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New port: math/py-algopy: Algorithmic Differentiation (AD) and Taylor polynomial approximations

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This commit is contained in:
Yuri Victorovich 2018-04-17 22:24:52 +00:00
parent ff9707fa61
commit ab3dbf7f9c
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=467660
4 changed files with 34 additions and 0 deletions
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1
math/Makefile
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@ -645,6 +645,7 @@
SUBDIR += py-PySCIPOpt SUBDIR += py-PySCIPOpt
SUBDIR += py-PyWavelets SUBDIR += py-PyWavelets
SUBDIR += py-Pyomo SUBDIR += py-Pyomo
SUBDIR += py-algopy
SUBDIR += py-altgraph SUBDIR += py-altgraph
SUBDIR += py-apgl SUBDIR += py-apgl
SUBDIR += py-basemap SUBDIR += py-basemap

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math/py-algopy/Makefile Normal file
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@ -0,0 +1,20 @@
# $FreeBSD$
PORTNAME= algopy
DISTVERSION= 0.5.7
CATEGORIES= math python
MASTER_SITES= CHEESESHOP
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
MAINTAINER= yuri@FreeBSD.org
COMMENT= Algorithmic Differentiation (AD) and Taylor polynomial approximations
LICENSE= BSD3CLAUSE
RUN_DEPENDS= ${PYNUMPY}
USES= python zip
USE_PYTHON= distutils autoplist
NO_ARCH= yes
.include <bsd.port.mk>

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math/py-algopy/distinfo Normal file
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TIMESTAMP = 1524003219
SHA256 (algopy-0.5.7.zip) = 6955f676fce3858fa3585cb7f3f7e1796cb93377d24016419b6699291584b7df
SIZE (algopy-0.5.7.zip) = 189516

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math/py-algopy/pkg-descr Normal file
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The purpose of AlgoPy is the evaluation of higher-order derivatives in the
forward and reverse mode of Algorithmic Differentiation (AD) of functions
that are implemented as Python programs. Particular focus are functions that
contain numerical linear algebra functions as they often appear in statistically
motivated functions. The intended use of AlgoPy is for easy prototyping at
reasonable execution speeds. More precisely, for a typical program a directional
derivative takes order 10 times as much time as time as the function evaluation.
This is approximately also true for the gradient.
WWW: https://pythonhosted.org/algopy/