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Wp _FLT$LL$I2H= LT$ mLT$ID$ E1E1E1D$ rCLL$HIHLL$I)D$ LT$8E1D$ wL\$?L\$HHD$H:k H5K L\$8H8>>L\$8LT$L1LLL$=ALL$IL?HH0k H5" H8=Hj H5 H8=H\$D$U E1E1D$ H@`H6 H H& HIH H9Xu)L=jI.LD$>> 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
int an integer is required name __loader__ loader __file__ origin __package__ parent __path__ submodule_search_locations Missing type object name '%U' is not defined numpy/random/_mt19937.c %s (%s:%d) %d.%d builtins cython_runtime __builtins__ 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 not found _ARRAY_API is NULL pointer __init__.pxd numpy.import_array _mt19937.pyx init numpy.random._mt19937 stringsource at least at most jumped __init__ __del__ numpy.random._mt19937.MT19937 state _legacy_seeding __reduce_cython__ __setstate_cython__ (7P7666CDCCCHPPPO8PQ=Q%QdQHQ7RR R\RKR``B]``d eeGe,efffffsk):0( 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 ImportError __pyx_vtable__ ValueError __reduce_ex__ TypeError __setstate__ operator __getstate__ squeeze casting asarray MT19937 unsafe uint32 __reduce__ __import__ format astype zeros state range order numpy jumps int64 index __enter__ dtype __class__ __test__ size seed safe __name__ __main__ __init__ __exit__ pos key get any __all__ np 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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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.
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