wddtw_alignment_path

wddtw_alignment_path(x: ndarray, y: ndarray, window: float | None = None, g: float = 0.05, itakura_max_slope: float | None = None) → tuple[list[tuple[int, int]], float]

Compute the WDDTW alignment path between two time series.

Parameters:

xnp.ndarray

First time series, shape (n_channels, n_timepoints) or (n_timepoints,).

ynp.ndarray

Second time series, shape (m_channels, m_timepoints) or (m_timepoints,).

windowfloat, default=None

The window to use for the bounding matrix. If None, no bounding matrix is used.

gfloat, default=0.05

Constant that controls the level of penalisation for the points with larger phase difference. Default is 0.05.

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 wddtw distance betweeen the two time series.

Raises:

ValueError

If x and y are not 1D or 2D arrays. If n_timepoints or m_timepoints are less than 2.

Examples

>>> import numpy as np
>>> from aeon.distances import wddtw_alignment_path
>>> x = np.array([[1, 2, 3, 6]])
>>> y = np.array([[1, 2, 3, 4]])
>>> path, dist = wddtw_alignment_path(x, y)
>>> path
[(0, 0), (1, 1)]