1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022012 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 


22  using System.Collections.Generic;


23  using System.Linq;


24  using HeuristicLab.Common;


25  using HeuristicLab.Core;


26  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


27 


28  namespace HeuristicLab.Problems.DataAnalysis {


29  /// <summary>


30  /// Represents a weight calculator that gives every classification solution the same weight.


31  /// </summary>


32  [StorableClass]


33  [Item("MajorityVoteWeightCalculator", "Represents a weight calculator that gives every classification solution the same weight.")]


34  public class MajorityVoteWeightCalculator : ClassificationWeightCalculator {


35 


36  public MajorityVoteWeightCalculator()


37  : base() {


38  }


39 


40  [StorableConstructor]


41  protected MajorityVoteWeightCalculator(bool deserializing) : base(deserializing) { }


42  protected MajorityVoteWeightCalculator(MajorityVoteWeightCalculator original, Cloner cloner)


43  : base(original, cloner) {


44  }


45 


46  public override IDeepCloneable Clone(Cloner cloner) {


47  return new MajorityVoteWeightCalculator(this, cloner);


48  }


49 


50  protected override IEnumerable<double> CalculateWeights(IEnumerable<IClassificationSolution> classificationSolutions) {


51  return Enumerable.Repeat<double>(1, classificationSolutions.Count());


52  }


53 


54  public override double GetConfidence(IEnumerable<IClassificationSolution> solutions, int index, double estimatedClassValue, CheckPoint handler) {


55  if (solutions.Count() < 1)


56  return double.NaN;


57  var votingSolutions = solutions.Where(s => handler(s.ProblemData, index));


58  if (votingSolutions.Count() < 1)


59  return double.NaN;


60  Dataset dataset = votingSolutions.First().ProblemData.Dataset;


61  int correctEstimated = votingSolutions.Select(s => s.Model.GetEstimatedClassValues(dataset, Enumerable.Repeat(index, 1)).First())


62  .Where(x => x.Equals(estimatedClassValue))


63  .Count();


64  return ((double)correctEstimated / (double)votingSolutions.Count()  0.5) * 2;


65  }


66 


67  public override IEnumerable<double> GetConfidence(IEnumerable<IClassificationSolution> solutions, IEnumerable<int> indices, IEnumerable<double> estimatedClassValue, CheckPoint handler) {


68  if (solutions.Count() < 1)


69  return Enumerable.Repeat(double.NaN, indices.Count());


70 


71  List<int> indicesList = indices.ToList();


72  Dataset dataset = solutions.First().ProblemData.Dataset;


73  var solValues = solutions.ToDictionary(x => x, x => x.Model.GetEstimatedClassValues(dataset, indicesList).ToArray());


74  double[] estimatedClassValueArr = estimatedClassValue.ToArray();


75  double correctEstimated;


76  double[] confidences = new double[indices.Count()];


77 


78  for (int i = 0; i < indicesList.Count; i++) {


79  var votingSolutions = solValues.Where(x => handler(x.Key.ProblemData, indicesList[i]));


80  correctEstimated = votingSolutions.Where(x => DoubleExtensions.IsAlmost(x.Value[i], estimatedClassValueArr[i])).Count();


81  confidences[i] = (correctEstimated / (double)votingSolutions.Count()  0.5) * 2;


82  }


83 


84  return confidences;


85  }


86  }


87  }

