source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/GaussianProcessRegressionSolutionEstimatedValuesView.cs @ 13550

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 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 System.Windows.Forms;
using HeuristicLab.Data;
using HeuristicLab.Data.Views;
using HeuristicLab.MainForm;
using HeuristicLab.Problems.DataAnalysis.Views;

namespace HeuristicLab.Algorithms.DataAnalysis.Views {
  [View("Estimated Values")]
  [Content(typeof(GaussianProcessRegressionSolution), false)]
  public partial class GaussianProcessRegressionSolutionEstimatedValuesView : RegressionSolutionEstimatedValuesView {
    private const string TARGETVARIABLE_SERIES_NAME = "Target Variable";
    private const string ESTIMATEDVALUES_SERIES_NAME = "Estimated Values (all)";
    private const string ESTIMATEDVALUES_TRAINING_SERIES_NAME = "Estimated Values (training)";
    private const string ESTIMATEDVALUES_TEST_SERIES_NAME = "Estimated Values (test)";
    private const string ESTIMATEDVARIANCE_TRAINING_SERIES_NAME = "Estimated Variance (training)";
    private const string ESTIMATEDVARIANCE_TEST_SERIES_NAME = "Estimated Variance (test)";

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

    public GaussianProcessRegressionSolutionEstimatedValuesView()
      : base() {
      InitializeComponent();
    }

    #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;
        if (Content != null) {
          string[,] values = new string[Content.ProblemData.Dataset.Rows, 9];

          var trainingRows = Content.ProblemData.TrainingIndices;
          var testRows = Content.ProblemData.TestIndices;

          double[] target = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable).ToArray();
          var estimated = Content.EstimatedValues.GetEnumerator();
          var estimated_training = Content.EstimatedTrainingValues.GetEnumerator();
          var estimated_test = Content.EstimatedTestValues.GetEnumerator();
          var estimated_var_training = Content.GetEstimatedVariance(trainingRows).GetEnumerator();
          var estimated_var_test = Content.GetEstimatedVariance(testRows).GetEnumerator();

          foreach (var row in Content.ProblemData.TrainingIndices) {
            estimated_training.MoveNext();
            estimated_var_training.MoveNext();
            values[row, 3] = estimated_training.Current.ToString();
            values[row, 7] = estimated_var_training.Current.ToString();
          }

          foreach (var row in Content.ProblemData.TestIndices) {
            estimated_test.MoveNext();
            estimated_var_test.MoveNext();
            values[row, 4] = estimated_test.Current.ToString();
            values[row, 8] = estimated_var_test.Current.ToString();
          }

          foreach (var row in Enumerable.Range(0, Content.ProblemData.Dataset.Rows)) {
            estimated.MoveNext();
            double est = estimated.Current;
            double res = Math.Abs(est - target[row]);
            values[row, 0] = row.ToString();
            values[row, 1] = target[row].ToString();
            values[row, 2] = est.ToString();
            values[row, 5] = Math.Abs(res).ToString();
            values[row, 6] = Math.Abs(res / est).ToString();
          }

          matrix = new StringMatrix(values);
          matrix.ColumnNames = new string[] { "Id", TARGETVARIABLE_SERIES_NAME, ESTIMATEDVALUES_SERIES_NAME, ESTIMATEDVALUES_TRAINING_SERIES_NAME, ESTIMATEDVALUES_TEST_SERIES_NAME, "Absolute Error (all)", "Relative Error (all)", ESTIMATEDVARIANCE_TRAINING_SERIES_NAME, ESTIMATEDVARIANCE_TEST_SERIES_NAME };
          matrix.SortableView = true;
        }
        matrixView.Content = matrix;
      }
    }
    #endregion
  }
}