datasets-0.3.0: datasets.cabal

Name:                datasets
Version:             0.3.0
Synopsis:            Classical data sets for statistics and machine learning
Description:         Classical machine learning and statistics datasets from
                     the UCI Machine Learning Repository and other sources.
                     .
                     The @datasets@ package defines two different kinds of datasets:
                     .
                     * small data sets which are directly (or indirectly with `file-embed`)
                       embedded in the package as pure values and do not require network or IO to download
                       the data set. This includes Iris, Anscombe and OldFaithful.
                     .
                     * other data sets which need to be fetched over the network with
                     `Numeric.Datasets.getDataset` and are cached in a local temporary directory.
                     .
                     The @datafiles/@ directory of this package includes copies of a few famous datasets, such as Titanic, Nightingale and Michelson.
                     .
                     Example :
                     .
                     > import Numeric.Datasets (getDataset)
                     > import Numeric.Datasets.Iris (iris)
                     > import Numeric.Datasets.Abalone (abalone)
                     >
                     > main = do
                     >   -- The Iris data set is embedded
                     >   print (length iris)
                     >   print (head iris)
                     >   -- The Abalone dataset is fetched
                     >   abas <- getDataset abalone
                     >   print (length abas)
                     >   print (head abas)

License:             MIT
License-file:        LICENSE
Author:              Tom Nielsen <tanielsen@gmail.com>
Maintainer:          Marco Zocca <ocramz fripost org>
build-type:          Simple
Cabal-Version: 	     >= 1.10
homepage:            https://github.com/DataHaskell/dh-core
bug-reports:         https://github.com/DataHaskell/dh-core/issues
category:            Statistics, Machine Learning, Data Mining, Data
Tested-With:         GHC == 7.10.2, GHC == 7.10.3, GHC == 8.0.1, GHC == 8.4.3
extra-source-files:
                   changelog.md
                   datafiles/iris.data
                   datafiles/michelson.json
                   datafiles/nightingale.json
                   datafiles/titanic2_full.tsv
                   datafiles/netflix/training/mv_0000001.txt
                   datafiles/netflix/test/qualifying.txt
                   datafiles/netflix/movies/movie_titles.txt

source-repository head
  type:     git
  location: https://github.com/DataHaskell/dh-core/datasets

Library
   ghc-options:       -Wall -fno-warn-unused-imports
   hs-source-dirs:    src
   other-extensions: TemplateHaskell
   default-language:  Haskell2010

   Exposed-modules:
                   Numeric.Datasets
                 , Numeric.Datasets.Anscombe
                 , Numeric.Datasets.BostonHousing
                 , Numeric.Datasets.OldFaithful
                 , Numeric.Datasets.Abalone
                 , Numeric.Datasets.Adult
                 , Numeric.Datasets.BreastCancerWisconsin
                 , Numeric.Datasets.Car
                 , Numeric.Datasets.Coal
                 , Numeric.Datasets.CO2
                 , Numeric.Datasets.Gapminder
                 , Numeric.Datasets.Iris
                 , Numeric.Datasets.Michelson
                 , Numeric.Datasets.Mushroom                 
                 , Numeric.Datasets.Nightingale
                 , Numeric.Datasets.Quakes
                 , Numeric.Datasets.States
                 , Numeric.Datasets.Sunspots
                 , Numeric.Datasets.Titanic                 
                 , Numeric.Datasets.UN
                 , Numeric.Datasets.Vocabulary
                 , Numeric.Datasets.Wine
                 , Numeric.Datasets.WineQuality
                 , Numeric.Datasets.Netflix
   Build-depends:
                 base          >= 4.6 && < 5
               , aeson
               , attoparsec    >= 0.13
               , bytestring
               , cassava
               , data-default-class
               , directory
               , file-embed
               , filepath
               , hashable
               , microlens
               , req            >= 1.0.0
               , stringsearch
               , text
               , time
               , vector
                 -- , wreq