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;


23  using System.Collections.Generic;


24  using System.Linq;


25  using HeuristicLab.Common;


26  using HeuristicLab.Core;


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


28 


29  namespace HeuristicLab.Problems.DataAnalysis {


30  /// <summary>


31  ///


32  /// </summary>


33  [StorableClass]


34  [Item("PointCertaintyWeightCalculator", "")]


35  public class PointCertaintyWeightCalculator : WeightCalculator {


36 


37  public PointCertaintyWeightCalculator()


38  : base() {


39  }


40 


41  [StorableConstructor]


42  protected PointCertaintyWeightCalculator(bool deserializing) : base(deserializing) { }


43  protected PointCertaintyWeightCalculator(PointCertaintyWeightCalculator original, Cloner cloner)


44  : base(original, cloner) {


45  }


46 


47  public override IDeepCloneable Clone(Cloner cloner) {


48  return new PointCertaintyWeightCalculator(this, cloner);


49  }


50 


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


52  if (classificationSolutions.Count <= 0)


53  return new List<double>();


54 


55  if (!classificationSolutions.All(x => x is IDiscriminantFunctionClassificationSolution))


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


57 


58  ItemCollection<IDiscriminantFunctionClassificationSolution> discriminantSolutions = new ItemCollection<IDiscriminantFunctionClassificationSolution>();


59  foreach (var solution in classificationSolutions) {


60  discriminantSolutions.Add((IDiscriminantFunctionClassificationSolution)solution);


61  }


62 


63  List<double> weights = new List<double>();


64  IClassificationProblemData problemData = classificationSolutions.ElementAt(0).ProblemData;


65  IEnumerable<double> targetValues = GetValues(problemData.Dataset.GetDoubleValues(problemData.TargetVariable).ToList(), problemData.TrainingIndizes);


66  IEnumerator<double> trainingValues;


67  double avg = problemData.ClassValues.Average();


68 


69  foreach (var solution in discriminantSolutions) {


70  IEnumerator<double> estimatedTrainingVal = solution.EstimatedTrainingValues.GetEnumerator();


71  IEnumerator<double> estimatedTrainingClassVal = solution.EstimatedTrainingClassValues.GetEnumerator();


72 


73  trainingValues = targetValues.GetEnumerator();


74  double curWeight = 0.0;


75  while (estimatedTrainingVal.MoveNext() && estimatedTrainingClassVal.MoveNext() && trainingValues.MoveNext()) {


76  if (estimatedTrainingClassVal.Current.Equals(trainingValues.Current)) {


77  curWeight += 0.5;


78  double distanceToPoint = Math.Abs(estimatedTrainingVal.Current  avg);


79  double distanceToClass = Math.Abs(trainingValues.Current  avg);


80  if (trainingValues.Current > avg && estimatedTrainingVal.Current > avg


81   trainingValues.Current < avg && estimatedTrainingVal.Current < avg)


82  curWeight += distanceToPoint < distanceToClass ? (0.5 / distanceToClass) * distanceToPoint : 0.5;


83  }


84  }


85  weights.Add(curWeight);


86  }


87  return weights;


88  }


89 


90  private IEnumerable<double> GetValues(IList<double> targetValues, IEnumerable<int> indizes) {


91  return from i in indizes


92  select targetValues[i];


93  }


94  }


95  }

