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.dev202308291692362912-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308291692362912-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.dev202308291692362912-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.dev202308291692362912-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308291692362912-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.dev202308291692362912-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.dev202308291692362912-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.dev202308291692362912-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.dev202308291692362912-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308291692362912-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.dev202308291692362912-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.dev202308291692362912-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 ca47862d990c2e90d17eabf2ccac1e1a21d46f5d81a1b763cd4e428b60d6a641
MD5 6176feea3bc8b2c3effe55b935f41305
BLAKE2b-256 028eaa18a38a2595f6b5a6606cb76976291b62eab5b65d5579b0ce9abb6954aa

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 33f2adc4b64645656bcc3eedbb69dd5e794d12ce6f41ca0368f72bcf09bb9a29
MD5 e8e71a51c6082e91bbb5ca725c467f4d
BLAKE2b-256 1f85bbd52ad9e586eadea4a1d25c17406b9f410972cc5137a8c20879dade2c8c

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a90d756011722e0842952700ca7fdc58693f72d18e105e8a79af2f2d2ca7479a
MD5 a77328bcd792775f85bfc56fd797b00d
BLAKE2b-256 1235a942e6d159ef402e243bdb5295eadfb22a1fef17e666b76d8dcce8601a54

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3db0b59f9ec8e2729b0e51969cb9ab1504173675d75e972fdfaa438c08128683
MD5 1733d9aead3cf07e8da721cd5ee568c8
BLAKE2b-256 40270886985ff7ac481f11d8096327a68dee7a5d3f5be4721a3057dd2da7eb48

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1a24746df94b6ad8610fce6458473d795850a7b40ddb7215cceba1dba3ec0991
MD5 1c49ea0a0a76f9ff86f826c0413b0e8c
BLAKE2b-256 54022039a19bc1f10c4f94a2e678f0034afe96d5f0a7fe4ac6d7985f73cb2560

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 d5559f4dab0fedf2553a0e69f8c15a272559e87d4e0424d809cf334c51323843
MD5 a4c09ed0906aa9c7daa7dcf603823b0b
BLAKE2b-256 3ef77d0a10014468cb188fd68fc7593846dcc672d6b2b5800375dfddb2161d25

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8629fa97dd62e2ac9329542f7942bc0213e04f6597d89899ee844c0c77214227
MD5 9cf06c245c89280a8ba95dbd9b175469
BLAKE2b-256 08d864ec802a198e6450bcdc2c6ce4bd77803ffda59426ae7dfe6c5e105317bc

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0a8e3b70caa6cb16f273bc37d643cdaa66b2bf49db1161d72aaa87983b15679e
MD5 ed5df5187f1ff18c96fe58ab391bb903
BLAKE2b-256 31aaa6b46a2b8e3124b646c5430c86f4875cb21d7184171dc339fd17ada8d72a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 769ccec8f147b51d7d1f46d48ec73784edca43bcf3aeab18560135badde29948
MD5 7135ddf99aac5acee586abd8386eb411
BLAKE2b-256 ee35f04b0a2d39206e6a11b488040b6e73219c4f7d37fae169184235e40ee356

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c30f3308c9e1c8c73c00f618941b4b803a6302f6e06e1dbd17a73e82a0232c81
MD5 8c823d09b6c9b1a0743831623ebcb6ee
BLAKE2b-256 a6a180a0c296827f2a595d7e3a82899571a2043fd5bce0b147c955173302ddf4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a48e107560b3b30355d61edc8de9f9dd996dc68f9adb7a6257de5f231cc242a4
MD5 4f3633504cb005e520b188fa1531cc73
BLAKE2b-256 150b4f6baa67358229f8e02c7ccc8974bbc49f0f0032451332a6d3bae5c54be1

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5f8fc95f8e4fc6c3bc615eb5cc01a5edccd4f8bc83f139d2d38dfa13948737f1
MD5 ef8f8f285f724bd03f27341be503e691
BLAKE2b-256 30d2e3d839f3f297c0d3ed550534d9054acc28a3b9f1f63c29a6467a66a7e29f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5504e3779bcd6dd14fa03b129b0644330e15a2a23247acbbdaa7ed482b7d0cea
MD5 435c95ccac7eeba846ccb1a08b58607e
BLAKE2b-256 614b0b38a068e7352cc9af98b01d52e8779c252da984bc89ac7ff07ff90110df

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 46b0803bf0468e75449d9f681f71043ec4caab517fd57231e586b542f1628fbb
MD5 320d2a2a7bf905ea8cc9d92c8fc9faa7
BLAKE2b-256 8423f52d80300deb577aaf06fd5f8f2bf25178f263c052429c5a07c1262a01fd

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 cef6f014a3e4060188168eb71cf487e06f467587dbfda1eedce636de35086e87
MD5 19fedc3654b2430ac29906189d622ea8
BLAKE2b-256 166d7c12d631eb06d485240e141577ba81f4c8b460fbaf65a2a0afc1c7806bdd

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f8eb77337a6a4f956efc5be615c9aafb1ef62f1247b916f2d672cf5f15312f35
MD5 9403d205425b87f20cb47f76fec5fa8d
BLAKE2b-256 894bae6d2b83c50c51ab0e1e9551a32c4346bcca9e70c3ea34caede9e4169aa7

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ce4a208a8d30cf37e6772ed30fe19599805b171b9b19c56500709ade00781fb0
MD5 d7549e430e12ae61a05dff4c6bfebe24
BLAKE2b-256 aa3624518e439496f78a3af0e8c28b6ebdf7654d2d6f6a0bda10c2979f3db41b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bebf81cd0926b5f41b997d1d65a87bb5c8749bf2caa8fcfcaefba61318a2238b
MD5 d0dbb02eaf791335d313204e2f5cc897
BLAKE2b-256 a46a9c702ce276d26b575453a5f4db82fd9fd637ebe050de9eeb30c889f104b7

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308291692362912-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0eb8b2b1a388e48a33c180a74b5d11937413bfba2b648f82047717ff07cdc649
MD5 718830de81c9044345f76711101487a6
BLAKE2b-256 05bc54d88ae88579794f3a88d9d2b1d4351be85b811663b8f606d4715be5b136

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