MIT

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.

Great Documentation0

Easy to Use0

Performant0

Highly Customizable0

Bleeding Edge0

Responsive Maintainers0

Poor Documentation0

Hard to Use0

Slow0

Buggy0

Abandoned0

Unwelcoming Community0