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
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"description": "\npyAgrum\n=======\n\n``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.\n\nImportant\n=========\n\nSince pyAgrum 2.0.0, the package name follows PEP8 rules and is now ``pyagrum`` (lowercase). \nPlease use ``import pyagrum`` instead of ``import pyAgrum`` in your code.\n\nSee the `CHANGELOG <https://gitlab.com/agrumery/aGrUM/-/blob/master/CHANGELOG.md?ref_type=heads#changelog-for-200>`_ for more details.\n\nExample\n=======\n\n.. code:: python\n\n import pyagrum as gum\n\n # Creating BayesNet with 4 variables\n bn=gum.BayesNet('WaterSprinkler')\n print(bn)\n\n # Adding nodes the long way\n c=bn.add(gum.LabelizedVariable('c','cloudy ?',[\"Yes\",\"No\"]))\n print(c)\n\n # Adding nodes the short way\n s, r, w = [ bn.add(name, 2) for name in \"srw\" ]\n print (s,r,w)\n print (bn)\n\n # Addings arcs c -> s, c -> r, s -> w, r -> w\n bn.addArc(c,s)\n for link in [(c,r),(s,w),(r,w)]:\n bn.addArc(*link)\n print(bn)\n\n # or, equivalenlty, creating the BN with 4 variables, and the arcs in one line\n bn=gum.fastBN(\"w<-r<-c{Yes|No}->s->w\")\n\n # Filling CPTs\n bn.cpt(\"c\").fillWith([0.5,0.5])\n bn.cpt(\"s\")[0,:]=0.5 # equivalent to [0.5,0.5]\n bn.cpt(\"s\")[{\"c\":1}]=[0.9,0.1]\n bn.cpt(\"w\")[0,0,:] = [1, 0] # r=0,s=0\n bn.cpt(\"w\")[0,1,:] = [0.1, 0.9] # r=0,s=1\n bn.cpt(\"w\")[{\"r\":1,\"s\":0}] = [0.1, 0.9] # r=1,s=0\n bn.cpt(\"w\")[1,1,:] = [0.01, 0.99] # r=1,s=1\n bn.cpt(\"r\")[{\"c\":0}]=[0.8,0.2]\n bn.cpt(\"r\")[{\"c\":1}]=[0.2,0.8]\n\n # Saving BN as a BIF file\n gum.saveBN(bn,\"WaterSprinkler.bif\")\n\n # Loading BN from a BIF file\n bn2=gum.loadBN(\"WaterSprinkler.bif\")\n\n # Inference\n ie=gum.LazyPropagation(bn)\n ie.makeInference()\n print (ie.posterior(\"w\"))\n\n # Adding hard evidence\n ie.setEvidence({\"s\": 1, \"c\": 0})\n ie.makeInference()\n print(ie.posterior(\"w\"))\n\n # Adding soft and hard evidence\n ie.setEvidence({\"s\": [0.5, 1], \"c\": 0})\n ie.makeInference()\n print(ie.posterior(\"w\"))\n\nLICENSE\n=======\n\nCopyright (C) 2005-2024 by Pierre-Henri WUILLEMIN et Christophe GONZALES\n{prenom.nom}_at_lip6.fr\n\nThe aGrUM/pyAgrum library and all its derivatives are distributed under the dual LGPLv3+MIT license, see LICENSE.LGPL and LICENSE.MIT.\n\nYou 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.\nIf you wish to integrate the aGrUM library into your product without being affected by this license, please contact us (info@agrum.org).\n\nThis library depends on different third-party codes. See src/aGrUM/tools/externals for specific COPYING and explicit permission of\nthe authors, if needed.\n\nIf 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\ncodes as long as you use only their aGrUM/pyAgrum interfaces and not their native interfaces.\n\nAuthors\n=======\n\n- Pierre-Henri Wuillemin\n- Christophe Gonzales\n\nMaintainers\n===========\n\n- Lionel Torti\n- Gaspard Ducamp\n",
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