Source: python-gplearn
Maintainer: Debian Python Team <team+python@tracker.debian.org>
Uploaders:
 Yogeswaran Umasankar <yogu@debian.org>,
Homepage: https://github.com/trevorstephens/gplearn
Vcs-Git: https://salsa.debian.org/python-team/packages/python-gplearn.git
Vcs-Browser: https://salsa.debian.org/python-team/packages/python-gplearn
Section: python
Priority: optional
Build-Depends:
 debhelper-compat (= 13),
 dh-sequence-python3,
 python3-all,
 python3-joblib,
 python3-numpydoc <!nodoc>,
 python3-pytest <!nocheck>,
 python3-setuptools,
 python3-sklearn,
 python3-sphinx <!nodoc>,
 python3-sphinx-autodoc2 <!nodoc>,
 python3-sphinx-rtd-theme <!nodoc>,
Rules-Requires-Root: no
Standards-Version: 4.7.0
Testsuite: autopkgtest-pkg-pybuild

Package: python3-gplearn
Architecture: all
Depends:
 ${misc:Depends},
 ${python3:Depends},
Pre-Depends:
 ${misc:Pre-Depends},
Description: Genetic Programming in Python, with a scikit-learn inspired API
 `gplearn` implements Genetic Programming in Python, with a
 `scikit-learn <http://scikit-learn.org>`_ inspired and
 compatible API.
 While Genetic Programming (GP) can be used
 to perform a `very wide variety of tasks
 <http://www.genetic-programming.org/combined.php>`_, gplearn
 is purposefully constrained to solving symbolic regression
 problems. This is motivated by the scikit-learn ethos, of
 having powerful estimators that are straight-forward to
 implement.
 Symbolic regression is a machine learning
 technique that aims to identify an underlying mathematical
 expression that best describes a relationship. It begins by
 building a population of naive random formulas to represent
 a relationship between known independent variables and their
 dependent variable targets in order to predict new data.
 Each successive generation of programs is then evolved
 from the one that came before it by selecting the fittest
 individuals from the population to undergo genetic operations.

Package: python-gplearn-doc
Architecture: all
Section: doc
Depends:
 node-mathjax-full,
 ${misc:Depends},
 ${sphinxdoc:Depends},
Multi-Arch: foreign
Description: Documentation for python-gplearn
 `gplearn` implements Genetic Programming in Python, with a
 `scikit-learn <http://scikit-learn.org>`_ inspired and
 compatible API.
 While Genetic Programming (GP) can be used
 to perform a `very wide variety of tasks
 <http://www.genetic-programming.org/combined.php>`_, gplearn
 is purposefully constrained to solving symbolic regression
 problems. This is motivated by the scikit-learn ethos, of
 having powerful estimators that are straight-forward to
 implement.
 Symbolic regression is a machine learning
 technique that aims to identify an underlying mathematical
 expression that best describes a relationship. It begins by
 building a population of naive random formulas to represent
 a relationship between known independent variables and their
 dependent variable targets in order to predict new data.
 Each successive generation of programs is then evolved
 from the one that came before it by selecting the fittest
 individuals from the population to undergo genetic operations.
 .
 This package contains documentation for gplearn.