wdtw_pairwise_distance¶
- wdtw_pairwise_distance(X: ndarray | List[ndarray], y: ndarray | List[ndarray] | None = None, window: float | None = None, g: float = 0.05, itakura_max_slope: float | None = None) ndarray [source]¶
Compute the WDTW 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 wdtw pairwise distance between the instances of X is calculated.- windowfloat, default=None
The window to use for the bounding matrix. If None, no bounding matrix is used.
- gfloat, default=0.05
Constant that controls the level of penalisation for the points with larger phase difference. Default is 0.05.
- itakura_max_slopefloat, default=None
Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1.
- Returns:
- np.ndarray (n_cases, n_cases)
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]) >>> wdtw_pairwise_distance(X, y_univariate) array([[144.37763524], [ 70.74504127], [ 23.10042164]])
>>> # 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])] >>> wdtw_pairwise_distance(X) array([[ 0. , 20.25043711, 139.70656066], [ 20.25043711, 0. , 39.64543037], [139.70656066, 39.64543037, 0. ]])