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| cmake/ | H | 08-Sep-2021 | - | 1,015 | 803 | |
| doc/ | H | 08-Sep-2021 | - | 15,718 | 13,105 | |
| extensions/ | H | 08-Sep-2021 | - | 1,644 | 1,029 | |
| generated-files/ | H | 08-Sep-2021 | - | 174,947 | 157,226 | |
| pyLibs/ | H | 08-Sep-2021 | - | 860,198 | 856,453 | |
| swigsrc/ | H | 08-Sep-2021 | - | 2,559 | 1,810 | |
| testunits/ | H | 03-May-2022 | - | 71,817 | 70,038 | |
| wheelhouse/ | H | 08-Sep-2021 | - | 262 | 197 | |
| README.md | H A D | 08-Sep-2021 | 1,014 | 14 | 9 | |
| pyAgrum.i | H A D | 08-Sep-2021 | 3 KiB | 99 | 41 |
README.md
1# pyAgrum 2 3[](https://pypi.org/project/pyAgrum/) 4[](https://pypi.org/project/pyAgrum/) 5 6pyAgrum is a Python wrapper for the C++ aGrUM library (using SWIG interface generator). It provides a high-level interface to the part of aGrUM allowing to create, model, learn, use, calculate with and embed Bayesian Networks and other graphical models. Some specific (python and C++) codes are added in order to simplify and extend the aGrUM API. 7 8Several topics have been added to pyAgrum (as pure python modules using pyAgrum) : 9 10- Scikit-learn-compliant probabilistic classifiers based on Bayesian networks, 11- Probabilistic causality (causal networks, do-calculus), 12- dynamic Bayesian network. 13 14See the [tutorials as jupyter notebooks](https://webia.lip6.fr/~phw//aGrUM/docs/last/notebooks/Tutorial.ipynb.html) for more details.
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