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diff --git a/python-pymc3-hmm.spec b/python-pymc3-hmm.spec new file mode 100644 index 0000000..078c333 --- /dev/null +++ b/python-pymc3-hmm.spec @@ -0,0 +1,213 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pymc3-hmm +Version: 0.2.5 +Release: 1 +Summary: Hidden Markov Models in PyMC3 +License: Apache Software License +URL: http://github.com/AmpersandTV/pymc3-hmm +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/90/ba/cbb44a1adc4e16d0bba7dc610c0fcb00a3fbc0755bce0da38451577472f6/pymc3-hmm-0.2.5.tar.gz +BuildArch: noarch + + +%description + +[](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples) + +# PyMC3 HMM + +Hidden Markov models in [PyMC3](https://github.com/pymc-devs/pymc3). + +### Features +- Fully implemented PyMC3 `Distribution` classes for HMM state sequences (`DiscreteMarkovChain`) and mixtures that are driven by them (`SwitchingProcess`) +- A forward-filtering backward-sampling (FFBS) implementation (`FFBSStep`) that works with NUTS—or any other PyMC3 sampler +- A conjugate Dirichlet transition matrix sampler (`TransMatConjugateStep`) +- Support for time-varying transition matrices in the FFBS sampler and all the relevant `Distribution` classes + +To use these distributions and step methods in your PyMC3 models, simply import them from the `pymc3_hmm` package. + +See the [examples directory](https://nbviewer.jupyter.org/github/AmpersandTV/pymc3-hmm/tree/main/examples/) for demonstrations of the aforementioned features. You can also use [Binder](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples) to run the examples yourself. + +## Installation + +Currently, the package can be installed via `pip` directly from GitHub +```shell +$ pip install git+https://github.com/AmpersandTV/pymc3-hmm +``` + +## Development + +First, pull in the source from GitHub: + +```python +$ git clone git@github.com:AmpersandTV/pymc3-hmm.git +``` + +Next, you can run `make conda` or `make venv` to set up a virtual environment. + +Once your virtual environment is set up, install the project, its dependencies, and the `pre-commit` hooks: + +```bash +$ pip install -r requirements.txt +$ pre-commit install --install-hooks +``` + + + +After making changes, be sure to run `make black` in order to automatically format the code and then `make check` to run the linters and tests. + +## License + +[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0) + +%package -n python3-pymc3-hmm +Summary: Hidden Markov Models in PyMC3 +Provides: python-pymc3-hmm +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pymc3-hmm + +[](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples) + +# PyMC3 HMM + +Hidden Markov models in [PyMC3](https://github.com/pymc-devs/pymc3). + +### Features +- Fully implemented PyMC3 `Distribution` classes for HMM state sequences (`DiscreteMarkovChain`) and mixtures that are driven by them (`SwitchingProcess`) +- A forward-filtering backward-sampling (FFBS) implementation (`FFBSStep`) that works with NUTS—or any other PyMC3 sampler +- A conjugate Dirichlet transition matrix sampler (`TransMatConjugateStep`) +- Support for time-varying transition matrices in the FFBS sampler and all the relevant `Distribution` classes + +To use these distributions and step methods in your PyMC3 models, simply import them from the `pymc3_hmm` package. + +See the [examples directory](https://nbviewer.jupyter.org/github/AmpersandTV/pymc3-hmm/tree/main/examples/) for demonstrations of the aforementioned features. You can also use [Binder](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples) to run the examples yourself. + +## Installation + +Currently, the package can be installed via `pip` directly from GitHub +```shell +$ pip install git+https://github.com/AmpersandTV/pymc3-hmm +``` + +## Development + +First, pull in the source from GitHub: + +```python +$ git clone git@github.com:AmpersandTV/pymc3-hmm.git +``` + +Next, you can run `make conda` or `make venv` to set up a virtual environment. + +Once your virtual environment is set up, install the project, its dependencies, and the `pre-commit` hooks: + +```bash +$ pip install -r requirements.txt +$ pre-commit install --install-hooks +``` + + + +After making changes, be sure to run `make black` in order to automatically format the code and then `make check` to run the linters and tests. + +## License + +[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0) + +%package help +Summary: Development documents and examples for pymc3-hmm +Provides: python3-pymc3-hmm-doc +%description help + +[](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples) + +# PyMC3 HMM + +Hidden Markov models in [PyMC3](https://github.com/pymc-devs/pymc3). + +### Features +- Fully implemented PyMC3 `Distribution` classes for HMM state sequences (`DiscreteMarkovChain`) and mixtures that are driven by them (`SwitchingProcess`) +- A forward-filtering backward-sampling (FFBS) implementation (`FFBSStep`) that works with NUTS—or any other PyMC3 sampler +- A conjugate Dirichlet transition matrix sampler (`TransMatConjugateStep`) +- Support for time-varying transition matrices in the FFBS sampler and all the relevant `Distribution` classes + +To use these distributions and step methods in your PyMC3 models, simply import them from the `pymc3_hmm` package. + +See the [examples directory](https://nbviewer.jupyter.org/github/AmpersandTV/pymc3-hmm/tree/main/examples/) for demonstrations of the aforementioned features. You can also use [Binder](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples) to run the examples yourself. + +## Installation + +Currently, the package can be installed via `pip` directly from GitHub +```shell +$ pip install git+https://github.com/AmpersandTV/pymc3-hmm +``` + +## Development + +First, pull in the source from GitHub: + +```python +$ git clone git@github.com:AmpersandTV/pymc3-hmm.git +``` + +Next, you can run `make conda` or `make venv` to set up a virtual environment. + +Once your virtual environment is set up, install the project, its dependencies, and the `pre-commit` hooks: + +```bash +$ pip install -r requirements.txt +$ pre-commit install --install-hooks +``` + + + +After making changes, be sure to run `make black` in order to automatically format the code and then `make check` to run the linters and tests. + +## License + +[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0) + +%prep +%autosetup -n pymc3-hmm-0.2.5 + +%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-pymc3-hmm -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.5-1 +- Package Spec generated |