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 System.Threading;


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 regression data analysis algorithm.


35  /// </summary>


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


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


38  [StorableClass]


39  public sealed class NearestNeighbourRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {


40  private const string KParameterName = "K";


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


42  private const string WeightsParameterName = "Weights";


43 


44  #region parameter properties


45  public IFixedValueParameter<IntValue> KParameter {


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


47  }


48 


49  public IValueParameter<DoubleArray> WeightsParameter {


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


51  }


52  #endregion


53  #region properties


54  public int K {


55  get { return KParameter.Value.Value; }


56  set {


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


58  else KParameter.Value.Value = value;


59  }


60  }


61 


62  public DoubleArray Weights {


63  get { return WeightsParameter.Value; }


64  set { WeightsParameter.Value = value; }


65  }


66  #endregion


67 


68  [StorableConstructor]


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


70  private NearestNeighbourRegression(NearestNeighbourRegression original, Cloner cloner)


71  : base(original, cloner) {


72  }


73  public NearestNeighbourRegression()


74  : base() {


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


76  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)"));


77  Problem = new RegressionProblem();


78  }


79 


80  [StorableHook(HookType.AfterDeserialization)]


81  private void AfterDeserialization() {


82  // BackwardsCompatibility3.3


83  #region Backwards compatible code, remove with 3.4


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


85  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)"));


86  }


87  #endregion


88  }


89 


90  public override IDeepCloneable Clone(Cloner cloner) {


91  return new NearestNeighbourRegression(this, cloner);


92  }


93 


94  #region nearest neighbour


95  protected override void Run(CancellationToken cancellationToken) {


96  double[] weights = null;


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


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


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


100  }


101 


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


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


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


105  }


106 


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


108  return new NearestNeighbourModel(problemData.Dataset,


109  problemData.TrainingIndices,


110  k,


111  problemData.TargetVariable,


112  problemData.AllowedInputVariables,


113  weights);


114  }


115  #endregion


116  }


117  }

