wddtw_cost_matrix#

wddtw_cost_matrix(x: ndarray, y: ndarray, window: float = None, g: float = 0.05) → ndarray[source]#

Compute the wddtw cost matrix 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, defaults=None: The window to use for the bounding matrix. If None, no bounding matrix is used.
g: float, defaults=0.05: Constant that controls the level of penalisation for the points with larger phase difference. Default is 0.05.

Returns:

np.ndarray (n_timepoints_x, n_timepoints_y): wddtw 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 wddtw_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]])
>>> wddtw_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.]])