Pre-release
This release is a pre-release and may not be stable for production use.
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
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.
Maintainers
Lionel Torti
Gaspard Ducamp
1 maintainer
Metadata
1 maintainer
Release files for pyAgrum-nightly 1.13.0.9.dev202404101712167003
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 114.4 MB
Release files / pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp312-cp312-win_amd64.whl
| Download URL | pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp312-cp312-win_amd64.whl |
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| Size | 2.7 MB |
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Release files / pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-win_amd64.whl
| Download URL | pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-win_amd64.whl |
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Release files / pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-manylinux2014_x86_64.whl
| Download URL | pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-manylinux2014_x86_64.whl |
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Release files / pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-manylinux2014_aarch64.whl
| Download URL | pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-manylinux2014_aarch64.whl |
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Release files / pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-macosx_11_0_arm64.whl
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Release files / pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-macosx_10_9_x86_64.whl
| Download URL | pyAgrum_nightly-1.13.0.9.dev202404101712167003-cp38-cp38-macosx_10_9_x86_64.whl |
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