Source: torch3
Section: libs
Priority: optional
Maintainer: Cosimo Alfarano <kalfa@debian.org>
Build-Depends: debhelper (>= 10)
Standards-Version: 3.8.0

Package: libtorch3c2
Section: libs
Architecture: any
Depends: ${shlibs:Depends} ${misc:Depends}
Provides: libtorch
Conflicts: libtorch3
Description: State of the art machine learning library - runtime library
 Torch is a machine-learning library, written in C++.  Its aim is to
 provide the state-of-the-art of the best algorithms for
 machine-learning. 
 .
  * 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 runtime library.

Package: libtorch3-dev
Section: devel
Architecture: any
Depends: libtorch3c2 (= ${binary:Version})
Provides: libtorch-dev
Conflicts: libtorch1-dev, libtorch-dev
Description: State of the art machine learning library - development 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.)