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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/MultiObjectiveSymbolicRegressionProblem.cs @ 5656

Last change on this file since 5656 was 5625, checked in by mkommend, 14 years ago

#1418: Reorganized branch and removed CreateAble-Attribute from outdated classes.

File size: 5.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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  [StorableClass]
33  public class MultiObjectiveSymbolicRegressionProblem : SymbolicRegressionProblemBase, IMultiObjectiveHeuristicOptimizationProblem {
34
35    #region Parameter Properties
36    public ValueParameter<BoolArray> MaximizationParameter {
37      get { return (ValueParameter<BoolArray>)Parameters["Maximization"]; }
38    }
39    IParameter IMultiObjectiveHeuristicOptimizationProblem.MaximizationParameter {
40      get { return MaximizationParameter; }
41    }
42    public new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator> EvaluatorParameter {
43      get { return (ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>)Parameters["Evaluator"]; }
44    }
45    IParameter IHeuristicOptimizationProblem.EvaluatorParameter {
46      get { return EvaluatorParameter; }
47    }
48    #endregion
49
50    #region Properties
51    public new IMultiObjectiveSymbolicRegressionEvaluator Evaluator {
52      get { return EvaluatorParameter.Value; }
53      set { EvaluatorParameter.Value = value; }
54    }
55    IMultiObjectiveEvaluator IMultiObjectiveHeuristicOptimizationProblem.Evaluator {
56      get { return EvaluatorParameter.Value; }
57    }
58    IEvaluator IHeuristicOptimizationProblem.Evaluator {
59      get { return EvaluatorParameter.Value; }
60    }
61    #endregion
62
63    [StorableConstructor]
64    protected MultiObjectiveSymbolicRegressionProblem(bool deserializing) : base(deserializing) { }
65    protected MultiObjectiveSymbolicRegressionProblem(MultiObjectiveSymbolicRegressionProblem original, Cloner cloner)
66      : base(original, cloner) {
67      RegisterParameterEvents();
68      RegisterParameterValueEvents();
69    }
70    public MultiObjectiveSymbolicRegressionProblem()
71      : base() {
72      var evaluator = new MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator();
73      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 })));
74      Parameters.Add(new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>("Evaluator", "The operator which should be used to evaluate symbolic regression solutions.", evaluator));
75
76      evaluator.QualitiesParameter.ActualName = "TrainingRSquared/Size";
77
78      ParameterizeEvaluator();
79
80      RegisterParameterEvents();
81      RegisterParameterValueEvents();
82    }
83
84    public override IDeepCloneable Clone(Cloner cloner) {
85      return new MultiObjectiveSymbolicRegressionProblem(this, cloner);
86    }
87
88    private void RegisterParameterValueEvents() {
89      EvaluatorParameter.ValueChanged += new EventHandler(EvaluatorParameter_ValueChanged);
90    }
91
92    private void RegisterParameterEvents() {
93    }
94
95    #region event handling
96    protected override void OnDataAnalysisProblemChanged(EventArgs e) {
97      base.OnDataAnalysisProblemChanged(e);
98      // paritions could be changed
99      ParameterizeEvaluator();
100    }
101    protected override void OnSolutionParameterNameChanged(EventArgs e) {
102      ParameterizeEvaluator();
103    }
104
105    protected override void OnEvaluatorChanged(EventArgs e) {
106      base.OnEvaluatorChanged(e);
107      ParameterizeEvaluator();
108      RaiseEvaluatorChanged(e);
109    }
110    #endregion
111
112    #region event handlers
113    private void EvaluatorParameter_ValueChanged(object sender, EventArgs e) {
114      OnEvaluatorChanged(e);
115    }
116    #endregion
117
118    #region Helpers
119    [StorableHook(HookType.AfterDeserialization)]
120    private void AfterDeserializationHook() {
121      RegisterParameterEvents();
122      RegisterParameterValueEvents();
123    }
124
125    private void ParameterizeEvaluator() {
126      Evaluator.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
127      Evaluator.RegressionProblemDataParameter.ActualName = DataAnalysisProblemDataParameter.Name;
128      Evaluator.SamplesStartParameter.Value = TrainingSamplesStart;
129      Evaluator.SamplesEndParameter.Value = TrainingSamplesEnd;
130    }
131    #endregion
132  }
133}
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