Initial configuration commit
This commit is contained in:
commit
31c8abea59
266 changed files with 780274 additions and 0 deletions
16
typings/numpy/random/__init__.pyi
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16
typings/numpy/random/__init__.pyi
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"""
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This type stub file was generated by pyright.
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"""
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from numpy._pytesttester import PytestTester
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from numpy.random._generator import Generator as Generator, default_rng as default_rng
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from numpy.random._mt19937 import MT19937 as MT19937
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from numpy.random._pcg64 import PCG64 as PCG64, PCG64DXSM as PCG64DXSM
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from numpy.random._philox import Philox as Philox
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from numpy.random._sfc64 import SFC64 as SFC64
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from numpy.random.bit_generator import BitGenerator as BitGenerator, SeedSequence as SeedSequence
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from numpy.random.mtrand import RandomState as RandomState, beta as beta, binomial as binomial, bytes as bytes, chisquare as chisquare, choice as choice, dirichlet as dirichlet, exponential as exponential, f as f, gamma as gamma, geometric as geometric, get_bit_generator as get_bit_generator, get_state as get_state, gumbel as gumbel, hypergeometric as hypergeometric, laplace as laplace, logistic as logistic, lognormal as lognormal, logseries as logseries, multinomial as multinomial, multivariate_normal as multivariate_normal, negative_binomial as negative_binomial, noncentral_chisquare as noncentral_chisquare, noncentral_f as noncentral_f, normal as normal, pareto as pareto, permutation as permutation, poisson as poisson, power as power, rand as rand, randint as randint, randn as randn, random as random, random_integers as random_integers, random_sample as random_sample, ranf as ranf, rayleigh as rayleigh, sample as sample, seed as seed, set_bit_generator as set_bit_generator, set_state as set_state, shuffle as shuffle, standard_cauchy as standard_cauchy, standard_exponential as standard_exponential, standard_gamma as standard_gamma, standard_normal as standard_normal, standard_t as standard_t, triangular as triangular, uniform as uniform, vonmises as vonmises, wald as wald, weibull as weibull, zipf as zipf
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__all__: list[str]
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__path__: list[str]
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test: PytestTester
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469
typings/numpy/random/_generator.pyi
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469
typings/numpy/random/_generator.pyi
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"""
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This type stub file was generated by pyright.
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"""
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from collections.abc import Callable
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from typing import Any, Literal, TypeVar, Union, overload
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from numpy import bool_, dtype, float32, float64, int16, int32, int64, int8, int_, ndarray, uint, uint16, uint32, uint64, uint8
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from numpy.random import BitGenerator, SeedSequence
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from numpy._typing import ArrayLike, _ArrayLikeFloat_co, _ArrayLikeInt_co, _DTypeLikeBool, _DTypeLikeInt, _DTypeLikeUInt, _DoubleCodes, _Float32Codes, _Float64Codes, _FloatLike_co, _Int16Codes, _Int32Codes, _Int64Codes, _Int8Codes, _IntCodes, _ShapeLike, _SingleCodes, _SupportsDType, _UInt16Codes, _UInt32Codes, _UInt64Codes, _UInt8Codes, _UIntCodes
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_ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any])
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_DTypeLikeFloat32 = Union[dtype[float32], _SupportsDType[dtype[float32]], type[float32], _Float32Codes, _SingleCodes,]
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_DTypeLikeFloat64 = Union[dtype[float64], _SupportsDType[dtype[float64]], type[float], type[float64], _Float64Codes, _DoubleCodes,]
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class Generator:
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def __init__(self, bit_generator: BitGenerator) -> None:
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...
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def __repr__(self) -> str:
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...
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def __str__(self) -> str:
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...
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def __getstate__(self) -> dict[str, Any]:
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...
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def __setstate__(self, state: dict[str, Any]) -> None:
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...
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def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]:
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...
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@property
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def bit_generator(self) -> BitGenerator:
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...
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def spawn(self, n_children: int) -> list[Generator]:
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...
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def bytes(self, length: int) -> bytes:
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...
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@overload
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def standard_normal(self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ...) -> float:
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...
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@overload
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def standard_normal(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_normal(self, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_normal(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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@overload
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def standard_normal(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]:
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...
