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

If you're not sure about the file name format, learn more about wheel file names.

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-macosx_11_0_arm64.whl (3.8 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-macosx_10_9_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 d0d5954c892fcbdffba8b7580f36507a7b8e10482812211922eee24fbd48dcfe
MD5 56a7ed0fcda87464a6d9e2457b6bbc0e
BLAKE2b-256 b7e2e068cf0a451232b612bd2be29f88a02c6e2063ce1129dc3d2c1bbc093cbe

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e9598b29dc44b2e68617f7dfb68b537371d62ce36c995e5f9d5331fde052c99c
MD5 99c47002279027b639c6c2380a1bdebe
BLAKE2b-256 708f84b67e0876fe6ea33c1a35fc310beb754314fe11479833ad3c3425909720

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 3bd441c06baf47fbf4648ff89906ffbd67eaf2d89a9a9778e3822c53e4f46be4
MD5 27c4858153cd91a6fc4b1097d11e4790
BLAKE2b-256 1220da490508f64f22ddcd80227ff81726427a4c2377600d6579301b80e2ff74

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0b0927c2a7d3e37cf9961fde320676671722bc44abcf95bfd2624135b0a561bf
MD5 4a790236ede4bf65994f73d9bc4984e8
BLAKE2b-256 c0c2cd057ced8033407e1d1a507aceb636eb1d326ba382307ce3935fabd4d26b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3f0c6c336b6e38afbddd413259614e320af4dccbd50634de152a0f502bb07ec6
MD5 c46cb5f0c02108cff746d9b76fae3afa
BLAKE2b-256 ae40e2c6455769cad8f2e6e52e71faad8fa356cdd94232748af4900e9bc02bd4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 5bd3b1ec14db2ef5bab89e230ed9f28d6c1ff41027ba0d7ce247c4935ae2e23c
MD5 6cb4a3cc75defd01be393bdd5f5c1fca
BLAKE2b-256 c3a86fa19472bf5b3fbb33c43cdb434d9828f4186af5c9502afc87f87665e427

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f3fcc4e3a223ca0f6c957878b1bc406bfa3532660a22d6b2644a970c41345602
MD5 22e1c111064ebd0011e169848a0aac8b
BLAKE2b-256 f95e7b9c047bfa6d296f0dd108ea841c37cc5bc0eefda040b93e4f8f624df107

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d4856d37eb2a877f822338c0a65bbae44cc389859aca8c75e6772e546fb3ccb7
MD5 6c8354af14ba2497b2e106c9bc8997d0
BLAKE2b-256 0b499fedb54bff11da50128696616b8f426bdec3599023a8a8505000bf87c18f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b7c98461701e31c0f737e579abbd362b7e582f2d6ddeeda5b5f7cbfacf68ad11
MD5 2ed3362066c135d8531c741b6aef267b
BLAKE2b-256 e2dad12aaf448849af5ab9f16a13c50bafc4cb245ca441f937642719b2cb8a87

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1d8197c50246cea86522a1eb9c2f47e4b2de82e1d26846f4665e40d643c1e462
MD5 608117df90cdcec9b9c5d8f137aeff33
BLAKE2b-256 094b3a2e9c05a1d0d30b3f1b33c216045a9d88761144791a41701b4ba4d68514

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 707e312c15f170615cbde289ddc6a6e68aa3f2a67ac620e955a71df9a515f07b
MD5 fb37012eed41f6ee4eba1124a962cb30
BLAKE2b-256 ad29887b2a8f320f851ba6a4ba5b71153334743ab7a71b4ea28d3a2c2194f653

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f25bb5ca6bf4a87916822a24323877f1340a28b94f0c75724ef7c90315fa6844
MD5 069ebb05fba4c2346f94737cf5e92925
BLAKE2b-256 741bbeb6addce990a9e1684772967aeec88d49516a6df34e0eef9c828449a57a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 1154bfd5903fe2d82a71bceeb0101c205c616b3501229110aa9fb66bd899a35d
MD5 f0647cc276dab49f6ab4cf2ea89f211e
BLAKE2b-256 129109f029d52a9473d55bb6d5d682190a0f0d6a43e27327c0961431054e6c08

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a8957e13e64db89d4cb6a022451979dc0c75080dbb32f774c1ddfb84b7ddf018
MD5 6210fdeac4bbca6d596a5e9df4fba67c
BLAKE2b-256 43a1c872ad2fd9781a360cba4b61f5aaa52b5a9e54ca6aec447d123498c15319

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 2f562f888c92e9bee28ae4f48eb04bcf2b1ebb0bb4d7f23aa218a56c002c81c8
MD5 c43bcbe7a5c1a2f34887a9c29560f728
BLAKE2b-256 b2b91a361489cef94c2513c1abaa07aec5b8a0ba0beda9fe6079baf732ebe61e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 304375dd4dc04da6540f178d6e70f760fa807277ba93afb3dc45115b0e8133a1
MD5 d69d6ad5be56946227e122bfce8bf94c
BLAKE2b-256 bfb13d0eb1d0420506ad31b1b7678241f1a4473329f45d6bd3229fadc7d1602f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8442b477bf8c7606913bc8bd4b9174d9e50cec4dbe86e28c980ba7ee7fc14dfa
MD5 2e962cfd0e35236f3be588247ee78951
BLAKE2b-256 988ed9e2cf03297cc309edfab1d8cfd1b40ca925a8263c36b085a0b915c1801d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 07b43666dc0a5b5c7dc06dc15aa7c15d8f66b005f25f19e16062265ec80e87da
MD5 70b17af5c3551dc4ac6d6747a7fba686
BLAKE2b-256 010a0b5602959cb046aa9158e124c14266060cbc2f7c4a19e731daaa36486efa

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1673224ccfccea936d5ba918bb10f603b3b710859a32b693a576daaddc5480d0
MD5 5e00fdb4c24db37cf78a96630fdfe6fe
BLAKE2b-256 defd1d2ab2a902e0a43712fa5e6125f9de415c87f3589b7e7eecc35347896a8d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202310051692362912-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 79c95462701f72380a60dfeb3366484f1e9c3c37df8e5754e5dd26b4d3941c89
MD5 737d3d8b5e78750b64ddb4434d7a4068
BLAKE2b-256 e7369d15e37f7feda5c8f8d90649e3171ec6c9b89427f7ddad5f41e3bdb17de3

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