[7127] | 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;
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| 23 | using System.Collections;
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| 24 | using System.Collections.Generic;
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| 25 | using System.Linq;
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| 26 | using HeuristicLab.Data;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
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| 29 | public class HighSenseampDelay : RegressionRealWorldBenchmark {
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| 30 |
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| 31 | private const string fileName = "senseamp_delay.csv";
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| 32 |
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| 33 | public HighSenseampDelay() {
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| 34 | Name = "McConaghy High senseamp_delay";
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| 35 | Description = "Paper: Deterministic Symbolic Regression Technology, Genetic Programming Theory and Practice IX" + Environment.NewLine
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| 36 | + "High-Dimensional Statistical Modeling and Analysis of Custom Integrated Circuits" + Environment.NewLine
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| 37 | + "Authors: T. McConaghy" + Environment.NewLine
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| 38 | + "Website: http://trent.st/ffx/";
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| 39 | }
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| 40 |
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| 41 | protected override List<IList> GetData() {
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| 42 | csvFileParser = Benchmark.getParserForFile(fileName);
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| 43 |
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| 44 | targetVariable = csvFileParser.VariableNames.First();
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| 45 | inputVariables = new List<string>(csvFileParser.VariableNames.Skip(1));
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| 46 | int trainingPartEnd = csvFileParser.Rows * 2 / 3;
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| 47 | trainingPartition = new IntRange(0, trainingPartEnd);
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| 48 | testPartition = new IntRange(trainingPartEnd, csvFileParser.Rows);
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| 49 |
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| 50 | return csvFileParser.Values;
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| 51 | }
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| 52 | }
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| 53 | }
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