Skip to main content

Dynamic Time Warping (DTW) algorithm with an O(N) time and memory complexity.

Project description

fastdtw

Python implementation of FastDTW [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.

Install

pip install fastdtw

Example

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)

References

[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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
fastdtw-0.3.2.tar.gz (118.4 kB) Copy SHA256 hash SHA256 Source None Jul 16, 2017

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page