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@overload
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def standard_exponential(self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., method: Literal["zig", "inv"] = ..., out: None = ...) -> float:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_exponential(self, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ..., *, method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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@overload
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def standard_exponential(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., method: Literal["zig", "inv"] = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random(self, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ...) -> float:
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...
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@overload
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def random(self, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random(self, size: _ShapeLike = ..., *, out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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@overload
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def random(self, size: _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def beta(self, a: _FloatLike_co, b: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def beta(self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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def exponential(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def integers(self, low: int, high: None | int = ...) -> int:
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...
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@overload
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def integers(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeBool = ..., endpoint: bool = ...) -> bool:
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...
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@overload
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def integers(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeInt | _DTypeLikeUInt = ..., endpoint: bool = ...) -> int:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: _DTypeLikeBool = ..., endpoint: bool = ...) -> ndarray[Any, dtype[bool_]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int8]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int16]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int32]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint8]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint16]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint32]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint64]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[int_]]:
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...
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@overload
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def integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., endpoint: bool = ...) -> ndarray[Any, dtype[uint]]:
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...
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@overload
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def choice(self, a: int, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> int:
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...
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@overload
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def choice(self, a: int, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> ndarray[Any, dtype[int64]]:
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...
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@overload
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def choice(self, a: ArrayLike, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> Any:
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...
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@overload
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def choice(self, a: ArrayLike, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ..., axis: int = ..., shuffle: bool = ...) -> ndarray[Any, Any]:
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...
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@overload
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def uniform(self, low: _FloatLike_co = ..., high: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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def uniform(self, low: _ArrayLikeFloat_co = ..., high: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def normal(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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def normal(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_gamma(self, shape: _FloatLike_co, size: None = ..., dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., out: None = ...) -> float:
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...
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@overload
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def standard_gamma(self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_gamma(self, shape: _ArrayLikeFloat_co, *, out: ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_gamma(self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., dtype: _DTypeLikeFloat32 = ..., out: None | ndarray[Any, dtype[float32]] = ...) -> ndarray[Any, dtype[float32]]:
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...
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|
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@overload
|
||||
def standard_gamma(self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., dtype: _DTypeLikeFloat64 = ..., out: None | ndarray[Any, dtype[float64]] = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float:
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...
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@overload
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||||
def gamma(self, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float:
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...
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@overload
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def f(self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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||||
def noncentral_f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float:
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...
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||||
@overload
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||||
def noncentral_f(self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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||||
...
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||||
|
||||
@overload
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||||
def chisquare(self, df: _FloatLike_co, size: None = ...) -> float:
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||||
...
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||||
@overload
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||||
def chisquare(self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
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||||
|
||||
@overload
|
||||
def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float:
|
||||
...
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||||
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||||
@overload
|
||||
def noncentral_chisquare(self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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||||
...
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||||
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||||
@overload
|
||||
def standard_t(self, df: _FloatLike_co, size: None = ...) -> float:
|
||||
...
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||||
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||||
@overload
|
||||
def standard_t(self, df: _ArrayLikeFloat_co, size: None = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
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||||
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||||
@overload
|
||||
def standard_t(self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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||||
...
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||||
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||||
@overload
|
||||
def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def vonmises(self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def pareto(self, a: _FloatLike_co, size: None = ...) -> float:
|
||||
...
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||||
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||||
@overload
|
||||
def pareto(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def weibull(self, a: _FloatLike_co, size: None = ...) -> float:
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||||
...
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||||
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||||
@overload
|
||||
def weibull(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
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||||
|
||||
@overload
|
||||
def power(self, a: _FloatLike_co, size: None = ...) -> float:
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||||
...
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||||
|
||||
@overload
|
||||
def power(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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||||
...
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||||
|
||||
@overload
|
||||
def standard_cauchy(self, size: None = ...) -> float:
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||||
...
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||||
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||||
@overload
|
||||
def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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||||
...
