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source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs @ 14559

Last change on this file since 14559 was 14308, checked in by gkronber, 8 years ago

#2652: merged r14235 from trunk to stable

File size: 5.2 KB
RevLine 
[6577]1#region License Information
2/* HeuristicLab
[14186]3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[6577]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 System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Optimization;
[8465]28using HeuristicLab.Parameters;
[6577]29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Problems.DataAnalysis;
31
32namespace HeuristicLab.Algorithms.DataAnalysis {
33  /// <summary>
[6583]34  /// Nearest neighbour classification data analysis algorithm.
[6577]35  /// </summary>
[13297]36  [Item("Nearest Neighbour Classification (kNN)", "Nearest neighbour classification data analysis algorithm (wrapper for ALGLIB).")]
[12708]37  [Creatable(CreatableAttribute.Categories.DataAnalysisClassification, Priority = 150)]
[6577]38  [StorableClass]
[6583]39  public sealed class NearestNeighbourClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {
40    private const string KParameterName = "K";
41    private const string NearestNeighbourClassificationModelResultName = "Nearest neighbour classification solution";
[14308]42    private const string WeightsParameterName = "Weights";
[6578]43
[14308]44
[6578]45    #region parameter properties
[6583]46    public IFixedValueParameter<IntValue> KParameter {
47      get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
[6578]48    }
[14308]49    public IValueParameter<DoubleArray> WeightsParameter {
50      get { return (IValueParameter<DoubleArray>)Parameters[WeightsParameterName]; }
51    }
[6578]52    #endregion
53    #region properties
[6583]54    public int K {
55      get { return KParameter.Value.Value; }
[6578]56      set {
[6583]57        if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K");
58        else KParameter.Value.Value = value;
[6578]59      }
60    }
[14308]61    public DoubleArray Weights {
62      get { return WeightsParameter.Value; }
63      set { WeightsParameter.Value = value; }
64    }
[6578]65    #endregion
66
[6577]67    [StorableConstructor]
[6583]68    private NearestNeighbourClassification(bool deserializing) : base(deserializing) { }
69    private NearestNeighbourClassification(NearestNeighbourClassification original, Cloner cloner)
[6577]70      : base(original, cloner) {
71    }
[6583]72    public NearestNeighbourClassification()
[6577]73      : base() {
[6583]74      Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));
[14308]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)"));
[6583]76      Problem = new ClassificationProblem();
[6577]77    }
78    [StorableHook(HookType.AfterDeserialization)]
[14308]79    private void AfterDeserialization() {
80      // BackwardsCompatibility3.3
81      #region Backwards compatible code, remove with 3.4
82      if (!Parameters.ContainsKey(WeightsParameterName)) {
83        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)"));
84      }
85      #endregion
86    }
[6577]87
88    public override IDeepCloneable Clone(Cloner cloner) {
[6583]89      return new NearestNeighbourClassification(this, cloner);
[6577]90    }
91
[6583]92    #region nearest neighbour
[6577]93    protected override void Run() {
[14308]94      double[] weights = null;
95      if (Weights != null) weights = Weights.CloneAsArray();
96      var solution = CreateNearestNeighbourClassificationSolution(Problem.ProblemData, K, weights);
[6583]97      Results.Add(new Result(NearestNeighbourClassificationModelResultName, "The nearest neighbour classification solution.", solution));
[6577]98    }
99
[14308]100    public static IClassificationSolution CreateNearestNeighbourClassificationSolution(IClassificationProblemData problemData, int k, double[] weights = null) {
[8465]101      var problemDataClone = (IClassificationProblemData)problemData.Clone();
[14308]102      return new NearestNeighbourClassificationSolution(Train(problemDataClone, k, weights), problemDataClone);
[8465]103    }
[6577]104
[14308]105    public static INearestNeighbourModel Train(IClassificationProblemData problemData, int k, double[] weights = null) {
[8465]106      return new NearestNeighbourModel(problemData.Dataset,
107        problemData.TrainingIndices,
108        k,
109        problemData.TargetVariable,
110        problemData.AllowedInputVariables,
[14308]111        weights,
[8465]112        problemData.ClassValues.ToArray());
[6577]113    }
114    #endregion
115  }
116}
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