} // maximize R² and minimize average similarity
public override IOperation InstrumentedApply()
{
IEnumerable <int> rows = GenerateRowsToEvaluate();
var solution = SymbolicExpressionTreeParameter.ActualValue;
var problemData = ProblemDataParameter.ActualValue;
var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
var estimationLimits = EstimationLimitsParameter.ActualValue;
var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
if (UseConstantOptimization)
{
SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
}
double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling);
if (DecimalPlaces >= 0)
{
r2 = Math.Round(r2, DecimalPlaces);
}
lock (locker) {
if (AverageSimilarityParameter.ActualValue == null)
{
var context = new ExecutionContext(null, SimilarityCalculator, ExecutionContext.Scope.Parent);
SimilarityCalculator.StrictSimilarity = StrictSimilarity;
SimilarityCalculator.Execute(context, CancellationToken);
}
}
var avgSimilarity = AverageSimilarityParameter.ActualValue.Value;
QualitiesParameter.ActualValue = new DoubleArray(new[] { r2, avgSimilarity });
return(base.InstrumentedApply());
}
} // maximize R² and minimize model complexity
public override IOperation InstrumentedApply()
{
IEnumerable <int> rows = GenerateRowsToEvaluate();
var solution = SymbolicExpressionTreeParameter.ActualValue;
var problemData = ProblemDataParameter.ActualValue;
var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
var estimationLimits = EstimationLimitsParameter.ActualValue;
var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
if (UseConstantOptimization)
{
SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
}
double[] qualities = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling, DecimalPlaces);
QualitiesParameter.ActualValue = new DoubleArray(qualities);
return(base.InstrumentedApply());
}
protected SymbolicRegressionConstantOptimizationEvaluator(SymbolicRegressionConstantOptimizationEvaluator original, Cloner cloner)
: base(original, cloner)
{
}
protected SymbolicRegressionConstantOptimizationEvaluator(SymbolicRegressionConstantOptimizationEvaluator original, Cloner cloner)
: base(original, cloner) {
}
