XCSF learning classifier system: rule-based evolutionary machine learning

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for rpreen from gravatar.com rpreen

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU General Public License v3 (GPLv3) (GPL-3.0)
  • Maintainer: Richard Preen
  • Tags divide and conquer, evolutionary algorithm, genetic programming, learning classifier system, least squares, machine learning, neural networks, neuroevolution, reinforcement learning, rule-based, supervised learning, stochastic gradient descent, XCS, XCSF
  • Requires: Python >=3.8
Classifiers

Project description

XCSF learning classifier system

An implementation of the XCSF learning classifier system that can be built as a stand-alone binary or as a Python library. XCSF is an accuracy-based online evolutionary machine learning system with locally approximating functions that compute classifier payoff prediction directly from the input state. It can be seen as a generalisation of XCS where the prediction is a scalar value. XCSF attempts to find solutions that are accurate and maximally general over the global input space, similar to most machine learning techniques. However, it maintains the additional power to adaptively subdivide the input space into simpler local approximations.

See the project wiki for details on features, how to build, run, and use as a Python library.

License Linux Build MacOS Build Windows Build Latest Version DOI

Codacy CodeFactor SonarCloud codecov Lines of Code

PyPI package Python versions Downloads

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for rpreen from gravatar.com rpreen

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU General Public License v3 (GPLv3) (GPL-3.0)
  • Maintainer: Richard Preen
  • Tags divide and conquer, evolutionary algorithm, genetic programming, learning classifier system, least squares, machine learning, neural networks, neuroevolution, reinforcement learning, rule-based, supervised learning, stochastic gradient descent, XCS, XCSF
  • Requires: Python >=3.8
Classifiers

Release history Release notifications | RSS feed

1.4.7

1.4.6

This version

1.4.5

1.4.4

1.4.3

1.4.2

1.4.1

1.3.1

1.3.0

1.2.9

1.2.8

1.2.7

1.2.6.post1

1.2.6

1.2.5

1.2.4

1.2.3

1.2.2

1.2.1

1.2.0

1.1.6

1.1.5

1.1.4

1.1.3.post1

1.1.3

1.1.2.post2

1.1.2

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xcsf-1.4.5.tar.gz (5.7 MB view details)

Uploaded Source

Built Distributions

xcsf-1.4.5-pp310-pypy310_pp73-win_amd64.whl (621.1 kB view details)

Uploaded PyPy Windows x86-64

xcsf-1.4.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (411.5 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-pp310-pypy310_pp73-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

xcsf-1.4.5-pp39-pypy39_pp73-win_amd64.whl (621.2 kB view details)

Uploaded PyPy Windows x86-64

xcsf-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (411.3 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

xcsf-1.4.5-pp38-pypy38_pp73-win_amd64.whl (621.2 kB view details)

Uploaded PyPy Windows x86-64

xcsf-1.4.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (410.9 kB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded PyPy macOS 10.9+ x86-64

xcsf-1.4.5-cp312-cp312-win_amd64.whl (628.1 kB view details)

Uploaded CPython 3.12 Windows x86-64

xcsf-1.4.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (417.2 kB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-cp312-cp312-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.12 macOS 10.9+ x86-64

xcsf-1.4.5-cp311-cp311-win_amd64.whl (628.3 kB view details)

Uploaded CPython 3.11 Windows x86-64

xcsf-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (417.5 kB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-cp311-cp311-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

xcsf-1.4.5-cp310-cp310-win_amd64.whl (627.2 kB view details)

Uploaded CPython 3.10 Windows x86-64

xcsf-1.4.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (416.1 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

xcsf-1.4.5-cp39-cp39-win_amd64.whl (627.5 kB view details)

Uploaded CPython 3.9 Windows x86-64

xcsf-1.4.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (416.3 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-cp39-cp39-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

xcsf-1.4.5-cp38-cp38-win_amd64.whl (627.1 kB view details)

Uploaded CPython 3.8 Windows x86-64

xcsf-1.4.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (416.0 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

xcsf-1.4.5-cp38-cp38-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file xcsf-1.4.5.tar.gz.

