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.dev202308071690302491-cp311-cp311-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308071690302491-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.dev202308071690302491-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.dev202308071690302491-cp310-cp310-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308071690302491-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.dev202308071690302491-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.dev202308071690302491-cp39-cp39-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308071690302491-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.dev202308071690302491-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.dev202308071690302491-cp38-cp38-win_amd64.whl (2.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyAgrum_nightly-1.9.0.9.dev202308071690302491-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.dev202308071690302491-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.dev202308071690302491-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 981795b7f791d266a853c777bf5cf75dfecd21bff04af6f776a1a875fb8e44fd
MD5 e344646226ba4bb1c58460cdeebe3789
BLAKE2b-256 2b6b8ea92b9e0ccc5dc35235c0849f2d8496f916073dfb223beb9b2b96459484

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f8f5f10eab95e3e7e20493676bd4b0b5a641dcac8bf29c1d193b96ef1542a2a7
MD5 70ce2a6a5a84b8bebe8223a83217d839
BLAKE2b-256 c045b116453e694849d5ca68d1a390db90854a37c9788473d84ee789b87cbc87

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 9f9c1fdb9106dcfd4a5ad31516ca8611e8b549edd15bf846011c798ffb45e445
MD5 a2ccc4119b511b9b78c75a8664a24523
BLAKE2b-256 f99d084124ad04b3a0aca36581ebafedc79113bf63f4e585a2deae18a8d65caa

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 95e424f31a3cf224c2021c1dd87e0d0b28a7989ffa8fa01745c58f90b6f8138b
MD5 68db6b98adf87dcc27dd84b0a954cf1a
BLAKE2b-256 1a9cee4b929c84dc5d45c08685721d786d54cc5a7bad40492c40f8f6e4ab4667

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b356256ccfd65b3f26ed6f4270a91819619a22467a7a12869b26b54baf6d24dd
MD5 ba2c7f1e7c4beff84f0a681389cc4167
BLAKE2b-256 8b7b6e692b6acf6c5fffa672ca501df710b74913fb65cc6109b05b82c80e8de7

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 d717b8c40a9dda8451373fe9ca5e7b186d88e1307e6a47ed49620519cc954f3e
MD5 7d2697b9f371e68c21d36c5a169ca444
BLAKE2b-256 84e3f6152965a65eb7ef973309df83daed2d7c1ece95ca97861e297b1e2823f5

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 337cffa1f78a15f183fc8e3fc17788694eaef5f537f8dcdbf63162c0125f05a6
MD5 3994873c2489f84f7efc528366a02eb8
BLAKE2b-256 a6360a9a153de9760fa402879494c1899827cf279295c363c0962bbd4a326268

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ec583a5260d14036a330151f956728b8715e7405a7e095cb7377e74b3b45b1d5
MD5 6b4c98496f619cad1172770435aeb9be
BLAKE2b-256 47f4e2a30c01f5bbb8e189e10ff43dc5d11a30dfcaaf756a0b00eb729c09f8dd

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 44a3ac8e680989bfe44bcd477514d607b810e360f1890eb666b0b8922c708ee1
MD5 cfe7125d77aef56f2460d7495a2fe9f1
BLAKE2b-256 16929372ec7a151a606673845a942420d7cb31ff7a8131b041f5b56a97f29425

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ae9b5245f0a718c0915878edec543548931bd746fbeac61bf9b28e460b6ebf08
MD5 31a3758b4e1c94192b9543145a48e8c6
BLAKE2b-256 28ff5d20216fd84f9b88cff21dd35642546b49d174c0143cf91fb640d7083958

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 4092de6b074cefffe7447f2174c293400f56544d4fd0e8ec89c72d01fd49cd89
MD5 468cd04a427a2583da42887c952bd658
BLAKE2b-256 d50ed0cbaf8757312066ad3e4e14fbdc77db4cb3ad9e60a913a786b49e344227

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 852f6562cd64f61b4e0082fce901a36e96bb38346afc218ee24cf9578a368fb9
MD5 d40a52e91831b09e1fa9d44a62bfe9ca
BLAKE2b-256 bd7f041af0a91a4321befa592affbd8ebdb8c252a720db5437e6f68c5b91d5c1

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 eccefa1025a1a4c824f8ded4de89551537974f6dfb445bae6a7fa33155c07643
MD5 d56ab5b308bb89e9cfda41b247f1a88b
BLAKE2b-256 f05bdf1ee0d98aba2378c7d7412fefc9cb8acc54457a41563de486778af99871

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3661ae3def2c56447d9b261616dc6a00001455ef5a15a380de8a93cc90686c03
MD5 6b5c14cb317d7f784035c49b12b78c4e
BLAKE2b-256 eb929e0dd61ac4c1fe1567eadf34baada1a771ca14e879fa1897997a4369b988

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6e6c79b9a87d95dd5f4229df3741aa65a7581353b142d8c73aad87d6c2fa4ef9
MD5 3fdb11377c5b089a4a98616fcb96e575
BLAKE2b-256 8db225473fd926ddbae9eb9d15295687de3f285db163727f26dc786f3759203f

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 d7b60e216c555711b9638cccc7caa1e99ac0f45e7568f26ebdb1bebfdf97a4e9
MD5 50702f1ecd400b759c47728a936d2c81
BLAKE2b-256 edfcf668bcf5337830cee04c6550099adeba617fb03c5c89efbcf1f7c2db45c0

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 77e571cc3d15b694e9e44b963862a4a2d6edf68a617bdb0f49997665ac9b6ec1
MD5 f12f1776b2a8edd6ef443a3435b3aea9
BLAKE2b-256 64bdee3c513c3ae6c8d8e7d37a850d30ec876befe31552a3b8f8419d165c25d7

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d219631185b1b1c9b8111063f85a3ffad79b21924874ec4e239556f9c8c239ad
MD5 df9d9ac9df7372e4ee1528811d64eafc
BLAKE2b-256 611c30a167019978638379b2c6c2662c64256663000a321a62386cb4bcba77c5

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e9154703708c119c15fcd0cd31c598e2e1866b1ec248a910945ebdc84a4152e5
MD5 8f988ddfa47df54611b04c36ba505f90
BLAKE2b-256 324a2049a0670c4a4b981b9c48c54e73401e1a237ff1ff04ae0d51c53c5d50d8

See more details on using hashes here.

File details

Details for the file pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyAgrum_nightly-1.9.0.9.dev202308071690302491-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 25b6167184a28b1af0d7b57823cccda12fd1bb11041ba47ba62731296b103c25
MD5 3ca5c6154230867f1eec4552d6d42b21
BLAKE2b-256 8dce93bd58026006a07fe6d48ef5eec970cd56197b21f19146ed91edc02106c8

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