twe_alignment_path#
- twe_alignment_path(x: ndarray, y: ndarray, window: float = None, nu: float = 0.001, lmbda: float = 1.0) Tuple[List[Tuple[int, int]], float] [source]#
Compute the twe alignment path between two time series.
- Parameters:
- x: np.ndarray, of shape (n_channels, n_timepoints) or (n_timepoints,)
First time series.
- y: np.ndarray, of shape (m_channels, m_timepoints) or (m_timepoints,)
Second time series.
- window: float, default=None
The window to use for the bounding matrix. If None, no bounding matrix is used.
- nu: float, defaults = 0.001
A non-negative constant which characterizes the stiffness of the elastic twe measure. Must be > 0.
- lmbda: float, defaults = 1.0
A constant penalty that punishes the editing efforts. Must be >= 1.0.
- Returns:
- List[Tuple[int, int]]
The alignment path between the two time series where each element is a tuple of the index in x and the index in y that have the best alignment according to the cost matrix.
- float
The twe distance betweeen the two time series.
- Raises:
- ValueError
If x and y are not 1D or 2D arrays.
Examples
>>> import numpy as np >>> from aeon.distances import twe_alignment_path >>> x = np.array([[1, 2, 3, 6]]) >>> y = np.array([[1, 2, 3, 4]]) >>> twe_alignment_path(x, y) ([(0, 0), (1, 1), (2, 2), (3, 3)], 2.0)