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+%global _empty_manifest_terminate_build 0
+Name:		python-gglasso
+Version:	0.1.9
+Release:	1
+Summary:	Algorithms for Single and Multiple Graphical Lasso problems.
+License:	MIT
+URL:		https://github.com/fabian-sp/GGLasso
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/1f/b9/5ac04eaf3cd77d04acf5c00b832cb5787208ee744697159e5afdff202d47/gglasso-0.1.9.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-numba
+Requires:	python3-pandas
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+Requires:	python3-networkx
+Requires:	python3-regain
+Requires:	python3-decorator
+Requires:	python3-sphinx
+Requires:	python3-sphinx-gallery
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-pytest
+Requires:	python3-pytest-cov
+
+%description
+
+# GGLasso
+
+[![PyPI version fury.io](https://badge.fury.io/py/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
+[![PyPI license](https://img.shields.io/pypi/l/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
+[![Python version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/)
+[![Documentation Status](https://readthedocs.org/projects/gglasso/badge/?version=latest)](http://gglasso.readthedocs.io/?badge=latest)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.03865/status.svg)](https://doi.org/10.21105/joss.03865)
+[![arXiv](https://img.shields.io/badge/arXiv-2011.00898-b31b1b.svg)](https://arxiv.org/abs/2110.10521)
+
+
+This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent 
+Graphical Lasso problems. <br>
+
+[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html)
+
+## Getting started
+
+### Install via pip
+
+The package is available on pip and can be installed with
+
+    pip install gglasso
+
+### Install from source
+
+Alternatively, you can install the package from source using the following commands:
+
+    git clone https://github.com/fabian-sp/GGLasso.git
+    pip install -r requirements.txt
+    python setup.py
+
+Test your installation with 
+
+    pytest gglasso/ -v
+
+
+### Advanced options
+
+When installing from source, you can also install dependencies with `conda` via the command
+
+	$ while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt
+
+If you wish to install `gglasso` in developer mode, i.e. not having to reinstall `gglasso` everytime the source code changes (either by remote or local changes), run
+
+    python setup.py clean --all develop clean --all
+
+## The `glasso_problem` class
+
+`GGLasso` can solve multiple problem forumulations, e.g. single and multiple Graphical Lasso problems as well as with and without latent factors. Therefore, the main entry point for the user is the `glasso_problem` class which chooses automatically the correct solver and model selection functionality. See [our documentation](https://gglasso.readthedocs.io/en/latest/problem-object.html) for all the details.
+
+
+## Algorithms
+
+`GGLasso` contains algorithms for Single and Multiple Graphical Lasso problems. Moreover, it allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type **sparse - low rank**. The following algorithms are contained in the package.
+<br>
+1) ADMM for Single Graphical Lasso<br>
+
+2) ADMM for Group and Fused Graphical Lasso<br>
+The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.<br>
+
+3) A Proximal Point method for Group and Fused Graphical Lasso<br>
+We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.<br>
+
+4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming<br>
+Method for problems where not all variables exist in all instances/datasets.  To use this, import `ext_ADMM_MGL` from `gglasso/solver/ext_admm_solver`.<br>
+
+## Citation
+
+If you use `GGLasso`, please consider the following citation
+
+    @article{Schaipp2021,
+      doi = {10.21105/joss.03865},
+      url = {https://doi.org/10.21105/joss.03865},
+      year = {2021},
+      publisher = {The Open Journal},
+      volume = {6},
+      number = {68},
+      pages = {3865},
+      author = {Fabian Schaipp and Oleg Vlasovets and Christian L. Müller},
+      title = {GGLasso - a Python package for General Graphical Lasso computation},
+      journal = {Journal of Open Source Software}
+    }
+
+
+## Community Guidelines
+
+1)  Contributions and suggestions to the software are always welcome.
+    Please, consult our [contribution guidelines](CONTRIBUTING.md) prior
+    to submitting a pull request.
+2)  Report issues or problems with the software using github’s [issue
+    tracker](https://github.com/fabian-sp/GGLasso/issues).
+3)  Contributors must adhere to the [Code of
+    Conduct](CODE_OF_CONDUCT.md).
+
+
+## References
+*  [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007).  Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432–441.
+*  [2] Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2):373–397.
