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Bug#881842: marked as done (O: shogun -- Large Scale Machine Learning Toolbox)

Your message dated Wed, 18 Dec 2019 06:03:40 +0000
with message-id <E1ihSQu-0000yd-91@fasolo.debian.org>
and subject line Bug#946927: Removed package(s) from unstable
has caused the Debian Bug report #881842,
regarding O: 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.

(NB: If you are a system administrator and have no idea what this
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immediately.)


-- 
881842: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=881842
Debian Bug Tracking System
Contact owner@bugs.debian.org with problems
--- Begin Message --- 
Package: wnpp

The current maintainer of 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: shogun
Binary: libshogun16, libshogun-dev, shogun-doc-en, shogun-doc-cn, libshogun-dbg, shogun-cmdline-static
Version: 3.2.0-7.5
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, ghostscript, libblas-dev, doxygen-latex, graphviz, libglpk-dev, libnlopt-dev, libbsd-dev, liblzo2-dev, zlib1g-dev, liblzma-dev, libxml2-dev, libjson-c-dev, cmake, libarpack2-dev, libsnappy-dev, libhdf5-dev (>= 1.8.8~) | libhdf5-serial-dev, libprotobuf-dev, protobuf-compiler, libcurl4-gnutls-dev, libbz2-dev, libcolpack-dev, clang [mips mipsel powerpc], python
Architecture: any all
Standards-Version: 3.9.5
Format: 3.0 (quilt)
Files:
 7c545a310e387fc7ee01e8f1cae6ff8d 2592 shogun_3.2.0-7.5.dsc
 1815f21cfe4d07edaa7c1ddf09732b58 3863400 shogun_3.2.0.orig.tar.xz
 9153daa3be77456e6a6406f1e9dd11bc 15944 shogun_3.2.0-7.5.debian.tar.xz
Vcs-Browser: http://bollin.googlecode.com/svn/shogun/trunk/
Vcs-Svn: http://bollin.googlecode.com/svn/shogun/trunk/
Checksums-Sha256:
 b4483aa10dbcccd8b38f9e44763734041c8c4d6c0fb155a0398385abedcab8a7 2592 shogun_3.2.0-7.5.dsc
 9ebb493bc56fb1c8c408e5c39da8aa75c767a9d64f8aae10d4fa9d280fa3f330 3863400 shogun_3.2.0.orig.tar.xz
 a205d2d812bbb576f5fd601f5acbb58908696900e764fb7053ed80466943fd44 15944 shogun_3.2.0-7.5.debian.tar.xz
Homepage: http://www.shogun-toolbox.org
Package-List: 
 libshogun-dbg deb debug extra arch=any
 libshogun-dev deb libdevel optional arch=any
 libshogun16 deb libs optional arch=any
 shogun-cmdline-static deb science optional arch=any
 shogun-doc-cn deb doc optional arch=all
 shogun-doc-en deb doc optional arch=all
Directory: pool/main/s/shogun
Priority: source
Section: science

Package: libshogun16
Source: shogun
Version: 3.2.0-7.5
Installed-Size: 15737
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Architecture: amd64
Depends: libarpack2 (>= 2.1), libatlas3-base, libbz2-1.0, libc6 (>= 2.23), libcolpack0v5, libcurl3-gnutls (>= 7.16.2), libgcc1 (>= 1:4.0), libglpk40 (>= 4.59), libgomp1 (>= 4.9), libhdf5-100, libjson-c3 (>= 0.10), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20120614), liblzo2-2, libnlopt0 (>= 2.2.4), libprotobuf10, libsnappy1v5, libstdc++6 (>= 5.2), libsz2, libxml2 (>= 2.7.4), zlib1g (>= 1:1.1.4)
Conflicts: libshogunui0, libshogunui1, libshogunui2, libshogunui3, libshogunui4, libshogunui5, libshogunui6
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 is the core
 library with the machine learning methods and ui helpers all interfaces are
 based on.
Description-md5: 6bb0422cfbb53c6d03535e4b9ea0892e
Homepage: http://www.shogun-toolbox.org
Tag: role::shared-lib
Section: libs
Priority: optional
Filename: pool/main/s/shogun/libshogun16_3.2.0-7.5_amd64.deb
Size: 3855610
MD5sum: 96d5cd8ed49caae2aa31ad895c14f25c
SHA256: 6aa382cfd13650a4717aab5be3d10b2e96fa9650e44ac672f5ca4398bb2b6eec

