#region License Information /* HeuristicLab * Copyright (C) 2002-2011 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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification { /// /// An operator that analyzes the validation best symbolic classification solution for multi objective symbolic classification problems. /// [Item("SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer", "An operator that analyzes the validation best symbolic classification solution for multi objective symbolic classification problems.")] [StorableClass] public sealed class SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveValidationBestSolutionAnalyzer, ISymbolicDataAnalysisBoundedOperator { private const string EstimationLimitsParameterName = "EstimationLimits"; private const string ApplyLinearScalingParameterName = "ApplyLinearScaling"; #region parameter properties public IValueLookupParameter EstimationLimitsParameter { get { return (IValueLookupParameter)Parameters[EstimationLimitsParameterName]; } } public IValueParameter ApplyLinearScalingParameter { get { return (IValueParameter)Parameters[ApplyLinearScalingParameterName]; } } #endregion #region properties public BoolValue ApplyLinearScaling { get { return ApplyLinearScalingParameter.Value; } } #endregion [StorableConstructor] private SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { } private SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer(SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { } public SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer() : base() { Parameters.Add(new ValueLookupParameter(EstimationLimitsParameterName, "The loewr and upper limit for the estimated values produced by the symbolic classification model.")); Parameters.Add(new ValueParameter(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false))); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer(this, cloner); } protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQualities) { var model = new SymbolicDiscriminantFunctionClassificationModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); var solution = new SymbolicDiscriminantFunctionClassificationSolution(model, ProblemDataParameter.ActualValue); if (ApplyLinearScaling.Value) { solution.ScaleModel(); } return solution; } } }