shark 3.1.3+ds1-2ubuntu5 source package in Ubuntu
Changelog
shark (3.1.3+ds1-2ubuntu5) artful; urgency=medium * No-change rebuild to pick up multiarchified libblas. -- Matthias Klose <email address hidden> Fri, 15 Sep 2017 10:19:13 +0200
Upload details
- Uploaded by:
- Matthias Klose
- Uploaded to:
- Artful
- Original maintainer:
- Ubuntu Developers
- Architectures:
- any all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| shark_3.1.3+ds1.orig.tar.gz | 13.2 MiB | 0d30fb6cf2f574fe74c85828b0e238d123a5c872b4b15a291dd19a85fc307e9a |
| shark_3.1.3+ds1-2ubuntu5.debian.tar.xz | 10.7 KiB | 751f7941a7482753ace5175b149aa5989d35bda9686473bea0b221de67caa547 |
| shark_3.1.3+ds1-2ubuntu5.dsc | 2.6 KiB | 5e9faaea56f976b2e63ac912a29fc1632c83740c11f032dbfec231d664b9c0b7 |
Available diffs
Binary packages built by this source
- libshark-dev: development files for Shark
Shark is a modular C++ library for the design and optimization of adaptive
systems. It provides methods for linear and nonlinear optimization, in
particular evolutionary and gradient-based algorithms, kernel-based learning
algorithms and neural networks, and various other machine learning techniques.
.
This package provides the development files.
- libshark0: Shark machine learning library
Shark is a modular C++ library for the design and optimization of adaptive
systems. It provides methods for linear and nonlinear optimization, in
particular evolutionary and gradient-based algorithms, kernel-based learning
algorithms and neural networks, and various other machine learning techniques.
.
This package provides the shared libraries.
- libshark0-dbgsym: No summary available for libshark0-dbgsym in ubuntu artful.
No description available for libshark0-dbgsym in ubuntu artful.
- shark-doc: documentation for Shark
Shark is a modular C++ library for the design and optimization of adaptive
systems. It provides methods for linear and nonlinear optimization, in
particular evolutionary and gradient-based algorithms, kernel-based learning
algorithms and neural networks, and various other machine learning techniques.
.
This package provides the documentation.
