public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling)
{
IEnumerable <double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
IEnumerable <double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
OnlineCalculatorError errorState;
double lowestClassValue = problemData.ClassValues.OrderBy(x => x).First();
double upmostClassValue = problemData.ClassValues.OrderByDescending(x => x).First();
double boundedMse;
if (applyLinearScaling)
{
var boundedMseCalculator = new OnlineBoundedMeanSquaredErrorCalculator(lowestClassValue, upmostClassValue);
CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, boundedMseCalculator, problemData.Dataset.Rows);
errorState = boundedMseCalculator.ErrorState;
boundedMse = boundedMseCalculator.BoundedMeanSquaredError;
}
else
{
IEnumerable <double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
boundedMse = OnlineBoundedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, lowestClassValue, upmostClassValue, out errorState);
}
if (errorState != OnlineCalculatorError.None)
{
return(Double.NaN);
}
return(boundedMse);
}
public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
OnlineCalculatorError errorState;
double lowestClassValue = problemData.ClassValues.OrderBy(x => x).First();
double upmostClassValue = problemData.ClassValues.OrderByDescending(x => x).First();
double boundedMse;
if (applyLinearScaling) {
var boundedMseCalculator = new OnlineBoundedMeanSquaredErrorCalculator(lowestClassValue, upmostClassValue);
CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, boundedMseCalculator, problemData.Dataset.Rows);
errorState = boundedMseCalculator.ErrorState;
boundedMse = boundedMseCalculator.BoundedMeanSquaredError;
} else {
IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
boundedMse = OnlineBoundedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, lowestClassValue, upmostClassValue, out errorState);
}
if (errorState != OnlineCalculatorError.None) return Double.NaN;
return boundedMse;
}
