--- shogun-0.4.4.orig/debian/rules +++ shogun-0.4.4/debian/rules @@ -0,0 +1,138 @@ +#!/usr/bin/make -f + +DEB_SHLIBDEPS_INCLUDE := $(shell octave-config -p OCTLIBDIR) /usr/lib/atlas +DEB_COMPRESS_EXCLUDE := .py +R_LIBRARY_DIR := /usr/lib/R/site-library + +include /usr/share/cdbs/1/rules/debhelper.mk +include /usr/share/cdbs/1/rules/patchsys-quilt.mk +include /usr/share/octave/debian/defs.make + +PYVER = $(shell pyversions -vr debian/control) +PYCONFIG = cp -a src build-python$(modular)$(pyver); \ + cd build-python$(modular)$(pyver); \ + ./configure --disable-cpudetection --prefix=/usr \ + --python=python$(pyver) --interface=python$(modular); \ + cd ..; +PYBUILD = $(MAKE) -C build-python$(modular)$(pyver); +PYINSTALL = $(MAKE) -C build-python$(modular)$(pyver) install \ + DESTDIR=$(CURDIR)/debian/shogun-python$(modular); + + +makebuilddir:: debian/stamp-nonfree +debian/stamp-nonfree: + @if [ -e src/classifier/svm/SVM_light.cpp ]; then \ + echo "Do you really want to build a nonfree version? (y/N): "; \ + read answer; \ + if [ "$$answer" != y ]; then \ + exit 1; \ + fi; \ + fi + touch $@ + +# python modular +configure/shogun-python-modular:: debian/stamp-configure-python-modular +debian/stamp-configure-python-modular: modular=-modular +debian/stamp-configure-python-modular: + set -x; $(foreach pyver, $(PYVER), $(PYCONFIG)) + touch $@ + +build/shogun-python-modular:: debian/stamp-build-python-modular +debian/stamp-build-python-modular: modular=-modular +debian/stamp-build-python-modular: + set -x; $(foreach pyver, $(PYVER), $(PYBUILD)) + touch $@ + +install/shogun-python-modular:: debian/stamp-install-python-modular +debian/stamp-install-python-modular: modular=-modular +debian/stamp-install-python-modular: + set -x; $(foreach pyver, $(PYVER), $(PYINSTALL)) + dh_pycentral -pshogun-python-modular + touch $@ + +# python +configure/shogun-python:: debian/stamp-configure-python +debian/stamp-configure-python: modular= +debian/stamp-configure-python: + set -x; $(foreach pyver, $(PYVER), $(PYCONFIG)) + touch $@ + +build/shogun-python:: debian/stamp-build-python +debian/stamp-build-python: modular= +debian/stamp-build-python: + set -x; $(foreach pyver, $(PYVER), $(PYBUILD)) + dh_pycentral -pshogun-python + touch $@ + +install/shogun-python:: debian/stamp-install-python +debian/stamp-install-python: modular= +debian/stamp-install-python: + set -x; $(foreach pyver, $(PYVER), $(PYINSTALL)) + touch $@ + +# readline +configure/shogun-readline:: debian/stamp-configure-readline +debian/stamp-configure-readline: + cp -a src build-readline + cd build-readline && ./configure --disable-cpudetection \ + --prefix=/usr --interface=cmdline + touch $@ + +build/shogun-readline:: debian/stamp-build-readline +debian/stamp-build-readline: + $(MAKE) -C build-readline + touch $@ + +install/shogun-readline:: debian/stamp-install-readline +debian/stamp-install-readline: + $(MAKE) -C build-readline install DESTDIR=$(CURDIR)/debian/shogun-readline + touch $@ + +# octave +configure/shogun-octave:: debian/stamp-configure-octave +debian/stamp-configure-octave: + # reintroduce tests; failing autobuilders are fun ... + ls -l /usr/bin/oct* + ls -l /etc/alternatives/oct* + cp -a src build-octave + cd build-octave && ./configure --disable-cpudetection \ + --prefix=/usr --interface=octave + touch $@ + +build/shogun-octave:: debian/stamp-build-octave +debian/stamp-build-octave: + $(MAKE) -C build-octave + touch $@ + +install/shogun-octave:: debian/stamp-install-octave +debian/stamp-install-octave: + dh_install -pshogun-octave build-octave/sg.oct $(OCTDIR) + octave-depends + touch $@ + +# r +configure/shogun-r:: debian/stamp-configure-r +debian/stamp-configure-r: + mkdir build-r + cp -a R src build-r + touch $@ + +build/shogun-r:: debian/stamp-build-r +debian/stamp-build-r: + $(MAKE) -C build-r/R sg/src CFGOPTS=--disable-cpudetection + find build-r/R/sg \( -name COPYING -o -name 'LICENSE*' \) -delete + touch $@ + +install/shogun-r:: debian/stamp-install-r +debian/stamp-install-r: + mkdir -p $(CURDIR)/debian/shogun-r$(R_LIBRARY_DIR) + R CMD INSTALL -l $(CURDIR)/debian/shogun-r$(R_LIBRARY_DIR) build-r/R/sg + touch $@ + +#get-orig-source: +# -uscan --rename --upstream-version 0 +# @echo successfully retrieved upstream tarball + +clean:: + $(RM) -r build-* debian/stamp-* + find debian -mindepth 2 -name 'shogun-*' -delete --- shogun-0.4.4.orig/debian/shogun.1 +++ shogun-0.4.4/debian/shogun.1 @@ -0,0 +1,73 @@ +.\" Hey, EMACS: -*- nroff -*- +.\" First parameter, NAME, should be all caps +.\" Second parameter, SECTION, should be 1-8, maybe w/ subsection +.\" other parameters are allowed: see man(7), man(1) +.TH SHOGUN 5 "August 1, 2007" +.\" Please adjust this date whenever revising the manpage. +.\" +.\" Some roff macros, for reference: +.\" .nh disable hyphenation +.\" .hy enable hyphenation +.\" .ad l left justify +.\" .ad b justify to both left and right margins +.\" .nf disable filling +.\" .fi enable filling +.\" .br insert line break +.\" .sp insert n+1 empty lines +.\" for manpage-specific macros, see man(7) +.SH NAME +shogun \- A Large Scale Machine Learning Toolbox +.SH SYNOPSIS +.B shogun +.RI [ options ] +.br +.SH DESCRIPTION +This manual page briefly documents the readline interface of +.B shogun +. +.PP +.\" TeX users may be more comfortable with the \fB\fP and +.\" \fI\fP escape sequences to invode bold face and italics, +.\" respectively. +\fBShogun\fP is a large scale machine learning toolbox with focus on large +scale kernel methods and especially on Support Vector Machines (SVM) with focus +to bioinformatics. It provides a generic SVM object interfacing to several +different SVM implementations. Each of the SVMs can be combined with a variety +of the many kernels implemented. It can deal with weighted linear combination +of a number of sub-kernels, each of which not necessarily working on the same +domain, where an optimal sub-kernel weighting can be learned using Multiple +Kernel Learning. Apart from SVM 2-class classification and regression +problems, a number of linear methods like Linear Discriminant Analysis (LDA), +Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms +to train hidden markov models are implemented. The input feature-objects +can be dense, sparse or strings and of type int/short/double/char and can +be converted into different feature types. Chains of preprocessors (e.g. +substracting the mean) can be attached to each feature object +allowing for on-the-fly pre-processing. + +.SH OPTIONS +A summary of options is included below. +.TP +.B \-h, \-\-help, /? +Show summary of options. +.TP +.B \-i +listen on tcp port 7367 (hex of sg) +.TP +.B filename +execute a script by reading commands from file +.TP +when no options are given the interactive readline interface will be entered +.TP +.SH SEE ALSO +.BR svm-train (1), +.BR svm-predict (1). +.BR svm-scale (1). +.TP +.SH AUTHOR +.br +shogun was written by Soeren Sonnenburg +and Gunnar Raetsch +.PP +This manual page was written by Soeren Sonnenburg , +for the Debian project (but may be used by others). --- shogun-0.4.4.orig/debian/shogun-python-modular.examples +++ shogun-0.4.4/debian/shogun-python-modular.examples @@ -0,0 +1 @@ +python-modular/examples/* --- shogun-0.4.4.orig/debian/orig-tar.sh +++ shogun-0.4.4/debian/orig-tar.sh @@ -0,0 +1,16 @@ +#!