#region License Information
/* HeuristicLab
* Copyright (C) 2002-2019 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 HEAL.Attic;
namespace HeuristicLab.Algorithms.DataAnalysis {
[StorableType("588270d5-61ee-4906-b30f-841f64cd6724")]
// 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)
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);
}
}