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%global _empty_manifest_terminate_build 0
Name: python-ddbscan
Version: 0.3.0
Release: 1
Summary: Discrete DBSCAN algorithm optimized for discrete and bounded data.
License: MIT
URL: https://github.com/cloudwalkio/ddbscan
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c1/d5/fea87ff9e2307f06867c4670651df25f4c8b47e979303d75d31e88d691b3/ddbscan-0.3.0.tar.gz
BuildArch: noarch
%description
|
This is a version of `DBSCAN`_ clustering algorithm optimized for discrete,
bounded data, reason why we call it Discrete DBSCAN (DDBSCAN). The base for
the current implementation is from `this source`_. The algorithm code is in
file ``ddbscan/ddbscan.py`` and can easily be read. The main algorithm itself
is in method ``compute()``, and can be understood following the links above
or reading papers describing it.
Another feature of this implementation is that it is designed towards online
learning. As a result, when we add points to our DDBSCAN object, we must pass
one point each time to method ``add_point``. See usage below.
%package -n python3-ddbscan
Summary: Discrete DBSCAN algorithm optimized for discrete and bounded data.
Provides: python-ddbscan
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ddbscan
|
This is a version of `DBSCAN`_ clustering algorithm optimized for discrete,
bounded data, reason why we call it Discrete DBSCAN (DDBSCAN). The base for
the current implementation is from `this source`_. The algorithm code is in
file ``ddbscan/ddbscan.py`` and can easily be read. The main algorithm itself
is in method ``compute()``, and can be understood following the links above
or reading papers describing it.
Another feature of this implementation is that it is designed towards online
learning. As a result, when we add points to our DDBSCAN object, we must pass
one point each time to method ``add_point``. See usage below.
%package help
Summary: Development documents and examples for ddbscan
Provides: python3-ddbscan-doc
%description help
|
This is a version of `DBSCAN`_ clustering algorithm optimized for discrete,
bounded data, reason why we call it Discrete DBSCAN (DDBSCAN). The base for
the current implementation is from `this source`_. The algorithm code is in
file ``ddbscan/ddbscan.py`` and can easily be read. The main algorithm itself
is in method ``compute()``, and can be understood following the links above
or reading papers describing it.
Another feature of this implementation is that it is designed towards online
learning. As a result, when we add points to our DDBSCAN object, we must pass
one point each time to method ``add_point``. See usage below.
%prep
%autosetup -n ddbscan-0.3.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-ddbscan -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.0-1
- Package Spec generated
|
