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-1.9.0.9.dev202308281692362912-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202308281692362912-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-1.9.0.9.dev202308281692362912-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 22d973dd53b559122fefe4689fa49a378bbe073be4b367a1c6e82bafe68e313e
MD5 a4c57e5aac3ad1808bcc7fb7dd846824
BLAKE2b-256 83553f15ae51a7922e6b81c6b94b79d124242fe071432d962b824a648c77f38e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 43a2712e9a01c574a3174ae96578b12cf89d4042253b79c1fc71c096a5cea583
MD5 c4c30eecaaf9475678ce993b1743c06d
BLAKE2b-256 d5820951e695118cef0d6b49d72a634e27f72cd012dc90fd588188122fbcbb8a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 2bd31d716ad7d689e1ac97a0f55ce78d013888d93148313a085303513bf479e1
MD5 a1224ecb3507af49481e3589f35fadbf
BLAKE2b-256 9b0b2968e93b9f8cb42cfe402424ed4b388753f01a1f925214e5182e1af45da6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 fa6db8dcea01787f2a6f8135ca49f5d267a6253c6588129a25fbe8e140453568
MD5 02593a7efb1e044b1aa178a10b50282f
BLAKE2b-256 49efe9e8b65930a4bb352f94ec2ccdc21b348a45f37fb5dee091f12b2b9fb664

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a5d0b68ba5f6ec236fcec6178668e2214d8ad041de1e3a93f3cf3438e26c55d5
MD5 de613eb9ab33b4676bea191d5ab31dc8
BLAKE2b-256 69bcd9f5e353fe2652ea2cabaec3698cce4d6c2f1210f0e2f7e38794026b114b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 4e37cfb5cc106e034ab401b725e022b0e0505fbce5ed6dd62e7c443fe45bee7f
MD5 b602ee818fee3b0746c2e05ede91c54d
BLAKE2b-256 ef73423e6e0391c5ffd47c3799f85e5492251ee0e70439d5be1edacfdcae3a5b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d359a1c607d7128a679f3b94c6b3d1109ab8e77818f9943afb6ccf758d98ab78
MD5 5cc88fe2734ea77d8c2b780190a188b6
BLAKE2b-256 49d0215fa8fcd489f8b5b1de6534c8424fed8d4804fb4bde9087697790999123

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 457c2c4833c5de9442059c37ce12179a5daa9185d1bf8ed60bd0211b18d0a261
MD5 45ca6d1e1bb4e04048c24b941d9f257a
BLAKE2b-256 306a0e06d0d19cfbea0a27fb5d3462b24830aaaf41a55158bad28e75ff14fc7d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f10d24f68aaec682cf0ea0c78dc4297406b33a8e716a6a995549dd16a994b148
MD5 e9b83f02ea699498dcc50108e7017cbe
BLAKE2b-256 376620c88026d645531d2aa922106de463f4cc727b3acb65abac6d52a4a99ff4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 cef8bbb2ac5e15577f3368a6d3a72ed7d6b0693c5c516c725de0c676b31cfda9
MD5 1c9a9e869aef8c44e6fdd35bc6ad5bf0
BLAKE2b-256 fc1ed92711ef8d7634fa8c0118881f841844a7089b7d1f20bef8a052a6172bf5

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 74b54b936405e16c7360d1d380e7b77ce8d9866566eb78c0542f652c9c5c2cb5
MD5 44f1ebce1ab40bbf41ce296570d09e36
BLAKE2b-256 6cb6c5a3d0290b72d99cc18cc35f6c39ee57bb2f8a060f2bef17f2b831f73b2c

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 345096353ea830dab92a6c8ef25fd440d04d48082839334a35c9cb5f0e58063c
MD5 161121a53619ce9be6b9952331dd54f4
BLAKE2b-256 643e11ace7388ef63d67b9eb2cda8ef6fb297e0562718fdcc01623b05bf90673

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d50cd4780184a45d57cdc4dce59e448b544b3696ad4c660de9f4ab2242ccba7a
MD5 48ca7e2c271095d474ee1c25d9395214
BLAKE2b-256 b94104de8b83040281f30e0be9e1de7509165c722eebe9d7b60bd4fcfbe8af8b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cbfcf76b7343621ab84ea8305dc3a6585a5f5f85ce7d141548aba02d67a8a9c9
MD5 b9c9062e92bbff5bb134db930d4a2d72
BLAKE2b-256 57a99cee7368e7f7d8bbb4667266514e47bd333e049d776753080d15a2f952f8

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a1c54f728b5c870b93a4af40fd4603625852006010a73a94ae15af8613cb3d9e
MD5 a5647f412ee059260262f70a3e8351d8
BLAKE2b-256 9a275f5e00fae334f035f2ae1f25cbc192294eb077f1a0df821f71728a59c8bd

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 e8bf7012df06cf83b9ce838cbb397ac55b560d13addedf4e2ee97a67b61346fa
MD5 b40badadcb23c534aaee9f68d3b5dc77
BLAKE2b-256 1c6215342f4c513b5168a749387eafb016a94e120432c6a5643524861c176bc4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a97d0cf511f607607f540c28d4cf7d783d71c45ad4fa83546ceb5e62bffac617
MD5 853a55e04d5c57cd70b3b8d2600b384b
BLAKE2b-256 21b0dad1c11acddad822fd189c6b7dc28bdfc22b652852228e62f9e2e82a51b9

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 caba1dbe226bcece2727ff1e271ddddcee589fe1f5eb7ae5c74894085cdfe831
MD5 24dcfe26286cc0733cbba41db72b1f7c
BLAKE2b-256 89b8c3dea27c4e7f43e37c7986c6d3a9b42424230286490e23074582de4f1ef6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6405ad59827c04edc28fce4a163b3a23eab1487030afd2b8dd620fa9d9c01cd1
MD5 a99efc58e152226f2a346437cbecf787
BLAKE2b-256 b681a40ecf0386e44c5108c4646dace00146623ea655fa3870d9a6f110ab0938

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308281692362912-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b7765d70806cd5bc6f0251ce83477684f65fac78f49def6503cd9d067bc45453
MD5 bd9ed52da4f76c6f980777c6b8c729d8
BLAKE2b-256 81f929872b3f489b1d175c4db233b8d876a53aae8137f042b1e3736f412f9168

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