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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Evaluators/SymbolicDataAnalysisEvaluator.cs @ 5823

Last change on this file since 5823 was 5823, checked in by gkronber, 13 years ago

#1418 fixed minor issues in symbolic data analysis classes.

File size: 6.1 KB
Line 
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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Operators;
30using HeuristicLab.Parameters;
31using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
32using HeuristicLab.Random;
33
34namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
35  [StorableClass]
36  public abstract class SymbolicDataAnalysisEvaluator<T> : SingleSuccessorOperator,
37    ISymbolicDataAnalysisEvaluator<T>, ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator
38  where T : class, IDataAnalysisProblemData {
39    private const string RandomParameterName = "Random";
40    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
41    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
42    private const string ProblemDataParameterName = "ProblemData";
43    private const string EstimationLimitsParameterName = "EstimationLimits";
44    private const string EvaluationPartitionParameterName = "EvaluationPartition";
45    private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
46
47    public override bool CanChangeName { get { return false; } }
48
49    #region parameter properties
50    public IValueLookupParameter<IRandom> RandomParameter {
51      get { return (IValueLookupParameter<IRandom>)Parameters[RandomParameterName]; }
52    }
53    public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
54      get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
55    }
56    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
57      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
58    }
59    public IValueLookupParameter<T> ProblemDataParameter {
60      get { return (IValueLookupParameter<T>)Parameters[ProblemDataParameterName]; }
61    }
62
63    public IValueLookupParameter<IntRange> EvaluationPartitionParameter {
64      get { return (IValueLookupParameter<IntRange>)Parameters[EvaluationPartitionParameterName]; }
65    }
66    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
67      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
68    }
69    public IValueLookupParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
70      get { return (IValueLookupParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
71    }
72    #endregion
73
74
75    [StorableConstructor]
76    protected SymbolicDataAnalysisEvaluator(bool deserializing) : base(deserializing) { }
77    protected SymbolicDataAnalysisEvaluator(SymbolicDataAnalysisEvaluator<T> original, Cloner cloner)
78      : base(original, cloner) {
79    }
80    public SymbolicDataAnalysisEvaluator()
81      : base() {
82      Parameters.Add(new ValueLookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
83      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic data analysis tree."));
84      Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic data analysis solution encoded as a symbolic expression tree."));
85      Parameters.Add(new ValueLookupParameter<T>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
86      Parameters.Add(new ValueLookupParameter<IntRange>(EvaluationPartitionParameterName, "The start index of the dataset partition on which the symbolic data analysis solution should be evaluated."));
87      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."));
88      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."));
89    }
90
91    protected IEnumerable<int> GenerateRowsToEvaluate() {
92      int seed = RandomParameter.ActualValue.Next();
93      int samplesStart = EvaluationPartitionParameter.ActualValue.Start;
94      int samplesEnd = EvaluationPartitionParameter.ActualValue.End;
95      int testPartitionStart = ProblemDataParameter.ActualValue.TestPartition.Start;
96      int testPartitionEnd = ProblemDataParameter.ActualValue.TestPartition.End;
97
98      if (samplesEnd < samplesStart) throw new ArgumentException("Start value is larger than end value.");
99      int count = (int)((samplesEnd - samplesStart) * RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value);
100      if (count == 0) count = 1;
101      return RandomEnumerable.SampleRandomNumbers(seed, samplesStart, samplesEnd, count)
102        .Where(i => i < testPartitionStart || testPartitionEnd <= i);
103    }
104  }
105}
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