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.dev202309081692362912-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309081692362912-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.dev202309081692362912-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.dev202309081692362912-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309081692362912-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.dev202309081692362912-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.dev202309081692362912-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309081692362912-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.dev202309081692362912-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.dev202309081692362912-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309081692362912-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.dev202309081692362912-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.dev202309081692362912-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 d0f8eef348a412ba8e41d4c8cbc576621e306015b993ce3e7f1425be64eca051
MD5 59ef43646cb1929fb3bcf9146337da2a
BLAKE2b-256 51ad8d3fc5145fec89fe9beaaa0764d525884e4469e02fa0267e77513168eddf

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ce25c39ffb88911f959bf7481c506e79501217d1af347afa7d49466eb7a83393
MD5 bf48b6816a97d81f6ff6e95438eb3653
BLAKE2b-256 e5844340bda3411c44bed243c07c1ce3e883c79c0ae185ccfabd18019010309e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d0ad7f356dd0ff89cf76727bfa7c196012e502ded54c2af8ac2a99de083bc897
MD5 16989c4f4abc18b922b28ad962a6fd25
BLAKE2b-256 483b5c0b6b1d866b2589b4a449aef01272ae2c1320d99344b8b9772259f64983

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 978ddd3848e383390b7ddb4647645eb69e38d1f075065bed717f6ef2119e7833
MD5 6137033ac7244ebef3012d9ea52f6af0
BLAKE2b-256 221a8cb583defe51f5859709f4f5301352de43b073ea3af5e3f6f4011c45e42d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 cbb7fe4b7dcca0060a1e3b953dda0406d8562815fecccb30cd1ac64e1a581760
MD5 cd2a4481f53d62ac22bcdd06b7749b0b
BLAKE2b-256 9a3b59d63a0c4f0ae3cfaf86da0421875c995dc9a5013ca4f8efad8ec6f5cbd6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 25da35ce38cd381387acc087b0779a0493a6276c8700389295938b5aa1192f5c
MD5 fadc839fb598eb6d0cdcfd9821ce6148
BLAKE2b-256 34a18e1b2668b63c3893858d616db7757f975ed8f075d85106c0d296dadb28ed

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 be391a764799f273c84f3e53a2c8b9cc3aff426de7f7e02d2127db9f7e6cf006
MD5 63b4eb100bd1c2ff1cd5451efd37d28d
BLAKE2b-256 4b27d95b5d222e39484226c54f5f654350e932b0cbac5e640b3c973929ed94bb

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0a0b8c9e9b7cda37ab2df5cd235932765fabb1295cb3e13c2f14f5a418852c8f
MD5 ea4c1f4aea884301b14fd4acfa6008ea
BLAKE2b-256 af727412689730ea0b4a1b329e6c190535ab16234d177f6b632e8be7426e553d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 135a8d70a16a99ba52dbe4e6b9f284e5c73055e8413a5c052e4d36cb8af33d72
MD5 0fca4869dcb794dea1570c659c890a35
BLAKE2b-256 678a77d41b09b386dd73fc9a0512f7eabd9f97ef4482aab4374dd1bff5ca3cd2

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4201090f83c18456180ad117a433b231ce6d47f8442e0adf5e1e25a73da5b50b
MD5 7c3a0e70637199b873caa12f843fa563
BLAKE2b-256 873e6d13e2abf44c40bb54de74c7f5c15f36b6f21a4f6201df1cc5f964e9c536

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 9b2b4e8aed2d18bc395d7f02d5176c2b39fa3399ac63b5b8d1d6133703f24613
MD5 923a0405a715746f116950a0a5ad04ba
BLAKE2b-256 36565cec66fbf36e4ee68b9fd219c7ac7ef314e6d3d7e14b4f91f1438fbe1011

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 185f2355a13ec07804a51a5cc4e85c7ef898bdb1307d95a4bb7f61f397b36a2a
MD5 72efb5dca95c6eceec698f94a99dad3b
BLAKE2b-256 dff2c3fdd07587d8afe6d032cb34aabaff25c295f19b2134aa8ff914f50b34cb

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0e1589e0c5421d50ed6d04059486233efa8c1cc3120beee8584030ab1574a9c5
MD5 4e7f4f7741e80b7fef0ee9d24bcfe5ff
BLAKE2b-256 135be6fa8c13bd678bcb33b6b2b286735fae709b039c59eab8a1afe0c381c670

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 37a88908e45a45f394a7b139fd62ef93838ef913769cc21368c13fb801215ca2
MD5 664cf3786a79759d549442a370d0d683
BLAKE2b-256 1091242d28ab0eb19e5bda238bfd7625d9fdfcb452c83c4db55878ecbc05da6d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 627374bfc8be083e94893306bef5892e933d9dae483d84afab2dd7901a50d4be
MD5 0288286b1be0fec4bdd0b25f40c184e6
BLAKE2b-256 95359c25d28f9c23f014ff0f9cf5cf4419354c264473931623e9e7823d7839ed

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 18a88e29fe955b3e11e95290094632534d7cd0f58d8740b09ee856d2ec188377
MD5 ef8d6208081ec25f6b937d44c182a7b7
BLAKE2b-256 fc39c54dd389c63856f723828d01d89af051334d818d976c8102a84764f32ed0

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6bf03ac4b4469b54b91e788871dd72d5c3245733dd84fe12aa21c6f1cc8c7960
MD5 81368c64ea0a4777d611211e85ec612d
BLAKE2b-256 7e182023a9adaee2e0be6357f79db60317dd9f064b57be6247c03afe6e337248

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 86a6e10693bee684d987efe0d94e04c01ec9bc70ccb78cbf1db03ce6cc939f41
MD5 bf0af00bdb9d76a04be82ab35939514c
BLAKE2b-256 0176d65ed3680c304024464fa9501144b1004008f6d22d9738ba5604ec2fc541

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ba9a0d359d69407e0ddbc0843aa14a65a627c2098320c3e7a6d59790a0c4b0b3
MD5 3a1a1ae610662796637d6e0cecccd7bc
BLAKE2b-256 b1ea24ff84d4f70b9a32ce7a580d6f9f3b51182cdf1b273c2e7b7885b169c0c6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309081692362912-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 59be2cc09bebccefdae94e535310a773a7aa9b8bc7b44408c3c2f95580ab4481
MD5 31403ec59d1b65e010bf755feb2b39e0
BLAKE2b-256 e6aa80058157450357b0d7872cdc40989e3dc7d1f156336a8868465af7c33cfa

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