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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/ILossFunction.cs @ 12873

Last change on this file since 12873 was 12873, checked in by gkronber, 9 years ago

#2450 derived ILossFunction from IItem to allow execution on hive without privileged flag (made an "after deserialization"-hook necessary to convert the parameter type)

File size: 1.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 * and the BEACON Center for the Study of Evolution in Action.
5 *
6 * This file is part of HeuristicLab.
7 *
8 * HeuristicLab is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * HeuristicLab is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the GNU General Public License
19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
20 */
21#endregion
22
23using System.Collections.Generic;
24using HeuristicLab.Core;
25
26namespace HeuristicLab.Algorithms.DataAnalysis {
27  // represents an interface for loss functions used by gradient boosting
28  // target represents the target vector  (original targets from the problem data, never changed)
29  // pred   represents the current vector of predictions (a weighted combination of models learned so far, this vector is updated after each step)
30  public interface ILossFunction : IItem {
31    // returns the loss of the current prediction vector
32    double GetLoss(IEnumerable<double> target, IEnumerable<double> pred);
33
34    // returns an enumerable of the loss gradient for each row
35    IEnumerable<double> GetLossGradient(IEnumerable<double> target, IEnumerable<double> pred);
36
37    // returns the optimal value for the partition of rows stored in idx[startIdx] .. idx[endIdx] inclusive
38    double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx);
39  }
40}
41
42
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