#region License Information /* HeuristicLab * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.IO; using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.Xml; using HeuristicLab.Problems.DataAnalysis.Symbolic; using HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis; using HeuristicLab.Selection; using Microsoft.VisualStudio.TestTools.UnitTesting; namespace HeuristicLab.Tests { [TestClass] public class GPTimeSeriesSampleTest { private const string SampleFileName = "OSGP_TimeSeries"; [TestMethod] [TestCategory("Samples.Create")] [TestProperty("Time", "medium")] public void CreateGpTimeSeriesSampleTest() { var ga = CreateGpTimeSeriesSample(); string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension); XmlGenerator.Serialize(ga, path); } [TestMethod] [TestCategory("Samples.Execute")] [TestProperty("Time", "long")] public void RunGpTimeSeriesSampleTest() { var osga = CreateGpTimeSeriesSample(); osga.SetSeedRandomly.Value = false; SamplesUtils.RunAlgorithm(osga); Assert.AreEqual(0.015441526903606416, SamplesUtils.GetDoubleResult(osga, "BestQuality"), 1E-8); Assert.AreEqual(0.017420834241279298, SamplesUtils.GetDoubleResult(osga, "CurrentAverageQuality"), 1E-8); Assert.AreEqual(0.065195703753298972, SamplesUtils.GetDoubleResult(osga, "CurrentWorstQuality"), 1E-8); Assert.AreEqual(92000, SamplesUtils.GetIntResult(osga, "EvaluatedSolutions")); } public static OffspringSelectionGeneticAlgorithm CreateGpTimeSeriesSample() { var problem = new SymbolicTimeSeriesPrognosisSingleObjectiveProblem(); problem.Name = "Symbolic time series prognosis problem (Mackey Glass t=17)"; problem.ProblemData.Name = "Mackey Glass t=17"; problem.MaximumSymbolicExpressionTreeLength.Value = 125; problem.MaximumSymbolicExpressionTreeDepth.Value = 12; problem.EvaluatorParameter.Value.HorizonParameter.Value.Value = 10; problem.ApplyLinearScaling.Value = true; foreach (var symbol in problem.SymbolicExpressionTreeGrammar.Symbols) { if (symbol is Exponential || symbol is Logarithm) { symbol.Enabled = false; } else if (symbol is AutoregressiveTargetVariable) { symbol.Enabled = true; var autoRegressiveSymbol = symbol as AutoregressiveTargetVariable; autoRegressiveSymbol.MinLag = -30; autoRegressiveSymbol.MaxLag = -1; } if (symbol is VariableBase) { var varSy = symbol as VariableBase; varSy.VariableChangeProbability = 1.0; // backwards compatibility } } var osga = new OffspringSelectionGeneticAlgorithm(); osga.Name = "Genetic Programming - Time Series Prediction (Mackey-Glass-17)"; osga.Description = "A genetic programming algorithm for creating a time-series model for the Mackey-Glass-17 time series."; osga.Problem = problem; SamplesUtils.ConfigureOsGeneticAlgorithmParameters (osga, popSize: 100, elites: 1, maxGens: 25, mutationRate: 0.15); osga.MaximumSelectionPressure.Value = 100; return osga; } } }