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.
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.
Maintainers
Lionel Torti
Gaspard Ducamp
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-win_amd64.whl.
File metadata
- Download URL: pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-win_amd64.whl
- Upload date:
- Size: 3.5 MB
- Tags: CPython 3.10+, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e8e39d4a160cdb0e4ce3d34950713153d6fa9526932951cc6b7caab7bce414fc
|
|
| MD5 |
328e462464fa133ce5c0a376bb98de89
|
|
| BLAKE2b-256 |
b9c9044c56ecb4eb20e210c5abf9096550c4ebab162a34d2397846806c7d97b1
|
File details
Details for the file pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-manylinux2014_x86_64.whl.
File metadata
- Download URL: pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-manylinux2014_x86_64.whl
- Upload date:
- Size: 5.4 MB
- Tags: CPython 3.10+
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7ba02cf84f3995d4501275db2a61a241ad16d2e029601820daf982c00f3f322f
|
|
| MD5 |
2a1d03c15ea3af3b36c108af07562c2a
|
|
| BLAKE2b-256 |
d20ed256502e7ee68227cfce094418956d7c3209e48af3952520b8b5f6bc1c14
|
File details
Details for the file pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-manylinux2014_aarch64.whl.
File metadata
- Download URL: pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-manylinux2014_aarch64.whl
- Upload date:
- Size: 4.6 MB
- Tags: CPython 3.10+
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1514560c13ef93784a0d84021f9e4258b3370e8d7773aecb4a833b6c6a95ea66
|
|
| MD5 |
1c1ecd6d1fd7433fe51dd0d6efed3ff3
|
|
| BLAKE2b-256 |
0107d918652042a3c824af81cdc8a249d17bccc4d28b7ba5b6ef8837081a6f7e
|
File details
Details for the file pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-macosx_11_0_arm64.whl.
File metadata
- Download URL: pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-macosx_11_0_arm64.whl
- Upload date:
- Size: 3.4 MB
- Tags: CPython 3.10+, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
71c12bd5434aaa8a7864a1c74d2e0cf4e5474ef104e66802f0fcd0fb90db3a02
|
|
| MD5 |
dda5c8013dcd0496594c20309a199808
|
|
| BLAKE2b-256 |
b47c7f147c70c1896bdfcc7671d1fafea9bc73f601e7b1d34b5807ac2987aa8a
|
File details
Details for the file pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-macosx_10_15_x86_64.whl.
File metadata
- Download URL: pyagrum_nightly-3.0.0.9.dev202608101784631574-cp310-abi3-macosx_10_15_x86_64.whl
- Upload date:
- Size: 3.8 MB
- Tags: CPython 3.10+, macOS 10.15+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.8.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0ea977651bba9bff14f04508c345b703356b394260086977e583e57376dd2b30
|
|
| MD5 |
f9b1af0066b60fe4b19e96d1729bfa90
|
|
| BLAKE2b-256 |
596be4d78d245ae518256389a00d0912b1095aa2f81df24d08cd69077d942b30
|
