alignment_path¶
- alignment_path(x: ndarray, y: ndarray, metric: str, **kwargs: Unpack) Tuple[List[Tuple[int, int]], float] [source]¶
Compute the alignment path and distance 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.
- metricstr
The distance metric to use. A list of valid distance metrics can be found in the documentation for
aeon.distances.get_distance_function
or by calling the functionaeon.distances.get_distance_function_names
.- kwargsany
Arguments for metric. Refer to each metrics documentation for a list of possible arguments.
- 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 dtw distance betweeen the two time series.
- Raises:
- ValueError
If x and y are not 1D, or 2D arrays. If metric is not one of the supported strings or a callable.
Examples
>>> import numpy as np >>> from aeon.distances import alignment_path >>> x = np.array([[1, 2, 3, 6]]) >>> y = np.array([[1, 2, 3, 4]]) >>> alignment_path(x, y, metric='dtw') ([(0, 0), (1, 1), (2, 2), (3, 3)], 4.0)