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%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 <Python_Bot@openeuler.org> - 2.0.0-1
- Package Spec generated
|
