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|
%global _empty_manifest_terminate_build 0
Name: python-veroku
Version: 1.0.171
Release: 1
Summary: An open source library for building and performing inference with probabilistic graphical models.
License: BSD 3-Clause License
URL: https://github.com/ejlouw/veroku
Source0: https://mirrors.aliyun.com/pypi/web/packages/82/55/ea160d2b13cc3179e6b3443d143b58bd4d815fc924229dfe7070c9849aea/veroku-1.0.171.tar.gz
BuildArch: noarch
Requires: python3-numdifftools
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-seaborn
Requires: python3-graphviz
Requires: python3-Pillow
Requires: python3-ipython
Requires: python3-pandas
Requires: python3-tqdm
Requires: python3-ipympl
Requires: python3-numpy
Requires: python3-networkx
%description
<div align="center">
<img src="logo.svg">
</div>
[comment]: # (doc-start)
### Installation
For installing through pip:
```bash
pip install veroku
```
To clone this git repo:
```
git clone https://github.com/ejlouw/veroku.git
cd veroku/
pip install -r requirements.txt
```
It is recommended to use a separate conda virtual environment when installing the dependencies, to avoid interfering
with existing packages. To get started with conda environments, see the
[installation guide](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).
For more information on using conda environments see
[managing environments guide](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
### Overview
<div style="text-align: justify">
Veroku is an open source library for building and performing inference with probabilistic graphical models (PGMs) in
python. PGMs provide a framework for performing efficient probabilistic inference with very high dimensional
distributions. A typical example of a well-known type of PGM is the Kalman filter that can be used to obtain
probabilistic estimates of a hidden state of a process or system, given noisy measurements. PGMs can in principle be
used for any problem that involves uncertainty and is therefore applicable to many problems.</div>
Veroku currently supports the following probability distributions:
* Categorical (sparse and dense implementations)
* Gaussian
* Gaussian mixture<sup>1</sup>
* Linear Gaussian<sup>2</sup>
* Non-linear Gaussian<sup>3</sup>
<sup>1</sup> This class still has some experimental functionality (specifically the Gaussian mixture division methods)
and is, therefore, still in the factors.experimental sub-package.
<sup>2</sup> Using the Gaussian class - see the Kalman filter example notebook.<br/>
<sup>3</sup>This implementation is still experimental (see the factors.experimental sub-package).
<div style="text-align: justify">
These distributions can be used as factors to represent a factorised distribution. These factors can be used, together
with the <code>cluster_graph</code> module to automatically create valid cluster graphs. Inference can be performed in these graphs
using message passing algorithms. Currently only the LBU (Loopy Belief Update) message-passing algorithm is supported.
</div>
<br/>
Example notebooks:
* [Toy example](https://github.com/ejlouw/veroku/blob/master/examples/slip_on_grass.ipynb)
* [Kalman filter](https://github.com/ejlouw/veroku/blob/master/examples/Kalman_filter.ipynb)
* [Sudoku](https://github.com/ejlouw/veroku/blob/master/examples/sudoku.ipynb)
### On the Roadmap
The following distributions, models and features are on the roadmap to be added to veroku:
* Conditional Gaussian
* Dirichlet distribution
* Wishart distribution
* Normal-Wishart distribution
* Plate models
* Structure Learning
* Causal Inference
### Dependencies
For the python dependencies see the [requirements](https://github.com/ejlouw/veroku/blob/master/requirements.txt) file.
The following additional dependencies are also required for some functionality (these are not installed automatically
with the `pip install`):
##### Graphviz
See https://graphviz.org/download/ for installation instructions.
### Contributing
If you would like to contribute to veroku, please see the [contributing guide](https://github.com/ejlouw/veroku/blob/master/contributing.md).
### License
Veroku is released under a 3-Clause BSD license. You can view the license
[here](https://github.com/ejlouw/veroku/blob/master/LICENSE).
%package -n python3-veroku
Summary: An open source library for building and performing inference with probabilistic graphical models.
Provides: python-veroku
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-veroku
<div align="center">
<img src="logo.svg">
</div>
[comment]: # (doc-start)
### Installation
For installing through pip:
```bash
pip install veroku
```
To clone this git repo:
```
git clone https://github.com/ejlouw/veroku.git
cd veroku/
pip install -r requirements.txt
```
It is recommended to use a separate conda virtual environment when installing the dependencies, to avoid interfering
with existing packages. To get started with conda environments, see the
[installation guide](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).
For more information on using conda environments see
[managing environments guide](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
### Overview
<div style="text-align: justify">
Veroku is an open source library for building and performing inference with probabilistic graphical models (PGMs) in
python. PGMs provide a framework for performing efficient probabilistic inference with very high dimensional
distributions. A typical example of a well-known type of PGM is the Kalman filter that can be used to obtain
probabilistic estimates of a hidden state of a process or system, given noisy measurements. PGMs can in principle be
used for any problem that involves uncertainty and is therefore applicable to many problems.</div>
Veroku currently supports the following probability distributions:
* Categorical (sparse and dense implementations)
* Gaussian
* Gaussian mixture<sup>1</sup>
* Linear Gaussian<sup>2</sup>
* Non-linear Gaussian<sup>3</sup>
<sup>1</sup> This class still has some experimental functionality (specifically the Gaussian mixture division methods)
and is, therefore, still in the factors.experimental sub-package.
