Python implementation of FastDTW <http://cs.fit.edu/~pkc/papers/tdm04.pdf>_ [1]_, which is an approximate Dynamic Time Warping (DTW) algorithm that provides optimal or near-optimal alignments with an O(N) time and memory complexity.
::
pip install fastdtw
::
import numpy as np from scipy.spatial.distance import euclidean
from fastdtw import fastdtw
x = np.array([[1,1], [2,2], [3,3], [4,4], [5,5]]) y = np.array([[2,2], [3,3], [4,4]]) distance, path = fastdtw(x, y, dist=euclidean) print(distance)
.. [1] Stan Salvador, and Philip Chan. "FastDTW: Toward accurate dynamic time warping in linear time and space." Intelligent Data Analysis 11.5 (2007): 561-580.
FAQs
Dynamic Time Warping (DTW) algorithm with an O(N) time and memory complexity.
We found that fastdtw demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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