857 lines
23 KiB
Python
857 lines
23 KiB
Python
from collections.abc import Callable
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from typing import Any, Literal, TypeAlias, TypeVar, overload
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import numpy as np
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from numpy import dtype, float32, float64, int64
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from numpy._typing import (
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ArrayLike,
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DTypeLike,
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NDArray,
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_ArrayLikeFloat_co,
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_ArrayLikeInt_co,
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_BoolCodes,
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_DoubleCodes,
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_DTypeLike,
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_DTypeLikeBool,
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_Float32Codes,
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_Float64Codes,
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_FloatLike_co,
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_Int8Codes,
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_Int16Codes,
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_Int32Codes,
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_Int64Codes,
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_IntPCodes,
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_ShapeLike,
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_SingleCodes,
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_SupportsDType,
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_UInt8Codes,
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_UInt16Codes,
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_UInt32Codes,
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_UInt64Codes,
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_UIntPCodes,
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)
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from numpy.random import BitGenerator, RandomState, SeedSequence
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_IntegerT = TypeVar("_IntegerT", bound=np.integer)
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_DTypeLikeFloat32: TypeAlias = (
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dtype[float32]
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| _SupportsDType[dtype[float32]]
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| type[float32]
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| _Float32Codes
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| _SingleCodes
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)
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_DTypeLikeFloat64: TypeAlias = (
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dtype[float64]
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| _SupportsDType[dtype[float64]]
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| type[float]
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| type[float64]
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| _Float64Codes
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| _DoubleCodes
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)
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class Generator:
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def __init__(self, bit_generator: BitGenerator) -> None: ...
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def __repr__(self) -> str: ...
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def __str__(self) -> str: ...
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def __getstate__(self) -> None: ...
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def __setstate__(self, state: dict[str, Any] | None) -> None: ...
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def __reduce__(self) -> tuple[
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Callable[[BitGenerator], Generator],
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tuple[BitGenerator],
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None]: ...
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@property
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def bit_generator(self) -> BitGenerator: ...
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def spawn(self, n_children: int) -> list[Generator]: ...
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def bytes(self, length: int) -> bytes: ...
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@overload
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def standard_normal( # type: ignore[misc]
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self,
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size: None = ...,
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dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
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out: None = ...,
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) -> float: ...
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@overload
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def standard_normal( # type: ignore[misc]
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self,
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size: _ShapeLike = ...,
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) -> NDArray[float64]: ...
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@overload
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def standard_normal( # type: ignore[misc]
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self,
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*,
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out: NDArray[float64] = ...,
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) -> NDArray[float64]: ...
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@overload
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def standard_normal( # type: ignore[misc]
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self,
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size: _ShapeLike = ...,
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dtype: _DTypeLikeFloat32 = ...,
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out: NDArray[float32] | None = ...,
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) -> NDArray[float32]: ...
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@overload
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def standard_normal( # type: ignore[misc]
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self,
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size: _ShapeLike = ...,
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dtype: _DTypeLikeFloat64 = ...,
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out: NDArray[float64] | None = ...,
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) -> NDArray[float64]: ...
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@overload
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def permutation(self, x: int, axis: int = ...) -> NDArray[int64]: ...
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@overload
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def permutation(self, x: ArrayLike, axis: int = ...) -> NDArray[Any]: ...
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@overload
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def standard_exponential( # type: ignore[misc]
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self,
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size: None = ...,
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dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
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method: Literal["zig", "inv"] = ...,
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out: None = ...,
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) -> float: ...
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@overload
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def standard_exponential(
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self,
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size: _ShapeLike = ...,
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) -> NDArray[float64]: ...
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@overload
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def standard_exponential(
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self,
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*,
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out: NDArray[float64] = ...,
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) -> NDArray[float64]: ...
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@overload
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def standard_exponential(
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self,
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size: _ShapeLike = ...,
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*,
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method: Literal["zig", "inv"] = ...,
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out: NDArray[float64] | None = ...,
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) -> NDArray[float64]: ...
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@overload
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def standard_exponential(
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self,
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size: _ShapeLike = ...,
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dtype: _DTypeLikeFloat32 = ...,
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method: Literal["zig", "inv"] = ...,
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out: NDArray[float32] | None = ...,
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) -> NDArray[float32]: ...
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@overload
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def standard_exponential(
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self,
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size: _ShapeLike = ...,
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dtype: _DTypeLikeFloat64 = ...,
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method: Literal["zig", "inv"] = ...,
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out: NDArray[float64] | None = ...,
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) -> NDArray[float64]: ...
