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Add py-bayesian-optimization 1.0.0

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Bayesian Optimization is a 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.

WWW: https://github.com/fmfn/BayesianOptimization
This commit is contained in:
Sunpoet Po-Chuan Hsieh 2019-01-17 19:20:56 +00:00
parent e26d68717a
commit b93cbf2342
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=490584
4 changed files with 38 additions and 0 deletions
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1
math/Makefile
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@ -686,6 +686,7 @@
SUBDIR += py-apgl
SUBDIR += py-basemap
SUBDIR += py-basemap-data
SUBDIR += py-bayesian-optimization
SUBDIR += py-bitmath
SUBDIR += py-bitvector
SUBDIR += py-bottleneck

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math/py-bayesian-optimization/Makefile Normal file
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@ -0,0 +1,24 @@
# Created by: Po-Chuan Hsieh <sunpoet@FreeBSD.org>
# $FreeBSD$
PORTNAME= bayesian-optimization
PORTVERSION= 1.0.0
CATEGORIES= math python
MASTER_SITES= CHEESESHOP
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
MAINTAINER= sunpoet@FreeBSD.org
COMMENT= Bayesian Optimization package
LICENSE= MIT
RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}numpy>=1.9.0:math/py-numpy@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}scikit-learn>=0.18.0:science/py-scikit-learn@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}scipy>=0.14.0:science/py-scipy@${PY_FLAVOR}
USES= python
USE_PYTHON= autoplist concurrent distutils
NO_ARCH= yes
.include <bsd.port.mk>

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math/py-bayesian-optimization/distinfo Normal file
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TIMESTAMP = 1547722783
SHA256 (bayesian-optimization-1.0.0.tar.gz) = 14a626073cd0c8de8bceb1f0c52f2d016b5ad976473905abb82a5c3e28467037
SIZE (bayesian-optimization-1.0.0.tar.gz) = 12739

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math/py-bayesian-optimization/pkg-descr Normal file
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Bayesian Optimization is a 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.
WWW: https://github.com/fmfn/BayesianOptimization