Source: python-shogun Section: python Priority: optional Maintainer: Soeren Sonnenburg 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.