clustering-0.2.0: clustering.cabal

-- Initial fastcluster.cabal generated by cabal init.  For further 
-- documentation, see http://haskell.org/cabal/users-guide/

name:                clustering
version:             0.2.0
synopsis:            High performance clustering algorithms
description:
  Following clutering methods are included in this library:
  .
    1 Agglomerative hierarchical clustering. Complete linkage O(n^2),
      Single linkage O(n^2), Average linkage O(n^2),
      Weighted linkage O(n^2), Ward's linkage O(n^2).
  .
    2 KMeans clustering.

license:             MIT
license-file:        LICENSE
author:              Kai Zhang
maintainer:          kai@kzhang.org
copyright:           (c) 2015 Kai Zhang
category:            Math
build-type:          Simple
-- extra-source-files:  
cabal-version:       >=1.10

library
  exposed-modules:     
    AI.Clustering.Hierarchical
    AI.Clustering.Hierarchical.Internal
    AI.Clustering.Hierarchical.Types
    AI.Clustering.KMeans
    AI.Clustering.KMeans.Internal
    AI.Clustering.KMeans.Types

--  other-modules:       

  build-depends:
      base >=4.0 && <5.0
    , binary
    , containers
    , matrices >=0.4.0
    , mwc-random
    , parallel
    , primitive
    , vector

  hs-source-dirs:      src
  ghc-options:         -Wall
  default-language:    Haskell2010

test-suite test
  type: exitcode-stdio-1.0
  hs-source-dirs: tests
  main-is: test.hs
  other-modules:
    Test.Hierarchical
    Test.KMeans
    Test.Utils

  default-language:    Haskell2010
  build-depends: 
      base
    , binary
    , mwc-random
    , matrices
    , vector
    , tasty
    , tasty-hunit
    , tasty-quickcheck
    , clustering
    , hierarchical-clustering
    , split
    , Rlang-QQ

benchmark bench
  type: exitcode-stdio-1.0
  hs-source-dirs: benchmarks
  ghc-options:  -threaded -rtsopts -with-rtsopts=-N2
  main-is: bench.hs
  other-modules:
    Bench.Hierarchical
    Bench.KMeans
    Bench.Utils

  default-language:    Haskell2010
  build-depends: 
      base
    , criterion
    , mwc-random
    , vector
    , clustering
    , hierarchical-clustering
    , matrices

source-repository  head
  type: git
  location: https://github.com/kaizhang/clustering.git