You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
282 lines
7.3 KiB
282 lines
7.3 KiB
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: ...
|
|
|