pyAgrum 2.3.2
Bayesian networks and other Probabilistic Graphical Models.
Requires Python: >=3.10
Keywords: probabilities probabilistic-graphical-models inference diagnosis


pyAgrum
=======

``pyAgrum`` is a scientific C++ and Python library dedicated to Bayesian Networks and other Probabilistic Graphical Models. 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.

Important
=========

Since pyAgrum 2.0.0, the package name follows PEP8 rules and is now ``pyagrum`` (lowercase). 
Please use ``import pyagrum`` instead of ``import pyAgrum`` in your code.

See the `CHANGELOG <https://gitlab.com/agrumery/aGrUM/-/blob/master/CHANGELOG.md?ref_type=heads#changelog-for-200>`_  for more details.

Example
=======

.. code:: python

    import pyagrum as gum

    # Creating BayesNet with 4 variables
    bn=gum.BayesNet('WaterSprinkler')
    print(bn)

    # Adding nodes the long way
    c=bn.add(gum.LabelizedVariable('c','cloudy ?',["Yes","No"]))
    print(c)

    # Adding nodes the short way
    s, r, w = [ bn.add(name, 2) for name in "srw" ]
    print (s,r,w)
    print (bn)

    # Addings arcs c -> s, c -> r, s -> w, r -> w
    bn.addArc(c,s)
    for link in [(c,r),(s,w),(r,w)]:
    bn.addArc(*link)
    print(bn)

    # or, equivalenlty, creating the BN with 4 variables, and the arcs in one line
    bn=gum.fastBN("w<-r<-c{Yes|No}->s->w")

    # Filling CPTs
    bn.cpt("c").fillWith([0.5,0.5])
    bn.cpt("s")[0,:]=0.5 # equivalent to [0.5,0.5]
    bn.cpt("s")[{"c":1}]=[0.9,0.1]
    bn.cpt("w")[0,0,:] = [1, 0] # r=0,s=0
    bn.cpt("w")[0,1,:] = [0.1, 0.9] # r=0,s=1
    bn.cpt("w")[{"r":1,"s":0}] = [0.1, 0.9] # r=1,s=0
    bn.cpt("w")[1,1,:] = [0.01, 0.99] # r=1,s=1
    bn.cpt("r")[{"c":0}]=[0.8,0.2]
    bn.cpt("r")[{"c":1}]=[0.2,0.8]

    # Saving BN as a BIF file
    gum.saveBN(bn,"WaterSprinkler.bif")

    # Loading BN from a BIF file
    bn2=gum.loadBN("WaterSprinkler.bif")

    # Inference
    ie=gum.LazyPropagation(bn)
    ie.makeInference()
    print (ie.posterior("w"))

    # Adding hard evidence
    ie.setEvidence({"s": 1, "c": 0})
    ie.makeInference()
    print(ie.posterior("w"))

    # Adding soft and hard evidence
    ie.setEvidence({"s": [0.5, 1], "c": 0})
    ie.makeInference()
    print(ie.posterior("w"))

LICENSE
=======

Copyright (C) 2005-2024 by Pierre-Henri WUILLEMIN et Christophe GONZALES
{prenom.nom}_at_lip6.fr

The aGrUM/pyAgrum library and all its derivatives are distributed under the dual LGPLv3+MIT license, see LICENSE.LGPL and LICENSE.MIT.

You can therefore integrate this library into your software solution but it will remain covered by either the LGPL v.3 license or the MIT license or, as aGrUM itself, by the dual LGPLv3+MIT license at your convenience.
If you wish to integrate the aGrUM library into your product without being affected by this license, please contact us (info@agrum.org).

This library depends on different third-party codes. See src/aGrUM/tools/externals for specific COPYING and explicit permission of
the authors, if needed.

If you use aGrUM/pyAgrum as a dependency of your own project, you are not contaminated by the GPL license of some of these third-party
codes as long as you use only their aGrUM/pyAgrum interfaces and not their native interfaces.

Authors
=======

-  Pierre-Henri Wuillemin
-  Christophe Gonzales

Maintainers
===========

- Lionel Torti
- Gaspard Ducamp