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.dev202309141692362912-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309141692362912-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.dev202309141692362912-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.dev202309141692362912-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309141692362912-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.dev202309141692362912-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.dev202309141692362912-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309141692362912-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.dev202309141692362912-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.dev202309141692362912-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309141692362912-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.dev202309141692362912-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.dev202309141692362912-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 19ece13354e360a649de6dc782129b53082716660fe734811c707953249e5761
MD5 4aebc0f5ed7cc68870ab3b2bf39d4a5e
BLAKE2b-256 50c361ea0a63064007eb9af07739cfe29977208718d82f90755e9921e2dbed94

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 94d949ed635421ebd2bd930920ee91d601a82f64a24d0e56a7788b9ca761f5ab
MD5 1dfae9b780d352f5d4e3e0e6c1ed59bd
BLAKE2b-256 63a1c5c2a56cb960fc40061b2c1fc2712c3f38a9ad73df4eb6f6bc06f4c5ee54

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c6b17dada7302361cfa6605d878c5daa218bfef55889d8ef15595d940269176d
MD5 675a5e859b436538217295eb4851cad1
BLAKE2b-256 7a9f6eb4debe6111ca0e659312ac75740426b084a4eef2665b6c4a268f1e0956

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c1bfc98ea981dd821ff5f5c4271028bb0b701528a96347f6474e15938ecebfe9
MD5 e954e60d9bcad6d23650d59a83355c9d
BLAKE2b-256 abf068b7833edd22119d67431a6d1cd1012ce4063877bda6eeea8391cff98340

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 2278416d244bda6f29165a746061df7d03ac7fce9dd227449339b3d38882ee00
MD5 38b29fe71998ecad43e6c412d5a6000b
BLAKE2b-256 be43e0ec5aa2255fee4fc9b99c37b7fc18d626665733432ba122f095953d259b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 b29d619b15f03573b19c766aad5b2cb1ca42fba6467cfba4eb5512f5e14bd304
MD5 a6ed051fa6e5f39df950b711153a1cff
BLAKE2b-256 00371c289a96305d1a4cc32374f8343e4edd207593e401ca1193f10848494822

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0c7c0e5ddd25c1bd9de81b25278aee478d21e6dfbe69b00504a2da20d9510d2d
MD5 129ffac05228f11acc3aace375e0c2c0
BLAKE2b-256 2aee96501de78a0e1e2442241365d490d101bcb33134665da49fc8b7fa130eb6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 969cf77b0f443c48b98f5149de89e1e0ed2da266b71ddd29193a984c921ee5ff
MD5 19b1887911a6478baa8b7e49e7e1c5ad
BLAKE2b-256 c0c538952ed9385847078b8596fe48222a8846797f6914fafb5b7fc4579437d9

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8221c02e811296aaa4b18683009d34a9f8d9f5fc0a24bf7908e362e01a42cc44
MD5 64220389e155d52e4e0e498ec083364d
BLAKE2b-256 3291e6b625fbbda9a8ffb78b172b64c50a8533109b5c650280bbdb61eee4f5e7

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 dbd175d6b2067d962f2d6c49a32b842f6964443296d6c9ab889fb531673c0fe3
MD5 aa843b7849d0faefc0506ed0513321e8
BLAKE2b-256 7f20b8110622af100a93e874ad1b52c09c6fe7b249edc44bed7a883fc5d4cd6a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 ff34d47d91c0483ed63a3956c09aefcdbe221794dc7dadd328ef64bb590e4e04
MD5 f9ef17dad82faec4d0a810604d955eca
BLAKE2b-256 a374498a4bd6ec29eba04221d3e8a7c332622dab075982f28e7393f6605866c6

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c642b89d90c4d3bf381e1025534a4d356320ad6b83d8544dadf8956fb081ecc9
MD5 d2f9b5d57c3c423c6d8fdcb912e43e0a
BLAKE2b-256 cf2ad26fb0624a31d9835e4ea389d8e77015bbf97af1e905706a15bb781caf94

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 479a3d01e276e8392419bc35b2da4e603e1ca73ec96fba7166fed75bdbdb6263
MD5 dc0410504b1f490b163e2e13518a1f41
BLAKE2b-256 4c617f8a7a884169f78e890af6da1c0a152767ea8290b9502d220e6212092414

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 36fc20a72a0b7fc95eb5260bac2f00e8d518630a1a6dd5ddaea7aba0256ab614
MD5 4386cb6a1f5d5b344ddb4ce1eb1cbd14
BLAKE2b-256 8dfa453ed30adefb6544a1336cbbcfc30b70371d40217cac45271f709bcd137c

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 24d22ec950a2e20f66857c7f4b956f66532d92ccff7fc4c80b9cb489b6e9c74e
MD5 77a3e5b4b191908a312e9fc5f3cd080b
BLAKE2b-256 49dd9b00b992e9250b2556e7aefef52999a579a6d1f916afc72ad9b576fa0681

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 aabe0b2730f2c75342022e2e46e05daced157db236a5213972d0d87c29459d6a
MD5 6302bafe1c6c1a5f93da196fac095a50
BLAKE2b-256 30562ed4b6dcc2f65ee04290ebdbfa5bd8ca902686b46a7bdd82ded84cfe4aa1

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1366bb03d8f655c0c04666b15523923e771eaab41bd55d6ad7af4677a3dfcb3a
MD5 de74bd9f0e3ee91fd3986910c55e0a17
BLAKE2b-256 5315b40f6973188bb7d14f14c07aadad991990dd6166f1e5bf10bf8dd00d1e95

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 421991208167c1c58ab5f6c51e3246a21a7058458089f3ccd9812ccd4ddb106b
MD5 4026f53ee9993f759b3c03a3e69d56ae
BLAKE2b-256 b27a7352a6746354088f2d13675135745d67d39a474a4433e7f10b9afc10000f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 023dda70114357bd0f4119a2d8cc75f61591d39a7142a2e4ed48cd0f2e8b4c48
MD5 9b13e051d7cd21bb07184bf1eab3870d
BLAKE2b-256 47fb1c4634c81313d5d0bc9976e883f674ff6d9c7e66e5f7494fe3ab1f231ce3

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309141692362912-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 008898335f30c28ca309b0dd53bbcb001d0d889ca7286ae242cbbf199e83af10
MD5 d7126a190e056560592095daf3c4f762
BLAKE2b-256 71e8868220f1a211890ae781f4046e2dc17790edfbd21e60ffbbfc253711e44d

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