/bin/sh + +# called by uscan with '--upstream-version' + +#tar xjf $3 +#cd shogun-$2 && make DEBIAN=yes vanilla-package +#rm -rf $3 +# +## move to directory 'tarballs' +#if [ -r .svn/deb-layout ]; then +# . .svn/deb-layout +# mv shogun-$2. $origDir +# echo "moved $3 to $origDir" +#fi + +exit 0 --- shogun-0.4.4.orig/debian/shogun-readline.manpages +++ shogun-0.4.4/debian/shogun-readline.manpages @@ -0,0 +1 @@ +debian/shogun.1 --- shogun-0.4.4.orig/debian/pycompat +++ shogun-0.4.4/debian/pycompat @@ -0,0 +1 @@ +2 --- shogun-0.4.4.orig/debian/shogun-r.examples +++ shogun-0.4.4/debian/shogun-r.examples @@ -0,0 +1 @@ +R/examples/* --- shogun-0.4.4.orig/debian/watch +++ shogun-0.4.4/debian/watch @@ -0,0 +1,3 @@ +version=3 +ftp://shogun-toolbox.org/shogun/releases/([\.0-9]*)/sources/shogun-([0-9]*.[\.0-9]*).tar.bz2 \ +debian debian/orig-tar.sh --- shogun-0.4.4.orig/debian/compat +++ shogun-0.4.4/debian/compat @@ -0,0 +1 @@ +5 --- shogun-0.4.4.orig/debian/shogun-octave.examples +++ shogun-0.4.4/debian/shogun-octave.examples @@ -0,0 +1,2 @@ +octave/examples/* +matlab/examples/* --- shogun-0.4.4.orig/debian/patches/series +++ shogun-0.4.4/debian/patches/series @@ -0,0 +1 @@ +wds.diff --- shogun-0.4.4.orig/debian/patches/wds.diff +++ shogun-0.4.4/debian/patches/wds.diff @@ -0,0 +1,13 @@ +Index: kernel/WeightedDegreePositionStringKernel.h +=================================================================== +--- ../src/kernel/WeightedDegreePositionStringKernel.h (revision 2459) ++++ ../src/kernel/WeightedDegreePositionStringKernel.h (working copy) +@@ -233,7 +233,7 @@ + DREAL compute_with_mismatch(CHAR* avec, INT alen, CHAR* bvec, INT blen) ; + DREAL compute_without_mismatch(CHAR* avec, INT alen, CHAR* bvec, INT blen) ; + DREAL compute_without_mismatch_matrix(CHAR* avec, INT alen, CHAR* bvec, INT blen) ; +- DREAL compute_without_mismatch_position_weights(CHAR* avec, DREAL *posweights_lhs, INT alen, CHAR* bvec, DREAL *posweights_lhs, INT blen) ; ++ DREAL compute_without_mismatch_position_weights(CHAR* avec, DREAL *posweights_lhs, INT alen, CHAR* bvec, DREAL *posweights_rhs, INT blen) ; + + virtual void remove_lhs() ; + virtual void remove_rhs() ; --- shogun-0.4.4.orig/debian/shogun-python.examples +++ shogun-0.4.4/debian/shogun-python.examples @@ -0,0 +1 @@ +python/examples/* --- shogun-0.4.4.orig/debian/changelog +++ shogun-0.4.4/debian/changelog @@ -0,0 +1,152 @@ +shogun (0.4.4-2ubuntu2) intrepid; urgency=low + + * gfortran transition on build dependecies: + - atlas3-base-dev -> libatlas-base-dev + - refblas3-dev -> libblas-dev + + -- Morten Kjeldgaard Tue, 09 Sep 2008 13:25:17 +0200 + +shogun (0.4.4-2ubuntu1) hardy; urgency=low + + * debian/control: + - Do not build-depend on python-numpy-{dev,ext}, build-depend + on python-numpy instead. + - Update Maintainer field as per spec. + + -- Luca Falavigna Wed, 06 Feb 2008 20:10:33 +0000 + +shogun (0.4.4-2) unstable; urgency=low + + [ Soeren Sonnenburg ] + * Depend on octave 3.0, thanks Thomas Weber (Closes: #457979) + * Fix FTBS for gcc-4.3 (Closes: #458192) + + [ Torsten Werner ] + * Change Depends: liblapack3-dev [arm]. + * Support newer versions of dpkg-shlibdeps. + * Change XS-Vcs to Vcs in debian/control. + + -- Torsten Werner Wed, 02 Jan 2008 20:31:20 +0100 + +shogun (0.4.4-1) unstable; urgency=low + + * New upstream version contains major bug fixes. + + -- Soeren Sonnenburg Fri, 23 Nov 2007 13:05:05 +0200 + +shogun (0.4.3-1) unstable; urgency=low + + * New upstream allows to disable custom cpu settings which allows + for a workaround the gcc-4.2 build failure on alpha. + + -- Soeren Sonnenburg Fri, 28 Sep 2007 09:53:35 +0200 + +shogun (0.4.2-1) unstable; urgency=low + + [Soeren Sonnenburg] + * New upstream version (Closes: #442990) + * Build R package in a separate directory (Closes: #442730) + + [Torsten Werner] + * minor fixes in debian/rules + + -- Soeren Sonnenburg Wed, 19 Sep 2007 09:08:42 +0200 + +shogun (0.4.1-1) unstable; urgency=low + + * new upstream version + + -- Soeren