217 lines
5.4 KiB
Python
217 lines
5.4 KiB
Python
|
import sys
|
||
|
|
from typing import (overload, Optional, Any, Union, Tuple, SupportsFloat,
|
||
|
|
Literal, Protocol, SupportsIndex)
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy.typing import ArrayLike, NDArray
|
||
|
|
|
||
|
|
# Anything that can be parsed by `np.float64.__init__` and is thus
|
||
|
|
# compatible with `ndarray.__setitem__` (for a float64 array)
|
||
|
|
_FloatValue = Union[None, str, bytes, SupportsFloat, SupportsIndex]
|
||
|
|
|
||
|
|
class _MetricCallback1(Protocol):
|
||
|
|
def __call__(
|
||
|
|
self, __XA: NDArray[Any], __XB: NDArray[Any]
|
||
|
|
) -> _FloatValue: ...
|
||
|
|
|
||
|
|
class _MetricCallback2(Protocol):
|
||
|
|
def __call__(
|
||
|
|
self, __XA: NDArray[Any], __XB: NDArray[Any], **kwargs: Any
|
||
|
|
) -> _FloatValue: ...
|
||
|
|
|
||
|
|
# TODO: Use a single protocol with a parameter specification variable
|
||
|
|
# once available (PEP 612)
|
||
|
|
_MetricCallback = Union[_MetricCallback1, _MetricCallback2]
|
||
|
|
|
||
|
|
_MetricKind = Literal[
|
||
|
|
'braycurtis',
|
||
|
|
'canberra',
|
||
|
|
'chebychev', 'chebyshev', 'cheby', 'cheb', 'ch',
|
||
|
|
'cityblock', 'cblock', 'cb', 'c',
|
||
|
|
'correlation', 'co',
|
||
|
|
'cosine', 'cos',
|
||
|
|
'dice',
|
||
|
|
'euclidean', 'euclid', 'eu', 'e',
|
||
|
|
'hamming', 'hamm', 'ha', 'h',
|
||
|
|
'minkowski', 'mi', 'm', 'pnorm',
|
||
|
|
'jaccard', 'jacc', 'ja', 'j',
|
||
|
|
'jensenshannon', 'js',
|
||
|
|
'kulsinski', 'kulczynski1',
|
||
|
|
'mahalanobis', 'mahal', 'mah',
|
||
|
|
'rogerstanimoto',
|
||
|
|
'russellrao',
|
||
|
|
'seuclidean', 'se', 's',
|
||
|
|
'sokalmichener',
|
||
|
|
'sokalsneath',
|
||
|
|
'sqeuclidean', 'sqe', 'sqeuclid',
|
||
|
|
'yule',
|
||
|
|
]
|
||
|
|
|
||
|
|
# Function annotations
|
||
|
|
|
||
|
|
def braycurtis(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def canberra(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
# TODO: Add `metric`-specific overloads
|
||
|
|
# Returns a float64 or float128 array, depending on the input dtype
|
||
|
|
@overload
|
||
|
|
def cdist(
|
||
|
|
XA: ArrayLike,
|
||
|
|
XB: ArrayLike,
|
||
|
|
metric: _MetricKind = ...,
|
||
|
|
*,
|
||
|
|
out: None | NDArray[np.floating[Any]] = ...,
|
||
|
|
p: float = ...,
|
||
|
|
w: Optional[ArrayLike] = ...,
|
||
|
|
V: Optional[ArrayLike] = ...,
|
||
|
|
VI: Optional[ArrayLike] = ...,
|
||
|
|
) -> NDArray[np.floating[Any]]: ...
|
||
|
|
@overload
|
||
|
|
def cdist(
|
||
|
|
XA: ArrayLike,
|
||
|
|
XB: ArrayLike,
|
||
|
|
metric: _MetricCallback,
|
||
|
|
*,
|
||
|
|
out: None | NDArray[np.floating[Any]] = ...,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> NDArray[np.floating[Any]]: ...
|
||
|
|
|
||
|
|
# TODO: Wait for dtype support; the return type is
|
||
|
|
# dependent on the input arrays dtype
|
||
|
|
def chebyshev(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> Any: ...
