Dowload WEKA tarball from AUR, and Change weka.sh to
#! /bin/sh # Note: the '-Xmx256m' flag is used to set the maximum memory that the java # program is allowed. When working with large data sets, this number may need # to be increased based on how much memory you wish to allow weka to have. # Specifying command line arguments will override the defaults shown here. # Example: '-Xmx512m' DEFAULT_ARGS='-Xms32m -Xmx256m' if [ $# -gt 0 ]; then ARGS="$@" else ARGS="$DEFAULT_ARGS" fi java -classpath "${CLASSROOT+CLASSROOT:}/usr/share/java/weka/weka.jar:/usr/share/java/libsvm.jar" weka.gui.GUIChooser $ARGS
Do not forget to change the MD5SUM of weka.sh in PKGBUILD file :) Modify LibSVM PKGBUILD to:
# Contributor: Pierre Gueth # Modified by: Yuanjie Huang pkgname=libsvm pkgver=2.9 pkgrel=2 pkgdesc="A library for Support Vector Machines classification, include binaries and python bindings." arch=(x86_64 i686) url="http://www.csie.ntu.edu.tw/~cjlin/libsvm/" license=('GPL') groups=() depends=() makedepends=() optdepends=() provides=() conflicts=() replaces=() backup=() options=() install= source=("http://www.csie.ntu.edu.tw/~cjlin/cgi-bin/libsvm.cgi?+http://www.csie.ntu.edu.tw/~cjlin/libsvm+tar.gz") noextract=() md5sums=('c48109c825d8326d71c8c8564589736d') build() { cd "$srcdir/$pkgname-$pkgver" make || return 1 install -D -m755 svm-train $pkgdir/usr/bin/svm-train install -D -m755 svm-predict $pkgdir/usr/bin/svm-predict install -D -m755 svm-scale $pkgdir/usr/bin/svm-scale install -D -m644 java/libsvm.jar $pkgdir/usr/share/java/libsvm.jar cd "python" python setup.py build || return 1 python setup.py install --root $pkgdir }
Then makepgk and install, have fun.