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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Views/3.3/ResultsView.cs @ 9813

Last change on this file since 9813 was 5275, checked in by gkronber, 14 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

File size: 4.2 KB
Line 
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
21using System;
22using System.Collections.Generic;
23using System.Windows.Forms;
24using HeuristicLab.Data;
25using HeuristicLab.MainForm;
26using HeuristicLab.MainForm.WindowsForms;
27using HeuristicLab.Problems.DataAnalysis.Evaluators;
28
29namespace HeuristicLab.Problems.DataAnalysis.Views {
30  [Content(typeof(DataAnalysisSolution), false)]
31  [View("Results View")]
32  public partial class ResultsView : AsynchronousContentView {
33    private List<string> rowNames = new List<string>() { "Mean squared error", "Pearson's R²", "Average relative error" };
34    private List<string> columnNames = new List<string>() { "Training", "Test" };
35
36    public ResultsView() {
37      InitializeComponent();
38    }
39
40    public new DataAnalysisSolution Content {
41      get { return (DataAnalysisSolution)base.Content; }
42      set { base.Content = value; }
43    }
44
45    protected override void RegisterContentEvents() {
46      base.RegisterContentEvents();
47      Content.ModelChanged += new EventHandler(Content_ModelChanged);
48      Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
49      Content.EstimatedValuesChanged += new EventHandler(Content_EstimatedValuesChanged);
50    }
51    protected override void DeregisterContentEvents() {
52      base.DeregisterContentEvents();
53      Content.ModelChanged -= new EventHandler(Content_ModelChanged);
54      Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
55      Content.EstimatedValuesChanged -= new EventHandler(Content_EstimatedValuesChanged);
56    }
57
58    private void Content_ModelChanged(object sender, EventArgs e) {
59      UpdateView();
60    }
61    private void Content_ProblemDataChanged(object sender, EventArgs e) {
62      UpdateView();
63    }
64    private void Content_EstimatedValuesChanged(object sender, EventArgs e) {
65      UpdateView();
66    }
67
68    protected override void OnContentChanged() {
69      base.OnContentChanged();
70      UpdateView();
71    }
72    private void UpdateView() {
73      if (Content != null) {
74        DoubleMatrix matrix = new DoubleMatrix(rowNames.Count, columnNames.Count);
75        matrix.RowNames = rowNames;
76        matrix.ColumnNames = columnNames;
77        matrix.SortableView = false;
78
79        IEnumerable<double> originalTrainingValues = Content.ProblemData.Dataset.GetEnumeratedVariableValues(Content.ProblemData.TargetVariable.Value, Content.ProblemData.TrainingIndizes);
80        IEnumerable<double> originalTestValues = Content.ProblemData.Dataset.GetEnumeratedVariableValues(Content.ProblemData.TargetVariable.Value, Content.ProblemData.TestIndizes);
81        matrix[0, 0] = SimpleMSEEvaluator.Calculate(originalTrainingValues, Content.EstimatedTrainingValues);
82        matrix[0, 1] = SimpleMSEEvaluator.Calculate(originalTestValues, Content.EstimatedTestValues);
83        matrix[1, 0] = SimpleRSquaredEvaluator.Calculate(originalTrainingValues, Content.EstimatedTrainingValues);
84        matrix[1, 1] = SimpleRSquaredEvaluator.Calculate(originalTestValues, Content.EstimatedTestValues);
85        matrix[2, 0] = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(originalTrainingValues, Content.EstimatedTrainingValues);
86        matrix[2, 1] = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(originalTestValues, Content.EstimatedTestValues);
87
88        matrixView.Content = matrix;
89      } else
90        matrixView.Content = null;
91    }
92  }
93}
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