source: branches/ClassificationModelComparison/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.cs @ 10556

Last change on this file since 10556 was 10556, checked in by mkommend, 8 years ago

#1998: Updated classification model comparison branch with trunk changes (remaining changes).

File size: 2.6 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.Collections.Generic;
23using System.Linq;
24using System.Windows.Forms;
25using HeuristicLab.MainForm;
26using HeuristicLab.MainForm.WindowsForms;
27namespace HeuristicLab.Problems.DataAnalysis.Views {
28  [View("Error Characteristics Curve")]
29  [Content(typeof(ITimeSeriesPrognosisSolution))]
30  public partial class TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView : RegressionSolutionErrorCharacteristicsCurveView {
31
32
33    public TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView()
34      : base() {
35      InitializeComponent();
36    }
37
38    public new ITimeSeriesPrognosisSolution Content {
39      get { return (ITimeSeriesPrognosisSolution)base.Content; }
40      set { base.Content = value; }
41    }
42    public new ITimeSeriesPrognosisProblemData ProblemData {
43      get {
44        if (Content == null) return null;
45        return Content.ProblemData;
46      }
47    }
48
49    protected override void UpdateChart() {
50      base.UpdateChart();
51      if (Content == null) return;
52
53      //AR1 model
54      double alpha, beta;
55      OnlineCalculatorError errorState;
56      IEnumerable<double> trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
57      OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
58      var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(ProblemData.TargetVariable, new double[] { beta }, alpha).CreateTimeSeriesPrognosisSolution(ProblemData);
59      AR1model.Name = "AR(1) Model";
60      AddRegressionSolution(AR1model);
61    }
62  }
63}
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