This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

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.
Release History

Release History

This version
History Node

0.3.2

History Node

0.3.1

History Node

0.3.0

History Node

0.2.2

History Node

0.2.1

History Node

0.2.0

History Node

0.1.0

Download Files

Download Files

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

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
fastdtw-0.3.2.tar.gz (118.4 kB) Copy SHA256 Checksum SHA256 Source Jul 16, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting