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-1.6.1.9.dev202302271677346080-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-macosx_11_0_arm64.whl (4.0 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-macosx_10_9_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 c2c612e49ad3a596e44e6bbd65f4a5508629a7c60181c07e4309b690f9188b7f
MD5 7610a20d3862a0ed0ea6345cdb6fa380
BLAKE2b-256 e169a2dd3a8b0c28f0882655fea9f9addca9c3a225af00cb2600789014b03387

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3397fdeaf133b8ba089ae01e6ebcf1870d4b1ea772f5f1a31a2cb0c071304440
MD5 cc66a3308acafc00cf3ec3cf648c4c09
BLAKE2b-256 52cb14edcf318d7600fe470613d18cb2709ddbf56831ae95d72bee4b721e790e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 6e957dcb5677084439d8f937c7fd75108322d5885473f2473aa4d2fc4a85f4fd
MD5 eddf6921a08d051a999f54e4daf8c317
BLAKE2b-256 ab0ddc345642d65a870e0403cf4bab0b2c89323066ad174d1b67806200d89910

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6dc1ebbfd93b1849e853cd8a0e49e01b1191c229df816bce55b82f1325c47e58
MD5 ec36df815e6518b68e1eb2edc5097821
BLAKE2b-256 51c7d5a9154dcaf7acc5ed1650bce0ffba8e7abd4ff1c76eada1becb149a74f4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b29d2d82aa7f226da6df83112a075f9d1efabb7e01bdcf853174547194453651
MD5 31247914c2732bb521add26f5cf6542e
BLAKE2b-256 e1115f7a407ce659f7e5008284f1eb534189880292be81ee3040bdf35d3bb7c2

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 d7df3b31a70a322763d2da195ed9329c41c7eff100e978f61a9c208bdd598ef4
MD5 2c93a31bc2aa5fadbf9e29272ae8f4ba
BLAKE2b-256 6d6c420ccbe044aef18cdb55eb9d8ed473ed95cecded17c1ba0aabcb5ebf686a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 94e69adf28317eedb28f21d55d06198ba87e2241f7edb145cc12b13f6cbd09be
MD5 a65453872459406eff8615d2ca4a6700
BLAKE2b-256 67aa40d1cd9062c76dc6889d31c5dce7315bf5618d8fda3d33951b653921197a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 04ac8bb5f7d00ef5cfe5953a99d3bdf072a02089b92955e681e4c2871259087c
MD5 9e6341992781ca82e67744417fca7252
BLAKE2b-256 11a52d27e42b7d744e9958bd0b698bad5edabbcb21309711c9c33cf772bf4ef9

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1877c0a36c0a2f32a45d539a9b08956caf0b64a61d4f243317bc0cf2810d9612
MD5 a557122abe03f290d21e59e848237cb0
BLAKE2b-256 3f6217db00384c743466cd63e985d506803958eedd4ffaf6ac9ff702cea2d09a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b8b0c4d476a930584b54913587aacd4820086cacc47b7f73bc01998c80a3936d
MD5 982c01ab04e8a6355e84634f990e9566
BLAKE2b-256 c79857581deb1aac0c91c480f78c3ac245746c66c57bf3eb6146d4f2f7d04c20

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 084135dd888ab8b35b99da16161a0d389d15ce05126d6a7334e55c817f8b9f9a
MD5 b0d97ef3bbf2128834393ad750790a3c
BLAKE2b-256 febd84f45422defdfa79dc62c074b2a77f064d3876831e7e513d7c8949e75e2e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7ab81b22ed8e66ecf2fed7d5b8fe558b7817ab12462ffe96d5d173995b3c957c
MD5 b8a7f607e5782bf9cd138b558fafde52
BLAKE2b-256 7f6798f3d61446414416dbda2b8acb7c1884b835647efea619d21d0792b795fc

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 df8ace3a9ae6feec52e6df752b25e77ba3d328227ba8cb3caef616f9a091c3d6
MD5 76f956a6c732f75ffe9e272e99a25b3f
BLAKE2b-256 739ca05b090c7c8fa5e3a9153e7ee9dfb19aca695ce7789817a8c27b410fda0e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f81b48463b215a5a7877295b82673101a5c3225845dce74ef5ad105bad51814d
MD5 ea09a89662f46f0bc894ccbd06825965
BLAKE2b-256 a24e172889928ce8c7d746407f4aa86fb6511b44373ddade09abdcf6f155c67f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3489ff52c11f42ce315355752ee54b3e84a4f65cf536e5b1c739b5f26fb04b8a
MD5 2b5677202fc8f0301c9f369685965d72
BLAKE2b-256 d5f14227f88b2ca4f60e82f4258c8320f8c956df25053f3892b254e9dcee6fa5

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 ab2beaf0dd625ffc185713b0ed06a03d212321fb644e227cebb469932e6765dd
MD5 882bf9879bcb7ddd0afdfa31d4836333
BLAKE2b-256 c1890aa5598e001151bc42e03addf788b5f5f5c1fa1940370b006667360e4bb4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f99ba7699be4022c5e9df540b4a1af2b8a7e095289350521244b2d49ddd20682
MD5 c69d9dd89be018a4db94ff0f971209da
BLAKE2b-256 0010e9542ad60fc7bb518d7d11af008b863a27fe51a74f65da941369049d9dc4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 81c1a24a035ac7f2958bb6ab1dcff978e49499ab29c2e5f0e7b85faa83b9dc4a
MD5 92efcad242e1c20542c31da90c30494c
BLAKE2b-256 684db50bced2419dd4389ea990bc75a1fc7093ba4c26e19162024026f8e31461

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1c8a058d684a4c958e4b9b279162aa9a7906c2922c3e5fd75e2cb8d5eb400b0b
MD5 f40cf3a455df35696827a6d7c3c965af
BLAKE2b-256 9b051fa46ecf3287095dd3646756d4d16764b19d32bd3bb3eed17d83aa14ebaa

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.6.1.9.dev202302271677346080-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ff753ced07a507f276e6db7265d7c1a57c816f8fff9d7f44e42732bc7df3f946
MD5 0307258d6022e4979836a0ed5b06e90e
BLAKE2b-256 fd5ded0e3a03caca90f2c212d39a9bf1b5f54d8d506f8e2ee64d43afcd8969f0

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