source: branches/2925_AutoDiffForDynamicalModels/HeuristicLab.Problems.DynamicalSystemsModelling/3.3/SolutionView.cs @ 17393

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion



using System;
using System.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Analysis.Views;
using HeuristicLab.Core.Views;
using HeuristicLab.Data;
using HeuristicLab.MainForm;
using HeuristicLab.MainForm.WindowsForms;

namespace HeuristicLab.Problems.DynamicalSystemsModelling {
  [View("Solution View")]
  [Content(typeof(Solution), true)]
  public sealed partial class SolutionView : ItemView {
    public new Solution Content {
      get { return (Solution)base.Content; }
      set { base.Content = value; }
    }

    public SolutionView() {
      InitializeComponent();
    }

    protected override void OnContentChanged() {
      base.OnContentChanged();
      if (Content == null) {
        episodeStartTextbox.Text = string.Empty;
        episodeEndTextbox.Text = string.Empty;
      } else {
        var episode = Content.TrainingEpisodes.First();
        episodeStartTextbox.Text = episode.Start.ToString();
        episodeEndTextbox.Text = episode.End.ToString();
      }
      UpdatePredictions();
    }

    private void UpdatePredictions() {
      tableLayoutPanel.Controls.Clear();
      if (Content == null) return;
      tableLayoutPanel.RowCount = Content.Trees.Length;
      var targetVars = Content.TargetVariables;
      var calculatedVars = Content.TargetVariables.Concat(Content.LatentVariables).ToArray();
      var ds = Content.ProblemData.Dataset;
      var predictionEpisode = new IntRange(int.Parse(episodeStartTextbox.Text), int.Parse(episodeEndTextbox.Text));

      trackBar.Minimum = 0;
      trackBar.Maximum = ds.Rows;
      var forecastHorizon = 0;
      int.TryParse(forecastTextbox.Text, out forecastHorizon);
      // trackBar.Value = predictionEpisode.Start;

      double snmse;
      var predictions = Content.Predict(predictionEpisode, forecastHorizon).ToArray();
      errorLabel.Text = $"SNMSE: {0.0:e4}";
      var trainingPredictions = predictions.Take(predictionEpisode.Size).ToArray();
      var forecastPredictions = predictions.Skip(predictionEpisode.Size).ToArray();
      for (int i = 0; i < calculatedVars.Length; i++) {
        var varName = calculatedVars[i];
        var tree = Content.Trees[i];
        var dt = new DataTable(varName);
        var trainingPredValues =
          Enumerable.Repeat(double.NaN, predictionEpisode.Start)
          .Concat(trainingPredictions.Select(pi => pi[i]))
          .Concat(Enumerable.Repeat(double.NaN, ds.Rows - predictionEpisode.Start - trainingPredictions.Length))
          .ToArray();

        var predRow = new DataRow(varName + " (training)", varName + " (training)", trainingPredValues);
        dt.Rows.Add(predRow);

        if (targetVars.Contains(varName)) {
          var targetValues = ds.GetReadOnlyDoubleValues(varName);
          var targetRow = new DataRow(varName + " (target)", varName + " (target)", targetValues);
          dt.Rows.Add(targetRow);
        }

        if (ds.Rows - predictionEpisode.End - forecastPredictions.Length > 0) {
          var forecastPredValues =
    Enumerable.Repeat(double.NaN, predictionEpisode.End)
    .Concat(forecastPredictions.Select(pi => pi[i]))
    .Concat(Enumerable.Repeat(double.NaN, ds.Rows - predictionEpisode.End - forecastPredictions.Length))
    .ToArray();

          var forecastRow = new DataRow(varName + " (forecast)", varName + " (forecast)", forecastPredValues);
          dt.Rows.Add(forecastRow);
        }

        var vizProp = new DataTableVisualProperties();
        dt.VisualProperties = vizProp;
        var dtv = new DataTableView();
        dtv.Content = dt;
        dtv.ShowChartOnly = true;
        dtv.Dock = System.Windows.Forms.DockStyle.Fill;
        tableLayoutPanel.Controls.Add(dtv, 0, i);
      }
    }

    protected override void OnLockedChanged() {
      base.OnLockedChanged();
    }

    private void updateButton_Click(object sender, System.EventArgs e) {
      UpdatePredictions();
    }

    private void trackBar_Validated(object sender, EventArgs e) {
      var start = int.Parse(episodeStartTextbox.Text);
      if (start == trackBar.Value) return;
      var end = int.Parse(episodeEndTextbox.Text);
      var delta = end - start;
      episodeStartTextbox.Text = (trackBar.Value).ToString();
      episodeEndTextbox.Text = (trackBar.Value + delta).ToString();
    }

    private void episodeStartTextbox_Validating(object sender, System.ComponentModel.CancelEventArgs e) {
      int res;
      e.Cancel = !int.TryParse(episodeStartTextbox.Text, out res);
    }

    private void episodeStartTextbox_Validated(object sender, EventArgs e) {
      trackBar.Value = int.Parse(episodeStartTextbox.Text);
    }
  }
}