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Timestamp:
09/17/12 11:18:40 (12 years ago)
Author:
mkommend
Message:

#1951:

  • Added linear scaling parameter to data analysis problems.
  • Adapted interfaces, evaluators and analyzers accordingly.
  • Added OnlineBoundedMeanSquaredErrorCalculator.
  • Adapted symbolic regression sample unit test.
File:
1 edited

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  • trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs

    r8639 r8664  
    3030namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
    3131  [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 { 
    4233    [StorableConstructor]
    4334    protected SymbolicRegressionSingleObjectiveEvaluator(bool deserializing) : base(deserializing) { }
    4435    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         if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
    81           linearScalingCalculator.Add(estimated, target);
    82         i++;
    83       }
    84       if (linearScalingCalculator.ErrorState == OnlineCalculatorError.None && (targetValuesEnumerator.MoveNext() || estimatedValuesEnumerator.MoveNext()))
    85         throw new ArgumentException("Number of elements in target and estimated values enumeration do not match.");
    86 
    87       double alpha = linearScalingCalculator.Alpha;
    88       double beta = linearScalingCalculator.Beta;
    89       if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) {
    90         alpha = 0.0;
    91         beta = 1.0;
    92       }
    93 
    94       //calculate the quality by using the passed online calculator
    95       targetValuesEnumerator = targetValues.GetEnumerator();
    96       var scaledBoundedEstimatedValuesEnumerator = Enumerable.Range(0, i).Select(x => cache[x] * beta + alpha)
    97         .LimitToRange(lowerEstimationLimit, upperEstimationLimit).GetEnumerator();
    98 
    99       while (targetValuesEnumerator.MoveNext() & scaledBoundedEstimatedValuesEnumerator.MoveNext()) {
    100         calculator.Add(targetValuesEnumerator.Current, scaledBoundedEstimatedValuesEnumerator.Current);
    101       }
    102     }
     36    protected SymbolicRegressionSingleObjectiveEvaluator(): base() {}   
    10337  }
    10438}
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