Cabal revisions of neural-0.1.0.1
Hackage metadata revisions edit the .cabal file after upload; each diff below is one revision.
revision 1
-name: neural-version: 0.1.0.1-cabal-version: >=1.10-build-type: Simple-license: MIT-license-file: LICENSE-copyright: Copyright: (c) 2016 Dr. Lars Bruenjes-maintainer: brunjlar@gmail.com-homepage: http://github.com/brunjlar/neural-synopsis: Neural Networks in native Haskell-description:- The goal of `neural` is to provide a modular and flexible neural network library written in native Haskell.- .- Features include- .- * /composability/ via- <https://hackage.haskell.org/package/base-4.9.0.0/docs/Control-Arrow.html Arrow> instances and- <https://hackage.haskell.org/package/pipes pipes>,- .- * /automatic differentiation/ for automatic gradient descent/ backpropagation training- (using Edward Kmett's fabulous <https://hackage.haskell.org/package/ad ad> library).- .- The idea is to be able to easily define new components and wire them up in flexible, possibly- complicated ways (convolutional deep networks etc.).- .- Two examples are included as proof of concept:- .- * A simple neural network that approximates the sqrt function on [0,4].- .- * A slightly more complicated neural network that solves the famous- <https://en.wikipedia.org/wiki/Iris_flower_data_set Iris flower> problem.- .- The library is still very much experimental at this point.-category: Machine Learning-author: Lars Bruenjes--source-repository head- type: git- location: https://github.com/brunjlar/neural.git--library- exposed-modules:- Numeric.Neural- Numeric.Neural.Layer- Numeric.Neural.Model- Numeric.Neural.Normalization- Numeric.Neural.Pipes- Data.MyPrelude- Data.Utils- Data.Utils.Analytic- Data.Utils.Arrow- Data.Utils.List- Data.Utils.Matrix- Data.Utils.Random- Data.Utils.Stack- Data.Utils.Statistics- Data.Utils.Traversable- Data.Utils.Vector- build-depends:- base >=4.7 && <5,- ad >=4.3.2 && <4.4,- array >=0.5.1.0 && <0.6,- deepseq >=1.4.1.1 && <1.5,- directory >=1.2.2.0 && <1.3,- filepath >=1.4.0.0 && <1.5,- ghc-typelits-natnormalise >=0.4.1 && <0.5,- hspec >=2.2.2 && <2.3,- lens ==4.13.*,- MonadRandom >=0.4.2.2 && <0.5,- mtl >=2.2.1 && <2.3,- parallel >=3.2.1.0 && <3.3,- pipes >=4.1.8 && <4.2,- profunctors ==5.2.*,- STMonadTrans >=0.3.3 && <0.4,- text >=1.2.2.1 && <1.3,- transformers >=0.4.2.0 && <0.5,- typelits-witnesses >=0.2.0.0 && <0.3,- vector >=0.11.0.0 && <0.12- default-language: Haskell2010- hs-source-dirs: src- ghc-options: -Wall -fexcess-precision -optc-O3 -optc-ffast-math--executable iris- main-is: iris.hs- build-depends:- base >=4.7 && <5,- attoparsec >=0.13.0.1 && <0.14,- neural >=0.1.0.1 && <0.2,- text >=1.2.2.1 && <1.3- default-language: Haskell2010- hs-source-dirs: examples/iris- ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math--executable sqrt- main-is: sqrt.hs- build-depends:- base >=4.7 && <5,- MonadRandom >=0.4.2.2 && <0.5,- neural >=0.1.0.1 && <0.2- default-language: Haskell2010- hs-source-dirs: examples/sqrt- ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math--test-suite neural-test- type: exitcode-stdio-1.0- main-is: Spec.hs- build-depends:- base >=4.7 && <5,- hspec >=2.2.2 && <2.3,- MonadRandom >=0.4.2.2 && <0.5,- neural >=0.1.0.1 && <0.2- default-language: Haskell2010- hs-source-dirs: test- other-modules:- Utils.MatrixSpec- Utils.VectorSpec- ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math-test-suite neural-doctest- type: exitcode-stdio-1.0- main-is: doctest.hs- build-depends:- base >=4.7 && <5,- doctest >=0.10.1 && <0.11,- neural >=0.1.0.1 && <0.2- default-language: Haskell2010- hs-source-dirs: doctest+name: neural +version: 0.1.0.1 +x-revision: 1 +cabal-version: >=1.10 +build-type: Simple +license: MIT +license-file: LICENSE +copyright: Copyright: (c) 2016 Lars Bruenjes +maintainer: brunjlar@gmail.com +homepage: https://github.com/brunjlar/neural +bug-reports: https://github.com/brunjlar/neural/issues +synopsis: