lcss_cost_matrix#

lcss_cost_matrix(x: ndarray, y: ndarray, window: float = None, epsilon: float = 1.0) → ndarray[source]#

Return the lcss cost matrix between x and y.

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.
windowfloat, defaults=None: The window to use for the bounding matrix. If None, no bounding matrix is used.
epsilon: float, defaults=1.: Matching threshold to determine if two subsequences are considered close enough to be considered ‘common’. The default is 1.

Returns:

np.ndarray: The lcss 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 lcss_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]])
>>> lcss_cost_matrix(x, y)
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
       [ 0.,  1.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.],
       [ 0.,  1.,  2.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.],
       [ 0.,  1.,  2.,  3.,  4.,  4.,  4.,  4.,  4.,  4.,  4.],
       [ 0.,  1.,  2.,  3.,  4.,  5.,  5.,  5.,  5.,  5.,  5.],
       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  6.,  6.,  6.,  6.],
       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  7.,  7.,  7.],
       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  8.,  8.],
       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.,  9.],
       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]])