{
"__type": "IngestedDoc",
"__tag": 4010,
"_content": {
"Notes": {
"__type": "Section",
"__tag": 4015,
"children": [
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "PCG-64 is a 128-bit implementation of O'Neill's permutation congruential generator ("
},
{
"__type": "FootnoteReference",
"__tag": 4066,
"label": "1"
},
{
"__type": "Text",
"__tag": 4046,
"value": ", "
},
{
"__type": "FootnoteReference",
"__tag": 4066,
"label": "2"
},
{
"__type": "Text",
"__tag": 4046,
"value": "). PCG-64 has a period of "
},
{
"__type": "InlineMath",
"__tag": 4057,
"value": "2^{128}"
},
{
"__type": "Text",
"__tag": 4046,
"value": " and supports advancing an arbitrary number of steps as well as "
},
{
"__type": "InlineMath",
"__tag": 4057,
"value": "2^{127}"
},
{
"__type": "Text",
"__tag": 4046,
"value": " streams. The specific member of the PCG family that we use is PCG XSL RR 128/64 as described in the paper ("
},
{
"__type": "FootnoteReference",
"__tag": 4066,
"label": "2"
},
{
"__type": "Text",
"__tag": 4046,
"value": ")."
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "CrossRef",
"__tag": 4002,
"value": "PCG64",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random._pcg64:PCG64"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " 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 "
},
{
"__type": "CrossRef",
"__tag": 4002,
"value": "Generator",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random._generator:Generator"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " or similar object that supports low-level access."
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "Supports the method "
},
{
"__type": "InlineRole",
"__tag": 4003,
"value": "advance",
"domain": null,
"role": "meth",
"inventory": null
},
{
"__type": "Text",
"__tag": 4046,
"value": " to advance the RNG an arbitrary number of steps. The state of the PCG-64 RNG is represented by 2 128-bit unsigned integers."
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Strong",
"__tag": 4048,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "State and Seeding"
}
]
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "The "
},
{
"__type": "CrossRef",
"__tag": 4002,
"value": "PCG64",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random._pcg64:PCG64"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " 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."
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "The input seed is processed by "
},
{
"__type": "CrossRef",
"__tag": 4002,
"value": "SeedSequence",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random.bit_generator:SeedSequence"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " to generate both values. The increment is not independently settable."
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Strong",
"__tag": 4048,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "Parallel Features"
}
]
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "The preferred way to use a BitGenerator in parallel applications is to use the "
},
{
"__type": "InlineRole",
"__tag": 4003,
"value": "SeedSequence.spawn",
"domain": null,
"role": null,
"inventory": null
},
{
"__type": "Text",
"__tag": 4046,
"value": " method to obtain entropy values, and to use these to generate new BitGenerators:"
}
]
},
{
"__type": "Code",
"__tag": 4050,
"value": ">>> from numpy.random import Generator, PCG64, SeedSequence\n>>> sg = SeedSequence(1234)\n>>> rg = [Generator(PCG64(s)) for s in sg.spawn(10)]",
"execution_status": null
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Strong",
"__tag": 4048,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "Compatibility Guarantee"
}
]
}
]
},
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "CrossRef",
"__tag": 4002,
"value": "PCG64",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random._pcg64:PCG64"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " makes a guarantee that a fixed seed will always produce the same random integer stream."
}
]
}
],
"title": [],
"level": 0,
"target": null
},
"Warns": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Raises": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Yields": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Methods": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Returns": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Summary": {
"__type": "Section",
"__tag": 4015,
"children": [
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "BitGenerator for the PCG-64 pseudo-random number generator."
}
]
}
],
"title": [],
"level": 0,
"target": null
},
"Receives": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Warnings": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Attributes": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Parameters": {
"__type": "Section",
"__tag": 4015,
"children": [
{
"__type": "Parameters",
"__tag": 4026,
"children": [
{
"__type": "DocParam",
"__tag": 4016,
"name": "seed",
"annotation": "{None, int, array_like[ints], SeedSequence}, optional",
"desc": [
{
"__type": "Paragraph",
"__tag": 4045,
"children": [
{
"__type": "Text",
"__tag": 4046,
"value": "A seed to initialize the "
},
{
"__type": "CrossRef",
"__tag": 4002,
"value": "BitGenerator",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random.bit_generator:BitGenerator"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": ". If None, then fresh, unpredictable entropy will be pulled from the OS. If an "
},
{
"__type": "InlineCode",
"__tag": 4051,
"value": "int"
},
{
"__type": "Text",
"__tag": 4046,
"value": " or "
},
{
"__type": "InlineCode",
"__tag": 4051,
"value": "array_like[ints]"
},
{
"__type": "Text",
"__tag": 4046,
"value": " is passed, then it will be passed to "
},
{
"__type": "CrossRef",
"__tag": 4002,
"value": "SeedSequence",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random.bit_generator:SeedSequence"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " to derive the initial "
},
{
"__type": "CrossRef",
"__tag": 4002,
"value": "BitGenerator",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random.bit_generator:BitGenerator"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " state. One may also pass in a "
},
{
"__type": "CrossRef",
"__tag": 4002,
"value": "SeedSequence",
"reference": {
"__type": "LocalRef",
"__tag": 4022,
"kind": "module",
"path": "numpy.random.bit_generator:SeedSequence"
},
"kind": "module"
},
{
"__type": "Text",
"__tag": 4046,
"value": " instance."
}
]
}
]
}
]
}
],
"title": [],
"level": 0,
"target": null
},
"Extended Summary": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"Other Parameters": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
}
},
"_ordered_sections": [
"Summary",
"Extended Summary",
"Parameters",
"Attributes",
"Methods",
"Returns",
"Yields",
"Receives",
"Other Parameters",
"Raises",
"Warns",
"Warnings",
"Notes"
],
"item_file": "/numpy/random/_pcg64.cpython-314-x86_64-linux-gnu.so",
"item_line": null,
"item_type": "class",
"aliases": [
"numpy.random.PCG64"
],
"example_section_data": {
"__type": "Section",
"__tag": 4015,
"children": [],
"title": [],
"level": 0,
"target": null
},
"see_also": [],
"signature": {
"__type": "SignatureNode",
"__tag": 4029,
"kind": "function",
"parameters": [
{
"__type": "SigParam",
"__tag": 4030,
"name": "seed",
"annotation": {
"__type": "Empty",
"__tag": 4031
},
"kind": "POSITIONAL_OR_KEYWORD",
"default": "None"
}
],
"return_annotation": {
"__type": "Empty",
"__tag": 4031
},
"target_name": "PCG64"
},
"references": [
".. [1] `\"PCG, A Family of Better Random Number Generators\"",
" `_",
".. [2] O'Neill, Melissa E. `\"PCG: A Family of Simple Fast Space-Efficient",
" Statistically Good Algorithms for Random Number Generation\"",
" `_"
],
"qa": "numpy.random._pcg64:PCG64",
"arbitrary": [],
"local_refs": [
"seed"
]
}