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|
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.
|
state
|
Get or set the PRNG state
|
|
Returns
|
-------
|
state : dict
|
Dictionary containing the information required to describe the
|
state of the PRNG
|
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"
|
<https://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>`_
|
numpy.random._pcg64.PCG64DXSM
|
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>`_
|
_pcg64 ¯( À* ` \) ` @*
|
` P- ` p- ` °+ ` + ` @, ` X, ` 8+ ` ø* ` Ì( P*
|
` * ` À( à) ` Ø( ` È+ ` H+ ` h)
|
` à+ ` ¼( ` t) ` è) |) ` ) ` ¤) ð( ` Ä( p+ ` à( ` ð) ` * ` ä( ` ) ` + ` + ` `*
|
¬) ` ) ` ) ` ¸) ` ) ` È( ` ) ` - ( ¸- # ø+ ` x, è( ` p* ` °* ` * ` È) ` X+ ` * ` ,) ` 4) ` à- P * ` * ` + ` @) ` ) ` (, , (+ P) ` Ô) ` 0* ` __module__ _cython_3_1_4._common_types_metatype 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_coroutine __dictoffset__ __vectorcalloffset__ __weaklistoffset__ __reduce__ _cython_3_1_4.cython_function_or_method 0( H( h( x( ( numpy/random/_pcg64.pyx ../numpy/__init__.cython-30.pxd <stringsource> cpython/type.pxd numpy/random/bit_generator.pxd Ø
|
+Q d ? gc np RNG __all__ get inc pop __func__ __init__ __main__ __name__ seed self __spec__ __test__ PCG64 __class__ delta dtype empty jumps numpy state enable jumped __module__ __reduce__ uint64 advance disable __getstate__ __qualname__ __set_name__ __setstate__ uinteger PCG64DXSM TypeError isenabled __pyx_state __reduce_ex__ ValueError has_uint32 __pyx_vtable__ ImportError Ð1ð4 J aØYd !Ø] ! 1àq PCG64.jumped _initializing _is_coroutine <stringsource> PCG64.advance bit_generator __reduce_cython__ generate_state __setstate_cython__ PCG64DXSM.jumped PCG64DXSM.advance asyncio.coroutines cline_in_traceback numpy.random._pcg64 state must be for a state must be a dict PCG64.__reduce_cython__ PCG64.__setstate_cython__ numpy/random/_pcg64.pyx AðH à Bf A S¨¨b°Ø %vTØ %vSØ$l¨.¸ ÀQÀaØ Ð # 1Øq AðH à Bf A S¨¨b°Ø %vTØ %vSØQat< ~°]À!À1Ø Ð # 1Øq PCG64DXSM.__reduce_cython__ PCG64DXSM.__setstate_cython__ numpy._core.multiarray failed to import numpy._core.umath failed to import self._bitgen,self.rng_state cannot be converted to a Python object for pickling
|
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.
|
jumped
|
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.
|
advance __reduce_cython__ __setstate_cython__
|
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.
|
numpy._core._multiarray_umath numpy.core._multiarray_umath _ARRAY_API _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 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. FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime __init__ numpy.random._pcg64.PCG64.__init__ numpy.random._pcg64.PCG64.jump_inplace numpy.random._pcg64.PCG64.jumped numpy.random._pcg64.PCG64.state.__get__ numpy.random._pcg64.PCG64.state.__set__ numpy.random._pcg64.PCG64.advance numpy.random._pcg64.PCG64.__reduce_cython__ numpy.random._pcg64.PCG64.__setstate_cython__ numpy.random._pcg64.PCG64DXSM.__init__ numpy.random._pcg64.PCG64DXSM.jump_inplace numpy.random._pcg64.PCG64DXSM.jumped numpy.random._pcg64.PCG64DXSM.state.__get__ numpy.random._pcg64.PCG64DXSM.state.__set__ numpy.random._pcg64.PCG64DXSM.advance numpy.random._pcg64.PCG64DXSM.__reduce_cython__ numpy.random._pcg64.PCG64DXSM.__setstate_cython__ __del__ numpy.random.bit_generator BitGenerator builtins type numpy dtype flatiter broadcast ndarray generic number integer signedinteger unsignedinteger inexact floating complexfloating flexible character ufunc SeedSequence SeedlessSequence numpy.random._common double POISSON_LAM_MAX LEGACY_POISSON_LAM_MAX uint64_t MAXSIZE PyObject *(PyObject *, PyObject *) wrap_int Interpreter change detected - this module can only be loaded into one interpreter per process. name __loader__ loader __file__ origin __package__ parent __path__ submodule_search_locations Module '_pcg64' has already been imported. Re-initialisation is not supported. cython_runtime __builtins__ numpy.random._pcg64 init numpy.random._pcg64 0x10000000000000000 0x9e3779b97f4a7c15f39cc0605cedc835 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). name '%U' is not defined while calling a Python object NULL result without error in PyObject_Call raise: arg 3 must be a traceback or None instance exception may not have a separate value calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException exception causes must derive from BaseException %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' at least at most exactly s %.200s() takes %.8s %zd positional argument%.1s (%zd given) Missing type object Cannot convert %.200s to %.200s keywords must be strings join() result is too long for a Python string %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd-%zd from PyObject base class '%.200s' is not a heap type 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 invalid vtable found for imported type multiple bases have vtable conflict: '%.200s' and '%.200s' Unable to initialize pickling for %.200s . No module named '%U' _cython_3_1_4 Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling __name__ must be set to a string object __qualname__ must be set to a string object function's dictionary may not be deleted setting function's dictionary to a non-dict __defaults__ must be set to a tuple object changes to cyfunction.__defaults__ will not currently affect the values used in function calls __kwdefaults__ must be set to a dict object changes to cyfunction.__kwdefaults__ will not currently affect the values used in function calls __annotations__ must be set to a dict object %.200s() %s (%zd given) %.200s() %s Bad call flags for CyFunction <cyfunction %U at %p> takes no arguments takes exactly one argument takes no keyword arguments unbound method %.200S() needs an argument needs an argument value too large to convert to int can't convert negative value to uint32_t does not match compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d __pyx_capi__ %.200s does not export expected C variable %.200s C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s) %.200s does not export expected C function %.200s C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s) __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. __int__ returned non-int (type %.200s) an integer is required < ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ@ @ p P x Ô°ïh 0 ìP ì8 P 4 ÌP óè oé é 9ë Oë î ï ;ï {ñ ñ ìñ <ò ò Õ @å $ó fó 0ã .text$mn 0ó 6 .text$mn$00 fó .text$x p .idata$5 p 8 .00cfg ¨ .CRT$XCA ° .CRT$XCZ ¸ .CRT$XIA À .CRT$XIZ È .CRT$XPA Ð .CRT$XPZ Ø .CRT$XTA à .CRT$XTZ ð J .rdata P l .rdata$voltmd ìP 4 .rdata$zzzdbg S .rtc$IAA (S .rtc$IZZ 0S .rtc$TAA 8S .rtc$TZZ @S 0 .xdata p_ \ .edata Ì_ P .idata$2 ` .idata$3 0` p .idata$4 e R .idata$6 À .data À °
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