1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022016 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;


23  using HeuristicLab.Common;


24  using HeuristicLab.Core;


25  using HeuristicLab.Data;


26  using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;


27  using HeuristicLab.Optimization.Operators;


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


29 


30  namespace HeuristicLab.Problems.DataAnalysis.Symbolic {


31  [Item("SymbolicExpressionTreePhenotypicSimilarityCalculator", "An operator that calculates the similarity betweeon two trees based on the correlation of their outputs.")]


32  [StorableClass]


33  public class SymbolicExpressionTreePhenotypicSimilarityCalculator : SolutionSimilarityCalculator {


34  [Storable]


35  public IDataAnalysisProblemData ProblemData { get; set; }


36  [Storable]


37  public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter { get; set; }


38 


39  protected override bool IsCommutative { get { return true; } }


40 


41  [StorableConstructor]


42  protected SymbolicExpressionTreePhenotypicSimilarityCalculator(bool deserializing) : base(deserializing) { }


43 


44  public SymbolicExpressionTreePhenotypicSimilarityCalculator(SymbolicExpressionTreePhenotypicSimilarityCalculator original, Cloner cloner)


45  : base(original, cloner) {


46  this.ProblemData = cloner.Clone(original.ProblemData);


47  this.Interpreter = cloner.Clone(original.Interpreter);


48  }


49 


50  public override IDeepCloneable Clone(Cloner cloner) {


51  return new SymbolicExpressionTreePhenotypicSimilarityCalculator(this, cloner);


52  }


53 


54  public SymbolicExpressionTreePhenotypicSimilarityCalculator() { }


55 


56  public double CalculateSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2) {


57  if (Interpreter == null  ProblemData == null)


58  throw new InvalidOperationException("Cannot calculate phenotypic similarity when no interpreter or problem data were set.");


59 


60  var v1 = Interpreter.GetSymbolicExpressionTreeValues(t1, ProblemData.Dataset, ProblemData.TrainingIndices);


61  var v2 = Interpreter.GetSymbolicExpressionTreeValues(t2, ProblemData.Dataset, ProblemData.TrainingIndices);


62 


63  if (v1.Variance().IsAlmost(0) && v2.Variance().IsAlmost(0))


64  return 1.0;


65 


66  OnlineCalculatorError error;


67  var r = OnlinePearsonsRCalculator.Calculate(v1, v2, out error);


68 


69  var r2 = error == OnlineCalculatorError.None ? r * r : 0;


70 


71  if (r2 > 1.0)


72  r2 = 1.0;


73 


74  return r2;


75  }


76 


77  public override double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution) {


78  if (leftSolution == rightSolution)


79  return 1.0;


80 


81  if (!leftSolution.Variables.ContainsKey("EstimatedValues")  !rightSolution.Variables.ContainsKey("EstimatedValues"))


82  throw new ArgumentException("No estimated values are present in the subscopes.");


83 


84  var leftValues = (DoubleArray)leftSolution.Variables["EstimatedValues"].Value;


85  var rightValues = (DoubleArray)rightSolution.Variables["EstimatedValues"].Value;


86 


87  if (leftValues.Variance().IsAlmost(0) && rightValues.Variance().IsAlmost(0))


88  return 1.0;


89 


90  OnlineCalculatorError error;


91  var r = OnlinePearsonsRCalculator.Calculate(leftValues, rightValues, out error);


92 


93  var r2 = error == OnlineCalculatorError.None ? r * r : 0;


94 


95  if (r2 > 1.0)


96  r2 = 1.0;


97 


98  return r2;


99  }


100  }


101  }

