514 lines
17 KiB
Python
514 lines
17 KiB
Python
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"""
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This type stub file was generated by pyright.
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"""
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import builtins
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from collections.abc import Callable
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from typing import Any, Literal, 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.bit_generator import BitGenerator
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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
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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 RandomState:
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_bit_generator: BitGenerator
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def __init__(self, seed: None | _ArrayLikeInt_co | 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], RandomState], tuple[str], dict[str, Any]]:
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...
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def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None:
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...
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@overload
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def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]:
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...
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@overload
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def get_state(self, legacy: Literal[True] = ...) -> dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]:
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...
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def set_state(self, state: dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]) -> None:
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...
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@overload
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def random_sample(self, size: None = ...) -> float:
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...
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@overload
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def random_sample(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random(self, size: None = ...) -> float:
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...
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@overload
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def random(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def beta(self, a: float, b: float, 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: float = ..., 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 standard_exponential(self, size: 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 tomaxint(self, size: None = ...) -> int:
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...
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@overload
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def tomaxint(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
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...
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@overload
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def randint(self, low: int, high: None | int = ...) -> int:
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...
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@overload
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def randint(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeBool = ...) -> bool:
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...
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@overload
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def randint(self, low: int, high: None | int = ..., size: None = ..., dtype: _DTypeLikeInt | _DTypeLikeUInt = ...) -> int:
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...
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@overload
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def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
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...
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@overload
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def randint(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ..., dtype: _DTypeLikeBool = ...) -> ndarray[Any, dtype[bool_]]:
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...
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@overload
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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]]:
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...
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@overload
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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]]:
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...
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@overload
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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]]:
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...
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@overload
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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]]:
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...
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@overload
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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]]:
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...
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@overload
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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]]:
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...
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@overload
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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]]:
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...
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@overload
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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]]:
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...
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@overload
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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_]]:
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...
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@overload
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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]]:
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...
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def bytes(self, length: int) -> builtins.bytes:
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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 = ...) -> 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 = ...) -> ndarray[Any, dtype[int_]]:
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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 = ...) -> 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 = ...) -> ndarray[Any, Any]:
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...
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@overload
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def uniform(self, low: float = ..., high: float = ..., 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 rand(self) -> float:
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...
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@overload
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def rand(self, *args: int) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def randn(self) -> float:
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...
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@overload
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def randn(self, *args: int) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int:
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...
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@overload
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def random_integers(self, low: _ArrayLikeInt_co, high: None | _ArrayLikeInt_co = ..., size: None | _ShapeLike = ...) -> ndarray[Any, dtype[int_]]:
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...
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@overload
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def standard_normal(self, size: 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 normal(self, loc: float = ..., scale: float = ..., 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: float, size: 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 gamma(self, shape: float, scale: float = ..., 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: float, dfden: float, 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: float, dfden: float, nonc: float, 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: float, 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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...
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@overload
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def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float:
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...
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@overload
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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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@overload
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def standard_t(self, df: float, size: None = ...) -> float:
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...
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@overload
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def standard_t(self, df: _ArrayLikeFloat_co, size: None = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_t(self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def vonmises(self, mu: float, kappa: float, size: None = ...) -> float:
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...
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@overload
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def vonmises(self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def pareto(self, a: float, size: None = ...) -> float:
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...
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@overload
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def pareto(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def weibull(self, a: float, size: None = ...) -> float:
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...
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@overload
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def weibull(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def power(self, a: float, size: None = ...) -> float:
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...
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@overload
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def power(self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def standard_cauchy(self, size: None = ...) -> float:
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...
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@overload
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def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]:
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...
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@overload
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def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float:
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...
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@overload
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def laplace(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 gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float:
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...
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@overload
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def gumbel(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 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:
|
||
|
|
...
|
||
|
|
|