persepron.hs

main = do
  print "Hello, World!"




-- training data とはfeature vector とラベルの組である。

-- feature vector
data FeatureVector = FeatureVector { x :: Double} deriving Show



-- 入力
type InputSpace = [FeatureVector]
-- newtype InputSpace = InputSpace [FeatureVector] deriving Show

inputSpace = map (FeatureVector) [1,2,3,4,5,6,7,8,9,10]


-- ラベル
type Label = Double

-- 出力
type OutputSpace = [Label]
-- newtype OutputSpace = OutputSpace [Label] deriving Show
outputSpace::OutputSpace
outputSpace = [2,4,6,8,10,12,14,16,18,20]


type TrainingExample = (FeatureVector, Label)
--newtype TrainingExample = TrainingExample (FeatureVector, Label) deriving Show
-- data TrainingExample = TrainingExample {featureVector :: FeatureVector, label :: Label} deriving Show

type TrainingExamples = [TrainingExample]
-- newtype TrainingExamples = TrainingExamples [TrainingExample] deriving Show
trainingExamples = zip inputSpace outputSpace



-- g:: InputSpace -> OutputSpace
-- gは仮説空間Gの元
type Hypothesis = FeatureVector -> Label
type HypothesisSpace = [Hypothesis]
-- newtype HypothesisSpace = HypothesisSpace [InputSpace -> OutputSpace]


-- 損失関数
-- 絶対値はどうやるの？
-- costFunctionはlossFunctionの別名
lossFunction:: Hypothesis -> TrainingExample -> Double
-- 損失２乗誤差
lossFunction f (x, y) = (y - f(x))^2

-- 標準誤差(sample error) 経験誤算、経験損失
sampleError:: Hypothesis -> TrainingExamples -> Double
sampleError f [] = 0
sampleError f dataList = sum(map (lossFunction f)  dataList) / realToFrac(length(dataList))

--  map (\(x,y) -> (x,2*y)) [(1,3),(2,4)]

type Parameter = (Double, Double)

makeHypothsis :: Parameter -> Hypothesis
makeHypothsis (a,b) = \fv -> a * x(fv) + b

errorFunc::Parameter -> TrainingExamples -> Double
errorFunc = lossFunction makeHypothsis

-- errorFunc::Parameter -> TrainingExamples -> Double
-- derrorFunc_a ((a,b), t) = errorFunc(a


parameters::[Parameter]
parameters = [(1,0),(1.5,0),(2.0,0),(2.5,0)]

gs :: HypothesisSpace
gs = map makeHypothsis parameters

reslist =  [sampleError g trainingExamples | g <- gs]

res = reslist !! 0
res2 = reslist !! 1

hypothesis_0::Hypothesis
hypothesis_0 = makeHypothsis (3,2)

result0 = sampleError hypothesis_0 trainingExamples

type LearningCoefficent
grade :: Parameter -> LearningCoefficent -> Error -> Parameter
grade ((a,b), e, ) = ()