pairwise_distance¶

pairwise_distance(x: ndarray, y: ndarray | None = None, method: str | Callable[[ndarray, ndarray, Any], float] | None = None, symmetric: bool = True, **kwargs: Unpack[DistanceKwargs]) → ndarray[source]¶

Compute the pairwise distance matrix between two time series.

Parameters:

Xnp.ndarray

A collection of time series instances of shape (n_cases, n_timepoints): or (n_cases, n_channels, n_timepoints).

ynp.ndarray or None, default=None

A single series or a collection of time series of shape (m_timepoints,) or (m_cases, m_timepoints) or (m_cases, m_channels, m_timepoints)

methodstr or Callable

The distance to use. A list of valid distance can be found in the documentation for aeon.distances.get_distance_function or by calling the function aeon.distances.get_distance_function_names.

symmetricbool, default=True

If True and a function is provided as the “method” paramter, then it will compute a symmetric distance matrix where d(x, y) = d(y, x). Only the lower triangle is calculated, and the upper triangle is ignored. If False and a function is provided as the “method” parameter, then it will compute an asymmetric distance matrix, and the entire matrix (including both upper and lower triangles) is returned.

kwargsAny

Extra arguments for distance. Refer to each distance documentation for a list of possible arguments.

Returns:

np.ndarray (n_cases, n_cases): pairwise matrix between the instances of X.

Raises:

ValueError: If X is not 2D or 3D array when only passing X. If X and y are not 1D, 2D or 3D arrays when passing both X and y. If distance is not a valid string or callable.

Examples

>>> import numpy as np
>>> from aeon.distances import pairwise_distance
>>> # Distance between each time series in a collection of time series
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> pairwise_distance(X, method='dtw')
array([[  0.,  26., 108.],
       [ 26.,   0.,  26.],
       [108.,  26.,   0.]])

>>> # Distance between two collections of time series
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> y = np.array([[[11, 12, 13]],[[14, 15, 16]], [[17, 18, 19]]])
>>> pairwise_distance(X, y, method='dtw')
array([[300., 507., 768.],
       [147., 300., 507.],
       [ 48., 147., 300.]])

>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> y_univariate = np.array([11, 12, 13])
>>> pairwise_distance(X, y_univariate, method='dtw')
array([[300.],
       [147.],
       [ 48.]])