Debian Science Machine learning packages

AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering, or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the "natural" classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided.

GNU Prolog is a free Prolog compiler with constraint solving over finite domains (FD) developed at INRIA by Daniel Diaz. GNU Prolog is based on two systems developed by the same author (with lot of code rewriting and a lot of new extensions): wamcc and clp(FD). Much work has been devoted to make it ISO compatible, full compliance being one of its goals.

This package contains the compiler and runtime system for the ISO standard version of GNU Prolog.

complearn is a library for parameter-free universal learning. Using this library, developers can access a wealth of powerful and general techniques in artificial intelligence and pattern recognition including fields such as genomics, language evolution, music recognition, and much more

This package provide files for translation from libcv-dev to subdivided packages.

This package contains the header files and static library needed to compile applications that use OpenCV (Open Computer Vision).

The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance.

OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction.

This library provides a framework for programming a wide variety of evolutionary algorithms, ranging from genetic algorithms to agent simulations. Evocosm is the foundation for Acovea

This package contains the files needed to develop code using libevocosm.

Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. A Python binding is available, and bindings for PHP, C++, .NET, Delphi, Octave, Ruby, Pure Data and Mathematica can be downloaded from FANN's homepage.

This package contains the header files and static libraries which are needed for developing libfann applications.

GAlib contains a set of C++ genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program using any representation and genetic operators. The documentation includes an extensive overview of how to implement a genetic algorithm as well as examples illustrating customizations to the GAlib classes.

This package contains the development files.

LIBLINEAR is a library for learning linear classifiers for large scale applications. It supports Support Vector Machines (SVM) with L2 and L1 loss, logistic regression, multi class classification and also Linear Programming Machines (L1-regularized SVMs). Its computational complexity scales linearly with the number of training examples making it one of the fastest SVM solvers around.

This package contains the header files and static libraries.

This library implements Optimized Cutting Plane Algorithm (OCAS) for training linear Support Vector Machine (SVM) classifiers from large-scale data. The computational effort of OCAS scales linearly with the number of training examples. It is one of the fastest SVM solvers around for solving linear and multiclass L2 regularized SVMs.

This package contains the header files and static libraries.

qsearch is a library for universal hierarchical clustering using an arbitrary distance matrix as input. It searches through the space of all possible unrooted trees of a given size and finds the closest match based on a weighted quartet cost function determined by the distance matrix. When used in combination with other feature extraction libraries such as libcomplearn this system can be used for fast and accurate phylogenetic reconstruction, linguistic analysis, or stemmatology.

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

This package contains development files of the mlp plug-in for ROOT, provides a Multi Layer Perceptron Neural Network package for ROOT.

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

This package contains development files of the Vmc library for ROOT.

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

The Toolkit for Multivariate Analysis (TMVA) provides a ROOT-integrated environment for the parallel processing and evaluation of MVA techniques to discriminate signal from background samples. It presently includes (ranked by complexity):

• Rectangular cut optimisation
• Correlated likelihood estimator (PDE approach)
• Multi-dimensional likelihood estimator (PDE - range-search approach)
• Fisher (and Mahalanobis) discriminant
• H-Matrix (chi-squared) estimator
• Artificial Neural Network (two different implementations)
• Boosted Decision Trees

The TMVA package includes an implementation for each of these discrimination techniques, their training and testing (performance evaluation). In addition all these methods can be tested in parallel, and hence their performance on a particular data set may easily be compared.

This package provides development files of TMVA package for ROOT.

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.

LIBSVM, a machine-learning library, is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection.

This package contains the development header files.

Torch is a machine-learning library, written in C++. Its aim is to provide the state-of-the-art of the best algorithms.

• Many gradient-based methods, including multi-layered perceptrons, radial basis functions, and mixtures of experts. Many small "modules" (Linear module, Tanh module, SoftMax module, ...) can be plugged together.
• Support Vector Machine, for classification and regression.
• Distribution package, includes Kmeans, Gaussian Mixture Models, Hidden Markov Models, and Bayes Classifier, and classes for speech recognition with embedded training.
• Ensemble models such as Bagging and Adaboost.
• Non-parametric models such as K-nearest-neighbors, Parzen Regression and Parzen Density Estimator.

This package is the Torch development package (header files and static library.)

Vision with Generic Algorithms (VIGRA) is a computer vision library that puts its main emphasis on flexible algorithms, because algorithms represent the principle know-how of this field. The library was consequently built using generic programming as introduced by Stepanov and Musser and exemplified in the C++ Standard Template Library. By writing a few adapters (image iterators and accessors) you can use VIGRA's algorithms on top of your data structures, within your environment.