Neural Networks in native Haskell +description: + The goal of `neural` is to provide a modular and flexible neural network library written in native Haskell. + . + Features include + . + * /composability/ via + <https://hackage.haskell.org/package/base-4.9.0.0/docs/Control-Arrow.html Arrow> instances and + <https://hackage.haskell.org/package/pipes pipes>, + . + * /automatic differentiation/ for automatic gradient descent/ backpropagation training + (using Edward Kmett's fabulous <https://hackage.haskell.org/package/ad ad> library). + . + The idea is to be able to easily define new components and wire them up in flexible, possibly + complicated ways (convolutional deep networks etc.). + . + Two examples are included as proof of concept: + . + * A simple neural network that approximates the sqrt function on [0,4]. + . + * A slightly more complicated neural network that solves the famous + <https://en.wikipedia.org/wiki/Iris_flower_data_set Iris flower> problem. + . + The library is still very much experimental at this point. +category: Machine Learning +author: Lars Bruenjes + +source-repository head + type: git + location: https://github.com/brunjlar/neural.git + +library + exposed-modules: + Numeric.Neural + Numeric.Neural.Layer + Numeric.Neural.Model + Numeric.Neural.Normalization + Numeric.Neural.Pipes + Data.MyPrelude + Data.Utils + Data.Utils.Analytic + Data.Utils.Arrow + Data.Utils.List + Data.Utils.Matrix + Data.Utils.Random + Data.Utils.Stack + Data.Utils.Statistics + Data.Utils.Traversable + Data.Utils.Vector + build-depends: + base >=4.7 && <5, + ad >=4.3.2 && <4.4, + array >=0.5.1.0 && <0.6, + deepseq >=1.4.1.1 && <1.5, + directory >=1.2.2.0 && <1.3, + filepath >=1.4.0.0 && <1.5, + ghc-typelits-natnormalise >=0.4.1 && <0.5, + hspec >=2.2.2 && <2.3, + lens ==4.13.*, + MonadRandom >=0.4.2.2 && <0.5, + mtl >=2.2.1 && <2.3, + parallel >=3.2.1.0 && <3.3, + pipes >=4.1.8 && <4.2, + profunctors ==5.2.*, + STMonadTrans >=0.3.3 && <0.4, + text >=1.2.2.1 && <1.3, + transformers >=0.4.2.0 && <0.5, + typelits-witnesses >=0.2.0.0 && <0.3, + vector >=0.11.0.0 && <0.12 + default-language: Haskell2010 + hs-source-dirs: src + ghc-options: -Wall -fexcess-precision -optc-O3 -optc-ffast-math + +executable iris + main-is: iris.hs + build-depends: + base >=4.7 && <5, + attoparsec >=0.13.0.1 && <0.14, + neural >=0.1.0.1 && <0.2, + text >=1.2.2.1 && <1.3 + default-language: Haskell2010 + hs-source-dirs: examples/iris + ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math + +executable sqrt + main-is: sqrt.hs + build-depends: + base >=4.7 && <5, + MonadRandom >=0.4.2.2 && <0.5, + neural >=0.1.0.1 && <0.2 + default-language: Haskell2010 + hs-source-dirs: examples/sqrt + ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math + +test-suite neural-test + type: exitcode-stdio-1.0 + main-is: Spec.hs + build-depends: + base >=4.7 && <5, + hspec >=2.2.2 && <2.3, + MonadRandom >=0.4.2.2 && <0.5, + neural >=0.1.0.1 && <0.2 + default-language: Haskell2010 + hs-source-dirs: test + other-modules: + Utils.MatrixSpec + Utils.VectorSpec + ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math +test-suite neural-doctest + type: exitcode-stdio-1.0 + main-is: doctest.hs + build-depends: + base >=4.7 && <5, + doctest >=0.10.1 && <0.11, + neural >=0.1.0.1 && <0.2 + default-language: Haskell2010 + hs-source-dirs: doctest ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math
revision 2
name: neural version: 0.1.0.1 -x-revision: 1 +x-revision: 2 cabal-version: >=1.10 build-type: Simple license: MIT type: git location: https://github.com/brunjlar/neural.git +source-repository this + type: git + location: https://github.com/brunjlar/neural.git + tag: 0.1.0.1 + library exposed-modules: Numeric.Neural neural >=0.1.0.1 && <0.2 default-language: Haskell2010 hs-source-dirs: doctest - ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math+ ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math