Source: simplebayes Section: python Priority: optional Maintainer: Thomas Perret Build-Depends: debhelper (>= 12), dh-python, python3-all, python3-setuptools, python3-mock, python3-sphinx, python3-sphinx-rtd-theme Standards-Version: 4.3.0 Homepage: https://github.com/hickeroar/simplebayes Vcs-Browser: https://salsa.debian.org/openpaperwork-team/simplebayes Vcs-Git: https://salsa.debian.org/openpaperwork-team/simplebayes.git Testsuite: autopkgtest-pkg-python Package: python3-simplebayes Architecture: all Depends: ${python3:Depends}, ${misc:Depends} Suggests: python-simplebayes-doc Description: Naïve bayesian text classifier for Python 3 A memory-based, optional-persistence naïve bayesian text classifier. This work is heavily inspired by the Python "redisbayes" module found here: https://github.com/jart/redisbayes and https://pypi.python.org/pypi/redisbayes This was written to alleviate the network/time requirements when using the bayesian classifier to classify large sets of text, or when attempting to train with very large sets of sample data. . This package installs the library for Python 3. Package: python-simplebayes-doc Architecture: all Section: doc Depends: ${sphinxdoc:Depends}, ${misc:Depends}, libjs-jquery, libjs-underscore Description: Naïve bayesian text classifier - documentation A memory-based, optional-persistence naïve bayesian text classifier. This work is heavily inspired by the Python "redisbayes" module found here: https://github.com/jart/redisbayes and https://pypi.python.org/pypi/redisbayes This was written to alleviate the network/time requirements when using the bayesian classifier to classify large sets of text, or when attempting to train with very large sets of sample data. . This is the common documentation package.