1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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.Data;
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26 |
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27 | namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
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28 | public class Housing : RegressionRealWorldBenchmark {
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29 |
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30 | private const string fileName = "housing.csv";
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31 |
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32 | public Housing() {
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33 | Name = "RealWorldProblem Housing";
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34 | //Description = "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
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35 | // + "Authors: Maarten Keijzer" + Environment.NewLine
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36 | // + "Function: f(x) = log(x)" + Environment.NewLine
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37 | // + "range(train): x = [0:1:100]" + Environment.NewLine
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38 | // + "range(test): x = [0:0.1:100]" + Environment.NewLine
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39 | // + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
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40 | }
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41 |
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42 | protected override List<IList> GetData() {
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43 | csvFileParser = Benchmark.getParserForFile(fileName);
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44 |
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45 | targetVariable = csvFileParser.VariableNames.Last();
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46 | inputVariables = new List<string>(csvFileParser.VariableNames.Take(csvFileParser.Columns - 1));
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47 | int trainingPartEnd = csvFileParser.Rows * 2 / 3;
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48 | trainingPartition = new IntRange(0, trainingPartEnd);
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49 | testPartition = new IntRange(trainingPartEnd, csvFileParser.Rows);
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50 |
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51 | return csvFileParser.Values.Skip(csvFileParser.Columns - 1).Union(csvFileParser.Values.Take(csvFileParser.Columns - 1)).ToList();
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52 | }
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53 | }
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54 | }
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