Changeset 5942 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification

Timestamp:

04/04/11 15:38:16 (14 years ago)

Author:

mkommend

Message:

#1453: Renamed IOnlineEvaluator to IOnlineCalculator

Location:

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4

Files:

• MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs (modified) (1 diff)
• MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs (modified) (1 diff)
• SymbolicDiscriminantFunctionClassificationModel.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs

@@ -56 +56 @@
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
       IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-      OnlineEvaluatorError errorState;
-      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues, out errorState);
-      if (errorState != OnlineEvaluatorError.None) mse = double.NaN;
+      OnlineCalculatorError errorState;
+      double mse = OnlineMeanSquaredErrorCalculator.Calculate(originalValues, boundedEstimationValues, out errorState);
+      if (errorState != OnlineCalculatorError.None) mse = double.NaN;
       return new double[2] { mse, solution.Length };
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

@@ -34 +34 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      OnlineEvaluatorError errorState;
-      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues, out errorState);
-      if (errorState != OnlineEvaluatorError.None) r2 = 0.0;
+      OnlineCalculatorError errorState;
+      double r2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState);
+      if (errorState != OnlineCalculatorError.None) r2 = 0.0;
       return new double[] { r2, solution.Length };
 

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs

@@ -56 +56 @@
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
       IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-      OnlineEvaluatorError errorState;
-      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues, out errorState);
-      if (errorState != OnlineEvaluatorError.None) return double.NaN;
+      OnlineCalculatorError errorState;
+      double mse = OnlineMeanSquaredErrorCalculator.Calculate(originalValues, boundedEstimationValues, out errorState);
+      if (errorState != OnlineCalculatorError.None) return double.NaN;
       else return mse;
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs

@@ -55 +55 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      OnlineEvaluatorError errorState;
-      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues, out errorState);
-      if (errorState != OnlineEvaluatorError.None) return 0.0;
+      OnlineCalculatorError errorState;
+      double r2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState);
+      if (errorState != OnlineCalculatorError.None) return 0.0;
       else return r2;
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs

@@ -124 +124 @@
       double alpha;
       double beta;
-      OnlineEvaluatorError errorState;
+      OnlineCalculatorError errorState;
       OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta, out errorState);
-      if (errorState != OnlineEvaluatorError.None) return;
+      if (errorState != OnlineCalculatorError.None) return;
 
       ConstantTreeNode alphaTreeNode = null;