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; } }