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| from collections.abc import Iterable
| from typing import (
| Literal as L,
| overload,
| TypeVar,
| Any,
| SupportsIndex,
| SupportsInt,
| )
|
| from numpy import (
| generic,
| floating,
| complexfloating,
| int32,
| float64,
| complex128,
| )
|
| from numpy.linalg import LinAlgError as LinAlgError
|
| from numpy._typing import (
| NDArray,
| ArrayLike,
| _ArrayLikeInt_co,
| _ArrayLikeFloat_co,
| _ArrayLikeComplex_co,
| _ArrayLikeTD64_co,
| _ArrayLikeObject_co,
| )
|
| _T = TypeVar("_T")
| _ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
|
| _2Tuple = tuple[_T, _T]
| _ModeKind = L["reduced", "complete", "r", "raw"]
|
| __all__: list[str]
|
| @overload
| def tensorsolve(
| a: _ArrayLikeInt_co,
| b: _ArrayLikeInt_co,
| axes: None | Iterable[int] =...,
| ) -> NDArray[float64]: ...
| @overload
| def tensorsolve(
| a: _ArrayLikeFloat_co,
| b: _ArrayLikeFloat_co,
| axes: None | Iterable[int] =...,
| ) -> NDArray[floating[Any]]: ...
| @overload
| def tensorsolve(
| a: _ArrayLikeComplex_co,
| b: _ArrayLikeComplex_co,
| axes: None | Iterable[int] =...,
| ) -> NDArray[complexfloating[Any, Any]]: ...
|
| @overload
| def solve(
| a: _ArrayLikeInt_co,
| b: _ArrayLikeInt_co,
| ) -> NDArray[float64]: ...
| @overload
| def solve(
| a: _ArrayLikeFloat_co,
| b: _ArrayLikeFloat_co,
| ) -> NDArray[floating[Any]]: ...
| @overload
| def solve(
| a: _ArrayLikeComplex_co,
| b: _ArrayLikeComplex_co,
| ) -> NDArray[complexfloating[Any, Any]]: ...
|
| @overload
| def tensorinv(
| a: _ArrayLikeInt_co,
| ind: int = ...,
| ) -> NDArray[float64]: ...
| @overload
| def tensorinv(
| a: _ArrayLikeFloat_co,
| ind: int = ...,
| ) -> NDArray[floating[Any]]: ...
| @overload
| def tensorinv(
| a: _ArrayLikeComplex_co,
| ind: int = ...,
| ) -> NDArray[complexfloating[Any, Any]]: ...
|
| @overload
| def inv(a: _ArrayLikeInt_co) -> NDArray[float64]: ...
| @overload
| def inv(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
| @overload
| def inv(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
|
| # TODO: The supported input and output dtypes are dependent on the value of `n`.
| # For example: `n < 0` always casts integer types to float64
| def matrix_power(
| a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
| n: SupportsIndex,
| ) -> NDArray[Any]: ...
|
| @overload
| def cholesky(a: _ArrayLikeInt_co) -> NDArray[float64]: ...
| @overload
| def cholesky(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
| @overload
| def cholesky(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
|
| @overload
| def qr(a: _ArrayLikeInt_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[float64]]: ...
| @overload
| def qr(a: _ArrayLikeFloat_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[floating[Any]]]: ...
| @overload
| def qr(a: _ArrayLikeComplex_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
|
| @overload
| def eigvals(a: _ArrayLikeInt_co) -> NDArray[float64] | NDArray[complex128]: ...
| @overload
| def eigvals(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]] | NDArray[complexfloating[Any, Any]]: ...
| @overload
| def eigvals(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
|
| @overload
| def eigvalsh(a: _ArrayLikeInt_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[float64]: ...
| @overload
| def eigvalsh(a: _ArrayLikeComplex_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[floating[Any]]: ...
|
| @overload
| def eig(a: _ArrayLikeInt_co) -> _2Tuple[NDArray[float64]] | _2Tuple[NDArray[complex128]]: ...
| @overload
| def eig(a: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]] | _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
| @overload
| def eig(a: _ArrayLikeComplex_co) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
|
| @overload
| def eigh(
| a: _ArrayLikeInt_co,
| UPLO: L["L", "U", "l", "u"] = ...,
| ) -> tuple[NDArray[float64], NDArray[float64]]: ...
| @overload
| def eigh(
| a: _ArrayLikeFloat_co,
| UPLO: L["L", "U", "l", "u"] = ...,
| ) -> tuple[NDArray[floating[Any]], NDArray[floating[Any]]]: ...
