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| | ;;; GNU Guix --- Functional package management for GNU
;;; Copyright © 2015 Ricardo Wurmus <rekado@elephly.net>
;;;
;;; This file is part of GNU Guix.
;;;
;;; GNU Guix is free software; you can redistribute it and/or modify it
;;; under the terms of the GNU General Public License as published by
;;; the Free Software Foundation; either version 3 of the License, or (at
;;; your option) any later version.
;;;
;;; GNU Guix is distributed in the hope that it will be useful, but
;;; WITHOUT ANY WARRANTY; without even the implied warranty of
;;; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
;;; GNU General Public License for more details.
;;;
;;; You should have received a copy of the GNU General Public License
;;; along with GNU Guix. If not, see <http://www.gnu.org/licenses/>.
(define-module (gnu packages machine-learning)
#:use-module ((guix licenses) #:prefix license:)
#:use-module (guix packages)
#:use-module (guix utils)
#:use-module (guix download)
#:use-module (guix build-system gnu)
#:use-module (gnu packages)
#: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
(name "libsvm")
(version "3.20")
(source
(origin
(method url-fetch)
(uri (string-append
"https://github.com/cjlin1/libsvm/archive/v"
(string-delete #\. version) ".tar.gz"))
(file-name (string-append name "-" version ".tar.gz"))
(sha256
(base32
"1jpjlql3frjza7zxzrqqr2firh44fjb8fqsdmvz6bjz7sb47zgp4"))))
(build-system gnu-build-system)
(arguments
`(#:tests? #f ;no "check" target
#:phases (modify-phases %standard-phases
(delete 'configure)
(replace
'install
(lambda* (#:key outputs #:allow-other-keys)
(let* ((out (assoc-ref outputs "out"))
(bin (string-append out "/bin/")))
(mkdir-p bin)
(for-each (lambda (file)
(copy-file file (string-append bin file)))
'("svm-train"
"svm-predict"
"svm-scale")))
#t)))))
(home-page "http://www.csie.ntu.edu.tw/~cjlin/libsvm/")
(synopsis "Library for Support Vector Machines")
(description
"LIBSVM is a machine learning library for support vector
classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and
distribution estimation (one-class SVM). It supports multi-class
classification.")
(license license:bsd-3)))
(define-public python-libsvm
(package (inherit libsvm)
(name "python-libsvm")
(build-system gnu-build-system)
(arguments
`(#:tests? #f ;no "check" target
#:make-flags '("-C" "python")
#:phases
(modify-phases %standard-phases
(delete 'configure)
(replace
'install
(lambda* (#:key inputs outputs #:allow-other-keys)
(let ((site (string-append (assoc-ref outputs "out")
"/lib/python"
(string-take
(string-take-right
(assoc-ref inputs "python") 5) 3)
"/site-packages/")))
(substitute* "python/svm.py"
(("../libsvm.so.2") "libsvm.so.2"))
(mkdir-p site)
(for-each (lambda (file)
(copy-file file (string-append site (basename file))))
(find-files "python" "\\.py"))
(copy-file "libsvm.so.2"
(string-append site "libsvm.so.2")))
#t)))))
(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)))
(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+)))
|