ufunc SeedSequence SeedlessSequence numpy.core._multiarray_umath _ARRAY_API _ARRAY_API is NULL pointer __init__.cython-30.pxd numpy.import_array _mt19937.pyx init numpy.random._mt19937 __setstate_cython__ <stringsource> exactly __reduce_cython__ at least at most jumped _legacy_seeding __del__ __init__ numpy.random._mt19937.MT19937 state
jumped(jumps=1)
Returns a new bit generator with the state jumped
The state of the returned bit generator is jumped as-if
2**(128 * jumps) random numbers have been generated.
Parameters
----------
jumps : integer, positive
Number of times to jump the state of the bit generator returned
Returns
-------
bit_generator : MT19937
New instance of generator jumped iter times
Notes
-----
The jump step is computed using a modified version of Matsumoto's
implementation of Horner's method. The step polynomial is precomputed
to perform 2**128 steps. The jumped state has been verified to match
the state produced using Matsumoto's original code.
References
----------
.. [1] Matsumoto, M, Generating multiple disjoint streams of
pseudorandom number sequences. Accessed on: May 6, 2020.
http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/JUMP/
.. [2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François
Panneton, Pierre L'Ecuyer, "Efficient Jump Ahead for F2-Linear
Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20,
No. 3, Summer 2008, pp. 385-390.
_legacy_seeding(seed)
Seed the generator in a backward compatible way. For modern
applications, creating a new instance is preferable. Calling this
overrides self._seed_seq
Parameters
----------
seed : {None, int, array_like}
Random seed initializing the pseudo-random number generator.
Can be an integer in [0, 2**32-1], array of integers in
[0, 2**32-1], a `SeedSequence, or ``None``. If `seed`
is ``None``, then fresh, unpredictable entropy will be pulled from
the OS.
Raises
------
ValueError
If seed values are out of range for the PRNG.
state is not a legacy MT19937 state self._bitgen cannot be converted to a Python object for pickling numpy.core.umath failed to import numpy.core.multiarray failed to import Seed must be between 0 and 2**32 - 1 state must be for a {0} PRNG Seed must be non-empty Seed array must be 1-d state must be a dict cline_in_traceback __setstate_cython__ generate_state __reduce_cython__ bit_generator _initializing ImportError __pyx_vtable__ ValueError __reduce_ex__ __pyx_state isenabled TypeError __setstate__ operator __getstate__ squeeze disable casting asarray MT19937 unsafe uint32 __reduce__ __import__ format enable astype zeros state range order numpy jumps int64 index __enter__ dtype __class__ __test__ __spec__ seed safe __name__ __main__ __init__ __exit__ pos key get any __all__ ? * np gc C A < @ ߰߰߰߰ ߰ c9r Ğp ' # Y>& Y" wR VH ( IH > 9m 1a VT3 {L |: g GQ } t M3 XaU ;p ;\ B a [>_ waAG B ϩ ]X ` (, ; }> YɁ i ޠs QHR ;hw s V l; ޑ 8 1 j ծ BF \ T<% \EC ̵s Ԃ D - F R l U p (= J& x { B ̫ Qg 0 ; ^R > L, }j :^ #2 Ø xw fJ U B @ ϡ LΒ( 1' hA` m# Ͼ r Q6 ȋ @XJ ; I o ) 9h M a Na, D a .p; f| 3( jC o I ÍI E Ĥ
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