File metadata

  • Download URL: xcsf-1.4.5.tar.gz
  • Upload date:
  • Size: 5.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for xcsf-1.4.5.tar.gz
Algorithm Hash digest
SHA256 bb980c6e0078a25c5383f7fa9a6a6c14c07f83692f7bbc63201a3f1b95880678
MD5 4a634ed5646c0be74cd3f54e4c340cff
BLAKE2b-256 109c7eaa9dc49001e408323ae811adcd85753d324acb11da3f571bd2eeb743c9

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp310-pypy310_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp310-pypy310_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 ec0f669e8b2d7dc39e85d17735c634ed36ac19776ac1e10534008d42465f5bc8
MD5 243a11f4941788079732aa8a0c5c36df
BLAKE2b-256 7057c7306fe5f221f25d412e3a4e8cf99baa561c1fccfd3b31f99e739cceaa16

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6e2b17bee0c637cb5075bd2149b68a6d89245d7661fe135dbbff79833136fd86
MD5 70fe76a0fe14f8a75093282f404311bc
BLAKE2b-256 4c013ca794177439f3a8adab02e11e00c4ab7b44c0015bf50673de7d637ce71a

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp310-pypy310_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp310-pypy310_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 27c40270911f4d1b05be8545d6502e0454ad339bc4232917b38e5909d82d06e5
MD5 fc6de373d20becad04252a6cf8080688
BLAKE2b-256 9aacb5a55f155c6bfd8c37e4e67508e710b0e156c690493eb6637a874f72e860

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp39-pypy39_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp39-pypy39_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 c658e6bd43776df6f72afc6935181571a0c9c7b85f2ba07785bb6dc528f1de50
MD5 79db363ad9497c0fff674a10a8c326e0
BLAKE2b-256 099f9e96711944d8b3ef41b2cc2ec339c6f47331d34bf0e4d05c66e767969ea4

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 02ce1ac09595f2cfb9571434293845df1221959f8d083bfe692990a0f2b2a82d
MD5 4d8411d0e5b7d94228f0e2d65d67fb89
BLAKE2b-256 d9e667c00cc07fcd26144eca127d46f88b5edd18212eda1bb89018b3e3a8b563

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5ed6fe97473d1c0a766d49ec6f3a08d5c2f8e1f0f52cbc8a4e1264cd50b3114a
MD5 31abaa0ff50dc9a9cd09326bd55fcf03
BLAKE2b-256 7fb4397df465f6c68744d5bd4024eeb217c38beeb3a54089db4b446b2d641db8

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp38-pypy38_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 9536cbc7f1825291fea221358c5aa6a1e383bec900a5e1d4e0350120fe5d1169
MD5 78d44706b261a03e3c9981425b253906
BLAKE2b-256 ca8ed8fe2e85d8480aef582a39fbff2b73325290fe585ec762e202fc7bae36fe

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2e16576f3bb3de1d403335a5deebd0045faa9f4322d89db61f6be452b9e545df
MD5 a3333be336bb69d4cc1de879013b7039
BLAKE2b-256 081e7ef941c5fcb5ac99b73063c7fc67948c24c0cf13123b545f33b6f2800014

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 d5c97d2feac05e537bd78e017d494c43be18c656b86e37fac2fd01c7472f3f17
MD5 6a7fdd3f70b96d9bc4101b1e39759500
BLAKE2b-256 d4e473fe8bd4893319c832cb15f176e48b1d2a2513a0a54e1435bce4ecb65203

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: xcsf-1.4.5-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 628.1 kB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for xcsf-1.4.5-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 3946d3d9a06cd342009e02412b24ffa6d8cddeb58ecf311fd5cf1ca5ccb3ac4b
MD5 6d4e496a6d3a465988cda672c68e5e0d
BLAKE2b-256 9e0cced125f37216564ddc74d25ebec0e16426a5114cdd3d9e53eb50da185048

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 579a5d3cc7b7f82ebc0c206f5a23142b70440d1f624470520551b5b2b42ea514
MD5 33b9eb050a1417caf8aff59fdbf18f28
BLAKE2b-256 c62b0c88ae73758de8d4489963fbd805915d331210d8260e71453f0eed6f3c51

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp312-cp312-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 43fcdaa40ebd9da8d823a4c754fed0397166b5b8a32e172cc1e69a465181cb65
MD5 c4250e8a4f237c7313d1a74d26c51a98
BLAKE2b-256 41cb8dd8ae32877cf4ea7e9fdf284ad9cabba07ca2b0b5223af0c3397393381d

