1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System.Linq;


23  using HeuristicLab.Common;


24  using HeuristicLab.Core;


25  using HeuristicLab.Data;


26  using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;


27  using HeuristicLab.Optimization;


28  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


29 


30  namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {


31  /// <summary>


32  /// Represents a symbolic classification solution (model + data) and attributes of the solution like accuracy and complexity


33  /// </summary>


34  [StorableClass]


35  [Item(Name = "SymbolicClassificationSolution", Description = "Represents a symbolic classification solution (model + data) and attributes of the solution like accuracy and complexity.")]


36  public sealed class SymbolicClassificationSolution : ClassificationSolution, ISymbolicClassificationSolution {


37  private const string ModelLengthResultName = "ModelLength";


38  private const string ModelDepthResultName = "ModelDepth";


39 


40  public new ISymbolicClassificationModel Model {


41  get { return (ISymbolicClassificationModel)base.Model; }


42  set { base.Model = value; }


43  }


44 


45  ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {


46  get { return (ISymbolicDataAnalysisModel)base.Model; }


47  }


48  public int ModelLength {


49  get { return ((IntValue)this[ModelLengthResultName].Value).Value; }


50  private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }


51  }


52 


53  public int ModelDepth {


54  get { return ((IntValue)this[ModelDepthResultName].Value).Value; }


55  private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }


56  }


57 


58  [StorableConstructor]


59  private SymbolicClassificationSolution(bool deserializing) : base(deserializing) { }


60  private SymbolicClassificationSolution(SymbolicClassificationSolution original, Cloner cloner)


61  : base(original, cloner) {


62  }


63  public SymbolicClassificationSolution(ISymbolicClassificationModel model, IClassificationProblemData problemData)


64  : base(model, problemData) {


65  foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTopLevelNode>())


66  node.SetGrammar(null);


67 


68  Add(new Result(ModelLengthResultName, "Length of the symbolic classification model.", new IntValue()));


69  Add(new Result(ModelDepthResultName, "Depth of the symbolic classification model.", new IntValue()));


70  RecalculateResults();


71  }


72 


73  public override IDeepCloneable Clone(Cloner cloner) {


74  return new SymbolicClassificationSolution(this, cloner);


75  }


76 


77  protected override void RecalculateResults() {


78  base.RecalculateResults();


79  ModelLength = Model.SymbolicExpressionTree.Length;


80  ModelDepth = Model.SymbolicExpressionTree.Depth;


81  }


82  }


83  }