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||||
|
||||
@overload
|
||||
def laplace(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def laplace(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
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||||
|
||||
@overload
|
||||
def gumbel(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def gumbel(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logistic(self, loc: _FloatLike_co = ..., scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logistic(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def lognormal(self, mean: _FloatLike_co = ..., sigma: _FloatLike_co = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def lognormal(self, mean: _ArrayLikeFloat_co = ..., sigma: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def rayleigh(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def wald(self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def wald(self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def triangular(self, left: _FloatLike_co, mode: _FloatLike_co, right: _FloatLike_co, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def triangular(self, left: _ArrayLikeFloat_co, mode: _ArrayLikeFloat_co, right: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def binomial(self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def negative_binomial(self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def negative_binomial(self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def poisson(self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def zipf(self, a: _FloatLike_co, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def zipf(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def geometric(self, p: _FloatLike_co, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def geometric(self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def hypergeometric(self, ngood: _ArrayLikeInt_co, nbad: _ArrayLikeInt_co, nsample: _ArrayLikeInt_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logseries(self, p: _FloatLike_co, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logseries(self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
def multivariate_normal(self, mean: _ArrayLikeFloat_co, cov: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., check_valid: Literal["warn", "raise", "ignore"] = ..., tol: float = ..., *, method: Literal["svd", "eigh", "cholesky"] = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
def multinomial(self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
def multivariate_hypergeometric(self, colors: _ArrayLikeInt_co, nsample: int, size: None | _ShapeLike = ..., method: Literal["marginals", "count"] = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
def dirichlet(self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
def permuted(self, x: ArrayLike, *, axis: None | int = ..., out: None | ndarray[Any, Any] = ...) -> ndarray[Any, Any]:
|
||||
...
|
||||
|
||||
def shuffle(self, x: ArrayLike, axis: int = ...) -> None:
|
||||
...
|
||||
|
||||
|
||||
|
||||
def default_rng(seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator = ...) -> Generator:
|
||||
...
|
||||
|
||||
38
typings/numpy/random/_mt19937.pyi
Normal file
38
typings/numpy/random/_mt19937.pyi
Normal file
|
|
@ -0,0 +1,38 @@
|
|||
"""
|
||||
This type stub file was generated by pyright.
|
||||
"""
|
||||
|
||||
from typing import Any, TypedDict
|
||||
from numpy import dtype, ndarray, uint32
|
||||
from numpy.random.bit_generator import BitGenerator, SeedSequence
|
||||
from numpy._typing import _ArrayLikeInt_co
|
||||
|
||||
class _MT19937Internal(TypedDict):
|
||||
key: ndarray[Any, dtype[uint32]]
|
||||
pos: int
|
||||
...
|
||||
|
||||
|
||||
class _MT19937State(TypedDict):
|
||||
bit_generator: str
|
||||
state: _MT19937Internal
|
||||
...
|
||||
|
||||
|
||||
class MT19937(BitGenerator):
|
||||
def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None:
|
||||
...
|
||||
|
||||
def jumped(self, jumps: int = ...) -> MT19937:
|
||||
...
|
||||
|
||||
@property
|
||||
def state(self) -> _MT19937State:
|
||||
...
|
||||
|
||||
@state.setter
|
||||
def state(self, value: _MT19937State) -> None:
|
||||
...
|
||||
|
||||
|
||||
|
||||
62
typings/numpy/random/_pcg64.pyi
Normal file
62
typings/numpy/random/_pcg64.pyi
Normal file
|
|
@ -0,0 +1,62 @@
|
|||
"""
|
||||
This type stub file was generated by pyright.
|
||||
"""
|
||||
|
||||
from typing import TypedDict
|
||||
from numpy.random.bit_generator import BitGenerator, SeedSequence
|
||||
from numpy._typing import _ArrayLikeInt_co
|
||||
|
||||
class _PCG64Internal(TypedDict):
|
||||
state: int
|
||||
inc: int
|
||||
...
|
||||
|
||||
|
||||
class _PCG64State(TypedDict):
|
||||
bit_generator: str
|
||||
state: _PCG64Internal
|
||||
has_uint32: int
|
||||
uinteger: int
|
||||
...
|
||||
|
||||
|
||||
class PCG64(BitGenerator):
|
||||
def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None:
|
||||
...
|
||||
|
||||
def jumped(self, jumps: int = ...) -> PCG64:
|
||||
...
|
||||
|
||||
@property
|
||||
def state(self) -> _PCG64State:
|
||||
...
|
||||
|
||||
@state.setter
|
||||
def state(self, value: _PCG64State) -> None:
|
||||
...
