1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022011 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 HeuristicLab.Common;


23  using HeuristicLab.Core;


24  using HeuristicLab.Data;


25  using HeuristicLab.Optimization;


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


27 


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


29  /// <summary>


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


31  /// </summary>


32  [StorableClass]


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


34  public sealed class SymbolicDiscriminantFunctionClassificationSolution : DiscriminantFunctionClassificationSolution, ISymbolicClassificationSolution {


35  private const string ModelLengthResultName = "Model Length";


36  private const string ModelDepthResultName = "Model Depth";


37 


38  public new ISymbolicDiscriminantFunctionClassificationModel Model {


39  get { return (ISymbolicDiscriminantFunctionClassificationModel)base.Model; }


40  set { base.Model = value; }


41  }


42 


43  ISymbolicClassificationModel ISymbolicClassificationSolution.Model {


44  get { return Model; }


45  }


46 


47  ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {


48  get { return Model; }


49  }


50  public int ModelLength {


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


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


53  }


54 


55  public int ModelDepth {


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


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


58  }


59  [StorableConstructor]


60  private SymbolicDiscriminantFunctionClassificationSolution(bool deserializing) : base(deserializing) { }


61  private SymbolicDiscriminantFunctionClassificationSolution(SymbolicDiscriminantFunctionClassificationSolution original, Cloner cloner)


62  : base(original, cloner) {


63  }


64  public SymbolicDiscriminantFunctionClassificationSolution(ISymbolicDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)


65  : base(model, problemData) {


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


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


68  RecalculateResults();


69  }


70 


71  public override IDeepCloneable Clone(Cloner cloner) {


72  return new SymbolicDiscriminantFunctionClassificationSolution(this, cloner);


73  }


74 


75  protected override void RecalculateResults() {


76  CalculateResults();


77  CalculateRegressionResults();


78  ModelLength = Model.SymbolicExpressionTree.Length;


79  ModelDepth = Model.SymbolicExpressionTree.Depth;


80  }


81 


82  }


83  }

