DeBaCl is an open-source Python library for DEnsity-BAsed CLustering with level set trees.
Level set trees are a statistically-principled way to represent the topology of a probability density function. This representation is particularly useful for several core tasks in statistics:
clustering, especially for data with multi-scale clustering behavior
describing data topology
exploratory data analysis
data visualization
anomaly detection
DeBaCl is a Python implementation of the Level Set Tree method, with an emphasis on computational speed, algorithmic simplicity, and extensibility. The code is available at:
https://github.com/CoAxLab/DeBaCl
and the API documentation is at:
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Release files for debacl 1.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| debacl-1.1.tar.gz | 22.5 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| debacl-1.1-py2-none-any.whl | Python 2 | none | any | Details |
Total release size: 48.1 kB
Release files / debacl-1.1.tar.gz
| Download URL | debacl-1.1.tar.gz |
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| Size | 22.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Release files / debacl-1.1-py2-none-any.whl
| Download URL | debacl-1.1-py2-none-any.whl |
|---|---|
| Size | 25.6 kB |
| Tags | Python 2 |
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SHA-256 checksum How to use checksums |
1119b150ae97687d10bc3b90ee2d43fe9bb64d95195b0ce357d413193e80dfb2
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BLAKE2b-256 checksum How to use checksums |
f1e1f5159e0a69c83440054bfde01e5b3b17d56068d33187298b7ca2f7a18d6b
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for debacl 1.1
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