Task: Machine Learning
Description: Debian Science Machine Learning packages
 This metapackage will install Debian packages which might be useful for
 scientists interested in machine learning.  Included packages range
 from knowledge-based (expert) inference systems to software
 implementing dominant nowadays statistical methods.

Depends: python-pyke, gprolog, yap
Comment: Prolog (and alike) systems for inductive reasoning

Depends: libtorch3-dev

Depends: libshogun-dev, libfann-dev, libsvm-dev, libcomplearn-dev, \
            libqsearch-dev, liblinear-dev, libocas-dev
Comment: above libraries have also variety of interfaces to high-level
 scripting languages (e.g. Python) and even possibly some interactive GUI

Depends: python-scikits-learn, python-mdp, python-mlpy
Comment: Native Python toolkits

Depends: weka

Depends: vowpal-wabbit

Depends: r-cran-mass, r-cran-bayesm, r-cran-class, r-cran-cluster, \
            r-cran-msm, r-cran-mcmcpack, r-cran-mnp, r-cran-amore
Comment: R packages

Depends: python-mvpa
Why: Framework for statistical learning analysis of large datasets.
Published-Title: PyMVPA: a unifying approach to the analysis of neuroscientific data
Published-Authors: Michael Hanke, Yaroslav O. Halchenko, Per B. Sederberg, Emanuele Olivetti, Ingo Fründ, Jochem W. Rieger, Christoph S. Herrmann, James V. Haxby, Stephen José Hanson, Stefan Pollmann
Published-In: Frontiers in Neuroinformatics, 3:3
Published-Year: 2009
Published-URL: http://www.frontiersin.org/neuroinformatics/paper/10.3389/neuro.11/003.2009/
Published-DOI: 10.3389/neuro.11.003.2009


Depends: python-scikits.statsmodels
Homepage: http://statsmodels.sourceforge.net/
Language: Python
License: BSD
Responsible: Yaroslav Halchenko <debian@onerussian.com>
Pkg-URL: http://neuro.debian.net/pkgs/python-scikits.statsmodels.html
WNPP: 570604
Why: Statistical models
Pkg-Description: classes and functions for the estimation of statistical models
 scikits.statsmodels is a pure Python package that provides classes
 and functions for the estimation of several categories of statistical
 models. These currently include linear regression models, OLS, GLS,
 WLS and GLS with AR(p) errors, generalized linear models for six
 distribution families and M-estimators for robust linear models. An
 extensive list of result statistics are available for each estimation
 problem.

Depends: libroot-tmva-dev, libroot-montecarlo-vmc-dev, libroot-math-mlp-dev
Comment: ROOT libraries

Depends: autoclass, mcl
Comment: Applications

Depends: scilab-ann

Depends: libga-dev, libevocosm-dev, pgapack, python-genetic, octave-ga, \
            python-pyevolve
Comment: Evolutionary algorithm libraries in various languages

Depends: flann
Homepage: http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
Language: C++
WNPP: 581903
License: BSD
Pkg-Description: Fast Library for Approximate Nearest Neighbors
 FLANN is a library for performing fast approximate nearest neighbor searches
 in high dimensional spaces. It contains a collection of algorithms we found
 to work best for nearest neighbor search and a system for automatically
 choosing the best algorithm and optimum parameters depending on the dataset.

Depends: lua-torch5
Homepage: http://torch5.sourceforge.net
Language: C, Lua
WNPP: 490204
License: BSD
Pkg-Description: A matlab-like environment for state-of-the-art machine learning algorithms.
 Torch5 provides a Matlab-like environment for state-of-the-art machine
 learning algorithms. It is easy to use and provides a very efficient
 implementation, thanks to an easy and fast scripting language (Lua) and
 a underlying C implementation.

 ; Added by blends-inject 0.0.6. [Please note here if modified manually]
Depends: lush
Why: LUSH is a generic Lisp environment for numeric computation, but
 because ML is of primary interest of the authors, thus lush contains
 quite a few ML libraries (ANN, SVM, etc).
Published-Authors: Leon Bottou and Yann LeCun
Published-Title: The Lush manual
Published-URL: http://lush.sf.net
Published-Year: 2002

Depends: libcv-dev, python-opencv
Why: OpenCV provides a set of ML methods for pattern recognition
 within the context of computer vision

Depends: libvigraimpex-dev, python-vigra
Why: VIGRA is a computer vision library that provides customizable algorithms
 and datastructures, allowing for easy adaptation in applications.

Depends: pybrain
Homepage: http://www.pybrain.org
Language: Python
WNPP: 587069
License: BSD
Pkg-Description:  Modular Machine Learning Library
 PyBrain is a modular machine learning library for Python. Its goal is to offer
 flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks
 and a variety of predefined environments to test and compare your algorithms. 
 .
 PyBrain currently features algorithms for Supervised Learning, Unsupervised
 Learning, Reinforcment Learning and Black-box Optimization.

Depends: libshark-dev
Homepage: http://shark-project.sourceforge.net
Language: C++
WNPP: 595485
License: GPL-2+
Pkg-Description: Library for Designing and Optimizing Adaptive Systems
 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.
 SHARK serves as a toolbox to support real world applications as well as
 research in different domains of computational intelligence and machine
 learning.

Suggests: science-statistics, science-numericalcomputation

Suggests: libacovea-dev

Suggests: science-typesetting
Meta-Suggests: svn://svn.debian.org/blends/projects/science/trunk/debian-science/tasks/typesetting

 ; Added by blends-inject 0.0.7. [now official package]
Depends: python-pebl

Depends: python-orange
License: GPLv3
Homepage: http://orange.biolab.si/
Pkg-URL: http://orange.biolab.si/debian/
Responsible: Mitar <mmitar@gmail.com>
Pkg-Description: Data mining framework
 Orange is a component-based data mining software. It includes a range
 of data visualization, exploration, preprocessing and modeling
 techniques. It can be used through a nice and intuitive user interface
 or, for more advanced users, as a module for Python programming language.
