%global _empty_manifest_terminate_build 0 Name: python-pymoo Version: 0.6.0.1 Release: 1 Summary: Multi-Objective Optimization in Python License: Apache License 2.0 URL: https://pymoo.org Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c1/cb/b382ee907d83cfb28c0c364155703395abe54688ffa3e1713fe62d90a7cd/pymoo-0.6.0.1.tar.gz Requires: python3-numpy Requires: python3-scipy Requires: python3-matplotlib Requires: python3-autograd Requires: python3-cma Requires: python3-alive-progress Requires: python3-dill Requires: python3-Deprecated %description Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features related to multi-objective optimization such as visualization and decision making. Installation ******************************************************************************** First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3. The official release is always available at PyPi: pip install -U pymoo For the current developer version: git clone https://github.com/anyoptimization/pymoo cd pymoo pip install . Since for speedup, some of the modules are also available compiled, you can double-check if the compilation worked. When executing the command, be sure not already being in the local pymoo directory because otherwise not the in site-packages installed version will be used. python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())" Usage ******************************************************************************** We refer here to our documentation for all the details. However, for instance, executing NSGA2: from pymoo.algorithms.moo.nsga2 import NSGA2 from pymoo.problems import get_problem from pymoo.optimize import minimize from pymoo.visualization.scatter import Scatter problem = get_problem("zdt1") algorithm = NSGA2(pop_size=100) res = minimize(problem, algorithm, ('n_gen', 200), seed=1, verbose=True) plot = Scatter() plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7) plot.add(res.F, color="red") plot.show() A representative run of NSGA2 looks as follows: |animation| Citation ******************************************************************************** If you have used our framework for research purposes, you can cite our publication by: | `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 `_ | | BibTex: @ARTICLE{pymoo, author={J. {Blank} and K. {Deb}}, journal={IEEE Access}, title={pymoo: Multi-Objective Optimization in Python}, year={2020}, volume={8}, number={}, pages={89497-89509}, } Contact ******************************************************************************** Feel free to contact me if you have any questions: | `Julian Blank `_ (blankjul [at] msu.edu) | Michigan State University | Computational Optimization and Innovation Laboratory (COIN) | East Lansing, MI 48824, USA %package -n python3-pymoo Summary: Multi-Objective Optimization in Python Provides: python-pymoo BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-pymoo Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features related to multi-objective optimization such as visualization and decision making. Installation ******************************************************************************** First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3. The official release is always available at PyPi: pip install -U pymoo For the current developer version: git clone https://github.com/anyoptimization/pymoo cd pymoo pip install . Since for speedup, some of the modules are also available compiled, you can double-check if the compilation worked. When executing the command, be sure not already being in the local pymoo directory because otherwise not the in site-packages installed version will be used. python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())" Usage ******************************************************************************** We refer here to our documentation for all the details. However, for instance, executing NSGA2: from pymoo.algorithms.moo.nsga2 import NSGA2 from pymoo.problems import get_problem from pymoo.optimize import minimize from pymoo.visualization.scatter import Scatter problem = get_problem("zdt1") algorithm = NSGA2(pop_size=100) res = minimize(problem, algorithm, ('n_gen', 200), seed=1, verbose=True) plot = Scatter() plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7) plot.add(res.F, color="red") plot.show() A representative run of NSGA2 looks as follows: |animation| Citation ******************************************************************************** If you have used our framework for research purposes, you can cite our publication by: | `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 `_ | | BibTex: @ARTICLE{pymoo, author={J. {Blank} and K. {Deb}}, journal={IEEE Access}, title={pymoo: Multi-Objective Optimization in Python}, year={2020}, volume={8}, number={}, pages={89497-89509}, } Contact ******************************************************************************** Feel free to contact me if you have any questions: | `Julian Blank `_ (blankjul [at] msu.edu) | Michigan State University | Computational Optimization and Innovation Laboratory (COIN) | East Lansing, MI 48824, USA %package help Summary: Development documents and examples for pymoo Provides: python3-pymoo-doc %description help Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features related to multi-objective optimization such as visualization and decision making. Installation ******************************************************************************** First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3. The official release is always available at PyPi: pip install -U pymoo For the current developer version: git clone https://github.com/anyoptimization/pymoo cd pymoo pip install . Since for speedup, some of the modules are also available compiled, you can double-check if the compilation worked. When executing the command, be sure not already being in the local pymoo directory because otherwise not the in site-packages installed version will be used. python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())" Usage ******************************************************************************** We refer here to our documentation for all the details. However, for instance, executing NSGA2: from pymoo.algorithms.moo.nsga2 import NSGA2 from pymoo.problems import get_problem from pymoo.optimize import minimize from pymoo.visualization.scatter import Scatter problem = get_problem("zdt1") algorithm = NSGA2(pop_size=100) res = minimize(problem, algorithm, ('n_gen', 200), seed=1, verbose=True) plot = Scatter() plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7) plot.add(res.F, color="red") plot.show() A representative run of NSGA2 looks as follows: |animation| Citation ******************************************************************************** If you have used our framework for research purposes, you can cite our publication by: | `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 `_ | | BibTex: @ARTICLE{pymoo, author={J. {Blank} and K. {Deb}}, journal={IEEE Access}, title={pymoo: Multi-Objective Optimization in Python}, year={2020}, volume={8}, number={}, pages={89497-89509}, } Contact ******************************************************************************** Feel free to contact me if you have any questions: | `Julian Blank `_ (blankjul [at] msu.edu) | Michigan State University | Computational Optimization and Innovation Laboratory (COIN) | East Lansing, MI 48824, USA %prep %autosetup -n pymoo-0.6.0.1 %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-pymoo -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 0.6.0.1-1 - Package Spec generated