reduce population to fix memory issues in higher depth case

lambda/src/LambdaDatasets/GermanDataset.hs

@@ -86,7 +86,7 @@ lE =
             ((Ref.SomeTypeRep (Ref.TypeRep @(Job))), [(fmap show (enumUniform UnemployedOrUnskilledNonResident HighlySkilled ))])
           ],
       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))),
-      maxDepth = 8,
+      maxDepth = 5,
       weights =
         ExpressionWeights
           { lambdaSpucker = 1,
@@ -189,7 +189,7 @@ evalResults ex trs = do
 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)
 evalResult ex tr result = ( tr,
       FittnesRes
-        { total = acc * 100 + (biasSmall - 1),
+        { total = score,
           fitnessTotal = fitness',
           fitnessMean = meanOfAccuricyPerClass resAndTarget,
           fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,

lambda/src/LambdaDatasets/IrisDataset.hs

@@ -53,7 +53,7 @@ lE =
             ((Ref.SomeTypeRep (Ref.TypeRep @(IrisClass))), [(fmap show (enumUniform Setosa Versicolor :: RVar IrisClass))])
           ],
       targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Float -> Float -> Float -> Float -> IrisClass))),
-      maxDepth = 8,
+      maxDepth = 5,
       weights =
         ExpressionWeights
           { lambdaSpucker = 1,
@@ -68,7 +68,7 @@ lEE :: LamdaExecutionEnv
 lEE =
   LamdaExecutionEnv
     { -- For now these need to define all available functions and types. Generic functions can be used.
-      imports = ["LambdaDatasets.IrisDefinition"],
+      imports = ["LambdaDatasets.IrisDataset"],
       training = True,
       trainingData =
         ( map fst (takeFraktion 0.8 irisTrainingData),
@@ -89,7 +89,7 @@ shuffledLEE = do
   itD <- smpl $ shuffle irisTrainingData
   return  LamdaExecutionEnv
     { -- For now these need to define all available functions and types. Generic functions can be used.
-      imports = ["LambdaDatasets.IrisDefinition"],
+      imports = ["LambdaDatasets.IrisDataset"],
       training = True,
       trainingData =
         ( map fst (takeFraktion 0.8 itD),
@@ -155,7 +155,7 @@ evalResults ex trs = do
 evalResult :: LamdaExecutionEnv -> TypeRequester -> (Float -> Float -> Float -> Float -> IrisClass) -> (TypeRequester, FittnesRes)
 evalResult ex tr result = ( tr,
       FittnesRes
-        { total = acc * 100 + (biasSmall - 1),
+        { total = score,
           fitnessTotal = fitness',
           fitnessMean = meanOfAccuricyPerClass resAndTarget,
           fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,

lambda/src/LambdaDatasets/NurseryDataset.hs

@@ -74,7 +74,7 @@ lE =
             ((Ref.SomeTypeRep (Ref.TypeRep @(Health))), [(fmap show (enumUniform NotRecommendHealth PriorityHealth ))])
           ],
       targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Parents -> HasNurs -> Form -> Children -> Housing -> Finance -> Social -> Health -> NurseryClass))),
-      maxDepth = 8,
+      maxDepth = 5,
       weights =
         ExpressionWeights
           { lambdaSpucker = 1,

lambda/src/Main.hs

@@ -8,8 +8,8 @@ import Pipes
 import Pretty
 import Protolude hiding (for)
 import System.IO
-import LambdaDatasets.IrisDataset
+-- import LambdaDatasets.IrisDataset
--- import LambdaDatasets.NurseryDataset
+import LambdaDatasets.NurseryDataset
 -- import LambdaDatasets.GermanDataset
 import Debug.Trace as DB
 import qualified Data.Map.Strict as Map