source: branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/NeighbourhoodWeightCalculator.cs @ 7491

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.DataAnalysis {
  /// <summary>
  /// 
  /// </summary>
  [StorableClass]
  [Item("NeighbourhoodWeightCalculator", "")]
  public class NeighbourhoodWeightCalculator : WeightCalculator {

    public NeighbourhoodWeightCalculator()
      : base() {
    }

    [StorableConstructor]
    protected NeighbourhoodWeightCalculator(bool deserializing) : base(deserializing) { }
    protected NeighbourhoodWeightCalculator(NeighbourhoodWeightCalculator original, Cloner cloner)
      : base(original, cloner) {
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new NeighbourhoodWeightCalculator(this, cloner);
    }

    public override IEnumerable<double> CalculateWeights(ItemCollection<IClassificationSolution> classificationSolutions) {
      if (classificationSolutions.Count <= 0)
        return new List<double>();

      if (!classificationSolutions.All(x => x is IDiscriminantFunctionClassificationSolution))
        return Enumerable.Repeat<double>(1, classificationSolutions.Count);

      ItemCollection<IDiscriminantFunctionClassificationSolution> discriminantSolutions = new ItemCollection<IDiscriminantFunctionClassificationSolution>();
      List<List<double>> estimatedTestValEnumerators = new List<List<double>>();
      List<List<double>> estimatedTestClassValEnumerators = new List<List<double>>();
      foreach (var solution in classificationSolutions) {
        discriminantSolutions.Add((IDiscriminantFunctionClassificationSolution)solution);
        estimatedTestValEnumerators.Add(discriminantSolutions.Last().EstimatedTestValues.ToList());
        estimatedTestClassValEnumerators.Add(discriminantSolutions.Last().EstimatedTestClassValues.ToList());
      }

      List<double> weights = Enumerable.Repeat<double>(0, classificationSolutions.Count()).ToList<double>();

      IClassificationProblemData problemData = classificationSolutions.ElementAt(0).ProblemData;
      IList<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable).ToList();
      List<double> testVal = GetValues(targetValues, problemData.TestIndizes).ToList();

      double pointAvg, help;
      int count;
      for (int point = 0; point < estimatedTestClassValEnumerators.First().Count; point++) {
        pointAvg = 0.0;
        count = 0;
        for (int solution = 0; solution < estimatedTestClassValEnumerators.Count; solution++) {
          if (estimatedTestClassValEnumerators[solution][point].Equals(testVal[point])) {
            pointAvg += estimatedTestValEnumerators[solution][point];
            count++;
          }
        }
        pointAvg /= (double)count;
        for (int solution = 0; solution < estimatedTestClassValEnumerators.Count; solution++) {
          if (estimatedTestClassValEnumerators[solution][point].Equals(testVal[point])) {
            weights[solution] += 0.5;
            help = Math.Abs(estimatedTestValEnumerators[solution][point] - 0.5);
            weights[solution] += help < 0.5 ? 0.5 - help : 0.0;
          }
        }
      }
      return weights.Select(x => x / weights.Sum());
    }

    private IEnumerable<double> GetValues(IList<double> targetValues, IEnumerable<int> indizes) {
      return from i in indizes
             select targetValues[i];
    }
  }
}