This package contains the header and development files needed to build programs and packages using VIGRA.

Lush is a programming language and environment that is based on the Lisp programming language. The lush language is small compared to ANSI Common Lisp and is optimized for numeric calculations. Lush includes a libraries for numerical analysis and building graphical user interfaces.

This package contains the binary executable.

The MCL package is an implementation of the MCL algorithm, and offers utilities for manipulating sparse matrices (the essential data structure in the MCL algorithm) and conducting cluster experiments.

MCL is currently being used in sciences like biology (protein family detection, genomics), computer science (node clustering in Peer-to-Peer networks), and linguistics (text analysis).

This package provides function to work with genetic algorithms in Octave, a numerical computation software. It provides the ga() function, which works similarly to other optimization functions in Octave.

This Octave add-on package is part of the Octave-Forge project.

PGAPack is a general-purpose, data-structure-neutral, parallel genetic algorithm package being developed at Argonne National Laboratory.

This package contains header files, manual pages, examples and tests. To use pgpack, you need to install the libpgapack-serial ('single cpu') or libpgapack-mpi ('parallel') package.

python-genetic provides genetic algorithms for Python, as often used in artificial intelligence. It should be able to solve any problem that consists in minimizing functions.

You'll find some demos using Genetic in this package, including an impressively simple program that provides a solution to the well-known TSP (Travelling Salesman Problem). Also, make sure to read demo/genetic_demo_2.py for the list of the special "magic" genes that make Genetic really fun and ... living !

Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers.

This package contains MDP for Python 2.

mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping.

mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, *RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators.

PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun).

While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets.

This package contains Python bindings for the OpenCV (Open Computer Vision) library.

The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance.

OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction.

Pebl is a Python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features:

• Can learn with observational and interventional data
• Handles missing values and hidden variables using exact and heuristic methods
• Provides several learning algorithms; makes creating new ones simple
• Has facilities for transparent parallel execution using several cluster/grid resources
• Calculates edge marginals and consensus networks
• Presents results in a variety of formats

Pyevolve was developed to be a complete genetic algorithm framework written in pure Python. It provides an easy-to-use API, implementing the most common features of GA, including various selectors and scaling schemes. It is also easily extendable, allowing users to create new representations and genetic operators. Various methods of interactive and non-interactive visualization are supported.

This package contains the Python modules.

Pyke introduces a form of Logic Programming (inspired by Prolog) to Python by providing a knowledge-based inference engine (or "expert system").

Unlike Prolog, Pyke integrates with Python code allowing one to invoke Pyke from Python and intermingle Python statements and expressions within your expert system rules.

PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace.

statsmodels Python module 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.

Vision with Generic Algorithms (VIGRA) is a computer vision library that puts its main emphasis on flexible algorithms, because algorithms represent the principle know-how of this field. The library was consequently built using generic programming as introduced by Stepanov and Musser and exemplified in the C++ Standard Template Library. By writing a few adapters (image iterators and accessors) you can use VIGRA's algorithms on top of your data structures, within your environment.

This package exports the functionality of the VIGRA library to Python.

This package was born to release the TAO robust neural network algorithm to the R users. It has grown and can be of interest for the users wanting to implement their own training algorithms as well as for those others whose needs lye only in the "user space".

The bayesm package covers many important models used in marketing and micro-econometrics applications. The package includes:

• Bayes Regression (univariate or multivariate dep var)
• Multinomial Logit (MNL) and Multinomial Probit (MNP)
• Multivariate Probit,
• Multivariate Mixtures of Normals
• Hierarchical Linear Models with normal prior and covariates
• Hierarchical Multinomial Logits with mixture of normals prior and covariates
• Bayesian analysis of choice-based conjoint data
• Bayesian treatment of linear instrumental variables models
• Analyis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)).

For further reference, consult the authors' book, Bayesian Statistics and Marketing by Allenby, McCulloch and Rossi.

The class package provides functions and datasets to support chapter 12 on 'Classification' in the book 'Modern Applied Statistics with S' (4th edition) by W.N. Venables and B.D. Ripley. The following URL provides more details about the book: URL: http://www.stats.ox.ac.uk/pub/MASS4

This package provides functions and datasets for cluster analysis originally written by Peter Rousseeuw, Anja Struyf and Mia Hubert.