|
||||
|
||||
def advance(self, delta: int) -> PCG64:
|
||||
...
|
||||
|
||||
|
||||
|
||||
class PCG64DXSM(BitGenerator):
|
||||
def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None:
|
||||
...
|
||||
|
||||
def jumped(self, jumps: int = ...) -> PCG64DXSM:
|
||||
...
|
||||
|
||||
@property
|
||||
def state(self) -> _PCG64State:
|
||||
...
|
||||
|
||||
@state.setter
|
||||
def state(self, value: _PCG64State) -> None:
|
||||
...
|
||||
|
||||
def advance(self, delta: int) -> PCG64DXSM:
|
||||
...
|
||||
|
||||
|
||||
|
||||
45
typings/numpy/random/_philox.pyi
Normal file
45
typings/numpy/random/_philox.pyi
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
"""
|
||||
This type stub file was generated by pyright.
|
||||
"""
|
||||
|
||||
from typing import Any, TypedDict
|
||||
from numpy import dtype, ndarray, uint64
|
||||
from numpy.random.bit_generator import BitGenerator, SeedSequence
|
||||
from numpy._typing import _ArrayLikeInt_co
|
||||
|
||||
class _PhiloxInternal(TypedDict):
|
||||
counter: ndarray[Any, dtype[uint64]]
|
||||
key: ndarray[Any, dtype[uint64]]
|
||||
...
|
||||
|
||||
|
||||
class _PhiloxState(TypedDict):
|
||||
bit_generator: str
|
||||
state: _PhiloxInternal
|
||||
buffer: ndarray[Any, dtype[uint64]]
|
||||
buffer_pos: int
|
||||
has_uint32: int
|
||||
uinteger: int
|
||||
...
|
||||
|
||||
|
||||
class Philox(BitGenerator):
|
||||
def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ..., counter: None | _ArrayLikeInt_co = ..., key: None | _ArrayLikeInt_co = ...) -> None:
|
||||
...
|
||||
|
||||
@property
|
||||
def state(self) -> _PhiloxState:
|
||||
...
|
||||
|
||||
@state.setter
|
||||
def state(self, value: _PhiloxState) -> None:
|
||||
...
|
||||
|
||||
def jumped(self, jumps: int = ...) -> Philox:
|
||||
...
|
||||
|
||||
def advance(self, delta: int) -> Philox:
|
||||
...
|
||||
|
||||
|
||||
|
||||
36
typings/numpy/random/_sfc64.pyi
Normal file
36
typings/numpy/random/_sfc64.pyi
Normal file
|
|
@ -0,0 +1,36 @@
|
|||
"""
|
||||
This type stub file was generated by pyright.
|
||||
"""
|
||||
|
||||
from typing import Any, TypedDict
|
||||
from numpy import dtype as dtype, ndarray as ndarray, uint64
|
||||
from numpy.random.bit_generator import BitGenerator, SeedSequence
|
||||
from numpy._typing import _ArrayLikeInt_co
|
||||
|
||||
class _SFC64Internal(TypedDict):
|
||||
state: ndarray[Any, dtype[uint64]]
|
||||
...
|
||||
|
||||
|
||||
class _SFC64State(TypedDict):
|
||||
bit_generator: str
|
||||
state: _SFC64Internal
|
||||
has_uint32: int
|
||||
uinteger: int
|
||||
...
|
||||
|
||||
|
||||
class SFC64(BitGenerator):
|
||||
def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None:
|
||||
...
|
||||
|
||||
@property
|
||||
def state(self) -> _SFC64State:
|
||||
...
|
||||
|
||||
@state.setter
|
||||
def state(self, value: _SFC64State) -> None:
|
||||
...
|
||||
|
||||
|
||||
|
||||
131
typings/numpy/random/bit_generator.pyi
Normal file
131
typings/numpy/random/bit_generator.pyi
Normal file
|
|
@ -0,0 +1,131 @@
|
|||
"""
|
||||
This type stub file was generated by pyright.