| @overload
| def eigh(
| a: _ArrayLikeComplex_co,
| UPLO: L["L", "U", "l", "u"] = ...,
| ) -> tuple[NDArray[floating[Any]], NDArray[complexfloating[Any, Any]]]: ...
|
| @overload
| def svd(
| a: _ArrayLikeInt_co,
| full_matrices: bool = ...,
| compute_uv: L[True] = ...,
| hermitian: bool = ...,
| ) -> tuple[
| NDArray[float64],
| NDArray[float64],
| NDArray[float64],
| ]: ...
| @overload
| def svd(
| a: _ArrayLikeFloat_co,
| full_matrices: bool = ...,
| compute_uv: L[True] = ...,
| hermitian: bool = ...,
| ) -> tuple[
| NDArray[floating[Any]],
| NDArray[floating[Any]],
| NDArray[floating[Any]],
| ]: ...
| @overload
| def svd(
| a: _ArrayLikeComplex_co,
| full_matrices: bool = ...,
| compute_uv: L[True] = ...,
| hermitian: bool = ...,
| ) -> tuple[
| NDArray[complexfloating[Any, Any]],
| NDArray[floating[Any]],
| NDArray[complexfloating[Any, Any]],
| ]: ...
| @overload
| def svd(
| a: _ArrayLikeInt_co,
| full_matrices: bool = ...,
| compute_uv: L[False] = ...,
| hermitian: bool = ...,
| ) -> NDArray[float64]: ...
| @overload
| def svd(
| a: _ArrayLikeComplex_co,
| full_matrices: bool = ...,
| compute_uv: L[False] = ...,
| hermitian: bool = ...,
| ) -> NDArray[floating[Any]]: ...
|
| # TODO: Returns a scalar for 2D arrays and
| # a `(x.ndim - 2)`` dimensionl array otherwise
| def cond(x: _ArrayLikeComplex_co, p: None | float | L["fro", "nuc"] = ...) -> Any: ...
|
| # TODO: Returns `int` for <2D arrays and `intp` otherwise
| def matrix_rank(
| A: _ArrayLikeComplex_co,
| tol: None | _ArrayLikeFloat_co = ...,
| hermitian: bool = ...,
| ) -> Any: ...
|
| @overload
| def pinv(
| a: _ArrayLikeInt_co,
| rcond: _ArrayLikeFloat_co = ...,
| hermitian: bool = ...,
| ) -> NDArray[float64]: ...
| @overload
| def pinv(
| a: _ArrayLikeFloat_co,
| rcond: _ArrayLikeFloat_co = ...,
| hermitian: bool = ...,
| ) -> NDArray[floating[Any]]: ...
| @overload
| def pinv(
| a: _ArrayLikeComplex_co,
| rcond: _ArrayLikeFloat_co = ...,
| hermitian: bool = ...,
| ) -> NDArray[complexfloating[Any, Any]]: ...
|
| # TODO: Returns a 2-tuple of scalars for 2D arrays and
| # a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
| def slogdet(a: _ArrayLikeComplex_co) -> _2Tuple[Any]: ...
|
| # TODO: Returns a 2-tuple of scalars for 2D arrays and
| # a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
| def det(a: _ArrayLikeComplex_co) -> Any: ...
|
| @overload
| def lstsq(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, rcond: None | float = ...) -> tuple[
| NDArray[float64],
| NDArray[float64],
| int32,
| NDArray[float64],
| ]: ...
| @overload
| def lstsq(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, rcond: None | float = ...) -> tuple[
| NDArray[floating[Any]],
| NDArray[floating[Any]],
| int32,
| NDArray[floating[Any]],
| ]: ...
| @overload
| def lstsq(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, rcond: None | float = ...) -> tuple[
| NDArray[complexfloating[Any, Any]],
| NDArray[floating[Any]],
| int32,
| NDArray[floating[Any]],
| ]: ...
|
| @overload
| def norm(
| x: ArrayLike,
| ord: None | float | L["fro", "nuc"] = ...,
| axis: None = ...,
| keepdims: bool = ...,
| ) -> floating[Any]: ...
| @overload
| def norm(
| x: ArrayLike,
| ord: None | float | L["fro", "nuc"] = ...,
| axis: SupportsInt | SupportsIndex | tuple[int, ...] = ...,
| keepdims: bool = ...,
| ) -> Any: ...
|
| # TODO: Returns a scalar or array
| def multi_dot(
| arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co],
| *,
| out: None | NDArray[Any] = ...,
| ) -> Any: ...
|
|
