Package: python-shogun
Version: 3.2.0-5.2
Architecture: i386
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Soeren Sonnenburg <sonne@debian.org>
Installed-Size: 16753
Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), libshogun16, libstdc++6 (>= 4.5), python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8)
Recommends: python-matplotlib, python-scipy
Provides: python2.7-shogun
Section: python
Priority: optional
Homepage: http://www.shogun-toolbox.org
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.
