private void CalculateResults()
{
ModelLength = Model.SymbolicExpressionTree.Length;
ModelDepth = Model.SymbolicExpressionTree.Depth;
EstimationLimits.Lower = Model.LowerEstimationLimit;
EstimationLimits.Upper = Model.UpperEstimationLimit;
TrainingUpperEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
TestUpperEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
TrainingLowerEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
TestLowerEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
TrainingNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TrainingIndices).Count(double.IsNaN);
TestNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TestIndices).Count(double.IsNaN);
}
protected void CalculateRegressionResults()
{
double[] estimatedTrainingValues = EstimatedTrainingValues.ToArray(); // cache values
double[] originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
double[] estimatedTestValues = EstimatedTestValues.ToArray(); // cache values
double[] originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray();
OnlineCalculatorError errorState;
double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
TestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
trainingNormalizedGini = double.NaN;
}
double testNormalizedGini = NormalizedGiniCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
if (errorState != OnlineCalculatorError.None)
{
testNormalizedGini = double.NaN;
}
TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
TestNormalizedGiniCoefficient = testNormalizedGini;
}
