[10072] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12009] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[10072] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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[10968] | 19 | *
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| 20 | * Author: Sabine Winkler
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[10072] | 21 | */
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| 22 | #endregion
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| 23 |
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| 24 | using System;
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| 25 | using System.Collections.Generic;
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| 26 | using System.Linq;
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| 27 | using HeuristicLab.Common;
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| 28 | using HeuristicLab.Core;
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| 29 | using HeuristicLab.Data;
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[10073] | 30 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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[10072] | 31 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 32 | using HeuristicLab.Operators;
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| 33 | using HeuristicLab.Optimization;
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| 34 | using HeuristicLab.Parameters;
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| 35 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[10073] | 36 | using HeuristicLab.Problems.DataAnalysis;
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| 37 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 38 | using HeuristicLab.Problems.GrammaticalEvolution.Mappers;
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[10072] | 39 | using HeuristicLab.Random;
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| 40 |
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[10073] | 41 | namespace HeuristicLab.Problems.GrammaticalEvolution {
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[10072] | 42 | [StorableClass]
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[10073] | 43 | public abstract class GESymbolicDataAnalysisEvaluator<T> : SingleSuccessorOperator,
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| 44 | IGESymbolicDataAnalysisEvaluator<T>, ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator, IStochasticOperator
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[10072] | 45 | where T : class, IDataAnalysisProblemData {
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| 46 | private const string RandomParameterName = "Random";
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| 47 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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| 48 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 49 | private const string ProblemDataParameterName = "ProblemData";
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[10073] | 50 | private const string IntegerVectorParameterName = "IntegerVector";
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| 51 | private const string GenotypeToPhenotypeMapperParameterName = "GenotypeToPhenotypeMapper";
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| 52 | private const string SymbolicExpressionTreeGrammarParameterName = "SymbolicExpressionTreeGrammar";
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| 53 |
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[10072] | 54 | private const string EstimationLimitsParameterName = "EstimationLimits";
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| 55 | private const string EvaluationPartitionParameterName = "EvaluationPartition";
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| 56 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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| 57 | private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
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| 58 | private const string ValidRowIndicatorParameterName = "ValidRowIndicator";
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| 59 |
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| 60 | public override bool CanChangeName { get { return false; } }
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| 61 |
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| 62 | #region parameter properties
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| 63 | ILookupParameter<IRandom> IStochasticOperator.RandomParameter {
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| 64 | get { return RandomParameter; }
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| 65 | }
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| 66 |
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| 67 | public IValueLookupParameter<IRandom> RandomParameter {
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| 68 | get { return (IValueLookupParameter<IRandom>)Parameters[RandomParameterName]; }
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| 69 | }
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| 70 | public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 71 | get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 72 | }
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| 73 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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| 74 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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| 75 | }
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| 76 | public IValueLookupParameter<T> ProblemDataParameter {
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| 77 | get { return (IValueLookupParameter<T>)Parameters[ProblemDataParameterName]; }
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| 78 | }
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[10073] | 79 | public ILookupParameter<IntegerVector> IntegerVectorParameter {
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| 80 | get { return (ILookupParameter<IntegerVector>)Parameters[IntegerVectorParameterName]; }
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| 81 | }
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| 82 | public ILookupParameter<IGenotypeToPhenotypeMapper> GenotypeToPhenotypeMapperParameter {
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| 83 | get { return (ILookupParameter<IGenotypeToPhenotypeMapper>)Parameters[GenotypeToPhenotypeMapperParameterName]; }
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| 84 | }
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| 85 | public IValueLookupParameter<ISymbolicDataAnalysisGrammar> SymbolicExpressionTreeGrammarParameter {
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| 86 | get { return (IValueLookupParameter<ISymbolicDataAnalysisGrammar>)Parameters[SymbolicExpressionTreeGrammarParameterName]; }
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| 87 | }
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[10072] | 88 |
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| 89 | public IValueLookupParameter<IntRange> EvaluationPartitionParameter {
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| 90 | get { return (IValueLookupParameter<IntRange>)Parameters[EvaluationPartitionParameterName]; }
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| 91 | }
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| 92 | public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
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| 93 | get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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| 94 | }
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| 95 | public IValueLookupParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 96 | get { return (IValueLookupParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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| 97 | }
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| 98 | public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
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| 99 | get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
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| 100 | }
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| 101 | public IValueLookupParameter<StringValue> ValidRowIndicatorParameter {
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| 102 | get { return (IValueLookupParameter<StringValue>)Parameters[ValidRowIndicatorParameterName]; }
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| 103 | }
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| 104 | #endregion
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| 105 |
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| 106 |
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| 107 | [StorableConstructor]
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[10073] | 108 | protected GESymbolicDataAnalysisEvaluator(bool deserializing) : base(deserializing) { }
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| 109 | protected GESymbolicDataAnalysisEvaluator(GESymbolicDataAnalysisEvaluator<T> original, Cloner cloner)
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[10072] | 110 | : base(original, cloner) {
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| 111 | }
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[10073] | 112 | public GESymbolicDataAnalysisEvaluator()
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[10072] | 113 | : base() {
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| 114 | Parameters.Add(new ValueLookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
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| 115 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic data analysis tree."));
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| 116 | Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic data analysis solution encoded as a symbolic expression tree."));
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| 117 | Parameters.Add(new ValueLookupParameter<T>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
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[10073] | 118 | Parameters.Add(new LookupParameter<IntegerVector>(IntegerVectorParameterName, "The symbolic data analysis solution encoded as an integer vector genome."));
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| 119 | Parameters.Add(new LookupParameter<IGenotypeToPhenotypeMapper>(GenotypeToPhenotypeMapperParameterName, "Maps the genotype (an integer vector) to the phenotype (a symbolic expression tree)."));
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| 120 | Parameters.Add(new ValueLookupParameter<ISymbolicDataAnalysisGrammar>(SymbolicExpressionTreeGrammarParameterName, "The tree grammar that defines the correct syntax of symbolic expression trees that should be created."));
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| 121 |
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[10072] | 122 | Parameters.Add(new ValueLookupParameter<IntRange>(EvaluationPartitionParameterName, "The start index of the dataset partition on which the symbolic data analysis solution should be evaluated."));
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| 123 | 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."));
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| 124 | 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."));
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| 125 | Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
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| 126 | 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)."));
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| 127 | }
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| 128 |
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| 129 | [StorableHook(HookType.AfterDeserialization)]
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| 130 | private void AfterDeserialization() {
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| 131 | }
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| 132 | }
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| 133 | }
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