ports - FreeBSD ports tree

	Commit message (Collapse)	Author	Age	Files	Lines
*	Remove # $FreeBSD$ from Makefiles.	Mathieu Arnold	2021-04-06	1	-1/+0
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*	Update to 0.8.27	Sunpoet Po-Chuan Hsieh	2021-02-17	2	-9/+9
\| \| \| \| \| \| \|	Changes: https://github.com/scikit-learn-contrib/hdbscan/releases Notes: svn path=/head/; revision=565617
*	Remove PYNUMPY	Sunpoet Po-Chuan Hsieh	2021-02-06	1	-2/+2
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*	Update to 0.8.26	Sunpoet Po-Chuan Hsieh	2020-03-31	2	-4/+4
\| \| \| \| \| \| \|	Changes: https://github.com/scikit-learn-contrib/hdbscan/releases Notes: svn path=/head/; revision=530070
*	Update to 0.8.25	Sunpoet Po-Chuan Hsieh	2020-03-28	2	-4/+4
\| \| \| \| \| \| \|	Changes: https://github.com/scikit-learn-contrib/hdbscan/releases Notes: svn path=/head/; revision=529420
*	Update version requirement of RUN_DEPENDS	Sunpoet Po-Chuan Hsieh	2020-01-12	1	-3/+3
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*	Unbreak bulk -a	Antoine Brodin	2019-12-29	1	-1/+1
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*	Add py-hdbscan 0.8.24	Sunpoet Po-Chuan Hsieh	2019-12-29	3	-0/+43
	HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any). WWW: https://github.com/scikit-learn-contrib/hdbscan Notes: svn path=/head/; revision=521283