source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs @ 14185

Last change on this file since 14185 was 14185, checked in by swagner, 3 years ago

#2526: Updated year of copyrights in license headers

File size: 3.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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
22using System;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Optimization;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis;
30
31namespace 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}
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