Debian Science Project
Summary
Machine learning
Debian Science - Machine Learning-pakker

Denne metapakke vil installere pakker nyttige for maskinlæring. Inkluderede pakkeinterval går fra videnbaseret (ekspert) følgeslutningssystemer til programimplementeringer for avanceret statistik, som dominerer nutidens metoder.

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Science to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Science mailing list

Links to other tasks

Debian Science Machine learning packages

Official Debian packages with high relevance

Autoclass
Automatisk klassificering eller clustering
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AutoClass løser problemet med automatisk opdagelse af klasser i data (også kaldet clustering eller uovervåget indlæring) i modsætning til generering af klassebeskrivelser fra mærkede eksempler (kaldet overvåget indlæring). Det sigter på at opdage »naturlige« klasser i dataene. AutoClass kan bruges på observationer af ting, der kan beskrives ved et antal egenskaber uden at referere til andre ting. De dataværdier, der svarer til hver egenskab er begrænset til enten at være numre eller elementer i et fast symbolsæt. For numeriske data skal man angive målefejlen.

Caffe-cpu
Hurtig, åben ramme for Deep Learning - metapakke
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Caffe er en dyb læringsramme lavet med omtanke for udtryk, hastighed og modulopbygning. Programmet er udviklet af Berkeley AI Research Lab (BAIR) og bidragsydere fra fællesskabet.

Denne metapakke henter CPU_ONLY-versionen af caffe: * caffe-tools-cpu

  • libcaffe-cpu*
  • python3-caffe-cpu Og foreslår disse pakker:

  • libcaffe-cpu-dev

  • caffe-doc

Bemærk denne CPU_ONLY-version kan ikke sameksistere med CUDA-versionen.

Please cite: Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama and Trevor Darrell: Caffe: Convolutional Architecture for Fast Feature Embedding. (eprint) arXiv preprint arXiv:1408.5093 (2014)
Gprolog
GNU Prolog-kompiler
Maintainer: Salvador Abreu
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GNU Prolog er en fri Prologkompiler med begrænsningsløsning over finitte domæner (FD). GNU Prolog overholder hovedsagelig ISO-standarden og er en del af Prolog Commons-initiativet.

Denne pakke indeholder kompileren og kørselstidssystemet for ISO- standardversionen for GNU Prolog, inklusive implementeringen af prototypemodulet.

Libcomplearn-dev
machine-learning through data compression development files
Maintainer: Rudi Cilibrasi
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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

Libcv-dev
Translation package for libcv-dev
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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.

Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
Registry entries: SciCrunch  OMICtools 
Libevocosm-dev
C++ framework for developing evolutionary algorithms
Maintainer: Al Stone (Chris Lamb)
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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.

Libfann-dev
Udviklingsbiblioteker og teksthovedfiler for FANN
Maintainer: Christian Kastner
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Fast Artificial Neural Network Library er et frit neuralt netværksbibliotek, udviklet i åben kildekode, som implementerer flerlags neurale netværk med kunstig intelligens i C og med understøttelse for både fuldt forbundne og delvist forbundne netværk. Kørsel på flere platforme i både faste og flydende heltal er understøttet. Biblioteket inkluderer en ramme for nem håndtering af træningsdatasæt. Det er nemt at bruge, alsidigt, veldokumenteret og hurtigt.

Denne pakke indeholder teksthovedfilerne og statiske biblioteker, som er krævet for at udvikle libfann-programmer.

Libga-dev
C++ Library of Genetic Algorithm Components
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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-dev
Udviklingsbiblioteker og hovedfiler for LIBLINEAR
Maintainer: Christian Kastner
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LIBLINEAR er et bibliotek for læring af lineære klassifikatorer for omfangsrige programmer. Biblioteket understøtter Support Vector Machines (SVM) med L2-og L1-tab. Logistisk regression, flerklasseklassifikation og også Linear Programming Machines (L1-regulerede SVM'er). Dets beregningsmæssige kompleksitet skalerer lineært med antallet af træningseksempler, hvilket gør det til et af de hurtigste SVM-løsere.

Denne pakke indeholder hovedfilerne og de statiske biblioteker.

