| from collections.abc import Callable |
| from typing import Any, Union, overload, TypeVar, Literal |
| |
| from numpy import ( |
| bool_, |
| dtype, |
| float32, |
| float64, |
| int8, |
| int16, |
| int32, |
| int64, |
| int_, |
| ndarray, |
| uint, |
| uint8, |
| uint16, |
| uint32, |
| uint64, |
| ) |
| from numpy.random import BitGenerator, SeedSequence |
| from numpy._typing import ( |
| ArrayLike, |
| _ArrayLikeFloat_co, |
| _ArrayLikeInt_co, |
| _DoubleCodes, |
| _DTypeLikeBool, |
| _DTypeLikeInt, |
| _DTypeLikeUInt, |
| _Float32Codes, |
| _Float64Codes, |
| _FloatLike_co, |
| _Int8Codes, |
| _Int16Codes, |
| _Int32Codes, |
| _Int64Codes, |
| _IntCodes, |
| _ShapeLike, |
| _SingleCodes, |
| _SupportsDType, |
| _UInt8Codes, |
| _UInt16Codes, |
| _UInt32Codes, |
| _UInt64Codes, |
| _UIntCodes, |
| ) |
| |
| _ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any]) |
| |
| _DTypeLikeFloat32 = Union[ |
| dtype[float32], |
| _SupportsDType[dtype[float32]], |
| type[float32], |
| _Float32Codes, |
| _SingleCodes, |
| ] |
| |
| _DTypeLikeFloat64 = Union[ |
| dtype[float64], |
| _SupportsDType[dtype[float64]], |
| type[float], |
| type[float64], |
| _Float64Codes, |
| _DoubleCodes, |
| ] |
| |
| class Generator: |
| def __init__(self, bit_generator: BitGenerator) -> None: ... |
| def __repr__(self) -> str: ... |
| def __str__(self) -> str: ... |
| def __getstate__(self) -> dict[str, Any]: ... |
| def __setstate__(self, state: dict[str, Any]) -> None: ... |
| def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]: ... |
| @property |
| def bit_generator(self) -> BitGenerator: ... |
| def spawn(self, n_children: int) -> list[Generator]: ... |
| def bytes(self, length: int) -> bytes: ... |
| @overload |
| def standard_normal( # type: ignore[misc] |
| self, |
| size: None = ..., |
| dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., |
| out: None = ..., |
| ) -> float: ... |
| @overload |
| def standard_normal( # type: ignore[misc] |
| self, |
| size: _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_normal( # type: ignore[misc] |
| self, |
| *, |
| out: ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_normal( # type: ignore[misc] |
| self, |
| size: _ShapeLike = ..., |
| dtype: _DTypeLikeFloat32 = ..., |
| out: None | ndarray[Any, dtype[float32]] = ..., |
| ) -> ndarray[Any, dtype[float32]]: ... |
| @overload |
| def standard_normal( # type: ignore[misc] |
| self, |
| size: _ShapeLike = ..., |
| dtype: _DTypeLikeFloat64 = ..., |
| out: None | ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]: ... |
| @overload |
| def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]: ... |
| @overload |
| def standard_exponential( # type: ignore[misc] |
| self, |
| size: None = ..., |
| dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., |
| method: Literal["zig", "inv"] = ..., |
| out: None = ..., |
| ) -> float: ... |
| @overload |
| def standard_exponential( |
| self, |
| size: _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_exponential( |
| self, |
| *, |
| out: ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_exponential( |
| self, |
| size: _ShapeLike = ..., |
| *, |
| method: Literal["zig", "inv"] = ..., |
| out: None | ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_exponential( |
| self, |
| size: _ShapeLike = ..., |
| dtype: _DTypeLikeFloat32 = ..., |
| method: Literal["zig", "inv"] = ..., |
| out: None | ndarray[Any, dtype[float32]] = ..., |
| ) -> ndarray[Any, dtype[float32]]: ... |
| @overload |
| def standard_exponential( |
| self, |
| size: _ShapeLike = ..., |
| dtype: _DTypeLikeFloat64 = ..., |
| method: Literal["zig", "inv"] = ..., |
| out: None | ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def random( # type: ignore[misc] |
| self, |
| size: None = ..., |
| dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., |
| out: None = ..., |
| ) -> float: ... |
| @overload |
| def random( |
| self, |
| *, |
| out: ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def random( |
| self, |
| size: _ShapeLike = ..., |
| *, |
| out: None | ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def random( |
| self, |
| size: _ShapeLike = ..., |
| dtype: _DTypeLikeFloat32 = ..., |
| out: None | ndarray[Any, dtype[float32]] = ..., |
| ) -> ndarray[Any, dtype[float32]]: ... |
| @overload |
| def random( |
| self, |
| size: _ShapeLike = ..., |
| dtype: _DTypeLikeFloat64 = ..., |
| out: None | ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def beta( |
| self, |
| a: _FloatLike_co, |
| b: _FloatLike_co, |
| size: None = ..., |
| ) -> float: ... # type: ignore[misc] |
| @overload |
| def beta( |
| self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ... # type: ignore[misc] |
| @overload |
| def exponential( |
| self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ... |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: int, |
| high: None | int = ..., |
