Skip to main content

Bayesian networks and other Probabilistic Graphical Models.

Project description

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 for more details.

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) 2005-2024 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

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-macosx_11_0_arm64.whl (4.2 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-macosx_10_15_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 d217afd44325efe205809b0dafe19e2bf6f43ffbe45bee1520e17c5384182802
MD5 461a8ff2957191645238970d95da379a
BLAKE2b-256 fba96b83064bb84745ae213226df10ab312a3b15ca0f2b3b259c2be94abcf32b

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8f7885aa5b581b0934a563e04c6eb3f2f6921f053717c4eab2eee4e8d78563a4
MD5 277fad47789a8a697dd2268852cd0003
BLAKE2b-256 0b0840de970e42c850a67a1992efb376189af8b66f183b6fd34803379d73a73e

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c257374821bc8bc8d64c4e73391a76894200dcbbedf15611d62201ddde409b5b
MD5 39ce52978359f482eebc9669ce5cec3d
BLAKE2b-256 49eb41f1e15128d1f9f341c2cd63cd9ba8c734a85cdd5848fd11014fd46e1f57

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 dceb3a0bd4e80ad199939c4655d9bd0bd56f8715caa5b042dd061767b3d35ba0
MD5 005d380b15a20143bdc44d2219ae75db
BLAKE2b-256 6e355aed4627e53bca6a1ab31d0f0f4ccfc96c9a0b6e9dfc3c51fac3cd6d8f03

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511271763730929-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 59b942d3e3e1547530e318d07e3cb555430c0089ce1f0535a336afa1967172fc
MD5 fc331f31cce3cea3d3f5aade123e6326
BLAKE2b-256 52a3c59eec6d043f1fbb99b581f7785b951f40091652030df0ca9aa4de17ec2c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page