Haskell Perceptron

gistfile1.hs

module Main where

import Debug.Trace

threshold = 0.5
learningRate = 0.1
weights = [0, 0, 0] :: [Double]
trainingSet = [
        ([1, 0, 0], 1), 
        ([1, 0, 1], 1), 
        ([1, 1, 0], 1), 
        ([1, 1, 1], 0) ] :: [ ([Double], Int) ]


dotProduct vec1 vec2 = sum $ zipWith (*) vec1 vec2

add v1 v2 = zipWith (+) v1 v2

timesScalar vector n = map (* n) vector

timesVector n vector = map (* n) vector

cmp o1 o2  
        | o1 < o2 = 1
        | otherwise = 0

train threshold learningRate (_, weights) (inputVector, desiredOutput) =
        traceShow ("s=", s, "n=", n, "e=", error) $
        (error /= 0, newWeights)
        where error = fromIntegral (desiredOutput - result)
              s = inputVector `dotProduct` weights
              n = s `cmp` threshold
              result = desiredOutput - n
              newWeights = weights `add` ((learningRate *  error) `timesVector` inputVector)
 
trainExampleSet threshold learningRate weights exampleSet =
        (isError, newWeights)  
        where
                newWeights = snd $ last singleExampleTrainingResults
                isError = all (\_ -> True) $ map fst singleExampleTrainingResults          
                singleExampleTrainingResults = scanl (train threshold learningRate) (True, weights) trainingSet     

classify example weights =
        dotProduct example weights

main::IO()
main = do 
        -- print $ trainExampleSet threshold learningRate weights trainingSet
        let learnedWeights = snd $ last $ take 10 $ scanl (\(isError, w) ts -> trainExampleSet threshold learningRate w ts) (True, weights) (cycle [trainingSet])
        print $ classify [1,1,1] learnedWeights