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Source: python-bayesian-optimization
Maintainer: Debian Python Team <team+python@tracker.debian.org>
Uploaders:
Yogeswaran Umasankar <yogu@debian.org>,
Homepage: https://github.com/bayesian-optimization/BayesianOptimization
Vcs-Git: https://salsa.debian.org/python-team/packages/python-bayesian-optimization.git
Vcs-Browser: https://salsa.debian.org/python-team/packages/python-bayesian-optimization
Section: python
Priority: optional
Build-Depends:
debhelper-compat (= 13),
pandoc <!nodoc>,
pybuild-plugin-pyproject,
python3-all,
python3-colorama,
python3-coverage <!nocheck>,
python3-ipython <!nodoc>,
python3-jupyter-core <!nocheck>,
python3-matplotlib <!nocheck>,
python3-myst-parser <!nodoc>,
python3-nbconvert <!nocheck>,
python3-nbformat <!nocheck>,
python3-nbsphinx <!nodoc>,
python3-numpy,
python3-poetry-core,
python3-pydocstyle <!nocheck>,
python3-pytest <!nocheck>,
python3-pytest-cov <!nocheck>,
python3-scipy,
python3-sklearn,
python3-sphinx <!nodoc>,
python3-sphinx-autodoc-typehints <!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-bayesian-optimization
Architecture: all
Depends:
${misc:Depends},
${python3:Depends},
Pre-Depends:
${misc:Pre-Depends},
Description: Bayesian Optimization package
Pure Python implementation of bayesian global optimization
with gaussian processes. This is a constrained global
optimization package built upon bayesian inference and
gaussian process, that attempts to find the maximum value
of an unknown function in as few iterations as possible.
This technique is particularly suited for optimization of
high cost functions, situations where the balance between
exploration and exploitation is important.
Package: python-bayesian-optimization-doc
Architecture: all
Section: doc
Depends:
libjs-requirejs,
node-mathjax-full,
${misc:Depends},
${sphinxdoc:Depends},
Multi-Arch: foreign
Description: Documentation for python-bayesian-optimization
Pure Python implementation of bayesian global optimization
with gaussian processes. This is a constrained global
optimization package built upon bayesian inference and
gaussian process, that attempts to find the maximum value
of an unknown function in as few iterations as possible.
This technique is particularly suited for optimization of
high cost functions, situations where the balance between
exploration and exploitation is important.
.
This package contains documentation for bayesian-optimization.
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