[6577] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[10556] | 3 | * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[6577] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Optimization;
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[8465] | 27 | using HeuristicLab.Parameters;
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[6577] | 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 | using HeuristicLab.Problems.DataAnalysis;
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| 30 |
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| 31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 32 | /// <summary>
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[6583] | 33 | /// Nearest neighbour regression data analysis algorithm.
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[6577] | 34 | /// </summary>
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[6583] | 35 | [Item("Nearest Neighbour Regression", "Nearest neighbour regression data analysis algorithm (wrapper for ALGLIB).")]
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[6577] | 36 | [Creatable("Data Analysis")]
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| 37 | [StorableClass]
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[6583] | 38 | public sealed class NearestNeighbourRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
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| 39 | private const string KParameterName = "K";
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| 40 | private const string NearestNeighbourRegressionModelResultName = "Nearest neighbour regression solution";
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[6578] | 41 |
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| 42 | #region parameter properties
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[6583] | 43 | public IFixedValueParameter<IntValue> KParameter {
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| 44 | get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
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[6578] | 45 | }
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| 46 | #endregion
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| 47 | #region properties
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[6583] | 48 | public int K {
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| 49 | get { return KParameter.Value.Value; }
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[6578] | 50 | set {
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[6583] | 51 | if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K");
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| 52 | else KParameter.Value.Value = value;
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[6578] | 53 | }
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| 54 | }
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| 55 | #endregion
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| 56 |
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[6577] | 57 | [StorableConstructor]
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[6583] | 58 | private NearestNeighbourRegression(bool deserializing) : base(deserializing) { }
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| 59 | private NearestNeighbourRegression(NearestNeighbourRegression original, Cloner cloner)
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[6577] | 60 | : base(original, cloner) {
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| 61 | }
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[6583] | 62 | public NearestNeighbourRegression()
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[6577] | 63 | : base() {
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[6583] | 64 | Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));
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[6577] | 65 | Problem = new RegressionProblem();
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| 66 | }
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| 67 | [StorableHook(HookType.AfterDeserialization)]
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| 68 | private void AfterDeserialization() { }
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| 69 |
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| 70 | public override IDeepCloneable Clone(Cloner cloner) {
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[6583] | 71 | return new NearestNeighbourRegression(this, cloner);
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[6577] | 72 | }
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| 73 |
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[6583] | 74 | #region nearest neighbour
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[6577] | 75 | protected override void Run() {
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[6583] | 76 | var solution = CreateNearestNeighbourRegressionSolution(Problem.ProblemData, K);
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| 77 | Results.Add(new Result(NearestNeighbourRegressionModelResultName, "The nearest neighbour regression solution.", solution));
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[6577] | 78 | }
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| 79 |
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[6583] | 80 | public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k) {
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[8465] | 81 | var clonedProblemData = (IRegressionProblemData)problemData.Clone();
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| 82 | return new NearestNeighbourRegressionSolution(clonedProblemData, Train(problemData, k));
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| 83 | }
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[6577] | 84 |
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[8465] | 85 | public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k) {
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| 86 | return new NearestNeighbourModel(problemData.Dataset,
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| 87 | problemData.TrainingIndices,
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| 88 | k,
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| 89 | problemData.TargetVariable,
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| 90 | problemData.AllowedInputVariables);
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[6577] | 91 | }
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| 92 | #endregion
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| 93 | }
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| 94 | }
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