/// <summary>
/// Calculates alpha and beta parameters to linearly scale elements of original to the scale and location of target
/// original[i] * beta + alpha
/// </summary>
/// <param name="original">Values that should be scaled</param>
/// <param name="target">Target values to which the original values should be scaled</param>
/// <param name="alpha">Additive constant for the linear scaling</param>
/// <param name="beta">Multiplicative factor for the linear scaling</param>
/// <param name="errorState">Flag that indicates if errors occurred in the calculation of the linea scaling parameters.</param>
public static void Calculate(IEnumerable <double> original, IEnumerable <double> target, out double alpha, out double beta, out OnlineCalculatorError errorState)
{
OnlineLinearScalingParameterCalculator calculator = new OnlineLinearScalingParameterCalculator();
IEnumerator <double> originalEnumerator = original.GetEnumerator();
IEnumerator <double> targetEnumerator = target.GetEnumerator();
// always move forward both enumerators (do not use short-circuit evaluation!)
while (originalEnumerator.MoveNext() & targetEnumerator.MoveNext())
{
double originalElement = originalEnumerator.Current;
double targetElement = targetEnumerator.Current;
calculator.Add(originalElement, targetElement);
if (calculator.ErrorState != OnlineCalculatorError.None)
{
break;
}
}
// check if both enumerators are at the end to make sure both enumerations have the same length
if (calculator.ErrorState == OnlineCalculatorError.None &&
(originalEnumerator.MoveNext() || targetEnumerator.MoveNext()))
{
throw new ArgumentException("Number of elements in original and target enumeration do not match.");
}
else
{
errorState = calculator.ErrorState;
alpha = calculator.Alpha;
beta = calculator.Beta;
}
}
protected OnlineLinearScalingParameterCalculator(OnlineLinearScalingParameterCalculator original, Cloner cloner)
: base(original, cloner)
{
targetMeanCalculator = cloner.Clone(original.targetMeanCalculator);
originalMeanAndVarianceCalculator = cloner.Clone(original.originalMeanAndVarianceCalculator);
originalTargetCovarianceCalculator = cloner.Clone(original.originalTargetCovarianceCalculator);
// do not reset the calculators here
}
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;
}
protected void CalculateTimeSeriesResults()
{
OnlineCalculatorError errorState;
double trainingMean = ProblemData.TrainingIndices.Any() ? ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average() : double.NaN;
var meanModel = new ConstantModel(trainingMean);
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);
#region Calculate training quality measures
if (ProblemData.TrainingIndices.Any())
{
IEnumerable <double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
IEnumerable <double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
IEnumerable <double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
IEnumerable <double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
}
#endregion
#region Calculate test quality measures
if (ProblemData.TestIndices.Any())
{
IEnumerable <double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
IEnumerable <double> testEstimatedValues = EstimatedTestValues.ToList();
IEnumerable <double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
IEnumerable <double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
}
#endregion
}
/// <summary>
/// Calculates alpha and beta parameters to linearly scale elements of original to the scale and location of target
/// original[i] * beta + alpha
/// </summary>
/// <param name="original">Values that should be scaled</param>
/// <param name="target">Target values to which the original values should be scaled</param>
/// <param name="alpha">Additive constant for the linear scaling</param>
/// <param name="beta">Multiplicative factor for the linear scaling</param>
/// <param name="errorState">Flag that indicates if errors occurred in the calculation of the linea scaling parameters.</param>
public static void Calculate(IEnumerable<double> original, IEnumerable<double> target, out double alpha, out double beta, out OnlineCalculatorError errorState) {
OnlineLinearScalingParameterCalculator calculator = new OnlineLinearScalingParameterCalculator();
IEnumerator<double> originalEnumerator = original.GetEnumerator();
IEnumerator<double> targetEnumerator = target.GetEnumerator();
// always move forward both enumerators (do not use short-circuit evaluation!)
while (originalEnumerator.MoveNext() & targetEnumerator.MoveNext()) {
double originalElement = originalEnumerator.Current;
double targetElement = targetEnumerator.Current;
calculator.Add(originalElement, targetElement);
if (calculator.ErrorState != OnlineCalculatorError.None) break;
}
// check if both enumerators are at the end to make sure both enumerations have the same length
if (calculator.ErrorState == OnlineCalculatorError.None &&
(originalEnumerator.MoveNext() || targetEnumerator.MoveNext())) {
throw new ArgumentException("Number of elements in original and target enumeration do not match.");
} else {
errorState = calculator.ErrorState;
alpha = calculator.Alpha;
beta = calculator.Beta;
}
}