<sup>2</sup> Using the Gaussian class - see the Kalman filter example notebook.<br/>
<sup>3</sup>This implementation is still experimental (see the factors.experimental sub-package).
<div style="text-align: justify">
These distributions can be used as factors to represent a factorised distribution. These factors can be used, together
with the <code>cluster_graph</code> module to automatically create valid cluster graphs. Inference can be performed in these graphs
using message passing algorithms. Currently only the LBU (Loopy Belief Update) message-passing algorithm is supported.
</div>
<br/>
Example notebooks:
* [Toy example](https://github.com/ejlouw/veroku/blob/master/examples/slip_on_grass.ipynb)
* [Kalman filter](https://github.com/ejlouw/veroku/blob/master/examples/Kalman_filter.ipynb)
* [Sudoku](https://github.com/ejlouw/veroku/blob/master/examples/sudoku.ipynb)
### On the Roadmap
The following distributions, models and features are on the roadmap to be added to veroku:
* Conditional Gaussian
* Dirichlet distribution
* Wishart distribution
* Normal-Wishart distribution
* Plate models
* Structure Learning
* Causal Inference
### Dependencies
For the python dependencies see the [requirements](https://github.com/ejlouw/veroku/blob/master/requirements.txt) file.
The following additional dependencies are also required for some functionality (these are not installed automatically
with the `pip install`):
##### Graphviz
See https://graphviz.org/download/ for installation instructions.
### Contributing
If you would like to contribute to veroku, please see the [contributing guide](https://github.com/ejlouw/veroku/blob/master/contributing.md).
### License
Veroku is released under a 3-Clause BSD license. You can view the license
[here](https://github.com/ejlouw/veroku/blob/master/LICENSE).
%package help
Summary: Development documents and examples for veroku
Provides: python3-veroku-doc
%description help
<div align="center">
<img src="logo.svg">
</div>
[comment]: # (doc-start)
### Installation
For installing through pip:
```bash
pip install veroku
```
To clone this git repo:
```
git clone https://github.com/ejlouw/veroku.git
cd veroku/
pip install -r requirements.txt
```
It is recommended to use a separate conda virtual environment when installing the dependencies, to avoid interfering
with existing packages. To get started with conda environments, see the
[installation guide](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).
For more information on using conda environments see
[managing environments guide](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
### Overview
<div style="text-align: justify">
Veroku is an open source library for building and performing inference with probabilistic graphical models (PGMs) in
python. PGMs provide a framework for performing efficient probabilistic inference with very high dimensional
distributions. A typical example of a well-known type of PGM is the Kalman filter that can be used to obtain
probabilistic estimates of a hidden state of a process or system, given noisy measurements. PGMs can in principle be
used for any problem that involves uncertainty and is therefore applicable to many problems.</div>
Veroku currently supports the following probability distributions:
* Categorical (sparse and dense implementations)
* Gaussian
* Gaussian mixture<sup>1</sup>
* Linear Gaussian<sup>2</sup>
* Non-linear Gaussian<sup>3</sup>
<sup>1</sup> This class still has some experimental functionality (specifically the Gaussian mixture division methods)
and is, therefore, still in the factors.experimental sub-package.
<sup>2</sup> Using the Gaussian class - see the Kalman filter example notebook.<br/>
<sup>3</sup>This implementation is still experimental (see the factors.experimental sub-package).
<div style="text-align: justify">
These distributions can be used as factors to represent a factorised distribution. These factors can be used, together
with the <code>cluster_graph</code> module to automatically create valid cluster graphs. Inference can be performed in these graphs
using message passing algorithms. Currently only the LBU (Loopy Belief Update) message-passing algorithm is supported.
</div>
<br/>
Example notebooks:
* [Toy example](https://github.com/ejlouw/veroku/blob/master/examples/slip_on_grass.ipynb)
* [Kalman filter](https://github.com/ejlouw/veroku/blob/master/examples/Kalman_filter.ipynb)
* [Sudoku](https://github.com/ejlouw/veroku/blob/master/examples/sudoku.ipynb)
### On the Roadmap
The following distributions, models and features are on the roadmap to be added to veroku:
* Conditional Gaussian
* Dirichlet distribution
* Wishart distribution
* Normal-Wishart distribution
* Plate models
* Structure Learning
* Causal Inference
### Dependencies
For the python dependencies see the [requirements](https://github.com/ejlouw/veroku/blob/master/requirements.txt) file.
The following additional dependencies are also required for some functionality (these are not installed automatically
with the `pip install`):
##### Graphviz
See https://graphviz.org/download/ for installation instructions.
### Contributing
If you would like to contribute to veroku, please see the [contributing guide](https://github.com/ejlouw/veroku/blob/master/contributing.md).
### License
Veroku is released under a 3-Clause BSD license. You can view the license
[here](https://github.com/ejlouw/veroku/blob/master/LICENSE).
%prep
%autosetup -n veroku-1.0.171
%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-veroku -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.171-1
- Package Spec generated
|
