#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 .
*/
#endregion
using System;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using System.Collections.Generic;
using System.Linq;
namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
///
/// Represents a solution for a symbolic regression problem which can be visualized in the GUI.
///
[Item("SymbolicRegressionSolution", "Represents a solution for a symbolic regression problem which can be visualized in the GUI.")]
[StorableClass]
public sealed class SymbolicRegressionSolution : DataAnalysisSolution {
[Storable]
private SymbolicRegressionModel model;
public SymbolicRegressionModel Model {
get { return model; }
set {
if (model != value) {
if (value == null) throw new ArgumentNullException();
model = value;
OnModelChanged(EventArgs.Empty);
}
}
}
public SymbolicRegressionSolution() : base() { }
public SymbolicRegressionSolution(DataAnalysisProblemData problemData, SymbolicRegressionModel model)
: base(problemData) {
this.model = model;
}
public event EventHandler ModelChanged;
private void OnModelChanged(EventArgs e) {
RecalculateEstimatedValues();
var listeners = ModelChanged;
if (listeners != null)
listeners(this, e);
}
protected override void OnProblemDataChanged(EventArgs e) {
RecalculateEstimatedValues();
}
private void RecalculateEstimatedValues() {
estimatedValues = model.GetEstimatedValues(ProblemData.Dataset, 0, ProblemData.Dataset.Rows).ToList();
OnEstimatedValuesChanged(EventArgs.Empty);
}
private List estimatedValues;
public override IEnumerable EstimatedValues {
get {
if (estimatedValues == null) RecalculateEstimatedValues();
return estimatedValues.AsEnumerable();
}
}
public override IEnumerable 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 EstimatedTestValues {
get {
if (estimatedValues == null) RecalculateEstimatedValues();
int start = ProblemData.TestSamplesStart.Value;
int n = ProblemData.TestSamplesEnd.Value - start;
return estimatedValues.Skip(start).Take(n).ToList();
}
}
public override IDeepCloneable Clone(Cloner cloner) {
SymbolicRegressionSolution clone = (SymbolicRegressionSolution)base.Clone(cloner);
clone.model = (SymbolicRegressionModel)model.Clone(cloner);
return clone;
}
}
}