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|
%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
[](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
[](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
[](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
* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 3.1.13-1
- Package Spec generated
|
