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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator.cs @ 5538

Last change on this file since 5538 was 5445, checked in by swagner, 14 years ago

Updated year of copyrights (#1406)

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis.Symbolic;
30using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
31
32namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
33  [Item("MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator", "Calculates the correlation coefficient r² and the number of variables of a symbolic regression solution.")]
34  [StorableClass]
35  public sealed class MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator : MultiObjectiveSymbolicRegressionEvaluator {
36    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
37    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
38
39    #region parameter properties
40    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
41      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
42    }
43    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
44      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
45    }
46    #endregion
47    #region properties
48    public DoubleValue UpperEstimationLimit {
49      get { return UpperEstimationLimitParameter.ActualValue; }
50    }
51    public DoubleValue LowerEstimationLimit {
52      get { return LowerEstimationLimitParameter.ActualValue; }
53    }
54    #endregion
55    [StorableConstructor]
56    private MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator(bool deserializing) : base(deserializing) { }
57    private MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator(MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator original, Cloner cloner)
58      : base(original, cloner) {
59    }
60    public MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator()
61      : base() {
62      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit that should be used as cut off value for the output values of symbolic expression trees."));
63      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit that should be used as cut off value for the output values of symbolic expression trees."));
64    }
65
66    public override IDeepCloneable Clone(Cloner cloner) {
67      return new MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator(this, cloner);
68    }
69
70    protected override double[] Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, Dataset dataset, StringValue targetVariable, IEnumerable<int> rows) {
71      double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
72      double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
73      double r2 = SymbolicRegressionPearsonsRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable.Value, rows);
74      return new double[2] { r2, solution.Size };
75    }
76  }
77}
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