#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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.Algorithms.DataAnalysis;
using HeuristicLab.MainForm;
using HeuristicLab.Problems.DataAnalysis.Views;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views {
  [View("Error Characteristics Curve")]
  [Content(typeof(ISymbolicRegressionSolution))]
  public partial class SymbolicRegressionSolutionErrorCharacteristicsCurveView : RegressionSolutionErrorCharacteristicsCurveView {
    private IRegressionSolution linearRegressionSolution;
    public SymbolicRegressionSolutionErrorCharacteristicsCurveView() {
      InitializeComponent();
    }

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

    protected override void OnContentChanged() {
      if (Content != null)
        linearRegressionSolution = CreateLinearRegressionSolution();
      else
        linearRegressionSolution = null;

      base.OnContentChanged();
    }

    protected override void UpdateChart() {
      base.UpdateChart();
      if (Content == null || linearRegressionSolution == null) return;
      AddRegressionSolution(linearRegressionSolution);
    }

    private IRegressionSolution CreateLinearRegressionSolution() {
      if (Content == null) throw new InvalidOperationException();
      double rmse, cvRmsError;
      var problemData = (IRegressionProblemData)ProblemData.Clone();
      if(!problemData.TrainingIndices.Any()) return null; // don't create an LR model if the problem does not have a training set (e.g. loaded into an existing model)

      //clear checked inputVariables
      foreach (var inputVariable in problemData.InputVariables.CheckedItems) {
        problemData.InputVariables.SetItemCheckedState(inputVariable.Value, false);
      }

      //check inputVariables used in the symbolic regression model
      var usedVariables =
        Content.Model.SymbolicExpressionTree.IterateNodesPostfix().OfType<VariableTreeNode>().Select(
          node => node.VariableName).Distinct();
      foreach (var variable in usedVariables) {
        problemData.InputVariables.SetItemCheckedState(
          problemData.InputVariables.First(x => x.Value == variable), true);
      }

      var solution = LinearRegression.CreateLinearRegressionSolution(problemData, out rmse, out cvRmsError);
      solution.Name = "Linear Model";
      return solution;
    }

    protected override void Content_ModelChanged(object sender, EventArgs e) {
      linearRegressionSolution = CreateLinearRegressionSolution();
      base.Content_ModelChanged(sender, e);
    }

    protected override void Content_ProblemDataChanged(object sender, EventArgs e) {
      linearRegressionSolution = CreateLinearRegressionSolution();
      base.Content_ProblemDataChanged(sender, e);
    }
  }
}