public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows, IntRange evaluationPartition, int horizon, bool applyLinearScaling) { var horizions = rows.Select(r => Math.Min(horizon, evaluationPartition.End - r)); IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows.Zip(horizions, Enumerable.Range).SelectMany(r => r)); IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows, horizions).SelectMany(x => x); OnlineCalculatorError errorState; double mse; if (applyLinearScaling && horizon == 1) { //perform normal evaluation and afterwards scale the solution and calculate the fitness value var mseCalculator = new OnlineMeanSquaredErrorCalculator(); CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows * horizon); errorState = mseCalculator.ErrorState; mse = mseCalculator.MeanSquaredError; } else if (applyLinearScaling) { //first create model to perform linear scaling and afterwards calculate fitness for the scaled model var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)solution.Clone(), interpreter, lowerEstimationLimit, upperEstimationLimit); model.Scale(problemData); var scaledSolution = model.SymbolicExpressionTree; estimatedValues = interpreter.GetSymbolicExpressionTreeValues(scaledSolution, problemData.Dataset, rows, horizions).SelectMany(x => x); var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState); } else { var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState); } if (errorState != OnlineCalculatorError.None) return Double.NaN; else return mse; }
protected override ISymbolicTimeSeriesPrognosisSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) { var model = new SymbolicTimeSeriesPrognosisModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue as ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); if (ApplyLinearScalingParameter.ActualValue.Value) { model.Scale(ProblemDataParameter.ActualValue); } return(new SymbolicTimeSeriesPrognosisSolution(model, (ITimeSeriesPrognosisProblemData)ProblemDataParameter.ActualValue.Clone())); }
public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable <int> rows, IntRange evaluationPartition, int horizon, bool applyLinearScaling) { var horizions = rows.Select(r => Math.Min(horizon, evaluationPartition.End - r)); IEnumerable <double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows.Zip(horizions, Enumerable.Range).SelectMany(r => r)); IEnumerable <double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows, horizions).SelectMany(x => x); OnlineCalculatorError errorState; double mse; if (applyLinearScaling && horizon == 1) //perform normal evaluation and afterwards scale the solution and calculate the fitness value { var mseCalculator = new OnlineMeanSquaredErrorCalculator(); CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows * horizon); errorState = mseCalculator.ErrorState; mse = mseCalculator.MeanSquaredError; } else if (applyLinearScaling) //first create model to perform linear scaling and afterwards calculate fitness for the scaled model { var model = new SymbolicTimeSeriesPrognosisModel(problemData.TargetVariable, (ISymbolicExpressionTree)solution.Clone(), interpreter, lowerEstimationLimit, upperEstimationLimit); model.Scale(problemData); var scaledSolution = model.SymbolicExpressionTree; estimatedValues = interpreter.GetSymbolicExpressionTreeValues(scaledSolution, problemData.Dataset, rows, horizions).SelectMany(x => x); var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState); } else { var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState); } if (errorState != OnlineCalculatorError.None) { return(Double.NaN); } else { return(mse); } }
protected SymbolicTimeSeriesPrognosisModel(SymbolicTimeSeriesPrognosisModel original, Cloner cloner) : base(original, cloner) { }