ddtw_cost_matrix¶

ddtw_cost_matrix(x: ndarray, y: ndarray | None = None, window: float | None = None, itakura_max_slope: float | None = None) → ndarray[source]¶

Compute the DDTW cost matrix between two time series.

This involves taking the difference of the series then using the same cost function as DTW.

Parameters:

xnp.ndarray: First time series, either univariate, shape (n_timepoints,), or multivariate, shape (n_channels, n_timepoints).
ynp.ndarray: Second time series, either univariate, shape (n_timepoints,), or multivariate, shape (n_channels, n_timepoints).
windowfloat, default=None: The window to use for the bounding matrix. If None, no bounding matrix is used.
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:

np.ndarray (n_timepoints, m_timepoints): ddtw cost matrix between x and y.

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 ddtw_cost_matrix
>>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> y = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> ddtw_cost_matrix(x, y)
array([[0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0.]])