682 lines
22 KiB
Python
682 lines
22 KiB
Python
from collections.abc import Callable
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from typing import Any, Union, overload, TypeVar, Literal
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from numpy import (
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bool_,
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dtype,
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float32,
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float64,
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int8,
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int16,
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int32,
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int64,
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int_,
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ndarray,
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uint,
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uint8,
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uint16,
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uint32,
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uint64,
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)
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from numpy.random import BitGenerator, SeedSequence
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from numpy._typing import (
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ArrayLike,
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_ArrayLikeFloat_co,
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_ArrayLikeInt_co,
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_DoubleCodes,
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_DTypeLikeBool,
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_DTypeLikeInt,
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_DTypeLikeUInt,
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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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_IntCodes,
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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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_UIntCodes,
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)
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_ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any])
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_DTypeLikeFloat32 = Union[
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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 = Union[
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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) -> dict[str, Any]: ...
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def __setstate__(self, state: dict[str, Any]) -> None: ...
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def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]: ...
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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[Any, dtype[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[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float32]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]: ...
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@overload
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def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, 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[Any, dtype[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[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float32]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[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[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float32]] = ...,
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) -> ndarray[Any, dtype[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: None | ndarray[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[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, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
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) -> ndarray[Any, dtype[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(
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self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: int,
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high: None | int = ...,
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) -> int: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: int,
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high: None | int = ...,
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size: None = ...,
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dtype: _DTypeLikeBool = ...,
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endpoint: bool = ...,
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) -> bool: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: int,
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high: None | int = ...,
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size: None = ...,
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dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
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endpoint: bool = ...,
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) -> int: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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) -> ndarray[Any, dtype[int64]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: _DTypeLikeBool = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[bool_]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[int8]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[int16]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[int32]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[int64]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[uint8]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[uint16]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[uint32]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[uint64]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[int_]]: ...
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@overload
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def integers( # type: ignore[misc]
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self,
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low: _ArrayLikeInt_co,
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high: None | _ArrayLikeInt_co = ...,
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size: None | _ShapeLike = ...,
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dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
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endpoint: bool = ...,
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) -> ndarray[Any, dtype[uint]]: ...
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# TODO: Use a TypeVar _T here to get away from Any output? Should be int->ndarray[Any,dtype[int64]], ArrayLike[_T] -> _T | ndarray[Any,Any]
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@overload
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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: None | _ArrayLikeFloat_co = ...,
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axis: int = ...,
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shuffle: bool = ...,
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) -> int: ...
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@overload
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def choice(
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self,
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a: int,
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size: _ShapeLike = ...,
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replace: bool = ...,
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p: None | _ArrayLikeFloat_co = ...,
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axis: int = ...,
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shuffle: bool = ...,
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) -> ndarray[Any, dtype[int64]]: ...
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@overload
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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 = ...,
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p: None | _ArrayLikeFloat_co = ...,
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axis: int = ...,
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shuffle: bool = ...,
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) -> Any: ...
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@overload
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def choice(
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self,
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a: ArrayLike,
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size: _ShapeLike = ...,
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replace: bool = ...,
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p: None | _ArrayLikeFloat_co = ...,
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axis: int = ...,
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shuffle: bool = ...,
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) -> ndarray[Any, Any]: ...
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@overload
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def uniform(
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self,
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low: _FloatLike_co = ...,
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high: _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 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: None | _ShapeLike = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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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
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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: None | _ShapeLike = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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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
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def standard_gamma(
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self,
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shape: _ArrayLikeFloat_co,
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size: None | _ShapeLike = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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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[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def standard_gamma(
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self,
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shape: _ArrayLikeFloat_co,
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size: None | _ShapeLike = ...,
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dtype: _DTypeLikeFloat32 = ...,
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out: None | ndarray[Any, dtype[float32]] = ...,
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) -> ndarray[Any, dtype[float32]]: ...
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@overload
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def standard_gamma(
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self,
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shape: _ArrayLikeFloat_co,
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size: None | _ShapeLike = ...,
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dtype: _DTypeLikeFloat64 = ...,
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out: None | ndarray[Any, dtype[float64]] = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc]
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@overload
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def gamma(
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self,
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shape: _ArrayLikeFloat_co,
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scale: _ArrayLikeFloat_co = ...,
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size: None | _ShapeLike = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
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@overload
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|
def f(
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self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
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) -> ndarray[Any, dtype[float64]]: ...
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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: ... # type: ignore[misc]
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@overload
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def noncentral_f(
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self,
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dfnum: _ArrayLikeFloat_co,
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dfden: _ArrayLikeFloat_co,
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nonc: _ArrayLikeFloat_co,
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size: None | _ShapeLike = ...,
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
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@overload
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|
def chisquare(
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self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
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@overload
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def noncentral_chisquare(
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self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
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@overload
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def standard_t(
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self, df: _ArrayLikeFloat_co, size: None = ...
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def standard_t(
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self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
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) -> ndarray[Any, dtype[float64]]: ...
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@overload
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def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
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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]]: ...
|
|
@overload
|
|
def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def pareto(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def weibull(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def power(self, a: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def power(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def standard_cauchy(self, size: None = ...) -> float: ... # type: ignore[misc]
|
|
@overload
|
|
def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[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: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[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: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[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: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[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: None | _ShapeLike = ...,
|
|
) -> ndarray[Any, dtype[float64]]: ...
|
|
@overload
|
|
def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc]
|
|
@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: ... # type: ignore[misc]
|
|
@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: ... # type: ignore[misc]
|
|
@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: ... # type: ignore[misc]
|
|
@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: ... # type: ignore[misc]
|
|
@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: ... # type: ignore[misc]
|
|
@overload
|
|
def poisson(
|
|
self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int64]]: ...
|
|
@overload
|
|
def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@overload
|
|
def zipf(
|
|
self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
|
|
) -> ndarray[Any, dtype[int64]]: ...
|
|
@overload
|
|
def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ... # type: ignore[misc]
|
|
@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: ... # type: ignore[misc]
|
|
@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: ... # type: ignore[misc]
|
|
@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: ...
|