Bayesian networks and other Probabilistic Graphical Models.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for Aspard from gravatar.com Aspard Avatar for ltorti from gravatar.com ltorti

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers
Report project as malware

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.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for Aspard from gravatar.com Aspard Avatar for ltorti from gravatar.com ltorti

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

2.3.2

2.3.1

2.3.0

2.2.1

2.2.0

2.1.1

2.1.0

2.0.1

2.0.0

1.99.9.91 yanked

Reason this release was yanked:

temporary version

1.17.2

1.17.1

1.17.0

1.16.0

1.15.1

1.15.0

1.14.1

1.14.0

1.13.2

1.13.1

1.13.0

1.12.1

1.12.0

1.11.0

1.10.0

This version

1.9.0

1.8.3

1.8.2

1.8.1

1.8.0

1.7.1

1.7.0

1.6.1

1.6.0

1.5.2

1.5.1

1.5.0

1.4.1

1.4.0

1.3.2

1.3.1

1.3.0

1.2.0

1.1.1

1.1.0

1.0.0

0.22.9

0.22.8

0.22.7

0.22.6

0.22.5

0.22.4

0.22.3

0.22.2

0.22.1

0.22.0

0.21.0

0.20.3

0.20.2

0.20.1

0.20.0

0.19.3

0.19.2

0.19.1

0.19.0

0.18.2

0.18.1

0.18.0

0.17.3

0.17.2

0.17.1

0.17.0

0.16.4

0.16.3

0.16.2

0.16.1

0.16.0

0.15.2

0.15.1

0.15.0

0.14.3

0.14.2

0.14.1

0.13.6

0.13.5

0.13.4.9

0.13.3

0.13.2.1

0.13.1.2

0.13.1.1

0.13.0.1

0.12.1.0

0.12.0.9.1

0.12.0.3

0.12.0.1

0.12.0

0.11.2

0.11.1.9

0.10.4.3

0.10.3.4

0.10.1.1

0.10.0.1

0.9.3.7

0.9.3.6

0.9.3.5

0.9.3.4

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-1.9.0-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11Windows x86-64

pyAgrum-1.9.0-cp311-cp311-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.11

pyAgrum-1.9.0-cp311-cp311-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.11

pyAgrum-1.9.0-cp311-cp311-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

pyAgrum-1.9.0-cp311-cp311-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

pyAgrum-1.9.0-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10Windows x86-64

pyAgrum-1.9.0-cp310-cp310-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.10

pyAgrum-1.9.0-cp310-cp310-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.10

pyAgrum-1.9.0-cp310-cp310-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

pyAgrum-1.9.0-cp310-cp310-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

pyAgrum-1.9.0-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9Windows x86-64

pyAgrum-1.9.0-cp39-cp39-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.9

pyAgrum-1.9.0-cp39-cp39-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.9

pyAgrum-1.9.0-cp39-cp39-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

pyAgrum-1.9.0-cp39-cp39-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

pyAgrum-1.9.0-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8Windows x86-64

pyAgrum-1.9.0-cp38-cp38-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.8

pyAgrum-1.9.0-cp38-cp38-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.8

pyAgrum-1.9.0-cp38-cp38-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

pyAgrum-1.9.0-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-1.9.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.9.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 2.6 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for pyAgrum-1.9.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 33c68ca8a571e9455007ff89aa555fccc823eac30d880e1f5f9c8431641b1d8a
MD5 83cc22835f6902966e1b8ae740ea9175
BLAKE2b-256 630940402fc84065332ee9f806a351933f496190c3ddab59f840c8b53582dd88

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 26fca50ec196ba148f034034d0efc65add9c68f3e861d2441ec066e129b25fa0
MD5 f292b85bb5e64bfba3152ef2be3b3881
BLAKE2b-256 a11f3703ee6d8fc1a9359908611dff5b1f4e5610a8c06920433037054e498f67

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 943fdffd9d95a9bd15cdd0c3552b7fcebeed16e0d99551bd0fdf6c0fc7054b30
MD5 be0619b1db250f8fe7cce1c087dae40d
BLAKE2b-256 ad35a286fabcef8682008a49782c29d98457a314dcf7291eb750b3f0d03704a9

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3f932d2de6b502164e5f88ba3c8e5753853a385103b0c618543dbac677399e38
MD5 e009bad70ccff5277eaf40c18a4c49cf
BLAKE2b-256 e98ed7626fed23b11f2cb622936641b1bf0c3076c2c2455bb8a0c5e48b97825f

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b2917bf79e41de185e9cbd60e25683fc9bdb780189dc752dcb64570355ded94c
MD5 912ce4fa95eb77f146a3f9ae8f19109e
BLAKE2b-256 aaa8e4d5f87b1bef4f110732d6906ae0ff6458603ec52284c9a4a8de3f87da3d

