{ "info": { "author": "Kazuaki Tanida", "author_email": "", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering" ], "description": "fastdtw\n-------\n\nPython implementation of `FastDTW\n`_ [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.\n\nInstall\n-------\n\n::\n\n pip install fastdtw\n\nExample\n-------\n\n::\n\n import numpy as np\n from scipy.spatial.distance import euclidean\n\n from fastdtw import fastdtw\n\n x = np.array([[1,1], [2,2], [3,3], [4,4], [5,5]])\n y = np.array([[2,2], [3,3], [4,4]])\n distance, path = fastdtw(x, y, dist=euclidean)\n print(distance)\n\nReferences\n----------\n\n.. 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