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@overload
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def random( # type: ignore[misc]
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self,
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size: None = ...,
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dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
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out: None = ...,
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) -> float: ...
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@overload
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def random(
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self,
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*,
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out: NDArray[float64] = ...,
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) -> NDArray[float64]: ...
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@overload
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def random(
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self,
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size: _ShapeLike = ...,
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*,
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out: NDArray[float64] | None = ...,
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) -> NDArray[float64]: ...
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@overload
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def random(
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self,
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size: _ShapeLike = ...,
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dtype: _DTypeLikeFloat32 = ...,
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out: NDArray[float32] | None = ...,
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) -> NDArray[float32]: ...
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@overload
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def random(
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self,
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size: _ShapeLike = ...,
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dtype: _DTypeLikeFloat64 = ...,
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out: NDArray[float64] | None = ...,
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) -> NDArray[float64]: ...
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@overload
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def beta(
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self,
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a: _FloatLike_co,
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b: _FloatLike_co,
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size: None = ...,
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) -> float: ... # type: ignore[misc]
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@overload
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def beta(
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self,
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a: _ArrayLikeFloat_co,
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b: _ArrayLikeFloat_co,
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size: _ShapeLike | None = ...
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) -> NDArray[float64]: ...
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@overload
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def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc]
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@overload
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def exponential(self, scale: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...) -> NDArray[float64]: ...
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#
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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dtype: _DTypeLike[np.int64] | _Int64Codes = ...,
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endpoint: bool = False,
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) -> np.int64: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: type[bool],
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endpoint: bool = False,
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) -> bool: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: type[int],
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endpoint: bool = False,
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) -> int: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _DTypeLike[np.bool] | _BoolCodes,
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endpoint: bool = False,
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) -> np.bool: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _DTypeLike[_IntegerT],
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endpoint: bool = False,
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) -> _IntegerT: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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dtype: _DTypeLike[np.int64] | _Int64Codes = ...,
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endpoint: bool = False,
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) -> NDArray[np.int64]: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _DTypeLikeBool,
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endpoint: bool = False,
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) -> NDArray[np.bool]: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _DTypeLike[_IntegerT],
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endpoint: bool = False,
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) -> NDArray[_IntegerT]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _Int8Codes,
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endpoint: bool = False,
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) -> np.int8: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _Int8Codes,
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endpoint: bool = False,
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) -> NDArray[np.int8]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _UInt8Codes,
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endpoint: bool = False,
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) -> np.uint8: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _UInt8Codes,
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endpoint: bool = False,
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) -> NDArray[np.uint8]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _Int16Codes,
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endpoint: bool = False,
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) -> np.int16: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _Int16Codes,
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endpoint: bool = False,
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) -> NDArray[np.int16]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _UInt16Codes,
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endpoint: bool = False,
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) -> np.uint16: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _UInt16Codes,
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endpoint: bool = False,
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) -> NDArray[np.uint16]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _Int32Codes,
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endpoint: bool = False,
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) -> np.int32: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _Int32Codes,
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endpoint: bool = False,
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) -> NDArray[np.int32]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _UInt32Codes,
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endpoint: bool = False,
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) -> np.uint32: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _UInt32Codes,
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endpoint: bool = False,
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) -> NDArray[np.uint32]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _UInt64Codes,
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endpoint: bool = False,
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) -> np.uint64: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _UInt64Codes,
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endpoint: bool = False,
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) -> NDArray[np.uint64]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _IntPCodes,
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endpoint: bool = False,
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) -> np.intp: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _IntPCodes,
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endpoint: bool = False,
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) -> NDArray[np.intp]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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*,
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dtype: _UIntPCodes,
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endpoint: bool = False,
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) -> np.uintp: ...
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@overload
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def integers(
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self,
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low: _ArrayLikeInt_co,
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high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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*,
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dtype: _UIntPCodes,
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endpoint: bool = False,
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) -> NDArray[np.uintp]: ...
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@overload
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def integers(
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self,
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low: int,
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high: int | None = None,
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size: None = None,
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dtype: DTypeLike = ...,
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|
endpoint: bool = False,
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) -> Any: ...
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@overload
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|
def integers(
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self,
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low: _ArrayLikeInt_co,
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|
high: _ArrayLikeInt_co | None = None,
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size: _ShapeLike | None = None,
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dtype: DTypeLike = ...,
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endpoint: bool = False,
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) -> NDArray[Any]: ...
