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

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

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 45cebd2b7127a06d8aca62383624c6e1ea96a954e47bfdb98c172d6cee2ae81d
MD5 9adaaa63b031654cb72563d3d968e795
BLAKE2b-256 28d067d092bb8c4f8920270c51eba71962a6e17c3efc7d5578f2617d359deef9

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f4eee30721803839501d8117e46283519e381589f8071e2aa323aa52f1cdc7cb
MD5 e3cf149ad9bcb01c0913cee96ff23117
BLAKE2b-256 e531faeabed9d8f5324f448d093a49dd27421d3c876509e1d11c31ebc7028c1f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 262074afcd8cca39630ebd554fac1345bbd4178248870df9c7545dc8effb8925
MD5 b73e362a3c82ed5c5309702a36d74673
BLAKE2b-256 a5ddbffccc4723c0b5b197a69626717fc765d7b5c23ba740c914aca7cebab72b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0410bfacce0221ab29e2bca24b4b2c9cf71b6130f26eb67df5273eef2db25625
MD5 81a5f07f98c3d0cfcddbd4c1be05d8e1
BLAKE2b-256 002955ea7c12d72f4e670317eeb280b0d1090da0785ee598f6019cb85ebf5ed6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7a5ffa940a2d5cd8504d9dbc1f93aa5a95af4eb03c5ef0026790283df761802a
MD5 6e3e25cff720b395072b9781b42003d7
BLAKE2b-256 e2b528e1acf9691b8f12b8652a3b3b7fcfa7a86373de7f6bc430c561dce4ecc8

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 fca2777f62724d90bcbb0b8dc57eedb7e81bb374254c04d808e74aff8ea33b3c
MD5 aef9b0b6a13482b3d418f7569bf21534
BLAKE2b-256 e032d03b24c97031e85fb1d9cda24576c9c85d479fb1de088cc62a1736fe8322

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e46d690622d58655cba81e787497af158d0ed5e2b39d791e17869e6e46919ff6
MD5 3bc89ae2ebffedc673e344807efdb834
BLAKE2b-256 270e8d6280ffc127f7bf42f7e581d493b29d07a5eaf116d873be24638d811f8d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b0fcba2e8de38b68d02d391b60ae911633adf237df96686d63562e5266ea25cd
MD5 141b2bb3d3ed4c7a8d44e34a8029ecb3
BLAKE2b-256 231ac3a9c7a43e3a440e19690326bb762f4c9d62277332c645af3c65d196492e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9874ed769b1f8a24c1af28765e3e06746db7d3d12c95d79abd1c365c571f6a91
MD5 a6582b812d7d100311a56f6867e328ee
BLAKE2b-256 8c3e70949fb7bee1aaf087f997efaf2647edb0ab66da1e9c45a9e3e1c03c6f11

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 28434678f62c388d6bebd9b3bc3e84ef7f221f6064640d483f70d097605dad0f
MD5 43691f420e7b853a214e01db41827ac6
BLAKE2b-256 1167d4d48b79e3be2eefd234a9a2e8ade54a3c278216b8a3f60617ca879ea234

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 de1f8bf846670dacad84c7dea9cc0fd3fecd23dcae83e453c75578322ce3206e
MD5 9523e04cc8102d780f723c54c2da5f8a
BLAKE2b-256 c6291151dfcd5a2503c7f8fb73dd9269be332e8a212db73dd03202ec3ecfe5da

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5003bdf9e8fee3e39de6393fb681fd2e216f95f929cec17484fd074fadc880c9
MD5 652b2b97955ec508a401891273f44847
BLAKE2b-256 ad6ff1dd997ed0cfe5f1f056c821f1aba26929e5b8b02fd39318f72530ba46c3

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ff7aa9c1312454c7cb872c331c742c8bf70150842ecd09de524401fddf388e9b
MD5 eebefea4a888e6f2170fa27d8b7becda
BLAKE2b-256 f7f7816f4a6797fa63b42d43fb9285a864ed59d59d24cd04b780a3f81c6825d9

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d254b8423e8632e377ef013fe1a505680b20ca31a3c82f770c2fb660194d0563
MD5 e6b13bea6e1fa13b16188ffd4125a80f
BLAKE2b-256 9efa367e6937ab28b1319190b8247b292a53820ee18ceb23bba056b16bd5f644

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 13e2cca27702404014303ff559e17c604b1f9e431564a639c7c3672edf2cd0b3
MD5 af97758582dcae07a9d39b9ea923100f
BLAKE2b-256 8cc7b5ab01fa7933b23a379fe0938488052bf8db9dad40f3ce748fbc76ed12fc

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 e871491bd583afb0a6204ff9d034d3bc255be518713d70d2efb5679110002677
MD5 f6a8b6d429e08eebb4c1d09cd111d49c
BLAKE2b-256 78627c03caef67dc558580b29d2844318208dba53f069fb443efaeddf3420e46

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6961f4c60fe99929e96515521d077a58738a9b6c54ed67a02c685c344b9d85d1
MD5 4534dc2a02ed11fb67743a0fa38e1147
BLAKE2b-256 986bd09eaaa5dfa4f14923d985fa95c77ed7721c87849897924ac8cba15ac0a1

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e95d1d828a35143c0072ee07421c7d20bea932c10f542830869f14c4fc594e6b
MD5 2f363a470c254fc3117db59b5297b134
BLAKE2b-256 64bb85c0765e44bbf01f970d530ea052268159389ba5c66ae33d9df8c51c7e3d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 51702f545b1b27bffd43285ae705534fa0ca17243d26f91deccbaa3e44b7417a
MD5 1eb87baf224a322dec27f795552e8277
BLAKE2b-256 1394b1cb6487178369b8b6d66bd18c06e4a3a56bea460612e55335edb783796c

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305281685116202-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 2d7cdc951e1ed23b4d1a70e1cf41918824b2451b0a76021fbe632074ced5aa4e
MD5 daa4ab7fff5a7fa33e75345b0bc83f74
BLAKE2b-256 b3a139f3e0ae138f266052e1907a5fb473f5693e05be3c1a02ce995f2a404d0d

See more details on using hashes here.

Supported by

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