|
||||
"""
|
||||
|
||||
import abc
|
||||
from threading import Lock
|
||||
from collections.abc import Callable, Mapping, Sequence
|
||||
from typing import Any, Literal, NamedTuple, TypeVar, TypedDict, Union, overload
|
||||
from numpy import dtype, ndarray, uint32, uint64
|
||||
from numpy._typing import _ArrayLikeInt_co, _ShapeLike, _SupportsDType, _UInt32Codes, _UInt64Codes
|
||||
|
||||
_T = TypeVar("_T")
|
||||
_DTypeLikeUint32 = Union[dtype[uint32], _SupportsDType[dtype[uint32]], type[uint32], _UInt32Codes,]
|
||||
_DTypeLikeUint64 = Union[dtype[uint64], _SupportsDType[dtype[uint64]], type[uint64], _UInt64Codes,]
|
||||
class _SeedSeqState(TypedDict):
|
||||
entropy: None | int | Sequence[int]
|
||||
spawn_key: tuple[int, ...]
|
||||
pool_size: int
|
||||
n_children_spawned: int
|
||||
...
|
||||
|
||||
|
||||
class _Interface(NamedTuple):
|
||||
state_address: Any
|
||||
state: Any
|
||||
next_uint64: Any
|
||||
next_uint32: Any
|
||||
next_double: Any
|
||||
bit_generator: Any
|
||||
...
|
||||
|
||||
|
||||
class ISeedSequence(abc.ABC):
|
||||
@abc.abstractmethod
|
||||
def generate_state(self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...) -> ndarray[Any, dtype[uint32 | uint64]]:
|
||||
...
|
||||
|
||||
|
||||
|
||||
class ISpawnableSeedSequence(ISeedSequence):
|
||||
@abc.abstractmethod
|
||||
def spawn(self: _T, n_children: int) -> list[_T]:
|
||||
...
|
||||
|
||||
|
||||
|
||||
class SeedlessSeedSequence(ISpawnableSeedSequence):
|
||||
def generate_state(self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...) -> ndarray[Any, dtype[uint32 | uint64]]:
|
||||
...
|
||||
|
||||
def spawn(self: _T, n_children: int) -> list[_T]:
|
||||
...
|
||||
|
||||
|
||||
|
||||
class SeedSequence(ISpawnableSeedSequence):
|
||||
entropy: None | int | Sequence[int]
|
||||
spawn_key: tuple[int, ...]
|
||||
pool_size: int
|
||||
n_children_spawned: int
|
||||
pool: ndarray[Any, dtype[uint32]]
|
||||
def __init__(self, entropy: None | int | Sequence[int] | _ArrayLikeInt_co = ..., *, spawn_key: Sequence[int] = ..., pool_size: int = ..., n_children_spawned: int = ...) -> None:
|
||||
...
|
||||
|
||||
def __repr__(self) -> str:
|
||||
...
|
||||
|
||||
@property
|
||||
def state(self) -> _SeedSeqState:
|
||||
...
|
||||
|
||||
def generate_state(self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...) -> ndarray[Any, dtype[uint32 | uint64]]:
|
||||
...
|
||||
|
||||
def spawn(self, n_children: int) -> list[SeedSequence]:
|
||||
...
|
||||
|
||||
|
||||
|
||||
class BitGenerator(abc.ABC):
|
||||
lock: Lock
|
||||
def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None:
|
||||
...
|
||||
|
||||
def __getstate__(self) -> dict[str, Any]:
|
||||
...
|
||||
|
||||
def __setstate__(self, state: dict[str, Any]) -> None:
|
||||
...
|
||||
|
||||
def __reduce__(self) -> tuple[Callable[[str], BitGenerator], tuple[str], tuple[dict[str, Any]]]:
|
||||
...
|
||||
|
||||
@abc.abstractmethod
|
||||
@property
|
||||
def state(self) -> Mapping[str, Any]:
|
||||
...
|
||||
|
||||
@state.setter
|
||||
def state(self, value: Mapping[str, Any]) -> None:
|
||||
...
|
||||
|
||||
@property
|
||||
def seed_seq(self) -> ISeedSequence:
|
||||
...
|
||||
|
||||
def spawn(self, n_children: int) -> list[BitGenerator]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random_raw(self, size: None = ..., output: Literal[True] = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random_raw(self, size: _ShapeLike = ..., output: Literal[True] = ...) -> ndarray[Any, dtype[uint64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random_raw(self, size: None | _ShapeLike = ..., output: Literal[False] = ...) -> None:
|
||||
...
