lcss_pairwise_distance#
- lcss_pairwise_distance(X: ndarray, y: ndarray = None, window: float = None, epsilon: float = 1.0) ndarray [source]#
Compute the lcss pairwise 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.
- epsilon: float, defaults=1.
Matching threshold to determine if two subsequences are considered close enough to be considered ‘common’. The default is 1.
- Returns:
- np.ndarray (n_instances, n_instances)
lcss 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 lcss_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]]]) >>> lcss_pairwise_distance(X) array([[0. , 0.66666667, 1. ], [0.66666667, 0. , 0.66666667], [1. , 0.66666667, 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]]]) >>> lcss_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]]) >>> lcss_pairwise_distance(X, y_univariate) array([[1.], [1.], [1.]])