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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/MultiObjectiveSymbolicRegressionProblem.cs @ 4857

Last change on this file since 4857 was 4722, checked in by swagner, 14 years ago

Merged cloning refactoring branch back into trunk (#922)

File size: 5.1 KB
RevLine 
[4128]1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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;
29
30namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
31  [Item("Symbolic Regression Problem (multi objective)", "Represents a multi objective symbolic regression problem.")]
32  [Creatable("Problems")]
33  [StorableClass]
34  public class MultiObjectiveSymbolicRegressionProblem : SymbolicRegressionProblemBase, IMultiObjectiveProblem {
35
36    #region Parameter Properties
37    public ValueParameter<BoolArray> MaximizationParameter {
38      get { return (ValueParameter<BoolArray>)Parameters["Maximization"]; }
39    }
40    IParameter IMultiObjectiveProblem.MaximizationParameter {
41      get { return MaximizationParameter; }
42    }
43    public new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator> EvaluatorParameter {
44      get { return (ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>)Parameters["Evaluator"]; }
45    }
46    IParameter IProblem.EvaluatorParameter {
47      get { return EvaluatorParameter; }
48    }
49    #endregion
50
51    #region Properties
52    public new IMultiObjectiveSymbolicRegressionEvaluator Evaluator {
53      get { return EvaluatorParameter.Value; }
54      set { EvaluatorParameter.Value = value; }
55    }
56    IMultiObjectiveEvaluator IMultiObjectiveProblem.Evaluator {
57      get { return EvaluatorParameter.Value; }
58    }
59    IEvaluator IProblem.Evaluator {
60      get { return EvaluatorParameter.Value; }
61    }
62    #endregion
63
64    [StorableConstructor]
65    protected MultiObjectiveSymbolicRegressionProblem(bool deserializing) : base(deserializing) { }
[4722]66    protected MultiObjectiveSymbolicRegressionProblem(MultiObjectiveSymbolicRegressionProblem original, Cloner cloner)
67      : base(original, cloner) {
68      RegisterParameterEvents();
69      RegisterParameterValueEvents();
70    }
[4128]71    public MultiObjectiveSymbolicRegressionProblem()
72      : base() {
[4166]73      var evaluator = new MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator();
74      Parameters.Add(new ValueParameter<BoolArray>("Maximization", "Set to false as the error of the regression model should be minimized.", new BoolArray(new bool[] { false, false })));
[4128]75      Parameters.Add(new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>("Evaluator", "The operator which should be used to evaluate symbolic regression solutions.", evaluator));
76
77      evaluator.QualitiesParameter.ActualName = "TrainingRSquared/Size";
78
79      ParameterizeEvaluator();
80
81      RegisterParameterEvents();
82      RegisterParameterValueEvents();
83    }
84
85    public override IDeepCloneable Clone(Cloner cloner) {
[4722]86      return new MultiObjectiveSymbolicRegressionProblem(this, cloner);
[4128]87    }
88
89    private void RegisterParameterValueEvents() {
90      EvaluatorParameter.ValueChanged += new EventHandler(EvaluatorParameter_ValueChanged);
91    }
92
93    private void RegisterParameterEvents() {
94    }
95
96    #region event handling
97    protected override void OnDataAnalysisProblemChanged(EventArgs e) {
98      base.OnDataAnalysisProblemChanged(e);
99      // paritions could be changed
100      ParameterizeEvaluator();
101    }
102    protected override void OnSolutionParameterNameChanged(EventArgs e) {
103      ParameterizeEvaluator();
104    }
105
[4545]106    protected override void OnEvaluatorChanged(EventArgs e) {
107      base.OnEvaluatorChanged(e);
[4128]108      ParameterizeEvaluator();
109      RaiseEvaluatorChanged(e);
110    }
111    #endregion
112
113    #region event handlers
114    private void EvaluatorParameter_ValueChanged(object sender, EventArgs e) {
115      OnEvaluatorChanged(e);
116    }
117    #endregion
118
119    #region Helpers
120    [StorableHook(HookType.AfterDeserialization)]
121    private void AfterDeserializationHook() {
122      RegisterParameterEvents();
123      RegisterParameterValueEvents();
124    }
125
126    private void ParameterizeEvaluator() {
127      Evaluator.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
128      Evaluator.RegressionProblemDataParameter.ActualName = DataAnalysisProblemDataParameter.Name;
129      Evaluator.SamplesStartParameter.Value = TrainingSamplesStart;
130      Evaluator.SamplesEndParameter.Value = TrainingSamplesEnd;
131    }
132    #endregion
133  }
134}
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