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@. D. H. L. P. T. X. \. `. d. h. l. p. t. x. ! |. " . # . $ . % . & . ' . ( . ) . * . + ¤. . ¨. / ¬. 0 °. 1 ´. 2 ¸. 3 ¼. 4 À. 5 Ä. 6 È. 9 Ì. : Ð. ; Ô. < Ø. = Ü. > à. @ ä. B è. C ì. D ð. E ô. F ø. G ü. H / L / M / O / P / Q / R / S / T / U $/ V (/ W ,/ X 0/ Y 4/ Z 8/ [ </ \ @/ ] D/ ^ H/ _ L/ ` P/ a T/ b X/ c \/ d `/ f d/ g h/ j l/ k p/ l t/ m x/ n |/ o / q / r / s / t / u / w / x / y / z ¤/ { ¨/ | ¬/ } °/ ~ ´/ ¸/ ¼/ À/ Ä/ È/ Ì/ Ð/ Ô/ Ø/ Ü/ à/ ä/ è/ ì/ ð/ ô/ ø/ ü/ 0 exactly name '%U' is not defined Missing type object numpy/random/_mt19937.pyx __setstate_cython__ dtype SeedSequence __name__ must be set to a string object %s() got multiple values for keyword argument '%U' __int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python. ndarray does not match Shared Cython type %.200s has the wrong size, try recompiling %.200s() %s numpy.random._mt19937.MT19937._legacy_seeding flexible numpy.core._multiarray_umath Failed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'. _cython_3_2_6 %.200s() keywords must be strings complexfloating SeedlessSeedSequence _mt19937 zlib __qualname__ numpy.random._mt19937.MT19937.__reduce_cython__ multiple bases have vtable conflict: '%.200s' and '%.200s' integer loader __package__ numpy.random._mt19937 <stringsource> __vectorcalloffset__ func_doc __dict__ __annotate__ at most %.200s() takes %.8s %zd positional argument%.1s (%zd given) floating _legacy_seeding jumped an integer is required numpy._core._multiarray_umath _ARRAY_API is NULL pointer 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.
state _cython_3_2_6._common_types_metatype s SeedlessSequence _ARRAY_API FATAL: module compiled as unknown endian takes no arguments __weaklistoffset__ func_closure needs an argument __file__ parent submodule_search_locations %.200s() %s (%zd given) numpy.random._mt19937.MT19937 func_dict __reduce_cython__ NULL result without error in PyObject_Call numpy.random._mt19937.MT19937.__init__ __loader__ func_name __globals__ __name__ flatiter __defaults__ changes to cyfunction.__kwdefaults__ will not currently affect the values used in function calls numpy.random._mt19937.MT19937.jumped numpy.random._mt19937.MT19937.state.__get__ number cython_runtime takes no keyword arguments %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject __init__ __path__ invalid vtable found for imported type character ufunc module compiled against ABI version 0x%x but this version of numpy is 0x%x Shared Cython type %.200s is not a type object __code__ __kwdefaults__ must be set to a dict object BitGenerator numpy.random._mt19937.MT19937.state.__set__ Unable to initialize pickling for %.200s origin init numpy.random._mt19937 compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d ../numpy/__init__.cython-30.pxd can't convert negative value to uint32_t __int__ returned non-int (type %.200s) numpy.random._mt19937.MT19937.__setstate_cython__ _is_coroutine instance exception may not have a separate value numpy _ARRAY_API is not PyCapsule object func_globals func_code Placeholder __annotate__ function to allow 'functools.wraps' to work on Cython functions. cython __annotate__ called without 'self' argument %.200s.%.200s is not a type object calling %R should have returned an instance of BaseException, not %R name takes exactly one argument __reduce__ __closure__ __qualname__ must be set to a string object at least unsignedinteger numpy.import_array builtins func_defaults __defaults__ must be set to a tuple object extension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base type type decompress unbound method %.200S() needs an argument __annotations__ must be set to a dict object broadcast FATAL: module compiled as little endian, but detected different endianness at runtime Module '_mt19937' has already been imported. Re-initialisation is not supported. __doc__ changes to cyfunction.__defaults__ will not currently affect the values used in function calls numpy.random.bit_generator
Get or set the PRNG state
Returns
-------
state : dict
Dictionary containing the information required to describe the
state of the PRNG
base class '%.200s' is not a heap type module was compiled against NumPy C-API version 0x%x (NumPy 1.23) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem. Interpreter change detected - this module can only be loaded into one interpreter per process. __builtins__ __module__ <cyfunction %U at %p> __kwdefaults__ %s() got an unexpected keyword argument '%U' while calling a Python object raise: exception class must be a subclass of BaseException __del__ _cython_3_2_6.cython_function_or_method Bad call flags for CyFunction Cannot convert %.200s to %.200s __annotate__ cannot be deleted numpy.random._mt19937.MT19937.jump_inplace signedinteger inexact keywords must be strings __annotations__ join() result is too long for a Python string generic $ ' " @ #
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_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.
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.
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