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authorMark H Weaver <mhw@netris.org>2018-04-30 04:03:54 -0400
committerMark H Weaver <mhw@netris.org>2018-04-30 04:03:54 -0400
commit3d5ad159b336a9903b31d0be7ae052dbc8d5bfcc (patch)
treeee7dce4e436490a1db5f18e4bfad55511d2fff32 /gnu/packages/machine-learning.scm
parentc77835db04ee20c0afe20600dc8f91a67bc2421e (diff)
parent8c21c64e59d3f4d223d8aeef91f06fdde7de1ab7 (diff)
Merge branch 'master' into core-updates
Diffstat (limited to 'gnu/packages/machine-learning.scm')
-rw-r--r--gnu/packages/machine-learning.scm44
1 files changed, 44 insertions, 0 deletions
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm
index d9b54d692f..fb511b4949 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -6,6 +6,7 @@
;;; Copyright © 2018 Tobias Geerinckx-Rice <me@tobias.gr>
;;; Copyright © 2018 Mark Meyer <mark@ofosos.org>
;;; Copyright © 2018 Ben Woodcroft <donttrustben@gmail.com>
+;;; Copyright © 2018 Fis Trivial <ybbs.daans@hotmail.com>
;;;
;;; This file is part of GNU Guix.
;;;
@@ -689,3 +690,46 @@ mining and data analysis.")
(define-public python2-scikit-learn
(package-with-python2 python-scikit-learn))
+
+(define-public python-autograd
+ (let* ((commit "442205dfefe407beffb33550846434baa90c4de7")
+ (revision "0")
+ (version (git-version "0.0.0" revision commit)))
+ (package
+ (name "python-autograd")
+ (home-page "https://github.com/HIPS/autograd")
+ (source (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url home-page)
+ (commit commit)))
+ (sha256
+ (base32
+ "189sv2xb0mwnjawa9z7mrgdglc1miaq93pnck26r28fi1jdwg0z4"))
+ (file-name (git-file-name name version))))
+ (version version)
+ (build-system python-build-system)
+ (native-inputs
+ `(("python-nose" ,python-nose)
+ ("python-pytest" ,python-pytest)))
+ (propagated-inputs
+ `(("python-future" ,python-future)
+ ("python-numpy" ,python-numpy)))
+ (arguments
+ `(#:phases (modify-phases %standard-phases
+ (replace 'check
+ (lambda _
+ (invoke "py.test" "-v"))))))
+ (synopsis "Efficiently computes derivatives of NumPy code")
+ (description "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 supports reverse-mode differentiation
+(a.k.a. backpropagation), which means it can efficiently take gradients of
+scalar-valued functions with respect to array-valued arguments, as well as
+forward-mode differentiation, and the two can be composed arbitrarily. The
+main intended application of Autograd is gradient-based optimization.")
+ (license license:expat))))
+
+(define-public python2-autograd
+ (package-with-python2 python-autograd))