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python-gplearn package

python-gplearn 0.4.2-2 source package in Ubuntu

Changelog

python-gplearn (0.4.2-2) unstable; urgency=medium

  * Source-only upload.

 -- Yogeswaran Umasankar <email address hidden>  Tue, 30 Jul 2024 00:10:00 +0000

Upload details

Uploaded by:
Debian Python Team
Uploaded to:
Sid
Original maintainer:
Debian Python Team
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release universe misc

Builds

Oracular: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
python-gplearn_0.4.2-2.dsc 2.3 KiB 5c1c58d7c6fc6211d037985bd98f68cc1d8245f6c5cf9c38697638302f12e7c3
python-gplearn_0.4.2.orig.tar.gz 2.1 MiB 0dbeecc9b648d26be6445fd3ac1d5744057faa67b26ce2369dbbd6df49560e74
python-gplearn_0.4.2-2.debian.tar.xz 3.1 KiB b740a229941a1ba25e3b9052ce684f9d0ea60a59c8e17c7e3c711521d0c4a8f6

Available diffs

No changes file available.

Binary packages built by this source

python-gplearn-doc: 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.

python3-gplearn: 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.