alpino-tools 0.0.4 → 0.0.5
raw patch · 2 files changed
+11/−12 lines, 2 filesdep ~enumeratorPVP ok
version bump matches the API change (PVP)
Dependency ranges changed: enumerator
API changes (from Hackage documentation)
Files
alpino-tools.cabal view
@@ -1,5 +1,5 @@ Name: alpino-tools-Version: 0.0.4+Version: 0.0.5 License: OtherLicense License-file: LICENSE Copyright: Copyright 2010 Daniël de Kok@@ -16,7 +16,7 @@ Exposed-Modules: Data.Alpino.Model, Data.Alpino.Model.Enumerator Build-Depends: base >= 4 && < 5, bytestring >= 0.9.1.7, utf8-string >= 0.3.6, bytestring-lexing >= 0.2.1,- enumerator >= 0.4.5 && < 0.5, transformers >= 0.2.2.0,+ enumerator >= 0.4.8 && < 0.5, transformers >= 0.2.2.0, containers >= 0.3.0.0, random >= 1.0.0.3, random-shuffle >= 0.0.2 HS-Source-Dirs: src
src/Data/Alpino/Model/Enumerator.hs view
@@ -32,9 +32,8 @@ import qualified Data.Alpino.Model as AM import qualified Data.ByteString as B import qualified Data.ByteString.UTF8 as BU-import qualified Data.Enumerator as E-import Data.Enumerator hiding (isEOF, head, length, map) import qualified Data.Enumerator.List as EL+import Data.Enumerator hiding (isEOF, head, length, map) import qualified Data.Set as Set import Data.Typeable import System.IO (isEOF)@@ -51,14 +50,14 @@ -- | Retrieve the best score from a list of training instances. bestScore :: (Monad m) => Enumeratee [AM.TrainingInstance] Double m b-bestScore = E.map AM.bestScore'+bestScore = EL.map AM.bestScore' -- | -- Filter features by exact names. A modifier function can be applied, -- for instance, the 'not' function would exclude the specified features. filterFeatures :: (Monad m) => (Bool -> Bool) -> Set.Set B.ByteString -> Enumeratee AM.TrainingInstance AM.TrainingInstance m b-filterFeatures f keepFeatures = E.map (AM.filterFeatures f keepFeatures)+filterFeatures f keepFeatures = EL.map (AM.filterFeatures f keepFeatures) -- | -- Filter features by their functor. A modifier function can be applied,@@ -66,7 +65,7 @@ filterFeaturesFunctor :: (Monad m) => (Bool -> Bool) -> Set.Set B.ByteString -> Enumeratee AM.TrainingInstance AM.TrainingInstance m b filterFeaturesFunctor f keepFeatures =- E.map (AM.filterFeaturesFunctor f keepFeatures)+ EL.map (AM.filterFeaturesFunctor f keepFeatures) -- | Enumeratee grouping chunks according to an equality function. groupBy :: (Monad m, Eq a) => (a -> a -> Bool) ->@@ -98,7 +97,7 @@ -- | Enumeratee that converts `AM.TrainingInstance` to `B.ByteString`. instanceGenerator :: (Monad m) => Enumeratee AM.TrainingInstance B.ByteString m b-instanceGenerator = E.map AM.trainingInstanceToBs+instanceGenerator = EL.map AM.trainingInstanceToBs -- | Enumerator of lines read from the standard input. lineEnum :: MonadIO m => Enumerator B.ByteString m b@@ -153,7 +152,7 @@ -- | Extract a random sample of @n@ instances from a context. randomSample :: (MonadIO m) => Int -> Enumeratee [AM.TrainingInstance] [AM.TrainingInstance] m b-randomSample n = mapM (liftIO . sampleFun)+randomSample n = EL.mapM (liftIO . sampleFun) where sampleFun :: [AM.TrainingInstance] -> IO [AM.TrainingInstance] sampleFun i = do gen <- getStdRandom split@@ -164,18 +163,18 @@ -- /0.0/ for the rest). scoreToBinary :: (Monad m) => Enumeratee [AM.TrainingInstance] [AM.TrainingInstance] m b-scoreToBinary = E.map AM.scoreToBinary+scoreToBinary = EL.map AM.scoreToBinary -- | -- Enumerator recalculating scores, dividing a score of /1.0/ uniformly -- over instances with the highest quality score. scoreToBinaryNorm :: (Monad m) => Enumeratee [AM.TrainingInstance] [AM.TrainingInstance] m b-scoreToBinaryNorm = E.map AM.scoreToBinaryNorm+scoreToBinaryNorm = EL.map AM.scoreToBinaryNorm -- | -- Enumerator that normalized instance scores over all instances -- in the list. scoreToNorm :: (Monad m) => Enumeratee [AM.TrainingInstance] [AM.TrainingInstance] m b-scoreToNorm = E.map AM.scoreToNorm+scoreToNorm = EL.map AM.scoreToNorm