%global _empty_manifest_terminate_build 0
Name:		python-penaltymodel
Version:	1.0.2
Release:	1
Summary:	Package for creating penalty models.
License:	Apache 2.0
URL:		https://github.com/dwavesystems/penaltymodel
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c9/bf/3bdd0c151f8d1a24d8a293a6ce03e2933099582abfe1bd177b2d05f0de89/penaltymodel-1.0.2.tar.gz
BuildArch:	noarch

Requires:	python3-dimod
Requires:	python3-homebase
Requires:	python3-networkx
Requires:	python3-numpy
Requires:	python3-scipy

%description
One approach to solve a constraint satisfaction problem (`CSP <https://en.wikipedia.org/wiki/Constraint_satisfaction_problem>`_) using an `Ising model <https://en.wikipedia.org/wiki/Ising_model>`_ or a `QUBO <https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization>`_, is to map each individual constraint in the CSP to a 'small' Ising model or QUBO. This mapping is called a *penalty model*.
Imagine that we want to map an AND clause to a QUBO. In other words, we want the solutions
to the QUBO (the solutions that minimize the energy) to be exactly the valid configurations
of an AND gate. Let ``z = AND(x_1, x_2)``.
Before anything else, let's import that package we will need.
    import penaltymodel
    import dimod
    import networkx as nx
Next, we need to determine the feasible configurations that we wish to target (by making the energy of these configuration in the binary quadratic low).

%package -n python3-penaltymodel
Summary:	Package for creating penalty models.
Provides:	python-penaltymodel
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-penaltymodel
One approach to solve a constraint satisfaction problem (`CSP <https://en.wikipedia.org/wiki/Constraint_satisfaction_problem>`_) using an `Ising model <https://en.wikipedia.org/wiki/Ising_model>`_ or a `QUBO <https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization>`_, is to map each individual constraint in the CSP to a 'small' Ising model or QUBO. This mapping is called a *penalty model*.
Imagine that we want to map an AND clause to a QUBO. In other words, we want the solutions
to the QUBO (the solutions that minimize the energy) to be exactly the valid configurations
of an AND gate. Let ``z = AND(x_1, x_2)``.
Before anything else, let's import that package we will need.
    import penaltymodel
    import dimod
    import networkx as nx
Next, we need to determine the feasible configurations that we wish to target (by making the energy of these configuration in the binary quadratic low).

%package help
Summary:	Development documents and examples for penaltymodel
Provides:	python3-penaltymodel-doc
%description help
One approach to solve a constraint satisfaction problem (`CSP <https://en.wikipedia.org/wiki/Constraint_satisfaction_problem>`_) using an `Ising model <https://en.wikipedia.org/wiki/Ising_model>`_ or a `QUBO <https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization>`_, is to map each individual constraint in the CSP to a 'small' Ising model or QUBO. This mapping is called a *penalty model*.
Imagine that we want to map an AND clause to a QUBO. In other words, we want the solutions
to the QUBO (the solutions that minimize the energy) to be exactly the valid configurations
of an AND gate. Let ``z = AND(x_1, x_2)``.
Before anything else, let's import that package we will need.
    import penaltymodel
    import dimod
    import networkx as nx
Next, we need to determine the feasible configurations that we wish to target (by making the energy of these configuration in the binary quadratic low).

%prep
%autosetup -n penaltymodel-1.0.2

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

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

%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.2-1
- Package Spec generated