|
||||
|
||||
@property
|
||||
def ctypes(self) -> _Interface:
|
||||
...
|
||||
|
||||
@property
|
||||
def cffi(self) -> _Interface:
|
||||
...
|
||||
|
||||
|
||||
|
||||
513
typings/numpy/random/mtrand.pyi
Normal file
513
typings/numpy/random/mtrand.pyi
Normal file
|
|
@ -0,0 +1,513 @@
|
|||
"""
|
||||
This type stub file was generated by pyright.
|
||||
"""
|
||||
|
||||
import builtins
|
||||
from collections.abc import Callable
|
||||
from typing import Any, Literal, Union, overload
|
||||
from numpy import bool_, dtype, float32, float64, int16, int32, int64, int8, int_, ndarray, uint, uint16, uint32, uint64, uint8
|
||||
from numpy.random.bit_generator import BitGenerator
|
||||
from numpy._typing import ArrayLike, _ArrayLikeFloat_co, _ArrayLikeInt_co, _DTypeLikeBool, _DTypeLikeInt, _DTypeLikeUInt, _DoubleCodes, _Float32Codes, _Float64Codes, _Int16Codes, _Int32Codes, _Int64Codes, _Int8Codes, _IntCodes, _ShapeLike, _SingleCodes, _SupportsDType, _UInt16Codes, _UInt32Codes, _UInt64Codes, _UInt8Codes, _UIntCodes
|
||||
|
||||
_DTypeLikeFloat32 = Union[dtype[float32], _SupportsDType[dtype[float32]], type[float32], _Float32Codes, _SingleCodes,]
|
||||
_DTypeLikeFloat64 = Union[dtype[float64], _SupportsDType[dtype[float64]], type[float], type[float64], _Float64Codes, _DoubleCodes,]
|
||||
class RandomState:
|
||||
_bit_generator: BitGenerator
|
||||
def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None:
|
||||
...
|
||||
|
||||
def __repr__(self) -> str:
|
||||
...
|
||||
|
||||
def __str__(self) -> str:
|
||||
...
|
||||
|
||||
def __getstate__(self) -> dict[str, Any]:
|
||||
...
|
||||
|
||||
def __setstate__(self, state: dict[str, Any]) -> None:
|
||||
...
|
||||
|
||||
def __reduce__(self) -> tuple[Callable[[str], RandomState], tuple[str], dict[str, Any]]:
|
||||
...
|
||||
|
||||
def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None:
|
||||
...
|
||||
|
||||
@overload
|
||||
def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def get_state(self, legacy: Literal[True] = ...) -> dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]:
|
||||
...
|
||||
|
||||
def set_state(self, state: dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]) -> None:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random_sample(self, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random_sample(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random(self, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def beta(self, a: float, b: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def beta(self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def exponential(self, scale: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def exponential(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_exponential(self, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def tomaxint(self, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def tomaxint(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: int, high: None | int = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeBool = ...) -> bool:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeInt | _DTypeLikeUInt = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: _DTypeLikeBool = ...) -> ndarray[Any, dtype[bool_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...) -> ndarray[Any, dtype[int8]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...) -> ndarray[Any, dtype[int16]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...) -> ndarray[Any, dtype[int32]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...) -> ndarray[Any, dtype[int64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...) -> ndarray[Any, dtype[uint8]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...) -> ndarray[Any, dtype[uint16]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...) -> ndarray[Any, dtype[uint32]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...) -> ndarray[Any, dtype[uint64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...) -> ndarray[Any, dtype[uint]]:
|
||||
...
|
||||
|
||||
def bytes(self, length: int) -> builtins.bytes:
|
||||
...
|
||||
|
||||
@overload
|
||||
def choice(self, a: int, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def choice(self, a: int, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def choice(self, a: ArrayLike, size: None = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ...) -> Any:
|
||||
...
