147 lines
5.5 KiB
Scheme
147 lines
5.5 KiB
Scheme
;;; GNU Guix --- Functional package management for GNU
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;;; Copyright © 2015 Ricardo Wurmus <rekado@elephly.net>
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;;;
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;;; This file is part of GNU Guix.
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;;;
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;;; GNU Guix is free software; you can redistribute it and/or modify it
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;;; under the terms of the GNU General Public License as published by
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;;; the Free Software Foundation; either version 3 of the License, or (at
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;;; your option) any later version.
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;;;
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;;; GNU Guix is distributed in the hope that it will be useful, but
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;;; WITHOUT ANY WARRANTY; without even the implied warranty of
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;;; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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;;; GNU General Public License for more details.
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;;;
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;;; You should have received a copy of the GNU General Public License
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;;; along with GNU Guix. If not, see <http://www.gnu.org/licenses/>.
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(define-module (gnu packages machine-learning)
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#:use-module ((guix licenses) #:prefix license:)
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#:use-module (guix packages)
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#:use-module (guix utils)
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#:use-module (guix download)
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#:use-module (guix build-system gnu)
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#:use-module (gnu packages)
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#:use-module (gnu packages boost)
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#:use-module (gnu packages compression)
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#:use-module (gnu packages gcc)
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#:use-module (gnu packages maths)
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#:use-module (gnu packages python)
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#:use-module (gnu packages xml))
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(define-public libsvm
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(package
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(name "libsvm")
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(version "3.20")
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(source
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(origin
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(method url-fetch)
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(uri (string-append
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"https://github.com/cjlin1/libsvm/archive/v"
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(string-delete #\. version) ".tar.gz"))
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(file-name (string-append name "-" version ".tar.gz"))
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(sha256
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(base32
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"1jpjlql3frjza7zxzrqqr2firh44fjb8fqsdmvz6bjz7sb47zgp4"))))
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(build-system gnu-build-system)
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(arguments
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`(#:tests? #f ;no "check" target
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#:phases (modify-phases %standard-phases
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(delete 'configure)
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(replace
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'install
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(lambda* (#:key outputs #:allow-other-keys)
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(let* ((out (assoc-ref outputs "out"))
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(bin (string-append out "/bin/")))
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(mkdir-p bin)
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(for-each (lambda (file)
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(copy-file file (string-append bin file)))
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'("svm-train"
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"svm-predict"
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"svm-scale")))
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#t)))))
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(home-page "http://www.csie.ntu.edu.tw/~cjlin/libsvm/")
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(synopsis "Library for Support Vector Machines")
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(description
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"LIBSVM is a machine learning library for support vector
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classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and
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distribution estimation (one-class SVM). It supports multi-class
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classification.")
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(license license:bsd-3)))
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(define-public python-libsvm
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(package (inherit libsvm)
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(name "python-libsvm")
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(build-system gnu-build-system)
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(arguments
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`(#:tests? #f ;no "check" target
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#:make-flags '("-C" "python")
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#:phases
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(modify-phases %standard-phases
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(delete 'configure)
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(replace
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'install
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(lambda* (#:key inputs outputs #:allow-other-keys)
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(let ((site (string-append (assoc-ref outputs "out")
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"/lib/python"
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(string-take
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(string-take-right
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(assoc-ref inputs "python") 5) 3)
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"/site-packages/")))
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(substitute* "python/svm.py"
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(("../libsvm.so.2") "libsvm.so.2"))
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(mkdir-p site)
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(for-each (lambda (file)
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(copy-file file (string-append site (basename file))))
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(find-files "python" "\\.py"))
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(copy-file "libsvm.so.2"
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(string-append site "libsvm.so.2")))
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#t)))))
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(inputs
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`(("python" ,python)))
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(synopsis "Python bindings of libSVM")))
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(define-public randomjungle
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(package
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(name "randomjungle")
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(version "2.1.0")
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(source
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(origin
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(method url-fetch)
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(uri (string-append
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"http://www.imbs-luebeck.de/imbs/sites/default/files/u59/"
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"randomjungle-" version ".tar_.gz"))
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(sha256
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(base32
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"12c8rf30cla71swx2mf4ww9mfd8jbdw5lnxd7dxhyw1ygrvg6y4w"))))
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(build-system gnu-build-system)
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(arguments
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`(#:configure-flags
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(list (string-append "--with-boost="
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(assoc-ref %build-inputs "boost")))
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#:phases
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(modify-phases %standard-phases
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(add-before
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'configure 'set-CXXFLAGS
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(lambda _
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(setenv "CXXFLAGS" "-fpermissive ")
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#t)))))
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(inputs
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`(("boost" ,boost)
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("gsl" ,gsl)
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("libxml2" ,libxml2)
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("zlib" ,zlib)))
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(native-inputs
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`(("gfortran" ,gfortran-4.8)))
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(home-page "http://www.imbs-luebeck.de/imbs/de/node/227/")
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(synopsis "Implementation of the Random Forests machine learning method")
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(description
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"Random Jungle is an implementation of Random Forests. It is supposed to
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analyse high dimensional data. In genetics, it can be used for analysing big
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Genome Wide Association (GWA) data. Random Forests is a powerful machine
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learning method. Most interesting features are variable selection, missing
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value imputation, classifier creation, generalization error estimation and
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sample proximities between pairs of cases.")
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(license license:gpl3+)))
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