#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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 System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification { /// /// Represents a symbolic classification model /// [StorableClass] [Item(Name = "SymbolicClassificationModel", Description = "Represents a symbolic classification model.")] public abstract class SymbolicClassificationModel : SymbolicDataAnalysisModel, ISymbolicClassificationModel { [Storable] private readonly string targetVariable; public string TargetVariable { get { return targetVariable; } } [StorableConstructor] protected SymbolicClassificationModel(bool deserializing) : base(deserializing) { targetVariable = string.Empty; } protected SymbolicClassificationModel(SymbolicClassificationModel original, Cloner cloner) : base(original, cloner) { targetVariable = original.targetVariable; } protected SymbolicClassificationModel(string targetVariable, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) : base(tree, interpreter, lowerEstimationLimit, upperEstimationLimit) { this.targetVariable = targetVariable; } public abstract IEnumerable GetEstimatedClassValues(IDataset dataset, IEnumerable rows); public abstract void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable rows); public abstract ISymbolicClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData); IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) { return CreateClassificationSolution(problemData); } public void Scale(IClassificationProblemData problemData) { Scale(problemData, problemData.TargetVariable); } } }