Libmlpack-dev
intuitive, fast, scalable C++ machine learning library (development libs)
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This package contains the mlpack Library development files.

Machine Learning Pack (mlpack) is an intuitive, fast, scalable C++ machine learning library, meant to be a machine learning analog to LAPACK. It aims to implement a wide array of machine learning methods and function as a "swiss army knife" for machine learning researchers.

Libocas-dev
Development libraries and header files for LIBOCAS
Maintainer: Christian Kastner
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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.

Libqsearch-dev
nondeterministic quartet tree search library for unrooted trees
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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.

Libroot-math-mlp-dev
Flerlags perceptron-udvidelse for ROOT - udviklingsfiler
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ROOT-systemet tilbyder et sæt af OO-rammeværktøjer med al den funktionalitet, som er krævet for at håndtere og analysere store mængder af data effektivt.

Denne pakke indeholder udviklingsfiler til mlp-udvidelsesmodulet for ROOT, der tilbyder en Multi Layer Perceptron Neural Network-pakke for ROOT.

Libroot-montecarlo-vmc-dev
Virtuel Monte-Carlo-bibliotek for ROOT - udviklingsfiler
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ROOT-systemet tilbyder et sæt af OO-rammeværktøjer med al den funktionalitet, som er krævet for at håndtere og analysere store mængder af data effektivt.

Denne pakke indeholder udviklingsfiler for Vmc-biblioteket for ROOT.

Libroot-tmva-dev
Toolkit for multivariate data analysis - development files
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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.

Libshark-dev
development files for Shark
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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.

Please cite: C. Igel, V. Heidrich-Meisner and T. Glasmachers: Shark (eprint) Journal of Machine Learning Research 9:993-996 (2008)
Libshogun-dev
Large Scale Machine Learning Toolbox
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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-dev
LIBSVM header files
Maintainer: Chen-Tse Tsai (Adrian Bunk)
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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.

Libtorch3-dev
State of the art machine learning library - development files
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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.)

Libvigraimpex-dev
Udviklingsfiler for C++-computervisionsbiblioteket
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Vision med generiske algoritmer (VIGRA) er et computervisionbibliotek, som placerer sit hovedfokus på fleksible algoritmer, da algoritmerne repræsenterer den grundlægende viden indenfor dette felt. Biblioteket blev som konsekvent bygget med brug af generisk programmering som introduceret af Stepanov og Musser og eksemplicificeret i C++ Standard Template Library. Ved at skrive nogle få adaptere (billediteratorer og accessorer) kan du bruge VIGRA's algoritmer oven på dine datastrukturer, inden i dit miljø.

Denne pakke indeholder teksthovederne og udviklingsfilerne krævet for at bygge programmer og pakker med brug af VIGRA.

Lua-torch-graph
Graph Computation Package for Torch Framework
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This package provides graphical computation for Torch.

This package also ships a graphviz interface, you need not graphviz to be able to use this library but, if you have it, you will be able to display the graphs that you have created.

Lua-torch-image
Image Load/Save Library for Torch Framework
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"image" is the Torch7 distribution package for processing images. It contains a wide variety of functions divided into the following categories:

  • Saving and loading images as JPEG, PNG, PPM and PGM;
  • Simple transformations like translation, scaling and rotation;
  • Parameterized transformations like convolutions and warping;
  • Simple Drawing Routines like drawing text or a rectangle on an image;
  • Graphical user interfaces like display and window;
  • Color Space Conversions from and to RGB, YUV, Lab, and HSL;
  • Tensor Constructors for creating Lenna, Fabio and Gaussian and Laplacian kernels;

Note that unless specified otherwise, this package deals with images of size nChannel x height x width.

Lua-torch-nn
Neural Network Package for Torch Framework
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This package provides an easy and modular way to build and train simple or complex neural networks using Torch Framework:

  • Modules are the bricks used to build neural networks. Each are themselves neural networks, but can be combined with other networks using containers to create complex neural networks:

  • Module: abstract class inherited by all modules.

  • Containers: container classes.
  • Transfer functions: non-linear functions.
  • Simple layers: simple network layer like Linear.
  • Table layers: layers for manipulating tables.
  • Convolution layers: several kinds of convolutions.