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: xcsf-1.4.5-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 628.3 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for xcsf-1.4.5-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 4997fd0e2e897964bf5ae5249fded4e37008b26e03270ff759a16b521c23ffc4
MD5 b02bfeaf3889eb1730c9f7d8f5615a50
BLAKE2b-256 2d949ea47574b04264cd99f71e5f7a0f72c746949a64acef037be4fdd45c65dc

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 82e1975228fd76b28078b38ad11dc506ce214731a22bf47cf926a9cfe9f08200
MD5 d3e95ed029807fc12f0564ad81598900
BLAKE2b-256 06dd73b3bbca864fd1b00a39401034f5a72aa88564cc091c6f8b7764aa7ae4dc

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 51c05cff81e7b0f699fb50922293b278b0b8a7450a08c0da8226595471b6c6ec
MD5 d5aa6ad8f067826c408db9b385073298
BLAKE2b-256 0f1cc54351b0ea338fd781c1c49c3194e011d53a7abcf3bced9df018b1e992e1

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: xcsf-1.4.5-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 627.2 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for xcsf-1.4.5-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 97438d1ae6d619bf9b9412a0adc712f3d3fb016c693bbb7d678d0d51bab0bb8b
MD5 ef45740d5478245ccf5b17f1b81df7ba
BLAKE2b-256 f583bcef366c4f4eafdc93ea342f63c85cd3671e991c97d4a3306649b8984f50

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d7961d7b6c9e2908a66e1dcdd97e2a54b766e6ddc8b135bf3121be342eed81af
MD5 d8a1d75c4cf89bbc9d1cda3636d06bcc
BLAKE2b-256 9708be9faf9d758195ca634162d4fabbc5e888340ac3912f404024e5e602321e

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1738a050088dafac28e0b66035ab9b2941345ae02225f299db6867d55d401a2a
MD5 8b717862fc2c6c38588a4b2660eb6e6a
BLAKE2b-256 71ca2a2610c8b445c9bafd8995cd0532f380929cf5f088bf4cd06489a2256c51

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: xcsf-1.4.5-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 627.5 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for xcsf-1.4.5-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 2423e157fd8c79493e7f4f36dc0f3352ea56c05ce78b1e758496bf00061c6da5
MD5 f22cfcea8d0f275afca8c0b29215939c
BLAKE2b-256 b78c5f77841062fbb628fb9222338fad72e0f4de8a988f41aa1dc59b7d2b0048

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 17fcff4627913c2e18df7c2116d84e34fa3602a3cbea2e462ce527eb1c3fd93d
MD5 d44ebe8d356ccbf99f5ff3ec88641c4c
BLAKE2b-256 1c40d4e0de33956c9c34976c0fff77c4e7178ed166b41b0926205b665ad80239

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 261056e985c10a621d96d9c6475e8102be4674edcd886c6b1a41dabcb1cf08d0
MD5 a889a3c7439eb73161b8cacca3047853
BLAKE2b-256 fe968aadc785139b8c4efb69a1263cee664dcbfa630fea7c06d812b0d881fbd1

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: xcsf-1.4.5-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 627.1 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for xcsf-1.4.5-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 9900a00b93c14f79191a6ad60667363cbdfbfcf46dc371cde91e9f6a754aebf5
MD5 e3e79031e45fbd4b83e95c4dddf479e4
BLAKE2b-256 e4d12812c3892117695ec932a377930e9b52fe3a136d6828a2539d237485dfc8

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 48f7243202f56e51a40285012f0ca1795c806c13edf5e4b2408e2ec1b8f5e6b0
MD5 4296bb70ff3e76d57a4ce9230870f6bd
BLAKE2b-256 46f63970ed31d0eadbdb2b88546115ee4888902f5aaf7cf79fb5043ffca88d93

See more details on using hashes here.

File details

Details for the file xcsf-1.4.5-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for xcsf-1.4.5-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3e1439672389937e218a95831e5bf6e3ff6164a5949543d78e1cf9fd055d5c9c
MD5 8d9813135f4f5ba0b457f9413ca624fc
BLAKE2b-256 0a28caaeb9782e6b4bbe8081657149d1cc26bcddbb24fa239805f8a0014e5335

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