1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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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 System.Linq;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Optimization;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 | using HeuristicLab.Problems.DataAnalysis;
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31 |
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32 | namespace HeuristicLab.Algorithms.DataAnalysis {
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33 | /// <summary>
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34 | /// Linear regression data analysis algorithm.
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35 | /// </summary>
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36 | [Item("Radial Basis Function Regression (RBF-R)", "Radial basis function regression data analysis algorithm (uses for ALGLIB).")]
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37 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 100)]
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38 | [StorableClass]
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39 | public sealed class RadialBasisRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
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40 | private const string RadialBasisRegressionModelResultName = "RBF regression solution";
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41 |
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42 |
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43 | #region Parameternames
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44 | private const string Kernelname = "Kernel";
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45 | #endregion
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46 |
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47 | #region Paramterproperties
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48 | public ValueParameter<IKernelFunction<double[]>> KernelParameter
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49 | {
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50 | get { return Parameters[Kernelname] as ValueParameter<IKernelFunction<double[]>>; }
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51 | }
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52 | #endregion
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53 |
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54 | #region Properties
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55 | public IKernelFunction<double[]> Kernel
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56 | {
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57 | get { return KernelParameter.Value; }
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58 | }
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59 |
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60 | #endregion
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61 |
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62 | [StorableConstructor]
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63 | private RadialBasisRegression(bool deserializing) : base(deserializing) { }
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64 | private RadialBasisRegression(RadialBasisRegression original, Cloner cloner)
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65 | : base(original, cloner) {
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66 | }
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67 | public RadialBasisRegression() {
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68 | Problem = new RegressionProblem();
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69 | Parameters.Add(new ValueParameter<IKernelFunction<double[]>>(Kernelname, "The radial basis function"));
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70 | var kernel = new PolysplineKernel<double[]>();
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71 | KernelParameter.Value = kernel;
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72 | kernel.BetaParameter.Value.Value = 1;
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73 | }
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74 | [StorableHook(HookType.AfterDeserialization)]
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75 | private void AfterDeserialization() { }
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76 |
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77 | public override IDeepCloneable Clone(Cloner cloner) {
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78 | return new RadialBasisRegression(this, cloner);
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79 | }
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80 |
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81 | #region regression
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82 | protected override void Run() {
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83 | double loocvrmse, cvRmsError;
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84 | var solution = CreateRadialBasisRegressionSolution(Problem.ProblemData, Kernel, out loocvrmse, out cvRmsError);
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85 | Results.Add(new Result(RadialBasisRegressionModelResultName, "The RBF regression solution.", solution));
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86 | Results.Add(new Result("LOOCVRMSE", "The root of the mean of squared errors of a leave-one-out-cross-validation on the trainingsset (This is not the RSME on the trainingset)", new DoubleValue(loocvrmse)));
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87 | Results.Add(new Result("Estimated root mean square error (cross-validation)", "The estimated root of the mean of squared errors of the linear regression solution via cross validation.", new DoubleValue(cvRmsError)));
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88 | }
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89 |
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90 | public static IConfidenceRegressionSolution CreateRadialBasisRegressionSolution(IRegressionProblemData problemData, IKernelFunction<double[]> kernel, out double loocvRmsError, out double cvRmsError) {
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91 | var model = new RadialBasisFunctionModel(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, kernel);
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92 | loocvRmsError = model.LeaveOneOutCrossValidationRootMeanSquaredError();
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93 | cvRmsError = Math.Sqrt(model.GetEstimatedValues(problemData.Dataset, problemData.TestIndices)
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94 | .Zip(problemData.TargetVariableTestValues, (a, b) => (a - b) * (a - b))
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95 | .Sum());
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96 | var solution = (RadialBasisFunctionRegressionSolution)model.CreateRegressionSolution((IRegressionProblemData)problemData.Clone());
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97 | solution.Model.Name = "Radial Basis Regression Model";
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98 | solution.Name = "Radial Basis Regression Solution";
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99 | return solution;
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100 | }
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101 | #endregion
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102 |
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103 | #region helpers
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104 | #endregion
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105 | }
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106 | }
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