Wed Aug 24 23:50:12 2016 UTC ()
Add py-autograd 1.1.5

Autograd can automatically differentiate native Python and Numpy
code. It can handle a large subset of Python's features, including
loops, ifs, recursion and closures, and it can even take derivatives
of derivatives of derivatives. It uses reverse-mode differentiation
(a.k.a. backpropagation), which means it can efficiently take
gradients of scalar-valued functions with respect to array-valued
arguments. The main intended application is gradient-based
optimization.


(markd)
diff -r0 -r1.1 pkgsrc/math/py-autograd/DESCR
diff -r0 -r1.1 pkgsrc/math/py-autograd/Makefile
diff -r0 -r1.1 pkgsrc/math/py-autograd/PLIST
diff -r0 -r1.1 pkgsrc/math/py-autograd/distinfo
File Added: pkgsrc/math/py-autograd/Attic/DESCR
Autograd can automatically differentiate native Python and Numpy
code. It can handle a large subset of Python's features, including
loops, ifs, recursion and closures, and it can even take derivatives
of derivatives of derivatives. It uses reverse-mode differentiation
(a.k.a. backpropagation), which means it can efficiently take
gradients of scalar-valued functions with respect to array-valued
arguments. The main intended application is gradient-based
optimization.
File Added: pkgsrc/math/py-autograd/Attic/Makefile
# $NetBSD: Makefile,v 1.1 2016/08/24 23:50:12 markd Exp $

DISTNAME=	autograd-1.1.5
PKGNAME=	${PYPKGPREFIX}-${DISTNAME}
CATEGORIES=	math
MASTER_SITES=	${MASTER_SITE_PYPI:=a/autograd/}

MAINTAINER=	pkgsrc-users@NetBSD.org
HOMEPAGE=	https://github.com/HIPS/autograd
COMMENT=	Efficiently computes derivatives of numpy code
LICENSE=	mit

