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@@ -151,6 +151,7 @@ data LamdaExecutionEnv = LamdaExecutionEnv
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data FittnesRes = FittnesRes
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{ total :: R,
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fitnessTotal :: R,
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costAccordingToDataset :: N,
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fitnessGeoMean :: R,
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fitnessMean :: R,
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accuracy :: R,
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@@ -189,8 +190,9 @@ evalResults ex trs = do
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evalResult :: LamdaExecutionEnv -> TypeRequester -> (AccountStatus -> Int -> CreditHistory -> Purpose -> Int -> Savings -> EmploymentStatus -> Int -> StatusAndSex -> OtherDebtors -> Int -> Property -> Int -> OtherPlans -> Housing -> Int -> Job -> Int -> Bool -> Bool -> GermanClass) -> (TypeRequester, FittnesRes)
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evalResult ex tr result = ( tr,
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FittnesRes
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{ total = acc * 100 + (biasSmall - 1),
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{ total = (biasSmall - 1) - (fromIntegral costAccordingToDS),
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fitnessTotal = fitness',
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costAccordingToDataset = costAccordingToDS,
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fitnessMean = meanOfAccuricyPerClass resAndTarget,
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fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,
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accuracy = acc,
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@@ -201,7 +203,8 @@ evalResult ex tr result = ( tr,
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where
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res = map (\(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) -> result a b c d e f g h i j k l m n o p q r s t) (fst (dset ex))
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resAndTarget = (zip (snd (dset ex)) res)
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acc = (foldr (\ts s -> if ((fst ts) == (snd ts)) then s + 1 else s) 0 resAndTarget) / fromIntegral (length resAndTarget)
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acc = (foldr (\(actual,predicted) s -> if (actual == predicted) then s + 1 else s) 0 resAndTarget) / fromIntegral (length resAndTarget)
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costAccordingToDS = (foldr (\(actual,predicted) s -> if ((actual) == (predicted)) then s else (if actual == Deny then s+5 else s+1)) 0 resAndTarget)
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biasSmall = exp ((-(fromIntegral (countTrsR tr))) / 1000) -- 0 (schlecht) bis 1 (gut)
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fitness' = meanOfAccuricyPerClass resAndTarget
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score = fitness' + (biasSmall - 1)
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