shape_dtw_pairwise_distance¶
- shape_dtw_pairwise_distance(X: ndarray | List[ndarray], y: ndarray | List[ndarray] | None = None, window: float | None = None, descriptor: str = 'identity', reach: int = 30, itakura_max_slope: float | None = None, transformation_precomputed: bool = False, transformed_x: ndarray | None = None, transformed_y: ndarray | None = None) ndarray [source]¶
Compute the ShapeDTW pairwise distance among a set of 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 shape-dtw 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. window is a percentage deviation, so if
window = 0.1
then 10% of the series length is the max warping allowed. is used.- descriptorstr, default=None (if None then identity is used).
Defines which transformation is applied on the sub-sequences. Valid descriptors are: [‘identity’]
Identity is simply a copying mechanism of the sub-sequence, no transformations are done. For now no other descriptors are implemented.
If not specified then identity is used.
- reachint, default=30.
Length of the sub-sequences.
- 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.
- transformation_precomputedbool, default = False
To choose if the transformation of the sub-sequences is pre-computed or not.
- transformed_xnp.ndarray, default = None
The transformation of X, ignored if transformation_precomputed is False.
- transformed_ynp.ndarray, default = None
The transformation of y, ignored if transformation_precomputed is False.
- Returns:
- np.ndarray
ShapeDTW 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 and y are not 1D or 2D arrays.
Examples
>>> import numpy as np >>> from aeon.distances import shape_dtw_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]]]) >>> shape_dtw_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]]]) >>> shape_dtw_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]) >>> shape_dtw_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])] >>> shape_dtw_pairwise_distance(X) array([[ 0., 43., 292.], [ 43., 0., 89.], [292., 89., 0.]])