1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2011 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.Collections;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Random;
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28 |
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29 | namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
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30 | public abstract class RegressionBenchmark : Benchmark, IRegressionBenchmarkProblemDataGenerator {
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31 |
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32 | #region properties
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33 | public abstract List<string> InputVariable { get; }
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34 | public abstract string TargetVariable { get; }
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35 | public abstract IntRange TrainingPartition { get; }
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36 | public abstract IntRange TestPartition { get; }
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37 | #endregion
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38 |
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39 | protected RegressionBenchmark() { }
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40 | protected RegressionBenchmark(RegressionBenchmark original, Cloner cloner)
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41 | : base(original, cloner) {
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42 | }
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43 |
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44 | protected abstract List<double> CalculateFunction(Dictionary<string, IList<double>> data);
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45 |
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46 | protected abstract Dictionary<string, IList<double>> GenerateInput(Dictionary<string, IList<double>> data);
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47 |
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48 | public IDataAnalysisProblemData GenerateProblemData() {
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49 | Dictionary<string, IList<double>> data = new Dictionary<string, IList<double>>();
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50 | data.Add(this.TargetVariable, new List<double>());
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51 | foreach (var variable in this.InputVariable) {
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52 | data.Add(variable, new List<double>());
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53 | }
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54 |
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55 | data = GenerateInput(data);
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56 |
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57 | List<IList> values = new List<IList>();
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58 | foreach (var valueList in data.Values) {
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59 | values.Add((IList)valueList);
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60 | }
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61 |
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62 | Dataset dataset = new Dataset(data.Keys, values);
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63 | dataset.Name = this.Name;
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64 |
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65 | RegressionProblemData problemData = new RegressionProblemData(dataset, dataset.DoubleVariables.Skip(1), dataset.DoubleVariables.First());
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66 |
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67 | problemData.Name = "Data generated for benchmark problem \"" + this.Name + "\"";
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68 |
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69 |
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70 | problemData.TestPartition.Start = this.TestPartition.Start;
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71 | problemData.TestPartition.End = this.TestPartition.End;
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72 |
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73 | problemData.TrainingPartition.Start = this.TrainingPartition.Start;
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74 | problemData.TrainingPartition.End = this.TrainingPartition.End;
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75 |
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76 | return problemData;
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77 | }
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78 |
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79 | //private Dictionary<string, IList<double>> CalculateValues(Dictionary<string, IList<double>> data, DatasetDefinition dataDef) {
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80 | // Random rand = new Random();
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81 | // var combinationDataSet = AllCombinationsOf(dataDef.RangeVariables.Values.Select(range => range.Values).ToList());
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82 | // int index = 0;
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83 | // var help = dataDef.RangeVariables.Keys;
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84 | // foreach (var dataSet in combinationDataSet) {
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85 | // data[help.ElementAt(index)] = dataSet;
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86 | // index++;
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87 | // }
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88 | // List<string> vars = new List<string>(dataDef.RandomVariables.Keys);
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89 | // for (int i = 0; i < dataDef.AmountOfPoints; i++) {
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90 | // foreach (var variable in vars) {
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91 | // data[variable].Add(dataDef.RandomVariables[variable].Next());
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92 | // }
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93 | // // data[TargetVariable].Add(CalculateFunction(data, vars));
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94 | // }
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95 | // int bla = 0;
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96 | // var test = data.Values.Select((ind) => (ind.ElementAt(bla)));
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97 |
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98 | // return data;
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99 | //}
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100 |
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101 | public static List<double> generateSteps(DoubleRange range, double stepWidth) {
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102 | return Enumerable.Range(0, (int)((range.End - range.Start) / stepWidth) + 1)
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103 | .Select(i => (range.Start + i * stepWidth))
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104 | .ToList<double>();
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105 | }
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106 |
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107 | public static List<double> generateUniformDistributedValues(int amount, DoubleRange range) {
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108 | List<double> values = new List<double>();
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109 | System.Random rand = new System.Random();
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110 | for (int i = 0; i < amount; i++) {
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111 | values.Add(rand.NextDouble() * (range.End - range.Start) + range.Start);
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112 | }
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113 | return values;
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114 | }
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115 |
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116 | public static List<double> generateNormalDistributedValues(int amount, double mu, double sigma) {
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117 | List<double> values = new List<double>();
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118 | FastRandom rand = new FastRandom();
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119 | for (int i = 0; i < amount; i++) {
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120 | values.Add(NormalDistributedRandom.NextDouble(rand, mu, sigma));
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121 | }
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122 | return values;
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123 | }
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124 |
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125 | public static List<List<double>> AllCombinationsOf(List<List<double>> sets) {
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126 |
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127 | var combinations = new List<List<double>>();
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128 |
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129 | foreach (var value in sets[0])
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130 | combinations.Add(new List<double> { value });
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131 |
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132 | foreach (var set in sets.Skip(1))
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133 | combinations = AddExtraSet(combinations, set);
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134 |
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135 | return combinations;
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136 | }
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137 |
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138 | private static List<List<double>> AddExtraSet
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139 | (List<List<double>> combinations, List<double> set) {
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140 | var newCombinations = from value in set
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141 | from combination in combinations
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142 | select new List<double>(combination) { value };
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143 |
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144 | return newCombinations.ToList();
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145 | }
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146 | }
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147 | }
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