  • Criterions compute a gradient according to a given loss function given an input and a target:

  • Criterions: a list of all criterions.

  • MSECriterion: the Mean Squared Error criterion used for regression;
  • ClassNLLCriterion: the Negative Log Likelihood criterion used for classification.

  • Additional documentation:

  • Overview of the package essentials including modules, containers and training.

  • Training: how to train a neural network using optim.
  • Testing: how to test your modules.
  • Experimental Modules: a package containing experimental modules and criteria.

This package is a core part of the Torch Framework.

Lua-torch-nngraph
Neural Network Graph Package for Torch Framework
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This package provides graphical computation for nn library in Torch. The aim of this library is to provide users of nn package with tools to easily create complicated architectures. Any given nn module is going to be bundled into a graph node. The __call__ operator of an instance of nn.Module is used to create architectures as if one is writing function calls.

Lua-torch-optim
Numeric Optimization Package for Torch Framework
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This package contains several optimization routines and a logger for Torch.

The following algorithms are provided:

  • Stochastic Gradient Descent
  • Averaged Stochastic Gradient Descent
  • L-BFGS
  • Congugate Gradients
  • AdaDelta
  • AdaGrad
  • Adam
  • AdaMax
  • FISTA with backtracking line search
  • Nesterov's Accelerated Gradient method
  • RMSprop
  • Rprop
  • CMAES All these algorithms are designed to support batch optimization as well as stochastic optimization. It's up to the user to construct an objective function that represents the batch, mini-batch, or single sample on which to evaluate the objective.

This package provides also logging and live plotting capabilities via the optim.Logger() function. Live logging is essential to monitor the network accuracy and cost function during training and testing, for spotting under- and over-fitting, for early stopping or just for monitoring the health of the current optimisation task.

Lua-torch-trepl
REPL Package for Torch Framework
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A pure Lua REPL (Read,Eval,Print-Loop) for LuaJIT, with heavy support for Torch types. It uses Readline for tab completion.

This package contains backend files to support the command line frontend 'th'.

Lua-torch-xlua
Lua Extension Package for Torch Framework
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Lua is pretty compact in terms of built-in functionalities: this package extends the table and string libraries, and provide other general purpose tools (progress bar, ...).

This package ships a set of useful extensions to Lua for Torch Framework.

Mcl
Markov Cluster-algoritmen
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Pakken MCL er en implementering af MCL-algoritmen, og tilbyder redskaber til manipulation af matricer med spredning (på engelsk »sparse matrices«, den essentielle datastruktur i MCL-algoritmen) og til at foretage eksperimenter med klynger.

MCL anvendes på nuværende tidspunkt i videnskaber som biologi (detektering af proteinfamilier, arvemasseforskning), datalogi (knudepunktklynger i »vært til vært«-netværk) og lingvistik (tekstanalyse).

The package is enhanced by the following packages: zoem
Please cite: Stijn van Dongen and Cei Abreu-Goodger: Using MCL to extract clusters from networks. (PubMed,eprint) Methods Mol Biol. 804:281-95 (2012)
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Octave-ga
Genetisk optimeringskode for Octave
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Denne pakke tilbyder funktionalitet til at arbejde med genetiske algoritmer i Octave, et numerisk beregningsprogram. Pakken tilbyder ga()- funktionen, som fungerer på samme måder for andre optimeringsfunktioner i Octave.

Denne Octave-udvidelsespakke er en del af projektet Octave-Forge.

Pgapack
Genetisk algoritmepakke til mange formål
Maintainer: Dirk Eddelbuettel
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PGAPack er en data-struktur-neutral, parallel genetisk algoritmepakke til mange formål, som bliver udviklet på Argonne National Laboratory.

Denne pakke indeholder hovedfiler, manualsider, eksempler og test. For at bruge pgpack skal du installere enten pakken libpgapack-serial (»en cpu«) eller pakken libpgapack-mpi (»parallel«).

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Python3-amp
Atomistic Machine-learning Package (python 3)
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Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. This project is being developed at Brown University in the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi, and is released under the GNU General Public License. Amp allows for the modular representation of the potential energy surface, allowing the user to specify or create descriptor and regression methods.

