pyAgrum-nightly


NamepyAgrum-nightly JSON
Version 1.12.1.9.dev202402231708630418 PyPI version JSON
download
home_pagehttps://agrum.gitlab.io/
SummaryBayesian networks and other Probabilistic Graphical Models.
upload_time2024-02-23 02:06:12
maintainer
docs_urlNone
authorPierre-Henri Wuillemin and Christophe Gonzales
requires_python
licenseLGPLv3
keywords probabilities probabilistic-graphical-models inference diagnosis
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            
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.

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) 2005,2023 by Pierre-Henri WUILLEMIN et Christophe GONZALES
{prenom.nom}_at_lip6.fr                                               

The aGrUM/pyAgrum library and all its derivatives are distributed under the LGPL3 license, see https://www.gnu.org/licenses/lgpl-3.0.en.html.

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\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) 2005,2023 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 LGPL3 license, see https://www.gnu.org/licenses/lgpl-3.0.en.html.\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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