#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.Collections.Generic; using HeuristicLab.Core; using HeuristicLab.Persistence; namespace HeuristicLab.Algorithms.DataAnalysis { // represents an interface for loss functions used by gradient boosting // target represents the target vector (original targets from the problem data, never changed) // pred represents the current vector of predictions (a weighted combination of models learned so far, this vector is updated after each step) [StorableType("b5737a42-af19-4f7f-b883-72320136db2e")] public interface ILossFunction : IItem { // returns the loss of the current prediction vector double GetLoss(IEnumerable target, IEnumerable pred); // returns an enumerable of the loss gradient for each row IEnumerable GetLossGradient(IEnumerable target, IEnumerable pred); // returns the optimal value for the partition of rows stored in idx[startIdx] .. idx[endIdx] inclusive double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx); } }