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README.txt
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README.txt
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Shark is a fast, modular, general open-source C++ machine
learning library.
Shark is licensed under the GNU Lesser General Public License, please
see the files COPYING and COPYING.LESSER, or visit
http://www.gnu.org/licenses .
Any application of the SHARK code toward military research and use is
expressly against the wishes of the SHARK development team.
INSTALLATION / DOCUMENTATION
----------------------------
The entry point to the Shark library documentation is located at
doc/index.html . For installation instructions, please click on
"Getting started" on that page. Short version of installation guide:
issue "ccmake ." in the main directory to select your build options,
and afterwards issue "make" in the main directory -- you should be
done (assuming Boost and CMake were installed). See the documentation
for detailed instructions.
BUILDING THE DOCUMENTATION: To build the documentation yourself (e.g.,
if you need to read it locally in order to install it, i.e., because
you don't have internet), see doc/README.txt
FILE STRUCTURE
--------------
README.txt This file (residing in the root directory of
the Shark library).
CMakeLists.txt Definitions for the CMake build system.
include/ This directory and its sub-directories hold
all include files of the library. Note that
some functionality is implemented in lower-
level Impl/ folders and inline .inl files.
lib/ The Shark library is placed in this directory.
In the source code distribution this directory
is initially empty, and the library is placed
into the directory as the results of
compilation. Binary distributions already
contain the library, pre-built in release mode.
doc/ All documentation files are found in this
sub-directory. In packaged versions of Shark
the html documentation is pre-built; the
repository provides the corresponding sources.
The documentation contains technical reference
documents for all classes and functions as well
as a collection of introductory and advanced
tutorials.
doc/index.html Entry point to the Shark documentation.
examples/ The examples directory contains example
use-cases of the most important algorithms
implemented in Shark. Besides exemplifying
powerful learning algorithms, these programs
are intended as starting points for
experimentation with the library. The
executables corresponding to the C++ example
programs are found in examples/bin/.
Test/ Shark comes with a large collection of unit
tests, all of which reside inside the Test
directory.
bin/ The binaries of the Shark unit tests are placed
here. Once the CMake build system is set up
(with the "ccmake" command or equivalent) the
whole test suite can be executed with the
command "make test", issued in the Shark root
directory.
src/ Source files of the Shark library. Note that
from Shark version 3 onwards large parts of the
library are templated and therefore header-only.
gpl-3.0.txt GNU general public license, version 3.
Note:
Depending of the type of Shark distribution (binary or source
package, or current repository snapshot) not all of theses files
and directories are present.
PACKAGE STRUCTURE
The organization of the include/ directory reflects the structure of
the Shark library. It consists of the following modules:
GENERAL INFRASTRUCTURE:
LinAlg Data structures and algorithms for typical
linear algebra computations. For (dense and
sparse) vector and matrix classes Shark relies
on Boost uBLAS. Many higher level algorithms
(such as singular value decomposition) are
still implemented by the library itself.
Statistics This component is new in Shark 3. It wraps the
capabilities of Boost accumulators, and it
provides tools that appear regularly in machine
learning, such as the Mann-Whitney U-test (also
known as the Wilcoxon rank-sum test).
LEARNING INFRASTRUCTURE:
Core The core module is the central place for all
top-level interfaces. In addition it holds a
few infrastructure classes, such as exceptions.
Data The data module hosts data containers that have
been specifically designed for the needs of
machine learning code. Also, data can be
imported and exported from and to different
standard machine learning data file formats.
MACHINE LEARNING:
Models Models are adaptive systems, the architectures
on top of which (machine) learning happens.
Shark features a rich set of models, from simple
linear maps to (feed-forward and recurrent)
neural networks, support vector machines, and
different types of trees. Models can also be
concatenated with data format converters and
other models.
ObjectiveFunctions This module collects different types of cost,
fitness, or objective functions for learning.
The bandwidth includes data-dependent error
functions based on simple loss functions,
cross-validation, area under the ROC curve, and
different objectives used for model selection.
Algorithms All actual learning algorithms reside in this
module. There are two main groups of learning
algorithms, namely iterative optimizers and
more specialized model trainers. General
optimizers are organized into direct search
and gradient-based optimization. Specialized
algorithms for linear programming (a part of
GLPK, the GNU linear programming kit) and
quadratic programming for training of non-linear
support vector machines are included. Shark
also ships with algorithms for efficient
nearest neighbor search.
Unsupervised This module contains the Shark implementation
of restricted Bolzmann machines (RBMs),
a recent experimental feature of Shark.
We use external libraries, images and code:
* Shark uses the `Doxygen documentation system http://www.doxygen.org
Doxygen is available under the LGPL, as Shark.
* Shark also uses the `Sphinx documentation system: http://sphinx.pocoo.org/
Sphinx is available under the 2-clause (simplified/Free) BSD license
http://www.opensource.org/licenses/bsd-license.php.
* The Shark documentation links between Sphinx and Doxygen using Doxylink
http://pypi.python.org/pypi/sphinxcontrib-doxylink written
by Matt Williams and released under a 2-clause (simplified/Free) BSD license
http://www.opensource.org/licenses/bsd-license.php.
* The website header is derived from the Mollio, http://mollio.org/ set
of html/css templates. Mollio is licensed under the
GPLv2 <http://www.gnu.org/licenses/gpl-2.0.html
* The page icon in the local table of contents is one of Nicolas Gallagher's
pure CSS GUI icons http://nicolasgallagher.com/pure-css-gui-icons/.
Nicolas Gallagher's work is licensed under the
GNU GPLv2 <http://www.gnu.org/licenses/gpl-2.0.html
* Download icon from the Steel System Icons set by Uriy1966,iconarchive.com
http://www.iconarchive.com/show/steel-system-icons-by-uriy1966/Download-icon.html
free for non-commercial use.