# Python: 3.11.9 (tags/v3.11.9:de54cf5, Apr 2 2024, 10:12:12) [MSC v.1938 64 bit (AMD64)] # Library: numpy, version: 1.26.4 # Module: numpy.random._philox, version: unspecified import typing import builtins as _mod_builtins import numpy.random.bit_generator as _mod_numpy_random_bit_generator class Philox(_mod_numpy_random_bit_generator.BitGenerator): '\n Philox(seed=None, counter=None, key=None)\n\n Container for the Philox (4x64) pseudo-random number generator.\n\n Parameters\n ----------\n seed : {None, int, array_like[ints], SeedSequence}, optional\n A seed to initialize the `BitGenerator`. If None, then fresh,\n unpredictable entropy will be pulled from the OS. If an ``int`` or\n ``array_like[ints]`` is passed, then it will be passed to\n `SeedSequence` to derive the initial `BitGenerator` state. One may also\n pass in a `SeedSequence` instance.\n counter : {None, int, array_like}, optional\n Counter to use in the Philox state. Can be either\n a Python int (long in 2.x) in [0, 2**256) or a 4-element uint64 array.\n If not provided, the RNG is initialized at 0.\n key : {None, int, array_like}, optional\n Key to use in the Philox state. Unlike ``seed``, the value in key is\n directly set. Can be either a Python int in [0, 2**128) or a 2-element\n uint64 array. `key` and ``seed`` cannot both be used.\n\n Attributes\n ----------\n lock: threading.Lock\n Lock instance that is shared so that the same bit git generator can\n be used in multiple Generators without corrupting the state. Code that\n generates values from a bit generator should hold the bit generator\'s\n lock.\n\n Notes\n -----\n Philox is a 64-bit PRNG that uses a counter-based design based on weaker\n (and faster) versions of cryptographic functions [1]_. Instances using\n different values of the key produce independent sequences. Philox has a\n period of :math:`2^{256} - 1` and supports arbitrary advancing and jumping\n the sequence in increments of :math:`2^{128}`. These features allow\n multiple non-overlapping sequences to be generated.\n\n ``Philox`` provides a capsule containing function pointers that produce\n doubles, and unsigned 32 and 64- bit integers. These are not\n directly consumable in Python and must be consumed by a ``Generator``\n or similar object that supports low-level access.\n\n **State and Seeding**\n\n The ``Philox`` state vector consists of a 256-bit value encoded as\n a 4-element uint64 array and a 128-bit value encoded as a 2-element uint64\n array. The former is a counter which is incremented by 1 for every 4 64-bit\n randoms produced. The second is a key which determined the sequence\n produced. Using different keys produces independent sequences.\n\n The input ``seed`` is processed by `SeedSequence` to generate the key. The\n counter is set to 0.\n\n Alternately, one can omit the ``seed`` parameter and set the ``key`` and\n ``counter`` directly.\n\n **Parallel Features**\n\n The preferred way to use a BitGenerator in parallel applications is to use\n the `SeedSequence.spawn` method to obtain entropy values, and to use these\n to generate new BitGenerators:\n\n >>> from numpy.random import Generator, Philox, SeedSequence\n >>> sg = SeedSequence(1234)\n >>> rg = [Generator(Philox(s)) for s in sg.spawn(10)]\n\n ``Philox`` can be used in parallel applications by calling the ``jumped``\n method to advances the state as-if :math:`2^{128}` random numbers have\n been generated. Alternatively, ``advance`` can be used to advance the\n counter for any positive step in [0, 2**256). When using ``jumped``, all\n generators should be chained to ensure that the segments come from the same\n sequence.\n\n >>> from numpy.random import Generator, Philox\n >>> bit_generator = Philox(1234)\n >>> rg = []\n >>> for _ in range(10):\n ... rg.append(Generator(bit_generator))\n ... bit_generator = bit_generator.jumped()\n\n Alternatively, ``Philox`` can be used in parallel applications by using\n a sequence of distinct keys where each instance uses different key.\n\n >>> key = 2**96 + 2**33 + 2**17 + 2**9\n >>> rg = [Generator(Philox(key=key+i)) for i in range(10)]\n\n **Compatibility Guarantee**\n\n ``Philox`` makes a guarantee that a fixed ``seed`` will always produce\n the same random integer stream.\n\n Examples\n --------\n >>> from numpy.random import Generator, Philox\n >>> rg = Generator(Philox(1234))\n >>> rg.standard_normal()\n 0.123 # random\n\n References\n ----------\n .. [1] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw,\n "Parallel Random Numbers: As Easy as 1, 2, 3," Proceedings of\n the International Conference for High Performance Computing,\n Networking, Storage and Analysis (SC11), New York, NY: ACM, 2011.\n ' def __init__(self, seed=..., counter=..., key=...) -> None: '\n Philox(seed=None, counter=None, key=None)\n\n Container for the Philox (4x64) pseudo-random number generator.\n\n Parameters\n ----------\n seed : {None, int, array_like[ints], SeedSequence}, optional\n A seed to initialize the `BitGenerator`. If None, then fresh,\n unpredictable entropy will be pulled from the OS. If an ``int`` or\n ``array_like[ints]`` is passed, then it will be passed to\n `SeedSequence` to derive the initial `BitGenerator` state. One may also\n pass in a `SeedSequence` instance.\n counter : {None, int, array_like}, optional\n Counter to use in the Philox state. Can be either\n a Python int (long in 2.x) in [0, 2**256) or a 4-element uint64 array.\n If not provided, the RNG is initialized at 0.\n key : {None, int, array_like}, optional\n Key to use in the Philox state. Unlike ``seed``, the value in key is\n directly set. Can be either a Python int in [0, 2**128) or a 2-element\n uint64 array. `key` and ``seed`` cannot both be used.