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# TODO: Use a TypeVar _T here to get away from Any output?
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# Should be int->NDArray[int64], ArrayLike[_T] -> _T | NDArray[Any]
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@overload
|
|
def choice(
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self,
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a: int,
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size: None = ...,
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replace: bool = ...,
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p: _ArrayLikeFloat_co | None = ...,
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axis: int = ...,
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|
shuffle: bool = ...,
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) -> int: ...
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@overload
|
|
def choice(
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self,
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a: int,
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|
size: _ShapeLike = ...,
|
|
replace: bool = ...,
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|
p: _ArrayLikeFloat_co | None = ...,
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axis: int = ...,
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|
shuffle: bool = ...,
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) -> NDArray[int64]: ...
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@overload
|
|
def choice(
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self,
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|
a: ArrayLike,
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|
size: None = ...,
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|
replace: bool = ...,
|
|
p: _ArrayLikeFloat_co | None = ...,
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axis: int = ...,
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|
shuffle: bool = ...,
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) -> Any: ...
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@overload
|
|
def choice(
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|
self,
|
|
a: ArrayLike,
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|
size: _ShapeLike = ...,
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|
replace: bool = ...,
|
|
p: _ArrayLikeFloat_co | None = ...,
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axis: int = ...,
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shuffle: bool = ...,
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) -> NDArray[Any]: ...
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@overload
|
|
def uniform(
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self,
|
|
low: _FloatLike_co = ...,
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|
high: _FloatLike_co = ...,
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|
size: None = ...,
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|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def uniform(
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self,
|
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low: _ArrayLikeFloat_co = ...,
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high: _ArrayLikeFloat_co = ...,
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size: _ShapeLike | None = ...,
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) -> NDArray[float64]: ...
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@overload
|
|
def normal(
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self,
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loc: _FloatLike_co = ...,
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scale: _FloatLike_co = ...,
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size: None = ...,
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) -> float: ... # type: ignore[misc]
|
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@overload
|
|
def normal(
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self,
|
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loc: _ArrayLikeFloat_co = ...,
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scale: _ArrayLikeFloat_co = ...,
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size: _ShapeLike | None = ...,
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) -> NDArray[float64]: ...
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@overload
|
|
def standard_gamma( # type: ignore[misc]
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self,
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shape: _FloatLike_co,
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|
size: None = ...,
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dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
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out: None = ...,
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) -> float: ...
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@overload
|
|
def standard_gamma(
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self,
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shape: _ArrayLikeFloat_co,
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|
size: _ShapeLike | None = ...,
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) -> NDArray[float64]: ...
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@overload
|
|
def standard_gamma(
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self,
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shape: _ArrayLikeFloat_co,
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|
*,
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out: NDArray[float64] = ...,
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) -> NDArray[float64]: ...
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@overload
|
|
def standard_gamma(
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self,
|
|
shape: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...,
|
|
dtype: _DTypeLikeFloat32 = ...,
|
|
out: NDArray[float32] | None = ...,
|
|
) -> NDArray[float32]: ...
|
|
@overload
|
|
def standard_gamma(
|
|
self,
|
|
shape: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...,
|
|
dtype: _DTypeLikeFloat64 = ...,
|
|
out: NDArray[float64] | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def gamma(
|
|
self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def gamma(
|
|
self,
|
|
shape: _ArrayLikeFloat_co,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def f(
|
|
self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def f(
|
|
self,
|
|
dfnum: _ArrayLikeFloat_co,
|
|
dfden: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def noncentral_f(
|
|
self,
|
|
dfnum: _FloatLike_co,
|
|
dfden: _FloatLike_co,
|
|
nonc: _FloatLike_co, size: None = ...
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def noncentral_f(
|
|
self,
|
|
dfnum: _ArrayLikeFloat_co,
|
|
dfden: _ArrayLikeFloat_co,
|
|
nonc: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def chisquare(
|
|
self, df: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def noncentral_chisquare(
|
|
self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def noncentral_chisquare(
|
|
self,
|
|
df: _ArrayLikeFloat_co,
|
|
nonc: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def standard_t(
|
|
self, df: _ArrayLikeFloat_co, size: None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def standard_t(
|
|
self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def vonmises(
|
|
self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def vonmises(
|
|
self,
|
|
mu: _ArrayLikeFloat_co,
|
|
kappa: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def pareto(
|
|
self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def weibull(
|
|
self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def power(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def power(
|
|
self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def standard_cauchy(self, size: _ShapeLike = ...) -> NDArray[float64]: ...
