Changeset 200

Timestamp:

04/28/08 09:16:33 (16 years ago)

Author:

gkronber

Message:

added a counter for the total number of evaluated nodes.

Location:

trunk/sources/HeuristicLab.StructureIdentification/Evaluation

Files:

• EarlyStoppingMeanSquaredErrorEvaluator.cs (modified) (2 diffs)
• GPEvaluatorBase.cs (modified) (2 diffs)
• MeanSquaredErrorEvaluator.cs (modified) (1 diff)

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/EarlyStoppingMeanSquaredErrorEvaluator.cs

@@ -63 +63 @@
 
         // check the limit and stop as soon as we hit the limit
-        if(errorsSquaredSum / dataset.Rows >= qualityLimit)
-          return errorsSquaredSum / (sample+1); // return estimated MSE (when the remaining errors are on average the same)
+        if(errorsSquaredSum / dataset.Rows >= qualityLimit) {
+          scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * sample+1;
+          return errorsSquaredSum / (sample + 1); // return estimated MSE (when the remaining errors are on average the same)
+        }
       }
       errorsSquaredSum /= dataset.Rows;
@@ -70 +72 @@
         errorsSquaredSum = double.MaxValue;
       }
+      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * dataset.Rows;
       return errorsSquaredSum;
     }

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/GPEvaluatorBase.cs

@@ -33 +33 @@
   public abstract class GPEvaluatorBase : OperatorBase {
     protected double maximumPunishment;
+    protected int treeSize;
+    protected double totalEvaluatedNodes;
 
     public GPEvaluatorBase()
       : base() {
       AddVariableInfo(new VariableInfo("FunctionTree", "The function tree that should be evaluated", typeof(IFunctionTree), VariableKind.In));
+      AddVariableInfo(new VariableInfo("TreeSize", "Size (number of nodes) of the tree to evaluate", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo("Dataset", "Dataset with all samples on which to apply the function", typeof(Dataset), VariableKind.In));
       AddVariableInfo(new VariableInfo("TargetVariable", "Index of the column of the dataset that holds the target variable", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo("PunishmentFactor", "Punishment factor for invalid estimations", typeof(DoubleData), VariableKind.In));
+      AddVariableInfo(new VariableInfo("TotalEvaluatedNodes", "Number of evaluated nodes", typeof(DoubleData), VariableKind.In | VariableKind.Out)); 
       AddVariableInfo(new VariableInfo("Quality", "The evaluated quality of the model", typeof(DoubleData), VariableKind.New));
     }
@@ -48 +52 @@
       IFunctionTree functionTree = GetVariableValue<IFunctionTree>("FunctionTree", scope, true);
       this.maximumPunishment = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data * dataset.GetRange(targetVariable);
+      this.treeSize = scope.GetVariableValue<IntData>("TreeSize", false).Data;
+      this.totalEvaluatedNodes = scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data;
 
       double result = Evaluate(scope, functionTree, targetVariable, dataset);

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/MeanSquaredErrorEvaluator.cs

@@ -64 +64 @@
         errorsSquaredSum = double.MaxValue;
       }
+      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * dataset.Rows;
       return errorsSquaredSum;
     }