msm_alignment_path#
- msm_alignment_path(x: ndarray, y: ndarray, window: float = None, independent: bool = True, c: float = 1.0) Tuple[List[Tuple[int, int]], float] [source]#
Compute the msm alignment path 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, default=None
The window to use for the bounding matrix. If None, no bounding matrix is used.
- independent: bool, defaults=True
Whether to use the independent or dependent MSM distance. The default is True (to use independent).
- c: float, defaults=1.
Cost for split or merge operation. Default is 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 msm 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 msm_alignment_path >>> x = np.array([[1, 2, 3, 6]]) >>> y = np.array([[1, 2, 3, 4]]) >>> msm_alignment_path(x, y) ([(0, 0), (1, 1), (2, 2), (3, 3)], 2.0)