Amp is designed to integrate closely with the Atomic Simulation Environment (ASE). As such, the interface is in pure python, although several compute-heavy parts of the underlying code also have fortran versions to accelerate the calculations. The close integration with ASE means that any calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem, and Gaussian ─ can easily be used as the parent method.

This package provides the python 3 modules.

Python3-genetic
genetic algorithms in Python
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Python3-genetic provides genetic algorithms for Python3, 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 !

Python3-keras
deep learning framework running on Theano or TensorFlow
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Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation.

Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing).

It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian).

Python3-lasagne
deep learning library build on the top of Theano (Python3 modules)
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Lasagne is a Python library to build and train deep (multi-layered) artificial neural networks on the top of Theano (math expression compiler). In comparison to other abstraction layers for that like e.g. Keras, it abstracts Theano as little as possible.

Lasagne supports networks like Convolutional Neural Networks (CNN, mostly used for image recognition resp. classification) and the Long Short-Term Memory type (LSTM, a subtype of Recurrent Neural Networks, RNN).

This package contains the modules for Python 3.

Python3-mdp
Modular toolkit for Data Processing
Maintainer: Tiziano Zito
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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 3.

The package is enhanced by the following packages: python3-sklearn
Python3-opencv
Python 3-bindinger for computer vision-biblioteket
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Denne pakke indeholder Python 3-bindinger for OpenCV-biblioteket (Open Computer Vision).

Open Computer Vision Library er en samling af algoritmer og eksempelkode for diverse computer vision-problemer. Biblioteket er kompatibelt med IPL (Intels Image Processing Library) og, hvis tilgængeligt, kan det bruger IPP (Intels Integrated Performance Primitives) for bedre ydelse.

OpenCV tilbyder datatyper og operatorer på lavt niveau, der kan flyttes samt et sæt af funktioner på højt niveau for videooptagelse, billedbehandling og analyse, strukturel analyse, bevægelsesanalyse og objektsporing, objektgenkendelse, kamerakalibrering og 3D-rekonstruktion.

Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
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Python3-sklearn
Pythonmoduler for maskinlæring og dataundersøgelse - Python 3
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Scikit-learn er en samling af Pythonmoduler, der er relevante for maskin/statistisk læring og dataundersøgelse. En ikke udtømmende liste over inkluderet funktionalitet:

  • Gaussianske blandede modeller
  • Manifold-læring
  • kNN
  • SVM (via LIBSVM)

Denne pakke indeholder Python 3-versionen.

The package is enhanced by the following packages: python3-sklearn-pandas
Python3-statsmodels
Python3 module for the estimation of statistical models
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statsmodels Python3 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 several distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem.

Please cite: Skipper Seabold and Josef Perktold: Statsmodels: Econometric and statistical modeling with python (eprint) (2010)
Python3-thinc
Practical Machine Learning for NLP in Python
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Thinc is the machine learning library powering spaCy https://spacy.io. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0 https://spacy.io/usage/v2.

Thinc is a practical toolkit for implementing models that follow the "Embed, encode, attend, predict" architecture. It's designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

R-cran-amore
GNU R: A MORE flexible neural network package
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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".

R-cran-bayesm
GNU R-pakke til bayesiansk statistik
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Pakken bayesm dækker mange vigtige modeller, der anvendes i markedsføring og mikro-økonometri applikationer. Pakken indeholder:

  • Bayes-regression (univariate eller multivariate dep var)
  • Multinomial Logit (MNL) og Multinomial Probit (MNP)
  • Multivariate Probit,
  • Multivariate blandinger af Normals
  • Hierarkisk lineære modeller med normale priors og kovariater
  • Hierarkisk Multinomial Logits med blanding af normale priors og kovariater
  • Bayesiansk analyse af valg-baserede conjoint data
  • Bayesian behandling af lineære instrumentale variable modeller
  • Analyis af Multivariate Ordinal-undersøgelsesdata med skalaforbrugs- heterogenitet (som i Rossi et al, JASA (01)).