Sonnenburg Sat, 01 Sep 2007 09:56:50 +0200 + +shogun (0.4.0-3) unstable; urgency=low + + * add manpage for shogun-readline interface + + -- Soeren Sonnenburg Sun, 05 Aug 2007 13:49:06 +0200 + +shogun (0.4.0-2) unstable; urgency=low + + * remove build dependency on octave2.1-forge, but rather make it a Recommends + + -- Soeren Sonnenburg Thu, 02 Aug 2007 08:25:20 +0200 + +shogun (0.4.0-1) unstable; urgency=low + + * new upstream version + * also add include matlab examples in the octave examples + + -- Soeren Sonnenburg Tue, 31 Jul 2007 17:33:47 +0200 + +shogun (0.3.2-3) unstable; urgency=low + + * Change Build-Depends: r-base-core from r-base-dev. + + -- Torsten Werner Sun, 3 Jun 2007 10:50:29 +0200 + +shogun (0.3.2-2) unstable; urgency=low + + * support for python 2.5 + * bump compat to 5, pycompat to 2 + + -- Soeren Sonnenburg Wed, 09 May 2007 19:49:26 +0200 + +shogun (0.3.2-1) unstable; urgency=low + + * new upstream version + + -- Torsten Werner Wed, 7 Mar 2007 19:05:03 +0100 + +shogun (0.3.1-1) unstable; urgency=low + + * new upstream release, fixes a build problem + + -- Torsten Werner Tue, 20 Feb 2007 21:01:14 +0100 + +shogun (0.3.0-1) unstable; urgency=low + + * new upstream release + * Remove Build-Depends: atlas3-base-dev for arm architecture. + * Updated debian/python*.examples to reflect upstream changes. + + -- Torsten Werner Wed, 14 Feb 2007 20:49:23 +0100 + +shogun (0.2.1+svn1952-1) unstable; urgency=low + + * new upstream version that builds on more platforms + + -- Torsten Werner Sun, 11 Feb 2007 16:41:54 +0100 + +shogun (0.2.1+svn1923-1) unstable; urgency=low + + * new upstream version + * new upstream URL + * updated to new build system, renaming the packages to + shogun-python-modular and shogun-python + + -- Soeren Sonnenburg Wed, 24 Jan 2007 10:38:50 +0100 + +shogun (0.2.1+svn1880-2) unstable; urgency=low + + * NOT RELEASED YET + * Add more python examples. + + -- Torsten Werner Wed, 10 Jan 2007 22:02:45 +0100 + +shogun (0.2.1+svn1880-1) unstable; urgency=low + + [ Torsten Werner ] + * new upstream version + * Fixed debian/copyright as requested by ftp-master Jörg Jaspert. + + [ Sören Sonnenburg ] + * Removed non-free code completely. + * Switch to svn revisions instead of dates. + + -- Torsten Werner Wed, 10 Jan 2007 20:24:13 +0100 + +shogun (0.2.1-1) unstable; urgency=low + + [ Torsten Werner ] + * Initial Release, closes: #388148. + + -- Torsten Werner Thu, 30 Nov 2006 20:20:39 +0100 --- shogun-0.4.4.orig/debian/copyright +++ shogun-0.4.4/debian/copyright @@ -0,0 +1,27 @@ +This package was initially debianized by Torsten Werner +and is now maintained by Soeren Sonnenburg . + +It was downloaded from http://www.shogun-toolbox.org +. + +The upstream Author is Soeren Sonnenburg . + +Copyright: (c) 2006 Soeren Sonnenburg + +License: + + This program 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 2 of the License, or + (at your option) any later version. + + This program 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. + + On Debian systems, you can find the GPL license in: + /usr/share/common-licenses/GPL + +The nonfree SVMlight files have been removed and the package is configured with +--disable-svmlight. --- shogun-0.4.4.orig/debian/control +++ shogun-0.4.4/debian/control @@ -0,0 +1,140 @@ +Source: shogun +Section: science +Priority: optional +Maintainer: Ubuntu MOTU Developers +XSBC-Original-Maintainer: Soeren Sonnenburg +Uploaders: Torsten Werner +Build-Depends: libatlas-base-dev [!arm], cdbs, debhelper, libreadline5-dev, + octave3.0-headers, python-all-dev, python-central (>= 0.5), python-numpy, + quilt, r-base-core, lapack3-dev [arm], libblas-dev, swig, xutils-dev +XS-Python-Version: all +Standards-Version: 3.7.3 +Homepage: http://www.shogun-toolbox.org +Vcs-Svn: https://bollin.googlecode.com/svn/shogun/trunk/ +Vcs-Browser: http://bollin.googlecode.com/svn/shogun/trunk/ + +Package: shogun-python-modular +Architecture: any +Depends: ${shlibs:Depends}, ${misc:Depends}, ${python:Depends} +Recommends: python-numpy, python-matplotlib +Provides: ${python:Provides} +XB-Python-Version: ${python:Versions} +Description: Large Scale Machine Learning Toolbox + SHOGUN - is a new machine learning toolbox with focus on large scale kernel + methods and especially on Support Vector Machines (SVM) with focus to + bioinformatics. It provides a generic SVM object interfacing to several + different SVM implementations. Each of the SVMs can be combined with a variety + of the many kernels implemented. It can deal with weighted linear combination + of a number of sub-kernels, each of which not necessarily working on the same + domain, where an optimal sub-kernel weighting can be learned using Multiple + Kernel Learning. Apart from SVM 2-class classification and regression + problems, a number of linear methods like Linear Discriminant Analysis (LDA), + Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to + train hidden markov models are implemented. The input feature-objects can be + dense, sparse or strings and of type int/short/double/char and can be + converted into different feature types. Chains of preprocessors (e.g. + substracting the mean) can be attached to each feature object allowing for + on-the-fly pre-processing. + . + SHOGUN comes in different flavours, a stand-a-lone version and also with + interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular + Python package employing swig. + +Package: shogun-python +Architecture: any +Depends: ${shlibs:Depends}, ${misc:Depends}, ${python:Depends} +Recommends: python-numpy, python-matplotlib +Provides: ${python:Provides} +XB-Python-Version: ${python:Versions} +Description: Large Scale Machine Learning Toolbox + SHOGUN - is a new machine learning toolbox with focus on large scale kernel + methods and especially on Support Vector Machines (SVM) with focus to + bioinformatics. It provides a generic SVM object interfacing to several + different SVM implementations. Each of the SVMs can be combined with a variety + of the many kernels implemented. It can deal with weighted linear combination + of a number of sub-kernels, each of which not necessarily working on the same + domain, where an optimal sub-kernel weighting can be learned using Multiple + Kernel Learning. Apart from SVM 2-class classification and regression + problems, a number of linear methods like Linear Discriminant Analysis (LDA), + Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to + train hidden markov models are implemented. The input feature-objects can be + dense, sparse or strings and of type int/short/double/char and can be + converted into different feature types. Chains of preprocessors (e.g. + substracting the mean) can be attached to each feature object allowing for + on-the-fly pre-processing. + . + SHOGUN comes in different flavours, a stand-a-lone version and also with + interfaces to Matlab(tm), R, Octave, Readline and Python. This is the static + Python package without using swig. + +Package: shogun-readline +Architecture: any +Depends: ${shlibs:Depends}, ${misc:Depends} +Description: Large Scale Machine Learning Toolbox + SHOGUN - is a new machine learning toolbox with focus on large scale kernel + methods and especially on Support Vector Machines (SVM) with focus to + bioinformatics. It provides a generic SVM object interfacing to several + different SVM implementations. Each of the SVMs can be combined with a variety + of the many kernels implemented. It can deal with weighted linear combination + of a number of sub-kernels, each of which