+* [3] Tomasi, F., Tozzo, V., Salzo, S., and Verri, A. (2018). Latent Variable Time-varying Network Inference. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM.
+* [4] Zhang, Y., Zhang, N., Sun, D., and Toh, K.-C. (2020). A proximal point dual Newton algorithm for solving group graphical Lasso problems. SIAM J. Optim., 30(3):2197–2220.
+
+
+
+
+%package -n python3-gglasso
+Summary:	Algorithms for Single and Multiple Graphical Lasso problems.
+Provides:	python-gglasso
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-gglasso
+
+# GGLasso
+
+[![PyPI version fury.io](https://badge.fury.io/py/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
+[![PyPI license](https://img.shields.io/pypi/l/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
+[![Python version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/)
+[![Documentation Status](https://readthedocs.org/projects/gglasso/badge/?version=latest)](http://gglasso.readthedocs.io/?badge=latest)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.03865/status.svg)](https://doi.org/10.21105/joss.03865)
+[![arXiv](https://img.shields.io/badge/arXiv-2011.00898-b31b1b.svg)](https://arxiv.org/abs/2110.10521)
+
+
+This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent 
+Graphical Lasso problems. <br>
+
+[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html)
+
+## Getting started
+
+### Install via pip
+
+The package is available on pip and can be installed with
+
+    pip install gglasso
+
+### Install from source
+
+Alternatively, you can install the package from source using the following commands:
+
+    git clone https://github.com/fabian-sp/GGLasso.git
+    pip install -r requirements.txt
+    python setup.py
+
+Test your installation with 
+
+    pytest gglasso/ -v
+
+
+### Advanced options
+
+When installing from source, you can also install dependencies with `conda` via the command
+
+	$ while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt
+
+If you wish to install `gglasso` in developer mode, i.e. not having to reinstall `gglasso` everytime the source code changes (either by remote or local changes), run
+
+    python setup.py clean --all develop clean --all
+
+## The `glasso_problem` class
+
+`GGLasso` can solve multiple problem forumulations, e.g. single and multiple Graphical Lasso problems as well as with and without latent factors. Therefore, the main entry point for the user is the `glasso_problem` class which chooses automatically the correct solver and model selection functionality. See [our documentation](https://gglasso.readthedocs.io/en/latest/problem-object.html) for all the details.
+
+
+## Algorithms
+
+`GGLasso` contains algorithms for Single and Multiple Graphical Lasso problems. Moreover, it allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type **sparse - low rank**. The following algorithms are contained in the package.
+<br>
+1) ADMM for Single Graphical Lasso<br>
+
+2) ADMM for Group and Fused Graphical Lasso<br>
+The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.<br>
+
+3) A Proximal Point method for Group and Fused Graphical Lasso<br>
+We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.<br>
+
+4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming<br>
+Method for problems where not all variables exist in all instances/datasets.  To use this, import `ext_ADMM_MGL` from `gglasso/solver/ext_admm_solver`.<br>
+
+## Citation
+
+If you use `GGLasso`, please consider the following citation
+
+    @article{Schaipp2021,
+      doi = {10.21105/joss.03865},
+      url = {https://doi.org/10.21105/joss.03865},
+      year = {2021},
+      publisher = {The Open Journal},
+      volume = {6},
+      number = {68},
+      pages = {3865},
+      author = {Fabian Schaipp and Oleg Vlasovets and Christian L. Müller},
+      title = {GGLasso - a Python package for General Graphical Lasso computation},
+      journal = {Journal of Open Source Software}
+    }
+
+
+## Community Guidelines
+
+1)  Contributions and suggestions to the software are always welcome.
+    Please, consult our [contribution guidelines](CONTRIBUTING.md) prior
+    to submitting a pull request.
+2)  Report issues or problems with the software using github’s [issue
+    tracker](https://github.com/fabian-sp/GGLasso/issues).
+3)  Contributors must adhere to the [Code of
+    Conduct](CODE_OF_CONDUCT.md).
+
+
+## References
+*  [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007).  Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432–441.
+*  [2] Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2):373–397.
+* [3] Tomasi, F., Tozzo, V., Salzo, S., and Verri, A. (2018). Latent Variable Time-varying Network Inference. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM.
