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2 Commits
iris_9
...
f6007318fa
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f6007318fa | ||
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2b7cf859ac |
@@ -86,13 +86,13 @@ lE =
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((Ref.SomeTypeRep (Ref.TypeRep @(Job))), [(fmap show (enumUniform UnemployedOrUnskilledNonResident HighlySkilled ))])
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((Ref.SomeTypeRep (Ref.TypeRep @(Job))), [(fmap show (enumUniform UnemployedOrUnskilledNonResident HighlySkilled ))])
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],
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],
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targetType = (Ref.SomeTypeRep (Ref.TypeRep @(AccountStatus -> Int -> CreditHistory -> Purpose -> Int -> Savings -> EmploymentStatus -> Int -> StatusAndSex -> OtherDebtors -> Int -> Property -> Int -> OtherPlans -> Housing -> Int -> Job -> Int -> Bool -> Bool -> GermanClass))),
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targetType = (Ref.SomeTypeRep (Ref.TypeRep @(AccountStatus -> Int -> CreditHistory -> Purpose -> Int -> Savings -> EmploymentStatus -> Int -> StatusAndSex -> OtherDebtors -> Int -> Property -> Int -> OtherPlans -> Housing -> Int -> Job -> Int -> Bool -> Bool -> GermanClass))),
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maxDepth = 9,
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maxDepth = 5,
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weights =
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weights =
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ExpressionWeights
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ExpressionWeights
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{ lambdaSpucker = 0,
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{ lambdaSpucker = 1,
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lambdaSchlucker = 10,
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lambdaSchlucker = 2,
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symbol = 100,
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symbol = 30,
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variable = 5,
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variable = 10,
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constant = 5
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constant = 5
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}
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}
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}
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}
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@@ -189,7 +189,7 @@ 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 :: 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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evalResult ex tr result = ( tr,
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FittnesRes
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FittnesRes
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{ total = acc * 100 + (biasSmall - 1),
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{ total = score,
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fitnessTotal = fitness',
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fitnessTotal = fitness',
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fitnessMean = meanOfAccuricyPerClass resAndTarget,
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fitnessMean = meanOfAccuricyPerClass resAndTarget,
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fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,
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fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,
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@@ -53,13 +53,13 @@ lE =
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((Ref.SomeTypeRep (Ref.TypeRep @(IrisClass))), [(fmap show (enumUniform Setosa Versicolor :: RVar IrisClass))])
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((Ref.SomeTypeRep (Ref.TypeRep @(IrisClass))), [(fmap show (enumUniform Setosa Versicolor :: RVar IrisClass))])
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],
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],
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targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Float -> Float -> Float -> Float -> IrisClass))),
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targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Float -> Float -> Float -> Float -> IrisClass))),
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maxDepth = 9,
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maxDepth = 5,
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weights =
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weights =
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ExpressionWeights
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ExpressionWeights
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{ lambdaSpucker = 0,
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{ lambdaSpucker = 1,
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lambdaSchlucker = 10,
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lambdaSchlucker = 2,
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symbol = 100,
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symbol = 30,
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variable = 5,
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variable = 10,
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constant = 5
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constant = 5
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}
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}
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}
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}
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@@ -68,7 +68,7 @@ lEE :: LamdaExecutionEnv
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lEE =
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lEE =
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LamdaExecutionEnv
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LamdaExecutionEnv
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{ -- For now these need to define all available functions and types. Generic functions can be used.
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{ -- For now these need to define all available functions and types. Generic functions can be used.
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imports = ["LambdaDatasets.IrisDefinition"],
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imports = ["LambdaDatasets.IrisDataset"],
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training = True,
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training = True,
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trainingData =
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trainingData =
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( map fst (takeFraktion 0.8 irisTrainingData),
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( map fst (takeFraktion 0.8 irisTrainingData),
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@@ -89,7 +89,7 @@ shuffledLEE = do
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itD <- smpl $ shuffle irisTrainingData
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itD <- smpl $ shuffle irisTrainingData
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return LamdaExecutionEnv
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return LamdaExecutionEnv
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{ -- For now these need to define all available functions and types. Generic functions can be used.
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{ -- For now these need to define all available functions and types. Generic functions can be used.
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imports = ["LambdaDatasets.IrisDefinition"],
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imports = ["LambdaDatasets.IrisDataset"],
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training = True,
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training = True,
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trainingData =
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trainingData =
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( map fst (takeFraktion 0.8 itD),
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( map fst (takeFraktion 0.8 itD),
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@@ -155,7 +155,7 @@ evalResults ex trs = do
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evalResult :: LamdaExecutionEnv -> TypeRequester -> (Float -> Float -> Float -> Float -> IrisClass) -> (TypeRequester, FittnesRes)
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evalResult :: LamdaExecutionEnv -> TypeRequester -> (Float -> Float -> Float -> Float -> IrisClass) -> (TypeRequester, FittnesRes)
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evalResult ex tr result = ( tr,
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evalResult ex tr result = ( tr,
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FittnesRes
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FittnesRes
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{ total = acc * 100 + (biasSmall - 1),
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{ total = score,
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fitnessTotal = fitness',
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fitnessTotal = fitness',
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fitnessMean = meanOfAccuricyPerClass resAndTarget,
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fitnessMean = meanOfAccuricyPerClass resAndTarget,
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fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,
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fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,
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@@ -74,13 +74,13 @@ lE =
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((Ref.SomeTypeRep (Ref.TypeRep @(Health))), [(fmap show (enumUniform NotRecommendHealth PriorityHealth ))])
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((Ref.SomeTypeRep (Ref.TypeRep @(Health))), [(fmap show (enumUniform NotRecommendHealth PriorityHealth ))])
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],
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],
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targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Parents -> HasNurs -> Form -> Children -> Housing -> Finance -> Social -> Health -> NurseryClass))),
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targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Parents -> HasNurs -> Form -> Children -> Housing -> Finance -> Social -> Health -> NurseryClass))),
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maxDepth = 9,
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maxDepth = 5,
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weights =
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weights =
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ExpressionWeights
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ExpressionWeights
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{ lambdaSpucker = 0,
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{ lambdaSpucker = 1,
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lambdaSchlucker = 10,
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lambdaSchlucker = 2,
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symbol = 100,
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symbol = 30,
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variable = 5,
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variable = 10,
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constant = 5
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constant = 5
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}
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}
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}
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}
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@@ -8,9 +8,9 @@ import Pipes
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import Pretty
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import Pretty
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import Protolude hiding (for)
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import Protolude hiding (for)
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import System.IO
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import System.IO
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import LambdaDatasets.IrisDataset
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-- import LambdaDatasets.IrisDataset
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-- import LambdaDatasets.NurseryDataset
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-- import LambdaDatasets.NurseryDataset
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-- import LambdaDatasets.GermanDataset
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import LambdaDatasets.GermanDataset
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import Debug.Trace as DB
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import Debug.Trace as DB
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import qualified Data.Map.Strict as Map
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import qualified Data.Map.Strict as Map
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