%global _empty_manifest_terminate_build 0
Name:		python-hmmlearn
Version:	0.2.8
Release:	1
Summary:	Hidden Markov Models in Python with scikit-learn like API
License:	new BSD
URL:		https://github.com/hmmlearn/hmmlearn
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/2b/f6/fe1f14c993ac95b9b33be2aa889c90bc607e1f38e3729f71c88bbaa7ff62/hmmlearn-0.2.8.tar.gz

Requires:	python3-numpy
Requires:	python3-scikit-learn
Requires:	python3-scipy
Requires:	python3-matplotlib
Requires:	python3-pydata-sphinx-theme
Requires:	python3-sphinx
Requires:	python3-sphinx-gallery
Requires:	python3-pytest

%description
| |GitHub| |PyPI|
| |Read the Docs| |Build| |CodeCov|
   image:: https://img.shields.io/badge/github-hmmlearn%2Fhmmlearn-brightgreen
   image:: https://img.shields.io/pypi/v/hmmlearn.svg
   image:: https://readthedocs.org/projects/hmmlearn/badge/?version=latest
   image:: https://img.shields.io/github/workflow/status/hmmlearn/hmmlearn/build
   image:: https://codecov.io/gh/hmmlearn/hmmlearn/master.svg
hmmlearn is a set of algorithms for **unsupervised** learning and inference
of Hidden Markov Models. For supervised learning learning of HMMs and similar
models see seqlearn_.
**Note**: This package is under limited-maintenance mode.

%package -n python3-hmmlearn
Summary:	Hidden Markov Models in Python with scikit-learn like API
Provides:	python-hmmlearn
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-hmmlearn
| |GitHub| |PyPI|
| |Read the Docs| |Build| |CodeCov|
   image:: https://img.shields.io/badge/github-hmmlearn%2Fhmmlearn-brightgreen
   image:: https://img.shields.io/pypi/v/hmmlearn.svg
   image:: https://readthedocs.org/projects/hmmlearn/badge/?version=latest
   image:: https://img.shields.io/github/workflow/status/hmmlearn/hmmlearn/build
   image:: https://codecov.io/gh/hmmlearn/hmmlearn/master.svg
hmmlearn is a set of algorithms for **unsupervised** learning and inference
of Hidden Markov Models. For supervised learning learning of HMMs and similar
models see seqlearn_.
**Note**: This package is under limited-maintenance mode.

%package help
Summary:	Development documents and examples for hmmlearn
Provides:	python3-hmmlearn-doc
%description help
| |GitHub| |PyPI|
| |Read the Docs| |Build| |CodeCov|
   image:: https://img.shields.io/badge/github-hmmlearn%2Fhmmlearn-brightgreen
   image:: https://img.shields.io/pypi/v/hmmlearn.svg
   image:: https://readthedocs.org/projects/hmmlearn/badge/?version=latest
   image:: https://img.shields.io/github/workflow/status/hmmlearn/hmmlearn/build
   image:: https://codecov.io/gh/hmmlearn/hmmlearn/master.svg
hmmlearn is a set of algorithms for **unsupervised** learning and inference
of Hidden Markov Models. For supervised learning learning of HMMs and similar
models see seqlearn_.
**Note**: This package is under limited-maintenance mode.

%prep
%autosetup -n hmmlearn-0.2.8

%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-hmmlearn -f filelist.lst
%dir %{python3_sitearch}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.8-1
- Package Spec generated