~ruther/guix-local

0931c6091ccf028b9556c0c3d7e1e3157034b97d — Ricardo Wurmus 10 years ago 71f80f5
gnu: Add randomjungle.

* gnu/packages/machine-learning.scm (randomjungle): New variable.
1 files changed, 49 insertions(+), 1 deletions(-)

M gnu/packages/machine-learning.scm
M gnu/packages/machine-learning.scm => gnu/packages/machine-learning.scm +49 -1
@@ 23,7 23,12 @@
  #:use-module (guix download)
  #:use-module (guix build-system gnu)
  #:use-module (gnu packages)
  #:use-module (gnu packages python))
  #:use-module (gnu packages boost)
  #:use-module (gnu packages compression)
  #:use-module (gnu packages gcc)
  #:use-module (gnu packages maths)
  #:use-module (gnu packages python)
  #:use-module (gnu packages xml))

(define-public libsvm
  (package


@@ 96,3 101,46 @@ classification.")
    (inputs
     `(("python" ,python)))
    (synopsis "Python bindings of libSVM")))

(define-public randomjungle
  (package
    (name "randomjungle")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (string-append
             "http://www.imbs-luebeck.de/imbs/sites/default/files/u59/"
             "randomjungle-" version ".tar_.gz"))
       (sha256
        (base32
         "12c8rf30cla71swx2mf4ww9mfd8jbdw5lnxd7dxhyw1ygrvg6y4w"))))
    (build-system gnu-build-system)
    (arguments
     `(#:configure-flags
       (list (string-append "--with-boost="
                            (assoc-ref %build-inputs "boost")))
       #:phases
       (modify-phases %standard-phases
         (add-before
          'configure 'set-CXXFLAGS
          (lambda _
            (setenv "CXXFLAGS" "-fpermissive ")
            #t)))))
    (inputs
     `(("boost" ,boost)
       ("gsl" ,gsl)
       ("libxml2" ,libxml2)
       ("zlib" ,zlib)))
    (native-inputs
     `(("gfortran" ,gfortran-4.8)))
    (home-page "http://www.imbs-luebeck.de/imbs/de/node/227/")
    (synopsis "Implementation of the Random Forests machine learning method")
    (description
     "Random Jungle is an implementation of Random Forests.  It is supposed to
analyse high dimensional data.  In genetics, it can be used for analysing big
Genome Wide Association (GWA) data.  Random Forests is a powerful machine
learning method.  Most interesting features are variable selection, missing
value imputation, classifier creation, generalization error estimation and
sample proximities between pairs of cases.")
    (license license:gpl3+)))