[5607] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[17181] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[5607] | 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
| 22 | using HeuristicLab.Common;
|
---|
| 23 | using HeuristicLab.Core;
|
---|
| 24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
| 25 | using HeuristicLab.Parameters;
|
---|
[17097] | 26 | using HEAL.Attic;
|
---|
[5607] | 27 |
|
---|
| 28 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
|
---|
| 29 | /// <summary>
|
---|
| 30 | /// An operator that analyzes the validation best symbolic regression solution for single objective symbolic regression problems.
|
---|
| 31 | /// </summary>
|
---|
| 32 | [Item("SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer", "An operator that analyzes the validation best symbolic regression solution for single objective symbolic regression problems.")]
|
---|
[17097] | 33 | [StorableType("75E2AFFF-95B2-4FA4-8544-CF2025B65820")]
|
---|
[5720] | 34 | public sealed class SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicRegressionSolution, ISymbolicRegressionSingleObjectiveEvaluator, IRegressionProblemData>,
|
---|
| 35 | ISymbolicDataAnalysisBoundedOperator {
|
---|
[5770] | 36 | private const string EstimationLimitsParameterName = "EstimationLimits";
|
---|
| 37 |
|
---|
[5720] | 38 | #region parameter properties
|
---|
[5770] | 39 | public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
|
---|
| 40 | get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
|
---|
[5720] | 41 | }
|
---|
| 42 | #endregion
|
---|
| 43 |
|
---|
[5607] | 44 | [StorableConstructor]
|
---|
[17097] | 45 | private SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer(StorableConstructorFlag _) : base(_) { }
|
---|
[5607] | 46 | private SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer(SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
|
---|
| 47 | public SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer()
|
---|
| 48 | : base() {
|
---|
[5770] | 49 | Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
|
---|
[5607] | 50 | }
|
---|
[5720] | 51 |
|
---|
[5607] | 52 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 53 | return new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer(this, cloner);
|
---|
| 54 | }
|
---|
| 55 |
|
---|
| 56 | protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
|
---|
[14027] | 57 | var model = new SymbolicRegressionModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
|
---|
[8972] | 58 | if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
|
---|
[5914] | 59 | return new SymbolicRegressionSolution(model, (IRegressionProblemData)ProblemDataParameter.ActualValue.Clone());
|
---|
[5607] | 60 | }
|
---|
| 61 | }
|
---|
| 62 | }
|
---|