source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionPrognosedValuesView.cs @ 7610

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 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.Collections.Generic;
using System.Linq;
using System.Windows.Forms;
using HeuristicLab.Data;
using HeuristicLab.Data.Views;
using HeuristicLab.MainForm;
using HeuristicLab.MainForm.WindowsForms;

namespace HeuristicLab.Problems.DataAnalysis.Views {
  [View("Estimated Values")]
  [Content(typeof(ITimeSeriesPrognosisSolution))]
  public partial class TimeSeriesPrognosisSolutionEstimatedValuesView : DataAnalysisSolutionEvaluationView {
    private const string TARGETVARIABLE_SERIES_NAME = "Target Variable";
    private const string PROGNOSEDVALUES_TRAINING_SERIES_NAME = "Prognosed Values (training)";
    private const string PROGNOSEDVALUES_TEST_SERIES_NAME = "Prognosed Values (test)";

    public new ITimeSeriesPrognosisSolution Content {
      get { return (ITimeSeriesPrognosisSolution)base.Content; }
      set {
        base.Content = value;
      }
    }

    private StringConvertibleMatrixView matrixView;

    public TimeSeriesPrognosisSolutionEstimatedValuesView()
      : base() {
      InitializeComponent();
      matrixView = new StringConvertibleMatrixView();
      matrixView.ShowRowsAndColumnsTextBox = false;
      matrixView.ShowStatisticalInformation = false;
      matrixView.Dock = DockStyle.Fill;
      this.Controls.Add(matrixView);
    }

    #region events
    protected override void RegisterContentEvents() {
      base.RegisterContentEvents();
      Content.ModelChanged += new EventHandler(Content_ModelChanged);
      Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
    }

    protected override void DeregisterContentEvents() {
      base.DeregisterContentEvents();
      Content.ModelChanged -= new EventHandler(Content_ModelChanged);
      Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
    }

    private void Content_ProblemDataChanged(object sender, EventArgs e) {
      OnContentChanged();
    }

    private void Content_ModelChanged(object sender, EventArgs e) {
      OnContentChanged();
    }

    protected override void OnContentChanged() {
      base.OnContentChanged();
      UpdateEstimatedValues();
    }

    private void UpdateEstimatedValues() {
      if (InvokeRequired) Invoke((Action)UpdateEstimatedValues);
      else {
        StringMatrix matrix = null;
        List<string> columnNames = new List<string>();
        if (Content != null) {
          columnNames.Add("Id");

          string[,] values = new string[Content.ProblemData.Dataset.Rows, 1 + 3 * Content.ProblemData.TargetVariables.Count()];
          foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows))
            values[row, 0] = row.ToString();

          var allPrognosedTraining = Content.PrognosedTrainingValues.SelectMany(x=>x).ToArray();
          var allPrognosedTest = Content.PrognosedTestValues.SelectMany(x => x).ToArray();

          int i = 0;
          int targetVariableIndex = 0;
          foreach (var targetVariable in Content.ProblemData.TargetVariables) {
            var prognosedTraining =
              allPrognosedTraining.Skip(targetVariableIndex).TakeEvery(Content.ProblemData.TargetVariables.Count());
            var prognosedTest =
              allPrognosedTest.Skip(targetVariableIndex).TakeEvery(Content.ProblemData.TargetVariables.Count());

            double[] target = Content.ProblemData.Dataset.GetDoubleValues(targetVariable).ToArray();

            var prognosedTrainingEnumerator = prognosedTraining.GetEnumerator();
            foreach (var row in Content.ProblemData.TrainingIndizes) {
              prognosedTrainingEnumerator.MoveNext();
              values[row, i + 2] = prognosedTrainingEnumerator.Current.ToString();
            }

            var prognosedTestEnumerator = prognosedTest.GetEnumerator();
            foreach (var row in Content.ProblemData.TestIndizes) {
              prognosedTestEnumerator.MoveNext();
              values[row, i + 3] = prognosedTestEnumerator.Current.ToString();
            }

            foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows)) {
              values[row, i + 1] = target[row].ToString();
            }

            columnNames.AddRange(new string[] { targetVariable + "(actual)", targetVariable + "(training)", targetVariable + "(test)" });
            i += 3;
            targetVariableIndex++;
          } // foreach


          matrix = new StringMatrix(values);
          matrix.ColumnNames = columnNames.ToArray();
          matrix.SortableView = true;

        } // if
        matrixView.Content = matrix;
      }
    }
    #endregion
  }
}