`pyts.metrics`.dtw_classic¶

pyts.metrics.dtw_classic(x, y, dist='square', return_cost=False, return_accumulated=False, return_path=False)[source]¶

Classic Dynamic Time Warping (DTW) distance between two samples.

Parameters:

x : array-like, shape = (n_timestamps,): First array.
y : array-like, shape = (n_timestamps,): Second array
dist : ‘square’, ‘absolute’ or callable (default = ‘square’): Distance used. If ‘square’, the squared difference is used. If ‘absolute’, the absolute difference is used. If callable, it must be a function with a numba.njit() decorator that takes as input two numbers (two arguments) and returns a number.
return_cost : bool (default = False): If True, the cost matrix is returned.
return_accumulated : bool (default = False): If True, the accumulated cost matrix is returned.
return_path : bool (default = False): If True, the optimal path is returned.

Returns:

dtw_dist : float: The DTW distance between the two arrays.
cost_mat : array, shape = (n_timestamps, n_timestamps): Cost matrix. Only returned if return_cost=True.
acc_cost_mat : array, shape = (n_timestamps, n_timestamps): Accumulated cost matrix. Only returned if return_accumulated=True.
path : array, shape = (2, path_length): The optimal path along the cost matrix. The first row consists of the indices of the optimal path for x while the second row consists of the indices of the optimal path for y. Only returned if return_path=True.

Examples

>>> from pyts.metrics import dtw_classic
>>> x = [0, 1, 1]
>>> y = [2, 0, 1]
>>> dtw_classic(x, y)
2.0

pyts.metrics.dtw_classic¶