source: branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/ShapeConstrainedRegressionSingleObjectiveProblem.cs @ 18095

#region License Information
/* HeuristicLab
 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
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 * HeuristicLab is free software: you can redistribute it and/or modify
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#endregion

using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  [Item("Shape-constrained symbolic regression problem (single-objective)", "Represents a single-objective shape-constrained regression problem.")]
  [StorableType("B35ADCA7-E902-4BEE-9DDE-DF8BBC1E27FE")]
  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 150)]
  public class ShapeConstrainedRegressionSingleObjectiveProblem : SymbolicRegressionSingleObjectiveProblem {
    [StorableConstructor]
    protected ShapeConstrainedRegressionSingleObjectiveProblem(StorableConstructorFlag _) : base(_) { }
    protected ShapeConstrainedRegressionSingleObjectiveProblem(ShapeConstrainedRegressionSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) { return new ShapeConstrainedRegressionSingleObjectiveProblem(this, cloner); }

    public ShapeConstrainedRegressionSingleObjectiveProblem()
      : base(new ShapeConstrainedRegressionProblemData(), new NMSESingleObjectiveConstraintsEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {

      ApplyLinearScalingParameter.Value.Value = true;
      Maximization.Value = false;
      SymbolicExpressionTreeGrammarParameter.Value = new LinearScalingGrammar();

      InitializeOperators();
    }


    private void InitializeOperators() {
      Operators.Add(new ShapeConstraintsAnalyzer());
      ParameterizeOperators();
    }

    public override void Load(IRegressionProblemData data) {
      var scProblemData = new ShapeConstrainedRegressionProblemData(data.Dataset, data.AllowedInputVariables, data.TargetVariable,
                                                                    data.TrainingPartition, data.TestPartition) {
        Name = data.Name,
        Description = data.Description
      };

      base.Load(scProblemData);
    }
  }
}