|
||||
|
||||
@overload
|
||||
def choice(self, a: ArrayLike, size: _ShapeLike = ..., replace: bool = ..., p: None | _ArrayLikeFloat_co = ...) -> ndarray[Any, Any]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def uniform(self, low: _ArrayLikeFloat_co = ..., high: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def rand(self) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def rand(self, *args: int) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randn(self) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def randn(self, *args: int) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def random_integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_normal(self, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_normal(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def normal(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_gamma(self, shape: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_gamma(self, shape: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def gamma(self, shape: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def f(self, dfnum: float, dfden: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def f(self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def noncentral_f(self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def chisquare(self, df: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def chisquare(self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def noncentral_chisquare(self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_t(self, df: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_t(self, df: _ArrayLikeFloat_co, size: None = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_t(self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def vonmises(self, mu: float, kappa: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def vonmises(self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def pareto(self, a: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def pareto(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def weibull(self, a: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def weibull(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def power(self, a: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def power(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_cauchy(self, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def laplace(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def gumbel(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logistic(self, loc: _ArrayLikeFloat_co = ..., scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def lognormal(self, mean: _ArrayLikeFloat_co = ..., sigma: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def rayleigh(self, scale: float = ..., size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def rayleigh(self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def wald(self, mean: float, scale: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def wald(self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float:
|
||||
...
|
||||
|
||||
@overload
|
||||
def triangular(self, left: _ArrayLikeFloat_co, mode: _ArrayLikeFloat_co, right: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def binomial(self, n: int, p: float, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def binomial(self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def negative_binomial(self, n: float, p: float, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def negative_binomial(self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def poisson(self, lam: float = ..., size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def poisson(self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def zipf(self, a: float, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def zipf(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def geometric(self, p: float, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def geometric(self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def hypergeometric(self, ngood: _ArrayLikeInt_co, nbad: _ArrayLikeInt_co, nsample: _ArrayLikeInt_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logseries(self, p: float, size: None = ...) -> int:
|
||||
...
|
||||
|
||||
@overload
|
||||
def logseries(self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
def multivariate_normal(self, mean: _ArrayLikeFloat_co, cov: _ArrayLikeFloat_co, size: None | _ShapeLike = ..., check_valid: Literal["warn", "raise", "ignore"] = ..., tol: float = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
def multinomial(self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
def dirichlet(self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
|
||||
...
|
||||
|
||||
def shuffle(self, x: ArrayLike) -> None:
|
||||
...
|
||||
|
||||
@overload
|
||||
def permutation(self, x: int) -> ndarray[Any, dtype[int_]]:
|
||||
...
|
||||
|
||||
@overload
|
||||
def permutation(self, x: ArrayLike) -> ndarray[Any, Any]:
|
||||
...
|
||||
|
||||
|
||||
|
||||
_rand: RandomState
|
||||
beta = ...
|
||||
binomial = ...
|
||||
bytes = ...
|
||||
chisquare = ...
|
||||
choice = ...
|
||||
dirichlet = ...
|
||||
exponential = ...
|
||||
f = ...
|
||||
gamma = ...
|
||||
get_state = ...
|
||||
geometric = ...
|
||||
gumbel = ...
|
||||
hypergeometric = ...
|
||||
laplace = ...
|
||||
logistic = ...
|
||||
lognormal = ...
|
||||
logseries = ...
|
||||
multinomial = ...
|
||||
multivariate_normal = ...
|
||||
negative_binomial = ...
|
||||
noncentral_chisquare = ...
|
||||
noncentral_f = ...
|
||||
normal = ...
|
||||
pareto = ...
|
||||
permutation = ...
|
||||
poisson = ...
|
||||
power = ...
|
||||
rand = ...
|
||||
randint = ...
|
||||
randn = ...
|
||||
random = ...
|
||||
random_integers = ...
|
||||
random_sample = ...
|
||||
rayleigh = ...
|
||||
seed = ...
|
||||
set_state = ...
|
||||
shuffle = ...
|
||||
standard_cauchy = ...
|
||||
standard_exponential = ...
|
||||
standard_gamma = ...
|
||||
standard_normal = ...
|
||||
standard_t = ...
|
||||
triangular = ...
|
||||
uniform = ...
|
||||
vonmises = ...
|
||||
wald = ...
|
||||
weibull = ...
|
||||
zipf = ...
|
||||
sample = ...
|
||||
ranf = ...
|
||||
def set_bit_generator(bitgen: BitGenerator) -> None:
|
||||
...
|
||||
|
||||
def get_bit_generator() -> BitGenerator:
|
||||
...
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue