1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022015 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.Linq;


24  using HeuristicLab.Common;


25  using HeuristicLab.Core;


26  using HeuristicLab.Data;


27  using HeuristicLab.Optimization;


28  using HeuristicLab.Parameters;


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


30  using HeuristicLab.Problems.DataAnalysis;


31 


32  namespace HeuristicLab.Algorithms.DataAnalysis {


33  /// <summary>


34  /// Nearest neighbour classification data analysis algorithm.


35  /// </summary>


36  [Item("Nearest Neighbour Classification", "Nearest neighbour classification data analysis algorithm (wrapper for ALGLIB).")]


37  [Creatable("Algorithms#Data Analysis#Classification", Priority = 20)]


38  [StorableClass]


39  public sealed class NearestNeighbourClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {


40  private const string KParameterName = "K";


41  private const string NearestNeighbourClassificationModelResultName = "Nearest neighbour classification solution";


42 


43  #region parameter properties


44  public IFixedValueParameter<IntValue> KParameter {


45  get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }


46  }


47  #endregion


48  #region properties


49  public int K {


50  get { return KParameter.Value.Value; }


51  set {


52  if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K");


53  else KParameter.Value.Value = value;


54  }


55  }


56  #endregion


57 


58  [StorableConstructor]


59  private NearestNeighbourClassification(bool deserializing) : base(deserializing) { }


60  private NearestNeighbourClassification(NearestNeighbourClassification original, Cloner cloner)


61  : base(original, cloner) {


62  }


63  public NearestNeighbourClassification()


64  : base() {


65  Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));


66  Problem = new ClassificationProblem();


67  }


68  [StorableHook(HookType.AfterDeserialization)]


69  private void AfterDeserialization() { }


70 


71  public override IDeepCloneable Clone(Cloner cloner) {


72  return new NearestNeighbourClassification(this, cloner);


73  }


74 


75  #region nearest neighbour


76  protected override void Run() {


77  var solution = CreateNearestNeighbourClassificationSolution(Problem.ProblemData, K);


78  Results.Add(new Result(NearestNeighbourClassificationModelResultName, "The nearest neighbour classification solution.", solution));


79  }


80 


81  public static IClassificationSolution CreateNearestNeighbourClassificationSolution(IClassificationProblemData problemData, int k) {


82  var problemDataClone = (IClassificationProblemData)problemData.Clone();


83  return new NearestNeighbourClassificationSolution(problemDataClone, Train(problemDataClone, k));


84  }


85 


86  public static INearestNeighbourModel Train(IClassificationProblemData problemData, int k) {


87  return new NearestNeighbourModel(problemData.Dataset,


88  problemData.TrainingIndices,


89  k,


90  problemData.TargetVariable,


91  problemData.AllowedInputVariables,


92  problemData.ClassValues.ToArray());


93  }


94  #endregion


95  }


96  }

