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|
%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
|
