edr_pairwise_distance#

edr_pairwise_distance(X: ndarray, y: ndarray = None, window: float = None, epsilon: float = None) → ndarray[source]#

Compute the pairwise edr distance between a set of 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.
window: float, default=None: The window to use for the bounding matrix. If None, no bounding matrix is used.
epsilonfloat, defaults = None: Matching threshold to determine if two subsequences are considered close enough to be considered ‘common’. If not specified as per the original paper epsilon is set to a quarter of the maximum standard deviation.

Returns:

np.ndarray (n_instances, n_instances): edr 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.

Examples

>>> import numpy as np
>>> from aeon.distances import edr_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]]])
>>> edr_pairwise_distance(X)
array([[0., 1., 1.],
       [1., 0., 1.],
       [1., 1., 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]]])
>>> edr_pairwise_distance(X, y)
array([[1., 1., 1.],
       [1., 1., 1.],
       [1., 1., 1.]])

>>> 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]])
>>> edr_pairwise_distance(X, y_univariate)
array([[1.],
       [1.],
       [1.]])