adtw_pairwise_distance¶
- adtw_pairwise_distance(X: ndarray | list[ndarray], y: ndarray | list[ndarray] | None = None, window: float | None = None, itakura_max_slope: float | None = None, warp_penalty: float = 1.0) ndarray [source]¶
Compute the ADTW 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 adtw pairwise distance between the instances of X is calculated.- windowfloat or None, default=None
The window to use for the bounding matrix. If None, no bounding matrix is used.
- 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.
- warp_penalty: float, default=1.0
Penalty for warping. A high value will mean less warping. warp less and if value is low then will encourage algorithm to warp more.
- Returns:
- np.ndarray
ADTW pairwise matrix between the instances of X of shape
(n_cases, n_cases)
or between X and y of shape(n_cases, n_cases)
.
- Raises:
- ValueError
If X is not 2D or 3D array and if y is not 1D, 2D or 3D arrays when passing y.
Examples
>>> import numpy as np >>> from aeon.distances import adtw_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]]]) >>> adtw_pairwise_distance(X) array([[ 0., 27., 108.], [ 27., 0., 27.], [108., 27., 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]]]) >>> adtw_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]) >>> adtw_pairwise_distance(X, y_univariate) array([[300.], [147.], [ 48.]])
>>> # 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])] >>> adtw_pairwise_distance(X) array([[ 0., 44., 294.], [ 44., 0., 87.], [294., 87., 0.]])