Key | Value |
---|---|
FileName | ./usr/share/doc/python-shogun/examples/python_modular/serialization_string_kernels_modular.py.gz |
FileSize | 1752 |
MD5 | 2BEF50BB844954C75A2F2B4B3D8D18A4 |
SHA-1 | 06243F1DFAA89D73C85D5C1F03BE7E9CEAC42D7E |
SHA-256 | 65749EB10AC05D95F7E532AEE46604C4E66AA6EF1E4E845207026CC799FC04CB |
SSDEEP | 48:X5p4fNr8w62H4YOHxOni4fNP4FTUhxZ3L/8vY74YXjATWJ1n:J6NI12HROHxceGhxtUv44YcY1n |
TLSH | T165310B7845E70AA5AD41B2BD955A92E18024F0F3F7663DC77E600C340EC030EA4742BE |
hashlookup:parent-total | 15 |
hashlookup:trust | 100 |
The searched file hash is included in 15 parent files which include package known and seen by metalookup. A sample is included below:
Key | Value |
---|---|
FileSize | 2563344 |
MD5 | F4FA950E6FECE56A9E19E1A4C9EB6E6A |
PackageDescription | 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | 0E0591F1E7D5CD0E2FF810FA2E59E4B91CF765BD |
SHA-256 | D10D0FF8BF6E5AD78647C95DE97C3DF535D9B890167CDB5223E3408E72D323F3 |
Key | Value |
---|---|
FileSize | 3109708 |
MD5 | E7000B55376AD3241135FD947E6D9FE4 |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | 45156560C9F042CD03A613195572BCFEBBD00F6F |
SHA-256 | 666A00D2FC804252A6933CE53EC1DAE177ADAE4C644670450F38A66C8178729F |
Key | Value |
---|---|
FileSize | 2447164 |
MD5 | 2882063B9B00409EE12BDBD1C87AEB49 |
PackageDescription | 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2build2 |
SHA-1 | 9433EFE9646236116111ADD9A9601D4D71FBDFEC |
SHA-256 | 904F01FE07BFBF24FC39F5B79BF06D6CF2EBAF73F328262B063E3542DFDE24E3 |
Key | Value |
---|---|
FileSize | 3108572 |
MD5 | 27B4CE7086DEA1557BBC255FEA6DA2DB |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-4 |
SHA-1 | 96DA2072A1DC5787FFA030C0222D6408DB769E52 |
SHA-256 | DB44E3A34137AF56D276F1192D58925D84A0FBBD06BD7FC1E832D71FF674771C |
Key | Value |
---|---|
FileSize | 2561618 |
MD5 | C1354629C8E463623CD17F448B1251F7 |
PackageDescription | 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2build2 |
SHA-1 | 3D641A7DC80551DDEDB12097C87E06F85E52290A |
SHA-256 | 71B668FB034B36306545EF0590A74AC1A6CAF2A4BC28429417BF0D19B81CA21C |
Key | Value |
---|---|
FileSize | 3308066 |
MD5 | 716BC53E4BCF1C5180FA98BA7D883DA8 |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-4 |
SHA-1 | E3B54A50241D4664CAB26C128267A24F06F453F2 |
SHA-256 | AF53A196A81BAC8C0AD39EC14CFE0DC1A3AEF7979123FEAFDD0A1A9E47E49B85 |
Key | Value |
---|---|
FileSize | 3461062 |
MD5 | 4CBD5F15D6C34383AF785A2C99ED8B2E |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | A5ABE45A8C373B4DDECBFF083E792ECB04B320C2 |
SHA-256 | 48271F64F5A3A415E20AA05052ABD126B9EDC32F9CC0DB8580760FC6CFBC701F |
Key | Value |
---|---|
FileSize | 3255230 |
MD5 | F632FE9F948852B20EC1A2417D475804 |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | 54F6244044B0A6F8ABBD077F9AE4A48341B3A0F0 |
SHA-256 | 6EFC84F00120ABE3F173FE89CF9689B370B1EF88E974521D203C738CF730C588 |
Key | Value |
---|---|
FileSize | 3310786 |
MD5 | 55046B3D51A9F314FB217BDDC846E78E |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | 5DA43AA0325654F9C5DD5EE18E6E9B785295FC90 |
SHA-256 | DEA111356F3E19973277742FE0A4E7CFB447B5726BC30A2C9E18E1D7A43F9192 |
Key | Value |
---|---|
FileSize | 3126282 |
MD5 | 573881D086CD9DC751C1512BCC2A4F93 |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | A9BA014E34044877D472C2C95BE1E2F7E05A0B4E |
SHA-256 | 31ACB0AC94A3E5B3152BFDC00C8FAAB80BACA2B586E7AEF8709F1E66A8E32ECC |
Key | Value |
---|---|
FileSize | 3466904 |
MD5 | 9C32872908144320642D566FBA9359F3 |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-4 |
SHA-1 | 2073B038240200F87E8FC1F4553141E9359D7694 |
SHA-256 | A6B4AA9D554BAAD2BEA3EDB846AECB63DE74CE922EAB41183CEE2C1D325C6630 |
Key | Value |
---|---|
FileSize | 3126752 |
MD5 | 2EBB27AD9D216CF03FBB66689891C775 |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-4 |
SHA-1 | C6D1AFAEE8448E226E3B0E4B89ACAC42D293BBAA |
SHA-256 | 2D7D4A4B48A3B3535C8F096A541346E10A9E220546D9AB2CF3709EC0AB4B4889 |
Key | Value |
---|---|
FileSize | 3074246 |
MD5 | ED765C8A718C38B7BBE8053141184594 |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | 0651CB1BDA9DAA880564F978F2BC68DAA8A4444C |
SHA-256 | D61882A503776554D5972020C3EBD409BA8E2D5DA266042E39ECA93998EF9244 |
Key | Value |
---|---|
FileSize | 3376006 |
MD5 | 0F7D615D3B8542128FD3F7761ACB37CB |
PackageDescription | 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. |
PackageMaintainer | Soeren Sonnenburg <sonne@debian.org> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | F3E7F504BEA2FF22106E161A30ED49AC887A7670 |
SHA-256 | D2AA4DAE0F8A9A6E4E1F71D797CCA54F8329A615DA0F7E8FBC4FCDA7BE5BED9B |
Key | Value |
---|---|
FileSize | 2408936 |
MD5 | 5543E3C427DE9548E2F73AE72274A521 |
PackageDescription | 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. |
PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
PackageName | python-shogun |
PackageSection | python |
PackageVersion | 3.2.0-5.2 |
SHA-1 | E910FA9937D615EC5C126A557466AB8799E0473C |
SHA-256 | D14ABEF76D1B1431852F4E96AA9A9F912482E10AB4B60E34C2940E718E7BFFF6 |