|
||
|
|
|
||
|
|
# TODO: Wait for dtype support; the return type is
|
||
|
|
# dependent on the input arrays dtype
|
||
|
|
def cityblock(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> Any: ...
|
||
|
|
|
||
|
|
def correlation(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ..., centered: bool = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def cosine(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def dice(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> float: ...
|
||
|
|
|
||
|
|
def directed_hausdorff(
|
||
|
|
u: ArrayLike, v: ArrayLike, seed: Optional[int] = ...
|
||
|
|
) -> Tuple[float, int, int]: ...
|
||
|
|
|
||
|
|
def euclidean(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> float: ...
|
||
|
|
|
||
|
|
def hamming(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def is_valid_dm(
|
||
|
|
D: ArrayLike,
|
||
|
|
tol: float = ...,
|
||
|
|
throw: bool = ...,
|
||
|
|
name: Optional[str] = ...,
|
||
|
|
warning: bool = ...,
|
||
|
|
) -> bool: ...
|
||
|
|
|
||
|
|
def is_valid_y(
|
||
|
|
y: ArrayLike,
|
||
|
|
warning: bool = ...,
|
||
|
|
throw: bool = ...,
|
||
|
|
name: Optional[str] = ...,
|
||
|
|
) -> bool: ...
|
||
|
|
|
||
|
|
def jaccard(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def jensenshannon(
|
||
|
|
p: ArrayLike, q: ArrayLike, base: Optional[float] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def kulsinski(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def kulczynski1(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def mahalanobis(
|
||
|
|
u: ArrayLike, v: ArrayLike, VI: ArrayLike
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def minkowski(
|
||
|
|
u: ArrayLike, v: ArrayLike, p: float = ..., w: Optional[ArrayLike] = ...
|
||
|
|
) -> float: ...
|
||
|
|
|
||
|
|
def num_obs_dm(d: ArrayLike) -> int: ...
|
||
|
|
|
||
|
|
def num_obs_y(Y: ArrayLike) -> int: ...
|
||
|
|
|
||
|
|
# TODO: Add `metric`-specific overloads
|
||
|
|
@overload
|
||
|
|
def pdist(
|
||
|
|
X: ArrayLike,
|
||
|
|
metric: _MetricKind = ...,
|
||
|
|
*,
|
||
|
|
out: None | NDArray[np.floating[Any]] = ...,
|
||
|
|
p: float = ...,
|
||
|
|
w: Optional[ArrayLike] = ...,
|
||
|
|
V: Optional[ArrayLike] = ...,
|
||
|
|
VI: Optional[ArrayLike] = ...,
|
||
|
|
) -> NDArray[np.floating[Any]]: ...
|
||
|
|
@overload
|
||
|
|
def pdist(
|
||
|
|
X: ArrayLike,
|
||
|
|
metric: _MetricCallback,
|
||
|
|
*,
|
||
|
|
out: None | NDArray[np.floating[Any]] = ...,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> NDArray[np.floating[Any]]: ...
|
||
|
|
|
||
|
|
def seuclidean(
|
||
|
|
u: ArrayLike, v: ArrayLike, V: ArrayLike
|
||
|
|
) -> float: ...
|
||
|
|
|
||
|
|
def sokalmichener(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> float: ...
|
||
|
|
|
||
|
|
def sokalsneath(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def sqeuclidean(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> np.float64: ...
|
||
|
|
|
||
|
|
def squareform(
|
||
|
|
X: ArrayLike,
|
||
|
|
force: Literal["no", "tomatrix", "tovector"] = ...,
|
||
|
|
checks: bool = ...,
|
||
|
|
) -> NDArray[Any]: ...
|
||
|
|
|
||
|
|
def rogerstanimoto(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> float: ...
|
||
|
|
|
||
|
|
def russellrao(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> float: ...
|
||
|
|
|
||
|
|
def yule(
|
||
|
|
u: ArrayLike, v: ArrayLike, w: Optional[ArrayLike] = ...
|
||
|
|
) -> float: ...
|