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).
NOTE: If this package has multiple flavors (see below), then use one of them instead of the name specified above. NOTE: This is a Python port. Instead of py312-hdbscan listed in the above command, you can pick from the names under the Packages section.
math/py-hdbscan: Fix b0b4d9e1eae890d0de1591e20fb37358439a2ff6
- Update version requirement of *_DEPENDS
- Remove pyproject.toml patch
Note that math/py-numpy has PORTEPOCH=1.
all: remove explicit versions in USES=python for "3.x+"
The logic in USES=python will automatically convert this to 3.8+ by
itself.
Adjust two ports that only had Python 3.7 mentioned but build fine
on Python 3.8 too.
finance/quickfix: mark BROKEN with PYTHON
libtool: compile: c++ -DHAVE_CONFIG_H -I. -I../.. -I -I. -I.. -I../.. -I../C++
-DLIBICONV_PLUG -DPYTHON_MAJOR_VERSION=3 -Wno-unused-variable
-Wno-maybe-uninitialized -O2 -pipe -DLIBICONV_PLUG -fstack-protector-strong
-fno-strict-aliasing -DLIBICONV_PLUG -Wall -ansi
-Wno-unused-command-line-argument -Wpointer-arith -Wwrite-strings
-Wno-overloaded-virtual -Wno-deprecated-declarations -Wno-deprecated -std=c++0x
-MT _quickfix_la-QuickfixPython.lo -MD -MP -MF
.deps/_quickfix_la-QuickfixPython.Tpo -c QuickfixPython.cpp -fPIC -DPIC -o
.libs/_quickfix_la-QuickfixPython.o
warning: unknown warning option '-Wno-maybe-uninitialized'; did you mean
'-Wno-uninitialized'? [-Wunknown-warning-option]
QuickfixPython.cpp:175:11: fatal error: 'Python.h' file not found
^~~~~~~~~~
1 warning and 1 error generated.
Reviewed by: portmgr, vishwin, yuri
Differential Revision: <https://reviews.freebsd.org/D40568>
Remove WWW entries moved into port Makefiles
Commit b7f05445c00f has added WWW entries to port Makefiles based on
WWW: lines in pkg-descr files.
This commit removes the WWW: lines of moved-over URLs from these
pkg-descr files.
Approved by: portmgr (tcberner)
Add WWW entries to port Makefiles
It has been common practice to have one or more URLs at the end of the
ports' pkg-descr files, one per line and prefixed with "WWW:". These
URLs should point at a project website or other relevant resources.
Access to these URLs required processing of the pkg-descr files, and
they have often become stale over time. If more than one such URL was
present in a pkg-descr file, only the first one was tarnsfered into
the port INDEX, but for many ports only the last line did contain the
port specific URL to further information.
There have been several proposals to make a project URL available as
a macro in the ports' Makefiles, over time.
(Only the first 15 lines of the commit message are shown above )
math: remove 'Created by' lines
A big Thank You to the original contributors of these ports:
* Aaron Dalton <aaron@FreeBSD.org>
* Aaron Dalton <aaron@daltons.ca>
* Alessando Sagratini <ale_sagra@hotmail.com>
* Alex Dupre <ale@FreeBSD.org>
* Alexey Dokuchaev <danfe@FreeBSD.org>
* Amarendra Godbole <amarendra.godbole@gmail.com>
* Anders Nordby <anders@FreeBSD.org>
* Andreas Fehlner (fehlner@gmx.de)
* Andrew L. Neporada <andrew@chg.ru>
* Andrey <gugu@zoo.rambler.ru>
* Andrey Zakhvatov
(Only the first 15 lines of the commit message are shown above )
Add py-hdbscan 0.8.24
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