.include "../../lang/python/egg.mk"
.include "../../math/py-numpy/buildlink3.mk"
.include "../../mk/bsd.pkg.mk"
File Added: pkgsrc/math/py-autograd/Attic/PLIST
@comment $NetBSD: PLIST,v 1.1 2016/08/24 23:50:12 markd Exp $
${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
${PYSITELIB}/${EGG_INFODIR}/requires.txt
${PYSITELIB}/${EGG_INFODIR}/top_level.txt
${PYSITELIB}/autograd/__init__.py
${PYSITELIB}/autograd/__init__.pyc
${PYSITELIB}/autograd/__init__.pyo
${PYSITELIB}/autograd/container_types.py
${PYSITELIB}/autograd/container_types.pyc
${PYSITELIB}/autograd/container_types.pyo
${PYSITELIB}/autograd/convenience_wrappers.py
${PYSITELIB}/autograd/convenience_wrappers.pyc
${PYSITELIB}/autograd/convenience_wrappers.pyo
${PYSITELIB}/autograd/core.py
${PYSITELIB}/autograd/core.pyc
${PYSITELIB}/autograd/core.pyo
${PYSITELIB}/autograd/numpy/__init__.py
${PYSITELIB}/autograd/numpy/__init__.pyc
${PYSITELIB}/autograd/numpy/__init__.pyo
${PYSITELIB}/autograd/numpy/complex_array_node.py
${PYSITELIB}/autograd/numpy/complex_array_node.pyc
${PYSITELIB}/autograd/numpy/complex_array_node.pyo
${PYSITELIB}/autograd/numpy/fft.py
${PYSITELIB}/autograd/numpy/fft.pyc
${PYSITELIB}/autograd/numpy/fft.pyo
${PYSITELIB}/autograd/numpy/gpu_array_node.py
${PYSITELIB}/autograd/numpy/gpu_array_node.pyc
${PYSITELIB}/autograd/numpy/gpu_array_node.pyo
${PYSITELIB}/autograd/numpy/linalg.py
${PYSITELIB}/autograd/numpy/linalg.pyc
${PYSITELIB}/autograd/numpy/linalg.pyo
${PYSITELIB}/autograd/numpy/numpy_extra.py
${PYSITELIB}/autograd/numpy/numpy_extra.pyc
${PYSITELIB}/autograd/numpy/numpy_extra.pyo
${PYSITELIB}/autograd/numpy/numpy_grads.py
${PYSITELIB}/autograd/numpy/numpy_grads.pyc
${PYSITELIB}/autograd/numpy/numpy_grads.pyo
${PYSITELIB}/autograd/numpy/numpy_wrapper.py
${PYSITELIB}/autograd/numpy/numpy_wrapper.pyc
${PYSITELIB}/autograd/numpy/numpy_wrapper.pyo
${PYSITELIB}/autograd/numpy/random.py
${PYSITELIB}/autograd/numpy/random.pyc
${PYSITELIB}/autograd/numpy/random.pyo
${PYSITELIB}/autograd/numpy/use_gpu_numpy.py
${PYSITELIB}/autograd/numpy/use_gpu_numpy.pyc
${PYSITELIB}/autograd/numpy/use_gpu_numpy.pyo
${PYSITELIB}/autograd/scipy/__init__.py
${PYSITELIB}/autograd/scipy/__init__.pyc
${PYSITELIB}/autograd/scipy/__init__.pyo
${PYSITELIB}/autograd/scipy/linalg.py
${PYSITELIB}/autograd/scipy/linalg.pyc
${PYSITELIB}/autograd/scipy/linalg.pyo
${PYSITELIB}/autograd/scipy/misc.py
${PYSITELIB}/autograd/scipy/misc.pyc
${PYSITELIB}/autograd/scipy/misc.pyo
${PYSITELIB}/autograd/scipy/signal.py
${PYSITELIB}/autograd/scipy/signal.pyc
${PYSITELIB}/autograd/scipy/signal.pyo
${PYSITELIB}/autograd/scipy/special.py
${PYSITELIB}/autograd/scipy/special.pyc
${PYSITELIB}/autograd/scipy/special.pyo
${PYSITELIB}/autograd/scipy/stats/__init__.py
${PYSITELIB}/autograd/scipy/stats/__init__.pyc
${PYSITELIB}/autograd/scipy/stats/__init__.pyo
${PYSITELIB}/autograd/scipy/stats/dirichlet.py
${PYSITELIB}/autograd/scipy/stats/dirichlet.pyc
${PYSITELIB}/autograd/scipy/stats/dirichlet.pyo
${PYSITELIB}/autograd/scipy/stats/multivariate_normal.py
${PYSITELIB}/autograd/scipy/stats/multivariate_normal.pyc
${PYSITELIB}/autograd/scipy/stats/multivariate_normal.pyo
${PYSITELIB}/autograd/scipy/stats/norm.py
${PYSITELIB}/autograd/scipy/stats/norm.pyc
${PYSITELIB}/autograd/scipy/stats/norm.pyo
${PYSITELIB}/autograd/scipy/stats/t.py
${PYSITELIB}/autograd/scipy/stats/t.pyc
${PYSITELIB}/autograd/scipy/stats/t.pyo
${PYSITELIB}/autograd/util.py
${PYSITELIB}/autograd/util.pyc
${PYSITELIB}/autograd/util.pyo
File Added: pkgsrc/math/py-autograd/Attic/distinfo
$NetBSD: distinfo,v 1.1 2016/08/24 23:50:12 markd Exp $

SHA1 (autograd-1.1.5.tar.gz) = 1ed7727ac1d634b47b9ebe7244a851e76e3edd81
RMD160 (autograd-1.1.5.tar.gz) = 27ae3c0ef6a69141c1dddaa5975640f35ad63d94
SHA512 (autograd-1.1.5.tar.gz) = 4c41363acc2fbddad9bf587b6f6b9dbe151c0c1ef95059b192262f6d4eec2309e69d906f40bb3b39677323735af20ba7706993267e2b91607b251b09ea61aa7c
Size (autograd-1.1.5.tar.gz) = 24986 bytes