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BHcH Q% Љ34LFA3 Dn(HcH Q% Љ3tot H !FLJ % Љƒ303 AWp AVAU)ATUSD D)A9\HcMcȽoLND)N,MM)INdE1H@CoCo fCII9uAAtQFC,McHc,B1,EZD9~3B,EAHcMcFD1A9}FEMcMcFF1D9LcҽoENO$)N<I)N?L|$N&DMM)I\A9AEFAAN&@A)9EME1EAIfDCoCofCIM9uEAEE9tSFGdMcMcF$F1$EZA9}3E$ EAMcMcFF1A9}FEMcMcFF19¸MT*oLcځpApLML@IJDLH)Ho)ЃApJ@A)ӁoDO1EAIfDoAofAHL9uD؃A9tO F HMcF D1 Bot0D DMcHB1BntAHcMcB1[]A\A]A^A_@mHcMcȽoLN)N,MM)INdE1HCoCo$fCII9uAAtQFC,McHc,B1,EZD9~3B,EAHcMcFD1D9~FEMcMcFF1D9 LcAoDNKl E)N@L4A)LL)H9AA@AAֽA)9DMMN>> 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 Bad call flags for CyFunction__loader__loader__file__origin__package__parent__path__submodule_search_locations%.200s() needs an argumentkeywords must be stringsMissing type objectintan integer is requiredname '%U' is not defined%s (%s:%d)_mt19937.pyxbuiltinscython_runtime__builtins__does not match_cython_3_0_114294967295numpy.random.bit_generatorBitGeneratornumpydtypeflatiterbroadcastndarraygenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncSeedSequenceSeedlessSequencenumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointer__init__.cython-30.pxdnumpy.import_arrayinit numpy.random._mt19937__setstate_cython__exactly__reduce_cython__at leastat mostjumped_legacy_seeding__del____init____reduce____module__func_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutinenumpy.random._mt19937.MT19937state 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. 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