For yderligere reference, se forfatternes bog, Bayesian Statistics And Marketing af Allenby, McCulloch og Rossi.

R-cran-class
GNU R-pakke for klassifikation
Maintainer: Dirk Eddelbuettel
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Pakken class tilbyder funktioner og datasæt til at understøtte kapitel 12 om »Klassifikation« i bogen »Modern Applied Statistics with S« (4. udgave) af W.N. Veneables og B.D. Ripley. Den følgende adresse tilbyder yderligere detaljer om bogen: http://www.stats.ox.ac.uk/pub/MASS4

R-cran-cluster
GNU R-pakke for klyngeanalyse af Rousseeuw et al
Maintainer: Dirk Eddelbuettel
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Denne pakke tilbyder funktioner og datasæt for klyngeanalyse oprindelig skrevet af Peter Rousseeuw, Anja Struyf og Mia Hubert.

Denne pakke er del af et sæt af pakker, som »anbefales« af R Core, og leveres med kildeudgivelser via opstrøm af R.

R-cran-gbm
GNU R package providing Generalized Boosted Regression Models
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This package implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

R-cran-mass
GNU R-pakke med Venables og Ripleys MASS
Maintainer: Dirk Eddelbuettel
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Pakken MASS tilbyder funktioner og datasæt som understøtter bogen »Modern Applied Statistics with S« (4. udgave) af W. N. Venables og B. D. Ripley. Den følgende adresse oplyser flere detaljer om bogen: Adresse: http://www.stats.ox.ac.uk/pub/MASS4

The package is enhanced by the following packages: r-cran-pscl
R-cran-mcmcpack
R-rutiner for Markov-kæde Monte Carlo-modelestimering
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Dette er et sæt af rutiner for GNU R, som implementerer diverse statistiske og økonometriske modeller, der bruger Markov-kæde Monte Carlo-estimering (MCMC), som giver mulighed for at »løse« modeller, som ellers ville være umedgørlige med traditionelle teknikker, specielt problemer indenfor bayesiansk statistik (hvor en eller flere »priors« bruges som en del af estimeringsproceduren, i stedet for en antagelse om uvidenhed om »sande« punktskøn), skønt MCMC også kan anvendes til at løse statistiske problemer indenfor hyppighed med uinformative »priors«. MCMC-teknikker foretrækkes også over direkte estimering, når der er manglende data.

I øjeblikket er et antal økologiske inferensrutiner (EI) implementeret (for estimering af attributter eller opførsel på individniveau fra opsamlede data, såsom valgte afkast eller optællingsresultater) samt som modeller for traditionelle lineære panel- og tværsnitsdata, nogle visualiseringsrutiner for EI-diagnostik, to post-response (eller ideal-punkts estimering) teorimodeller, metrisk, ordenstal, og blandet respons faktoranalyse, og modeller for Gaussian- (lineær) og Poisson-regression, logistisk regression (eller logit), og binære og ordinal-svar probit-modeller.

De foreslåede pakker (r-cran-bayesm, -eco og -mnp) indeholder yderligere modeller, som kan være nyttige for dem interesseret i denne pakke.

The package is enhanced by the following packages: r-cran-mcmc r-cran-mnp
R-cran-metrics
GNU R evaluation metrics for machine learning
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An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.

R-cran-mlbench
GNU R Machine Learning Benchmark Problems
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This GNU R package provices a collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.

R-cran-mlr
Machine learning in GNU R
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Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

R-cran-mnp
GNU R-pakke for tilpasning af multinomial probit-modeller (MNP)
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MNP er en R-pakke som tilpasser Bayesian Multinomial Probit-modeller (MNP) via Markov chain Monte Carlo (MCMC). Sammen med den gængse multinomial probit-model, så kan den også tilpasse modeller med forskellige valgsæt for hver observation og fuldstændig eller delvis ordning af alle tilgængelige alternativer. Estimeringen er baseret på den effektive marginal algoritme for dataforøgning, som er udviklet af Imai og van Dyk (2004).

