#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 OnlineCovarianceCalculator : DeepCloneable, IOnlineCalculator { private double xMean, yMean, Cn; private int n; public double Covariance { get { return n > 0 ? Cn / n : 0.0; } } public OnlineCovarianceCalculator() { Reset(); } protected OnlineCovarianceCalculator(OnlineCovarianceCalculator original, Cloner cloner) : base(original, cloner) { Cn = original.Cn; xMean = original.xMean; yMean = original.yMean; n = original.n; errorState = original.errorState; } public override IDeepCloneable Clone(Cloner cloner) { return new OnlineCovarianceCalculator(this, cloner); } #region IOnlineCalculator Members private OnlineCalculatorError errorState; public OnlineCalculatorError ErrorState { get { return errorState; } } public double Value { get { return Covariance; } } public void Reset() { n = 0; Cn = 0.0; xMean = 0.0; yMean = 0.0; errorState = OnlineCalculatorError.InsufficientElementsAdded; } public void Add(double x, double y) { if (double.IsNaN(y) || double.IsInfinity(y) || double.IsNaN(x) || double.IsInfinity(x) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) { errorState = errorState | OnlineCalculatorError.InvalidValueAdded; } else { n++; errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded); // n >= 1 // online calculation of tMean xMean = xMean + (x - xMean) / n; double delta = y - yMean; // delta = (y - yMean(n-1)) yMean = yMean + delta / n; // online calculation of covariance Cn = Cn + delta * (x - xMean); // C(n) = C(n-1) + (y - yMean(n-1)) (t - tMean(n)) } } #endregion public static double Calculate(IEnumerable first, IEnumerable second, out OnlineCalculatorError errorState) { IEnumerator firstEnumerator = first.GetEnumerator(); IEnumerator secondEnumerator = second.GetEnumerator(); OnlineCovarianceCalculator covarianceCalculator = new OnlineCovarianceCalculator(); // always move forward both enumerators (do not use short-circuit evaluation!) while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) { double x = secondEnumerator.Current; double y = firstEnumerator.Current; covarianceCalculator.Add(x, y); if (covarianceCalculator.ErrorState != OnlineCalculatorError.None) break; } // check if both enumerators are at the end to make sure both enumerations have the same length if (covarianceCalculator.ErrorState == OnlineCalculatorError.None && (secondEnumerator.MoveNext() || firstEnumerator.MoveNext())) { throw new ArgumentException("Number of elements in first and second enumeration doesn't match."); } else { errorState = covarianceCalculator.ErrorState; return covarianceCalculator.Covariance; } } } }