implement output computation

1 files changed, 50 insertions, 1 deletions

diff --git a/src/Mind.hs b/src/Mind.hs
index 2971476..1ee70bc 100644
--- a/src/Mind.hs
+++ b/src/Mind.hs
@@ -11,6 +11,7 @@ module Mind
   ) where
 
 import Data.Ix
+import Data.Maybe
 
 -- index different neuron types
 data NeuronIndex = Input Int | Internal Int | Output Int deriving (Show, Eq)
@@ -76,4 +77,52 @@ insertEdge ns i e
 
 -- network computation
 compute :: Network -> [Float] -> [Float] -> Maybe ([Float], [Float])
-compute net input state = undefined
+compute net input state = Just $ (output net input state, [])
+
+
+type InputState = ([Float], [Float])
+type NewState = [Maybe Float]
+
+
+output net input state = 
+  let 
+    state' = replicate (length $ internalNeurons net) Nothing
+    numOutput = length $ outputNeurons net
+  in 
+    map ((fst . getValue net (input, state) state') . Output) [0..numOutput-1]
+
+
+updateValue :: NewState -> Int -> Float -> NewState
+updateValue state' index value =
+  let (front, _:back) = splitAt index state'
+  in front ++ (Just value):back
+
+
+getValue :: Network -> InputState -> NewState -> NeuronIndex -> (Float, NewState)
+getValue _ (input, _) state' (Input x) = (input !! x, state')
+getValue net inputState state' (Internal x) =
+  let cached = state' !! x
+  in
+    if isJust cached then (fromJust cached, state')
+    else let 
+      (value, ns) = foldEdges net inputState state' (internalNeurons net !! x)
+      nss = updateValue ns x value
+    in (value, nss)
+getValue net inputState state' (Output x) =
+  foldEdges net inputState state' (outputNeurons net !! x)
+
+
+
+foldEdges:: Network -> InputState -> NewState -> [Edge] -> (Float, NewState)
+foldEdges net (input, state) state' edges =
+  let 
+    (total, ns) = foldl
+      (\(total, ns) (Edge (source, w)) ->
+        let 
+          (value, ns') = getValue net (input, state) ns source
+          total' = (w * value) + total
+        in (total', ns')
+      )
+      (0, state')
+      edges
+  in (tanh total, ns)