\n\n Attributes\n ----------\n lock: threading.Lock\n Lock instance that is shared so that the same bit git generator can\n be used in multiple Generators without corrupting the state. Code that\n generates values from a bit generator should hold the bit generator\'s\n lock.\n\n Notes\n -----\n Philox is a 64-bit PRNG that uses a counter-based design based on weaker\n (and faster) versions of cryptographic functions [1]_. Instances using\n different values of the key produce independent sequences. Philox has a\n period of :math:`2^{256} - 1` and supports arbitrary advancing and jumping\n the sequence in increments of :math:`2^{128}`. These features allow\n multiple non-overlapping sequences to be generated.\n\n ``Philox`` provides a capsule containing function pointers that produce\n doubles, and unsigned 32 and 64- bit integers. These are not\n directly consumable in Python and must be consumed by a ``Generator``\n or similar object that supports low-level access.\n\n **State and Seeding**\n\n The ``Philox`` state vector consists of a 256-bit value encoded as\n a 4-element uint64 array and a 128-bit value encoded as a 2-element uint64\n array. The former is a counter which is incremented by 1 for every 4 64-bit\n randoms produced. The second is a key which determined the sequence\n produced. Using different keys produces independent sequences.\n\n The input ``seed`` is processed by `SeedSequence` to generate the key. The\n counter is set to 0.\n\n Alternately, one can omit the ``seed`` parameter and set the ``key`` and\n ``counter`` directly.\n\n **Parallel Features**\n\n The preferred way to use a BitGenerator in parallel applications is to use\n the `SeedSequence.spawn` method to obtain entropy values, and to use these\n to generate new BitGenerators:\n\n >>> from numpy.random import Generator, Philox, SeedSequence\n >>> sg = SeedSequence(1234)\n >>> rg = [Generator(Philox(s)) for s in sg.spawn(10)]\n\n ``Philox`` can be used in parallel applications by calling the ``jumped``\n method to advances the state as-if :math:`2^{128}` random numbers have\n been generated. Alternatively, ``advance`` can be used to advance the\n counter for any positive step in [0, 2**256). When using ``jumped``, all\n generators should be chained to ensure that the segments come from the same\n sequence.\n\n >>> from numpy.random import Generator, Philox\n >>> bit_generator = Philox(1234)\n >>> rg = []\n >>> for _ in range(10):\n ... rg.append(Generator(bit_generator))\n ... bit_generator = bit_generator.jumped()\n\n Alternatively, ``Philox`` can be used in parallel applications by using\n a sequence of distinct keys where each instance uses different key.\n\n >>> key = 2**96 + 2**33 + 2**17 + 2**9\n >>> rg = [Generator(Philox(key=key+i)) for i in range(10)]\n\n **Compatibility Guarantee**\n\n ``Philox`` makes a guarantee that a fixed ``seed`` will always produce\n the same random integer stream.\n\n Examples\n --------\n >>> from numpy.random import Generator, Philox\n >>> rg = Generator(Philox(1234))\n >>> rg.standard_normal()\n 0.123 # random\n\n References\n ----------\n .. [1] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw,\n "Parallel Random Numbers: As Easy as 1, 2, 3," Proceedings of\n the International Conference for High Performance Computing,\n Networking, Storage and Analysis (SC11), New York, NY: ACM, 2011.\n ' ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce_cython__(self) -> typing.Any: ... def __setstate_cython__(self) -> typing.Any: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def advance(self, delta) -> typing.Any: ' Advance the underlying RNG as-if delta draws have occurred.\n\n Parameters\n ----------\n delta : integer, positive\n Number of draws to advance the RNG. Must be less than the\n size state variable in the underlying RNG.\n\n Returns\n -------\n self : Philox\n RNG advanced delta steps\n\n Notes\n -----\n Advancing a RNG updates the underlying RNG state as-if a given\n number of calls to the underlying RNG have been made. In general\n there is not a one-to-one relationship between the number output\n random values from a particular distribution and the number of\n draws from the core RNG. This occurs for two reasons:\n\n * The random values are simulated using a rejection-based method\n and so, on average, more than one value from the underlying\n RNG is required to generate an single draw.\n * The number of bits required to generate a simulated value\n differs from the number of bits generated by the underlying\n RNG. For example, two 16-bit integer values can be simulated\n from a single draw of a 32-bit RNG.\n\n Advancing the RNG state resets any pre-computed random numbers.\n This is required to ensure exact reproducibility.\n ' ... def jumped(self, jumps=...) -> typing.Any: ' Returns a new bit generator with the state jumped\n\n The state of the returned bit generator is jumped as-if\n (2**128) * jumps random numbers have been generated.\n\n Parameters\n ----------\n jumps : integer, positive\n Number of times to jump the state of the bit generator returned\n\n Returns\n -------\n bit_generator : Philox\n New instance of generator jumped iter times\n ' ... @property def state(self) -> typing.Any: '\n Get or set the PRNG state\n\n Returns\n -------\n state : dict\n Dictionary containing the information required to describe the\n state of the PRNG\n ' ... def __getattr__(self, name) -> typing.Any: ... __all__: list __doc__: typing.Any __file__: str __name__: str __package__: str __test__: dict def __getattr__(name) -> typing.Any: ...