variation 4

lambda/src/LambdaDatasets/GermanDataset.hs

@@ -89,10 +89,10 @@ lE =
       maxDepth = 8,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 0,
+          { lambdaSpucker = 10,
-            lambdaSchlucker = 10,
+            lambdaSchlucker = 1,
-            symbol = 100,
+            symbol = 20,
-            variable = 5,
+            variable = 100,
             constant = 5
           }
     }
@@ -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

@@ -56,10 +56,10 @@ lE =
       maxDepth = 8,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 0,
+          { lambdaSpucker = 10,
-            lambdaSchlucker = 10,
+            lambdaSchlucker = 1,
-            symbol = 100,
+            symbol = 20,
-            variable = 5,
+            variable = 100,
             constant = 5
           }
     }
@@ -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

@@ -77,10 +77,10 @@ lE =
       maxDepth = 8,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 0,
+          { lambdaSpucker = 10,
-            lambdaSchlucker = 10,
+            lambdaSchlucker = 1,
-            symbol = 100,
+            symbol = 20,
-            variable = 5,
+            variable = 100,
             constant = 5
           }
     }

lambda/src/Main.hs

@@ -35,7 +35,7 @@ options =
       ( long "population-size"
           <> short 'p'
           <> metavar "N"
-          <> value 100
+          <> value 400
           <> help "Population size"
       )
 
@@ -59,7 +59,7 @@ main =
       selectionType = Tournament 3,
       termination = (steps (iterations opts)),
       poulationSize = (populationSize opts),
-      stepSize = 90,
+      stepSize = 120,
       elitismRatio = 5/100
     }
     pop' <- runEffect (for (run cfg) logCsv)