dtaidistance.dtw_ndim

Dynamic Time Warping (DTW) for N-dimensional series.

author:Wannes Meert
copyright:Copyright 2017-2018 KU Leuven, DTAI Research Group.
license:Apache License, Version 2.0, see LICENSE for details.
dtaidistance.dtw_ndim.distance(s1, s2, window=None, max_dist=None, max_step=None, max_length_diff=None, penalty=None, psi=None, use_c=False)

Dynamic Time Warping using multidimensional sequences.

cost = EuclideanDistance(s1[i], s2[j])

See dtaidistance.dtw.distance() for parameters.

dtaidistance.dtw_ndim.distance_matrix(s, max_dist=None, max_length_diff=None, window=None, max_step=None, penalty=None, psi=None, block=None, parallel=False, use_c=False, show_progress=False)

Dynamic Time Warping distance matrix using multidimensional sequences.

cost = EuclideanDistance(s1[i], s2[j])

See dtaidistance.dtw.distance_matrix() for parameters.

dtaidistance.dtw_ndim.warping_paths(s1, s2, window=None, max_dist=None, max_step=None, max_length_diff=None, penalty=None, psi=None)

Dynamic Time Warping (keep full matrix) using multidimensional sequences.

cost = EuclideanDistance(s1[i], s2[j])

See dtaidistance.dtw.warping_paths() for parameters.