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

1.9.0

1.8.3

1.8.2

1.8.1

1.8.0

1.7.1

1.7.0

1.6.1

This version

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-1.6.0-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11Windows x86-64

pyAgrum-1.6.0-cp311-cp311-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.11

pyAgrum-1.6.0-cp311-cp311-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.11

pyAgrum-1.6.0-cp311-cp311-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

pyAgrum-1.6.0-cp311-cp311-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

pyAgrum-1.6.0-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10Windows x86-64

pyAgrum-1.6.0-cp310-cp310-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.10

pyAgrum-1.6.0-cp310-cp310-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.10

pyAgrum-1.6.0-cp310-cp310-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

pyAgrum-1.6.0-cp310-cp310-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

pyAgrum-1.6.0-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9Windows x86-64

pyAgrum-1.6.0-cp39-cp39-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.9

pyAgrum-1.6.0-cp39-cp39-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.9

pyAgrum-1.6.0-cp39-cp39-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

pyAgrum-1.6.0-cp39-cp39-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

pyAgrum-1.6.0-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8Windows x86-64

pyAgrum-1.6.0-cp38-cp38-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.8

pyAgrum-1.6.0-cp38-cp38-manylinux2014_aarch64.whl (5.2 MB view details)

Uploaded CPython 3.8

pyAgrum-1.6.0-cp38-cp38-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

pyAgrum-1.6.0-cp38-cp38-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file pyAgrum-1.6.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.6.0-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/3.11.2

File hashes

Hashes for pyAgrum-1.6.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 47ebed86f2b81c7b77cc439442f0753c68b55f644f76d5335b23851116ac59e4
MD5 a69be8896f09d64d62421cad0742972f
BLAKE2b-256 f7a17d157ada13ab662017f214d6d66df710923a83d400f4d7e3a0466a1fc2ad

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 653880ef77205aefd95b6ad2b6c52d6718bd5460995b7c199ae59ed571bb569c
MD5 1dd018d5ba7ece387a15e1ac72cfceb7
BLAKE2b-256 ef74addfcc60bd09277f69460e7883512a85722daaf3d3e75e0b85b888963a38

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8db8b41a514c8a2f936cb3c2febce6cb274d094ecb02726db58eb1af574581e5
MD5 e57a360bc40600a0ac325312f7f860e0
BLAKE2b-256 8c9d0a60722215076feb153f34116611a7ce515e31aeae55d0abd2de0064e24d

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d5e2c8dd477db730d3ba7285fcbc0f7cfc7a2ce863bcf3b666ce9b3952bd862f
MD5 5c1626e78f50975927fface2489d0fb1
BLAKE2b-256 97a0c1798e46095fbfb62a908979d84d7686c74a6df264e1b7e47d2f5eebe9f5

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6f125414ba95326fa7f427309698737fbbbf64d67493413823af74a602daa943
MD5 b07917f6149fb428a669112dfb997f16
BLAKE2b-256 669225e51ace0920103f4ad16a43b45f97d0f453374219d5ccb9cd838217adc5

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.6.0-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/3.11.2

File hashes

Hashes for pyAgrum-1.6.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 5d365db206c05cbaa331f79e0417e8df3afabaa4e3fd92727da3a4aa0678a4eb
MD5 6142d959784ae0b5f3b7ee55bf6633e9
BLAKE2b-256 bcea4899ee3d9eb3e0dd2cb354096191555363520f10c65017366c373f2fc9b9

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 060637ec12abc60b1f36875b8fa8bc90b409a27c58e4aa31b8e0a1794cdb4a81
MD5 f3b2a2610679ea778f0bd2e2f5f910be
BLAKE2b-256 276fb6ff652bbd4a36cb331b885f7a3cb31895797ef7e28ba860110be18bcb4f

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 7ac4d1f5fb0f9971f8354ad30e1d9f4db88d0807a156ea245d6d548c60894840
MD5 7a1c0273a3fa14a7b740e414b3cd9980
BLAKE2b-256 e8b635db306a2490cd3286f396138a4ba64b24dc7e5aa3096df016e2f47bc59f

