%global _empty_manifest_terminate_build 0 Name: python-glimix-core Version: 3.1.13 Release: 1 Summary: Fast inference over mean and covariance parameters for Generalised Linear Mixed Models License: MIT URL: https://github.com/limix/glimix-core Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b6/2a/c77df3eff97040fdd82bde3dac78c8130823230e56fce7d3ef86b09a17a9/glimix-core-3.1.13.tar.gz BuildArch: noarch Requires: python3-brent-search Requires: python3-liknorm Requires: python3-ndarray-listener Requires: python3-numpy Requires: python3-numpy-sugar Requires: python3-optimix Requires: python3-pytest Requires: python3-pytest-doctestplus Requires: python3-scipy Requires: python3-tqdm %description # glimix-core [![Documentation](https://readthedocs.org/projects/glimix-core/badge/?version=latest)](https://glimix-core.readthedocs.io/en/latest/?badge=latest) Fast inference over mean and covariance parameters for Generalised Linear Mixed Models. It implements the mathematical tricks of [FaST-LMM](https://github.com/MicrosoftGenomics/FaST-LMM) for the special case of Linear Mixed Models with a linear covariance matrix and provides an interface to perform inference over millions of covariates in seconds. The Generalised Linear Mixed Model inference is implemented via Expectation Propagation and also makes use of several mathematical tricks to handle large data sets with thousands of samples and millions of covariates. ## Install There are two main ways of installing it. Via [pip](https://pypi.python.org/pypi/pip): ```bash pip install glimix-core ``` Or via [conda](http://conda.pydata.org/docs/index.html): ```bash conda install -c conda-forge glimix-core ``` ## Running the tests After installation, you can test it ```bash python -c "import glimix_core; glimix_core.test()" ``` as long as you have [pytest](https://docs.pytest.org/en/latest/). ## Usage Here it is a very simple example to get you started: ```python >>> from numpy import array, ones >>> from numpy_sugar.linalg import economic_qs_linear >>> from glimix_core.lmm import LMM >>> >>> X = array([[1, 2], [3, -1], [1.1, 0.5], [0.5, -0.4]], float) >>> QS = economic_qs_linear(X, False) >>> X = ones((4, 1)) >>> y = array([-1, 2, 0.3, 0.5]) >>> lmm = LMM(y, X, QS) >>> lmm.fit(verbose=False) >>> lmm.lml() -2.2726234086180557 ``` We also provide an extensive [documentation](http://glimix-core.readthedocs.org/) about the library. ## Authors * [Danilo Horta](https://github.com/horta) ## License This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/glimix-core/master/LICENSE.md). %package -n python3-glimix-core Summary: Fast inference over mean and covariance parameters for Generalised Linear Mixed Models Provides: python-glimix-core BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-glimix-core # glimix-core [![Documentation](https://readthedocs.org/projects/glimix-core/badge/?version=latest)](https://glimix-core.readthedocs.io/en/latest/?badge=latest) Fast inference over mean and covariance parameters for Generalised Linear Mixed Models. It implements the mathematical tricks of [FaST-LMM](https://github.com/MicrosoftGenomics/FaST-LMM) for the special case of Linear Mixed Models with a linear covariance matrix and provides an interface to perform inference over millions of covariates in seconds. The Generalised Linear Mixed Model inference is implemented via Expectation Propagation and also makes use of several mathematical tricks to handle large data sets with thousands of samples and millions of covariates. ## Install There are two main ways of installing it. Via [pip](https://pypi.python.org/pypi/pip): ```bash pip install glimix-core ``` Or via [conda](http://conda.pydata.org/docs/index.html): ```bash conda install -c conda-forge glimix-core ``` ## Running the tests After installation, you can test it ```bash python -c "import glimix_core; glimix_core.test()" ``` as long as you have [pytest](https://docs.pytest.org/en/latest/). ## Usage Here it is a very simple example to get you started: ```python >>> from numpy import array, ones >>> from numpy_sugar.linalg import economic_qs_linear >>> from glimix_core.lmm import LMM >>> >>> X = array([[1, 2], [3, -1], [1.1, 0.5], [0.5, -0.4]], float) >>> QS = economic_qs_linear(X, False) >>> X = ones((4, 1)) >>> y = array([-1, 2, 0.3, 0.5]) >>> lmm = LMM(y, X, QS) >>> lmm.fit(verbose=False) >>> lmm.lml() -2.2726234086180557 ``` We also provide an extensive [documentation](http://glimix-core.readthedocs.org/) about the library. ## Authors * [Danilo Horta](https://github.com/horta) ## License This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/glimix-core/master/LICENSE.md). %package help Summary: Development documents and examples for glimix-core Provides: python3-glimix-core-doc %description help # glimix-core [![Documentation](https://readthedocs.org/projects/glimix-core/badge/?version=latest)](https://glimix-core.readthedocs.io/en/latest/?badge=latest) Fast inference over mean and covariance parameters for Generalised Linear Mixed Models. It implements the mathematical tricks of [FaST-LMM](https://github.com/MicrosoftGenomics/FaST-LMM) for the special case of Linear Mixed Models with a linear covariance matrix and provides an interface to perform inference over millions of covariates in seconds. The Generalised Linear Mixed Model inference is implemented via Expectation Propagation and also makes use of several mathematical tricks to handle large data sets with thousands of samples and millions of covariates. ## Install There are two main ways of installing it. Via [pip](https://pypi.python.org/pypi/pip): ```bash pip install glimix-core ``` Or via [conda](http://conda.pydata.org/docs/index.html): ```bash conda install -c conda-forge glimix-core ``` ## Running the tests After installation, you can test it ```bash python -c "import glimix_core; glimix_core.test()" ``` as long as you have [pytest](https://docs.pytest.org/en/latest/). ## Usage Here it is a very simple example to get you started: ```python >>> from numpy import array, ones >>> from numpy_sugar.linalg import economic_qs_linear >>> from glimix_core.lmm import LMM >>> >>> X = array([[1, 2], [3, -1], [1.1, 0.5], [0.5, -0.4]], float) >>> QS = economic_qs_linear(X, False) >>> X = ones((4, 1)) >>> y = array([-1, 2, 0.3, 0.5]) >>> lmm = LMM(y, X, QS) >>> lmm.fit(verbose=False) >>> lmm.lml() -2.2726234086180557 ``` We also provide an extensive [documentation](http://glimix-core.readthedocs.org/) about the library. ## Authors * [Danilo Horta](https://github.com/horta) ## License This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/glimix-core/master/LICENSE.md). %prep %autosetup -n glimix-core-3.1.13 %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-glimix-core -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 31 2023 Python_Bot - 3.1.13-1 - Package Spec generated