Metadata-Version: 2.4
Name: probabilistic_model
Version: 7.2.0
Summary: Models for probabilistic reasoning
Author-email: Tom Schierenbeck <tom_sch@uni-bremen.de>
Project-URL: Source, https://github.com/cram2/cognitive_robot_abstract_machine
Project-URL: Bug Tracker, https://github.com/cram2/cognitive_robot_abstract_machine/issues
Keywords: probabilistic models,probabilistic machine learning,probability theory,variables,reasoning under uncertainty
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: random-events>=4.1.2
Requires-Dist: networkx
Requires-Dist: plotly
Requires-Dist: scipy
Requires-Dist: typing_extensions
Requires-Dist: numpy
Requires-Dist: sortedcontainers
Requires-Dist: tqdm
Requires-Dist: tabulate
Requires-Dist: N2G
Requires-Dist: python-igraph
Requires-Dist: rustworkx>=0.16.0
Requires-Dist: scikit-learn>=1.6.1
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Requires-Dist: equinox>=0.12.1
Requires-Dist: mlflow>=2.22.0
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Requires-Dist: pyjpt
Requires-Dist: mlflow-skinny>=2.22.0
Requires-Dist: dnutils
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Requires-Dist: deprecated
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Requires-Dist: qt-material
Requires-Dist: superqt

# Welcome to the Probabilistic Model's package. 
[![Python Tests](https://github.com/tomsch420/probabilistic_model/actions/workflows/python-tests.yml/badge.svg)](https://github.com/tomsch420/probabilistic_model/actions/workflows/python-tests.yml)

This package contains an interface for any kind of probabilistic models.
The aim of this package is to provide a clean, unifying, well documented API to
probabilistic models. Just like sklearn does for classical machine learning models.

Read the docs here https://probabilistic-model.readthedocs.io/en/latest/.

## Development

### Testing

The project uses unittest for testing. To run the tests locally:

```bash
pip install -r requirements-dev.txt
pip install -e .
python -m unittest discover test
```

### Continuous Integration

This project uses GitHub Actions for continuous integration. The CI pipeline runs automatically on push to main/master branches and on pull requests to these branches. It sets up a Python environment, installs dependencies, and runs the tests.

You can see the status of the CI pipeline in the GitHub Actions tab of the repository.
