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.

4 files changed, 110 insertions, 0 deletions

diff --git a/math/py-autograd/DESCR b/math/py-autograd/DESCR
new file mode 100644
index 00000000000..ce620a70f94
--- /dev/null
+++ b/math/py-autograd/DESCR
@@ -0,0 +1,8 @@
+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.
diff --git a/math/py-autograd/Makefile b/math/py-autograd/Makefile
new file mode 100644
index 00000000000..debf499c9b0
--- /dev/null
+++ b/math/py-autograd/Makefile
@@ -0,0 +1,15 @@
+# $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"
diff --git a/math/py-autograd/PLIST b/math/py-autograd/PLIST
new file mode 100644
index 00000000000..ff13226e565
--- /dev/null
+++ b/math/py-autograd/PLIST
@@ -0,0 +1,81 @@
+@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
+${PYSITELIB}/autograd/util.pyo
diff --git a/math/py-autograd/distinfo b/math/py-autograd/distinfo
new file mode 100644
index 00000000000..c37b66f3b67
--- /dev/null
+++ b/math/py-autograd/distinfo
@@ -0,0 +1,6 @@
+$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