R-cran-msm
GNU R Multi-state Markov og skjulte Markovmodeller i sammenhængende tid
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Funktioner for tilpasning af generel Markov i sammenhængende tid og skjulte Markov flertilstandsmodeller til data i længderetningen. Både overgangsrater for Markov og den skjulte Markov-resultatproces kan modelleres i form af kovariater. Et udvalg af observationsskemaer er understøttet, inklusive processer observeret på arbitrære tidspunkter, fuldstændig-observerede processer, og censorerede tilstande.

Please cite: Christopher H. Jackson: Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software 38(8):1-29 (2011)
R-cran-tgp
GNU R Bayesian treed Gaussian process models
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Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions.

Root-system
Metapakke til at installere alle ROOT-pakker
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ROOT-systemet tilbyder et sæt af OO-rammeværktøjer med al den funktionalitet, som er krævet for at håndtere og analysere store mængder af data effektivt.

Med dataene defineret som et sæt af objekter, kan specialiserede lagermetoder give direkte adgang til de separate attributter for de valgte objekter, uden at skulle røre den store mængde af data. Inkluderet er historgrammetoder i 1, 2 og 3 dimensioner, kurvetilpasning, funktionsevaluering, minimering, grafik- og visualiseringsklasser, der giver mulighed for nemt at oprette et analyseprogram, som kan forespørge og behandle dataene interaktivt eller i jobtilstand.

Kommandosproget, skriptsproget (eller makro) og programmeringssproget er alle C++, takket være den indbyggede CINT C++-fortolker. Denne fortolker fjerner den tidsforbrugende kompilering/lænke-cyklus, hvilket giver mulighed for hurtigere udarbejdelse af prototyper for makroer og tilbyder et godt miljø til at lære C++. Hvis yderligere ydelse er krævet, kan de interaktive makroer kompileres med en C++-kompiler.

Systemet er blevet designet på en sådan måde, at du kan forespørge dens databaser parallelt på MPP-maskiner eller på klynger af arbejdsstationer eller kraftige pc'er. ROOT er et åbent system, som dynamisk kan udvides ved at lænke eksterne biblioteker. Dette gør ROOT til en ekstraordinær platform hvorpå der kan bygges dataindhentelse, simulering og dataanalyse.

Denne pakke er en metapakke til at sikre installationen af alle mulige ROOT-pakker på et system.

Scilab-ann
Scilab-modul for neurale netværk med kunstig intelligens
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Svn

Dette modul implementerer funktioner for neurale netværk med kunstig intelligens i sproget Scilab. Nuværende funktioner:

  • Kun lagdelte feedforward-netværk er understøttet direkte i øjeblikket (for andre brug de tilbudte »hooks«)
  • Ubegrænset antal lag
  • Ubegrænset antal af neuroner per hvert lag separat
  • Brugerdefineret aktiveringsfunktion (standard er logistik)
  • Brugerdefineret fejlfunktion (standard er SSE)
  • Algoritmer implementeret indtil videre:
  • standard (vanilla) med eller uden bias, online eller job
  • momentum med eller uden bias, online eller job
  • SuperSAB med eller uden bias, online eller job
  • konjugerede gradienter
  • Jacobian-beregning
  • Beregning af resultatet af multiplikation mellem »vektor« og Hessian
  • Nogle hjælpefunktioner
Torch-core-free
Scientific Computing Framework For Luajit - grundkomponenter
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Torch er en videnskabelig beregningsramme med bred understøttelse for maskinlæringsalgoritmer som sætter GPU'er først. Rammen er nem at bruge og effektivt, takket være et nemt og hurtigt skriptsprog, LuaJIT og en underliggende C/CUDA-implementering.

Et overblik over grundfunktioner:

  • en funktionsrig N-dimensionel tabel
  • masser af rutiner for indeksering, opdeling, transposing, ...
  • imponerende grænseflade til C, via LuaJIT
  • lineær algebrarutiner
  • neuralt netværk og energibaserede modeller
  • numeriske optimeringsrutiner
  • Hurtig og effektiv GPU-understøttelse
  • Kan indlejres, med porte til iOS-, Android- og FPGA-motorer

Formålet med Torch er at have maksimal fleksibilitet og hastighed i bygning af dine videnskabelige algoritmer mens processen er ekstrem simpel. Torch har et stort økosystem af fællesskabsdrevne pakker i maskinlæring, computervision, signalbehandling, parallel behandling, billede, video, lyd og netværk blandt andre og bygget oven på Lua- fællesskabet.

