diff options
| author | yuri <yuri@FreeBSD.org> | 2018-04-18 06:24:52 +0800 |
|---|---|---|
| committer | yuri <yuri@FreeBSD.org> | 2018-04-18 06:24:52 +0800 |
| commit | 71d5efe6b705d20a5928a22df3520fe496a6557f (patch) | |
| tree | 6094911adefcb6f11bea9496bf4938658801d041 /math/py-algopy | |
| parent | ee871dc01c60d082c14b8403d2775ddb781920ad (diff) | |
| download | freebsd-ports-gnome-71d5efe6b705d20a5928a22df3520fe496a6557f.tar.gz freebsd-ports-gnome-71d5efe6b705d20a5928a22df3520fe496a6557f.tar.zst freebsd-ports-gnome-71d5efe6b705d20a5928a22df3520fe496a6557f.zip | |
New port: math/py-algopy: Algorithmic Differentiation (AD) and Taylor polynomial approximations
Diffstat (limited to 'math/py-algopy')
| -rw-r--r-- | math/py-algopy/Makefile | 20 | ||||
| -rw-r--r-- | math/py-algopy/distinfo | 3 | ||||
| -rw-r--r-- | math/py-algopy/pkg-descr | 10 |
3 files changed, 33 insertions, 0 deletions
diff --git a/math/py-algopy/Makefile b/math/py-algopy/Makefile new file mode 100644 index 000000000000..0bfa25cbb48f --- /dev/null +++ b/math/py-algopy/Makefile @@ -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> diff --git a/math/py-algopy/distinfo b/math/py-algopy/distinfo new file mode 100644 index 000000000000..3ff9366933d8 --- /dev/null +++ b/math/py-algopy/distinfo @@ -0,0 +1,3 @@ +TIMESTAMP = 1524003219 +SHA256 (algopy-0.5.7.zip) = 6955f676fce3858fa3585cb7f3f7e1796cb93377d24016419b6699291584b7df +SIZE (algopy-0.5.7.zip) = 189516 diff --git a/math/py-algopy/pkg-descr b/math/py-algopy/pkg-descr new file mode 100644 index 000000000000..214144cdf392 --- /dev/null +++ b/math/py-algopy/pkg-descr @@ -0,0 +1,10 @@ +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/ |