Added unit test and travis-ci config.

.travis.yml

@@ -0,0 +1 @@
+language: haskell

RANSAC.cabal

@@ -1,5 +1,5 @@
 name:                RANSAC
-version:             0.1.0.0
+version:             0.1.0.1
 synopsis:            The RANSAC algorithm for parameter estimation.
 description:         The RANdom SAmple Consensus (RANSAC) algorithm for
                      estimating the parameters of a mathematical model
@@ -25,9 +25,19 @@ source-repository head
 
 library
   exposed-modules:     Numeric.Ransac
-  build-depends:       base >= 4.6 && < 5, 
+  build-depends:       base >= 4.5 && < 5, 
                        vector >= 0.10, 
                        random >= 1.0
   hs-source-dirs:      src
   default-language:    Haskell2010
-  ghc-options:         -Wall
+  ghc-options:         -Wall
+
+test-suite tests
+  type:             exitcode-stdio-1.0
+  hs-source-dirs:   tests
+  main-is:          SmokeTest.hs
+  ghc-options:      -Wall
+  default-language: Haskell2010
+  build-depends:    base >= 4.5 && < 5,
+                    test-framework, test-framework-hunit, HUnit,
+                    linear, vector, lens, RANSAC

tests/SmokeTest.hs

@@ -0,0 +1,47 @@
+-- |Basic linear fit unit test. There is no noise in the data set, so
+-- it is very unlikely that the correct model will not be found.
+module Main where
+import Control.Applicative
+import Control.Lens (view)
+import qualified Data.Foldable as F
+import Data.Vector.Storable (Vector)
+import qualified Data.Vector.Storable as V
+import Linear
+import Test.Framework (defaultMain)
+import Test.Framework.Providers.HUnit (hUnitTestToTests)
+import Test.HUnit (Test, (~?))
+import Numeric.Ransac
+
+type Point = V2 Float
+
+sq :: Float -> Float
+sq x = x * x
+
+-- | Fit a 2D line to a collection of 'Point's.
+fitLine :: Vector Point -> Maybe (V2 Float)
+fitLine pts = (!* b) <$> inv22 a
+  where sx = V.sum $ V.map (view _x) pts
+        a = V2 (V2 (V.sum (V.map (sq . view _x) pts)) sx)
+               (V2 sx (fromIntegral (V.length pts)))
+        b = V2 (V.sum (V.map F.product pts))
+               (V.sum (V.map (view _y) pts))
+
+-- | Compute the error of a 'Point' with respect to a hypothesized
+-- linear model.
+ptError :: V2 Float -> Point -> Float
+ptError (V2 m b) (V2 x y) = sq $ y - (m*x+b)
+
+test1 :: Test
+test1 = tst ~? "noise-free linear fit"
+  where model = ransac 20 2 0.8 fitLine ptError (< 0.1) pts
+        pts :: Vector Point
+        pts = V.generate 100 mkPoint
+        mkPoint i = let x = fromIntegral i * 0.1
+                    in V2 x (3 * x + 1)
+        tst = do mm <- model
+                 return $ case mm of
+                   Nothing -> False
+                   Just (m,_) -> qd (V2 3 1) m < 0.1
+
+main :: IO ()
+main = defaultMain $ hUnitTestToTests test1