{"id":20388177,"url":"https://github.com/operalib/operalib","last_synced_at":"2025-04-12T10:36:11.447Z","repository":{"id":57449302,"uuid":"53597336","full_name":"operalib/operalib","owner":"operalib","description":"Learning with operator-valued kernels","archived":false,"fork":false,"pushed_at":"2023-01-31T08:47:06.000Z","size":34180,"stargazers_count":22,"open_issues_count":6,"forks_count":3,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-02-14T11:36:59.296Z","etag":null,"topics":["features","fourier","kernel","kernel-methods","learning-algorithm","machine-learning-library","operator-valued","random","rff","scikit-learn"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/operalib.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-03-10T15:55:28.000Z","updated_at":"2024-10-08T21:31:16.000Z","dependencies_parsed_at":"2023-02-16T17:31:10.018Z","dependency_job_id":null,"html_url":"https://github.com/operalib/operalib","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/operalib%2Foperalib","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/operalib%2Foperalib/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/operalib%2Foperalib/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/operalib%2Foperalib/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/operalib","download_url":"https://codeload.github.com/operalib/operalib/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248553590,"owners_count":21123480,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["features","fourier","kernel","kernel-methods","learning-algorithm","machine-learning-library","operator-valued","random","rff","scikit-learn"],"created_at":"2024-11-15T03:07:45.800Z","updated_at":"2025-04-12T10:36:11.396Z","avatar_url":"https://github.com/operalib.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":".. -*- mode: rst -*-\n\n.. |Travis| image:: https://travis-ci.org/operalib/operalib.svg?branch=master\n.. _Travis: https://travis-ci.org/operalib/operalib\n\n.. |Codecov| image:: https://codecov.io/gh/operalib/operalib/branch/master/graph/badge.svg\n.. _Codecov: https://codecov.io/gh/operalib/operalib\n\n.. |CircleCI| image:: https://circleci.com/gh/operalib/operalib/tree/master.svg?style=shield\u0026circle-token=:circle-token\n.. _CircleCI: https://circleci.com/gh/operalib/operalib\n\n.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg\n.. _Python27: https://github.com/operalib/operalib\n\n.. |Python36| image:: https://img.shields.io/badge/python-3.6-blue.svg\n.. _Python36: https://github.com/operalib/operalib\n\n.. |PyPi| image:: https://badge.fury.io/py/operalib.svg\n.. _PyPi: https://badge.fury.io/py/operalib\n\nOperalib\n========\n|PyPi|_ |Travis|_ |Codecov|_ |CircleCI|_ |Python27|_ |Python36|_\n\nOperalib is a library for structured learning and prediction for\n`python \u003chttps://www.python.org\u003e`_ based on operator-valued kernels (OVKs).\nOVKs are an extension of scalar kernels to matrix-valued kernels.\nThe idea is to predict silmultaneously several targets while, for instance,\nencoding the output structure with the operator-valued kernel.\n\nWe aim at providing an easy-to-use standard implementation of operator-valued\nkernel methods. Operalib is designed for compatilibity to\n`scikit-learn \u003chttp://scikit-learn.org\u003e`_ interface and conventions.\nIt uses `numpy \u003chttp://www.numpy.org\u003e`_,\n`scipy \u003chttp://www.scipy.org\u003e`_ and `cvxopt \u003chttp://www.cvxopt.org\u003e`_ as\nunderlying libraries.\n\nThe project is developed by the\n`AROBAS \u003chttps://www.ibisc.univ-evry.fr/arobas\u003e`_ group of the\n`IBISC laboratory \u003chttps://www.ibisc.univ-evry.fr/en/start\u003e`_ of the\nUniversity of Evry, France.\n\nDocumentation\n=============\nIs available at: http://operalib.github.io/operalib/documentation/.\n\nInstall\n=======\nThe package is available on PyPi, and the installation should be as simple as::\n\n  pip install operalib\n\nTo install from the sources in your home directory, use::\n\n  pip install .\n\nTo install for all users on Unix/Linux::\n\n  python setup.py build\n  python setup.py install\n\n.. For more detailed installation instructions,\n.. see the web page http://scikit-learn.org/stable/install.html\n\nGIT\n~~~\n\nYou can check the latest sources with the command::\n\n    git clone https://github.com/operalib/operalib\n\nor through ssh, instead of https, if you have write privileges::\n\n    git clone git@github.com:operalib/operalib.git\n\nReferences\n==========\nA non-exhaustive list of publications related to operator-valued kernel is\navailable here:\n\nhttp://operalib.github.io/operalib/documentation/reference_papers/index.html.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foperalib%2Foperalib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Foperalib%2Foperalib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foperalib%2Foperalib/lists"}