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.dev202305311685427304-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305311685427304-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.dev202305311685427304-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.dev202305311685427304-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305311685427304-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.dev202305311685427304-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.dev202305311685427304-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305311685427304-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.dev202305311685427304-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.dev202305311685427304-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.8.1.9.dev202305311685427304-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.dev202305311685427304-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.dev202305311685427304-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 ef9215ec3a9306bec04109b0fb4beac1a496e845c6077952ccb2732beaff1102
MD5 6b918cde22267c56c162cfc4362f0b8c
BLAKE2b-256 8310b1384ce82531415dd90baf5d4e201b3350cc9013e2e74d30e42ccec6bf58

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 31130fda257d7b3b8beb0c5bc5d006ef22c1075a3870210d8c13f9efddb4b5ec
MD5 4934996e07e04876a921b5d3f1e45f96
BLAKE2b-256 77577234b5bc3970fdb40821510e93d84eb3d5447b59c6bf927182db8f06b581

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ab845ab307bc6e2a41cd2170652e8c6a32e55ccf054c46fc9a659e14d86158e5
MD5 413e96b3e617388b524c0bc15b841c47
BLAKE2b-256 f696757a89f23bf7f0bb5498f0a6b2a31c0a58f3a00c01271f33159fbebcfcbb

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c388db639dbeaf3b922d72e5bc02bcc9bd6142d7bc0cb8493f9fa7895c3378d6
MD5 97607b3c1056b535ca5e1e476edf07d5
BLAKE2b-256 86e0a703be9e92a2790fc8c9376db46ed241c9e10938f6671f3fe2580f6f3436

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9c6822db78c4e6c54730ff20243ae47d0147f6710b9b64495dafe23d7befeed5
MD5 50d1c1f696f777ffd9b778d664751f63
BLAKE2b-256 73082e5c77818ab2a52819b7bfa31140f278782e614a35f86b36390b5ee62ef9

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 ebd926fb09f3a9156609fabe4f46f9f19c2d2ee5e232486e4580ce8f7fb0938a
MD5 4f7b9177f32c6668d9668e25ec771f3e
BLAKE2b-256 20868fad3f80e41d0540c066068aa95306c9f0ce2c54d18501e5500c860e3096

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f4c033e49f50eab619ba64bf95a0e49d2bac97a8973d3146c5f4e2e1db3e8af1
MD5 c1ceb1c74ef3dea546691e0af06ce45f
BLAKE2b-256 8c35c95c04354e651c4940e447266913663316ad951317a4838d764083dcab60

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0a36a1f5d1a99deb472857b00222125dfebfbd1c1c983a2818d14f7446f59b28
MD5 eaf05a844abba0b4f85137e165cb6426
BLAKE2b-256 df93567b641821b8657ee5fef78c85ae90df196500a538e86d0c4877e94682a2

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 82514180ae53f1a6a27420d0705d7fdf3154771adb4f2cb544bdb91ce05b8b70
MD5 9eddce5dafc3f899a3bf1cf4c756f972
BLAKE2b-256 f0e557bdc74f57ccbbfa030a402d9ae877befbaff0bce319ac9182e295f5c3e0

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a80d44984034fe06beb3c486ce4276d070440d3cf8d9b10e6e94e0fd16777df0
MD5 8e702a4e582e369e8bb9b39d02393b34
BLAKE2b-256 9cf94a7bc9cc471bd369d452454ff00ddfa47fd7fa61b0316fe1db71f189b8c9

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 2b005e378f82df435f4afdcb56bbf527446d1553fe7a31996328fa24a1905ef7
MD5 d2bf4af05a0352d92727af39a1b2151a
BLAKE2b-256 ca54408dd42ea13c44115c0d4c39c6734522f79b532cae881d5c36e714a81a7a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d557e2f572dff78fcedc6c4b2939a0f783f6e8e26c42aff8f168cc88b11a0a83
MD5 07ca116bf1c88cb8b886d2c755e3b009
BLAKE2b-256 4990f7fffdc17b4f3f6e3138aee24dee804b346e33ab138132e0662300e10fd4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b2271fd1206417e4c32dac45047f3560ce6fef453fcc47ab3add5dfc397c694d
MD5 76ef6c23e8fd186faee51fbabac4d015
BLAKE2b-256 4831649ea9ff446bbc41aee4e6d4f21fbedd0e5556e586f3e04d691e413c9945

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c3755e035bdc1e4fd5fffdff83f5b06253605fa53c52692023acfb993fe668ad
MD5 37be9c5b0d09ac356e5e5bc6fb425f4c
BLAKE2b-256 5c656d08f3e1a04d3373da1331a6d4d9f83a55c2e3bb302afdf0485f54b0974d

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 572789abe91325940331d3ac0b64ff6fa8f6e8a3bb8aa1c2bf5692b298483f60
MD5 5d8ba3eab7851f5fd022d4593fb826e2
BLAKE2b-256 e5b76af3c4af68c7da7692b990621b682b8b35938b314bfaa6d64010c77f2f9f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 1bde803a0cf53877c2e16a46d7ef6510aa4e70a0dc2b3ebf1d05eae8cd2a96c4
MD5 bf2d37bfd961c25658b709055eed13bb
BLAKE2b-256 c9ab8ec8063c4bc4cc821116e9215fa355bf4a3be8cbb7b22e6a19be62507cbf

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d5f422a284e93b1225cd634782691fa29f490b4b1ba3e84a404d8acdd36fb423
MD5 5337bf59e6e25e4330ce77249b00aecd
BLAKE2b-256 ac0875a9a690ce12d637ce402a098dcf6833430108cee0fffd63cb8ca0464ed6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d38a3ae7ccef14700886ff0fcf9d4ca5d5f942e59bb01c2d51394c25c8c550af
MD5 00e5c521588ddba696db6b72439e640e
BLAKE2b-256 92f5a9a788aedd30a7d58b7b3b4087c330b797e9753815a36009f9f63aaad77b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 99fef8d7c7d23c83b3603892f85f946ff25b4268fc63aa640a3ba0bd9744bc8d
MD5 9eb6237a7ba86873b5f20235982dcf1d
BLAKE2b-256 c0e4adf56f5e7ace462d64b820bf533df318df9bf63f8599a7b082245a238048

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.8.1.9.dev202305311685427304-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6adfdac117dffd76df7d9507a53d71e010c5598a99d00fa5a6ef8c5a13d0820a
MD5 f9787d607f5cfbf505ef59e3ec8fe0cf
BLAKE2b-256 8f421e7001e03a6e15cb47e1f1ff4e96de875f2ec2255aeceb4489352ff21f5b

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