feat(Numeric): move all modules to Numeric

examples/424encoder.hs

@@ -1,5 +1,5 @@
 module Main where
-  import Sibe
+  import Numeric.Sibe
   import Numeric.LinearAlgebra
   import Data.List
   import Debug.Trace

examples/naivebayes-doc-classifier.hs

@@ -1,8 +1,7 @@
 module Main
   where
-    -- import Sibe
+    import Numeric.Sibe.NLP
-    import Sibe.NLP
+    import Numeric.Sibe.NaiveBayes
-    import Sibe.NaiveBayes
     import Text.Printf
     import Data.List
     import Data.Maybe

examples/notmnist.hs

@@ -3,7 +3,7 @@
 {-# LANGUAGE ScopedTypeVariables #-}
 
 module Main where
-  import Sibe
+  import Numeric.Sibe
   import Numeric.LinearAlgebra
   import Data.List
   import Debug.Trace

examples/word2vec.hs

@@ -3,9 +3,9 @@
 {-# LANGUAGE ScopedTypeVariables #-}
 
 module Main where
-  import Sibe
+  import Numeric.Sibe
-  import Sibe.Word2Vec
+  import Numeric.Sibe.Word2Vec
-  import Sibe.Utils
+  import Numeric.Sibe.Utils
   import Data.Default.Class
   import qualified Data.Vector.Storable as V
   import Data.List (sortBy)

examples/xor.hs

@@ -1,5 +1,5 @@
 module Main where
-  import Sibe
+  import Numeric.Sibe
   import Numeric.LinearAlgebra
   import Data.List
   import Debug.Trace

sibe.cabal

@@ -1,5 +1,5 @@
 name:                sibe
-version:             0.1.0.1
+version:             0.2.0.0
 synopsis:            Machine Learning algorithms
 description:         Haskell Machine Learning
 homepage:            https://github.com/mdibaiee/sibe
@@ -15,7 +15,7 @@ cabal-version:       >=1.10
 
 library
   hs-source-dirs:      src
-  exposed-modules:     Sibe, Sibe.NaiveBayes, Sibe.NLP, Sibe.Word2Vec, Sibe.Utils
+  exposed-modules:     Numeric.Sibe, Numeric.Sibe.NaiveBayes, Numeric.Sibe.NLP, Numeric.Sibe.Word2Vec, Numeric.Sibe.Utils
   build-depends:       base >= 4.7 && < 5
                      , hmatrix
                      , random

src/Numeric/Sibe.hs

@@ -3,7 +3,7 @@
 {-# LANGUAGE DataKinds #-}
 {-# LANGUAGE TypeOperators #-}
 
-module Sibe
+module Numeric.Sibe
     (Network(..),
      Layer(..),
      Input,

src/Numeric/Sibe/NLP.hs

@@ -1,4 +1,4 @@
-module Sibe.NLP
+module Numeric.Sibe.NLP
   (Class,
    Document(..),
    accuracy,
@@ -13,7 +13,7 @@ module Sibe.NLP
    ngramText,
   )
   where
-    import Sibe.Utils
+    import Numeric.Sibe.Utils
     import Data.List
     import Debug.Trace
     import Data.List.Split

src/Numeric/Sibe/NaiveBayes.hs

@@ -1,4 +1,4 @@
-module Sibe.NaiveBayes
+module Numeric.Sibe.NaiveBayes
   (Document(..),
    NB(..),
    initialize,
@@ -18,8 +18,8 @@ module Sibe.NaiveBayes
    removeStopwords,
   )
   where
-    import Sibe.Utils
+    import Numeric.Sibe.Utils
-    import Sibe.NLP
+    import Numeric.Sibe.NLP
     import Data.List
     import Debug.Trace
     import qualified Data.Set as Set

src/Numeric/Sibe/Utils.hs

@@ -1,4 +1,4 @@
-module Sibe.Utils
+module Numeric.Sibe.Utils
   ( similarity
   , ordNub
   , onehot

src/Numeric/Sibe/Word2Vec.hs

@@ -1,12 +1,12 @@
 {-# LANGUAGE ScopedTypeVariables #-}
 
-module Sibe.Word2Vec
+module Numeric.Sibe.Word2Vec
   ( word2vec
   , Word2Vec (..)
   , W2VMethod (..)
   ) where
-    import Sibe
+    import Numeric.Sibe
-    import Sibe.Utils
+    import Numeric.Sibe.Utils
     import Debug.Trace
     import Data.Char
     import Data.Maybe

src/Sibe/Word2Vec.hs.backup

@@ -1,44 +0,0 @@
-module Sibe.Word2Vec
-  (word2vec,
-   mapTuple
-  ) where
-    import Sibe
-    import Sibe.NLP
-    import Debug.Trace
-    import Data.Char
-    import Data.Maybe
-    import Data.List
-    import Numeric.LinearAlgebra hiding (find)
-    import qualified Data.Vector.Storable as V
-
-    word2vec docs session = do
-      let cooccurence = concat $ map co docs
-          a = (sigmoid, sigmoid')
-          o = (softmax, crossEntropy')
-          window = 2
-          s = session { training = cooccurence
-                      , test     = cooccurence
-                      , network  = buildNetwork 0 (-1, 1) n [(n, 300, (id, id))] (300, n, (softmax, crossEntropy'))
-                      }
-      print $ network s
-      newses <- run gd s
-      return (newses, cooccurence, vocabulary, vocvec)
-      where
-        n = length vocabulary
-        vocabulary = ordNub . words . map toLower . concatMap (++ " ") $ docs
-        vocvec = zip vocabulary $ map tovec [0..n]
-        tovec i = replicate i 0 ++ [1] ++ replicate (n - i - 1) 0
-        co d =
-          let p = pairs d
-          in map (\(a, [b, c]) -> (f a, V.concat [f b, f c])) p
-          where 
-            f w = vector . snd . fromJust $ find ((== w) . fst) vocvec
-        pairs d = concatMap iter [0..length ws]
-          where 
-            ws = words $ map toLower d
-            iter i
-              | i > 0 && i < length ws - 1 = [(ws !! i, [ws !! (i - 1), ws !! (i + 1)])]
-              | otherwise = []
-
-    mapTuple :: (a -> b) -> (a, a) -> (b, b)
-    mapTuple f (a, b) = (f a, f b)