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

The MASS package provides functions and datasets to support the book 'Modern Applied Statistics with S' (4th edition) by W.N. Venables and B.D. Ripley. The following URL provides more details about the book: URL: http://www.stats.ox.ac.uk/pub/MASS4

This is a set of routines for GNU R that implement various statistical and econometric models using Markov chain Monte Carlo (MCMC) estimation, which allows "solving" models that would otherwise be intractable with traditional techniques, particularly problems in Bayesian statistics (where one or more "priors" are used as part of the estimation procedure, instead of an assumption of ignorance about the "true" point estimates), although MCMC can also be used to solve frequentist statistical problems with uninformative priors. MCMC techniques are also preferable over direct estimation in the presence of missing data.

Currently implemented are a number of ecological inference (EI) routines (for estimating individual-level attributes or behavior from aggregate data, such as electoral returns or census results), as well as models for traditional linear panel and cross-sectional data, some visualization routines for EI diagnostics, two item-response theory (or ideal-point estimation) models, metric, ordinal, and mixed-response factor analysis, and models for Gaussian (linear) and Poisson regression, logistic regression (or logit), and binary and ordinal-response probit models.

The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.

MNP is an R package that fits Bayesian Multinomial Probit (MNP) models via Markov chain Monte Carlo (MCMC). Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation and complete or partial ordering of all the available alternatives. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2004).

Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data efficiently.

With the data defined as a set of objects, specialized storage methods can give direct access to the separate attributes of the selected objects, without having to touch the bulk of the data. Included are histogramming methods in 1, 2 and 3 dimensions, curve fitting, function evaluation, minimization, graphics and visualization classes to allow the easy creation of an analysis system that can query and process the data interactively or in batch mode.

The command language, the scripting (or macro) language, and the programming language are all C++, thanks to the built-in CINT C++ interpreter. This interpreter removes the time consuming compile/link cycle, allowing for fast prototyping of the macros, and providing a good environment to learn C++. If more performance is needed, the interactively developed macros can be compiled using a C++ compiler.

The system has been designed in such a way that it can query its databases in parallel on MPP machines or on clusters of workstations or high-end PCs. ROOT is an open system that can be dynamically extended by linking external libraries. This makes ROOT a premier platform on which to build data acquisition, simulation and data analysis systems.

This package is a metapackage to ensure the installation of all possible ROOT packages on a system.

This module implements artificial neural networks capabilities into the Scilab language. Current features are:

• Only layered feedforward networks are supported directly at the moment (for others use the "hooks" provided)
• Unlimited number of layers
• Unlimited number of neurons per each layer separately
• User defined activation function (defaults to logistic)
• User defined error function (defaults to SSE)
• Algorithms implemented so far:
• standard (vanilla) with or without bias, on-line or batch
• momentum with or without bias, on-line or batch
• SuperSAB with or without bias, on-line or batch
• Conjugate gradients
• Jacobian computation
• Computation of result of multiplication between "vector" and Hessian
• Some helper functions provided

Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features:

• flexible input data specification
• speedy learning
• scalability (bounded memory footprint, suitable for distributed computation)
• feature pairing

Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes.

Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets.

This package contains the binaries and examples.

High-performance Prolog compiler developed at LIACC/Universidade do Porto and at COPPE Sistemas/UFRJ. The YAP Prolog engine is based in the Warren Abstract Machine, with several optimizations for better performance. YAP follows the Edinburgh tradition, and is largely compatible with the ISO-Prolog standard and with Quintus and SICStus Prolog.

YAP features a constraint solver over real numbers, and support for constraint handling rules (CHR).

The ACOVEA (Analysis of Compiler Options via Evolutionary Algorithm) library that implements a genetic algorithm to find the "best" options for compiling programs with the GNU Compiler Collection (GCC) C and C++ compilers. "Best," in this context, is defined as those options that produce the fastest executable program from a given source code.

libacovea is part of a C++ framework that can be extended to test other programming languages and non-GCC compilers.

This package contains the development files for libacovea.

This metapackage will install Debian Science packages useful for numerical computation. The packages provide an array oriented calculation and visualisation system for scientific computing and data analysis. These packages are similar to commercial systems such as Matlab and IDL.

This metapackage is part of the Debian Pure Blend "Debian Science" and installs packages related to statistics. This task is a general task which might be useful for any scientific work. It depends from a lot of R packages as well as from other tools which are useful to do statistics. Moreover the Science Mathematics task is suggested to optionally install all mathematics related software.

This metapackage will install Debian Science packages related to typesetting. You might also be interested in the use::typesetting debtag.

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.

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.

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.

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.

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.