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source: branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/Binary/SolutionModel/Univariate/StaticAPI/Trainer.cs @ 14450

Last change on this file since 14450 was 14450, checked in by abeham, 8 years ago

#2701: working on MemPR implementation

File size: 2.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using HeuristicLab.Algorithms.MemPR.Interfaces;
25using HeuristicLab.Core;
26using HeuristicLab.Encodings.BinaryVectorEncoding;
27
28namespace HeuristicLab.Algorithms.MemPR.Binary.SolutionModel.Univariate {
29  public enum ModelBiasOptions { Rank, Fitness }
30
31  public static class Trainer {
32    public static ISolutionModel<BinaryVector> TrainBiased(ModelBiasOptions modelBias, IRandom random, bool maximization, IEnumerable<BinaryVector> population, IEnumerable<double> qualities) {
33      switch (modelBias) {
34        case ModelBiasOptions.Rank:
35          return UnivariateModel.CreateWithRankBias(random, maximization, population, qualities);
36        case ModelBiasOptions.Fitness:
37          return UnivariateModel.CreateWithFitnessBias(random, maximization, population, qualities);
38        default:
39          throw new InvalidOperationException(string.Format("Unknown bias option {0}", modelBias));
40      }
41    }
42
43    public static ISolutionModel<BinaryVector> TrainUnbiased(IRandom random, IEnumerable<BinaryVector> population) {
44      return UnivariateModel.CreateWithoutBias(random, population);
45    }
46  }
47}
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