Changeset 8550 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4

Timestamp:

08/31/12 13:52:24 (12 years ago)

Author:

mkommend

Message:

#1919: Changed threshold calculation methods to instance instead of static methods.

Location:

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

Files:

• MultiObjective/SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer.cs (modified) (1 diff)
• MultiObjective/SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer.cs (modified) (1 diff)
• SymbolicDiscriminantFunctionClassificationModel.cs (modified) (1 diff)

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

@@ -79 +79 @@
       if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
 
-      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
+      model.SetAccuracyMaximizingThresholds(ProblemDataParameter.ActualValue);
       return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
     }

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

@@ -68 +68 @@
       if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
 
-      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
+      model.SetAccuracyMaximizingThresholds(ProblemDataParameter.ActualValue);
       return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
     }

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

@@ -77 +77 @@
       if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
 
-      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
+      model.SetAccuracyMaximizingThresholds(ProblemDataParameter.ActualValue);
       return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
     }

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

@@ -62 +62 @@
       if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
 
-      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
+      model.SetAccuracyMaximizingThresholds(ProblemDataParameter.ActualValue);
       return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
     }

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

@@ -68 +68 @@
       if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
 
-      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
+      model.SetAccuracyMaximizingThresholds(ProblemDataParameter.ActualValue);
       return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
     }

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

@@ -62 +62 @@
       if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
 
-      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
+      model.SetAccuracyMaximizingThresholds(ProblemDataParameter.ActualValue);
       return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
     }

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

@@ -122 +122 @@
     #endregion
 
-    public static void SetAccuracyMaximizingThresholds(IDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData) {
+    public void SetAccuracyMaximizingThresholds(IClassificationProblemData problemData) {
       double[] classValues;
       double[] thresholds;
       var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices);
-      var estimatedTrainingValues = model.GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices);
+      var estimatedTrainingValues = GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices);
       AccuracyMaximizationThresholdCalculator.CalculateThresholds(problemData, estimatedTrainingValues, targetClassValues, out classValues, out thresholds);
 
-      model.SetThresholdsAndClassValues(thresholds, classValues);
-    }
-
-    public static void SetClassDistributionCutPointThresholds(IDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData) {
+      SetThresholdsAndClassValues(thresholds, classValues);
+    }
+
+    public void SetClassDistributionCutPointThresholds(IClassificationProblemData problemData) {
       double[] classValues;
       double[] thresholds;
       var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices);
-      var estimatedTrainingValues = model.GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices);
+      var estimatedTrainingValues = GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices);
       NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(problemData, estimatedTrainingValues, targetClassValues, out classValues, out thresholds);
 
-      model.SetThresholdsAndClassValues(thresholds, classValues);
+      SetThresholdsAndClassValues(thresholds, classValues);
     }