added takevector and dropvector functions

src/AI/Learning/LinearRegression.hs

@@ -191,7 +191,7 @@ ridgeRegress x y useConst lambda
                       (_,r) = qr x
                       rr = takeRows n r
                       ww = if useConst
-                            then diag (fromList (0 : replicate (n-1) 1))
+                            then diag $ join [0, constant 1 (n-1)]
                             else ident n
                    in (trans rr <> rr + lambda `scale` ww) <\> trans x <> y
 

src/AI/Learning/LogisticRegression.hs

@@ -76,10 +76,9 @@ lrLogLikRegularized y x useConst lambda theta = (cost, grad)
     where
         m = fromIntegral (rows x)
         h = sigmoid $ x <> theta
-        w = if useConst
-            then join [0, constant 1 (dim theta - 1)]
-            else constant 1 (dim theta)
-        theta' = w * theta
+        theta' = if useConst
+                    then join [0, dropVector 1 theta]
+                    else theta
         cost   = sumVector (y * log h + (1-y) * log (1-h)) / m + (sumVector $ mapVector (^2) theta') / (2 * m)
         grad   = (1/m `scale` (y - h) <> x) + (lambda / m) `scale` theta'
 

src/AI/Util/Matrix.hs

@@ -4,15 +4,6 @@ module AI.Util.Matrix where
 
 import Foreign.Storable (Storable)
 import Numeric.LinearAlgebra
-import Numeric.LinearAlgebra.Util (ones,zeros)
-
--- |Create an @rxc@ matrix of zeros.
---zeros :: (Num a, Container Vector a)  => Int -> Int -> Matrix a
---zeros r c = konst 0 (r,c)
-
----- |Create an @rxc@ matrix of ones.
---ones :: (Num a, Container Vector a) => Int -> Int -> Matrix a
---ones r c = konst 1 (r,c)
 
 -- |Return the size of a matrix as a 2-tuple.
 size :: Matrix a -> (Int,Int)
@@ -30,6 +21,12 @@ vertcat = fromBlocks . map return
 -- Functions on Vectors --
 --------------------------
 
+takeVector :: Storable a => Int -> Vector a -> Vector a
+takeVector n v = subVector 0 n v
+
+dropVector :: Storable a => Int -> Vector a -> Vector a
+dropVector n v = subVector n (dim v - n) v
+
 sumVector :: (Num a, Storable a) => Vector a -> a
 sumVector xs = foldVector (+) 0 xs