Package: libshogun-dev
Source: shogun
Version: 3.2.0-7.5
Installed-Size: 5196
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Architecture: amd64
Depends: libshogun16 (= 3.2.0-7.5)
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
 includes the developer files required to create stand-a-lone executables.
Description-md5: bfc80b06b9c1b287d681524474be7ec9
Homepage: http://www.shogun-toolbox.org
Tag: devel::library, role::devel-lib
Section: libdevel
Priority: optional
Filename: pool/main/s/shogun/libshogun-dev_3.2.0-7.5_amd64.deb
Size: 1533904
MD5sum: 973093fd583745acd0604ffcd05d729e
SHA256: 00f9c3836cc4b514ab03beaaa05ca4d3e22f036e11bb74dd4b7a314511fbf639

Package: shogun-doc-en
Source: shogun
Version: 3.2.0-7.5
Installed-Size: 238884
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Architecture: all
Replaces: shogun-doc
Recommends: libshogun-dev
Conflicts: shogun-doc
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 is the English
 user and developer documentation.
Description-md5: 301b3aa7b294b5e8a9c5538100845ced
Homepage: http://www.shogun-toolbox.org
Tag: devel::doc, made-of::html, role::documentation
Section: doc
Priority: optional
Filename: pool/main/s/shogun/shogun-doc-en_3.2.0-7.5_all.deb
Size: 28485200
MD5sum: de8d9db4a2bd542ba57b9e8ff921269b
SHA256: c8375b7b8fac24a37bf3ec023d2a1f3d0c9dea290588a66120916a4f730c391a

Package: shogun-doc-cn
Source: shogun
Version: 3.2.0-7.5
Installed-Size: 238418
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Architecture: all
Recommends: libshogun-dev
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 is the
 Chinese user and developer documentation.
Description-md5: 7efd1cc18219e0ce334e65b6e7fed49c
Homepage: http://www.shogun-toolbox.org
Tag: culture::chinese, devel::doc, made-of::html, role::documentation
Section: doc
Priority: optional
Filename: pool/main/s/shogun/shogun-doc-cn_3.2.0-7.5_all.deb
Size: 28494384
MD5sum: 88bce7f17ff3a6d52d39415710668437
SHA256: 2971e5390895ed5e61f62e742e2d5b4539692ec5cb3584349a2ad081ddb6b438

Package: libshogun-dbg
Source: shogun
Version: 3.2.0-7.5
Installed-Size: 59169
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Architecture: amd64
Replaces: shogun-dbg
Depends: libshogun16 (= 3.2.0-7.5)
Breaks: shogun-dbg
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 debug symbols for all interfaces.
Description-md5: c7102983b8576cbbe6c93467d8eb1daf
Homepage: http://www.shogun-toolbox.org
Build-Ids: 92326b04df754e0961927e47dc1ad4d815d45930
Tag: role::debug-symbols
Section: debug
Priority: optional
Filename: pool/main/s/shogun/libshogun-dbg_3.2.0-7.5_amd64.deb
Size: 56773696
MD5sum: 0575f5ad2a64173569497d1adc19f272
SHA256: 3f81dd60a34e610b6aca307d2653a719349b218e2caa14176db47e8981f8d445

Package: shogun-cmdline-static
Source: shogun
Version: 3.2.0-7.5
Installed-Size: 1084
Maintainer: Soeren Sonnenburg <sonne@debian.org>
Architecture: amd64
Replaces: shogun-cmdline
Depends: libarpack2 (>= 2.1), libatlas3-base, libbz2-1.0, libc6 (>= 2.14), libcolpack0v5, libcurl3-gnutls (>= 7.16.2), libgcc1 (>= 1:3.0), libglpk40 (>= 4.59), libgomp1 (>= 4.2.1), libhdf5-100, libjson-c3 (>= 0.10), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libnlopt0 (>= 2.2.4), libprotobuf10, libshogun16 (= 3.2.0-7.5), libsnappy1v5, libstdc++6 (>= 4.1.1), libsz2, libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4)
Conflicts: shogun-cmdline
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 is the Readline
 package.
Description-md5: 77514a757d989aed0db98766f5adb36f
Homepage: http://www.shogun-toolbox.org
Section: science
Priority: optional
Filename: pool/main/s/shogun/shogun-cmdline-static_3.2.0-7.5_amd64.deb
Size: 960240
MD5sum: 2ae23d537bc82e47f422e45a236ac0aa
SHA256: 4900796aa417e62a133301a4ef3a021ee0a2121d2111470475fd4624b7292685

Attachment: signature.asc
Description: PGP signature


--- End Message ---
--- Begin Message --- 
Version: 3.2.0-9+rm

Dear submitter,

as the package 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/946927

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

Please note that the changes have been done on the master archive and
will not propagate to any mirrors until the next dinstall run at the
earliest.

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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