source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionPrognosedValuesView.cs @ 7100

Last change on this file since 7100 was 7100, checked in by gkronber, 9 years ago

#1081 worked on multi-variate time series prognosis

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
21using System;
22using System.Collections.Generic;
23using System.Linq;
24using System.Windows.Forms;
25using HeuristicLab.Data;
26using HeuristicLab.Data.Views;
27using HeuristicLab.MainForm;
28using HeuristicLab.MainForm.WindowsForms;
29
30namespace HeuristicLab.Problems.DataAnalysis.Views {
31  [View("Estimated Values")]
32  [Content(typeof(ITimeSeriesPrognosisSolution))]
33  public partial class TimeSeriesPrognosisSolutionEstimatedValuesView : DataAnalysisSolutionEvaluationView {
34    private const string TARGETVARIABLE_SERIES_NAME = "Target Variable";
35    private const string PROGNOSEDVALUES_TRAINING_SERIES_NAME = "Prognosed Values (training)";
36    private const string PROGNOSEDVALUES_TEST_SERIES_NAME = "Prognosed Values (test)";
37
38    public new ITimeSeriesPrognosisSolution Content {
39      get { return (ITimeSeriesPrognosisSolution)base.Content; }
40      set {
41        base.Content = value;
42      }
43    }
44
45    private StringConvertibleMatrixView matrixView;
46
47    public TimeSeriesPrognosisSolutionEstimatedValuesView()
48      : base() {
49      InitializeComponent();
50      matrixView = new StringConvertibleMatrixView();
51      matrixView.ShowRowsAndColumnsTextBox = false;
52      matrixView.ShowStatisticalInformation = false;
53      matrixView.Dock = DockStyle.Fill;
54      this.Controls.Add(matrixView);
55    }
56
57    #region events
58    protected override void RegisterContentEvents() {
59      base.RegisterContentEvents();
60      Content.ModelChanged += new EventHandler(Content_ModelChanged);
61      Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
62    }
63
64    protected override void DeregisterContentEvents() {
65      base.DeregisterContentEvents();
66      Content.ModelChanged -= new EventHandler(Content_ModelChanged);
67      Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
68    }
69
70    private void Content_ProblemDataChanged(object sender, EventArgs e) {
71      OnContentChanged();
72    }
73
74    private void Content_ModelChanged(object sender, EventArgs e) {
75      OnContentChanged();
76    }
77
78    protected override void OnContentChanged() {
79      base.OnContentChanged();
80      UpdateEstimatedValues();
81    }
82
83    private void UpdateEstimatedValues() {
84      if (InvokeRequired) Invoke((Action)UpdateEstimatedValues);
85      else {
86        StringMatrix matrix = null;
87        List<string> columnNames = new List<string>();
88        if (Content != null) {
89          columnNames.Add("Id");
90
91          string[,] values = new string[Content.ProblemData.Dataset.Rows, 1 + 3 * Content.ProblemData.TargetVariables.Count()];
92          foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows))
93            values[row, 0] = row.ToString();
94
95          var prognosedTraining = Content.PrognosedTrainingValues.ToArray();
96          var prognosedTest = Content.PrognosedTestValues.ToArray();
97
98          int i = 0;
99          int targetVariableIndex = 0;
100          foreach (var targetVariable in Content.ProblemData.TargetVariables) {
101            double[] target = Content.ProblemData.Dataset.GetDoubleValues(targetVariable).ToArray();
102
103            var prognosedTrainingEnumerator = prognosedTraining[targetVariableIndex].GetEnumerator();
104            foreach (var row in Content.ProblemData.TrainingIndizes) {
105              prognosedTrainingEnumerator.MoveNext();
106              values[row, i + 2] = prognosedTrainingEnumerator.Current.ToString();
107            }
108
109            var prognosedTestEnumerator = prognosedTest[targetVariableIndex].GetEnumerator();
110            foreach (var row in Content.ProblemData.TestIndizes) {
111              prognosedTestEnumerator.MoveNext();
112              values[row, i + 3] = prognosedTestEnumerator.Current.ToString();
113            }
114
115            foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows)) {
116              values[row, i + 1] = target[row].ToString();
117            }
118
119            columnNames.AddRange(new string[] { targetVariable + "(actual)", targetVariable + "(training)", targetVariable + "(test)" });
120            i += 3;
121            targetVariableIndex++;
122          } // foreach
123
124
125          matrix = new StringMatrix(values);
126          matrix.ColumnNames = columnNames.ToArray();
127          matrix.SortableView = true;
128
129        } // if
130        matrixView.Content = matrix;
131      }
132    }
133    #endregion
134  }
135}
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