Changeset 8664 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs
- Timestamp:
- 09/17/12 11:18:40 (12 years ago)
- File:
-
- 1 edited
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trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs
r7259 r8664 20 20 #endregion 21 21 22 using System; 22 23 using System.Collections.Generic; 23 24 using HeuristicLab.Common; … … 47 48 IEnumerable<int> rows = GenerateRowsToEvaluate(); 48 49 var solution = SymbolicExpressionTreeParameter.ActualValue; 49 double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows );50 double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value); 50 51 QualityParameter.ActualValue = new DoubleValue(quality); 51 52 return base.Apply(); 52 53 } 53 54 54 public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows ) {55 public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) { 55 56 IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows); 56 IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows); 57 IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); 57 IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows); 58 58 OnlineCalculatorError errorState; 59 double mse = OnlineMeanSquaredErrorCalculator.Calculate(originalValues, boundedEstimationValues, out errorState); 60 if (errorState != OnlineCalculatorError.None) return double.NaN; 61 else return mse; 59 60 double mse; 61 if (applyLinearScaling) { 62 var mseCalculator = new OnlineMeanSquaredErrorCalculator(); 63 CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows); 64 errorState = mseCalculator.ErrorState; 65 mse = mseCalculator.MeanSquaredError; 66 } else { 67 IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); 68 mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState); 69 } 70 if (errorState != OnlineCalculatorError.None) return Double.NaN; 71 return mse; 62 72 } 63 73 … … 65 75 SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; 66 76 EstimationLimitsParameter.ExecutionContext = context; 77 ApplyLinearScalingParameter.ExecutionContext = context; 67 78 68 double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows );79 double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value); 69 80 70 81 SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; 71 82 EstimationLimitsParameter.ExecutionContext = null; 83 ApplyLinearScalingParameter.ExecutionContext = null; 72 84 73 85 return mse;
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