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  private const string WeightsParameterName = "Weights";


42 


43  #region parameter properties


44  public IFixedValueParameter<IntValue> KParameter {


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


46  }


47 


48  public IValueParameter<DoubleArray> WeightsParameter {


49  get { return (IValueParameter<DoubleArray>)Parameters[WeightsParameterName]; }


50  }


51  #endregion


52  #region properties


53  public int K {


54  get { return KParameter.Value.Value; }


55  set {


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


57  else KParameter.Value.Value = value;


58  }


59  }


60 


61  public DoubleArray Weights {


62  get { return WeightsParameter.Value; }


63  set { WeightsParameter.Value = value; }


64  }


65  #endregion


66 


67  [StorableConstructor]


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


69  private NearestNeighbourRegression(NearestNeighbourRegression original, Cloner cloner)


70  : base(original, cloner) {


71  }


72  public NearestNeighbourRegression()


73  : base() {


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


75  Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));


76  Problem = new RegressionProblem();


77  }


78 


79  [StorableHook(HookType.AfterDeserialization)]


80  private void AfterDeserialization() {


81  // BackwardsCompatibility3.3


82  #region Backwards compatible code, remove with 3.4


83  if (!Parameters.ContainsKey(WeightsParameterName)) {


84  Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));


85  }


86  #endregion


87  }


88 


89  public override IDeepCloneable Clone(Cloner cloner) {


90  return new NearestNeighbourRegression(this, cloner);


91  }


92 


93  #region nearest neighbour


94  protected override void Run() {


95  double[] weights = null;


96  if (Weights != null) weights = Weights.CloneAsArray();


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


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


99  }


100 


101  public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k, double[] weights = null) {


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


103  return new NearestNeighbourRegressionSolution(Train(problemData, k, weights), clonedProblemData);


104  }


105 


106  public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k, double[] weights = null) {


107  return new NearestNeighbourModel(problemData.Dataset,


108  problemData.TrainingIndices,


109  k,


110  problemData.TargetVariable,


111  problemData.AllowedInputVariables,


112  weights);


113  }


114  #endregion


115  }


116  }

