wdtw_cost_matrix#
- wdtw_cost_matrix(x: ndarray, y: ndarray, window: float = None, g: float = 0.05) ndarray[source]#
Compute the wdtw 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)
wdtw cost matrix between x and y.
- Raises:
- ValueError
If x and y are not 1D or 2D arrays.
Examples
>>> import numpy as np >>> from aeon.distances import wdtw_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]]) >>> wdtw_cost_matrix(x, y) array([[ 0. , 0.450166 , 2.30044662, 6.57563393, 14.37567559, 26.87567559, 45.32558186, 71.04956205, 105.44507215, 149.98162593], [ 0.450166 , 0. , 0.450166 , 2.30044662, 6.57563393, 14.37567559, 26.87567559, 45.32558186, 71.04956205, 105.44507215], [ 2.30044662, 0.450166 , 0. , 0.450166 , 2.30044662, 6.57563393, 14.37567559, 26.87567559, 45.32558186, 71.04956205], [ 6.57563393, 2.30044662, 0.450166 , 0. , 0.450166 , 2.30044662, 6.57563393, 14.37567559, 26.87567559, 45.32558186], [ 14.37567559, 6.57563393, 2.30044662, 0.450166 , 0. , 0.450166 , 2.30044662, 6.57563393, 14.37567559, 26.87567559], [ 26.87567559, 14.37567559, 6.57563393, 2.30044662, 0.450166 , 0. , 0.450166 , 2.30044662, 6.57563393, 14.37567559], [ 45.32558186, 26.87567559, 14.37567559, 6.57563393, 2.30044662, 0.450166 , 0. , 0.450166 , 2.30044662, 6.57563393], [ 71.04956205, 45.32558186, 26.87567559, 14.37567559, 6.57563393, 2.30044662, 0.450166 , 0. , 0.450166 , 2.30044662], [105.44507215, 71.04956205, 45.32558186, 26.87567559, 14.37567559, 6.57563393, 2.30044662, 0.450166 , 0. , 0.450166 ], [149.98162593, 105.44507215, 71.04956205, 45.32558186, 26.87567559, 14.37567559, 6.57563393, 2.30044662, 0.450166 , 0. ]])