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Source: python-shogun
Section: python
Priority: optional
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Build-Depends: libatlas-base-dev [!powerpc !alpha !arm !armel !armhf !sh4] | liblapack-dev,
libeigen3-dev, debhelper (>= 9), libreadline-dev | libreadline5-dev, libblas-dev,
libglpk-dev, libnlopt-dev, libshogun-dev (>= 3.2.0~), liblzo2-dev, zlib1g-dev, liblzma-dev,
libxml2-dev, libjson-c-dev | libjson0-dev, cmake, libarpack2-dev, libsnappy-dev,
libhdf5-dev (>= 1.8.8~) | libhdf5-serial-dev, swig3.0 (>= 3.0.2-1~),
python-numpy (>= 1:1.7.1-1~), python-all-dev (>= 2.7.0-1~),
libprotobuf-dev, protobuf-compiler, libcurl4-gnutls-dev, libbz2-dev, libcolpack-dev,
clang [mips mipsel powerpc]
#python3-numpy (>= 1:1.7.1-1~), python3-all-dev (>= 3.3.0-1~),
X-Python-Version: >= 2.7
#X-Python3-Version: >= 3.3
Standards-Version: 3.9.5
Homepage: http://www.shogun-toolbox.org
Vcs-Svn: http://bollin.googlecode.com/svn/python-shogun/trunk/
Vcs-Browser: http://bollin.googlecode.com/svn/python-shogun/trunk/
Package: python-shogun
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}, ${python:Depends}, libshogun16
Recommends: python-matplotlib, python-scipy
Provides: ${python:Provides}
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 package contains
the static and the modular Python interfaces.
Package: python-shogun-dbg
Architecture: any
Priority: extra
Section: debug
Depends: ${misc:Depends}, python-shogun (= ${binary:Version})
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 package contains
the debug symbols for the static and the modular Python interfaces.
#Package: python3-shogun
#Architecture: any
#Depends: ${shlibs:Depends}, ${misc:Depends}, ${python3:Depends}, libshogun16
#Recommends: python3-matplotlib, python3-scipy
#Provides: ${python3:Provides}
#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 package contains
# the static and the modular Python 3 interfaces.
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