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6960d058e223c53daf259b5e34648453c2ca2fb097203ca551c0b350a017c7ad
MD5 4fe8fa6bec7a65f995113806296249d4
BLAKE2b-256 3bd1fed9f1ef34a98480cd2a55942e2666b3ebb498d2f6005b4eaaef32635dcb

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 cf211c01265fecb18e5c2d64b06fd65c0a909dfb80514ae1709aca58785ce04c
MD5 031ca1e3c2e68385f3c1b05c34ad8eb0
BLAKE2b-256 5d87e597657b427e1fff06cb2089b4054b5114575eaba36408d12edd44918108

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.6.0-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/3.11.2

File hashes

Hashes for pyAgrum-1.6.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 9ade96d656f15bc88ab00be3fc9f84ef4f522e6ecd5da993f3e64da4e20c28d1
MD5 26cac2f4e893748b505bf579b0584699
BLAKE2b-256 595fdd7d8474e168e5484cd8d1eaff7043c62b65e1822d00cd047c2b98e3f692

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e9fc084dfd63460f605cacca255bd1d266bf21a658995a07e52f7d9a1b8655e5
MD5 522ace2340f5041de6f450a40a5f1449
BLAKE2b-256 1801af2b63597e8e47e700fca48c742e49c1d70b0084e9266e470574398df229

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b8c29017f4de52fc64cbb2fd02860d17c09f2ac1cb68b61f8d8ed0e390079c6d
MD5 6908da8c8fb1e9597bb3ac34ceb8ae1b
BLAKE2b-256 510604bc7e235c717cd34c3f565fd0b182a8514347fa474513380edeceb2306f

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 71ebc1b58733bf49263b6b77c8c8e1f2998be6783a82e9d117c773b1a05f8ee7
MD5 28192239139beb7749a3b0ce96a65c2a
BLAKE2b-256 bb325dfbe23be2c58551db74bdea917262a8c96a2ffb8cd7835af23d9bc0a7a3

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0e62f25ef9d2cb255c2ce0f9b6d45ee9537d93a51fb4d1c2f2b025b730b8ff87
MD5 331148b72f5d321deef73353c615eba1
BLAKE2b-256 1cd1c91abf3f6e71862e44fb95fadca7d27aa51391837dc025ef50ee7d6513eb

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyAgrum-1.6.0-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/3.11.2

File hashes

Hashes for pyAgrum-1.6.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 503e4a696316ee38aeca06443c8af6645a6be1aaa5c9b6078d9b736b05618baa
MD5 20551771b08416dd93438fd8a6fac0a0
BLAKE2b-256 9708c061e11c963243c033a192fa00afc637b9c229de83d79626fdd029c94e9e

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 800787061d93b13c33c342f545b4fcf936f5170a8cacb3953f0c1148c5c80744
MD5 e4aae426cc3f2bbeb567fb3ffc6e79cf
BLAKE2b-256 01fac6e7f1671c79eb78977326a6a539c7204565fda729658a50a1879c4c5fa9

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 505123b882923da60db597385319735c31e354a354c8fb2cef9e096e49cd0dc0
MD5 6b82e04dc6a12f5bd9056aa29f7fc602
BLAKE2b-256 972131a7395b9573103aa828cf481f25af9a43226257c17f4aee653a2c49ebed

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 50e6762156b76d6331091f55ef71033bbf2b48d283103b5c7572b24a6aebfa97
MD5 5d6d10f9948e55d2dfde3baf062f98d8
BLAKE2b-256 a465dc7d0b75fadf328da9b99d224a3d61a5ab9c036a5d1be2a34b2dbd776cb7

See more details on using hashes here.

File details

Details for the file pyAgrum-1.6.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum-1.6.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5f97330a07af36652870b5567a2aecf56e3f3dac49c23bff6205349d2a409373
MD5 c7ff66227975df3f8468c2c6225166f2
BLAKE2b-256 35e97b7448feebd64d32cab921317c48f2aed2271bc5a0b83244fd6542fd2ba8

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