%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 ![Build Status](https://github.com/AmpersandTV/pymc3-hmm/workflows/PyMC3-HMM/badge.svg) [![Binder](https://mybinder.org/badge_logo.svg)](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 ![Build Status](https://github.com/AmpersandTV/pymc3-hmm/workflows/PyMC3-HMM/badge.svg) [![Binder](https://mybinder.org/badge_logo.svg)](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 ![Build Status](https://github.com/AmpersandTV/pymc3-hmm/workflows/PyMC3-HMM/badge.svg) [![Binder](https://mybinder.org/badge_logo.svg)](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 - 0.2.5-1 - Package Spec generated