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.9.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 2.6 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for pyAgrum-1.9.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 e1902a357218b11122f5daafc149f82c613c6d400f2cdfe40314720d0246ad6b
MD5 2f5d30edd3e1f1a91801b510394f9852
BLAKE2b-256 41ce542c689bdcc45ba630de9e90a812154614c598da1f340a9121193aea5892

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1721e0ad5ce086c0dbf1c537ab6af3f132fd5c74d6ca612f355a659ed96bdddb
MD5 3251241dbcf3b5ae9db9975a248a53b8
BLAKE2b-256 fe65462dd978d74d53aae9ebb3ea115a4694d06327d4a0921c5976bf4771c645

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 9db48befa8682774df43adecc02ae705df6502b8f3663661ca412c63cedc4bae
MD5 9e93a2340997c83bcc8143f4241e0113
BLAKE2b-256 90f906d2423aa2cc14ae71311cdd048c6b8e173ab46a4752aaa1d4d9ca193c91

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0d0b007b8ee7c3955c22c7cc1a14336d5c1a840d0a8fc141b3983ec2b0201e31
MD5 5078faa7bddc2a0cde3f4027289dadd0
BLAKE2b-256 9029f6e25f19339e4d24b49fc701c54e517d0471e72280bab15102864284d6e9

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 27d5976b1472d4392b1631aca46ca86822ef51d7fcf5f28ba66a1c8fc5796e7a
MD5 5e8becd0ad0fbdfa197d5c2251541667
BLAKE2b-256 10a6200ee6f85386aa504d89adb2ff8606b3e88e8c54625ca661d7538373e90d

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.9.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 2.6 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for pyAgrum-1.9.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 dea41ea926eeda4bee4885291728a79f2dc6206e06f0767eb98d29b7e80cb9b4
MD5 e5b25b3004ec113bfd336a85e1477636
BLAKE2b-256 ac65a6d2f515980adfc34b791b928c6bdc0b9a9fc744dca9ad9a1efa0b6000db

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b3270e4ca446742f2383050bab8d023ce37925ce84ff32e5a23cc4890441b724
MD5 12d8e388a1109f796538344a7a826278
BLAKE2b-256 8a210c18fe36c4a7205a67029122c7e159b52eaef6c108e07ced4d598504734e

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 77941b4128b5fb8912bc0ff65524278b2ddfedae97b51f1bf56ae45ebeca6b1e
MD5 921c5088eb01f0d64b7df28407c90ead
BLAKE2b-256 fd572a640d4ed7db5579f0ed313c58659e34bed208c8003971381adef3d3bee5

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cfe5fff1d971432852afbbfda408804190e4e91106ecf3d716cd12763daf54ab
MD5 c8da0e579c391e5aa40f05f12306c9bb
BLAKE2b-256 d3128834e8f6bce5583352e94a839f4ec17cf90268e2255908038b0546d38d6c

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4f2b9ce54d7c0856a248e8e540aca03fb46ee1eb1643cbe0ae56be77a90ec5cf
MD5 3e99861de709e413b2c10e4d435fb635
BLAKE2b-256 b1ab5243180095d3714e604900d9b5936c4fcb7e7f1a351933907a58cfbbef4b

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.9.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 2.6 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for pyAgrum-1.9.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 33dc69f1bb732bb7c316b0cd776b76a588298fdfb708f1af2662a3b0a77faf67
MD5 f00ab3309bf584cf9616b3f74a6cf34c
BLAKE2b-256 07b375efcf39e700c5881d832aeb075b5e39f1b477e27f8d5b4316ab2afc9c7d

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e1ccf9d25d125de4d8feb43b24b5406e2090654544f04eff174905b065dacc19
MD5 d10ee5a8dcb87099cf3e51ccab25be68
BLAKE2b-256 19e7d61bfc93e59a5c30b58c7eb126b0fff94d3855c604915ccd369b36a82263

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ad3e46305b9258e4b1e5f341e3aaa4ba0d9b00fec6129b8161658feaa8c8dbc2
MD5 34c85b9d68780ad32aaac197854570a7
BLAKE2b-256 f7a52c0a80b39f71d98c120763a4d0690d5917e831644eb4c8fb30fc336035e2

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d1c34ea8bdddcbc05285900a0f0f67cafcbb7a83d917b46273c8fe5894e6c242
MD5 a1249dff0921ef161ca9aaf1343a3c9a
BLAKE2b-256 da903507bccaca52733b59f40f594be974968bceba86b0d6690dff47207a4c64

See more details on using hashes here.

File details

Details for the file pyAgrum-1.9.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.9.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ac775787d6da83ac5810abf02d234b1ee4bf3d17a02779dcd0cb091f95c86e2f
MD5 0446600aa80824f5c240a477589cbd6a
BLAKE2b-256 fd91a20c9c607645709e921fe21254e0bc2bc983f87612090d54c2190c6788db

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