machine-learning: Add agrum.

* guix-science/packages/machine-learning.scm (agrum): New variable.

1 files changed, 44 insertions, 0 deletions

diff --git a/guix-science/packages/machine-learning.scm b/guix-science/packages/machine-learning.scm
index a12c53b..729e032 100644
--- a/guix-science/packages/machine-learning.scm
+++ b/guix-science/packages/machine-learning.scm
@@ -15,6 +15,7 @@
   #:use-module (gnu packages digest)
   #:use-module (gnu packages docker)
   #:use-module (gnu packages gcc)
+  #:use-module (gnu packages graphviz)
   #:use-module (gnu packages guile)
   #:use-module (gnu packages image)
   #:use-module (gnu packages image-processing)
@@ -39,6 +40,7 @@
   #:use-module (gnu packages rpc)
   #:use-module (gnu packages sphinx)
   #:use-module (gnu packages statistics)
+  #:use-module (gnu packages swig)
   #:use-module (gnu packages time)
   #:use-module (gnu packages tls)
   #:use-module (gnu packages web)
@@ -60,6 +62,48 @@
   #:use-module (guix-science packages statistics)
   #:use-module ((srfi srfi-1) #:select (alist-delete)))
 
+(define-public agrum
+  (package
+    (name "agrum")
+    (version "3.0.0")
+    (source
+     (origin
+       (method git-fetch)
+       (uri (git-reference
+             (url "https://gitlab.com/agrumery/aGrUM.git")
+             (commit version)))
+       (file-name (git-file-name name version))
+       (sha256
+        (base32 "16ag9ypvz0xjq7dzdvwxcvn4bsmbhhxilfxfc0iw58ggimdcgibc"))))
+    (build-system cmake-build-system)
+    (arguments
+     (list
+      #:test-exclude "gumTest"
+      #:configure-flags
+      #~(list "-DBUILD_PYTHON:BOOL=ON")))
+    (propagated-inputs (list python-matplotlib python-matplotlib-inline
+                             python-numpy python-pydot python-scikit-learn))
+    (native-inputs (list python python-ipython swig-4.4))
+    (home-page "https://pyagrum.gitlab.io/")
+    (synopsis "C++ Library for Probabilistic Graphical Models")
+    (description
+     "@code{aGrUM} is a C++ library for graphical models.  It is designed for
+easily building applications using graphical models such as Bayesian networks,
+influence diagrams, credal networks, Markov random fields, decision trees, GAI
+networks, (Factored) Markov decision processes, etc.
+
+Features:
+
+@itemize
+@item Dedicated data structures
+@item Lightweight directed/undirected graphs
+@item Extensible multidimensional matrix
+@item Bayesian Network algorithms
+@item Research tools (random generation, introspection)
+@item Integration tools (listeners, multiple formats)
+@end itemize")
+    (license (list license:lgpl3+ license:expat))))
+
 (define-public python-accelerate
   (package
     (name "python-accelerate")