Changeset 5729 for branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective

Timestamp:

03/17/11 12:25:30 (14 years ago)

Author:

gkronber

Message:

#1418 moved liner scaling method into symbolic regression model and fixed bug in interactive solution simplifier

Location:

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective

Files:

• SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer.cs (modified) (2 diffs)
• SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs (modified) (2 diffs)

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer.cs

@@ -90 +90 @@
       Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic regression model."));
       Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic regression model."));
-      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(false)));
+      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
     }
 
@@ -99 +99 @@
     protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
       var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreter, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
-      return new SymbolicRegressionSolution(model, ProblemData);
+      var solution = new SymbolicRegressionSolution(model, ProblemData);
+      if (ApplyLinearScaling.Value)
+        solution.ScaleModel();
+      return solution;
     }
   }

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs

@@ -74 +74 @@
       Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic regression model."));
       Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic regression model."));
-      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(false)));
+      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
     }
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -82 +82 @@
     protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
       var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreter, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
-      return new SymbolicRegressionSolution(model, ProblemData);
+      var solution = new SymbolicRegressionSolution(model, ProblemData);
+      if (ApplyLinearScaling.Value)
+        solution.ScaleModel();
+      return solution;
     }
   }