#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 . */ #endregion using System; using System.Linq; using System.Windows.Forms; using System.Windows.Forms.DataVisualization.Charting; 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(); //clear checked inputVariables foreach (var inputVariable in problemData.InputVariables.CheckedItems) { problemData.InputVariables.SetItemCheckedState(inputVariable.Value, false); } var laggedVariables = Content.Model.SymbolicExpressionTree.IterateNodesPostfix() .OfType() .Select(n => n.Lag) .Where(l => l < 0); if (laggedVariables.Any()) throw new NotSupportedException("The symbolic regression solution contains lagged variables."); //check inputVariables used in the symbolic regression model var usedVariables = Content.Model.SymbolicExpressionTree.IterateNodesPostfix().OfType().Select( node => node.VariableName).Distinct(); foreach (var variable in usedVariables) { problemData.InputVariables.SetItemCheckedState( problemData.InputVariables.Where(x => x.Value == variable).First(), 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); } private void chart_MouseDown(object sender, MouseEventArgs e) { if (e.Clicks < 2) return; HitTestResult result = chart.HitTest(e.X, e.Y); if (result.ChartElementType != ChartElementType.LegendItem) return; if (result.Series.Name != linearRegressionSolution.Name) return; MainFormManager.MainForm.ShowContent((IRegressionSolution)result.Series.Tag); } } }