source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/SymbolicRegressionSolution.cs @ 4275

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 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.Drawing;
using System.Linq;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;

namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
  /// <summary>
  /// Represents a solution for a symbolic regression problem which can be visualized in the GUI.
  /// </summary>
  [Item("SymbolicRegressionSolution", "Represents a solution for a symbolic regression problem which can be visualized in the GUI.")]
  [StorableClass]
  public sealed class SymbolicRegressionSolution : DataAnalysisSolution {
    public SymbolicRegressionSolution() : base() { }
    public SymbolicRegressionSolution(DataAnalysisProblemData problemData, SymbolicRegressionModel model, double lowerEstimationLimit, double upperEstimationLimit)
      : base(problemData, lowerEstimationLimit, upperEstimationLimit) {
      this.Model = model;
    }

    public override Image ItemImage {
      get { return HeuristicLab.Common.Resources.VS2008ImageLibrary.Function; }
    }

    public new SymbolicRegressionModel Model {
      get { return (SymbolicRegressionModel)base.Model; }
      set { base.Model = value; }
    }

    protected override void RecalculateEstimatedValues() {
      int minLag = 0;
      var laggedTreeNodes = Model.SymbolicExpressionTree.IterateNodesPrefix().OfType<LaggedVariableTreeNode>();
      if (laggedTreeNodes.Any())
        minLag = laggedTreeNodes.Min(node => node.Lag);
      IEnumerable<double> calculatedValues =
          from x in Model.GetEstimatedValues(ProblemData, 0 - minLag, ProblemData.Dataset.Rows)
          let boundedX = Math.Min(UpperEstimationLimit, Math.Max(LowerEstimationLimit, x))
          select double.IsNaN(boundedX) ? UpperEstimationLimit : boundedX;
      estimatedValues = Enumerable.Repeat(double.NaN, Math.Abs(minLag)).Concat(calculatedValues).ToList();
      OnEstimatedValuesChanged();
    }

    private List<double> estimatedValues;
    public override IEnumerable<double> EstimatedValues {
      get {
        if (estimatedValues == null) RecalculateEstimatedValues();
        return estimatedValues.AsEnumerable();
      }
    }

    public override IEnumerable<double> EstimatedTrainingValues {
      get {
        if (estimatedValues == null) RecalculateEstimatedValues();
        int start = ProblemData.TrainingSamplesStart.Value;
        int n = ProblemData.TrainingSamplesEnd.Value - start;
        return estimatedValues.Skip(start).Take(n).ToList();
      }
    }

    public override IEnumerable<double> EstimatedTestValues {
      get {
        if (estimatedValues == null) RecalculateEstimatedValues();
        int start = ProblemData.TestSamplesStart.Value;
        int n = ProblemData.TestSamplesEnd.Value - start;
        return estimatedValues.Skip(start).Take(n).ToList();
      }
    }
  }
}