%global _empty_manifest_terminate_build 0
Name:		python-cma
Version:	3.3.0
Release:	1
Summary:	CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python
License:	BSD
URL:		https://github.com/CMA-ES/pycma
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/46/b8/dc832cf0881641355c5121b390942de858079de08720bc2a6030c6c85153/cma-3.3.0.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-moarchiving
Requires:	python3-matplotlib

%description
A stochastic numerical optimization algorithm for difficult (non-convex,
ill-conditioned, multi-modal, rugged, noisy) optimization problems in
continuous search spaces, implemented in Python.
Typical domain of application are bound-constrained or unconstrained
objective functions with:
* search space dimension between, say, 5 and (a few) 100,
* no gradients available,
* at least, say, 100 times dimension function evaluations needed to
  get satisfactory solutions,
* non-separable, ill-conditioned, or rugged/multi-modal landscapes.
The CMA-ES is quite reliable, however for small budgets (fewer function
evaluations than, say, 100 times dimension) or in very small dimensions
better (i.e. faster) methods are available.
The ``pycma`` module provides two independent implementations of the 
CMA-ES algorithm in the classes ``cma.CMAEvolutionStrategy`` and 
``cma.purecma.CMAES``. 

%package -n python3-cma
Summary:	CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python
Provides:	python-cma
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-cma
A stochastic numerical optimization algorithm for difficult (non-convex,
ill-conditioned, multi-modal, rugged, noisy) optimization problems in
continuous search spaces, implemented in Python.
Typical domain of application are bound-constrained or unconstrained
objective functions with:
* search space dimension between, say, 5 and (a few) 100,
* no gradients available,
* at least, say, 100 times dimension function evaluations needed to
  get satisfactory solutions,
* non-separable, ill-conditioned, or rugged/multi-modal landscapes.
The CMA-ES is quite reliable, however for small budgets (fewer function
evaluations than, say, 100 times dimension) or in very small dimensions
better (i.e. faster) methods are available.
The ``pycma`` module provides two independent implementations of the 
CMA-ES algorithm in the classes ``cma.CMAEvolutionStrategy`` and 
``cma.purecma.CMAES``. 

%package help
Summary:	Development documents and examples for cma
Provides:	python3-cma-doc
%description help
A stochastic numerical optimization algorithm for difficult (non-convex,
ill-conditioned, multi-modal, rugged, noisy) optimization problems in
continuous search spaces, implemented in Python.
Typical domain of application are bound-constrained or unconstrained
objective functions with:
* search space dimension between, say, 5 and (a few) 100,
* no gradients available,
* at least, say, 100 times dimension function evaluations needed to
  get satisfactory solutions,
* non-separable, ill-conditioned, or rugged/multi-modal landscapes.
The CMA-ES is quite reliable, however for small budgets (fewer function
evaluations than, say, 100 times dimension) or in very small dimensions
better (i.e. faster) methods are available.
The ``pycma`` module provides two independent implementations of the 
CMA-ES algorithm in the classes ``cma.CMAEvolutionStrategy`` and 
``cma.purecma.CMAES``. 

%prep
%autosetup -n cma-3.3.0

%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-cma -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Fri Apr 07 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3.0-1
- Package Spec generated