source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs @ 10492

Last change on this file since 10492 was 10492, checked in by bburlacu, 7 years ago

#2156: Modified the InteractiveSymbolicDataAnalysisSolutionSimplifierView to use the unified method of calculating impact and replacement values.

File size: 3.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Problems.DataAnalysis.Symbolic.Views;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views {
29  public partial class InteractiveSymbolicRegressionSolutionSimplifierView : InteractiveSymbolicDataAnalysisSolutionSimplifierView {
30    private readonly SymbolicRegressionSolutionImpactValuesCalculator calculator;
31
32    public new SymbolicRegressionSolution Content {
33      get { return (SymbolicRegressionSolution)base.Content; }
34      set { base.Content = value; }
35    }
36
37    public InteractiveSymbolicRegressionSolutionSimplifierView()
38      : base() {
39      InitializeComponent();
40      this.Caption = "Interactive Regression Solution Simplifier";
41      calculator = new SymbolicRegressionSolutionImpactValuesCalculator();
42    }
43
44    protected override void UpdateModel(ISymbolicExpressionTree tree) {
45      var model = new SymbolicRegressionModel(tree, Content.Model.Interpreter, Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit);
46      model.Scale(Content.ProblemData);
47      Content.Model = model;
48    }
49
50    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree) {
51      return tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToDictionary(
52        n => n,
53        n => calculator.CalculateReplacementValue(Content.Model, n, Content.ProblemData, Content.ProblemData.TrainingIndices)
54        );
55    }
56
57    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree) {
58      var values = CalculateImpactAndReplacementValues(tree);
59      return values.ToDictionary(x => x.Key, x => x.Value.Item1);
60    }
61
62    protected override Dictionary<ISymbolicExpressionTreeNode, Tuple<double, double>> CalculateImpactAndReplacementValues(ISymbolicExpressionTree tree) {
63      var impactAndReplacementValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, double>>();
64      foreach (var node in tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix()) {
65        double impactValue, replacementValue;
66        calculator.CalculateImpactAndReplacementValues(Content.Model, node, Content.ProblemData, Content.ProblemData.TrainingIndices, out impactValue, out replacementValue);
67        impactAndReplacementValues.Add(node, new Tuple<double, double>(impactValue, replacementValue));
68      }
69      return impactAndReplacementValues;
70    }
71
72    protected override void btnOptimizeConstants_Click(object sender, EventArgs e) {
73      var model = Content.Model;
74      SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(model.Interpreter, model.SymbolicExpressionTree, Content.ProblemData, Content.ProblemData.TrainingIndices,
75        applyLinearScaling: true, maxIterations: 50, upperEstimationLimit: model.UpperEstimationLimit, lowerEstimationLimit: model.LowerEstimationLimit);
76      UpdateModel(Content.Model.SymbolicExpressionTree);
77    }
78  }
79}
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