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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 and 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.
_legacy_seeding jumped numpy.random._mt19937.MT19937._legacy_seeding while calling a Python object NULL result without error in PyObject_Call value too large to convert to uint32_t can't convert negative value to uint32_t hasattr(): attribute name must be string calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException Missing type object Cannot convert %.200s to %.200s numpy.random._mt19937.MT19937.jumped %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' %s() got multiple values for keyword argument '%U' at least at most %.200s() takes %.8s %zd positional argument%.1s (%zd given) s numpy.random._mt19937.MT19937.__reduce_cython__ numpy.random._mt19937.MT19937.__setstate_cython__
Get or set the PRNG state
Returns
-------
state : dict
Dictionary containing the information required to describe the
state of the PRNG
numpy.random._mt19937.MT19937.state.__get__ __del__ numpy.random._mt19937.MT19937.state.__set__ value too large to convert to int numpy.random._mt19937.MT19937.__init__ Unable to initialize pickling for %s type flatiter broadcast ndarray generic number integer signedinteger unsignedinteger inexact floating complexfloating flexible character ufunc SeedSequence SeedlessSequence invalid vtable found for imported type numpy.import_array numpy.core._multiarray_umath _ARRAY_API _ARRAY_API not found _ARRAY_API is not PyCapsule object _ARRAY_API is NULL pointer module compiled against ABI version 0x%x but this version of numpy is 0x%x module compiled against API version 0x%x but this version of numpy is 0x%x FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime %s (%s:%d) 4 4 ÑX! a $ L L °É L & À' `) @* p5 °5 0; < Ph @i °j ðk Àl °m n p àr °s u w àw ` °£ °¤ ¼Ë ÆË ÐË ÚË äË îË øË Ì Ì Ì Ì *Ì 4Ì >Ì HÌ RÌ \Ì fÌ pÌ zÌ Ì Ì Ì ¢Ì ¬Ì ¶Ì ÀÌ ÊÌ ÔÌ ÞÌ èÌ òÌ üÌ Í Í Í $Í .Í 8Í BÍ LÍ VÍ `Í jÍ tÍ ~Í Í Í Í ¦Í °Í ºÍ ÄÍ ÎÍ ØÍ âÍ ìÍ öÍ Î
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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 big 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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