not necessarily working on the same + domain, where an optimal sub-kernel weighting can be learned using Multiple + Kernel Learning. Apart from SVM 2-class classification and regression + problems, a number of linear methods like Linear Discriminant Analysis (LDA), + Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to + train hidden markov models are implemented. The input feature-objects can be + dense, sparse or strings and of type int/short/double/char and can be + converted into different feature types. Chains of preprocessors (e.g. + substracting the mean) can be attached to each feature object allowing for + on-the-fly pre-processing. + . + SHOGUN comes in different flavours, a stand-a-lone version and also with + interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Readline + package. + +Package: shogun-octave +Architecture: any +Depends: ${shlibs:Depends}, ${misc:Depends}, ${octave:Depends} +Description: Large Scale Machine Learning Toolbox + SHOGUN - is a new machine learning toolbox with focus on large scale kernel + methods and especially on Support Vector Machines (SVM) with focus to + bioinformatics. It provides a generic SVM object interfacing to several + different SVM implementations. Each of the SVMs can be combined with a variety + of the many kernels implemented. It can deal with weighted linear combination + of a number of sub-kernels, each of which not necessarily working on the same + domain, where an optimal sub-kernel weighting can be learned using Multiple + Kernel Learning. Apart from SVM 2-class classification and regression + problems, a number of linear methods like Linear Discriminant Analysis (LDA), + Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to + train hidden markov models are implemented. The input feature-objects can be + dense, sparse or strings and of type int/short/double/char and can be + converted into different feature types. Chains of preprocessors (e.g. + substracting the mean) can be attached to each feature object allowing for + on-the-fly pre-processing. + . + SHOGUN comes in different flavours, a stand-a-lone version and also with + interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Octave + package. + +Package: shogun-r +Architecture: any +Depends: ${shlibs:Depends}, ${misc:Depends}, r-base-core +Description: Large Scale Machine Learning Toolbox + SHOGUN - is a new machine learning toolbox with focus on large scale kernel + methods and especially on Support Vector Machines (SVM) with focus to + bioinformatics. It provides a generic SVM object interfacing to several + different SVM implementations. Each of the SVMs can be combined with a variety + of the many kernels implemented. It can deal with weighted linear combination + of a number of sub-kernels, each of which not necessarily working on the same + domain, where an optimal sub-kernel weighting can be learned using Multiple + Kernel Learning. Apart from SVM 2-class classification and regression + problems, a number of linear methods like Linear Discriminant Analysis (LDA), + Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to + train hidden markov models are implemented. The input feature-objects can be + dense, sparse or strings and of type int/short/double/char and can be + converted into different feature types. Chains of preprocessors (e.g. + substracting the mean) can be attached to each feature object allowing for + on-the-fly pre-processing. + . + SHOGUN comes in different flavours, a stand-a-lone version and also with + interfaces to Matlab(tm), R, Octave, Readline and Python. This is the R + package. --- shogun-0.4.4.orig/debian/README.Debian +++ shogun-0.4.4/debian/README.Debian @@ -0,0 +1 @@ +The nonfree SVMlight files have been removed from the upstream archive.