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.dev202309051692362912-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309051692362912-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.dev202309051692362912-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.dev202309051692362912-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309051692362912-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.dev202309051692362912-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.dev202309051692362912-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309051692362912-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.dev202309051692362912-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.dev202309051692362912-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202309051692362912-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.dev202309051692362912-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.dev202309051692362912-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 6a0ebf62c98ad137f60e46024e1ffe2b8dc540ada5425d8f1cc3236721bd6cc5
MD5 91c05708034a4be7c3f48a043a18867e
BLAKE2b-256 793175cc20588cd0916a40a7163fcf9a0ee25f249d1972f133e2c011aed72364

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 291d22a67df86c737bda7794203375d3f36d8f6bc02a1b5ca6bcb7a7f5055ef2
MD5 e18f3c18746be7ffc8ce415affa8b910
BLAKE2b-256 36854034e3e3ab71d9b8ef7821dbed649d6265a721f61a0a5a73d393c2c20b87

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 18fd0b643a05b1f0c96bbfaa86ce3c227b6ddc84316743d7730fcabbde5764cb
MD5 311558081cc1c76a0ee67b6b92a27809
BLAKE2b-256 f232ad86536ca0bfdf0358c029fd84c416eafcbfd7524fa8f24012061547d11b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 589164bd4ba4ef8b53a3653a2681cdb07787e69326e9690c0717d864819b0f76
MD5 e07dab12e32a9f4835db6861b67b0c47
BLAKE2b-256 2cf50c60e78d5c3da1802a4987f1d667abd6c7e146974a563af35b9bbfe6ce7a

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f0e1fc9aba8251875dc43ce512f5ecede8f0d7a43248dba10c8ab699df1e9e46
MD5 2c4022b9b409ceaef68f5daddf4e568c
BLAKE2b-256 c94881cda9d00dde4f0c88e1a2eab690aa85321145c8ebe9ff8be92e3a144cfb

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 d72c7abd7134f3ac6df681a7598960fdd3dcfe8606d3ad7a69573b62a4f09af7
MD5 3bdbe70273d3aa6305ace47e30b75af4
BLAKE2b-256 46ae65c1aded567a1e1ea09193c4895d9aa36bc06845bddf09627e3a4dee1925

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 61f15a78a48e5a858a4993772f31c4bdc7913c6a075e783faaa9b86d6a21b2ec
MD5 debb0e0c76d612d6dd56c1d77626bea2
BLAKE2b-256 776d33807cfd5a5087777c2302f4f32bb6131f50c1797b39c75a0c052f2e8f80

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5249861f823a18da40011bfccd3c9d663fc4d3f61465cb8af2496e4387628ca1
MD5 dfb7312a956d6991e7084e3adf75b83c
BLAKE2b-256 0831fba97abda2142cef55455255a9af0ca4a29f21fe401c790a8b4a2f2d82e7

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ec45e05c22523a8416dc8ab7288ec6f29497669af1e8a063f97f18badd01a30e
MD5 c543ffb6d3895d8290518a396027cf38
BLAKE2b-256 4d9e0c2ea64223c1b398286451e76f1655969be8bc6ae69f420e65cacf6ba74b

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c4bcba531137a597ed9900b26b7d63833745d7b678f428598a3026a6a3d51976
MD5 8ac8b06f6855f52654eb90f5f8cb324e
BLAKE2b-256 478ea5761ad5400b56be539250033ed943974720ec2bed981c88ab784a8f7d3f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 34a72edabebcc7fa50179a57cb6b98c7f617a72cd06007e47ebd8d847f93b6b7
MD5 f2ad1f5f9da5349c6ffe89fb826c558d
BLAKE2b-256 8fc3b6a238f378c76ee0f647462500bc32bebafc097a16f7e3824e193de91bf1

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 451542aa222c6cedadafaf045d9d8fdfb35c37178d5bb9917738f459b5f9029b
MD5 917ba0e91fe56e12284312236bfb9c38
BLAKE2b-256 7e9a29cf51deb3f7ffc5db4a4d7793c94bcddac18e222dbb4bae3186ba17d070

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b32cd5ccd9546eee117ce7bc2c7cbc7b8e97d667e48e03a0eea8351905bdc2f2
MD5 c14a06d88428d3cfea908cf9539c43f4
BLAKE2b-256 74bfa4274696a31316b54a917d2335b77fa62f1ddf15190081ec4a9b3b2cdfbe

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 00bc30bd2b0219d011bb348d958a56b7c94e6f1f3122766beab60bfb3d2b0966
MD5 37d9b5b0c2f3f6f07b712e7ab10901b4
BLAKE2b-256 15ff245ed0d9e16d0821da3e11638154ecd9446ce794e61400e6b3166f64d1f4

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 16821796dab2a17fd7cc5c030ae284f38c12683951cbc0985c5b7811ae05f995
MD5 715a3d5cd813c7cb68f50fc2f9cdbcbf
BLAKE2b-256 e1393fba1618d2d347ffc27e2a1f29830b528119dc937a8335614f771320e8c5

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 f631c9b5247533c16dbe62570504f08d8f3f16bb44de9ecb10ffd92d781003d2
MD5 e3b5b9f871b485f1cbaf01b90908d066
BLAKE2b-256 1d85b6bdb1f62744fd145376218d2552f3b36ef789593371f0545d1915122e4e

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ec01a8c64e89fea987ea4773b8603423644223ed6513562954b75a475dbc54a8
MD5 5b5de5edaaee39e8c84e9996c597948b
BLAKE2b-256 d0aa550eaa7b079928866d7d61c99cf8488c890a101b3ec29196c15bd3bf38e8

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f09d855266425247479baacbb5a364179d499b53ebbaa8a66cee58ce2a171a12
MD5 23cdd81ffbe22a65221420b6042b2f39
BLAKE2b-256 0f7a2a8e110cd33b1ad7aa8f8b0e70b203348a5ddde9e292cf016477ec7b7ea5

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 83899d58fa19056c8c6759db2b2278026e3824609d16ef6139e8fc6a4770936e
MD5 98b12459683572a6287a3e115b9c5d68
BLAKE2b-256 939bd728d32c1dcb091eae0a9c8e01b81a9f0830e73e9cb498f7d86303404264

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202309051692362912-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1bc8bf8626f0e4b8cdfdde0c1832bff7bac0d2eb4bea5f464bdffc912196cf22
MD5 96db3d37dfda88d47718646d10214d2e
BLAKE2b-256 146a8f6796c5ee1090479915a2e292cc799addb2b31404848a189c5cba770956

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