1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022016 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 HeuristicLab.Common;


24  using HeuristicLab.Core;


25  using HeuristicLab.Data;


26  using HeuristicLab.Optimization;


27  using HeuristicLab.Parameters;


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


29  using HeuristicLab.Problems.DataAnalysis;


30 


31  namespace HeuristicLab.Algorithms.DataAnalysis {


32  /// <summary>


33  /// Nearest neighbour regression data analysis algorithm.


34  /// </summary>


35  [Item("Nearest Neighbour Regression (kNN)", "Nearest neighbour regression data analysis algorithm (wrapper for ALGLIB).")]


36  [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 150)]


37  [StorableClass]


38  public sealed class NearestNeighbourRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {


39  private const string KParameterName = "K";


40  private const string NearestNeighbourRegressionModelResultName = "Nearest neighbour regression solution";


41 


42  #region parameter properties


43  public IFixedValueParameter<IntValue> KParameter {


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


45  }


46  #endregion


47  #region properties


48  public int K {


49  get { return KParameter.Value.Value; }


50  set {


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


52  else KParameter.Value.Value = value;


53  }


54  }


55  #endregion


56 


57  [StorableConstructor]


58  private NearestNeighbourRegression(bool deserializing) : base(deserializing) { }


59  private NearestNeighbourRegression(NearestNeighbourRegression original, Cloner cloner)


60  : base(original, cloner) {


61  }


62  public NearestNeighbourRegression()


63  : base() {


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


65  Problem = new RegressionProblem();


66  }


67  [StorableHook(HookType.AfterDeserialization)]


68  private void AfterDeserialization() { }


69 


70  public override IDeepCloneable Clone(Cloner cloner) {


71  return new NearestNeighbourRegression(this, cloner);


72  }


73 


74  #region nearest neighbour


75  protected override void Run() {


76  var solution = CreateNearestNeighbourRegressionSolution(Problem.ProblemData, K);


77  Results.Add(new Result(NearestNeighbourRegressionModelResultName, "The nearest neighbour regression solution.", solution));


78  }


79 


80  public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k) {


81  var clonedProblemData = (IRegressionProblemData)problemData.Clone();


82  return new NearestNeighbourRegressionSolution(Train(problemData, k), clonedProblemData);


83  }


84 


85  public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k) {


86  return new NearestNeighbourModel(problemData.Dataset,


87  problemData.TrainingIndices,


88  k,


89  problemData.TargetVariable,


90  problemData.AllowedInputVariables);


91  }


92  #endregion


93  }


94  }

