pairwise_distance#

pairwise_distance(x: ndarray, y: Optional[ndarray] = None, metric: Optional[Union[str, Callable[[ndarray, ndarray, Any], float]]] = None, **kwargs: Any) → ndarray[source]#

Compute the pairwise distance matrix between two time series.

Parameters:

X: np.ndarray, of shape (n_instances, n_channels, n_timepoints) or: (n_instances, n_timepoints)

A collection of time series instances.
y: np.ndarray, of shape (m_instances, m_channels, m_timepoints) or: (m_instances, m_timepoints) or (m_timepoints,), default=None

A collection of time series instances.
metric: str or Callable: The distance metric to use. If a string is given, the value must be one of the following strings: ‘euclidean’, ‘squared’, ‘dtw’, ‘ddtw’, ‘wdtw’, ‘wddtw’, ‘lcss’, ‘edr’, ‘erp’, ‘msm’ If a callable is given, the value must be a function that accepts two numpy arrays and **kwargs returns a float.
kwargs: Any: Extra arguments for metric. Refer to each metric documentation for a list of possible arguments.

Returns:

np.ndarray (n_instances, n_instances): 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 metric 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, metric='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, metric='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],[14, 15, 16], [17, 18, 19]])
>>> pairwise_distance(X, y_univariate, metric='dtw')
array([[300.],
       [147.],
       [ 48.]])