%global _empty_manifest_terminate_build 0 Name: python-BMI500caonia Version: 2.0.0 Release: 1 Summary: BMI500 HW4 License: MIT License URL: https://github.com/shaoyanpan/BMI500-HW4 Source0: https://mirrors.nju.edu.cn/pypi/web/packages/35/d7/c2b35fc365a85f6b4cd09937dfe9c41e8f52e45574121747edf0077dc6a5/BMI500caonia-2.0.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-matplotlib Requires: python3-pandas Requires: python3-scipy Requires: python3-check-manifest %description # The Kmeans unsupervised clustering package # Contents This package is an example Kmeans' package for BMI500's project in Emory University. The package will automatically download the iris data collected from UCI. The dataset contains three classes of flowers, and the clustering algorithm is to seperate each group of the flowers. It does not necessary tell you what kind of flower is, but will tell you which flowers are in a same group. # FAQ ## How to install? In your command line, type "pip install BMI500caonia" ## How to use python from BMI500caonia import BMI500clustering BMI500clustering.Kmeans_run(n, iteration, random_state) (n is the number of clusters, iteration is the number of iteration, random_state is the number of random initializations) ## Running time and hardware requirement The running time is 14 seconds in Titan'x 12 GB gpu and Intel Iris 16 GB cpu. ## Future work The function should be modified to be more flexible in the future, so the user can customize parameters. %package -n python3-BMI500caonia Summary: BMI500 HW4 Provides: python-BMI500caonia BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-BMI500caonia # The Kmeans unsupervised clustering package # Contents This package is an example Kmeans' package for BMI500's project in Emory University. The package will automatically download the iris data collected from UCI. The dataset contains three classes of flowers, and the clustering algorithm is to seperate each group of the flowers. It does not necessary tell you what kind of flower is, but will tell you which flowers are in a same group. # FAQ ## How to install? In your command line, type "pip install BMI500caonia" ## How to use python from BMI500caonia import BMI500clustering BMI500clustering.Kmeans_run(n, iteration, random_state) (n is the number of clusters, iteration is the number of iteration, random_state is the number of random initializations) ## Running time and hardware requirement The running time is 14 seconds in Titan'x 12 GB gpu and Intel Iris 16 GB cpu. ## Future work The function should be modified to be more flexible in the future, so the user can customize parameters. %package help Summary: Development documents and examples for BMI500caonia Provides: python3-BMI500caonia-doc %description help # The Kmeans unsupervised clustering package # Contents This package is an example Kmeans' package for BMI500's project in Emory University. The package will automatically download the iris data collected from UCI. The dataset contains three classes of flowers, and the clustering algorithm is to seperate each group of the flowers. It does not necessary tell you what kind of flower is, but will tell you which flowers are in a same group. # FAQ ## How to install? In your command line, type "pip install BMI500caonia" ## How to use python from BMI500caonia import BMI500clustering BMI500clustering.Kmeans_run(n, iteration, random_state) (n is the number of clusters, iteration is the number of iteration, random_state is the number of random initializations) ## Running time and hardware requirement The running time is 14 seconds in Titan'x 12 GB gpu and Intel Iris 16 GB cpu. ## Future work The function should be modified to be more flexible in the future, so the user can customize parameters. %prep %autosetup -n BMI500caonia-2.0.0 %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-BMI500caonia -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 2.0.0-1 - Package Spec generated