squared_pairwise_distance#
- squared_pairwise_distance(X: ndarray, y: ndarray = None) ndarray [source]#
Compute the squared pairwise distance between a set of time series.
- Parameters:
- Xnp.ndarray, of shape (n_instances, n_channels, n_timepoints) or
(n_instances, n_timepoints) or (n_timepoints,)
A collection of time series instances.
- ynp.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.
- Returns:
- np.ndarray (n_instances, n_instances)
squared 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 squared_pairwise_distance >>> X = np.array([[[1, 2, 3, 4]],[[4, 5, 6, 3]], [[7, 8, 9, 3]]]) >>> squared_pairwise_distance(X) array([[ 0., 28., 109.], [ 28., 0., 27.], [109., 27., 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]]]) >>> squared_pairwise_distance(X, y) 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]]) >>> squared_pairwise_distance(X, y_univariate) array([[300.], [147.], [ 48.]])