pydtw | lightweight unconstrained Dynamic Time Warping bindings | Machine Learning library

by gravitino C++ Version: Current License: No License

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kandi X-RAY | pydtw Summary

pydtw is a C++ library typically used in Artificial Intelligence, Machine Learning, Numpy applications. pydtw has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

lightweight unconstrained and constrained Dynamic Time Warping bindings for Python. Have a look at the development branch featuring native numpy support, multivariate measues etc.

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Support

pydtw has a low active ecosystem.

It has 34 star(s) with 13 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pydtw is current.

Quality

pydtw has 0 bugs and 0 code smells.

Security

pydtw has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pydtw code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pydtw does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

pydtw releases are not available. You will need to build from source code and install.

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pydtw Key Features

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pydtw Examples and Code Snippets

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Community Discussions

Trending Discussions on pydtw

Different results and performances with different libraries

QUESTION

Different results and performances with different libraries

Asked 2019-Apr-11 at 12:07

I'm comparing the libraries dtaidistance, fastdtw and cdtw for DTW computations. This is my code:

...

ANSWER

Answered 2018-Oct-16 at 13:58

Edit: what are the units of the time measurements? I believe that you compared them as they were all in the same unit. Probably the dtw.distance is, for example, in microseconds, while the other answers are in milliseconds, and you thought that dtw.distance performed slower, when it is actually the opposite.

There are different methodologies to measure the distance between two points. It could be based on standard deviation or just euclidian distance. Here is a list of many of those distance.

Some of them might be more computational intensive than others, and also have different meanings. Fast dtw, for example, uses as a third input the type of distance that you want, as described on their github

Source https://stackoverflow.com/questions/52479046

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pydtw

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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