wdtw_pairwise_distance#
- wdtw_pairwise_distance(X: ndarray, y: ndarray = None, window: float = None, g: float = 0.05) ndarray [source]#
Compute the wdtw 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.
- g: float, defaults=0.05
Constant that controls the level of penalisation for the points with larger phase difference. Default is 0.05.
- Returns:
- np.ndarray (n_instances, n_instances)
wdtw 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 wdtw_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]]]) >>> wdtw_pairwise_distance(X) array([[ 0. , 12.61266072, 51.97594869], [12.61266072, 0. , 12.61266072], [51.97594869, 12.61266072, 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]]]) >>> wdtw_pairwise_distance(X, y) array([[144.37763524, 243.99820355, 369.60674621], [ 70.74504127, 144.37763524, 243.99820355], [ 23.10042164, 70.74504127, 144.37763524]])
>>> 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]]) >>> wdtw_pairwise_distance(X, y_univariate) array([[144.37763524], [ 70.74504127], [ 23.10042164]])