| ) -> int: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: int, |
| high: None | int = ..., |
| size: None = ..., |
| dtype: _DTypeLikeBool = ..., |
| endpoint: bool = ..., |
| ) -> bool: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: int, |
| high: None | int = ..., |
| size: None = ..., |
| dtype: _DTypeLikeInt | _DTypeLikeUInt = ..., |
| endpoint: bool = ..., |
| ) -> int: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[int64]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: _DTypeLikeBool = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[bool_]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[int8]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[int16]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[int32]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[int64]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[uint8]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[uint16]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[uint32]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[uint64]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[int_]]: ... |
| @overload |
| def integers( # type: ignore[misc] |
| self, |
| low: _ArrayLikeInt_co, |
| high: None | _ArrayLikeInt_co = ..., |
| size: None | _ShapeLike = ..., |
| dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ..., |
| endpoint: bool = ..., |
| ) -> ndarray[Any, dtype[uint]]: ... |
| # 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] |
| @overload |
| def choice( |
| self, |
| a: int, |
| size: None = ..., |
| replace: bool = ..., |
| p: None | _ArrayLikeFloat_co = ..., |
| axis: int = ..., |
| shuffle: bool = ..., |
| ) -> int: ... |
| @overload |
| def choice( |
| self, |
| a: int, |
| size: _ShapeLike = ..., |
| replace: bool = ..., |
| p: None | _ArrayLikeFloat_co = ..., |
| axis: int = ..., |
| shuffle: bool = ..., |
| ) -> ndarray[Any, dtype[int64]]: ... |
| @overload |
| def choice( |
| self, |
| a: ArrayLike, |
| size: None = ..., |
| replace: bool = ..., |
| p: None | _ArrayLikeFloat_co = ..., |
| axis: int = ..., |
| shuffle: bool = ..., |
| ) -> Any: ... |
| @overload |
| def choice( |
| self, |
| a: ArrayLike, |
| size: _ShapeLike = ..., |
| replace: bool = ..., |
| p: None | _ArrayLikeFloat_co = ..., |
| axis: int = ..., |
| shuffle: bool = ..., |
| ) -> ndarray[Any, Any]: ... |
| @overload |
| def uniform( |
| self, |
| low: _FloatLike_co = ..., |
| high: _FloatLike_co = ..., |
| size: None = ..., |
| ) -> float: ... # type: ignore[misc] |
| @overload |
| def uniform( |
| self, |
| low: _ArrayLikeFloat_co = ..., |
| high: _ArrayLikeFloat_co = ..., |
| size: None | _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def normal( |
| self, |
| loc: _FloatLike_co = ..., |
| scale: _FloatLike_co = ..., |
| size: None = ..., |
| ) -> float: ... # type: ignore[misc] |
| @overload |
| def normal( |
| self, |
| loc: _ArrayLikeFloat_co = ..., |
| scale: _ArrayLikeFloat_co = ..., |
| size: None | _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_gamma( # type: ignore[misc] |
| self, |
| shape: _FloatLike_co, |
| size: None = ..., |
| dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ..., |
| out: None = ..., |
| ) -> float: ... |
| @overload |
| def standard_gamma( |
| self, |
| shape: _ArrayLikeFloat_co, |
| size: None | _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_gamma( |
| self, |
| shape: _ArrayLikeFloat_co, |
| *, |
| out: ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def standard_gamma( |
| self, |
| shape: _ArrayLikeFloat_co, |
| size: None | _ShapeLike = ..., |
| dtype: _DTypeLikeFloat32 = ..., |
| out: None | ndarray[Any, dtype[float32]] = ..., |
| ) -> ndarray[Any, dtype[float32]]: ... |
| @overload |
| def standard_gamma( |
| self, |
| shape: _ArrayLikeFloat_co, |
| size: None | _ShapeLike = ..., |
| dtype: _DTypeLikeFloat64 = ..., |
| out: None | ndarray[Any, dtype[float64]] = ..., |
| ) -> ndarray[Any, dtype[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: None | _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[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: None | _ShapeLike = ... |
| ) -> ndarray[Any, dtype[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: None | _ShapeLike = ..., |
| ) -> ndarray[Any, dtype[float64]]: ... |
| @overload |
| def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ... # type: ignore[misc] |
| @overload |
| def chisquare( |
| self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ... |
| ) -> ndarray[Any, dtype[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: None | _ShapeLike = ... |
| ) -> ndarray[Any, dtype[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[Any, dtype[float64]]: ... |
| @overload |
| def standard_t( |
| self, df: _ArrayLikeFloat_co, size: _ShapeLike = ... |
| ) -> ndarray[Any, dtype[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: 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: ... |