+* [4] Zhang, Y., Zhang, N., Sun, D., and Toh, K.-C. (2020). A proximal point dual Newton algorithm for solving group graphical Lasso problems. SIAM J. Optim., 30(3):2197–2220.
+
+
+
+
+%package help
+Summary:	Development documents and examples for gglasso
+Provides:	python3-gglasso-doc
+%description help
+
+# GGLasso
+
+[![PyPI version fury.io](https://badge.fury.io/py/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
+[![PyPI license](https://img.shields.io/pypi/l/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
+[![Python version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/)
+[![Documentation Status](https://readthedocs.org/projects/gglasso/badge/?version=latest)](http://gglasso.readthedocs.io/?badge=latest)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.03865/status.svg)](https://doi.org/10.21105/joss.03865)
+[![arXiv](https://img.shields.io/badge/arXiv-2011.00898-b31b1b.svg)](https://arxiv.org/abs/2110.10521)
+
+
+This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent 
+Graphical Lasso problems. <br>
+
+[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html)
+
+## Getting started
+
+### Install via pip
+
+The package is available on pip and can be installed with
+
+    pip install gglasso
+
+### Install from source
+
+Alternatively, you can install the package from source using the following commands:
+
+    git clone https://github.com/fabian-sp/GGLasso.git
+    pip install -r requirements.txt
+    python setup.py
+
+Test your installation with 
+
+    pytest gglasso/ -v
+
+
+### Advanced options
+
+When installing from source, you can also install dependencies with `conda` via the command
+
+	$ while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt
+
+If you wish to install `gglasso` in developer mode, i.e. not having to reinstall `gglasso` everytime the source code changes (either by remote or local changes), run
+
+    python setup.py clean --all develop clean --all
+
+## The `glasso_problem` class
+
+`GGLasso` can solve multiple problem forumulations, e.g. single and multiple Graphical Lasso problems as well as with and without latent factors. Therefore, the main entry point for the user is the `glasso_problem` class which chooses automatically the correct solver and model selection functionality. See [our documentation](https://gglasso.readthedocs.io/en/latest/problem-object.html) for all the details.
+
+
+## Algorithms
+
+`GGLasso` contains algorithms for Single and Multiple Graphical Lasso problems. Moreover, it allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type **sparse - low rank**. The following algorithms are contained in the package.
+<br>
+1) ADMM for Single Graphical Lasso<br>
+
+2) ADMM for Group and Fused Graphical Lasso<br>
+The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.<br>
+
+3) A Proximal Point method for Group and Fused Graphical Lasso<br>
+We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.<br>
+
+4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming<br>
+Method for problems where not all variables exist in all instances/datasets.  To use this, import `ext_ADMM_MGL` from `gglasso/solver/ext_admm_solver`.<br>
+
+## Citation
+
+If you use `GGLasso`, please consider the following citation
+
+    @article{Schaipp2021,
+      doi = {10.21105/joss.03865},
+      url = {https://doi.org/10.21105/joss.03865},
+      year = {2021},
+      publisher = {The Open Journal},
+      volume = {6},
+      number = {68},
+      pages = {3865},
+      author = {Fabian Schaipp and Oleg Vlasovets and Christian L. Müller},
+      title = {GGLasso - a Python package for General Graphical Lasso computation},
+      journal = {Journal of Open Source Software}
+    }
+
+
+## Community Guidelines
+
+1)  Contributions and suggestions to the software are always welcome.
+    Please, consult our [contribution guidelines](CONTRIBUTING.md) prior
+    to submitting a pull request.
+2)  Report issues or problems with the software using github’s [issue
+    tracker](https://github.com/fabian-sp/GGLasso/issues).
+3)  Contributors must adhere to the [Code of
+    Conduct](CODE_OF_CONDUCT.md).
+
+
+## References
+*  [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007).  Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432–441.
+*  [2] Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2):373–397.
+* [3] Tomasi, F., Tozzo, V., Salzo, S., and Verri, A. (2018). Latent Variable Time-varying Network Inference. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM.
+* [4] Zhang, Y., Zhang, N., Sun, D., and Toh, K.-C. (2020). A proximal point dual Newton algorithm for solving group graphical Lasso problems. SIAM J. Optim., 30(3):2197–2220.
+
+
+
+
+%prep
+%autosetup -n gglasso-0.1.9
+
+%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-gglasso -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.9-1
+- Package Spec generated