Skip to main content

Bayesian networks and other Probabilistic Graphical Models.

Project description

Description: 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 the C++ aGrUM library 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. The module is mainly generated by the SWIG interface generator.

Release history Release notifications | RSS feed

This version

1.3.0

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-1.3.0-cp310-cp310-win_amd64.whl (2.5 MB view details)

Uploaded CPython 3.10Windows x86-64

pyAgrum-1.3.0-cp310-cp310-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.10

pyAgrum-1.3.0-cp310-cp310-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.10

pyAgrum-1.3.0-cp310-cp310-macosx_11_0_arm64.whl (3.9 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

pyAgrum-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

pyAgrum-1.3.0-cp39-cp39-win_amd64.whl (2.5 MB view details)

Uploaded CPython 3.9Windows x86-64

pyAgrum-1.3.0-cp39-cp39-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.9

pyAgrum-1.3.0-cp39-cp39-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.9

pyAgrum-1.3.0-cp39-cp39-macosx_11_0_arm64.whl (3.9 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

pyAgrum-1.3.0-cp39-cp39-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

pyAgrum-1.3.0-cp38-cp38-win_amd64.whl (2.5 MB view details)

Uploaded CPython 3.8Windows x86-64

pyAgrum-1.3.0-cp38-cp38-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.8

pyAgrum-1.3.0-cp38-cp38-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.8

pyAgrum-1.3.0-cp38-cp38-macosx_11_0_arm64.whl (3.9 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

pyAgrum-1.3.0-cp38-cp38-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file pyAgrum-1.3.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.3.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for pyAgrum-1.3.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 75871def85f4d59977e1fe9eedb973f005435aa3e5297770a52b3837721d5412
MD5 f3aa1ad667b1d4f6ed1b7bd3f6fe97ac
BLAKE2b-256 e684d78f9a234c0d623661147f2756da461999ff0353a25ce7e17d223aa00c0d

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0ab85fb09b36c67eb5bca729ee25e9db09dafebbb6d572878b55cb45cb0abbd2
MD5 81db13d9cd53a2b2581ac5db6131957f
BLAKE2b-256 f18a1bf951e88788ae3c34ff9c3f779dd0f66f3404a9c3f62564f5dee601e27f

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0c8f56e21309a1a53447d79eae23aa484df7f77b21ec4009ca777972e21b11ef
MD5 cd8ca3c3aa80ef581f7f73789e47a0b9
BLAKE2b-256 13c4f4772dd49f7f8bdf1e605d82bdc4419d2230d2da506cc42d024e6df69e30

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c364463c4123d40705156bc0357eb1219dc24f258ef13be1b0056379b8f8b05c
MD5 4c5d0a737fe7465a16d8785d3a75aae4
BLAKE2b-256 18a11d567f007829c457129de0fee715aa0093afc4dd18d4712aaac4ad5ad809

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 92c252366f762401c69c4bbd28f51a3c8baf33529b687cc9baa4ff05fa5bbb91
MD5 3e8063b834a6462d45cd84e8bb1d261c
BLAKE2b-256 f917c655694fe95b52ac6728391ace167d58c982fcd74a62ecc86bd5d2b0b69b

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.3.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for pyAgrum-1.3.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 301c76e1c14c0e88787a16b307f1ea015336289e1ac8d73a84b45a8fc3368696
MD5 f34891a52667ce3cce6890f18822638a
BLAKE2b-256 71c092cc6a2381f3f52b8d8213905afa7285020a5ef341a6f989ce0c7590f58c

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 beace02065b4e707d11d7ab0fc3808c20b2ad1e52d2462eb8f84e03ecb17516c
MD5 783960b999b0650f9a714b2a2ad98175
BLAKE2b-256 9dea2b89ddc0148afa84de27e8809c14e4ead052acb35a7e2cb2dd74223958c3

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 92d616e87792c7289d1bc4156f3cf918c0e3c69f432b30f4c0ada30b3c82d2e3
MD5 b10c323293b4704a342956b29ff0bec8
BLAKE2b-256 13ac2544d7283919be985faddf1df8ad352a0e61351f2dbdb65fd932c2d3fd9b

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8aa82af2e08c36a443dae07509d5d166dbc324d96fc41917812cbd5b85352f51
MD5 12e4ea943da4eaa11449c7fcf177aa31
BLAKE2b-256 45e9e08e97ee026bc7e9aa227ddcd91b24f2b6c334825d7feb2ec844d290d5a8

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 44df738f96c22a0d0400a24e24c3b55c929ca886a6bc83bc241787509cf1c369
MD5 4838dbc640a178a6bbe51b56790f21a0
BLAKE2b-256 ed884acdae6c96dd21408bd1015e79d22c938d470bca7b1bc62ec875f7e1973f

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.3.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 2.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.5

File hashes

Hashes for pyAgrum-1.3.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 9b73f0ccb4c914a698e3d755cc68b178698a70be35de3c337bd1c15d6bbe2f33
MD5 a4ac6b0d85d3dc0b2ac57036eb50aadf
BLAKE2b-256 06dfe21c95155007c0da8187155bf331fdec3ffce382324b6de01adbed056f9a

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c56b57dc14a3cd1396c98c22e8ded28af25750f9301e2f65b881c7bd949a70fa
MD5 80cb2668509bfc0c1f3abfb2b89f59de
BLAKE2b-256 d5147876cae80f54d42a53703570318599b46385f2f8171205ce21e8dc26dea1

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 da87cbe7d80566bbfde51f0de02148f2b424580133846e4915dd94bffe4ee1f9
MD5 30886c6e19757d39e93be7f1421f26b4
BLAKE2b-256 f64bf82adb4d4a1b1ea7e72511a85812ede0c4a14f4968e4868ab15a97703c3f

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f13387546944dde66126c988625358dfb007c1f61e4d97310b542cb803724226
MD5 765cbbc85463ec1b3b65bf499b65204e
BLAKE2b-256 8f20c08baf94ed8ae3d34c58fb5b0e7d0f67801018ad2a34cabd2a0ac625f6db

See more details on using hashes here.

File details

Details for the file pyAgrum-1.3.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.3.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 2b1c6b0ec9c03b025dc5c72a0a5e1466e1a608083c12120ae76c3b21d11577a0
MD5 706e2addc5696d8c41e41b9fe8700344
BLAKE2b-256 65a06d21bfa411d0c58eaa810a17fef8321f19e7406dbd4b66e68c5528889119

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