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Bug#881841: marked as done (O: python-shogun -- Large Scale Machine Learning Toolbox)

Your message dated Fri, 09 Aug 2019 16:54:02 +0000
with message-id <E1hw89S-000Beo-N0@fasolo.debian.org>
and subject line Bug#934257: Removed package(s) from unstable
has caused the Debian Bug report #881841,
regarding O: python-shogun -- Large Scale Machine Learning Toolbox
to be marked as done.

This means that you claim that the problem has been dealt with.
If this is not the case it is now your responsibility to reopen the
Bug report if necessary, and/or fix the problem forthwith.

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-- 
881841: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=881841
Debian Bug Tracking System
Contact owner@bugs.debian.org with problems
--- Begin Message --- 
Package: wnpp

The current maintainer of python-shogun, Soeren Sonnenburg <sonne@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 <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]
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 <sonne@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 <sonne@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

Attachment: signature.asc
Description: PGP signature


--- End Message ---
--- Begin Message --- 
Version: 3.2.0-5.2+rm

Dear submitter,

as the package python-shogun has just been removed from the Debian archive
unstable we hereby close the associated bug reports.  We are sorry
that we couldn't deal with your issue properly.

For details on the removal, please see https://bugs.debian.org/934257

The version of this package that was in Debian prior to this removal
can still be found using http://snapshot.debian.org/.

This message was generated automatically; if you believe that there is
a problem with it please contact the archive administrators by mailing
ftpmaster@ftp-master.debian.org.

Debian distribution maintenance software
pp.
Scott Kitterman (the ftpmaster behind the curtain)

--- End Message ---
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