Changeset 5439 for trunk/sources

Timestamp:

02/04/11 23:31:10 (13 years ago)

Author:

gkronber

Message:

#1040 Not directly related to NSGA-II implementation but not worth a separate ticket. Made minor improvements in multi-objective symbolic regression problem to make it work better with NSGA-II.

Location:

trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic

Files:

• Evaluators/MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• Evaluators/MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator.cs (modified) (1 diff)
• MultiObjectiveSymbolicRegressionProblem.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator.cs

@@ -72 +72 @@
       double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
       double mse = SymbolicRegressionMeanSquaredErrorEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable.Value, rows);
-      List<string> vars = new List<string>();
-      solution.Root.ForEachNodePostfix(n => {
-        var varNode = n as VariableTreeNode;
-        if (varNode != null && !vars.Contains(varNode.VariableName)) {
-          vars.Add(varNode.VariableName);
-        }
-      });
-      return new double[2] { mse, vars.Count };
+      return new double[2] { mse, solution.Size };
     }
   }

trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator.cs

@@ -72 +72 @@
       double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
       double r2 = SymbolicRegressionPearsonsRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable.Value, rows);
-      List<string> vars = new List<string>();
-      solution.Root.ForEachNodePostfix(n => {
-        var varNode = n as VariableTreeNode;
-        if (varNode != null && !vars.Contains(varNode.VariableName)) {
-          vars.Add(varNode.VariableName);
-        }
-      });
-      return new double[2] { r2, vars.Count };
+      return new double[2] { r2, solution.Size };
     }
   }

trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/MultiObjectiveSymbolicRegressionProblem.cs

@@ -71 +71 @@
     public MultiObjectiveSymbolicRegressionProblem()
       : base() {
-      var evaluator = new MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator();
-      Parameters.Add(new ValueParameter<BoolArray>("Maximization", "Set to false as the error of the regression model should be minimized.", new BoolArray(new bool[] { false, false })));
+      var evaluator = new MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator();
+      Parameters.Add(new ValueParameter<BoolArray>("Maximization", "Set to false as the error of the regression model should be minimized.", new BoolArray(new bool[] { true, false })));
       Parameters.Add(new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>("Evaluator", "The operator which should be used to evaluate symbolic regression solutions.", evaluator));