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