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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: 4.7.2
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.
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