similarity_measures | Quantify the difference between two arbitrary curves | Monitoring library

by cjekel Jupyter Notebook Version: 0.7.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | similarity_measures Summary

similarity_measures is a Jupyter Notebook library typically used in Performance Management, Monitoring applications. similarity_measures has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Quantify the difference between two arbitrary curves in space

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similarity_measures has a low active ecosystem.

It has 196 star(s) with 34 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 12 open issues and 14 have been closed. On average issues are closed in 9 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of similarity_measures is 0.7.0

Quality

similarity_measures has 0 bugs and 0 code smells.

Security

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

similarity_measures code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

similarity_measures is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

similarity_measures releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 2613 lines of code, 41 functions and 9 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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similarity_measures Key Features

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similarity_measures Examples and Code Snippets

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Community Discussions

Trending Discussions on similarity_measures

What is the correct way to format the parameters for DTW in Similarity Measures?

QUESTION

What is the correct way to format the parameters for DTW in Similarity Measures?

Asked 2021-Jun-01 at 17:44

I am trying to use the DTW algorithm from the Similarity Measures library. However, I get hit with an error that states a 2-Dimensional Array is required. I am not sure I understand how to properly format the data, and the documentation is leaving me scratching my head.

https://github.com/cjekel/similarity_measures/blob/master/docs/similaritymeasures.html

According to the documentation the function takes two arguments (exp_data and num_data ) for the data set, which makes sense. What doesn't make sense to me is:

exp_data : array_like

Curve from your experimental data. exp_data is of (M, N) shape, where M is the number of data points, and N is the number of dimensions

This is the same for both the exp_data and num_data arguments.

So, for further clarification, let's say I am implementing the fastdtw library. It looks like this:

...

ANSWER

Answered 2021-Jun-01 at 17:44

It appears the solution in my case was to include the index in the array. For example, if your data looks like this:

Source https://stackoverflow.com/questions/67744927

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

Vulnerabilities

No vulnerabilities reported

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This is by no means a complete list of all possible similarity measures. For instance the SciPy Hausdorff distance is an alternative similarity measure useful if you don't know the beginning and ending of each curve. There are many more possible functions out there. Feel free to send PRs for other functions in literature!.

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