%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.nju.edu.cn/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
[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
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.
Veroku currently supports the following probability distributions:
* Categorical (sparse and dense implementations)
* Gaussian
* Gaussian mixture1
* Linear Gaussian2
* Non-linear Gaussian3
1 This class still has some experimental functionality (specifically the Gaussian mixture division methods)
and is, therefore, still in the factors.experimental sub-package.
2 Using the Gaussian class - see the Kalman filter example notebook.
3This implementation is still experimental (see the factors.experimental sub-package).
These distributions can be used as factors to represent a factorised distribution. These factors can be used, together
with the cluster_graph 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.
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
[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
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.
Veroku currently supports the following probability distributions:
* Categorical (sparse and dense implementations)
* Gaussian
* Gaussian mixture1
* Linear Gaussian2
* Non-linear Gaussian3
1 This class still has some experimental functionality (specifically the Gaussian mixture division methods)
and is, therefore, still in the factors.experimental sub-package.
2 Using the Gaussian class - see the Kalman filter example notebook.
3This implementation is still experimental (see the factors.experimental sub-package).
These distributions can be used as factors to represent a factorised distribution. These factors can be used, together
with the cluster_graph 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.
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
[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
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.
Veroku currently supports the following probability distributions:
* Categorical (sparse and dense implementations)
* Gaussian
* Gaussian mixture1
* Linear Gaussian2
* Non-linear Gaussian3
1 This class still has some experimental functionality (specifically the Gaussian mixture division methods)
and is, therefore, still in the factors.experimental sub-package.
2 Using the Gaussian class - see the Kalman filter example notebook.
3This implementation is still experimental (see the factors.experimental sub-package).
These distributions can be used as factors to represent a factorised distribution. These factors can be used, together
with the cluster_graph 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.
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 May 18 2023 Python_Bot - 1.0.171-1
- Package Spec generated
