#region License Information /* HeuristicLab * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using HeuristicLab.Common; namespace HeuristicLab.Problems.DataAnalysis { public class OnlineBoundedMeanSquaredErrorCalculator : DeepCloneable, IOnlineCalculator { private double errorSum; private int n; public double BoundedMeanSquaredError { get { return n > 0 ? errorSum / n : 0.0; } } public double LowerBound { get; private set; } public double UpperBound { get; private set; } public OnlineBoundedMeanSquaredErrorCalculator(double lowerBound, double upperBound) { LowerBound = lowerBound; UpperBound = upperBound; Reset(); } protected OnlineBoundedMeanSquaredErrorCalculator(OnlineBoundedMeanSquaredErrorCalculator original, Cloner cloner) : base(original, cloner) { LowerBound = original.LowerBound; UpperBound = original.UpperBound; n = original.n; errorSum = original.errorSum; errorState = original.ErrorState; } public override IDeepCloneable Clone(Cloner cloner) { return new OnlineBoundedMeanSquaredErrorCalculator(this, cloner); } #region IOnlineCalculator Members private OnlineCalculatorError errorState; public OnlineCalculatorError ErrorState { get { return errorState; } } public double Value { get { return BoundedMeanSquaredError; } } public void Reset() { n = 0; errorSum = 0.0; errorState = OnlineCalculatorError.InsufficientElementsAdded; } public void Add(double original, double estimated) { if (double.IsNaN(estimated) || double.IsInfinity(estimated) || double.IsNaN(original) || double.IsInfinity(original) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) { errorState = errorState | OnlineCalculatorError.InvalidValueAdded; } else { double error = estimated - original; if (estimated < LowerBound || estimated > UpperBound) errorSum += Math.Abs(error); else errorSum += error * error; n++; errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded); // n >= 1 } } #endregion public static double Calculate(IEnumerable originalValues, IEnumerable estimatedValues, double lowerBound, double upperBound, out OnlineCalculatorError errorState) { IEnumerator originalEnumerator = originalValues.GetEnumerator(); IEnumerator estimatedEnumerator = estimatedValues.GetEnumerator(); OnlineBoundedMeanSquaredErrorCalculator boundedMseCalculator = new OnlineBoundedMeanSquaredErrorCalculator(lowerBound, upperBound); // always move forward both enumerators (do not use short-circuit evaluation!) while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) { double original = originalEnumerator.Current; double estimated = estimatedEnumerator.Current; boundedMseCalculator.Add(original, estimated); if (boundedMseCalculator.ErrorState != OnlineCalculatorError.None) break; } // check if both enumerators are at the end to make sure both enumerations have the same length if (boundedMseCalculator.ErrorState == OnlineCalculatorError.None && (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())) { throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match."); } else { errorState = boundedMseCalculator.ErrorState; return boundedMseCalculator.BoundedMeanSquaredError; } } } }