private void CalculateTrainingPrognosisResults()
{
OnlineCalculatorError errorState;
var problemData = Solution.ProblemData;
if (!problemData.TrainingIndices.Any())
{
return;
}
var model = Solution.Model;
//mean model
double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
var meanModel = new ConstantModel(trainingMean);
//AR1 model
double alpha, beta;
IEnumerable <double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
var trainingHorizions = problemData.TrainingIndices.Select(r => Math.Min(trainingHorizon, problemData.TrainingPartition.End - r)).ToList();
IEnumerable <IEnumerable <double> > trainingTargetValues = problemData.TrainingIndices.Zip(trainingHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
IEnumerable <IEnumerable <double> > trainingEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
IEnumerable <IEnumerable <double> > trainingMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
IEnumerable <IEnumerable <double> > trainingAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
IEnumerable <double> originalTrainingValues = trainingTargetValues.SelectMany(x => x).ToList();
IEnumerable <double> estimatedTrainingValues = trainingEstimatedValues.SelectMany(x => x).ToList();
double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
PrognosisTrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
PrognosisTrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
PrognosisTrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingDirectionalSymmetry : 0.0;
PrognosisTrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
PrognosisTrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingWeightedDirectionalSymmetry : 0.0;
PrognosisTrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
PrognosisTrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticAR1 : double.PositiveInfinity;
PrognosisTrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
PrognosisTrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticMean : double.PositiveInfinity;
}
private void CalculateTestPrognosisResults() {
OnlineCalculatorError errorState;
var problemData = Solution.ProblemData;
if (!problemData.TestIndices.Any()) return;
var model = Solution.Model;
var testHorizions = problemData.TestIndices.Select(r => Math.Min(testHorizon, problemData.TestPartition.End - r)).ToList();
IEnumerable<IEnumerable<double>> testTargetValues = problemData.TestIndices.Zip(testHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
IEnumerable<IEnumerable<double>> testEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
IEnumerable<double> testStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
IEnumerable<double> originalTestValues = testTargetValues.SelectMany(x => x).ToList();
IEnumerable<double> estimatedTestValues = testEstimatedValues.SelectMany(x => x).ToList();
double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
PrognosisTestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
PrognosisTestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
PrognosisTestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
PrognosisTestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
PrognosisTestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestDirectionalSymmetry : 0.0;
PrognosisTestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
PrognosisTestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestWeightedDirectionalSymmetry : 0.0;
if (problemData.TrainingIndices.Any()) {
//mean model
double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
var meanModel = new ConstantModel(trainingMean);
//AR1 model
double alpha, beta;
IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
}
}