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dev-python/hdbscan: new package (DANDI ephys stack)

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Package-Manager: Portage-3.0.28, Repoman-3.0.3
Signed-off-by: Horea Christian <chr@chymera.eu>
This commit is contained in:
Horea Christian 2021-10-12 15:46:26 -04:00
parent fc23864e95
commit 3765ea2e04
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dev-python/hdbscan/ChangeLog Normal file
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*hdbscan-0.8.26 (12 Oct 2021)
12 Oct 2021; <chymera@gentoo.org> +hdbscan-0.8.26.ebuild, +metadata.xml:
dev-python/hdbscan: new package (DANDI ephys stack)

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DIST hdbscan-0.8.26.tar.gz 10776639 SHA256 2fd10906603b6565ee138656b6d59df3494c03c5e8099aede400d50b13af912b SHA512 7337f4246511d22e8e11308e7776aad6929f26674daecc1e174b01673782837815a40820a6565406315b1ae14ed059c4945810ac997abfde9e24abda352de622 WHIRLPOOL 792b2cfb440a5cfd17ef66c32418a1f59f5184a72e27c7afcb59433f51a4f6e432ef35cc91c5c6f4117c85d918a4a886ab99552b93b94e7cbcc5e5601aa31fef

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dev-python/hdbscan/hdbscan-0.8.26.ebuild Normal file
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# Copyright 2021 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
PYTHON_COMPAT=( python3_{8..10} )
inherit distutils-r1
DESCRIPTION="A high performance implementation of HDBSCAN clustering."
HOMEPAGE="https://github.com/scikit-learn-contrib/hdbscan"
SRC_URI="https://github.com/scikit-learn-contrib/hdbscan/archive/refs/tags/${PV}.tar.gz -> ${P}.tar.gz"
LICENSE="BSD"
SLOT="0"
KEYWORDS="~amd64 ~x86"
IUSE="test"
RESTRICT="test"
# Tests fail, reported to upstream
# https://github.com/scikit-learn-contrib/hdbscan/issues/501
DEPEND=""
RDEPEND="
dev-python/cython[${PYTHON_USEDEP}]
dev-python/joblib[${PYTHON_USEDEP}]
dev-python/numpy[${PYTHON_USEDEP}]
dev-python/scipy[${PYTHON_USEDEP}]
dev-python/six[${PYTHON_USEDEP}]
sci-libs/scikit-learn[${PYTHON_USEDEP}]
"
BDEPEND=""
distutils_enable_tests pytest

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<?xml version='1.0' encoding='UTF-8'?>
<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
<pkgmetadata>
<maintainer type="person">
<email>gentoo@chymera.eu</email>
<name>Horea Christian</name>
</maintainer>
<maintainer type="project">
<email>sci@gentoo.org</email>
<name>Gentoo Science Project</name>
</maintainer>
<longdescription lang="en">
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).
</longdescription>
<upstream>
<remote-id type="github">scikit-learn-contrib/hdbscan</remote-id>
</upstream>
</pkgmetadata>