cabal-version: >=1.10 name: datasets version: 0.4.0 license: MIT license-file: LICENSE maintainer: Marco Zocca author: Tom Nielsen tested-with: ghc ==7.10.2 ghc ==7.10.3 ghc ==8.0.1 ghc ==8.4.3 homepage: https://github.com/DataHaskell/dh-core bug-reports: https://github.com/DataHaskell/dh-core/issues 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) category: Statistics, Machine Learning, Data Mining, Data build-type: Simple 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 exposed-modules: Numeric.Dataloader Numeric.Datasets Numeric.Datasets.Anscombe Numeric.Datasets.BostonHousing Numeric.Datasets.CIFAR10 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.Internal.Streaming 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 hs-source-dirs: src other-modules: Streaming.Instances default-language: Haskell2010 other-extensions: TemplateHaskell ghc-options: -Wall -fno-warn-unused-imports build-depends: base >=4.6 && <5, aeson >=1.4.2.0, attoparsec >=0.13, bytestring >=0.10.8.2, cassava >=0.5.1.0, deepseq >=1.4.4.0, directory >=1.3.3.0, exceptions >=0.10.0, file-embed >=0.0.11, filepath >=1.4.2.1, hashable >=1.2.7.0, JuicyPixels >=3.3.3, microlens >=0.4.10, mtl >=2.2.2, mwc-random >=0.14.0.0, parallel >=3.2.2.0, req >=2.0.0, safe-exceptions >=0.1.7.0, streaming >=0.2.2.0, streaming-attoparsec >=1.0.0, streaming-bytestring >=0.1.6, streaming-cassava >=0.1.0.1, streaming-commons >=0.2.1.0, stringsearch >=0.3.6.6, tar >=0.5.1.0, text >=1.2.3.1, time >=1.8.0.2, transformers >=0.5.5.0, vector >=0.12.0.2, safe-exceptions >=0.1.7.0, zlib >=0.6.2 test-suite spec type: exitcode-stdio-1.0 main-is: Spec.hs hs-source-dirs: test default-language: Haskell2010 default-extensions: OverloadedStrings ghc-options: -threaded -rtsopts -with-rtsopts=-N build-depends: base >=4.6 && <5, QuickCheck >=2.12.6.1, hspec >=2.6.0 benchmark bench type: exitcode-stdio-1.0 main-is: Main.hs hs-source-dirs: bench other-modules: Numeric.Dataloader.Benchmark default-language: Haskell2010 ghc-options: -Wall -O2 -rtsopts -threaded build-depends: base >=4.6 && <5, criterion >=1.5.3.0, datasets -any, deepseq >=1.4.4.0, directory >=1.3.3.0, filepath >=1.4.2.1, JuicyPixels >=3.3.3, mwc-random >=0.14.0.0, req >=2.0.0, safe-exceptions >=0.1.7.0, streaming >=0.2.2.0