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| | (| 0| 8| @| H| P| X| `| h| p| x| | | | " | # | $ ¨| % °| & ¸| ' À| ) È| * Ð| + Ø| , à| . è| / ð| 0 ø| 1 } 2 } 4 } 5 } 8 } 9 (} : 0} ; 8} < @} = H} > P} ? X} @ `} A h} B p} C x} D } E } G } J } K } M ¨} N °} O ¸} P À} Q È} R Ð} S Ø} T à} U è} W ð} X ø} Y ~ Z ~ [ ~ \ ~ ^ ~ _ (~ ` 0~ b 8~ c @~ d H~ e P~ g X~ h `~ i h~ j p~ k x~ o ~ r ~ s ~ t ~ v Module '_pcg64' has already been imported. Re-initialisation is not supported. module compiled against API version 0x%x but this version of numpy is 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 . name '%U' is not defined exactly Missing type object numpy.random._pcg64.PCG64.state.__set__ SeedSequence %s() got multiple values for keyword argument '%U' numpy.random._pcg64.PCG64.__setstate_cython__ __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. __%.4s__ returned non-%.4s (type %.200s) numpy.random._pcg64.PCG64DXSM.__init__ ndarray does not match 0x10000000000000000 numpy.core._multiarray_umath numpy.random._pcg64.PCG64DXSM.advance flexible C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s) %.200s() keywords must be strings numpy.random._pcg64.PCG64DXSM numpy.random._pcg64.PCG64DXSM.__reduce_cython__ complexfloating multiple bases have vtable conflict: '%.200s' and '%.200s' integer %.200s does not export expected C function %.200s loader __package__ <stringsource> at most %.200s() takes %.8s %zd positional argument%.1s (%zd given) floating LEGACY_POISSON_LAM_MAX _ARRAY_API is NULL pointer jumped an integer is required numpy.random._pcg64.PCG64DXSM.jump_inplace POISSON_LAM_MAX %s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject 0x9e3779b97f4a7c15f39cc0605cedc835 _ARRAY_API FATAL: module compiled as unknown endian s SeedlessSequence wrap_int __file__ parent submodule_search_locations NULL result without error in PyObject_Call
PCG64DXSM(seed=None)
BitGenerator for the PCG-64 DXSM 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.
Notes
-----
PCG-64 DXSM is a 128-bit implementation of O'Neill's permutation congruential
generator ([1]_, [2]_). PCG-64 DXSM has a period of :math:`2^{128}` and supports
advancing an arbitrary number of steps as well as :math:`2^{127}` streams.
The specific member of the PCG family that we use is PCG CM DXSM 128/64. It
differs from ``PCG64`` in that it uses the stronger DXSM output function,
a 64-bit "cheap multiplier" in the LCG, and outputs from the state before
advancing it rather than advance-then-output.
``PCG64DXSM`` provides a capsule containing function pointers that produce
doubles, and unsigned 32 and 64- bit integers. These are not
directly consumable in Python and must be consumed by a ``Generator``
or similar object that supports low-level access.
Supports the method :meth:`advance` to advance the RNG an arbitrary number of
steps. The state of the PCG-64 DXSM RNG is represented by 2 128-bit unsigned
integers.
**State and Seeding**
The ``PCG64DXSM`` state vector consists of 2 unsigned 128-bit values,
which are represented externally as Python ints. One is the state of the
PRNG, which is advanced by a linear congruential generator (LCG). The
second is a fixed odd increment used in the LCG.
The input seed is processed by `SeedSequence` to generate both values. The
increment is not independently settable.
**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, PCG64DXSM, SeedSequence
>>> sg = SeedSequence(1234)
>>> rg = [Generator(PCG64DXSM(s)) for s in sg.spawn(10)]
**Compatibility Guarantee**
``PCG64DXSM`` makes a guarantee that a fixed seed will always produce
the same random integer stream.
References
----------
.. [1] `"PCG, A Family of Better Random Number Generators"
<http://www.pcg-random.org/>`_
.. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient
Statistically Good Algorithms for Random Number Generation"
<https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>`_
__loader__ numpy.random._pcg64.PCG64DXSM.state.__set__ double PyObject *(PyObject *, PyObject *) numpy.random._pcg64.PCG64.__init__ flatiter _pcg64.pyx int numpy.random._pcg64.PCG64DXSM.jumped number C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s) %s (%s:%d) cython_runtime %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject __path__ module compiled against ABI version 0x%x but this version of numpy is 0x%x invalid vtable found for imported type character ufunc BitGenerator numpy.random._pcg64.PCG64.jumped numpy.random._pcg64.PCG64.advance Unable to initialize pickling for %.200s _pcg64 origin numpy.random._pcg64 compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d numpy.random._pcg64.PCG64.state.__get__ can't convert negative value to uint32_t _ARRAY_API is not PyCapsule object instance exception may not have a separate value uint64_t %.200s.%.200s is not a type object numpy.random._pcg64.PCG64.jump_inplace numpy.random._pcg64.PCG64.__reduce_cython__ calling %R should have returned an instance of BaseException, not %R numpy/random/_pcg64.cpython-312.so.p/numpy/random/_pcg64.pyx.c name at least value too large to convert to uint32_t numpy.random._pcg64.PCG64DXSM.__setstate_cython__ unsignedinteger MAXSIZE builtins 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 FATAL: module compiled as little endian, but detected different endianness at runtime value too large to convert to int numpy.random._pcg64.PCG64DXSM.state.__get__ broadcast %.200s does not export expected C variable %.200s init numpy.random._pcg64 numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it). 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 numpy.random._common Interpreter change detected - this module can only be loaded into one interpreter per process. __builtins__ while calling a Python object numpy.random._pcg64.PCG64
PCG64(seed=None)
BitGenerator for the PCG-64 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.
