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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/MultiObjectiveSymbolicRegressionProblem.cs @ 5867

Last change on this file since 5867 was 5809, checked in by mkommend, 14 years ago

#1418: Reintegrated branch into trunk.

File size: 5.3 KB
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[4128]1#region License Information
2/* HeuristicLab
[5445]3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[4128]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
22using System;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Optimization;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
[5809]29using HeuristicLab.PluginInfrastructure;
[4128]30
31namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
32  [Item("Symbolic Regression Problem (multi objective)", "Represents a multi objective symbolic regression problem.")]
33  [StorableClass]
[5809]34  [NonDiscoverableType]
35  public class MultiObjectiveSymbolicRegressionProblem : SymbolicRegressionProblemBase, IMultiObjectiveHeuristicOptimizationProblem {
[4128]36
37    #region Parameter Properties
38    public ValueParameter<BoolArray> MaximizationParameter {
39      get { return (ValueParameter<BoolArray>)Parameters["Maximization"]; }
40    }
[5809]41    IParameter IMultiObjectiveHeuristicOptimizationProblem.MaximizationParameter {
[4128]42      get { return MaximizationParameter; }
43    }
44    public new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator> EvaluatorParameter {
45      get { return (ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>)Parameters["Evaluator"]; }
46    }
[5809]47    IParameter IHeuristicOptimizationProblem.EvaluatorParameter {
[4128]48      get { return EvaluatorParameter; }
49    }
50    #endregion
51
52    #region Properties
53    public new IMultiObjectiveSymbolicRegressionEvaluator Evaluator {
54      get { return EvaluatorParameter.Value; }
55      set { EvaluatorParameter.Value = value; }
56    }
[5809]57    IMultiObjectiveEvaluator IMultiObjectiveHeuristicOptimizationProblem.Evaluator {
[4128]58      get { return EvaluatorParameter.Value; }
59    }
[5809]60    IEvaluator IHeuristicOptimizationProblem.Evaluator {
[4128]61      get { return EvaluatorParameter.Value; }
62    }
63    #endregion
64
65    [StorableConstructor]
66    protected MultiObjectiveSymbolicRegressionProblem(bool deserializing) : base(deserializing) { }
[4722]67    protected MultiObjectiveSymbolicRegressionProblem(MultiObjectiveSymbolicRegressionProblem original, Cloner cloner)
68      : base(original, cloner) {
69      RegisterParameterEvents();
70      RegisterParameterValueEvents();
71    }
[4128]72    public MultiObjectiveSymbolicRegressionProblem()
73      : base() {
[5439]74      var evaluator = new MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator();
75      Parameters.Add(new ValueParameter<BoolArray>("Maximization", "Set to false as the error of the regression model should be minimized.", new BoolArray(new bool[] { true, false })));
[4128]76      Parameters.Add(new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>("Evaluator", "The operator which should be used to evaluate symbolic regression solutions.", evaluator));
77
78      evaluator.QualitiesParameter.ActualName = "TrainingRSquared/Size";
79
80      ParameterizeEvaluator();
81
82      RegisterParameterEvents();
83      RegisterParameterValueEvents();
84    }
85
86    public override IDeepCloneable Clone(Cloner cloner) {
[4722]87      return new MultiObjectiveSymbolicRegressionProblem(this, cloner);
[4128]88    }
89
90    private void RegisterParameterValueEvents() {
91      EvaluatorParameter.ValueChanged += new EventHandler(EvaluatorParameter_ValueChanged);
92    }
93
94    private void RegisterParameterEvents() {
95    }
96
97    #region event handling
98    protected override void OnDataAnalysisProblemChanged(EventArgs e) {
99      base.OnDataAnalysisProblemChanged(e);
100      // paritions could be changed
101      ParameterizeEvaluator();
102    }
103    protected override void OnSolutionParameterNameChanged(EventArgs e) {
104      ParameterizeEvaluator();
105    }
106
[4545]107    protected override void OnEvaluatorChanged(EventArgs e) {
108      base.OnEvaluatorChanged(e);
[4128]109      ParameterizeEvaluator();
110      RaiseEvaluatorChanged(e);
111    }
112    #endregion
113
114    #region event handlers
115    private void EvaluatorParameter_ValueChanged(object sender, EventArgs e) {
116      OnEvaluatorChanged(e);
117    }
118    #endregion
119
120    #region Helpers
121    [StorableHook(HookType.AfterDeserialization)]
122    private void AfterDeserializationHook() {
123      RegisterParameterEvents();
124      RegisterParameterValueEvents();
125    }
126
127    private void ParameterizeEvaluator() {
128      Evaluator.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
129      Evaluator.RegressionProblemDataParameter.ActualName = DataAnalysisProblemDataParameter.Name;
130      Evaluator.SamplesStartParameter.Value = TrainingSamplesStart;
131      Evaluator.SamplesEndParameter.Value = TrainingSamplesEnd;
132    }
133    #endregion
134  }
135}
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