#region License Information /* HeuristicLab * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * and the BEACON Center for the Study of Evolution in Action. * * 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 System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item("Squared error loss", "")] public sealed class SquaredErrorLoss : Item, ILossFunction { public SquaredErrorLoss() { } public double GetLoss(IEnumerable target, IEnumerable pred) { var targetEnum = target.GetEnumerator(); var predEnum = pred.GetEnumerator(); double s = 0; while (targetEnum.MoveNext() & predEnum.MoveNext()) { double res = targetEnum.Current - predEnum.Current; s += res * res; // (res)^2 } if (targetEnum.MoveNext() | predEnum.MoveNext()) throw new ArgumentException("target and pred have different lengths"); return s; } public IEnumerable GetLossGradient(IEnumerable target, IEnumerable pred) { var targetEnum = target.GetEnumerator(); var predEnum = pred.GetEnumerator(); while (targetEnum.MoveNext() & predEnum.MoveNext()) { yield return 2.0 * (targetEnum.Current - predEnum.Current); // dL(y, f(x)) / df(x) = 2 * res } if (targetEnum.MoveNext() | predEnum.MoveNext()) throw new ArgumentException("target and pred have different lengths"); } // targetArr and predArr are not changed by LineSearch public double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx) { if (targetArr.Length != predArr.Length) throw new ArgumentException("target and pred have different lengths"); // line search for squared error loss // for a given partition of rows the optimal constant that should be added to the current prediction values is the average of the residuals double s = 0.0; int n = 0; for (int i = startIdx; i <= endIdx; i++) { int row = idx[i]; s += (targetArr[row] - predArr[row]); n++; } return s / n; } #region item implementation [StorableConstructor] private SquaredErrorLoss(bool deserializing) : base(deserializing) { } private SquaredErrorLoss(SquaredErrorLoss original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SquaredErrorLoss(this, cloner); } #endregion } }