public static double Calculate(IEnumerable <double> startValues, IEnumerable <IEnumerable <double> > actualContinuations, IEnumerable <IEnumerable <double> > predictedContinuations, out OnlineCalculatorError errorState) { IEnumerator <double> startValueEnumerator = startValues.GetEnumerator(); IEnumerator <IEnumerable <double> > actualContinuationsEnumerator = actualContinuations.GetEnumerator(); IEnumerator <IEnumerable <double> > predictedContinuationsEnumerator = predictedContinuations.GetEnumerator(); OnlineDirectionalSymmetryCalculator dsCalculator = new OnlineDirectionalSymmetryCalculator(); // always move forward all enumerators (do not use short-circuit evaluation!) while (startValueEnumerator.MoveNext() & actualContinuationsEnumerator.MoveNext() & predictedContinuationsEnumerator.MoveNext()) { dsCalculator.Add(startValueEnumerator.Current, actualContinuationsEnumerator.Current, predictedContinuationsEnumerator.Current); if (dsCalculator.ErrorState != OnlineCalculatorError.None) { break; } } // check if all enumerators are at the end to make sure both enumerations have the same length if (dsCalculator.ErrorState == OnlineCalculatorError.None && (startValueEnumerator.MoveNext() || actualContinuationsEnumerator.MoveNext() || predictedContinuationsEnumerator.MoveNext())) { throw new ArgumentException("Number of elements in startValues, actualContinuations and estimatedValues predictedContinuations doesn't match."); } else { errorState = dsCalculator.ErrorState; return(dsCalculator.DirectionalSymmetry); } }
protected OnlineDirectionalSymmetryCalculator(OnlineDirectionalSymmetryCalculator original, Cloner cloner = null) : base(original, cloner) { n = original.n; nCorrect = original.nCorrect; errorState = original.errorState; }
public static double Calculate(double startValue, IEnumerable <double> actualContinuation, IEnumerable <double> predictedContinuation, out OnlineCalculatorError errorState) { OnlineDirectionalSymmetryCalculator dsCalculator = new OnlineDirectionalSymmetryCalculator(); dsCalculator.Add(startValue, actualContinuation, predictedContinuation); errorState = dsCalculator.ErrorState; return(dsCalculator.DirectionalSymmetry); }
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 }
public static double Calculate(IEnumerable<double> startValues, IEnumerable<IEnumerable<double>> actualContinuations, IEnumerable<IEnumerable<double>> predictedContinuations, out OnlineCalculatorError errorState) { IEnumerator<double> startValueEnumerator = startValues.GetEnumerator(); IEnumerator<IEnumerable<double>> actualContinuationsEnumerator = actualContinuations.GetEnumerator(); IEnumerator<IEnumerable<double>> predictedContinuationsEnumerator = predictedContinuations.GetEnumerator(); OnlineDirectionalSymmetryCalculator dsCalculator = new OnlineDirectionalSymmetryCalculator(); // always move forward all enumerators (do not use short-circuit evaluation!) while (startValueEnumerator.MoveNext() & actualContinuationsEnumerator.MoveNext() & predictedContinuationsEnumerator.MoveNext()) { dsCalculator.Add(startValueEnumerator.Current, actualContinuationsEnumerator.Current, predictedContinuationsEnumerator.Current); if (dsCalculator.ErrorState != OnlineCalculatorError.None) break; } // check if all enumerators are at the end to make sure both enumerations have the same length if (dsCalculator.ErrorState == OnlineCalculatorError.None && (startValueEnumerator.MoveNext() || actualContinuationsEnumerator.MoveNext() || predictedContinuationsEnumerator.MoveNext())) { throw new ArgumentException("Number of elements in startValues, actualContinuations and estimatedValues predictedContinuations doesn't match."); } else { errorState = dsCalculator.ErrorState; return dsCalculator.DirectionalSymmetry; } }
public static double Calculate(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> predictedContinuation, out OnlineCalculatorError errorState) { OnlineDirectionalSymmetryCalculator dsCalculator = new OnlineDirectionalSymmetryCalculator(); dsCalculator.Add(startValue, actualContinuation, predictedContinuation); errorState = dsCalculator.ErrorState; return dsCalculator.DirectionalSymmetry; }