twe_alignment_path¶
- twe_alignment_path(x: ndarray, y: ndarray, window: float | None = None, nu: float = 0.001, lmbda: float = 1.0, itakura_max_slope: float | None = None) tuple[list[tuple[int, int]], float] [source]¶
Compute the TWE alignment path between two time series.
- Parameters:
- xnp.ndarray, of shape (n_channels, n_timepoints) or (n_timepoints,)
First time series.
- ynp.ndarray, of shape (m_channels, m_timepoints) or (m_timepoints,)
Second time series.
- windowfloat, default=None
The window to use for the bounding matrix. If None, no bounding matrix is used.
- nufloat, default=0.001
A non-negative constant which characterizes the stiffness of the elastic twe method. Must be > 0.
- lmbdafloat, default=1.0
A constant penalty that punishes the editing efforts. Must be >= 1.0.
- 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:
- 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)