1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System.Collections.Generic;
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23 | using System.Linq;
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24 | using HeuristicLab.MainForm;
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25 |
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26 | namespace HeuristicLab.Problems.DataAnalysis.Views {
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27 | [View("Error Characteristics Curve")]
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28 | [Content(typeof(ITimeSeriesPrognosisSolution))]
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29 | public partial class TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView : RegressionSolutionErrorCharacteristicsCurveView {
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30 |
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31 |
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32 | public TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView()
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33 | : base() {
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34 | InitializeComponent();
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35 | }
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36 |
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37 | public new ITimeSeriesPrognosisSolution Content {
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38 | get { return (ITimeSeriesPrognosisSolution)base.Content; }
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39 | set { base.Content = value; }
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40 | }
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41 | public new ITimeSeriesPrognosisProblemData ProblemData {
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42 | get {
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43 | if (Content == null) return null;
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44 | return Content.ProblemData;
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45 | }
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46 | }
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47 |
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48 | protected override void UpdateChart() {
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49 | base.UpdateChart();
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50 | if (Content == null) return;
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51 |
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52 | IEnumerable<double> trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
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53 | if (trainingStartValues.Any()) {
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54 | //AR1 model
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55 | double alpha, beta;
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56 | OnlineCalculatorError errorState;
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57 | OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
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58 | var ar1model = new TimeSeriesPrognosisAutoRegressiveModel(ProblemData.TargetVariable, new double[] { beta }, alpha).CreateTimeSeriesPrognosisSolution(ProblemData);
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59 | ar1model.Name = "AR(1) Model";
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60 | AddRegressionSolution(ar1model);
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61 | }
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62 | }
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63 | }
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64 | }
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