Notes
-----
PCG-64 is a 128-bit implementation of O'Neill's permutation congruential
generator ([1]_, [2]_). PCG-64 has a period of :math:`2^{128}` and supports
advancing an arbitrary number of steps as well as :math:`2^{127}` streams.
The specific member of the PCG family that we use is PCG XSL RR 128/64
as described in the paper ([2]_).
``PCG64`` provides a capsule containing function pointers that produce
doubles, and unsigned 32 and 64- bit integers. These are not
directly consumable in Python and must be consumed by a ``Generator``
or similar object that supports low-level access.
Supports the method :meth:`advance` to advance the RNG an arbitrary number of
steps. The state of the PCG-64 RNG is represented by 2 128-bit unsigned
integers.
**State and Seeding**
The ``PCG64`` state vector consists of 2 unsigned 128-bit values,
which are represented externally as Python ints. One is the state of the
PRNG, which is advanced by a linear congruential generator (LCG). The
second is a fixed odd increment used in the LCG.
The input seed is processed by `SeedSequence` to generate both values. The
increment is not independently settable.
**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, PCG64, SeedSequence
>>> sg = SeedSequence(1234)
>>> rg = [Generator(PCG64(s)) for s in sg.spawn(10)]
**Compatibility Guarantee**
``PCG64`` makes a guarantee that a fixed seed will always produce
the same random integer stream.
References
----------
.. [1] `"PCG, A Family of Better Random Number Generators"
<http://www.pcg-random.org/>`_
.. [2] O'Neill, Melissa E. `"PCG: A Family of Simple Fast Space-Efficient
Statistically Good Algorithms for Random Number Generation"
<https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>`_
%s() got an unexpected keyword argument '%U' raise: exception class must be a subclass of BaseException __del__ Cannot convert %.200s to %.200s signedinteger inexact generic __pyx_capi__ ImportError PCG64 PCG64DXSM TypeError ValueError * ? __all__ bit_generator __class__ cline_in_traceback delta disable empty enable format gc generate_state get __getstate__ has_uint32 __import__ inc _initializing isenabled jumps __main__ __name__ np numpy.core.multiarray failed to import numpy.core.umath failed to import __pyx_state __pyx_vtable__ __reduce__ __reduce_ex__ seed self._bitgen,self.rng_state cannot be converted to a Python object for pickling __setstate__ __spec__ state must be a dict state must be for a {0} RNG __test__ uint64 uinteger
jumped(jumps=1)
Returns a new bit generator with the state jumped.
Jumps the state as-if jumps * 210306068529402873165736369884012333109
random numbers have been generated.
Parameters
----------
jumps : integer, positive
Number of times to jump the state of the bit generator returned
Returns
-------
bit_generator : PCG64
New instance of generator jumped iter times
Notes
-----
The step size is phi-1 when multiplied by 2**128 where phi is the
golden ratio.
advance
advance(delta)
Advance the underlying RNG as-if delta draws have occurred.
Parameters
----------
delta : integer, positive
Number of draws to advance the RNG. Must be less than the
size state variable in the underlying RNG.
Returns
-------
self : PCG64
RNG advanced delta steps
Notes
-----
Advancing a RNG updates the underlying RNG state as-if a given
number of calls to the underlying RNG have been made. In general
there is not a one-to-one relationship between the number output
random values from a particular distribution and the number of
draws from the core RNG. This occurs for two reasons:
* The random values are simulated using a rejection-based method
and so, on average, more than one value from the underlying
RNG is required to generate an single draw.
* The number of bits required to generate a simulated value
differs from the number of bits generated by the underlying
RNG. For example, two 16-bit integer values can be simulated
from a single draw of a 32-bit RNG.
Advancing the RNG state resets any pre-computed random numbers.
This is required to ensure exact reproducibility.
__reduce_cython__ __setstate_cython__ state __init__
jumped(jumps=1)
Returns a new bit generator with the state jumped.
Jumps the state as-if jumps * 210306068529402873165736369884012333109
random numbers have been generated.
Parameters
----------
jumps : integer, positive
Number of times to jump the state of the bit generator returned
Returns
-------
bit_generator : PCG64DXSM
New instance of generator jumped iter times
Notes
-----
The step size is phi-1 when multiplied by 2**128 where phi is the
golden ratio.
advance(delta)
Advance the underlying RNG as-if delta draws have occurred.
Parameters
----------
delta : integer, positive
Number of draws to advance the RNG. Must be less than the
size state variable in the underlying RNG.
Returns
-------
self : PCG64
RNG advanced delta steps
Notes
-----
Advancing a RNG updates the underlying RNG state as-if a given
number of calls to the underlying RNG have been made. In general
there is not a one-to-one relationship between the number output
random values from a particular distribution and the number of
draws from the core RNG. This occurs for two reasons:
* The random values are simulated using a rejection-based method
and so, on average, more than one value from the underlying
RNG is required to generate an single draw.
* The number of bits required to generate a simulated value
differs from the number of bits generated by the underlying
RNG. For example, two 16-bit integer values can be simulated
from a single draw of a 32-bit RNG.
Advancing the RNG state resets any pre-computed random numbers.
This is required to ensure exact reproducibility.
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