<pre style='margin:0'>
Christopher Nielsen (mascguy) pushed a commit to branch master
in repository macports-ports.
</pre>
<p><a href="https://github.com/macports/macports-ports/commit/6cc55dbfd36d6f6208dbc20dff32542dc6e1e67a">https://github.com/macports/macports-ports/commit/6cc55dbfd36d6f6208dbc20dff32542dc6e1e67a</a></p>
<pre style="white-space: pre; background: #F8F8F8">The following commit(s) were added to refs/heads/master by this push:
<span style='display:block; white-space:pre;color:#404040;'> new 6cc55dbfd36 py-hdbscan: Update to version 0.8.28, add Python 39 310
</span>6cc55dbfd36 is described below
<span style='display:block; white-space:pre;color:#808000;'>commit 6cc55dbfd36d6f6208dbc20dff32542dc6e1e67a
</span>Author: Steven Thomas Smith <s.t.smith@ieee.org>
AuthorDate: Tue Sep 6 07:08:06 2022 -0400
<span style='display:block; white-space:pre;color:#404040;'> py-hdbscan: Update to version 0.8.28, add Python 39 310
</span>---
python/py-hdbscan/Portfile | 40 ++++++++++++++++++++--------------------
1 file changed, 20 insertions(+), 20 deletions(-)
<span style='display:block; white-space:pre;color:#808080;'>diff --git a/python/py-hdbscan/Portfile b/python/py-hdbscan/Portfile
</span><span style='display:block; white-space:pre;color:#808080;'>index 4aafa27a23b..c3ab6e6cc6e 100644
</span><span style='display:block; white-space:pre;background:#e0e0ff;'>--- a/python/py-hdbscan/Portfile
</span><span style='display:block; white-space:pre;background:#e0e0ff;'>+++ b/python/py-hdbscan/Portfile
</span><span style='display:block; white-space:pre;background:#e0e0e0;'>@@ -4,40 +4,40 @@ PortSystem 1.0
</span> PortGroup github 1.0
PortGroup python 1.0
<span style='display:block; white-space:pre;background:#ffe0e0;'>-github.setup scikit-learn-contrib hdbscan 0.8.24
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+github.setup scikit-learn-contrib hdbscan 0.8.28
</span> name py-${github.project}
revision 0
categories-append science
<span style='display:block; white-space:pre;background:#ffe0e0;'>-platforms darwin
</span> license BSD
<span style='display:block; white-space:pre;background:#e0ffe0;'>+supported_archs arm64 x86_64
</span>
<span style='display:block; white-space:pre;background:#ffe0e0;'>-python.versions 37 38
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+python.versions 37 38 39 310
</span>
maintainers nomaintainer
description A high performance implementation of HDBSCAN clustering.
<span style='display:block; white-space:pre;background:#ffe0e0;'>-long_description HDBSCAN - Hierarchical Density-Based Spatial\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- Clustering of Applications with Noise. Performs\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- DBSCAN over varying epsilon values and integrates\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- the result to find a clustering that gives the\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- best stability over epsilon. This allows HDBSCAN\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- to find clusters of varying densities (unlike\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- DBSCAN), and be more robust to parameter\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- selection. In practice this means that HDBSCAN\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- returns a good clustering straight away with\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- little or no parameter tuning -- and the primary\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- parameter, minimum cluster size, is intuitive and\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- easy to select. HDBSCAN is ideal for exploratory\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- data analysis\; it's a fast and robust algorithm\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- that you can trust to return meaningful clusters\
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+long_description HDBSCAN - Hierarchical Density-Based Spatial \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ Clustering of Applications with Noise. Performs \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ DBSCAN over varying epsilon values and integrates \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ the result to find a clustering that gives the \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ best stability over epsilon. This allows HDBSCAN \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ to find clusters of varying densities (unlike \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ DBSCAN), and be more robust to parameter \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ selection. In practice this means that HDBSCAN \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ returns a good clustering straight away with \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ little or no parameter tuning -- and the primary \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ parameter, minimum cluster size, is intuitive and \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ easy to select. HDBSCAN is ideal for exploratory \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ data analysis\; it's a fast and robust algorithm \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ that you can trust to return meaningful clusters \
</span> (if there are any).
homepage https://hdbscan.readthedocs.io/en/latest/
<span style='display:block; white-space:pre;background:#ffe0e0;'>-checksums rmd160 0059cef4f9a2a870a9d7c103ee4220bb8ad796a2 \
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- sha256 29a07d47a4ff2d9e4938bb8d6f6b3c02e77b13669b0dc4cd3b2d43531c3aa957 \
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- size 10431303
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+checksums rmd160 60f6e4824305a204e8ea38274acdd01bc309d716 \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ sha256 a1d44adedc1372220a2b21ded510af5b927ba1a1b31990cee0261b11878a892f \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ size 11666990
</span>
if {${name} ne ${subport}} {
depends_build-append \
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