%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 * Wed May 10 2023 Python_Bot - 0.3.0-1 - Package Spec generated