[11091] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16057] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[11091] | 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.IO;
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| 23 | using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
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| 24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 25 | using HeuristicLab.Persistence.Default.Xml;
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| 26 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 27 | using HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis;
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| 28 | using HeuristicLab.Selection;
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| 29 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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| 30 |
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| 31 | namespace HeuristicLab.Tests {
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| 32 | [TestClass]
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| 33 | public class GPTimeSeriesSampleTest {
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[11514] | 34 | private const string SampleFileName = "OSGP_TimeSeries";
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[11091] | 35 |
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| 36 | [TestMethod]
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| 37 | [TestCategory("Samples.Create")]
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| 38 | [TestProperty("Time", "medium")]
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| 39 | public void CreateGpTimeSeriesSampleTest() {
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| 40 | var ga = CreateGpTimeSeriesSample();
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[11514] | 41 | string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
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[11091] | 42 | XmlGenerator.Serialize(ga, path);
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| 43 | }
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| 44 | [TestMethod]
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| 45 | [TestCategory("Samples.Execute")]
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| 46 | [TestProperty("Time", "long")]
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| 47 | public void RunGpTimeSeriesSampleTest() {
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| 48 | var osga = CreateGpTimeSeriesSample();
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| 49 | osga.SetSeedRandomly.Value = false;
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| 50 | SamplesUtils.RunAlgorithm(osga);
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| 51 |
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[14529] | 52 | Assert.AreEqual(0.015441526903606416, SamplesUtils.GetDoubleResult(osga, "BestQuality"), 1E-8);
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| 53 | Assert.AreEqual(0.017420834241279298, SamplesUtils.GetDoubleResult(osga, "CurrentAverageQuality"), 1E-8);
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| 54 | Assert.AreEqual(0.065195703753298972, SamplesUtils.GetDoubleResult(osga, "CurrentWorstQuality"), 1E-8);
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| 55 | Assert.AreEqual(92000, SamplesUtils.GetIntResult(osga, "EvaluatedSolutions"));
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[11091] | 56 | }
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| 57 |
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| 58 | public static OffspringSelectionGeneticAlgorithm CreateGpTimeSeriesSample() {
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| 59 | var problem = new SymbolicTimeSeriesPrognosisSingleObjectiveProblem();
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| 60 | problem.Name = "Symbolic time series prognosis problem (Mackey Glass t=17)";
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| 61 | problem.ProblemData.Name = "Mackey Glass t=17";
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| 62 | problem.MaximumSymbolicExpressionTreeLength.Value = 125;
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| 63 | problem.MaximumSymbolicExpressionTreeDepth.Value = 12;
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| 64 | problem.EvaluatorParameter.Value.HorizonParameter.Value.Value = 10;
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[14529] | 65 | problem.ApplyLinearScaling.Value = true;
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[11091] | 66 |
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| 67 | foreach (var symbol in problem.SymbolicExpressionTreeGrammar.Symbols) {
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| 68 | if (symbol is Exponential || symbol is Logarithm) {
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| 69 | symbol.Enabled = false;
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| 70 | } else if (symbol is AutoregressiveTargetVariable) {
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| 71 | symbol.Enabled = true;
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| 72 | var autoRegressiveSymbol = symbol as AutoregressiveTargetVariable;
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| 73 | autoRegressiveSymbol.MinLag = -30;
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| 74 | autoRegressiveSymbol.MaxLag = -1;
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| 75 | }
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[14832] | 76 | if (symbol is VariableBase) {
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| 77 | var varSy = symbol as VariableBase;
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| 78 | varSy.VariableChangeProbability = 1.0; // backwards compatibility
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| 79 | }
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[11091] | 80 | }
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| 81 |
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| 82 | var osga = new OffspringSelectionGeneticAlgorithm();
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| 83 | osga.Name = "Genetic Programming - Time Series Prediction (Mackey-Glass-17)";
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| 84 | osga.Description = "A genetic programming algorithm for creating a time-series model for the Mackey-Glass-17 time series.";
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| 85 | osga.Problem = problem;
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| 86 | SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>
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| 87 | (osga, popSize: 100, elites: 1, maxGens: 25, mutationRate: 0.15);
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| 88 | osga.MaximumSelectionPressure.Value = 100;
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| 89 | return osga;
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| 90 |
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| 91 | }
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| 92 | }
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| 93 | }
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