I hjertet af Torch er det populære neurale netværk og optimeringsbiblioteker som er simple at bruge, mens de har maksimal fleksibilitet i implementering af komplekse neurale netværkstopologier. Du kan bygge arbitrære grafer over neurale netværk og parallelisere dem over CPu'er og GPU'er på en effektiv måde.

Denne pakke er en metapakke, som henter de følgende grundlæggende og frie moduler for dig: cwrap, paths, sys, xlua, torch7, nn, graph, nngraph, optim, sundown, dok, trepl, image.

Bemærk at cutorch (CUDA-motor for torch) og cunn (CUDA-motor for neuralt netværk) ikke er til stede i denne metapakke - de vil blive medtaget i metapakken torch-core-contrib engang i fremtiden.

Toulbar2
Præcis kombinatorisk optimering for grafiske modeller
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Toulbar2 er et præcist diskret optimeringsværktøj for grafiske modeller såsom Cost Function Networks, Markov Random Fields, Weighted Constraint Satisfaction Problems og Bayesian Nets.

Vowpal-wabbit
hurtig og skalérbar algoritme til netbaseret maskinlæring
Maintainer: Yaroslav Halchenko
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Vowpal Wabbit er en algoritme til hurtig, netbaseret maskinlæring. Kerne- algoritmen er en specialiseret gradientnedstigning (GD) på en tabsfunktion (flere er tilgængelige). VW-funktioner:

  • fleksibel specifikation af inddata
  • hurtig læring
  • skalérbarhed (fast hukommelsesforbrug, egner sig til distribuerede beregninger)
  • funktionsbindinger
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Weka
Algoritmer for maskinlæring for dataindekseringsopgaver
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Weka er en samling af algoritmer for maskinlæring i Java, som enten kan bruges fra kommandolinjen eller kaldes fra din egen javakode. Weka er også velegnet for udvikling af ny maskinlæringsskemaer.

Implementerede skemaer dækker beslutningstræ-induktorer, regeltilegnelse, modeltræ-generatorer, understøtter vektormaskiner, lokalt vægtet regression, instansbaseret læring, »bagging«, »boosting« og stabling (»stacking«). Også inkluderet er klyngemetoder og en tilegnelse af associationsregler. Udover reelle læringsskemaer indeholder Weka også en stor række af værktøjer, som kan bruges for præbehandling af datasæt.

Denne pakke indeholder de binære filer og eksempler.

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Yap
Højtydende Prologsystem
Maintainer: Ralf Treinen
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Højtydende Prologkompilere udviklet på LIACC/Universidade do Porto og på COPPE Sistemas/UFRJ. YAP Prolog-motoren er baseret i Warren Abstract Machine, men har flere optimeringer for bedre ydelse. YAP følger Edinburghtraditionen og er stort set kompatibel med ISO-Prolog-standarden og med Quintus og SICStus Prolog.

YAP har en begrænsningsløser til reelle tal og understøttelse for begrænsningshåndteringsregler (CHR).

Official Debian packages with lower relevance

Ask
Adaptive Sampling Kit for store eksperimentelle rum
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Adaptive Sampling Kit (ASK) er et værktøjssæt for sampling af store eksperimentelle rum. Når rummet er lille, kan svaret måles for hvert punkt i rummet. Når rummet er stort, er en omfattende måling enten ikke mulig i form af kørselstid eller simpelthen ikke praktisk. ASK forsøger at finde en god tilnærmelse for svaret ved kun at sample en lille del af rummet. ASK har flere aktive læringsalgoritmer til at prioritere undersøgelsen af de interessante dele af det eksperimentelle rum.

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Libacovea-dev
library for analyzing compiler options via evolutionary algorithms
Maintainer: Al Stone
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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.

