fix Iris

lambda/src/LambdaDatasets/GermanDataset.hs

@@ -86,13 +86,13 @@ 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 = 5,
+      maxDepth = 9,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 1,
-            lambdaSchlucker = 2,
-            symbol = 30,
-            variable = 10,
+          { lambdaSpucker = 0,
+            lambdaSchlucker = 10,
+            symbol = 100,
+            variable = 5,
             constant = 5
           }
     }

lambda/src/LambdaDatasets/IrisDataset.hs

@@ -53,13 +53,13 @@ lE =
             ((Ref.SomeTypeRep (Ref.TypeRep @(IrisClass))), [(fmap show (enumUniform Setosa Versicolor :: RVar IrisClass))])
           ],
       targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Float -> Float -> Float -> Float -> IrisClass))),
-      maxDepth = 5,
+      maxDepth = 9,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 1,
-            lambdaSchlucker = 2,
-            symbol = 30,
-            variable = 10,
+          { lambdaSpucker = 0,
+            lambdaSchlucker = 10,
+            symbol = 100,
+            variable = 5,
             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.IrisDataset"],
+      imports = ["LambdaDatasets.IrisDefinition"],
       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.IrisDataset"],
+      imports = ["LambdaDatasets.IrisDefinition"],
       training = True,
       trainingData =
         ( map fst (takeFraktion 0.8 itD),

lambda/src/LambdaDatasets/NurseryDataset.hs

@@ -74,13 +74,13 @@ 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 = 5,
+      maxDepth = 9,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 1,
-            lambdaSchlucker = 2,
-            symbol = 30,
-            variable = 10,
+          { lambdaSpucker = 0,
+            lambdaSchlucker = 10,
+            symbol = 100,
+            variable = 5,
             constant = 5
           }
     }

lambda/src/Main.hs

@@ -8,8 +8,8 @@ import Pipes
 import Pretty
 import Protolude hiding (for)
 import System.IO
-import LambdaDatasets.IrisDataset
--- import LambdaDatasets.NurseryDataset
+-- import LambdaDatasets.IrisDataset
+import LambdaDatasets.NurseryDataset
 -- import LambdaDatasets.GermanDataset
 import Debug.Trace as DB
 import qualified Data.Map.Strict as Map
@@ -35,7 +35,7 @@ options =
       ( long "population-size"
           <> short 'p'
           <> metavar "N"
-          <> value 400
+          <> value 100
           <> help "Population size"
       )
 
@@ -59,7 +59,7 @@ main =
       selectionType = Tournament 3,
       termination = (steps (iterations opts)),
       poulationSize = (populationSize opts),
-      stepSize = 120,
+      stepSize = 90,
       elitismRatio = 5/100
     }
     pop' <- runEffect (for (run cfg) logCsv)