rm(sin): remove sin example

fix(ignoreBiases): was ignoring nodes, lol
fix(w2v): better logging and implementation

examples/sin.hs

@@ -1,35 +0,0 @@
-module Main where
-  import Sibe
-  import Numeric.LinearAlgebra
-  import Data.List
-  import Debug.Trace
-
-  main = do
-    let alpha = 0.5
-        epochs = 1000
-        a = (sigmoid, sigmoid')
-        lo = (sigmoid, (\_ -> 1)) -- cross entropy
-        -- a = (relu, relu')
-        rnetwork = randomNetwork 0 (-1, 1) 1 [(50, a)] (1, lo)
-
-        inputs = map (\a -> vector [a]) (reverse [0, 30, 45, 60, 90])
-        labels = map (\deg -> vector $ [sin $ deg * pi/180]) (reverse [0, 30, 45, 60, 90])
-
-        initial_cost = zipWith crossEntropy (map (`forward` rnetwork) inputs) labels
-
-    network <- run session inputs rnetwork labels alpha epochs
-
-    let results = map (`forward` network) inputs
-        rounded = map (map round . toList) results
-
-        cost = zipWith crossEntropy (map (`forward` network) inputs) labels
-
-    putStrLn "parameters: "
-    putStrLn $ "- inputs: " ++ show inputs
-    putStrLn $ "- labels: " ++ show labels
-    putStrLn $ "- learning rate: " ++ show alpha
-    putStrLn $ "- epochs: " ++ show epochs
-    {-putStrLn $ "- initial cost (cross-entropy): " ++ show initial_cost-}
-    putStrLn "results: "
-    putStrLn $ "- actual result: " ++ show results
-    {-putStrLn $ "- cost (cross-entropy): " ++ show cost-}

examples/word2vec.hs

@@ -15,20 +15,43 @@ module Main where
   import Data.List.Split
 
   main = do
-    {-ds <- do
-        content <- readFile "examples/doc-classifier-data/data-reuters"
-        let splitted = splitOn (replicate 10 '-' ++ "\n") content
-            d = concatMap (tail . lines) (take 100 splitted)
-        return d-}
-    let ds = ["I like deep learning", "I like NLP", "I enjoy flying"]
+    sws <- lines <$> readFile "examples/stopwords"
+    {-ds <- do-}
+        {-content <- readFile "examples/doc-classifier-data/data-reuters"-}
+        {-let splitted = splitOn (replicate 10 '-' ++ "\n") content-}
+            {-d = concatMap (tail . lines) (take 100 splitted)-}
+        {-return $ removeWords sws d-}
+    --let ds = ["I like deep learning", "I like NLP", "I enjoy flying"]
+    let ds = ["the king loves the queen", "the queen loves the king",
+              "the dwarf hates the king", "the queen hates the dwarf",
+              "the dwarf poisons the king", "the dwarf poisons the queen"]
 
-    let session = def { learningRate = 0.8
-                      , batchSize = 10
-                      , epochs = 1000
+    let session = def { learningRate = 0.1
+                      , batchSize = 16
+                      , epochs = 100
                       } :: Session
-        w2v = def { docs = ds }:: Word2Vec
+        w2v = def { docs = ds
+                  , dimensions = 50
+                  , method = SkipGram
+                  , window = 3
+                  } :: Word2Vec
 
 
-    r <- word2vec w2v session
-    {-print r-}
+    (computed, vocvec) <- word2vec w2v session
+    
+    mapM_ (\(w, v) -> do
+                    putStr $ w ++ ": "
+                    let similarities = map (similarity v . snd) computed
+                    let sorted = sortBy (compare `on` similarity v . snd) computed
+                    print . take 2 . drop 1 . reverse $ map fst sorted
+          ) computed
+
     return ()
+
+  removeWords :: [String] -> [String] -> [String]
+  removeWords ws documents =
+    map (rm ws) documents
+    where
+        rm list text =
+          unwords $ filter (`notElem` list) (words text)
+