Libdlib-dev
C++ toolkit for machine learning and computer vision - development
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Dlib is a general purpose cross-platform open source software library written in the C++ programming language. It now contains software components for dealing with networking, threads, graphical interfaces, complex data structures, linear algebra, statistical machine learning, image processing, data mining, XML and text parsing, numerical optimization, Bayesian networks, and numerous other tasks.

This package contains the development headers.

Libfclib-dev
read and write problems from the Friction Contact Library (headers)
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fclib is an open source collection of Frictional Contact (FC) problems stored in a specific HDF5 format, and an open source light implementation of Input/Output functions in C Language to read and write problems.

The goal of this work is to set up a collection of 2D and 3D Frictional Contact (FC) problems in order to set up a list of benchmarks; provide a standard framework for testing available and new algorithms; and share common formulations of problems in order to exchange data.

Fclib is an open-source scientific software primarily targeted at modeling and simulating nonsmooth dynamical systems

This package includes the libfclib development headers.

Libmkldnn-dev
Intel Math Kernel Library for Deep Neural Networks (dev)
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

DNN functionality optimized for Intel architecture is also included in Intel(R) Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible with Intel MKL-DNN and does not include certain new and experimental features.

This package contains the header files, and symbol links to the shared object.

Libxsmm-dev
Matrix-operationer og dyb lærings-primitiver - udviklingsfiler
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LIBXSMM er et bibliotek målrettet Intel Architecture for specialiserede tætte og tynde matrixoperationer og dyb lærings-primitiver.

Dnene pakke indeholder de statiske biblioteker og teksthovedfiler.

Python3-hdmedians
high-dimensional medians in Python3
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Various definitions for a high-dimensional median exist and this Python package provides a number of fast implementations of these definitions. Medians are extremely useful due to their high breakdown point (up to 50% contamination) and have a number of nice applications in machine learning, computer vision, and high-dimensional statistics.

This package currently has implementations of medoid and geometric median with support for missing data using NaN.

Science-numericalcomputation
Debian Science Numerical Computation-pakker
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Denne metapakke vil installere Debian Science-pakker nyttige til numerisk beregning. Pakkerne tilbyder en tabelorienteret beregning og et visualiseringssystem til videnskabelig beregning og dataanalyse. Disse pakker svarer til kommercielle systemer såsom Matlab og IDL.

Science-statistics
Debians videnskabelige statistikpakker
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Denne metapakke er en del af Debians Pure Blend »Debian Science« og installerer pakker relateret til statistik. Denne opgave er en generel opgave, som kan være nyttig for videnskabelig arbejde. Den afhænger af en masse R-pakker samt andre værktøjer, som er nyttige til at udføre statistik. Derudover foreslås Videnskabelig matematik-opgaven som valgfri installation af alle matematikrelaterede programmer.

Science-typesetting
Debian Science - opsætningspakker
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Denne metapakke vil installere Debian Science-pakker relateret til opsætning. Du er måske også interesseret i deb-mærket use::typesetting.

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Caffe-cuda
Fast, open framework for Deep Learning (Meta)
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Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley AI Research Lab (BAIR) and community contributors.

This metapackage pulls CUDA version of caffe:

  • caffe-tools-cuda
  • libcaffe-cuda*
  • python3-caffe-cuda And suggests these packages:

  • libcaffe-cuda-dev

  • caffe-doc

Note, this CUDA version cannot co-exist with the CPU_ONLY version.

Please cite: Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama and Trevor Darrell: Caffe: Convolutional Architecture for Fast Feature Embedding. (eprint) arXiv preprint arXiv:1408.5093 (2014)

Packaging has started and developers might try the packaging code in VCS

Spacy
Industrial-strength Natural Language Processing (NLP)
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Version: 2.0.17-1

spaCy is a library for advanced Natural Language Processing in Python and Cython. It’s built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 30+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration.

Unofficial packages built by somebody else

Python3-orange
Data mining framework
Responsible: Mitar
License: GPLv3

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.

No known packages available but some record of interest (WNPP bug)

Flann - wnpp
Fast Library for Approximate Nearest Neighbors
License: BSD
Debian package not available
Language: C++

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.

Pybrain - wnpp
Modular Machine Learning Library
License: BSD
Debian package not available
Language: Python

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.

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