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source: branches/1888_OaaS/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionModel.cs @ 16375

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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
22using System.Collections.Generic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
29  /// <summary>
30  /// Represents a symbolic regression model
31  /// </summary>
32  [StorableClass]
33  [Item(Name = "Symbolic Regression Model", Description = "Represents a symbolic regression model.")]
34  public class SymbolicRegressionModel : SymbolicDataAnalysisModel, ISymbolicRegressionModel {
35    [Storable]
36    private double lowerEstimationLimit;
37    public double LowerEstimationLimit { get { return lowerEstimationLimit; } }
38    [Storable]
39    private double upperEstimationLimit;
40    public double UpperEstimationLimit { get { return upperEstimationLimit; } }
41
42    [StorableConstructor]
43    protected SymbolicRegressionModel(bool deserializing) : base(deserializing) { }
44    protected SymbolicRegressionModel(SymbolicRegressionModel original, Cloner cloner)
45      : base(original, cloner) {
46      this.lowerEstimationLimit = original.lowerEstimationLimit;
47      this.upperEstimationLimit = original.upperEstimationLimit;
48    }
49    public SymbolicRegressionModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
50      double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
51      : base(tree, interpreter) {
52      this.lowerEstimationLimit = lowerEstimationLimit;
53      this.upperEstimationLimit = upperEstimationLimit;
54    }
55
56    public override IDeepCloneable Clone(Cloner cloner) {
57      return new SymbolicRegressionModel(this, cloner);
58    }
59
60    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
61      return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows)
62        .LimitToRange(lowerEstimationLimit, upperEstimationLimit);
63    }
64
65    public ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
66      return new SymbolicRegressionSolution(this, new RegressionProblemData(problemData));
67    }
68    IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
69      return CreateRegressionSolution(problemData);
70    }
71
72    public void Scale(IRegressionProblemData problemData) {
73      Scale(problemData, problemData.TargetVariable);
74    }
75  }
76}
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