|
|
@overload
|
|
def laplace(
|
|
self,
|
|
loc: _FloatLike_co = ...,
|
|
scale: _FloatLike_co = ...,
|
|
size: None = ...,
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def laplace(
|
|
self,
|
|
loc: _ArrayLikeFloat_co = ...,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def gumbel(
|
|
self,
|
|
loc: _FloatLike_co = ...,
|
|
scale: _FloatLike_co = ...,
|
|
size: None = ...,
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def gumbel(
|
|
self,
|
|
loc: _ArrayLikeFloat_co = ...,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def logistic(
|
|
self,
|
|
loc: _FloatLike_co = ...,
|
|
scale: _FloatLike_co = ...,
|
|
size: None = ...,
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def logistic(
|
|
self,
|
|
loc: _ArrayLikeFloat_co = ...,
|
|
scale: _ArrayLikeFloat_co = ...,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def lognormal(
|
|
self,
|
|
mean: _FloatLike_co = ...,
|
|
sigma: _FloatLike_co = ...,
|
|
size: None = ...,
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def lognormal(
|
|
self,
|
|
mean: _ArrayLikeFloat_co = ...,
|
|
sigma: _ArrayLikeFloat_co = ...,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def rayleigh(
|
|
self, scale: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def wald(
|
|
self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def wald(
|
|
self,
|
|
mean: _ArrayLikeFloat_co,
|
|
scale: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def triangular(
|
|
self,
|
|
left: _FloatLike_co,
|
|
mode: _FloatLike_co,
|
|
right: _FloatLike_co,
|
|
size: None = ...,
|
|
) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def triangular(
|
|
self,
|
|
left: _ArrayLikeFloat_co,
|
|
mode: _ArrayLikeFloat_co,
|
|
right: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[float64]: ...
|
|
@overload
|
|
def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def binomial(
|
|
self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[int64]: ...
|
|
@overload
|
|
def negative_binomial(
|
|
self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...
|
|
) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def negative_binomial(
|
|
self,
|
|
n: _ArrayLikeFloat_co,
|
|
p: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...
|
|
) -> NDArray[int64]: ...
|
|
@overload
|
|
def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def poisson(
|
|
self, lam: _ArrayLikeFloat_co = ..., size: _ShapeLike | None = ...
|
|
) -> NDArray[int64]: ...
|
|
@overload
|
|
def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def zipf(
|
|
self, a: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[int64]: ...
|
|
@overload
|
|
def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def geometric(
|
|
self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[int64]: ...
|
|
@overload
|
|
def hypergeometric(
|
|
self, ngood: int, nbad: int, nsample: int, size: None = ...
|
|
) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def hypergeometric(
|
|
self,
|
|
ngood: _ArrayLikeInt_co,
|
|
nbad: _ArrayLikeInt_co,
|
|
nsample: _ArrayLikeInt_co,
|
|
size: _ShapeLike | None = ...,
|
|
) -> NDArray[int64]: ...
|
|
@overload
|
|
def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def logseries(
|
|
self, p: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[int64]: ...
|
|
def multivariate_normal(
|
|
self,
|
|
mean: _ArrayLikeFloat_co,
|
|
cov: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...,
|
|
check_valid: Literal["warn", "raise", "ignore"] = ...,
|
|
tol: float = ...,
|
|
*,
|
|
method: Literal["svd", "eigh", "cholesky"] = ...,
|
|
) -> NDArray[float64]: ...
|
|
def multinomial(
|
|
self, n: _ArrayLikeInt_co,
|
|
pvals: _ArrayLikeFloat_co,
|
|
size: _ShapeLike | None = ...
|
|
) -> NDArray[int64]: ...
|
|
def multivariate_hypergeometric(
|
|
self,
|
|
colors: _ArrayLikeInt_co,
|
|
nsample: int,
|
|
size: _ShapeLike | None = ...,
|
|
method: Literal["marginals", "count"] = ...,
|
|
) -> NDArray[int64]: ...
|
|
def dirichlet(
|
|
self, alpha: _ArrayLikeFloat_co, size: _ShapeLike | None = ...
|
|
) -> NDArray[float64]: ...
|
|
def permuted(
|
|
self, x: ArrayLike, *, axis: int | None = ..., out: NDArray[Any] | None = ...
|
|
) -> NDArray[Any]: ...
|
|
def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ...
|
|
|
|
def default_rng(
|
|
seed: _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator | RandomState | None = ...
|
|
) -> Generator: ...
|