euclidean_pairwise_distance¶
- euclidean_pairwise_distance(X: ndarray | list[ndarray], y: ndarray | list[ndarray] | None = None) ndarray [source]¶
Compute the Euclidean pairwise distance between a set of time series.
- Parameters:
- Xnp.ndarray or List of np.ndarray
A collection of time series instances of shape
(n_cases, n_timepoints)
or(n_cases, n_channels, n_timepoints)
.- ynp.ndarray or List of np.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)
. If None, then the euclidean pairwise distance between the instances of X is calculated.
- Returns:
- np.ndarray (n_cases, n_cases)
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]) >>> euclidean_pairwise_distance(X, y_univariate) array([[17.32050808], [12.12435565], [ 6.92820323]])
>>> # Distance between each TS in a collection of unequal-length time series >>> X = [np.array([1, 2, 3]), np.array([4, 5, 6, 7]), np.array([8, 9, 10, 11, 12])] >>> euclidean_pairwise_distance(X) array([[ 0. , 5.19615242, 12.12435565], [ 5.19615242, 0. , 8. ], [12.12435565, 8. , 0. ]])