Changeset 8915 for branches/HeuristicLab.TreeSimplifier/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs
- Timestamp:
- 11/15/12 16:47:25 (11 years ago)
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- 1 edited
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branches/HeuristicLab.TreeSimplifier/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs
r8127 r8915 30 30 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { 31 31 [StorableClass] 32 public abstract class SymbolicRegressionSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>, ISymbolicRegressionSingleObjectiveEvaluator { 33 private const string ApplyLinearScalingParameterName = "ApplyLinearScaling"; 34 public IFixedValueParameter<BoolValue> ApplyLinearScalingParameter { 35 get { return (IFixedValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; } 36 } 37 public bool ApplyLinearScaling { 38 get { return ApplyLinearScalingParameter.Value.Value; } 39 set { ApplyLinearScalingParameter.Value.Value = value; } 40 } 41 32 public abstract class SymbolicRegressionSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>, ISymbolicRegressionSingleObjectiveEvaluator { 42 33 [StorableConstructor] 43 34 protected SymbolicRegressionSingleObjectiveEvaluator(bool deserializing) : base(deserializing) { } 44 35 protected SymbolicRegressionSingleObjectiveEvaluator(SymbolicRegressionSingleObjectiveEvaluator original, Cloner cloner) : base(original, cloner) { } 45 protected SymbolicRegressionSingleObjectiveEvaluator() 46 : base() { 47 Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating.", new BoolValue(true))); 48 ApplyLinearScalingParameter.Hidden = true; 49 } 50 51 [StorableHook(HookType.AfterDeserialization)] 52 private void AfterDeserialization() { 53 if (!Parameters.ContainsKey(ApplyLinearScalingParameterName)) { 54 Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating.", new BoolValue(false))); 55 ApplyLinearScalingParameter.Hidden = true; 56 } 57 } 58 59 [ThreadStatic] 60 private static double[] cache; 61 62 protected static void CalculateWithScaling(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues, 63 double lowerEstimationLimit, double upperEstimationLimit, 64 IOnlineCalculator calculator, int maxRows) { 65 if (cache == null || cache.GetLength(0) < maxRows) { 66 cache = new double[maxRows]; 67 } 68 69 //calculate linear scaling 70 //the static methods of the calculator could not be used as it performs a check if the enumerators have an equal amount of elements 71 //this is not true if the cache is used 72 int i = 0; 73 var linearScalingCalculator = new OnlineLinearScalingParameterCalculator(); 74 var targetValuesEnumerator = targetValues.GetEnumerator(); 75 var estimatedValuesEnumerator = estimatedValues.GetEnumerator(); 76 while (targetValuesEnumerator.MoveNext() && estimatedValuesEnumerator.MoveNext()) { 77 double target = targetValuesEnumerator.Current; 78 double estimated = estimatedValuesEnumerator.Current; 79 cache[i] = estimated; 80 linearScalingCalculator.Add(estimated, target); 81 i++; 82 } 83 double alpha = linearScalingCalculator.Alpha; 84 double beta = linearScalingCalculator.Beta; 85 86 //calculate the quality by using the passed online calculator 87 targetValuesEnumerator = targetValues.GetEnumerator(); 88 var scaledBoundedEstimatedValuesEnumerator = Enumerable.Range(0, i).Select(x => cache[x] * beta + alpha) 89 .LimitToRange(lowerEstimationLimit, upperEstimationLimit).GetEnumerator(); 90 91 while (targetValuesEnumerator.MoveNext() & scaledBoundedEstimatedValuesEnumerator.MoveNext()) { 92 calculator.Add(targetValuesEnumerator.Current, scaledBoundedEstimatedValuesEnumerator.Current); 93 } 94 } 36 protected SymbolicRegressionSingleObjectiveEvaluator(): base() {} 95 37 } 96 38 }
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