Package: wnpp The current maintainer of python-shogun, Soeren Sonnenburg <so...@debian.org>, is apparently not active anymore. Therefore, I orphan this package now.
Maintaining a package requires time and skills. Please only adopt this package if you will have enough time and attention to work on it. If you want to be the new maintainer, please see https://www.debian.org/devel/wnpp/#howto-o for detailed instructions how to adopt a package properly. Some information about this package: Package: python-shogun Binary: python-shogun, python-shogun-dbg Version: 3.2.0-5.2 Maintainer: Soeren Sonnenburg <so...@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] Architecture: any Standards-Version: 3.9.5 Format: 3.0 (quilt) Files: 3eb667507ac71a549a81fabb71e67649 2498 python-shogun_3.2.0-5.2.dsc cc9a0fef2b87be3f791d1aed2e8de34c 1359052 python-shogun_3.2.0.orig.tar.xz f01279a828de1098cdb19541c7f21b34 9440 python-shogun_3.2.0-5.2.debian.tar.xz Vcs-Browser: http://bollin.googlecode.com/svn/python-shogun/trunk/ Vcs-Svn: http://bollin.googlecode.com/svn/python-shogun/trunk/ Checksums-Sha256: 58a9cc9ce7e7aa81357c2c44849ca08db937e398fc3b03db03baf864a1e23b5e 2498 python-shogun_3.2.0-5.2.dsc 0f4f39c941ad7ff7be74731d530db07447c02c12227994731402716a7cbbf73a 1359052 python-shogun_3.2.0.orig.tar.xz ef4e65beca68eb0a74d396def9c325fd68cb23181fb670ca0e590c92c71d81df 9440 python-shogun_3.2.0-5.2.debian.tar.xz Homepage: http://www.shogun-toolbox.org Package-List: python-shogun deb python optional arch=any python-shogun-dbg deb debug extra arch=any Directory: pool/main/p/python-shogun Priority: optional Section: misc Package: python-shogun Version: 3.2.0-5.2 Installed-Size: 18825 Maintainer: Soeren Sonnenburg <so...@debian.org> Architecture: amd64 Provides: python2.7-shogun Depends: libc6 (>= 2.14), 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 Description-en: 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. Description-md5: 5b94f29b021a8bdc343c6ffa0b259ffd Homepage: http://www.shogun-toolbox.org Section: science Priority: optional Filename: pool/main/p/python-shogun/python-shogun_3.2.0-5.2_amd64.deb Size: 3461062 MD5sum: 4cbd5f15d6c34383af785a2c99ed8b2e SHA256: 48271f64f5a3a415e20aa05052abd126b9edc32f9cc0db8580760fc6cfbc701f Package: python-shogun-dbg Source: python-shogun Version: 3.2.0-5.2 Installed-Size: 5871 Maintainer: Soeren Sonnenburg <so...@debian.org> Architecture: amd64 Depends: python-shogun (= 3.2.0-5.2) Description-en: 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. Description-md5: 3979e7348b2d7ed916b630fe648d7189 Homepage: http://www.shogun-toolbox.org Tag: role::debug-symbols Section: debug Priority: optional Filename: pool/main/p/python-shogun/python-shogun-dbg_3.2.0-5.2_amd64.deb Size: 3563174 MD5sum: d39e5907b952573baef798d1c3377b9d SHA256: f1450706a0e1e3108aa4237367ba8d7b1431fc44cc01ce1dca5ec610488762e2
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