euclidean_pairwise_distance#
- euclidean_pairwise_distance(X: ndarray, y: ndarray = None) ndarray [source]#
Compute the Euclidean pairwise distance between a set of time series.
- Parameters:
- Xnp.ndarray
A collection of time series instances of shape
(n_instances, n_timepoints)
or(n_instances, 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_instances, m_timepoints)
or(m_instances, m_channels, m_timepoints)
. If None, then the Euclidean pairwise distance between the instances of X is calculated.
- Returns:
- np.ndarray (n_instances, n_instances)
euclidean 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 euclidean_pairwise_distance >>> X = np.array([[[1, 2, 3, 4]],[[4, 5, 6, 3]], [[7, 8, 9, 3]]]) >>> euclidean_pairwise_distance(X) array([[ 0. , 5.29150262, 10.44030651], [ 5.29150262, 0. , 5.19615242], [10.44030651, 5.19615242, 0. ]])
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) >>> y = np.array([[[11, 12, 13]],[[14, 15, 16]], [[17, 18, 19]]]) >>> euclidean_pairwise_distance(X, y) array([[17.32050808, 22.5166605 , 27.71281292], [12.12435565, 17.32050808, 22.5166605 ], [ 6.92820323, 12.12435565, 17.32050808]])
>>> 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]]) >>> euclidean_pairwise_distance(X, y_univariate) array([[17.32050808], [12.12435565], [ 6.92820323]])