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source: branches/3044_variableScaling/HeuristicLab.Problems.GrammaticalEvolution/3.4/SymbolicRegression/GESymbolicDataAnalysisEvaluator.cs @ 17456

Last change on this file since 17456 was 17180, checked in by swagner, 5 years ago

#2875: Removed years in copyrights

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 * Author: Sabine Winkler
21 */
22#endregion
23
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.IntegerVectorEncoding;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Operators;
30using HeuristicLab.Optimization;
31using HeuristicLab.Parameters;
32using HEAL.Attic;
33using HeuristicLab.Problems.DataAnalysis;
34using HeuristicLab.Problems.DataAnalysis.Symbolic;
35using HeuristicLab.Problems.GrammaticalEvolution.Mappers;
36
37namespace HeuristicLab.Problems.GrammaticalEvolution {
38  [StorableType("3E723725-9141-4259-BB1D-BACE36657086")]
39  public abstract class GESymbolicDataAnalysisEvaluator<T> : SingleSuccessorOperator,
40    IGESymbolicDataAnalysisEvaluator<T>, ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator, IStochasticOperator
41  where T : class, IDataAnalysisProblemData {
42    private const string RandomParameterName = "Random";
43    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
44    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
45    private const string ProblemDataParameterName = "ProblemData";
46    private const string IntegerVectorParameterName = "IntegerVector";
47    private const string GenotypeToPhenotypeMapperParameterName = "GenotypeToPhenotypeMapper";
48    private const string SymbolicExpressionTreeGrammarParameterName = "SymbolicExpressionTreeGrammar";
49
50    private const string EstimationLimitsParameterName = "EstimationLimits";
51    private const string EvaluationPartitionParameterName = "EvaluationPartition";
52    private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
53    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
54    private const string ValidRowIndicatorParameterName = "ValidRowIndicator";
55
56    public override bool CanChangeName { get { return false; } }
57
58    #region parameter properties
59    ILookupParameter<IRandom> IStochasticOperator.RandomParameter {
60      get { return RandomParameter; }
61    }
62
63    public IValueLookupParameter<IRandom> RandomParameter {
64      get { return (IValueLookupParameter<IRandom>)Parameters[RandomParameterName]; }
65    }
66    public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
67      get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
68    }
69    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
70      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
71    }
72    public IValueLookupParameter<T> ProblemDataParameter {
73      get { return (IValueLookupParameter<T>)Parameters[ProblemDataParameterName]; }
74    }
75    public ILookupParameter<IntegerVector> IntegerVectorParameter {
76      get { return (ILookupParameter<IntegerVector>)Parameters[IntegerVectorParameterName]; }
77    }
78    public ILookupParameter<IGenotypeToPhenotypeMapper> GenotypeToPhenotypeMapperParameter {
79      get { return (ILookupParameter<IGenotypeToPhenotypeMapper>)Parameters[GenotypeToPhenotypeMapperParameterName]; }
80    }
81    public IValueLookupParameter<ISymbolicDataAnalysisGrammar> SymbolicExpressionTreeGrammarParameter {
82      get { return (IValueLookupParameter<ISymbolicDataAnalysisGrammar>)Parameters[SymbolicExpressionTreeGrammarParameterName]; }
83    }
84
85    public IValueLookupParameter<IntRange> EvaluationPartitionParameter {
86      get { return (IValueLookupParameter<IntRange>)Parameters[EvaluationPartitionParameterName]; }
87    }
88    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
89      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
90    }
91    public IValueLookupParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
92      get { return (IValueLookupParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
93    }
94    public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
95      get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
96    }
97    public IValueLookupParameter<StringValue> ValidRowIndicatorParameter {
98      get { return (IValueLookupParameter<StringValue>)Parameters[ValidRowIndicatorParameterName]; }
99    }
100    #endregion
101
102
103    [StorableConstructor]
104    protected GESymbolicDataAnalysisEvaluator(StorableConstructorFlag _) : base(_) { }
105    protected GESymbolicDataAnalysisEvaluator(GESymbolicDataAnalysisEvaluator<T> original, Cloner cloner)
106      : base(original, cloner) {
107    }
108    public GESymbolicDataAnalysisEvaluator()
109      : base() {
110      Parameters.Add(new ValueLookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
111      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic data analysis tree."));
112      Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic data analysis solution encoded as a symbolic expression tree."));
113      Parameters.Add(new ValueLookupParameter<T>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
114      Parameters.Add(new LookupParameter<IntegerVector>(IntegerVectorParameterName, "The symbolic data analysis solution encoded as an integer vector genome."));
115      Parameters.Add(new LookupParameter<IGenotypeToPhenotypeMapper>(GenotypeToPhenotypeMapperParameterName, "Maps the genotype (an integer vector) to the phenotype (a symbolic expression tree)."));
116      Parameters.Add(new ValueLookupParameter<ISymbolicDataAnalysisGrammar>(SymbolicExpressionTreeGrammarParameterName, "The tree grammar that defines the correct syntax of symbolic expression trees that should be created."));
117
118      Parameters.Add(new ValueLookupParameter<IntRange>(EvaluationPartitionParameterName, "The start index of the dataset partition on which the symbolic data analysis solution should be evaluated."));
119      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The upper and lower limit that should be used as cut off value for the output values of symbolic data analysis trees."));
120      Parameters.Add(new ValueLookupParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index."));
121      Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
122      Parameters.Add(new ValueLookupParameter<StringValue>(ValidRowIndicatorParameterName, "An indicator variable in the data set that specifies which rows should be evaluated (those for which the indicator <> 0) (optional)."));
123    }
124
125    [StorableHook(HookType.AfterDeserialization)]
126    private void AfterDeserialization() {
127    }
128  }
129}
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