minkowski_pairwise_distance¶
- minkowski_pairwise_distance(X: ndarray | List[ndarray], y: ndarray | List[ndarray] | None = None, p: float = 2.0, w: ndarray | None = None) ndarray [source]¶
Compute the Minkowski 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 minkoski pairwise distance between the instances of X is calculated.- pfloat, default=2.0
The order of the norm of the difference (default is 2.0, which represents the Euclidean distance).
- wnp.ndarray, default=None
An array of weights, applied to each pairwise calculation. The weights should match the shape of the time series in X and y.
- Returns:
- np.ndarray
Minkowski pairwise distance matrix between the instances of X (and y if provided).
- Raises:
- ValueError
If X (and y if provided) are not 1D, 2D or 3D arrays. If p is less than 1. If w is provided and its shape does not match the instances in X and y.
Examples
>>> import numpy as np >>> from aeon.distances import minkowski_pairwise_distance >>> X = np.array([[[1, 2, 3, 4]],[[4, 5, 6, 3]], [[7, 8, 9, 3]]]) >>> minkowski_pairwise_distance(X, p=1) array([[ 0., 10., 19.], [10., 0., 9.], [19., 9., 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]]]) >>> minkowski_pairwise_distance(X, y,p=2) array([[17.32050808, 22.5166605 , 27.71281292], [12.12435565, 17.32050808, 22.5166605 ], [ 6.92820323, 12.12435565, 17.32050808]])
>>> X = np.array([[1, 2, 3], [4, 5, 6]]) >>> y = np.array([[11, 12, 13], [14, 15, 16]]) >>> w = np.array([[21, 22, 23], [24, 25, 26]]) >>> minkowski_pairwise_distance(X, y, p=2, w=w) array([[ 81.24038405, 105.61249926], [ 60.62177826, 86.60254038]])
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]]) >>> y_univariate = np.array([11, 12, 13]) >>> minkowski_pairwise_distance(X, y_univariate, p=1) array([[30.], [21.], [12.]])
>>> # 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])] >>> minkowski_pairwise_distance(X) array([[ 0. , 5.19615242, 12.12435565], [ 5.19615242, 0. , 8. ], [12.12435565, 8. , 0. ]])